From d23f9c4d337c63f71c96b209c9cffc7f92b593c7 Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Fri, 1 Mar 2024 18:03:13 +0000 Subject: [PATCH 01/37] Version funcional --- docker/Dockerfile.jupyter | 104 +--- docker/Dockerfile.jupyter.base | 63 +++ docker/Dockerfile.rstudio | 63 +-- docker/Dockerfile.rstudio.base | 88 +++ docker/docker-compose.yml | 33 +- docker/entrypoint.sh | 6 +- nbs_pipeline/01_dataset_artifact.ipynb | 530 +++++------------- .../02c_encoder_MVP-sliding_window_view.ipynb | 457 +++++++-------- nbs_pipeline/config/base.yaml | 4 +- r_shiny_app/server.R | 57 +- 10 files changed, 572 insertions(+), 833 deletions(-) create mode 100644 docker/Dockerfile.jupyter.base create mode 100644 docker/Dockerfile.rstudio.base diff --git a/docker/Dockerfile.jupyter b/docker/Dockerfile.jupyter index c3787d9b..ce1fcef4 100644 --- a/docker/Dockerfile.jupyter +++ b/docker/Dockerfile.jupyter @@ -8,37 +8,15 @@ # Base image # ############## ##--- Setup Ubuntu -ARG CUDA_VERSION -FROM nvidia/cuda:${CUDA_VERSION} +FROM misantamaria/dvats-jupyter:cuda12.2.0-ubuntu20.04 #--- Tags LABEL maintainer="vrodriguezf " LABEL cuda_version=${CUDA_VERSION} -LABEL log_path=${log_path} -##---Initialize bash -LABEL cuda_version=${CUDA_VERSION} -RUN echo "Cuda version: $CUDA_VERSION" ##---Initialize bash SHELL [ "/bin/bash", "--login", "-c" ] -################## -# Packages setup # -################## -#TODO: Automatizar que lo coja de /etc/timezone -#Only neccesary for libarchive & libtiff if error appears -ARG TZ=Etc/UTC - -RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone \ - && apt-get update --fix-missing \ - #if errors with libtiff library - #&& apt-get install -y wget bzip2 curl git sudo libarchive-dev libtiff5-dev \ - && apt-get install -y wget bzip2 curl git sudo libarchive-dev libtiff5-dev zsh\ - #Otherwise - # && apt-get install -y wget bzip2 curl git sudo \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* - #-- Environmental variables for wandb ENV LC_ALL=C.UTF-8 \ LANG=C.UTF-8 @@ -67,66 +45,36 @@ USER $USER # Add the jupyterlab settings COPY --chown=$uid:$gid docker/jupyter_config $HOME/.jupyter -##################### -# Install miniconda # -##################### - -ENV MINICONDA_VERSION=4.10.3 \ -#ENV MINICONDA_VERSION=23.9.0 \ - CONDA_DIR=$HOME/miniconda3 \ - # Make non-activate conda commands available - PATH=$CONDA_DIR/bin:$PATH \ - PROJECT_DIR=$HOME - -RUN echo "HOME: ${HOME} | CONDA_DIR = ${CONDA_DIR}" \ - #-- Install MINICONDA - && wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-py38_$MINICONDA_VERSION-Linux-x86_64.sh -O ~/miniconda.sh \ - && chmod +x ~/miniconda.sh \ - && ~/miniconda.sh -b -p $CONDA_DIR \ - && rm ~/miniconda.sh - - # Make conda activate command available from /bin/bash --login shells -RUN echo ". $CONDA_DIR/etc/profile.d/conda.sh" >> ~/.profile - # make conda activate command available from /bin/bash --interative shells -RUN conda init bash \ - # create a project directory inside user home + +############################# +# Ensure miniconda for user # +############################# + +#ENV MINICONDA_VERSION=4.10.3 \ +# CONDA_DIR=/opt/miniconda3 + +#ENV PATH=$CONDA_DIR/bin:$PATH \ +ENV PROJECT_DIR=${HOME} +#ENV ENV_PREFIX=/opt/env + +RUN echo "--> Activate conda" + +# Make conda activate command available from /bin/bash --interative shells +RUN conda init bash \ + # Create a project directory inside user home && mkdir -p $PROJECT_DIR -WORKDIR $PROJECT_DIR -########################## -# Install & update MAMBA # -########################## -ENV ENV_PREFIX $PROJECT_DIR/env -RUN conda install --name base --channel conda-forge mamba \ - && mamba update --name base --channel defaults conda -#-- Build the mamba environment -RUN mamba install conda-lock -c conda-forge -COPY --chown=$UID:$GID docker/environment.yml docker/requirements.txt /tmp/ -#RUN mamba lock -f /tmp/environment.yml --lockfile /tmp/environment.lock -#RUN mamba create --prefix ${ENV_PREFIX} --file /tmp/environment.lock -RUN mamba env create --prefix ${ENV_PREFIX} --file /tmp/environment.yml -RUN conda clean --all --yes - -# run the postBuild script to install the JupyterLab extensions -COPY --chown=$UID:$GID docker/postBuild /usr/local/bin -RUN chmod u+x /usr/local/bin/postBuild \ - && conda activate $ENV_PREFIX \ - && /usr/local/bin/postBuild \ - && conda deactivate \ - # Make bash automatically activate the conda environment - && echo "conda activate $ENV_PREFIX" >> ~/.bashrc -RUN chmod u+x /usr/local/bin/postBuild \ - && conda activate $ENV_PREFIX \ - && /usr/local/bin/postBuild \ - && conda deactivate \ - # Make bash automatically activate the conda environment - && echo "conda activate $ENV_PREFIX" >> ~/.bashrc - -RUN conda list --prefix ${ENV_PREFIX} +RUN echo "--> WORKDIR" +WORKDIR ${HOME} COPY --chown=$UID:$GID docker/entrypoint.sh /usr/local/bin -RUN chmod u+x /usr/local/bin/entrypoint.sh +# Make bash automatically activate the conda environment +RUN echo "conda activate $ENV_PREFIX" >> ~/.bashrc \ + && echo "--> Exec entrypoint" \ + && chmod u+x /usr/local/bin/entrypoint.sh + +ENV PATH /usr/local/share/miniconda3/envs/env/bin:$PATH ENTRYPOINT [ "/usr/local/bin/entrypoint.sh" ] diff --git a/docker/Dockerfile.jupyter.base b/docker/Dockerfile.jupyter.base new file mode 100644 index 00000000..8f6c463c --- /dev/null +++ b/docker/Dockerfile.jupyter.base @@ -0,0 +1,63 @@ +############################################ +# CONDA & MINICONDA WITH PYBASE DOCKERFILE # +# For Jupyter service # +############################################ +############## +# Base image # +############## +##--- Setup Ubuntu +ARG CUDA_VERSION=$CUDA_VERSION + +FROM nvidia/cuda:${CUDA_VERSION} + +##--- Tags +LABEL maintainer="vrodriguezf " +LABEL cuda_version=${CUDA_VERSION} + +##--- Setup bash +SHELL [ "/bin/bash", "--login", "-c" ] + +################## +# Packages setup # +################## +#TODO: Automatizar que lo coja de /etc/timezone +ARG TZ=Etc/UTC +RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone \ + && apt-get update --fix-missing \ + && apt-get install -y wget bzip2 curl git sudo libarchive-dev libtiff5-dev zsh python3 python3-pip\ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +#-- Environmental variables +ENV LC_ALL=C.UTF-8 \ + LANG=C.UTF-8 \ + MINICONDA_VERSION=4.10.3 \ + HOME=/home\ + CONDA_DIR=/usr/local/share/miniconda3 + +ENV PATH=$CONDA_DIR/bin:$PATH \ + ENV_PREFIX=${CONDA_DIR}/envs/env + + +##################### +# Install miniconda # +##################### +RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-py38_$MINICONDA_VERSION-Linux-x86_64.sh -O /usr/local/share/miniconda.sh \ + && chmod +x /usr/local/share/miniconda.sh \ + && /usr/local/share/miniconda.sh -b -p $CONDA_DIR \ + && rm /usr/local/share/miniconda.sh \ + && echo ". $CONDA_DIR/etc/profile.d/conda.sh" >> /usr/local/share/.profile + +RUN echo PATH: ${PATH} +RUN conda init bash + +WORKDIR /usr/local/share/ + +########################## +# Install & update MAMBA # +########################## +COPY ./docker/environment.yml ./docker/requirements.txt /tmp/ +RUN conda install --name base --channel conda-forge mamba \ + && mamba update --name base --channel defaults conda \ + && mamba env create --prefix ${ENV_PREFIX} --file /tmp/environment.yml \ + && conda clean --all --yes \ No newline at end of file diff --git a/docker/Dockerfile.rstudio b/docker/Dockerfile.rstudio index e3c0834f..f235ca7e 100644 --- a/docker/Dockerfile.rstudio +++ b/docker/Dockerfile.rstudio @@ -8,38 +8,19 @@ ############## # Base image # ############## -#FROM vrodriguezf/rstudio-server-shiny-development -# Adds rstudio server, tiyverse, devtools to rocker/cuda -FROM rocker/ml:4.2 - +##--- Setup Ubuntu & Preliminary libraries +FROM misantamaria/dvats-rstudio:rocker-ml4.2 SHELL [ "/bin/bash", "--login", "-c" ] -RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys A4B469963BF863CC -RUN apt-get update -RUN apt-get install -y python3-pip -RUN python3 -m pip install --upgrade pip -RUN apt-get install -y python3-venv libxt-dev -## Install R packages - -#COPY docker/DESCRIPTION /tmp/ -COPY --chown=${UID}:${GID} docker/DESCRIPTION /tmp/ - -RUN ls -la /tmp/ && sleep 5 - -#RUN R -e "install.packages(deps_path = '/tmp/DESCRIPTION', remotes::dev_package_deps(dependencies = TRUE), repos = NULL)" - -RUN R -e "devtools::install_deps('/tmp/', dependencies = TRUE)" - -#RUN R -e "install.packages(c('shiny', 'pals', 'shinyWidgets', 'dygraphs', 'shinycssloaders', 'shinyjs', 'Rcpp', 'reactlog', 'pals', 'feather', 'fasttime', 'zoo', 'shinyWidgets'))" -#RUN R -e "remotes::install_github('r-lib/later')" -#RUN R -e "remotes::install_github('apache/arrow/r')" - -## Install reticulate and create virtual environment using default Ubuntu installed Python -#RUN R -e "install.packages('reticulate')" - +## Add Rstudio Server user ARG USER=user ARG UID=1000 ARG GID=1000 + +ENV USER=$USER +ENV UID=$UID +ENV GID=$GID + ENV HOME /home/$USER ENV ENV_PREFIX $HOME/env @@ -54,30 +35,12 @@ RUN adduser --disabled-password \ RUN adduser $USER sudo RUN echo "$USER ALL=(ALL) NOPASSWD:ALL" >> /etc/sudoers - -ARG RETICULATE_MINICONDA_PATH=/usr/local/share/r-miniconda -ARG RETICULATE_PYTHON_ENV=/usr/virtualenvs/venv_shiny_app -ENV RETICULATE_PYTHON=${RETICULATE_PYTHON_ENV}/bin/python/ - -#RUN R -e "reticulate::virtualenv_create(envname='${RETICULATE_PYTHON_ENV}', python='/usr/bin/python3')" -#RUN R -e "reticulate::virtualenv_install(c('numpy', 'pandas', 'wandb', 'hdbscan'), envname='${RETICULATE_PYTHON_ENV}')" - -RUN echo "RETICULATE_PYTHON_ENV=${RETICULATE_PYTHON_ENV}" >> ${HOME}/.Renviron -RUN echo "RETICULATE_PYTHON=${RETICULATE_PYTHON}" >> ${HOME}/.Renviron - -## Export W&B environment variable to Rstudio - -ARG WANDB_API_KEY -RUN echo "WANDB_API_KEY=${WANDB_API_KEY}" >> /${HOME}/.Renviron - -# use an entrypoint script to insure conda environment is properly activated at runtime - +# Use an entrypoint script to insure conda environment is properly activated at runtime COPY --chown=${UID}:${GID} docker/entrypoint-rstudio.sh /usr/local/bin RUN chmod u+x /usr/local/bin/entrypoint-rstudio.sh -ENTRYPOINT [ "/usr/local/bin/entrypoint-rstudio.sh" ] -# Rewrite the miniconda path environment in case it has been redefined in the compose file -RUN echo "RETICULATE_MINICONDA_PATH=${RETICULATE_MINICONDA_PATH}" >> ${HOME}/.Renviron +WORKDIR $HOME + +# Initialize R-Stusio Server +ENTRYPOINT [ "/usr/local/bin/entrypoint-rstudio.sh" ] -# make non-activate conda commands available -ENV PATH=${RETICULATE_MINICONDA_PATH}/bin:${PATH} \ No newline at end of file diff --git a/docker/Dockerfile.rstudio.base b/docker/Dockerfile.rstudio.base new file mode 100644 index 00000000..7419baf4 --- /dev/null +++ b/docker/Dockerfile.rstudio.base @@ -0,0 +1,88 @@ +######################################################### +# CONDA & MINICONDA & R packages WITH PYBASE DOCKERFILE # +# For Rstudio service # +######################################################### +FROM rocker/ml:4.2 + +##--- Tags +LABEL maintainer="vrodriiguezf /etc/timezone \ + && apt-get update --fix-missing \ + && apt-get install -y \ + wget bzip2 curl git \ + sudo libarchive-dev libtiff5-dev \ + zsh python3 python3-pip\ + && python3 -m pip install --upgrade pip \ + && apt-get install -y python3-venv libxt-dev \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +#-- Environment variables +#--- Wandb +ENV LC_ALL=C.UTF-8 \ + LANG=C.UTF-8 \ + #--- Miniconda installation + MINICONDA_VERSION=4.10.3 \ + HOME=/home\ + CONDA_DIR=/usr/local/share/miniconda3 + +ENV PATH=$CONDA_DIR/bin:$PATH \ + #---Mamba installation + ENV_PREFIX=${CONDA_DIR}/envs/env + +#-- Copy tmp configuration files +COPY ./docker/environment.yml ./docker/requirements.txt ./docker/DESCRIPTION /tmp/ + +##################### +# Install miniconda # +##################### +RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-py38_$MINICONDA_VERSION-Linux-x86_64.sh -O /usr/local/share/miniconda.sh \ + && chmod +x /usr/local/share/miniconda.sh \ + && /usr/local/share/miniconda.sh -b -p $CONDA_DIR \ + && rm /usr/local/share/miniconda.sh \ + && echo ". $CONDA_DIR/etc/profile.d/conda.sh" >> /usr/local/share/.profile +RUN conda init bash + +WORKDIR /usr/local/share/ + +########################## +# Install & update MAMBA # +########################## + +RUN echo "--> Mamba install & Update" \ + && conda install --name base --channel conda-forge mamba \ + && mamba update --name base --channel defaults conda \ + #Build the mamba environment + && mamba env create --prefix ${ENV_PREFIX} --file /tmp/environment.yml \ + && conda clean --all --yes + +############################################### +## INSTALL R PACKAGES & Make conda available ## +############################################### +ARG RETICULATE_MINICONDA_PATH=${CONDA_DIR} \ + RETICULATE_PYTHON_ENV=${ENV_PREFIX} + +ENV RETICULATE_PYTHON=${RETICULATE_PYTHON_ENV}/bin/python/ \ + PATH=${RETICULATE_MINICONDA_PATH}/bin:${PATH} + +#-- Update R-environment +RUN R -e "devtools::install_deps('/tmp/', dependencies = TRUE)" \ + && echo "RETICULATE_PYTHON_ENV=${RETICULATE_PYTHON_ENV}" >> ${HOME}/.Renviron \ + && echo "RETICULATE_PYTHON=${RETICULATE_PYTHON}" >> ${HOME}/.Renviron \ + && echo "RETICULATE_MINICONDA_PATH=${RETICULATE_MINICONDA_PATH}" >> ${HOME}/.Renviron + +COPY ../ /root/work + +RUN pip install -e /root/work \ No newline at end of file diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml index a0e93071..6e7620f8 100644 --- a/docker/docker-compose.yml +++ b/docker/docker-compose.yml @@ -6,10 +6,12 @@ services: - uid=${USER_ID} - gid=${GROUP_ID} - CUDA_VERSION=12.2.0-devel-ubuntu20.04 - - JUPYTER_TOKEN=${JUPYTER_TOKEN} + - JUPYTER_TOKEN=${JUPYTER_TOKEN} context: ../ + labels: + - username=${USER_NAME} dockerfile: docker/Dockerfile.jupyter - image: dvats-jupyter:12.2.0-devel-ubuntu20.04 + image: dvats-jupyter:${USER_NAME} ports: - "${JUPYTER_PORT}:8888" environment: @@ -21,11 +23,10 @@ services: - WANDB_DIR=/home/${USER_NAME}/work - JUPYTER_TOKEN=${JUPYTER_TOKEN} volumes: - - ../:/home/${USER}/work + - ../:/home/${USER_NAME}/work - ${LOCAL_DATA_PATH}:/home/${USER_NAME}/data/ - - conda-env:/home/${USER_NAME}/env - - miniconda:/home/${USER_NAME}/miniconda3 - - lib:/home/${USER_NAME}/lib + - conda-env:/usr/local/share/miniconda3/envs/env + - miniconda:/usr/local/share/miniconda3 init: true stdin_open: true tty: true @@ -43,16 +44,17 @@ services: dockerfile: docker/Dockerfile.rstudio args: - WANDB_API_KEY=${WANDB_API_KEY} # - - RETICULATE_PYTHON_ENV=/home/${USER_NAME}/env - - RETICULATE_MINICONDA_PATH=/home/${USER_NAME}/miniconda3 + - RETICULATE_PYTHON_ENV=/home/env + - RETICULATE_MINICONDA_PATH=/home/miniconda3 - USER=${USER_NAME} #* - UID=${USER_ID} #* - GID=${GROUP_ID} #* - image: dvats-r:rocker-ml_4.2 + image: dvats-rstudio:${USER_NAME} ports: - "${RSTUDIO_PORT}:8787" #* environment: - WANDB_ENTITY=${WANDB_ENTITY} + - WANDB_API_KEY=${WANDB_API_KEY} - WANDB_PROJECT=${WANDB_PROJECT} - USER=${USER_NAME} #* - USERID=${USER_ID} #* @@ -64,14 +66,13 @@ services: # - CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES} - GH_TOKEN=${GH_TOKEN} #* # TODO (28/03/2023): I don't know why it is not working without this - - ENV_VARS=WANDB_ENTITY,WANDB_PROJECT,USER,USERID,GROUPID,PASSWORD,ROOT,CUDA_VISIBLE_DEVICES + - ENV_VARS=WANDB_ENTITY,WANDB_API_KEY,WANDB_PROJECT,USER,USERID,GROUPID,PASSWORD,ROOT,CUDA_VISIBLE_DEVICES volumes: - - ../r_shiny_app:/home/${USER_NAME}/app #* - - ${LOCAL_DATA_PATH}:/home/${USER_NAME}/data/ #* + - ../r_shiny_app:/home/${USER_NAME}/app + - ${LOCAL_DATA_PATH}:/home/${USER_NAME}/data/ - ../dvats:/home/${USER_NAME}/dvats - - conda-env:/home/${USER_NAME}/env - - miniconda:/home/${USER_NAME}/miniconda3 #:ro - - lib:/home/${USER_NAME}/lib + - conda-env:/usr/local/share/miniconda3/envs/env + - miniconda:/home/user/local/share/miniconda3 deploy: resources: #limits: @@ -85,4 +86,4 @@ services: volumes: conda-env: miniconda: - lib: \ No newline at end of file + lib: diff --git a/docker/entrypoint.sh b/docker/entrypoint.sh index ba057ac4..0ec13b73 100644 --- a/docker/entrypoint.sh +++ b/docker/entrypoint.sh @@ -1,7 +1,7 @@ #!/bin/bash --login set -e -echo $ENV_PREFIX -conda activate $ENV_PREFIX -conda list +#echo $ENV_PREFIX +#conda list +ls -la /home/$USER/work pip install -e /home/$USER/work exec "$@" diff --git a/nbs_pipeline/01_dataset_artifact.ipynb b/nbs_pipeline/01_dataset_artifact.ipynb index facc32cf..c9e87388 100644 --- a/nbs_pipeline/01_dataset_artifact.ipynb +++ b/nbs_pipeline/01_dataset_artifact.ipynb @@ -89,13 +89,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "pre_configured_case = True\n", - "case_id = 1\n", - "frequency_factor = 5\n", + "case_id = 7\n", + "frequency_factor = 1\n", "frequency_factor_change_alias = True" ] }, @@ -150,47 +150,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Selecting monash_solar_10_minutes_0\n", - "--> Frequency factor config\n", - "Freq factor: 5\n", - "frequency_factor_change_alias: True\n", - "--> Frequency factor resampling frequency\n", - "Freq factor: 5\n", - "freq_original: 10min\n", - "freq_new: 0 days 00:50:00\n", - "suffix: 50m\n", - "resampling_freq: 50T\n", - "Frequency factor resampling frequency -->\n", - "resampling_freq: 10min\n", - "name: Monash-Solar_10_minutes-50m\n", - "path: ~/data/solar_10_minutes_dataset.tsf\n", - "Frequency factor config -->\n", - "missing_values_technique: None\u001b[0m\n", - "\u001b[94mdata_fpath: ~/data/australian_electricity_demand_dataset.tsf\u001b[0m -> ~/data/solar_10_minutes_dataset.tsf\u001b[0m\n", + "Selecting stumpy_toy_0\n", + "start_date: None\u001b[0m\n", + "\u001b[94mfreq: 1h\u001b[0m -> 1s\u001b[0m\n", + "use_wandb: True\u001b[0m\n", + "range_testing: None\u001b[0m\n", "csv_config: {}\u001b[0m\n", - "date_offset: None\u001b[0m\n", - "data_cols: [0]\u001b[0m\n", "missing_values_constant: None\u001b[0m\n", "test_split: None\u001b[0m\n", - "start_date: None\u001b[0m\n", - "date_format: %Y-%m-%d %H:%M:%S\u001b[0m\n", "time_col: None\u001b[0m\n", - "\u001b[94mresampling_freq: None\u001b[0m -> 50T\u001b[0m\n", - "range_testing: None\u001b[0m\n", - "\u001b[94martifact_name: Monash-Australian_electricity_demand\u001b[0m -> Monash-Solar_10_minutes-50m\u001b[0m\n", - "range_training: None\u001b[0m\n", - "normalize_training: False\u001b[0m\n", + "resampling_freq: None\u001b[0m\n", + "data_fpath: ~/data/australian_electricity_demand_dataset.tsf\u001b[0m\n", "wandb_artifacts_path: ./data/wandb_artifacts\u001b[0m\n", + "normalize_training: False\u001b[0m\n", "joining_train_test: False\u001b[0m\n", - "use_wandb: True\u001b[0m\n", - "\u001b[94mfreq: 1h\u001b[0m -> 10min\u001b[0m\n" + "date_format: %Y-%m-%d %H:%M:%S\u001b[0m\n", + "range_training: None\u001b[0m\n", + "date_offset: None\u001b[0m\n", + "\u001b[94mdata_cols: [0]\u001b[0m -> []\u001b[0m\n", + "missing_values_technique: None\u001b[0m\n", + "\u001b[94martifact_name: Monash-Australian_electricity_demand\u001b[0m -> toy\u001b[0m\n" ] } ], "source": [ - "config = cfg.get_artifact_config_sd2a(print_flag = False)\n", + "config = cfg_.get_artifact_config_sd2a(print_flag = False)\n", "if pre_configured_case: \n", - " cfg.force_artifact_config_sd2a(\n", + " cfg_.force_artifact_config_sd2a(\n", " config = config, \n", " id = case_id, \n", " print_flag = print_flag, \n", @@ -200,6 +186,15 @@ " )" ] }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "config.data_fpath = '~/data/toy.csv'" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -224,39 +219,28 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/macu/data/solar_10_minutes_dataset.tsf\n", - "# Dataset Information\n", - "# The original solar dataset contains approximately 6000 simulated time series representing 5-minute solar power and hourly day-ahead forecasts of photovoltaic (PV) power plants in United States in 2006.\n", - "# This file contains the aggregated version of a subset of the original dataset used by Lai et al. (2017).\n", - "# This contains 137 time series representing the solar power production recorded per every 10 minutes in Alabama state in 2006.\n", - "#\n", - "# For more details, please refer to\n", - "# National Renewable Energy Laboratory, 2020. Solar power data for integration studies. Accessed: 2020-04-30. URL https://www.nrel.gov/grid/solar-power-data.html\n", - "# Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04\n", - "# Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015\n", - "#\n", - "@relation Solar\n", - "@attribute series_name string\n", - "@attribute start_timestamp date\n", - "Timestamp 0 2006-01-01 00:00:01\n", - "1 2006-01-01 00:00:01\n", - "2 2006-01-01 00:00:01\n", - "3 2006-01-01 00:00:01\n", - "4 2006-01-01 00:00:01\n", - " ... \n", - "132 2006-01-01 00:00:01\n", - "133 2006-01-01 00:00:01\n", - "134 2006-01-01 00:00:01\n", - "135 2006-01-01 00:00:01\n", - "136 2006-01-01 00:00:01\n", - "Name: start_timestamp, Length: 137, dtype: datetime64[ns]\n" + "/home/agutierrez/data/toy.csv\n", + "T3,T2,T1\n", + "0.7418217407048813,0.6371797479205732,0.5651174160530583\n", + "0.7397310623320076,0.6294149853417309,0.49351340038822233\n", + "0.7187567854120941,0.5392199104076484,0.46934998330275307\n", + "0.7301690144996238,0.5776701391250909,0.4440996674413469\n", + "0.7524056920505833,0.5701801791118106,0.37300780215582824\n", + "0.7173686807875511,0.5439381598630367,0.4250306471976907\n", + "0.6828549014634744,0.5968434716370429,0.35396080886927506\n", + "0.7013592638393263,0.5281288533936315,0.2995819396437861\n", + "0.6411104193217257,0.5656793700127485,0.30444732370665484\n", + "0.6089596256371969,0.576548578046155,0.3270511350770369\n", + "0.6034718811428907,0.6077293874759166,0.35334613064970627\n", + "0.6556284395994489,0.7144243525282192,0.30579381196757144\n", + "Error while converting file. Maybe not a tsf: Missing attribute section. Attribute section must come before data.\n" ] } ], @@ -284,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -308,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -316,7 +300,7 @@ "output_type": "stream", "text": [ "File loaded successfully\n", - "(52560, 137)\n" + "(550, 3)\n" ] }, { @@ -340,171 +324,53 @@ " \n", " \n", " \n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", - " 8\n", - " 9\n", - " ...\n", - " 127\n", - " 128\n", - " 129\n", - " 130\n", - " 131\n", - " 132\n", - " 133\n", - " 134\n", - " 135\n", - " 136\n", + " T3\n", + " T2\n", + " T1\n", " \n", " \n", " \n", " \n", " 0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 0.741822\n", + " 0.637180\n", + " 0.565117\n", " \n", " \n", " 1\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 0.739731\n", + " 0.629415\n", + " 0.493513\n", " \n", " \n", " 2\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 0.718757\n", + " 0.539220\n", + " 0.469350\n", " \n", " \n", " 3\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 0.730169\n", + " 0.577670\n", + " 0.444100\n", " \n", " \n", " 4\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 0.752406\n", + " 0.570180\n", + " 0.373008\n", " \n", " \n", "\n", - "

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" ], "text/plain": [ "" @@ -1203,7 +949,7 @@ { "data": { "text/html": [ - " View project at https://wandb.ai/mi-santamaria/deepvats" + " View project at https://wandb.ai/angel-gutierrez/ream" ], "text/plain": [ "" @@ -1215,7 +961,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/mi-santamaria/deepvats/runs/mfkyp7cp" + " View run at https://wandb.ai/angel-gutierrez/ream/runs/bn8e6lvh" ], "text/plain": [ "" @@ -1224,13 +970,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'stream.Stream' object attribute 'write' is read-only\n" - ] - }, { "data": { "text/html": [ @@ -1246,7 +985,7 @@ { "data": { "text/html": [ - " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/mfkyp7cp
Synced 5 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)" + " View run 01_dataset_artifact at: https://wandb.ai/angel-gutierrez/ream/runs/bn8e6lvh
Synced 5 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" @@ -1258,7 +997,7 @@ { "data": { "text/html": [ - "Find logs at: /home/macu/work/wandb/run-20240105_125214-mfkyp7cp/logs" + "Find logs at: /home/agutierrez/work/wandb/run-20240229_191007-bn8e6lvh/logs" ], "text/plain": [ "" @@ -1286,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1295,7 +1034,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1331,7 +1070,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1339,6 +1078,13 @@ " import os\n", " os._exit(00)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb b/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb index 77764791..da105294 100644 --- a/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb +++ b/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb @@ -112,8 +112,8 @@ "outputs": [], "source": [ "pre_configured_case = True\n", - "case_id = 1\n", - "frequency_factor = 5\n", + "case_id = 7\n", + "frequency_factor = 1\n", "frequency_factor_change_alias = True" ] }, @@ -191,7 +191,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "GPU | Used mem: 262\n", + "GPU | Used mem: 260\n", "GPU | Used mem: 24576\n", "GPU | Memory Usage: [\u001b[90m--------------------\u001b[0m] \u001b[90m1%\u001b[0m\n" ] @@ -225,13 +225,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "alias: solar_4_seconds\n", - "fname: solar_4_seconds_dataset\n", - "ftype: .tsf\n", - "freq: 4s\n", + "alias: toy\n", + "fname: toy\n", + "ftype: .csv\n", "cols: []\n", + "freq: 1s\n", "time_col: None\n", - "mvp: {'batch_size': 512, 'n_epoch': 100, 'ws': [450, 900], 'stride': 450}\n", + "mvp: {'batch_size': 512, 'n_epoch': 100, 'ws': [10, 1008], 'stride': 1}\n", "dcae: {'batch_size': 512, 'n_epoch': 100, 'stride': 48, 'w': 224, 'delta': 60, 'nfs': [64, 32, 16], 'kss': [10, 5, 5], 'output_filter_size': 10, 'top_k': [2, 2, 4], 'pool_szs': [2, 2, 4]}\n" ] } @@ -288,30 +288,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "alias: solar_4_seconds\n", + "alias: toy\n", "analysis_mode: online\n", "batch_size: 512\n", "epochs: 100\n", "mask_future: False\n", "mask_stateful: True\n", "mask_sync: False\n", - "mvp_ws: [450, 900]\n", + "mvp_ws: [10, 1008]\n", "norm_by_sample: False\n", "norm_use_single_batch: False\n", "r: 0.71\n", - "stride: 450\n", - "train_artifact: mi-santamaria/deepvats/solar_4_seconds-20s:latest\n", + "stride: 1\n", + "train_artifact: angel-gutierrez/ream/toy:latest\n", "valid_artifact: None\n", "use_wandb: True\n", "valid_size: 0.2\n", - "w: 900\n", + "w: 1008\n", "wandb_group: None\n", - "artifact_name: solar_4_seconds-20s\n", + "artifact_name: toy\n", "data_cols: []\n", - "freq: 4s\n", + "freq: 1s\n", "time_col: None\n", "csv_config: {}\n", - "resampling_freq: 20S\n", "norm_use_by_single_batch: (False,)\n" ] } @@ -330,13 +329,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "wandb: Currently logged in as: mi-santamaria. Use `wandb login --relogin` to force relogin\n" + "wandb: Currently logged in as: angel-gutierrez. Use `wandb login --relogin` to force relogin\n" ] }, { "data": { "text/html": [ - "wandb version 0.16.1 is available! To upgrade, please run:\n", + "wandb version 0.16.3 is available! To upgrade, please run:\n", " $ pip install wandb --upgrade" ], "text/plain": [ @@ -361,7 +360,7 @@ { "data": { "text/html": [ - "Run data is saved locally in /home/macu/work/wandb/run-20240105_144924-wka202yd" + "Run data is saved locally in /home/agutierrez/work/wandb/run-20240229_191559-otjx7kp3" ], "text/plain": [ "" @@ -373,7 +372,7 @@ { "data": { "text/html": [ - "Syncing run 02c_encoder_MVP-sliding_window_view to Weights & Biases (docs)
" + "Syncing run 02c_encoder_MVP-sliding_window_view to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -385,7 +384,7 @@ { "data": { "text/html": [ - " View project at https://wandb.ai/mi-santamaria/deepvats" + " View project at https://wandb.ai/angel-gutierrez/ream" ], "text/plain": [ "" @@ -397,7 +396,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/mi-santamaria/deepvats/runs/wka202yd" + " View run at https://wandb.ai/angel-gutierrez/ream/runs/otjx7kp3" ], "text/plain": [ "" @@ -459,30 +458,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "alias: solar_4_seconds\n", + "alias: toy\n", "analysis_mode: online\n", "batch_size: 512\n", "epochs: 100\n", "mask_future: False\n", "mask_stateful: True\n", "mask_sync: False\n", - "mvp_ws: [450, 900]\n", + "mvp_ws: [10, 1008]\n", "norm_by_sample: False\n", "norm_use_single_batch: False\n", "r: 0.71\n", - "stride: 450\n", - "train_artifact: mi-santamaria/deepvats/solar_4_seconds-20s:latest\n", + "stride: 1\n", + "train_artifact: angel-gutierrez/ream/toy:latest\n", "valid_artifact: None\n", "use_wandb: True\n", "valid_size: 0.2\n", - "w: 900\n", + "w: 1008\n", "wandb_group: None\n", - "artifact_name: solar_4_seconds-20s\n", + "artifact_name: toy\n", "data_cols: []\n", - "freq: 4s\n", + "freq: 1s\n", "time_col: None\n", "csv_config: {}\n", - "resampling_freq: 20S\n", "norm_use_by_single_batch: [False]\n" ] } @@ -495,6 +493,26 @@ { "cell_type": "code", "execution_count": 13, + "id": "285b5137-62e8-40fa-b610-6c126600bb5b", + "metadata": {}, + "outputs": [], + "source": [ + "config.mvp_ws = [10, 30]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "41448690-f245-497e-9587-cf8231524006", + "metadata": {}, + "outputs": [], + "source": [ + "config.w = 30" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "id": "2cd3daa2-d550-424a-9113-df1d9c7bef14", "metadata": {}, "outputs": [], @@ -504,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "id": "78dced3c-8280-460e-bd11-8188495bf470", "metadata": {}, "outputs": [], @@ -514,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "8acf714f-0fe1-4aed-8b57-23ccd31b22fe", "metadata": {}, "outputs": [ @@ -529,26 +547,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "(1479445, 1)\n" + "(550, 3)\n" ] }, { "data": { "text/plain": [ - "" + "[550 rows x 3 columns]>" ] }, "metadata": {}, @@ -584,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "id": "2ca6f7fd-ca88-449a-81db-9ebcce6beb80", "metadata": {}, "outputs": [ @@ -592,9 +610,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "df_train ~ (1479445, 1)\n", - "window_sizes = [450, 900]\n", - "wlen = 900\n" + "df_train ~ (550, 3)\n", + "window_sizes = [10, 30]\n", + "wlen = 30\n" ] } ], @@ -607,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "973a9e71-2e00-4cbd-a93b-2824e2f45c4e", "metadata": {}, "outputs": [], @@ -617,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "id": "3deacf2c-348f-414c-8cdd-2de61c49733a", "metadata": {}, "outputs": [ @@ -625,8 +643,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "X ~ (1478545, 1, 900)\n", - "stride ~ 450\n" + "X ~ (520, 3, 30)\n", + "stride ~ 1\n" ] } ], @@ -648,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "id": "9ff39cfd-bab8-4b9e-96c4-7a8600652afe", "metadata": {}, "outputs": [], @@ -669,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "id": "8d25ab5a-b1e6-4eb7-8542-e4dc87f2a883", "metadata": {}, "outputs": [ @@ -677,8 +695,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "X ~ (1478545, 1, 900)\n", - "X_strided ~ (3286, 1, 900)\n" + "X ~ (520, 3, 30)\n", + "X_strided ~ (520, 3, 30)\n" ] } ], @@ -716,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "id": "854c7680-e590-449a-a454-6b6a7595b0ea", "metadata": {}, 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1307544, 1307994, 1308444, 1308894,\n", - " 1309344, 1309794, 1310244, 1310694, 1311144, 1311594, 1312044,\n", - " 1312494, 1312944, 1313394, 1313844, 1314294, 1314744, 1315194,\n", - " 1315644, 1316094, 1316544, 1316994, 1317444, 1317894, 1318344,\n", - " 1318794, 1319244, 1319694, 1320144, 1320594, 1321044, 1321494,\n", - " 1321944, 1322394, 1322844, 1323294, 1323744, 1324194, 1324644,\n", - " 1325094, 1325544, 1325994, 1326444, 1326894, 1327344, 1327794,\n", - " 1328244, 1328694, 1329144, 1329594, 1330044, 1330494, 1330944,\n", - " 1331394, 1331844, 1332294, 1332744, 1333194, 1333644, 1334094,\n", - " 1334544, 1334994, 1335444, 1335894, 1336344, 1336794, 1337244,\n", - " 1337694, 1338144, 1338594, 1339044, 1339494, 1339944, 1340394,\n", - " 1340844, 1341294, 1341744, 1342194, 1342644, 1343094, 1343544,\n", - " 1343994, 1344444, 1344894, 1345344, 1345794, 1346244, 1346694,\n", - " 1347144, 1347594, 1348044, 1348494, 1348944, 1349394, 1349844,\n", - " 1350294, 1350744, 1351194, 1351644, 1352094, 1352544, 1352994,\n", - " 1353444, 1353894, 1354344, 1354794, 1355244, 1355694, 1356144,\n", - " 1356594, 1357044, 1357494, 1357944, 1358394, 1358844, 1359294,\n", - " 1359744, 1360194, 1360644, 1361094, 1361544, 1361994, 1362444,\n", - " 1362894, 1363344, 1363794, 1364244, 1364694, 1365144, 1365594,\n", - " 1366044, 1366494, 1366944, 1367394, 1367844, 1368294, 1368744,\n", - " 1369194, 1369644, 1370094, 1370544, 1370994, 1371444, 1371894,\n", - " 1372344, 1372794, 1373244, 1373694, 1374144, 1374594, 1375044,\n", - " 1375494, 1375944, 1376394, 1376844, 1377294, 1377744, 1378194,\n", - " 1378644, 1379094, 1379544, 1379994, 1380444, 1380894, 1381344,\n", - " 1381794, 1382244, 1382694, 1383144, 1383594, 1384044, 1384494,\n", - " 1384944, 1385394, 1385844, 1386294, 1386744, 1387194, 1387644,\n", - " 1388094, 1388544, 1388994, 1389444, 1389894, 1390344, 1390794,\n", - " 1391244, 1391694, 1392144, 1392594, 1393044, 1393494, 1393944,\n", - " 1394394, 1394844, 1395294, 1395744, 1396194, 1396644, 1397094,\n", - " 1397544, 1397994, 1398444, 1398894, 1399344, 1399794, 1400244,\n", - " 1400694, 1401144, 1401594, 1402044, 1402494, 1402944, 1403394,\n", - " 1403844, 1404294, 1404744, 1405194, 1405644, 1406094, 1406544,\n", - " 1406994, 1407444, 1407894, 1408344, 1408794, 1409244, 1409694,\n", - " 1410144, 1410594, 1411044, 1411494, 1411944, 1412394, 1412844,\n", - " 1413294, 1413744, 1414194, 1414644, 1415094, 1415544, 1415994,\n", - " 1416444, 1416894, 1417344, 1417794, 1418244, 1418694, 1419144,\n", - " 1419594, 1420044, 1420494, 1420944, 1421394, 1421844, 1422294,\n", - " 1422744, 1423194, 1423644, 1424094, 1424544, 1424994, 1425444,\n", - " 1425894, 1426344, 1426794, 1427244, 1427694, 1428144, 1428594,\n", - " 1429044, 1429494, 1429944, 1430394, 1430844, 1431294, 1431744,\n", - " 1432194, 1432644, 1433094, 1433544, 1433994, 1434444, 1434894,\n", - " 1435344, 1435794, 1436244, 1436694, 1437144, 1437594, 1438044,\n", - " 1438494, 1438944, 1439394, 1439844, 1440294, 1440744, 1441194,\n", - " 1441644, 1442094, 1442544, 1442994, 1443444, 1443894, 1444344,\n", - " 1444794, 1445244, 1445694, 1446144, 1446594, 1447044, 1447494,\n", - " 1447944, 1448394, 1448844, 1449294, 1449744, 1450194, 1450644,\n", - " 1451094, 1451544, 1451994, 1452444, 1452894, 1453344, 1453794,\n", - " 1454244, 1454694, 1455144, 1455594, 1456044, 1456494, 1456944,\n", - " 1457394, 1457844, 1458294, 1458744, 1459194, 1459644, 1460094,\n", - " 1460544, 1460994, 1461444, 1461894, 1462344, 1462794, 1463244,\n", - " 1463694, 1464144, 1464594, 1465044, 1465494, 1465944, 1466394,\n", - " 1466844, 1467294, 1467744, 1468194, 1468644, 1469094, 1469544,\n", - " 1469994, 1470444, 1470894, 1471344, 1471794, 1472244, 1472694,\n", - " 1473144, 1473594, 1474044, 1474494, 1474944, 1475394, 1475844,\n", - " 1476294, 1476744, 1477194, 1477644, 1478094, 1478544], dtype=int32))" + "((#416) [0,1,2,3,4,5,6,7,8,9...],\n", + " (#104) [416,417,418,419,420,421,422,423,424,425...])" ] }, "metadata": {}, @@ -874,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "id": "c7c3cd99", "metadata": {}, "outputs": [], @@ -892,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "id": "b97038fe-116f-4d6c-8569-e9a9c015a434", "metadata": {}, "outputs": [], @@ -912,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "id": "668e7b5a", "metadata": {}, "outputs": [ @@ -920,12 +854,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "(1478545, 1, 900)\n" + "(520, 3, 30)\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -959,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "id": "f9e0746c-7d67-474b-85db-f65e5fe147b8", "metadata": {}, "outputs": [ @@ -967,8 +901,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "X ~ (1478545, 1, 900)\n", - "dls batch size 512\n" + "X ~ (520, 3, 30)\n", + "dls batch size 416\n" ] } ], @@ -996,7 +930,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "id": "68800c87-ba6e-4097-bfb0-6862a0fb1a2c", "metadata": {}, "outputs": [], @@ -1022,17 +956,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "id": "a291d3f9-ab4a-4233-9fe7-2febc19e80f9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0.5, 900]" + "[0.3333333333333333, 30]" ] }, - "execution_count": 28, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1055,7 +989,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "id": "f5f2b562-c1d8-4b01-aa69-6aa974ee959b", "metadata": {}, "outputs": [], @@ -1085,7 +1019,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 32, "id": "3deaf33e-2d3d-4fac-a10f-21704a2cca60", "metadata": {}, "outputs": [ @@ -1093,9 +1027,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "obtained percentage [0.5, 900]\n", - "obtained absolute [450, 900]\n", - "learn dls.bs 512\n" + "obtained percentage [0.3333333333333333, 30]\n", + "obtained absolute [10, 30]\n", + "learn dls.bs 416\n" ] } ], @@ -1124,7 +1058,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 33, "id": "cb9dd304-6691-49b7-9ffc-f004757f3cd8", "metadata": {}, "outputs": [], @@ -1145,13 +1079,13 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 34, "id": "563efbd4-bc93-4969-b7e8-bed4b191291e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -1186,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 35, "id": "ab1d5cc8-fac5-4f9d-bbd9-4083c2c60d8a", "metadata": {}, "outputs": [ @@ -1194,18 +1128,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "w 900 mvp_ws [450, 900]\n", - "expected [450, 900]\n", - "450 900\n" + "w 30 mvp_ws [10, 30]\n", + "expected [10, 30]\n", + "10 30\n" ] }, { "data": { "text/plain": [ - "482" + "10" ] }, - "execution_count": 33, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1221,7 +1155,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 36, "id": "385e5d4c-84de-440c-9572-8688951c2873", "metadata": {}, "outputs": [ @@ -1229,8 +1163,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "obtained [0.5, 900]\n", - "obtained [450, 900]\n" + "obtained [0.3333333333333333, 30]\n", + "obtained [10, 30]\n" ] } ], @@ -1243,7 +1177,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 37, "id": "ce3a3112-ba6b-4756-9ce1-e15e4c2af1fd", "metadata": {}, "outputs": [ @@ -1272,7 +1206,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 38, "id": "9272a132-9026-419c-81b0-d16d03894f70", "metadata": {}, "outputs": [ @@ -1280,8 +1214,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[450, 900]\n", - "847\n" + "[10, 30]\n", + "15\n" ] } ], @@ -1304,7 +1238,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 39, "id": "ca10238a-f7f2-4fa2-9497-1b59f83cc6b2", "metadata": {}, "outputs": [ @@ -1312,7 +1246,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "obtained [0.5, 900]\n" + "obtained [0.3333333333333333, 30]\n" ] } ], @@ -1323,7 +1257,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "id": "f4402b8e-a0e3-40ac-b208-8172d9ba0457", "metadata": {}, "outputs": [ @@ -1331,7 +1265,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "GPU | Used mem: 428\n", + "GPU | Used mem: 406\n", "GPU | Used mem: 24576\n", "GPU | Memory Usage: [\u001b[90m--------------------\u001b[0m] \u001b[90m2%\u001b[0m\n" ] @@ -1343,7 +1277,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 41, "id": "d4870d10-dcd3-4cfc-937e-3db61ed57f3a", "metadata": {}, "outputs": [ @@ -1351,12 +1285,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "█\r" + "Epoch 99/101 : \r" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1371,7 +1305,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 42, "id": "fc5f53b3-5c75-4d9b-94c9-0385f65dd22f", "metadata": {}, "outputs": [ @@ -1379,7 +1313,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "obtained [450, 900]\n" + "obtained [10, 30]\n" ] } ], @@ -1390,7 +1324,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 43, "id": "bd3a6e2e-6b3b-44f4-9f8a-7585931b2c5b", "metadata": {}, "outputs": [ @@ -1398,11 +1332,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "obtained percentage [450, 900]\n", - "obtained absolute [405000, 900]\n", - "[450, 900]\n", - "[0.5, 900]\n", - "450\n" + "obtained percentage [10, 30]\n", + "obtained absolute [300, 30]\n", + "[10, 30]\n", + "[0.3333333333333333, 30]\n", + "10\n" ] } ], @@ -1414,7 +1348,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 44, "id": "c26833f1-ba03-470e-b209-b20ddebc7294", "metadata": {}, "outputs": [ @@ -1422,9 +1356,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "GPU | Used mem: 24256\n", + "GPU | Used mem: 1150\n", "GPU | Used mem: 24576\n", - "GPU | Memory Usage: [\u001b[91m███████████████████-\u001b[0m] \u001b[91m99%\u001b[0m\n" + "GPU | Memory Usage: [\u001b[90m█-------------------\u001b[0m] \u001b[90m5%\u001b[0m\n" ] } ], @@ -1434,7 +1368,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 45, "id": "610dd97f-1cb2-4364-9748-db882ac6162a", "metadata": {}, "outputs": [ @@ -1443,12 +1377,12 @@ "output_type": "stream", "text": [ "epoch train_loss valid_loss time \n", - "0 0.768875 0.671882 00:00 \n" + "0 1.365752 1.132565 00:00 \n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1460,43 +1394,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "1 0.507787 0.668131 00:00 \n", - "2 0.415524 0.645501 00:00 \n", - "3 0.346389 0.583259 00:00 \n", - "4 0.300052 0.445173 00:00 \n", - "5 0.262728 0.240250 00:00 \n", - "6 0.231390 0.183625 00:00 \n", - "7 0.205598 0.126813 00:00 \n", - "8 0.183196 0.034046 00:00 \n", - "9 0.164534 0.067192 00:00 \n", - "10 0.148830 0.030189 00:00 \n", - "11 0.134748 0.031246 00:00 \n", - "12 0.123263 0.027264 00:00 \n", - "13 0.112746 0.036742 00:00 \n", - "14 0.103428 0.011268 00:00 \n", - "15 0.095276 0.016156 00:00 \n", - "16 0.088213 0.015701 00:00 \n", - "17 0.081752 0.012160 00:00 \n", - "18 0.076217 0.010347 00:00 \n", - "19 0.070990 0.008814 00:00 \n", - "20 0.066182 0.007139 00:00 \n", - "21 0.062174 0.005264 00:00 \n", - "22 0.058196 0.006014 00:00 \n", - "23 0.054945 0.012115 00:00 \n", - "24 0.051672 0.008815 00:00 \n", - "25 0.048885 0.005845 00:00 \n", - "26 0.046318 0.009210 00:00 \n", - "27 0.044430 0.017570 00:00 \n", - "28 0.042263 0.019851 00:00 \n", - "29 0.040466 0.036854 00:00 \n", - "30 0.039081 0.014727 00:00 \n", - "31 0.037566 0.016116 00:00 \n", - "No improvement since epoch 21: early stopping\n" + "1 1.318081 1.195328 00:00 \n", + "2 1.268504 1.164641 00:00 \n", + "3 1.246782 1.232104 00:00 \n", + "4 1.207111 1.243713 00:00 \n", + "5 1.142787 1.182389 00:00 \n", + "6 1.070203 1.253534 00:00 \n", + "7 1.004350 1.232320 00:00 \n", + "8 0.945626 1.223679 00:00 \n", + "9 0.899184 1.195473 00:00 \n", + "10 0.858344 1.219938 00:00 \n", + "No improvement since epoch 0: early stopping\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1511,7 +1424,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 46, "id": "e92b920f-09d3-4bdc-9dbe-0d0ecf673138", "metadata": {}, "outputs": [ @@ -1519,9 +1432,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "GPU | Used mem: 8690\n", + "GPU | Used mem: 1150\n", "GPU | Used mem: 24576\n", - "GPU | Memory Usage: [\u001b[94m███████-------------\u001b[0m] \u001b[94m35%\u001b[0m\n" + "GPU | Memory Usage: [\u001b[90m█-------------------\u001b[0m] \u001b[90m5%\u001b[0m\n" ] } ], @@ -1539,7 +1452,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 47, "id": "f2ab4815-050b-475e-b0e7-7278fbae4215", "metadata": {}, "outputs": [ @@ -1547,11 +1460,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "obtained percentage [450, 900]\n", - "obtained absolute [405000, 900]\n", - "[450, 900]\n", - "[0.5, 900]\n", - "450\n" + "obtained percentage [10, 30]\n", + "obtained absolute [300, 30]\n", + "[10, 30]\n", + "[0.3333333333333333, 30]\n", + "10\n" ] } ], @@ -1563,7 +1476,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 48, "id": "b546f8d3", "metadata": {}, "outputs": [ @@ -1577,10 +1490,10 @@ { "data": { "text/plain": [ - "(#1) [0.34929099678993225]" + "(#1) [1.3280434608459473]" ] }, - "execution_count": 46, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1599,13 +1512,13 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 49, "id": "bfdf3667-a698-451a-9900-0ac1d6fa0cf5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1629,7 +1542,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 50, "id": "bf3bf350-4f52-4a18-ad19-9d260bd060d1", "metadata": { "editable": true, @@ -1642,10 +1555,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 48, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -1657,7 +1570,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 51, "id": "9cc0de64-aced-433e-9c10-28fbcf4f9a91", "metadata": {}, "outputs": [], @@ -1686,7 +1599,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 52, "id": "ddfe12cd-6e5f-40eb-8f84-12d2db7a355b", "metadata": {}, "outputs": [ @@ -1702,6 +1615,20 @@ "metadata": {}, "output_type": "display_data" }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "575318ac1caf44d1aa1a645503aaef66", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='0.006 MB of 0.006 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -1710,7 +1637,7 @@ " .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n", " .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n", " \n", - "

Run summary:


epoch32
eps_01e-05
eps_11e-05
lr_00.00118
lr_10.00118
mom_00.85201
mom_10.85201
raw_loss0.0275
sqr_mom_00.99
sqr_mom_10.99
train_loss0.03757
train_samples_per_sec3867.31928
valid_loss0.01612
wd_00.01
wd_10.01

" + "

Run summary:


epoch11
eps_01e-05
eps_11e-05
lr_00.00045
lr_10.00045
mom_00.91545
mom_10.91545
raw_loss0.49227
sqr_mom_00.99
sqr_mom_10.99
train_loss0.85834
train_samples_per_sec20864.12903
valid_loss1.21994
wd_00.01
wd_10.01

" ], "text/plain": [ "" @@ -1722,7 +1649,7 @@ { "data": { "text/html": [ - " View run 02c_encoder_MVP-sliding_window_view at: https://wandb.ai/mi-santamaria/deepvats/runs/wka202yd
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" + " View run 02c_encoder_MVP-sliding_window_view at: https://wandb.ai/angel-gutierrez/ream/runs/otjx7kp3
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" @@ -1734,7 +1661,7 @@ { "data": { "text/html": [ - "Find logs at: /home/macu/work/wandb/run-20240105_144924-wka202yd/logs" + "Find logs at: /home/agutierrez/work/wandb/run-20240229_191559-otjx7kp3/logs" ], "text/plain": [ "" diff --git a/nbs_pipeline/config/base.yaml b/nbs_pipeline/config/base.yaml index f98264a6..aabd3e95 100644 --- a/nbs_pipeline/config/base.yaml +++ b/nbs_pipeline/config/base.yaml @@ -13,8 +13,8 @@ user_preferences: # Whether to use or not wandb for experiment tracking use_wandb: &use_wandb true wdb: - user: &wdb_user mi-santamaria - project_name: &wdb_project deepvats + user: &wdb_user angel-gutierrez + project_name: &wdb_project ream version: &wdb_version 'latest' #'6' # 'online' if the model will be tested on future data, or 'offline' if not mode: &wdb_mode 'online' diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index 735f3fb9..641a4ce7 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -713,37 +713,40 @@ shinyServer(function(input, output, session) { }) window_list <- reactive({ - print("--> window_list") - on.exit(print("window_list -->")) - # Get the window indices - req(length(embedding_ids() > 0)) - embedding_idxs = embedding_ids() - window_indices = get_window_indices(embedding_idxs, input$wlen, input$stride) - # Put all the indices in one list and remove duplicates - unlist_window_indices = unique(unlist(window_indices)) - # Calculate a vector of differences to detect idx where a new window should be created - diff_vector <- diff(unlist_window_indices,1) - # Take indexes where the difference is greater than one (that represent a change of window) - idx_window_limits <- which(diff_vector!=1) - # Include the first and last index to have a whole set of indexes. - idx_window_limits <- c(1, idx_window_limits, length(unlist_window_indices)) - # Create a reduced window list - reduced_window_list <- vector(mode = "list", length = length(idx_window_limits)-1) - # Populate the first element of the list with the idx of the first window. - reduced_window_list[[1]] <- c(unlist_window_indices[idx_window_limits[1]], - unlist_window_indices[idx_window_limits[1+1]]) + print("--> window_list") + on.exit(print("window_list -->")) + # Get the window indices + req(length(embedding_ids() > 0)) + embedding_idxs = embedding_ids() + window_indices = get_window_indices(embedding_idxs, input$wlen, input$stride) + # Put all the indices in one list and remove duplicates + unlist_window_indices = unique(unlist(window_indices)) + # Calculate a vector of differences to detect idx where a new window should be created + diff_vector <- diff(unlist_window_indices,1) + # Take indexes where the difference is greater than one (that represent a change of window) + idx_window_limits <- which(diff_vector!=1) + # Include the first and last index to have a whole set of indexes. + idx_window_limits <- c(1, idx_window_limits, length(unlist_window_indices)) + # Create a reduced window list + reduced_window_list <- vector(mode = "list", length = length(idx_window_limits)-1) + # Populate the first element of the list with the idx of the first window. + reduced_window_list[[1]] = c( + isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[1]+1]], + isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[2]]] + ) + if (length(idx_window_limits) > 2) { # Populate the rest of the list for (i in 2:(length(idx_window_limits)-1)){ - reduced_window_list[[i]]<- c( - #unlist_window_indices[idx_window_limits[i]+1], - #unlist_window_indices[idx_window_limits[i+1]] - as.Date(isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[i]+1]]), - as.Date(isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[i+1]]]) - ) + reduced_window_list[[i]]<- c( + #unlist_window_indices[idx_window_limits[i]+1], + #unlist_window_indices[idx_window_limits[i+1]] + isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[i]+1]], + isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[i+1]]] + ) } - reduced_window_list + } + reduced_window_list }) - # Generate timeseries data for dygraph dygraph ts_plot <- reactive({ From ea24680e13787caf46e5885a872f34ed3a4c77be Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Mon, 18 Mar 2024 19:18:15 +0000 Subject: [PATCH 02/37] =?UTF-8?q?Proyecci=C3=B3n=203d?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- r_shiny_app/global.R | 3 +++ r_shiny_app/server.R | 49 ++++++++++++++++++++++++++++++++++++++------ r_shiny_app/ui.R | 9 ++++---- 3 files changed, 50 insertions(+), 11 deletions(-) diff --git a/r_shiny_app/global.R b/r_shiny_app/global.R index e8b0cf11..28e36155 100644 --- a/r_shiny_app/global.R +++ b/r_shiny_app/global.R @@ -16,6 +16,9 @@ library(shinyWidgets) library(RColorBrewer) library(pals) library(stringr) +#options(rgl.useNULL = TRUE) +#library(rgl) +library(plotly) ##################QUITAR CUANDO YA TIRE library(reactlog) library(feather) diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index 641a4ce7..5bb83dc8 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -459,6 +459,10 @@ shinyServer(function(input, output, session) { ) #result <- system(python_string) + #------------------------------------------------ + num_columns <- ncol(result) + print(paste("--------------------------ncol2 ", num_columns)) + #------------------------------------------------------------------- t_embs_1 <- Sys.time() diff <- t_embs_1 - t_embs_0 diff_secs <- as.numeric(diff, units = "secs") @@ -488,7 +492,8 @@ shinyServer(function(input, output, session) { print_flag = TRUE, n_neighbors = input$prj_n_neighbors, min_dist = input$prj_min_dist, - random_state= as.integer(input$prj_random_state) + random_state= as.integer(input$prj_random_state), + n_components = as.integer(3) ), TSNE = dvats$get_TSNE_prjs( X = embs, @@ -502,7 +507,7 @@ shinyServer(function(input, output, session) { ) ) res = res %>% as.data.frame # TODO: This should be a matrix for improved efficiency - colnames(res) = c("xcoord", "ycoord") + colnames(res) = c("xcoord", "ycoord", "zcoord") on.exit({print(" prj_object -->"); flush.console()}) flush.console() #browser() @@ -532,7 +537,8 @@ shinyServer(function(input, output, session) { print_flag = TRUE, n_neighbors = input$prj_n_neighbors, min_dist = input$prj_min_dist, - random_state= as.integer(input$prj_random_state) + random_state= as.integer(input$prj_random_state), + n_components = as.integer(3) ), TSNE = dvats$get_TSNE_prjs( X = embs, @@ -545,8 +551,12 @@ shinyServer(function(input, output, session) { random_state=as.integer(input$prj_random_state) ) ) + #----------------------------------------------------------------------------------- + #print(paste("---------------------ncol", ncol(res))) + #print(res) + #----------------------------------------------------------------------------------- res = res %>% as.data.frame # TODO: This should be a matrix for improved efficiency - colnames(res) = c("xcoord", "ycoord") + colnames(res) = c("xcoord", "ycoord", "zcoord") t_prj_1 = Sys.time() on.exit({print(paste0(" prj_object | ", t_prj_1-t_prj_0, " seconds -->")); flush.console()}) flush.console() @@ -592,6 +602,10 @@ shinyServer(function(input, output, session) { prjs <- prj_object() req(input$dataset, input$encoder, input$wlen, input$stride) print("Projections | before switch") + #------------------------------------------ + num_columns <- ncol(prjs) + print(paste("----------------", num_columns)) + #------------------------------------------ switch(clustering_options$selected, precomputed_clusters = { filename <- req(selected_clusters_labels_ar())$metadata$ref$hash @@ -631,8 +645,7 @@ shinyServer(function(input, output, session) { print(paste0("Projections | Repeat projections with CPU because of low quality clusters | score ", score)) } prjs$cluster <- clusters$labels_ - - + }) on.exit({print("Projections -->"); flush.console()}) @@ -1086,5 +1099,29 @@ shinyServer(function(input, output, session) { get_ts_plot_name(dataset_name, encoder_name) }) + # Dentro de la función reactive para la generación de la proyección de embedding + embedding_3d <- reactive({ + # Obtener la proyección de embedding en tres dimensiones + prj <- req(projections()) + print("------------------------------") + str(prj) + print("------------------------------") + # Suponiendo que las coordenadas de la proyección en tres dimensiones están en las primeras tres columnas + prj_3d <- prj[, 1:3] # Seleccionar las primeras tres columnas + colnames(prj_3d) <- c("xcoord", "ycoord", "zcoord") # Asignar nombres a las columnas + prj_3d + }) + + # Dentro de la función renderPlotly para visualizar la proyección en 3D + output$embedding_plot_3d <- renderPlotly({ + # Obtener la proyección en tres dimensiones + prj_3d <- embedding_3d() + + # Crear un gráfico 3D con Plotly + plot_ly(data = prj_3d, x = ~xcoord, y = ~ycoord, z = ~zcoord, type = "scatter3d", mode = "markers", marker = list(size = 5)) + }) + + + }) diff --git a/r_shiny_app/ui.R b/r_shiny_app/ui.R index 431b4fd3..85650f90 100644 --- a/r_shiny_app/ui.R +++ b/r_shiny_app/ui.R @@ -78,7 +78,7 @@ shinyUI(fluidPage( tags$b("Set height of the projections plot (px):"), numericInput("embedding_plot_height", label = "Height",value =400), hr(), - tags$b("Configure aestethics"), + tags$b("Configure aesthetics"), sliderInput("path_line_size", label = "path_line_size", value = DEFAULT_VALUES$path_line_size, min=0, max=5, step = 0.01), sliderInput("path_alpha", label = "path_alpha", @@ -89,8 +89,7 @@ shinyUI(fluidPage( value = DEFAULT_VALUES$point_size, min=0, max=10, step = 0.5), checkboxInput("show_lines", "Show lines", value = TRUE), actionButton('savePlot', 'Save embedding projections plot'), - - actionBttn(inputId = "update_prj_graph",label = "Update aestethics",style = "simple", + actionBttn(inputId = "update_prj_graph",label = "Update aesthetics",style = "simple", color = "primary",icon = icon("bar-chart"),size = "xs", block = TRUE), circle = FALSE, status = "primary", icon = icon("gear"), width = "300px",size = "xs", @@ -123,7 +122,7 @@ shinyUI(fluidPage( column(3) ), fluidRow( - uiOutput("projections_plot_ui") + plotlyOutput("embedding_plot_3d") # Agregamos la salida del gráfico en 3D ) ), fluidRow(h3("Original data")), @@ -176,4 +175,4 @@ shinyUI(fluidPage( ) -)) +)) \ No newline at end of file From 074c9895d3f13f4dc3ab23a692f862d160bc8dad Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Tue, 4 Jun 2024 18:11:29 +0000 Subject: [PATCH 03/37] Refactoring 3d graphic code and add button to change between graphics --- r_shiny_app/server.R | 2016 +++++++++++++++++++++--------------------- r_shiny_app/ui.R | 16 +- 2 files changed, 1026 insertions(+), 1006 deletions(-) diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index 5bb83dc8..6e115c72 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -12,1034 +12,1034 @@ source("./server-helper.R") shinyServer(function(input, output, session) { - options(shiny.verbose = TRUE) - #options(shiny.error = function() { - # traceback() - # stopApp() - #}) + options(shiny.verbose = TRUE) + #options(shiny.error = function() { + # traceback() + # stopApp() + #}) - ###################### - # REACTIVES VALUES # - ###################### - - # Reactive values created to update the current range of the main slider input - #slider_range <- reactiveValues(min_value = 1, max_value = 2) - - # Reactive value created to keep updated the selected precomputed clusters_labels artifact - precomputed_clusters <- reactiveValues(selected = NULL) - - - # Reactive value created to keep updated the selected clustering option - clustering_options <- reactiveValues(selected = "no_clusters") - - - # Reactive value created to configure the graph brush - ranges <- reactiveValues(x = NULL, y = NULL) - - - # Reactive value created to configure clusters options - clusters_config <- reactiveValues( - metric_hdbscan = DEFAULT_VALUES$metric_hdbscan, - min_cluster_size_hdbscan = DEFAULT_VALUES$min_cluster_size_hdbscan, - min_samples_hdbscan = DEFAULT_VALUES$min_samples_hdbscan, - cluster_selection_epsilon_hdbscan = DEFAULT_VALUES$cluster_selection_epsilon_hdbscan + ###################### + # REACTIVES VALUES # + ###################### + + # Reactive values created to update the current range of the main slider input + #slider_range <- reactiveValues(min_value = 1, max_value = 2) + + # Reactive value created to keep updated the selected precomputed clusters_labels artifact + precomputed_clusters <- reactiveValues(selected = NULL) + + + # Reactive value created to keep updated the selected clustering option + clustering_options <- reactiveValues(selected = "no_clusters") + + + # Reactive value created to configure the graph brush + ranges <- reactiveValues(x = NULL, y = NULL) + + + # Reactive value created to configure clusters options + clusters_config <- reactiveValues( + metric_hdbscan = DEFAULT_VALUES$metric_hdbscan, + min_cluster_size_hdbscan = DEFAULT_VALUES$min_cluster_size_hdbscan, + min_samples_hdbscan = DEFAULT_VALUES$min_samples_hdbscan, + cluster_selection_epsilon_hdbscan = DEFAULT_VALUES$cluster_selection_epsilon_hdbscan + ) + + # Reactive values created to configure the appearance of the projections graph. + config_style <- reactiveValues( + path_line_size = DEFAULT_VALUES$path_line_size, + path_alpha = DEFAULT_VALUES$path_alpha, + point_alpha = DEFAULT_VALUES$point_alpha, + point_size = DEFAULT_VALUES$point_size + ) + + # Reactive value created to store time series selected variables + ts_variables <- reactiveValues(selected = NULL) + + # Reactive value created to store the encoder_input + X <- reactiveVal() + + # Reactive value created to store encoder artifact stride + enc_ar_stride <- eventReactive(enc_ar(), { + stride = ceiling(enc_ar()$metadata$stride/2) + }) + + # Time series artifact + ts_ar <- eventReactive( + input$dataset, + { + req(input$dataset) + ar <- api$artifact(input$dataset, type='dataset') + on.exit({print("eventReactive ts_ar -->"); flush.console()}) + ar + }, label = "ts_ar") + + + # Reactive value for indexing saved projections plot + prj_plot_id <- reactiveVal(0) + + ################################# + # OBSERVERS & OBSERVERS EVENTS # + ################################# + observeEvent( + req(exists("encs_l")), + { + freezeReactiveValue(input, "dataset") + print("observeEvent encoders list enc_l | update dataset list | after freeze") + updateSelectizeInput( + session = session, + inputId = "dataset", + choices = encs_l %>% + map(~.$metadata$train_artifact) %>% + set_names() + ) + on.exit({print("observeEvent encoders list encs_l | update dataset list -->"); flush.console()}) + }, + label = "input_dataset" + ) + + observeEvent(input$dataset, { + #req(encs_l) + print("--> observeEvent input_dataset | update encoder list") + print(input$dataset) + freezeReactiveValue(input, "encoder") + print(paste0("observeEvent input_dataset | update encoders for dataset ", input$dataset)) + updateSelectizeInput( + session = session, + inputId = "encoder", + choices = encs_l %>% + keep(~ .$metadata$train_artifact == input$dataset) %>% + #map(~ .$metadata$enc_artifact) %>% + names ) - - # Reactive values created to configure the appearance of the projections graph. - config_style <- reactiveValues( - path_line_size = DEFAULT_VALUES$path_line_size, - path_alpha = DEFAULT_VALUES$path_alpha, - point_alpha = DEFAULT_VALUES$point_alpha, - point_size = DEFAULT_VALUES$point_size + ### TODO: Ver cómo poner bien esta ñapa para que no se actualizen los gráficos antes que el stride + updateSliderInput(session, "stride", value = 0) + ################ + on.exit( + {print("observeEvent input_dataset | update encoder list -->"); flush.console()} ) - - # Reactive value created to store time series selected variables - ts_variables <- reactiveValues(selected = NULL) - - # Reactive value created to store the encoder_input - X <- reactiveVal() - - # Reactive value created to store encoder artifact stride - enc_ar_stride <- eventReactive(enc_ar(), { - stride = ceiling(enc_ar()$metadata$stride/2) - }) - - # Time series artifact - ts_ar <- eventReactive( - input$dataset, - { - req(input$dataset) - ar <- api$artifact(input$dataset, type='dataset') - on.exit({print("eventReactive ts_ar -->"); flush.console()}) - ar - }, label = "ts_ar") - - - # Reactive value for indexing saved projections plot - prj_plot_id <- reactiveVal(0) - - ################################# - # OBSERVERS & OBSERVERS EVENTS # - ################################# - observeEvent( - req(exists("encs_l")), - { - freezeReactiveValue(input, "dataset") - print("observeEvent encoders list enc_l | update dataset list | after freeze") - updateSelectizeInput( - session = session, - inputId = "dataset", - choices = encs_l %>% - map(~.$metadata$train_artifact) %>% - set_names() - ) - on.exit({print("observeEvent encoders list encs_l | update dataset list -->"); flush.console()}) - }, - label = "input_dataset" + }, label = "input_encoder") + + observeEvent( + input$encoder, + { + #req(input$dataset, encs_l) + #enc_ar = req(enc_ar()) + print("--> observeEvent input_encoder | update wlen") + freezeReactiveValue(input, "wlen") + print("observeEvent input_encoder | update wlen | Before enc_ar") + enc_ar = enc_ar() + print(paste0("observeEvent input_encoder | update wlen | enc_ar: ", enc_ar)) + print("observeEvent input_encoder | update wlen | Set wlen slider values") + if (is.null(enc_ar$metadata$mvp_ws)) { + print("observeEvent input_encoder | update wlen | Set wlen slider values from w | ") + enc_ar$metadata$mvp_ws = c(enc_ar$metadata$w, enc_ar$metadata$w) + } + print(paste0("observeEvent input_encoder | update wlen | enc_ar$metadata$mvp_ws ", enc_ar$metadata$mvp_ws )) + wmin <- enc_ar$metadata$mvp_ws[1] + wmax <- enc_ar$metadata$mvp_ws[2] + wlen <- enc_ar$metadata$w + print(paste0("observeEvent input_encoder | update wlen | Update slider input (", wmin, ", ", wmax, " ) -> ", wlen )) + updateSliderInput(session = session, inputId = "wlen", + min = wmin, + max = wmax, + value = wlen + ) + updateSliderInput( + session = session, inputId = "stride", + min = 1, max = input$wlen, + value = enc_ar_stride() + ) + on.exit({print("observeEvent input_encoder | update wlen -->"); flush.console()}) + } + ) + + # Obtener el valor de stride + enc_ar_stride = reactive({ + print("--> reactive enc_ar_stride") + stride = ceiling(enc_ar()$metadata$mvp_ws[2]/2) #<- enc_ar()$metadata$stride + on.exit({print(paste0("reactive_enc_ar_stride | --> ", stride)); flush.console()}) + stride + }) + + observeEvent(input$wlen, { + req(input$wlen) + print(paste0("--> observeEvent input_wlen | update slide stride value | wlen ", input$wlen)) + tryCatch({ + old_value = input$stride + if (input$stride == 0 | input$stride == 1){ + old_value = enc_ar_stride() + print(paste0("enc_ar_stride: ", old_value)) + } + freezeReactiveValue(input, "stride") + print(paste0("oserveEvent input_wlen | update slide stride value | Update stride to ", old_value)) + updateSliderInput( + session = session, inputId = "stride", + min = 1, max = input$wlen, + value = ifelse(old_value <= input$wlen, old_value, 1) + ) + }, + error = function(e){ + print(paste0("observeEvent input_wlen | update slide stride value | Error | ", e$message)) + }, + warning = function(w) { + message(paste0("observeEvent input_wlen | update slide stride value | Warning | ", w$message)) + } ) - - observeEvent(input$dataset, { - #req(encs_l) - print("--> observeEvent input_dataset | update encoder list") - print(input$dataset) - freezeReactiveValue(input, "encoder") - print(paste0("observeEvent input_dataset | update encoders for dataset ", input$dataset)) - updateSelectizeInput( - session = session, - inputId = "encoder", - choices = encs_l %>% - keep(~ .$metadata$train_artifact == input$dataset) %>% - #map(~ .$metadata$enc_artifact) %>% - names - ) - ### TODO: Ver cómo poner bien esta ñapa para que no se actualizen los gráficos antes que el stride - updateSliderInput(session, "stride", value = 0) - ################ - on.exit( - {print("observeEvent input_dataset | update encoder list -->"); flush.console()} - ) - }, label = "input_encoder") - - observeEvent( - input$encoder, - { - #req(input$dataset, encs_l) - #enc_ar = req(enc_ar()) - print("--> observeEvent input_encoder | update wlen") - freezeReactiveValue(input, "wlen") - print("observeEvent input_encoder | update wlen | Before enc_ar") - enc_ar = enc_ar() - print(paste0("observeEvent input_encoder | update wlen | enc_ar: ", enc_ar)) - print("observeEvent input_encoder | update wlen | Set wlen slider values") - if (is.null(enc_ar$metadata$mvp_ws)) { - print("observeEvent input_encoder | update wlen | Set wlen slider values from w | ") - enc_ar$metadata$mvp_ws = c(enc_ar$metadata$w, enc_ar$metadata$w) - } - print(paste0("observeEvent input_encoder | update wlen | enc_ar$metadata$mvp_ws ", enc_ar$metadata$mvp_ws )) - wmin <- enc_ar$metadata$mvp_ws[1] - wmax <- enc_ar$metadata$mvp_ws[2] - wlen <- enc_ar$metadata$w - print(paste0("observeEvent input_encoder | update wlen | Update slider input (", wmin, ", ", wmax, " ) -> ", wlen )) - updateSliderInput(session = session, inputId = "wlen", - min = wmin, - max = wmax, - value = wlen - ) - updateSliderInput( - session = session, inputId = "stride", - min = 1, max = input$wlen, - value = enc_ar_stride() - ) - on.exit({print("observeEvent input_encoder | update wlen -->"); flush.console()}) - } + on.exit({print(paste0( + "observeEvent input_wlen | update slide stride value | Finally | wlen min ", + 1, " max ", input$wlen, " current value ", input$stride, " -->")); flush.console()}) + }) + + # Update "metric_hdbscan" selectInput when the app is loaded + observe({ + updateSelectInput( + session = session, + inputId = "metric_hdbscan", + choices = names(req(hdbscan_metrics)) ) - - # Obtener el valor de stride - enc_ar_stride = reactive({ - print("--> reactive enc_ar_stride") - stride = ceiling(enc_ar()$metadata$mvp_ws[2]/2) #<- enc_ar()$metadata$stride - on.exit({print(paste0("reactive_enc_ar_stride | --> ", stride)); flush.console()}) - stride - }) - - observeEvent(input$wlen, { - req(input$wlen) - print(paste0("--> observeEvent input_wlen | update slide stride value | wlen ", input$wlen)) - tryCatch({ - old_value = input$stride - if (input$stride == 0 | input$stride == 1){ - old_value = enc_ar_stride() - print(paste0("enc_ar_stride: ", old_value)) - } - freezeReactiveValue(input, "stride") - print(paste0("oserveEvent input_wlen | update slide stride value | Update stride to ", old_value)) - updateSliderInput( - session = session, inputId = "stride", - min = 1, max = input$wlen, - value = ifelse(old_value <= input$wlen, old_value, 1) - ) - }, - error = function(e){ - print(paste0("observeEvent input_wlen | update slide stride value | Error | ", e$message)) - }, - warning = function(w) { - message(paste0("observeEvent input_wlen | update slide stride value | Warning | ", w$message)) - } - ) - on.exit({print(paste0( - "observeEvent input_wlen | update slide stride value | Finally | wlen min ", - 1, " max ", input$wlen, " current value ", input$stride, " -->")); flush.console()}) - }) - - # Update "metric_hdbscan" selectInput when the app is loaded - observe({ - updateSelectInput( - session = session, - inputId = "metric_hdbscan", - choices = names(req(hdbscan_metrics)) - ) - }) - # Update the range of point selection when there is new data - # observeEvent(X(), { - # #max_ = ts_ar()$metadata$TS$n_samples - # max_ = dim(X())[[1]] - # freezeReactiveValue(input, "points_emb") - # updateSliderInput(session = session, inputId = "points_emb", - # min = 1, max = max_, value = c(1, max_)) - # }) - - # Update selected time series variables and update interface config - observeEvent(tsdf(), { - print("--> observeEvent tsdf | update select variables") - on.exit({print("--> observeEvent tsdf | update select variables -->"); flush.console()}) - freezeReactiveValue(input, "select_variables") - #ts_variables$selected = names(tsdf())[names(tsdf()) != "timeindex"] - ts_variables$selected = names(isolate(tsdf())) - print(paste0("observeEvent tsdf | select variables ", ts_variables$selected)) - updateCheckboxGroupInput( - session = session, - inputId = "select_variables", - choices = ts_variables$selected, - selected = ts_variables$selected - ) - }, label = "select_variables") - - # Update precomputed_clusters reactive value when the input changes - observeEvent(input$clusters_labels_name, { - print("--> observe | precomputed_cluster selected ") - precomputed_clusters$selected <- req(input$clusters_labels_name) - print(paste0("observe | precomputed_cluster selected --> | ", precomputed_cluster$selected)) - }) + }) + # Update the range of point selection when there is new data + # observeEvent(X(), { + # #max_ = ts_ar()$metadata$TS$n_samples + # max_ = dim(X())[[1]] + # freezeReactiveValue(input, "points_emb") + # updateSliderInput(session = session, inputId = "points_emb", + # min = 1, max = max_, value = c(1, max_)) + # }) + + # Update selected time series variables and update interface config + observeEvent(tsdf(), { + print("--> observeEvent tsdf | update select variables") + on.exit({print("--> observeEvent tsdf | update select variables -->"); flush.console()}) + freezeReactiveValue(input, "select_variables") + #ts_variables$selected = names(tsdf())[names(tsdf()) != "timeindex"] + ts_variables$selected = names(isolate(tsdf())) + print(paste0("observeEvent tsdf | select variables ", ts_variables$selected)) + updateCheckboxGroupInput( + session = session, + inputId = "select_variables", + choices = ts_variables$selected, + selected = ts_variables$selected + ) + }, label = "select_variables") + + # Update precomputed_clusters reactive value when the input changes + observeEvent(input$clusters_labels_name, { + print("--> observe | precomputed_cluster selected ") + precomputed_clusters$selected <- req(input$clusters_labels_name) + print(paste0("observe | precomputed_cluster selected --> | ", precomputed_cluster$selected)) + }) + + + # Update clustering_options reactive value when the input changes + observe({ + print("--> Observe clustering options") + clustering_options$selected <- req(input$clustering_options) + print("Observe clustering options -->") + }) + + # Update clusters_config reactive values when user clicks on "calculate_clusters" button + observeEvent(input$calculate_clusters, { + print("--> observe event calculate_clusters | update clusters_config") + clusters_config$metric_hdbscan <- req(input$metric_hdbscan) + clusters_config$min_cluster_size_hdbscan <- req(input$min_cluster_size_hdbscan) + clusters_config$min_samples_hdbscan <- req(input$min_samples_hdbscan) + clusters_config$cluster_selection_epsilon_hdbscan <- req(input$cluster_selection_epsilon_hdbscan) + #on.exit({print("observe event calculate_clusters | update clusters_config -->")) + }) + + + # Observe the events related to zoom the projections graph + observeEvent(input$zoom_btn, { + print("--> observeEvent zoom_btn") + brush <- input$projections_brush + if (!is.null(brush)) { + if(isTRUE(input$zoom_btn)){ + ranges$x <- c(brush$xmin, brush$xmax) + ranges$y <- c(brush$ymin, brush$ymax) + }else { + ranges$x <- NULL + ranges$y <- NULL + } + + } else { + ranges$x <- NULL + ranges$y <- NULL + } + }) + + + # Observe the events related to change the appearance of the projections graph + observeEvent(input$update_prj_graph,{ + style_values <- list(path_line_size = input$path_line_size , + path_alpha = input$path_alpha, + point_alpha = input$point_alpha, + point_size = input$point_size) - # Update clustering_options reactive value when the input changes - observe({ - print("--> Observe clustering options") - clustering_options$selected <- req(input$clustering_options) - print("Observe clustering options -->") + if (!is.null(style_values)) { + config_style$path_line_size <- style_values$path_line_size + config_style$path_alpha <- style_values$path_alpha + config_style$point_alpha <- style_values$point_alpha + config_style$point_size <- style_values$point_size + } else { + config_style$path_line_size <- NULL + config_style$path_alpha <- NULL + config_style$point_alpha <- NULL + config_style$point_size <- NULL + } + }) + + + # Update ts_variables reactive value when time series variable selection changes + observeEvent(input$select_variables, { + ts_variables$selected <- input$select_variables + }) + + + # Observe to check/uncheck all variables + observeEvent(input$selectall,{ + req(tsdf) + ts_variables$selected <- names(isolate(tsdf())) + if(input$selectall %%2 == 0){ + updateCheckboxGroupInput(session = session, + inputId = "select_variables", + choices = ts_variables$selected, + selected = ts_variables$selected) + } else { + updateCheckboxGroupInput(session = session, + inputId = "select_variables", + choices = ts_variables$selected, + selected = NULL) + } + }) + # Observe to update encoder input (enc_input = X()) + observe({ #Event(input$dataset, input$encoder, input$wlen, input$stride, { + req(input$wlen != 0, input$stride != 0, input$stride != 1) + print(paste0("Check reactiveness | X | wlen, stride |")) + if ( + is.null(X()) || + !identical( + input$dataset, isolate(input$dataset)) || + !identical(input$encoder, isolate(input$encoder)) || + input$wlen != isolate(input$wlen) || + input$stride != isolate(input$stride) + ) { + print("--> ReactiveVal X | Update Sliding Window") + print(paste0("reactive X | wlen ", input$wlen, " | stride ", input$stride, " | Let's prepare data")) + print("reactive X | SWV") + + t_x_0 <- Sys.time() + + enc_input = dvats$exec_with_feather_k_output( + function_name = "prepare_forecasting_data", + module_name = "tsai.data.preparation", + path = file.path(DEFAULT_PATH_WANDB_ARTIFACTS, ts_ar()$metadata$TS$hash), + k_output = as.integer(0), + print_flag = TRUE, + time_flag = TRUE, + fcst_history = input$wlen + ) + + t_x_1 <- Sys.time() + t_sliding_window_view = t_x_1 - t_x_0 + print(paste0("reactive X | SWV: ", t_sliding_window_view, " secs ")) + + print(paste0("reactive X | Update sliding window | Apply stride ", input$stride," | enc_input ~ ", dim(enc_input), "-->")) + print("| Update | X" ) + on.exit({print("| Outside| X"); flush.console()}) + X(enc_input) + } + X() + }) + + ############### + # REACTIVES # + ############### + + # Get timeseries artifact metadata + ts_ar_config = reactive({ + print("--> reactive ts_ar_config | List used artifacts") + ts_ar = req(ts_ar()) + print(paste0("reactive ts_ar_config | List used artifacts | hash", ts_ar$hash)) + list_used_arts = ts_ar$metadata$TS + list_used_arts$vars = ts_ar$metadata$TS$vars %>% stringr::str_c(collapse = "; ") + list_used_arts$name = ts_ar$name + list_used_arts$aliases = ts_ar$aliases + list_used_arts$artifact_name = ts_ar$name + list_used_arts$id = ts_ar$id + list_used_arts$created_at = ts_ar$created_at + list_used_arts + on.exit({print("reactive ts_ar_config -->"); flush.console()}) + }) + + # Get encoder artifact + enc_ar <- eventReactive ( + input$encoder, + { + print(paste0("eventReactive enc_ar | Enc. Artifact: ", input$encoder)) + result <- tryCatch({ + api$artifact(input$encoder, type = 'learner') + }, error = function(e){ + print(paste0("eventReactive enc_ar | Error: ", e$message)) + NULL + }) + on.exit({print("envent reactive enc_ar -->"); flush.console()}) + result + }, + ignoreInit = T + ) + + # Encoder + enc <- eventReactive( + enc_ar(), + { + req(input$dataset, input$encoder) + print("--> eventReactive enc | load encoder ") + encoder_artifact <- enc_ar() + enc <- py_load_object( + file.path( + DEFAULT_PATH_WANDB_ARTIFACTS, + encoder_artifact$metadata$ref$hash + ) + ) + on.exit({print("eventReactive enc | load encoder -->"); flush.console()}) + enc }) + + + + embs <- reactive({ + req(X(), enc_l <- enc()) + print("--> reactive embs | get embeddings") + if (torch$cuda$is_available()){ + print(paste0("CUDA devices: ", torch$cuda$device_count())) + } else { + print("CUDA NOT AVAILABLE") + } + t_embs_0 <- Sys.time() + print( + paste0( + "reactive embs | get embeddings | Just about to get embedings. Device number: ", + torch$cuda$current_device() + ) + ) - # Update clusters_config reactive values when user clicks on "calculate_clusters" button - observeEvent(input$calculate_clusters, { - print("--> observe event calculate_clusters | update clusters_config") - clusters_config$metric_hdbscan <- req(input$metric_hdbscan) - clusters_config$min_cluster_size_hdbscan <- req(input$min_cluster_size_hdbscan) - clusters_config$min_samples_hdbscan <- req(input$min_samples_hdbscan) - clusters_config$cluster_selection_epsilon_hdbscan <- req(input$cluster_selection_epsilon_hdbscan) - #on.exit({print("observe event calculate_clusters | update clusters_config -->")) - }) + print("reactive embs | get embeddings | Get batch size and dataset") + dataset_logged_by <- enc_ar()$logged_by() + bs = dataset_logged_by$config$batch_size + stride = input$stride - # Observe the events related to zoom the projections graph - observeEvent(input$zoom_btn, { - - print("--> observeEvent zoom_btn") - brush <- input$projections_brush - if (!is.null(brush)) { - if(isTRUE(input$zoom_btn)){ - ranges$x <- c(brush$xmin, brush$xmax) - ranges$y <- c(brush$ymin, brush$ymax) - }else { - ranges$x <- NULL - ranges$y <- NULL - } - - } else { - ranges$x <- NULL - ranges$y <- NULL - } - }) + print(paste0("reactive embs | get embeddings (set stride set batch size) | Stride ", input$stride, " | batch size: ", bs )) + enc_input = X() + #chunk_max = 10000000 + #shape <- dim(enc_input) + #print(paste0("reactive embs | get embeddings (set stride set batch size) | enc_input shape: ", shape )) + #chunk_size_ = min(shape[1]*shape[2],chunk_max/(shape[1]*shape[2])) + #N = max(3200,floor(chunk_size_/32)) + chunk_size = 10000000 #N*32 + #print(paste0("reactive embs | get embeddings (set stride set batch size) | Chunk_size ", chunk_size, " | shape[1]*shape[2]: ", shape[1]*shape[2] )) + print(paste0("reactive embs | get embeddings (set stride set batch size) | Chunk_size ", chunk_size)) + # python_string = paste0(" + #import dvats.all + cpu_flag = ifelse(input$cpu_flag == "CPU", TRUE, FALSE) + result = dvats$get_enc_embs_set_stride_set_batch_size( + X = X(), + print_flag = TRUE, + enc_learn = enc_l, + stride = input$stride, + batch_size = bs, + cpu = cpu_flag, + print_flag = FALSE, + time_flag = TRUE, + chunk_size = chunk_size, + check_memory_usage = TRUE + ) + #result <- system(python_string) + #------------------------------------------------ + num_columns <- ncol(result) + print(paste("--------------------------ncol2 ", num_columns)) + #------------------------------------------------------------------- + t_embs_1 <- Sys.time() + diff <- t_embs_1 - t_embs_0 + diff_secs <- as.numeric(diff, units = "secs") + diff_mins <- as.numeric(diff, units = "mins") + print(paste0("get_enc_embs total time: ", diff_secs, " secs thus ", diff_mins, " mins")) + X <- NULL + gc(verbose=TRUE) + on.exit({print("reactive embs | get embeddings -->"); flush.console()}) + result + }) + + prj_object_cpu <- reactive({ + embs = req(embs(), input$dr_method) + embs = embs[complete.cases(embs),] + print("--> prj_object") + #print(embs) #-- + #print(paste0("--> prj_object | UMAP params ", str(umap_params_))) + print("--> prj_object | UMAP params ") - # Observe the events related to change the appearance of the projections graph - observeEvent(input$update_prj_graph,{ - style_values <- list(path_line_size = input$path_line_size , - path_alpha = input$path_alpha, - point_alpha = input$point_alpha, - point_size = input$point_size) - - if (!is.null(style_values)) { - config_style$path_line_size <- style_values$path_line_size - config_style$path_alpha <- style_values$path_alpha - config_style$point_alpha <- style_values$point_alpha - config_style$point_size <- style_values$point_size - } else { - config_style$path_line_size <- NULL - config_style$path_alpha <- NULL - config_style$point_alpha <- NULL - config_style$point_size <- NULL - } - }) + res = switch( input$dr_method, + #### Comprobando parametros para saber por qué salen diferentes los embeddings + ######### Comprobando los parámetros + #UMAP = dvats$get_UMAP_prjs(input_data = embs, cpu=F, n_neighbors = 15, min_dist = 0.1, random_state=as.integer(1234)), + UMAP = dvats$get_UMAP_prjs( + input_data = embs, + cpu = TRUE, + print_flag = TRUE, + n_neighbors = input$prj_n_neighbors, + min_dist = input$prj_min_dist, + random_state= as.integer(input$prj_random_state), + n_components = as.integer(3) + ), + TSNE = dvats$get_TSNE_prjs( + X = embs, + cpu = TRUE, + random_state=as.integer(input$prj_random_state) + ), + PCA = dvats$get_PCA_prjs( + X = embs, + cpu = TRUE, + random_state=as.integer(input$prj_random_state) + ) + ) + res = res %>% as.data.frame # TODO: This should be a matrix for improved efficiency + colnames(res) = c("xcoord", "ycoord", "zcoord") + on.exit({print(" prj_object -->"); flush.console()}) + flush.console() + #browser() + res + }) + + prj_object <- reactive({ + req(embs(), input$dr_method) + print("--> prj_object") + t_prj_0 = Sys.time() + embs = req(embs()) + print("prj_object | Before complete cases ") + embs = embs[complete.cases(embs),] + #print(embs) #-- + #print(paste0("--> prj_object | UMAP params ", str(umap_params_))) + print("prj_object | Before switch ") + cpu_flag = ifelse(input$cpu_flag == "CPU", TRUE, FALSE) - # Update ts_variables reactive value when time series variable selection changes - observeEvent(input$select_variables, { - ts_variables$selected <- input$select_variables + res = switch( input$dr_method, + #### Comprobando parametros para saber por qué salen diferentes los embeddings + ######### Comprobando los parámetros + #UMAP = dvats$get_UMAP_prjs(input_data = embs, cpu=F, n_neighbors = 15, min_dist = 0.1, random_state=as.integer(1234)), + UMAP = dvats$get_UMAP_prjs( + input_data = embs, + cpu = cpu_flag, + print_flag = TRUE, + n_neighbors = input$prj_n_neighbors, + min_dist = input$prj_min_dist, + random_state= as.integer(input$prj_random_state), + n_components = as.integer(3) + ), + TSNE = dvats$get_TSNE_prjs( + X = embs, + cpu=FALSE, + random_state=as.integer(input$prj_random_state) + ), + PCA = dvats$get_PCA_prjs( + X = embs, + cpu=FALSE, + random_state=as.integer(input$prj_random_state) + ) + ) + #----------------------------------------------------------------------------------- + #print(paste("---------------------ncol", ncol(res))) + #print(res) + #----------------------------------------------------------------------------------- + res = res %>% as.data.frame # TODO: This should be a matrix for improved efficiency + colnames(res) = c("xcoord", "ycoord", "zcoord") + t_prj_1 = Sys.time() + on.exit({print(paste0(" prj_object | ", t_prj_1-t_prj_0, " seconds -->")); flush.console()}) + flush.console() + res + }) + + + + # Load and filter TimeSeries object from wandb + tsdf <- reactive( + { + req(input$encoder, ts_ar()) + ts_ar <- req(ts_ar()) + print(paste0("--> Reactive tsdf | ts artifact ", ts_ar)) + flush.console() + + t_init <- Sys.time() + path = file.path(DEFAULT_PATH_WANDB_ARTIFACTS, ts_ar$metadata$TS$hash) + print(paste0("Reactive tsdf | Read feather ", path )) + flush.console() + df <- read_feather(path, as_data_frame = TRUE, mmap = TRUE) %>% rename('timeindex' = `__index_level_0__`) + t_end = Sys.time() + print(paste0("Reactive tsdf | Read feather | Execution time: ", t_end - t_init, " seconds")) + flush.console() + + t_end = Sys.time() + on.exit({print(paste0("Reactive tsdf | Column to index | Execution time: ", t_end - t_init, " seconds"));flush.console()}) + df }) + + # Auxiliary object for the interaction ts->projections + tsidxs_per_embedding_idx <- reactive({ + req(input$wlen != 0, input$stride != 0) + get_window_indices(1:nrow(isolate(projections())), w = input$wlen, s = input$stride) + }) + + # Filter the embedding points and calculate/show the clusters if conditions are met. + projections <- reactive({ + print("--> Projections") + req(prj_object(), input$dr_method) + #prjs <- req(prj_object()) %>% slice(input$points_emb[[1]]:input$points_emb[[2]]) + print("Projections | before prjs") + prjs <- prj_object() + req(input$dataset, input$encoder, input$wlen, input$stride) + print("Projections | before switch") + #------------------------------------------ + num_columns <- ncol(prjs) + print(paste("----------------", num_columns)) + #------------------------------------------ + switch(clustering_options$selected, + precomputed_clusters = { + filename <- req(selected_clusters_labels_ar())$metadata$ref$hash + clusters_labels <- py_load_object(filename = file.path(DEFAULT_PATH_WANDB_ARTIFACTS, filename)) + #prjs$cluster <- clusters_labels[input$points_emb[[1]]:input$points_emb[[2]]] + prjs$cluster <- clusters_labels + }, + calculate_clusters = { + clusters = hdbscan$HDBSCAN( + min_cluster_size = as.integer(clusters_config$min_cluster_size_hdbscan), + min_samples = as.integer(clusters_config$min_samples_hdbscan), + cluster_selection_epsilon = clusters_config$cluster_selection_epsilon_hdbscan, + metric = clusters_config$metric_hdbscan + )$fit(prjs) + score = 0 + unique_labels <- unique(clusters$labels_) + total_unique_labels <- length(unique_labels) + if(total_unique_labels > 1){ + score = dvats$cluster_score(prjs, clusters$labels_, TRUE) + } + print(paste0("Projections | Score ", score)) + if (score <= 0) { + print(paste0("Projections | Repeat projections with CPU because of low quality clusters | score ", score)) + prjs <- prj_object_cpu() + clusters = hdbscan$HDBSCAN( + min_cluster_size = as.integer(clusters_config$min_cluster_size_hdbscan), + min_samples = as.integer(clusters_config$min_samples_hdbscan), + cluster_selection_epsilon = clusters_config$cluster_selection_epsilon_hdbscan, + metric = clusters_config$metric_hdbscan + )$fit(prjs) + score = 0 + unique_labels <- unique(clusters$labels_) + total_unique_labels <- length(unique_labels) + if(total_unique_labels > 1){ + score = dvats$cluster_score(prjs, clusters$labels_, TRUE) + } + print(paste0("Projections | Repeat projections with CPU because of low quality clusters | score ", score)) + } + prjs$cluster <- clusters$labels_ + + }) + on.exit({print("Projections -->"); flush.console()}) + prjs + }) + + # Update the colour palette for the clusters + update_palette <- reactive({ + prjs <- req(projections()) + if ("cluster" %in% names(prjs)) { + unique_labels <- unique(prjs$cluster) + print(unique_labels) + ## IF the value "-1" exists, assign the first element of mycolors to #000000, if not, assign the normal colorRampPalette + if (as.integer(-1) %in% unique_labels) + colour_palette <- append("#000000", colorRampPalette(brewer.pal(12,"Paired"))(length(unique_labels)-1)) + else + colour_palette <- colorRampPalette(brewer.pal(12,"Paired"))(length(unique_labels)) + } + else + colour_palette <- "red" - # Observe to check/uncheck all variables - observeEvent(input$selectall,{ - req(tsdf) - ts_variables$selected <- names(isolate(tsdf())) - if(input$selectall %%2 == 0){ - updateCheckboxGroupInput(session = session, - inputId = "select_variables", - choices = ts_variables$selected, - selected = ts_variables$selected) - } else { - updateCheckboxGroupInput(session = session, - inputId = "select_variables", - choices = ts_variables$selected, - selected = NULL) - } - }) - # Observe to update encoder input (enc_input = X()) - observe({ #Event(input$dataset, input$encoder, input$wlen, input$stride, { - req(input$wlen != 0, input$stride != 0, input$stride != 1) - print(paste0("Check reactiveness | X | wlen, stride |")) - if ( - is.null(X()) || - !identical( - input$dataset, isolate(input$dataset)) || - !identical(input$encoder, isolate(input$encoder)) || - input$wlen != isolate(input$wlen) || - input$stride != isolate(input$stride) - ) { - print("--> ReactiveVal X | Update Sliding Window") - print(paste0("reactive X | wlen ", input$wlen, " | stride ", input$stride, " | Let's prepare data")) - print("reactive X | SWV") - - t_x_0 <- Sys.time() - - enc_input = dvats$exec_with_feather_k_output( - function_name = "prepare_forecasting_data", - module_name = "tsai.data.preparation", - path = file.path(DEFAULT_PATH_WANDB_ARTIFACTS, ts_ar()$metadata$TS$hash), - k_output = as.integer(0), - print_flag = TRUE, - time_flag = TRUE, - fcst_history = input$wlen - ) - - t_x_1 <- Sys.time() - t_sliding_window_view = t_x_1 - t_x_0 - print(paste0("reactive X | SWV: ", t_sliding_window_view, " secs ")) - - print(paste0("reactive X | Update sliding window | Apply stride ", input$stride," | enc_input ~ ", dim(enc_input), "-->")) - print("| Update | X" ) - on.exit({print("| Outside| X"); flush.console()}) - X(enc_input) - } - X() - }) - - ############### - # REACTIVES # - ############### + colour_palette + }) + + color_palete_window_plot <- colorRampPalette( + colors = c("blue", "green"), + space = "Lab" # Option used when colors do not represent a quantitative scale + ) + + start_date <- reactive({ + isolate(tsdf())$timeindex[1] + }) + + end_date <- reactive({ + end_date_id = 100000 + end_date_id = min(end_date_id, nrow(isolate(tsdf()))) + isolate(tsdf())$timeindex[end_date_id] + }) + + ts_plot_base <- reactive({ + print("--> ts_plot_base") + on.exit({print("ts_plot_base -->"); flush.console()}) + start_date =isolate(start_date()) + end_date = isolate(end_date()) + print(paste0("ts_plot_base | start_date: ", start_date, " end_date: ", end_date)) + t_init <- Sys.time() + tsdf_ <- isolate(tsdf()) %>% select(ts_variables$selected, - "timeindex") + tsdf_xts <- xts(tsdf_, order.by = tsdf()$timeindex) + t_end <- Sys.time() + print(paste0("ts_plot_base | tsdf_xts time", t_end-t_init)) + print(head(tsdf_xts)) + print(tail(tsdf_xts)) + ts_plt = dygraph( + tsdf_xts, + width="100%", height = "400px" + ) %>% + dyRangeSelector(c(start_date, end_date)) %>% + dyHighlight(hideOnMouseOut = TRUE) %>% + dyOptions(labelsUTC = FALSE ) %>% + dyCrosshair(direction = "vertical")%>% + dyLegend(show = "follow", hideOnMouseOut = TRUE) %>% + dyUnzoom() %>% + dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>% + dyCSS( + textConnection( + ".dygraph-legend > span { display: none; } + .dygraph-legend > span.highlight { display: inline; }" + ) + ) - # Get timeseries artifact metadata - ts_ar_config = reactive({ - print("--> reactive ts_ar_config | List used artifacts") - ts_ar = req(ts_ar()) - print(paste0("reactive ts_ar_config | List used artifacts | hash", ts_ar$hash)) - list_used_arts = ts_ar$metadata$TS - list_used_arts$vars = ts_ar$metadata$TS$vars %>% stringr::str_c(collapse = "; ") - list_used_arts$name = ts_ar$name - list_used_arts$aliases = ts_ar$aliases - list_used_arts$artifact_name = ts_ar$name - list_used_arts$id = ts_ar$id - list_used_arts$created_at = ts_ar$created_at - list_used_arts - on.exit({print("reactive ts_ar_config -->"); flush.console()}) - }) - - # Get encoder artifact - enc_ar <- eventReactive ( - input$encoder, - { - print(paste0("eventReactive enc_ar | Enc. Artifact: ", input$encoder)) - result <- tryCatch({ - api$artifact(input$encoder, type = 'learner') - }, error = function(e){ - print(paste0("eventReactive enc_ar | Error: ", e$message)) - NULL - }) - on.exit({print("envent reactive enc_ar -->"); flush.console()}) - result - }, - ignoreInit = T - ) - - # Encoder - enc <- eventReactive( - enc_ar(), - { - req(input$dataset, input$encoder) - print("--> eventReactive enc | load encoder ") - encoder_artifact <- enc_ar() - enc <- py_load_object( - file.path( - DEFAULT_PATH_WANDB_ARTIFACTS, - encoder_artifact$metadata$ref$hash - ) + }) + + embedding_ids <- reactive({ + print("--> embedding idx") + on.exit(print("embedding idx -->")) + bp = brushedPoints(prj_object(), input$projections_brush, allRows = TRUE) #%>% debounce(miliseconds) #Wait 1 seconds: 1000 + bp %>% rownames_to_column("index") %>% dplyr::filter(selected_ == TRUE) %>% pull(index) %>% as.integer + }) + + window_list <- reactive({ + print("--> window_list") + on.exit(print("window_list -->")) + # Get the window indices + req(length(embedding_ids() > 0)) + embedding_idxs = embedding_ids() + window_indices = get_window_indices(embedding_idxs, input$wlen, input$stride) + # Put all the indices in one list and remove duplicates + unlist_window_indices = unique(unlist(window_indices)) + # Calculate a vector of differences to detect idx where a new window should be created + diff_vector <- diff(unlist_window_indices,1) + # Take indexes where the difference is greater than one (that represent a change of window) + idx_window_limits <- which(diff_vector!=1) + # Include the first and last index to have a whole set of indexes. + idx_window_limits <- c(1, idx_window_limits, length(unlist_window_indices)) + # Create a reduced window list + reduced_window_list <- vector(mode = "list", length = length(idx_window_limits)-1) + # Populate the first element of the list with the idx of the first window. + reduced_window_list[[1]] = c( + isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[1]+1]], + isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[2]]] + ) + if (length(idx_window_limits) > 2) { + # Populate the rest of the list + for (i in 2:(length(idx_window_limits)-1)){ + reduced_window_list[[i]]<- c( + #unlist_window_indices[idx_window_limits[i]+1], + #unlist_window_indices[idx_window_limits[i+1]] + isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[i]+1]], + isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[i+1]]] ) - on.exit({print("eventReactive enc | load encoder -->"); flush.console()}) - enc - }) - + } + } + reduced_window_list + }) + + # Generate timeseries data for dygraph dygraph + ts_plot <- reactive({ + print("--> ts_plot | Before req 1") + on.exit({print("ts_plot -->"); flush.console()}) + req(tsdf(), ts_variables, input$wlen != 0, input$stride) - embs <- reactive({ - req(X(), enc_l <- enc()) - print("--> reactive embs | get embeddings") - if (torch$cuda$is_available()){ - print(paste0("CUDA devices: ", torch$cuda$device_count())) - } else { - print("CUDA NOT AVAILABLE") - } - t_embs_0 <- Sys.time() - print( - paste0( - "reactive embs | get embeddings | Just about to get embedings. Device number: ", - torch$cuda$current_device() - ) - ) - - print("reactive embs | get embeddings | Get batch size and dataset") - - dataset_logged_by <- enc_ar()$logged_by() - bs = dataset_logged_by$config$batch_size - stride = input$stride - - print(paste0("reactive embs | get embeddings (set stride set batch size) | Stride ", input$stride, " | batch size: ", bs )) - enc_input = X() - #chunk_max = 10000000 - #shape <- dim(enc_input) - #print(paste0("reactive embs | get embeddings (set stride set batch size) | enc_input shape: ", shape )) - #chunk_size_ = min(shape[1]*shape[2],chunk_max/(shape[1]*shape[2])) - #N = max(3200,floor(chunk_size_/32)) - chunk_size = 10000000 #N*32 - #print(paste0("reactive embs | get embeddings (set stride set batch size) | Chunk_size ", chunk_size, " | shape[1]*shape[2]: ", shape[1]*shape[2] )) - print(paste0("reactive embs | get embeddings (set stride set batch size) | Chunk_size ", chunk_size)) - # python_string = paste0(" - #import dvats.all - cpu_flag = ifelse(input$cpu_flag == "CPU", TRUE, FALSE) - result = dvats$get_enc_embs_set_stride_set_batch_size( - X = X(), - print_flag = TRUE, - enc_learn = enc_l, - stride = input$stride, - batch_size = bs, - cpu = cpu_flag, - print_flag = FALSE, - time_flag = TRUE, - chunk_size = chunk_size, - check_memory_usage = TRUE - ) - - #result <- system(python_string) - #------------------------------------------------ - num_columns <- ncol(result) - print(paste("--------------------------ncol2 ", num_columns)) - #------------------------------------------------------------------- - t_embs_1 <- Sys.time() - diff <- t_embs_1 - t_embs_0 - diff_secs <- as.numeric(diff, units = "secs") - diff_mins <- as.numeric(diff, units = "mins") - print(paste0("get_enc_embs total time: ", diff_secs, " secs thus ", diff_mins, " mins")) - X <- NULL - gc(verbose=TRUE) - on.exit({print("reactive embs | get embeddings -->"); flush.console()}) - result - }) - - prj_object_cpu <- reactive({ - embs = req(embs(), input$dr_method) - embs = embs[complete.cases(embs),] - print("--> prj_object") - #print(embs) #-- - #print(paste0("--> prj_object | UMAP params ", str(umap_params_))) - print("--> prj_object | UMAP params ") - - res = switch( input$dr_method, - #### Comprobando parametros para saber por qué salen diferentes los embeddings - ######### Comprobando los parámetros - #UMAP = dvats$get_UMAP_prjs(input_data = embs, cpu=F, n_neighbors = 15, min_dist = 0.1, random_state=as.integer(1234)), - UMAP = dvats$get_UMAP_prjs( - input_data = embs, - cpu = TRUE, - print_flag = TRUE, - n_neighbors = input$prj_n_neighbors, - min_dist = input$prj_min_dist, - random_state= as.integer(input$prj_random_state), - n_components = as.integer(3) - ), - TSNE = dvats$get_TSNE_prjs( - X = embs, - cpu = TRUE, - random_state=as.integer(input$prj_random_state) - ), - PCA = dvats$get_PCA_prjs( - X = embs, - cpu = TRUE, - random_state=as.integer(input$prj_random_state) - ) - ) - res = res %>% as.data.frame # TODO: This should be a matrix for improved efficiency - colnames(res) = c("xcoord", "ycoord", "zcoord") - on.exit({print(" prj_object -->"); flush.console()}) - flush.console() - #browser() - res - }) - - prj_object <- reactive({ - req(embs(), input$dr_method) - print("--> prj_object") - t_prj_0 = Sys.time() - embs = req(embs()) - print("prj_object | Before complete cases ") - embs = embs[complete.cases(embs),] - #print(embs) #-- - #print(paste0("--> prj_object | UMAP params ", str(umap_params_))) - print("prj_object | Before switch ") + ts_plt = ts_plot_base() + + print("ts_plot | bp") + #miliseconds <- ifelse(nrow(tsdf()) > 1000000, 2000, 1000) + + #if (!is.data.frame(bp)) {bp = bp_} + print("ts_plot | embedings idxs ") + embedding_idxs = embedding_ids() + # Calculate windows if conditions are met (if embedding_idxs is !=0, that means at least 1 point is selected) + print("ts_plot | Before if") + if ((length(embedding_idxs)!=0) & isTRUE(input$plot_windows)) { + reduced_window_list = req(window_list()) + print(paste0("ts_plot | reduced_window_list[1] = ", reduced_window_list[1])) + start_indices = min(sapply(reduced_window_list, function(x) x[1])) + end_indices = max(sapply(reduced_window_list, function(x) x[2])) + + view_size = end_indices-start_indices+1 + max_size = 10000 + + start_date = isolate(tsdf())$timeindex[start_indices] + end_date = isolate(tsdf())$timeindex[end_indices] + + print(paste0("ts_plot | reuced_window_list (", start_date, end_date, ")", "view size ", view_size, "max size ", max_size)) + + if (view_size > max_size) { + end_date = isolate(tsdf())$timeindex[start_indices + max_size - 1] + #range_color = "#FF0000" # Red + } + + range_color = "#CCEBD6" # Original + + + # # Plot the windows + count = 0 + for(ts_idxs in reduced_window_list) { + count = count + 1 + start_event_date = isolate(tsdf())$timeindex[head(ts_idxs, 1)] + end_event_date = isolate(tsdf())$timeindex[tail(ts_idxs, 1)] + ts_plt <- ts_plt %>% dyShading( + from = start_event_date, + to = end_event_date, + color = range_color + ) + ts_plt <- ts_plt %>% dyRangeSelector(c(start_date, end_date)) + #%>% dyEvent( + # start_event_date, + # label = paste0("SW-", count), + # labelLoc="bottom" , + # strokePattern = "solid", + # color = range_color + # ) %>% dyEvent( + # end_event_date, + # label = paste0("SW-",paste0("SW-", count), + # labelLoc="bottom", + # strokePattern = "solid"), + # color = range_color + # ) - cpu_flag = ifelse(input$cpu_flag == "CPU", TRUE, FALSE) - - res = switch( input$dr_method, - #### Comprobando parametros para saber por qué salen diferentes los embeddings - ######### Comprobando los parámetros - #UMAP = dvats$get_UMAP_prjs(input_data = embs, cpu=F, n_neighbors = 15, min_dist = 0.1, random_state=as.integer(1234)), - UMAP = dvats$get_UMAP_prjs( - input_data = embs, - cpu = cpu_flag, - print_flag = TRUE, - n_neighbors = input$prj_n_neighbors, - min_dist = input$prj_min_dist, - random_state= as.integer(input$prj_random_state), - n_components = as.integer(3) - ), - TSNE = dvats$get_TSNE_prjs( - X = embs, - cpu=FALSE, - random_state=as.integer(input$prj_random_state) - ), - PCA = dvats$get_PCA_prjs( - X = embs, - cpu=FALSE, - random_state=as.integer(input$prj_random_state) - ) - ) - #----------------------------------------------------------------------------------- - #print(paste("---------------------ncol", ncol(res))) - #print(res) - #----------------------------------------------------------------------------------- - res = res %>% as.data.frame # TODO: This should be a matrix for improved efficiency - colnames(res) = c("xcoord", "ycoord", "zcoord") - t_prj_1 = Sys.time() - on.exit({print(paste0(" prj_object | ", t_prj_1-t_prj_0, " seconds -->")); flush.console()}) - flush.console() - res - }) + } + + ts_plt <- ts_plt + # NOTE: This code block allows you to plot shadyng at once. + # The traditional method has to plot the dygraph n times + # (n being the number of rectangles to plot). With the adjacent + # code it is possible to plot the dygraph only once. Currently + # it does not work well because there are inconsistencies in the + # timezones of the time series and shiny (there is a two-hour shift[the current plot method works well]), + # which does not allow this method to be used correctly. If that + # were fixed in the future everything would work fine. + # num_rects <- length(reduced_window_list) + # rects_ini <- vector(mode = "list", length = num_rects) + # rects_fin <- vector(mode = "list", length = num_rects) + # for(i in 1:num_rects) { + # rects_ini[[i]] <- head(reduced_window_list[[i]],1) + # rects_fin[[i]] <- tail(reduced_window_list[[i]],1) + # } + # ts_plt <- vec_dyShading(ts_plt,rects_ini, rects_fin,"red", rownames(tsdf())) + } - + ts_plt + }) + + # Get projections plot name for saving + prjs_plot_name <- reactive({ + dataset_name <- basename(input$dataset) + encoder_name <- basename(input$encoder) + get_prjs_plot_name(dataset_name, encoder_name, clustering_options$selected, prjs_$cluster, prj_plot_id, input) + }) + + # Get timeserie plot name for saving + ts_plot_name <- reactive({ + dataset_name <- basename(input$dataset) + encoder_name <- basename(input$encoder) + get_ts_plot_name(dataset_name, encoder_name, prj_plot_id, input) + }) + + ############# + # OUTPUTS # + ############# + + output$windows_plot <- renderPlot({ + req(length(embedding_ids()) > 0) + reduced_window_list = req(window_list()) - # Load and filter TimeSeries object from wandb - tsdf <- reactive( - { - req(input$encoder, ts_ar()) - ts_ar <- req(ts_ar()) - print(paste0("--> Reactive tsdf | ts artifact ", ts_ar)) - flush.console() - - t_init <- Sys.time() - path = file.path(DEFAULT_PATH_WANDB_ARTIFACTS, ts_ar$metadata$TS$hash) - print(paste0("Reactive tsdf | Read feather ", path )) - flush.console() - df <- read_feather(path, as_data_frame = TRUE, mmap = TRUE) %>% rename('timeindex' = `__index_level_0__`) - t_end = Sys.time() - print(paste0("Reactive tsdf | Read feather | Execution time: ", t_end - t_init, " seconds")) - flush.console() - - t_end = Sys.time() - on.exit({print(paste0("Reactive tsdf | Column to index | Execution time: ", t_end - t_init, " seconds"));flush.console()}) - df - }) + # Convertir a fechas POSIXct + reduced_window_df <- do.call(rbind, lapply(reduced_window_list, function(x) { + data.frame( + start = as.POSIXct(isolate(tsdf())$timeindex[x[1]], origin = "1970-01-01"), + end = as.POSIXct(isolate(tsdf())$timeindex[x[2]], origin = "1970-01-01") + ) + })) - # Auxiliary object for the interaction ts->projections - tsidxs_per_embedding_idx <- reactive({ - req(input$wlen != 0, input$stride != 0) - get_window_indices(1:nrow(isolate(projections())), w = input$wlen, s = input$stride) - }) + # Establecer límites basados en los datos + first_date = min(reduced_window_df$start) + last_date = max(reduced_window_df$end) - # Filter the embedding points and calculate/show the clusters if conditions are met. - projections <- reactive({ - print("--> Projections") - req(prj_object(), input$dr_method) - #prjs <- req(prj_object()) %>% slice(input$points_emb[[1]]:input$points_emb[[2]]) - print("Projections | before prjs") - prjs <- prj_object() - req(input$dataset, input$encoder, input$wlen, input$stride) - print("Projections | before switch") - #------------------------------------------ - num_columns <- ncol(prjs) - print(paste("----------------", num_columns)) - #------------------------------------------ - switch(clustering_options$selected, - precomputed_clusters = { - filename <- req(selected_clusters_labels_ar())$metadata$ref$hash - clusters_labels <- py_load_object(filename = file.path(DEFAULT_PATH_WANDB_ARTIFACTS, filename)) - #prjs$cluster <- clusters_labels[input$points_emb[[1]]:input$points_emb[[2]]] - prjs$cluster <- clusters_labels - }, - calculate_clusters = { - clusters = hdbscan$HDBSCAN( - min_cluster_size = as.integer(clusters_config$min_cluster_size_hdbscan), - min_samples = as.integer(clusters_config$min_samples_hdbscan), - cluster_selection_epsilon = clusters_config$cluster_selection_epsilon_hdbscan, - metric = clusters_config$metric_hdbscan - )$fit(prjs) - score = 0 - unique_labels <- unique(clusters$labels_) - total_unique_labels <- length(unique_labels) - if(total_unique_labels > 1){ - score = dvats$cluster_score(prjs, clusters$labels_, TRUE) - } - print(paste0("Projections | Score ", score)) - if (score <= 0) { - print(paste0("Projections | Repeat projections with CPU because of low quality clusters | score ", score)) - prjs <- prj_object_cpu() - clusters = hdbscan$HDBSCAN( - min_cluster_size = as.integer(clusters_config$min_cluster_size_hdbscan), - min_samples = as.integer(clusters_config$min_samples_hdbscan), - cluster_selection_epsilon = clusters_config$cluster_selection_epsilon_hdbscan, - metric = clusters_config$metric_hdbscan - )$fit(prjs) - score = 0 - unique_labels <- unique(clusters$labels_) - total_unique_labels <- length(unique_labels) - if(total_unique_labels > 1){ - score = dvats$cluster_score(prjs, clusters$labels_, TRUE) - } - print(paste0("Projections | Repeat projections with CPU because of low quality clusters | score ", score)) - } - prjs$cluster <- clusters$labels_ - - }) - - on.exit({print("Projections -->"); flush.console()}) - prjs - }) + left = as.POSIXct(isolate(tsdf())$timeindex[1], origin = "1970-01-01") + right = as.POSIXct(isolate(tsdf())$timeindex[nrow(isolate(tsdf()))], origin = "1970-01-01") - # Update the colour palette for the clusters - update_palette <- reactive({ - prjs <- req(projections()) - if ("cluster" %in% names(prjs)) { - unique_labels <- unique(prjs$cluster) - print(unique_labels) - ## IF the value "-1" exists, assign the first element of mycolors to #000000, if not, assign the normal colorRampPalette - if (as.integer(-1) %in% unique_labels) - colour_palette <- append("#000000", colorRampPalette(brewer.pal(12,"Paired"))(length(unique_labels)-1)) - else - colour_palette <- colorRampPalette(brewer.pal(12,"Paired"))(length(unique_labels)) - } - else - colour_palette <- "red" - - colour_palette - }) + # Configuración del gráfico base + par(mar = c(5, 4, 4, 0) + 0.1) #Down Up Left Right + plt <- plot( + NA, + xlim = c(left, right), + ylim = c(0, 1), + type = "n", + xaxt = "n", yaxt = "n", + xlab = "", ylab = "", + bty = "n") + f = "%F %H:%M:%S" + axis(1, at = as.numeric(c(left, right)), labels = c(format(first_date, f), format(last_date, f)), cex.axis = 0.7) - color_palete_window_plot <- colorRampPalette( - colors = c("blue", "green"), - space = "Lab" # Option used when colors do not represent a quantitative scale + # Añadir líneas verticales + colors = color_palete_window_plot(2) + abline( + v = as.numeric(reduced_window_df$start), + col = rep(colors, length.out = nrow(reduced_window_df)), + lwd = 1 + ) + abline( + v = as.numeric(reduced_window_df$end), + col = rep(colors, length.out = nrow(reduced_window_df)), + lwd = 1 + ) + segments( + x0 = as.numeric(reduced_window_df$start), + x1 = as.numeric(reduced_window_df$end), + y0 = 0, + y1 = 0, + col = rep(colors, length.out = nrow(reduced_window_df)), + lwd = 1 + ) + text( + x = as.numeric(reduced_window_df$start), + y = 0, + srt = 90, + adj = c(1,0.5), + labels = paste0("SW-", seq_len(nrow(reduced_window_df)), format(reduced_window_df$start, f)), + cex = 1, + xpd = TRUE, + col = rep(colors, length.out = nrow(reduced_window_df)) ) - - start_date <- reactive({ - isolate(tsdf())$timeindex[1] - }) - - end_date <- reactive({ - end_date_id = 100000 - end_date_id = min(end_date_id, nrow(isolate(tsdf()))) - isolate(tsdf())$timeindex[end_date_id] - }) - - ts_plot_base <- reactive({ - print("--> ts_plot_base") - on.exit({print("ts_plot_base -->"); flush.console()}) - start_date =isolate(start_date()) - end_date = isolate(end_date()) - print(paste0("ts_plot_base | start_date: ", start_date, " end_date: ", end_date)) - t_init <- Sys.time() - tsdf_ <- isolate(tsdf()) %>% select(ts_variables$selected, - "timeindex") - tsdf_xts <- xts(tsdf_, order.by = tsdf()$timeindex) - t_end <- Sys.time() - print(paste0("ts_plot_base | tsdf_xts time", t_end-t_init)) - print(head(tsdf_xts)) - print(tail(tsdf_xts)) - ts_plt = dygraph( - tsdf_xts, - width="100%", height = "400px" - ) %>% - dyRangeSelector(c(start_date, end_date)) %>% - dyHighlight(hideOnMouseOut = TRUE) %>% - dyOptions(labelsUTC = FALSE ) %>% - dyCrosshair(direction = "vertical")%>% - dyLegend(show = "follow", hideOnMouseOut = TRUE) %>% - dyUnzoom() %>% - dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>% - dyCSS( - textConnection( - ".dygraph-legend > span { display: none; } - .dygraph-legend > span.highlight { display: inline; }" - ) - ) - - }) - - embedding_ids <- reactive({ - print("--> embedding idx") - on.exit(print("embedding idx -->")) - bp = brushedPoints(prj_object(), input$projections_brush, allRows = TRUE) #%>% debounce(miliseconds) #Wait 1 seconds: 1000 - bp %>% rownames_to_column("index") %>% dplyr::filter(selected_ == TRUE) %>% pull(index) %>% as.integer - }) - - window_list <- reactive({ - print("--> window_list") - on.exit(print("window_list -->")) - # Get the window indices - req(length(embedding_ids() > 0)) - embedding_idxs = embedding_ids() - window_indices = get_window_indices(embedding_idxs, input$wlen, input$stride) - # Put all the indices in one list and remove duplicates - unlist_window_indices = unique(unlist(window_indices)) - # Calculate a vector of differences to detect idx where a new window should be created - diff_vector <- diff(unlist_window_indices,1) - # Take indexes where the difference is greater than one (that represent a change of window) - idx_window_limits <- which(diff_vector!=1) - # Include the first and last index to have a whole set of indexes. - idx_window_limits <- c(1, idx_window_limits, length(unlist_window_indices)) - # Create a reduced window list - reduced_window_list <- vector(mode = "list", length = length(idx_window_limits)-1) - # Populate the first element of the list with the idx of the first window. - reduced_window_list[[1]] = c( - isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[1]+1]], - isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[2]]] - ) - if (length(idx_window_limits) > 2) { - # Populate the rest of the list - for (i in 2:(length(idx_window_limits)-1)){ - reduced_window_list[[i]]<- c( - #unlist_window_indices[idx_window_limits[i]+1], - #unlist_window_indices[idx_window_limits[i+1]] - isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[i]+1]], - isolate(tsdf())$timeindex[unlist_window_indices[idx_window_limits[i+1]]] - ) - } - } - reduced_window_list - }) - - # Generate timeseries data for dygraph dygraph - ts_plot <- reactive({ - print("--> ts_plot | Before req 1") - on.exit({print("ts_plot -->"); flush.console()}) - - req(tsdf(), ts_variables, input$wlen != 0, input$stride) - - ts_plt = ts_plot_base() - - print("ts_plot | bp") - #miliseconds <- ifelse(nrow(tsdf()) > 1000000, 2000, 1000) - - #if (!is.data.frame(bp)) {bp = bp_} - print("ts_plot | embedings idxs ") - embedding_idxs = embedding_ids() - # Calculate windows if conditions are met (if embedding_idxs is !=0, that means at least 1 point is selected) - print("ts_plot | Before if") - if ((length(embedding_idxs)!=0) & isTRUE(input$plot_windows)) { - reduced_window_list = req(window_list()) - print(paste0("ts_plot | reduced_window_list[1] = ", reduced_window_list[1])) - start_indices = min(sapply(reduced_window_list, function(x) x[1])) - end_indices = max(sapply(reduced_window_list, function(x) x[2])) - - view_size = end_indices-start_indices+1 - max_size = 10000 - - start_date = isolate(tsdf())$timeindex[start_indices] - end_date = isolate(tsdf())$timeindex[end_indices] - - print(paste0("ts_plot | reuced_window_list (", start_date, end_date, ")", "view size ", view_size, "max size ", max_size)) - - if (view_size > max_size) { - end_date = isolate(tsdf())$timeindex[start_indices + max_size - 1] - #range_color = "#FF0000" # Red - } - - range_color = "#CCEBD6" # Original - - - # # Plot the windows - count = 0 - for(ts_idxs in reduced_window_list) { - count = count + 1 - start_event_date = isolate(tsdf())$timeindex[head(ts_idxs, 1)] - end_event_date = isolate(tsdf())$timeindex[tail(ts_idxs, 1)] - ts_plt <- ts_plt %>% dyShading( - from = start_event_date, - to = end_event_date, - color = range_color - ) - ts_plt <- ts_plt %>% dyRangeSelector(c(start_date, end_date)) - #%>% dyEvent( - # start_event_date, - # label = paste0("SW-", count), - # labelLoc="bottom" , - # strokePattern = "solid", - # color = range_color - # ) %>% dyEvent( - # end_event_date, - # label = paste0("SW-",paste0("SW-", count), - # labelLoc="bottom", - # strokePattern = "solid"), - # color = range_color - # ) - - } - - ts_plt <- ts_plt - # NOTE: This code block allows you to plot shadyng at once. - # The traditional method has to plot the dygraph n times - # (n being the number of rectangles to plot). With the adjacent - # code it is possible to plot the dygraph only once. Currently - # it does not work well because there are inconsistencies in the - # timezones of the time series and shiny (there is a two-hour shift[the current plot method works well]), - # which does not allow this method to be used correctly. If that - # were fixed in the future everything would work fine. - # num_rects <- length(reduced_window_list) - # rects_ini <- vector(mode = "list", length = num_rects) - # rects_fin <- vector(mode = "list", length = num_rects) - # for(i in 1:num_rects) { - # rects_ini[[i]] <- head(reduced_window_list[[i]],1) - # rects_fin[[i]] <- tail(reduced_window_list[[i]],1) - # } - # ts_plt <- vec_dyShading(ts_plt,rects_ini, rects_fin,"red", rownames(tsdf())) - } - - ts_plt - }) - - # Get projections plot name for saving - prjs_plot_name <- reactive({ - dataset_name <- basename(input$dataset) - encoder_name <- basename(input$encoder) - get_prjs_plot_name(dataset_name, encoder_name, clustering_options$selected, prjs_$cluster, prj_plot_id, input) - }) - # Get timeserie plot name for saving - ts_plot_name <- reactive({ - dataset_name <- basename(input$dataset) - encoder_name <- basename(input$encoder) - get_ts_plot_name(dataset_name, encoder_name, prj_plot_id, input) - }) - - ############# - # OUTPUTS # - ############# - - output$windows_plot <- renderPlot({ - req(length(embedding_ids()) > 0) - reduced_window_list = req(window_list()) - - # Convertir a fechas POSIXct - reduced_window_df <- do.call(rbind, lapply(reduced_window_list, function(x) { - data.frame( - start = as.POSIXct(isolate(tsdf())$timeindex[x[1]], origin = "1970-01-01"), - end = as.POSIXct(isolate(tsdf())$timeindex[x[2]], origin = "1970-01-01") - ) - })) - - # Establecer límites basados en los datos - first_date = min(reduced_window_df$start) - last_date = max(reduced_window_df$end) + points(x = as.numeric(left),y = 0, col = "black", pch = 20, cex = 1) + points(x = as.numeric(right),y = 0, col = "black", pch = 20, cex = 1) + plt + }, + height=200 + ) + + output$windows_text <- renderUI({ + req(length(embedding_ids()) > 0) + reduced_window_list = req(window_list()) - left = as.POSIXct(isolate(tsdf())$timeindex[1], origin = "1970-01-01") - right = as.POSIXct(isolate(tsdf())$timeindex[nrow(isolate(tsdf()))], origin = "1970-01-01") - - # Configuración del gráfico base - par(mar = c(5, 4, 4, 0) + 0.1) #Down Up Left Right - plt <- plot( - NA, - xlim = c(left, right), - ylim = c(0, 1), - type = "n", - xaxt = "n", yaxt = "n", - xlab = "", ylab = "", - bty = "n") - f = "%F %H:%M:%S" - axis(1, at = as.numeric(c(left, right)), labels = c(format(first_date, f), format(last_date, f)), cex.axis = 0.7) - - # Añadir líneas verticales - colors = color_palete_window_plot(2) - abline( - v = as.numeric(reduced_window_df$start), - col = rep(colors, length.out = nrow(reduced_window_df)), - lwd = 1 - ) - abline( - v = as.numeric(reduced_window_df$end), - col = rep(colors, length.out = nrow(reduced_window_df)), - lwd = 1 - ) - segments( - x0 = as.numeric(reduced_window_df$start), - x1 = as.numeric(reduced_window_df$end), - y0 = 0, - y1 = 0, - col = rep(colors, length.out = nrow(reduced_window_df)), - lwd = 1 - ) - text( - x = as.numeric(reduced_window_df$start), - y = 0, - srt = 90, - adj = c(1,0.5), - labels = paste0("SW-", seq_len(nrow(reduced_window_df)), format(reduced_window_df$start, f)), - cex = 1, - xpd = TRUE, - col = rep(colors, length.out = nrow(reduced_window_df)) - ) - - points(x = as.numeric(left),y = 0, col = "black", pch = 20, cex = 1) - points(x = as.numeric(right),y = 0, col = "black", pch = 20, cex = 1) - plt - }, - height=200 - ) - - output$windows_text <- renderUI({ - req(length(embedding_ids()) > 0) - reduced_window_list = req(window_list()) - - # Crear un conjunto de etiquetas de texto con información de las ventanas - window_info <- lapply(1:length(reduced_window_list), function(i) { - window <- reduced_window_list[[i]] - start <- format(as.POSIXct(isolate(tsdf())$timeindex[window[1]], origin = "1970-01-01"), "%b %d") - end <- format(as.POSIXct(isolate(tsdf())$timeindex[window[2]], origin = "1970-01-01"), "%b %d") - color <- ifelse(i %% 2 == 0, "green", "blue") - HTML(paste0("
Window ", i, ": ", start, " - ", end, "
")) - }) - - # Devuelve todos los elementos de texto como una lista de HTML - do.call(tagList, window_info) + # Crear un conjunto de etiquetas de texto con información de las ventanas + window_info <- lapply(1:length(reduced_window_list), function(i) { + window <- reduced_window_list[[i]] + start <- format(as.POSIXct(isolate(tsdf())$timeindex[window[1]], origin = "1970-01-01"), "%b %d") + end <- format(as.POSIXct(isolate(tsdf())$timeindex[window[2]], origin = "1970-01-01"), "%b %d") + color <- ifelse(i %% 2 == 0, "green", "blue") + HTML(paste0("
Window ", i, ": ", start, " - ", end, "
")) }) - # Generate encoder info table - output$enc_info = renderDataTable({ - selected_encoder_name <- req(input$encoder) - on.exit({print("Encoder artiffact -->"); flush.console()}) - print(paste0("--> Encoder artiffact", selected_encoder_name)) - selected_encoder <- encs_l[[selected_encoder_name]] - encoder_metadata <- req(selected_encoder$metadata) - print(paste0("Encoder artiffact | encoder metadata ", selected_encoder_name)) - encoder_metadata %>%enframe() - }) + # Devuelve todos los elementos de texto como una lista de HTML + do.call(tagList, window_info) + }) + + # Generate encoder info table + output$enc_info = renderDataTable({ + selected_encoder_name <- req(input$encoder) + on.exit({print("Encoder artiffact -->"); flush.console()}) + print(paste0("--> Encoder artiffact", selected_encoder_name)) + selected_encoder <- encs_l[[selected_encoder_name]] + encoder_metadata <- req(selected_encoder$metadata) + print(paste0("Encoder artiffact | encoder metadata ", selected_encoder_name)) + encoder_metadata %>%enframe() + }) + + # Generate time series info table + output$ts_ar_info = renderDataTable({ + ts_ar_config() %>% enframe() + }) + + + + + + # Generate projections plot + output$projections_plot <- renderPlot({ + req(input$dataset, input$encoder, input$wlen != 0, input$stride != 0) + print("--> Projections_plot") + prjs_ <- req(projections()) + print("projections_plot | Prepare column highlights") + # Prepare the column highlight to color data + if (!is.null(input$ts_plot_dygraph_click)) { + selected_ts_idx = which(ts_plot()$x$data[[1]] == input$ts_plot_dygraph_click$x_closest_point) + projections_idxs = tsidxs_per_embedding_idx() %>% map_lgl(~ selected_ts_idx %in% .) + prjs_$highlight = projections_idxs + } else { + prjs_$highlight = FALSE + } + # Prepare the column highlight to color data. If input$generate_cluster has not been clicked + # the column cluster will not exist in the dataframe, so we create with the value FALSE + if(!("cluster" %in% names(prjs_))) + prjs_$cluster = FALSE + print("projections_plot | GoGo Plot!") + plt <- ggplot(data = prjs_) + + aes(x = xcoord, y = ycoord, fill = highlight, color = as.factor(cluster)) + + scale_colour_manual(name = "clusters", values = req(update_palette())) + + geom_point(shape = 21,alpha = config_style$point_alpha, size = config_style$point_size) + + scale_shape(solid = FALSE) + + #geom_path(size=config_style$path_line_size, colour = "#2F3B65",alpha = config_style$path_alpha) + + guides() + + scale_fill_manual(values = c("TRUE" = "green", "FALSE" = "NA"))+ + coord_cartesian(xlim = ranges$x, ylim = ranges$y, expand = TRUE)+ + theme_void() + + theme(legend.position = "none") - # Generate time series info table - output$ts_ar_info = renderDataTable({ - ts_ar_config() %>% enframe() - }) - - - + if (input$show_lines){ + #plt <- plt + geom_path(size=config_style$path_line_size, colour = "#2F3B65",alpha = config_style$path_alpha) + plt <- plt + geom_path(linewidth=config_style$path_line_size, colour = "#2F3B65",alpha = config_style$path_alpha) + } - - # Generate projections plot - output$projections_plot <- renderPlot({ - req(input$dataset, input$encoder, input$wlen != 0, input$stride != 0) - print("--> Projections_plot") - prjs_ <- req(projections()) - print("projections_plot | Prepare column highlights") - # Prepare the column highlight to color data - if (!is.null(input$ts_plot_dygraph_click)) { - selected_ts_idx = which(ts_plot()$x$data[[1]] == input$ts_plot_dygraph_click$x_closest_point) - projections_idxs = tsidxs_per_embedding_idx() %>% map_lgl(~ selected_ts_idx %in% .) - prjs_$highlight = projections_idxs - } else { - prjs_$highlight = FALSE - } - # Prepare the column highlight to color data. If input$generate_cluster has not been clicked - # the column cluster will not exist in the dataframe, so we create with the value FALSE - if(!("cluster" %in% names(prjs_))) - prjs_$cluster = FALSE - print("projections_plot | GoGo Plot!") - plt <- ggplot(data = prjs_) + - aes(x = xcoord, y = ycoord, fill = highlight, color = as.factor(cluster)) + - scale_colour_manual(name = "clusters", values = req(update_palette())) + - geom_point(shape = 21,alpha = config_style$point_alpha, size = config_style$point_size) + - scale_shape(solid = FALSE) + - #geom_path(size=config_style$path_line_size, colour = "#2F3B65",alpha = config_style$path_alpha) + - guides() + - scale_fill_manual(values = c("TRUE" = "green", "FALSE" = "NA"))+ - coord_cartesian(xlim = ranges$x, ylim = ranges$y, expand = TRUE)+ - theme_void() + - theme(legend.position = "none") - - if (input$show_lines){ - #plt <- plt + geom_path(size=config_style$path_line_size, colour = "#2F3B65",alpha = config_style$path_alpha) - plt <- plt + geom_path(linewidth=config_style$path_line_size, colour = "#2F3B65",alpha = config_style$path_alpha) - } - - observeEvent(input$savePlot, { - plt <- plt + theme(plot.background = element_rect(fill = "white")) - ggsave(filename = prjs_plot_name(), plot = plt, path = "../data/plots/") - }) - #observeEvent(c(input$dataset, input$encoder, clustering_options$selected), { - #req(input$dataset, input$encoder) - #print("!-- CUDA?: ", torch$cuda$is_available()) - #prjs_ <- req(projections()) - #filename <- prjs_plot_name() - #print(paste("saving embedding plot to ",filename)) - #ggsave(filename = filename, plot = plt, path="../data/plots/") - #print("Embeding plot saved") - #}) - - plt + observeEvent(input$savePlot, { + plt <- plt + theme(plot.background = element_rect(fill = "white")) + ggsave(filename = prjs_plot_name(), plot = plt, path = "../data/plots/") }) + #observeEvent(c(input$dataset, input$encoder, clustering_options$selected), { + #req(input$dataset, input$encoder) + #print("!-- CUDA?: ", torch$cuda$is_available()) + #prjs_ <- req(projections()) + #filename <- prjs_plot_name() + #print(paste("saving embedding plot to ",filename)) + #ggsave(filename = filename, plot = plt, path="../data/plots/") + #print("Embeding plot saved") + #}) - - # Render projections plot - output$projections_plot_ui <- renderUI( - { - plotOutput( - "projections_plot", - click = "projections_click", - brush = "projections_brush", - height = input$embedding_plot_height - ) %>% withSpinner() - } - ) + plt + }) + + + # Render projections plot + output$projections_plot_ui <- renderUI( + { + plotOutput( + "projections_plot", + click = "projections_click", + brush = "projections_brush", + height = input$embedding_plot_height + ) %>% withSpinner() + } + ) # Render information about the selected point in the time series graph output$point <- renderText({ @@ -1099,29 +1099,39 @@ shinyServer(function(input, output, session) { get_ts_plot_name(dataset_name, encoder_name) }) - # Dentro de la función reactive para la generación de la proyección de embedding + #---------------------GRAFICA 3D---------------------------------------------------- + embedding_3d <- reactive({ # Obtener la proyección de embedding en tres dimensiones prj <- req(projections()) print("------------------------------") - str(prj) + str(embedding_ids) print("------------------------------") # Suponiendo que las coordenadas de la proyección en tres dimensiones están en las primeras tres columnas prj_3d <- prj[, 1:3] # Seleccionar las primeras tres columnas colnames(prj_3d) <- c("xcoord", "ycoord", "zcoord") # Asignar nombres a las columnas prj_3d }) - - # Dentro de la función renderPlotly para visualizar la proyección en 3D - output$embedding_plot_3d <- renderPlotly({ - # Obtener la proyección en tres dimensiones - prj_3d <- embedding_3d() - - # Crear un gráfico 3D con Plotly - plot_ly(data = prj_3d, x = ~xcoord, y = ~ycoord, z = ~zcoord, type = "scatter3d", mode = "markers", marker = list(size = 5)) + + output$embedding_plot_3d <- renderPlotly({ + + prj_3d <- embedding_3d() + + # Crear un gráfico 3D con Plotly + plot <- plot_ly( + data = prj_3d, + x = ~xcoord, y = ~ycoord, z = ~zcoord, + type = "scatter3d", + mode = "lines+markers", + marker = list(color = "black", size = 5)# Definir color de los puntos + ) + }) + + # Inicializar el valor del botón toggle_graph + observe({ + if (is.null(input$toggle_graph)) { + updateNumericInput(session, "toggle_graph", value = 0) + } }) - - }) - diff --git a/r_shiny_app/ui.R b/r_shiny_app/ui.R index 85650f90..943e7f0e 100644 --- a/r_shiny_app/ui.R +++ b/r_shiny_app/ui.R @@ -122,8 +122,18 @@ shinyUI(fluidPage( column(3) ), fluidRow( - plotlyOutput("embedding_plot_3d") # Agregamos la salida del gráfico en 3D - ) + column(12, + actionButton("toggle_graph", "Toggle Graph"), # Botón para alternar entre los gráficos + conditionalPanel( + condition = "input.toggle_graph % 2 == 0", # Condición para mostrar el primer gráfico + uiOutput("projections_plot_ui") # Interfaz para el primer gráfico + ), + conditionalPanel( + condition = "input.toggle_graph % 2 != 0", # Condición para mostrar el segundo gráfico + plotlyOutput("embedding_plot_3d") # Segundo gráfico + ) + ) # Agregamos la salida del gráfico en 3D + ) ), fluidRow(h3("Original data")), fluidRow( @@ -175,4 +185,4 @@ shinyUI(fluidPage( ) -)) \ No newline at end of file +)) From 35fa35e9ea11d678b2753c30b8a487bfc87ad135 Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Tue, 4 Jun 2024 18:20:13 +0000 Subject: [PATCH 04/37] LoadData modal window and notebook execution --- docker/docker-compose.yml | 5 +- dvats/config.ipynb | 2841 +++++++++++++++++ dvats/config.py | 1909 ++++++++++++ nbs_pipeline/01_dataset_artifact.ipynb | 3901 ++++++++++++++++++++++-- nbs_pipeline/02b_encoder_MVP.ipynb | 495 +-- nbs_pipeline/base.yaml | 96 + nbs_pipeline/utils/config.py | 1001 +++++- nbs_pipeline/utils_nbs/config.ipynb | 848 +++-- r_shiny_app/global.R | 2 + r_shiny_app/server.R | 129 +- 10 files changed, 10300 insertions(+), 927 deletions(-) create mode 100644 dvats/config.ipynb create mode 100644 dvats/config.py create mode 100644 nbs_pipeline/base.yaml diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml index 6e7620f8..073e0145 100644 --- a/docker/docker-compose.yml +++ b/docker/docker-compose.yml @@ -68,9 +68,12 @@ services: # TODO (28/03/2023): I don't know why it is not working without this - ENV_VARS=WANDB_ENTITY,WANDB_API_KEY,WANDB_PROJECT,USER,USERID,GROUPID,PASSWORD,ROOT,CUDA_VISIBLE_DEVICES volumes: - - ../r_shiny_app:/home/${USER_NAME}/app + - ../r_shiny_app:/home/${USER_NAME}/work/app - ${LOCAL_DATA_PATH}:/home/${USER_NAME}/data/ - ../dvats:/home/${USER_NAME}/dvats + - ../nbs_pipeline:/home/${USER_NAME}/work/nbs_pipeline + - ../dvats:/home/${USER_NAME}/work/nbs_pipeline/dvats + #- ../nbs_pipeline/utils_nbs/config.ipynb:/home/${USER_NAME}/python/dvats/config.ipynb - conda-env:/usr/local/share/miniconda3/envs/env - miniconda:/home/user/local/share/miniconda3 deploy: diff --git a/dvats/config.ipynb b/dvats/config.ipynb new file mode 100644 index 00000000..1383ab40 --- /dev/null +++ b/dvats/config.ipynb @@ -0,0 +1,2841 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "9c2ed463-8d9f-40cd-be11-d8d1d510fcef", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp config" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "34d5df84-5213-4795-bb13-2bb0b7f922f4", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "f49d5247-f6be-4126-8480-b6b307798212", + "metadata": {}, + "source": [ + "# Configuration functions \n", + "> This notebook introduces configuration functions designed to leverage proposed .yaml files, providing a more transparent and automated approach to artefact definition. While this method offers enhanced clarity, the option for direct definition within each notebook is also fully supported and remains practical.\n", + ">\n", + "> TODO: Check for possible simplifications. " + ] + }, + { + "cell_type": "markdown", + "id": "e7c7184a-93ba-49c1-a769-0b53cad5e08e", + "metadata": {}, + "source": [ + "First, we import\n", + "- os and sys for basic access to files\n", + "- yaml for .yaml reading\n", + "- tsai.basics for using tsai artifacts" + ] + }, + { + "cell_type": "markdown", + "id": "7e6faaa9-abda-444c-ad5c-b1345ac9306b", + "metadata": {}, + "source": [ + "## Basic configuration and definitions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "97cc1c68-f710-46eb-9fc8-e57f7f020f83", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "import os\n", + "import yaml\n", + "import sys\n", + "from tsai.basics import *" + ] + }, + { + "cell_type": "markdown", + "id": "f64aea40-28b4-44cb-ada2-d77927836eed", + "metadata": {}, + "source": [ + "As the yml are saved in ../nbs_pipeline/config, we will need to add '..' path" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "af853d2e-758c-4691-9127-9239ab46544c", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "sys.path.append(os.path.abspath('../nbs_pipeline'))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9a9ca90e-bc12-4d4b-823e-f9b2694f3b27", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "config_path = os.path.expanduser('~/work/nbs_pipeline/config/')\n", + "config_base_filename = 'base'" + ] + }, + { + "cell_type": "markdown", + "id": "5556b132-40f4-4daa-a50f-f38833a24aa5", + "metadata": {}, + "source": [ + "An custom_error(message) function is added just for basic error handling" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a3a27344-8e4b-4738-badb-73c1cbe59199", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def custom_error(message: str):\n", + " \"\"\"\n", + " This message raises an exception ensuring red-coloured error message is displayed\n", + " \"\"\"\n", + " # Change to red color ANSI code\n", + " red_start = \"\\033[91m\"\n", + " # Back to original color ANSI code\n", + " reset = \"\\033[0m\"\n", + " raise Exception(red_start + message + reset)" + ] + }, + { + "cell_type": "markdown", + "id": "ed8af711-39dc-4d65-8142-04e8ec7fad8a", + "metadata": {}, + "source": [ + "## Yaml reading auxiliar functions\n", + "> Defining basics functions to read yml in order to build Attrdict's\n", + "\n", + "In the version of YAML we are utilising, the '!join' constructor is not present. Therefore, it becomes necessary to define it." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9c06dfbe-9eb0-47e2-8b02-06f977fa5267", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def join_constructor(loader: yaml.Loader, node: yaml.Node) -> str:\n", + " \"\"\"\n", + " This function adds the '!join' constructor to the YAML parsing process. \n", + " It constructs a sequence from the provided node and then joins the elements of the sequence into a single string.\n", + " \"\"\"\n", + " seq = loader.construct_sequence(node)\n", + " return ''.join(seq)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7efaffe9-b8a0-47f1-9082-9480fdf166f3", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def recursive_attrdict(d: dict) -> AttrDict:\n", + " \"\"\" Recursively converts a dictionary into an AttrDict, including all nested dictionaries. \"\"\"\n", + " if isinstance(d, dict):\n", + " return AttrDict({k: recursive_attrdict(v) for k, v in d.items()})\n", + " return d" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e338cefb-6a7b-42b7-be07-aebc0a89ee62", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def replace_includes_with_content(\n", + " filename: str, \n", + " path: str = \"./\", \n", + " print_flag: bool = False\n", + ") -> str:\n", + " \"\"\"\n", + " This function processes a YAML file.\n", + " It replaces '!include' directives with the content of the specified files. \n", + " It takes a filename and a path, reads the file, and iteratively replaces any '!include' directives with the content of the included files.\n", + " The final processed content, with all '!include' directives substituted is returned as a string.\n", + " \"\"\"\n", + " if (print_flag):\n", + " print(\"... About to replace includes with content\")\n", + " with open(path+filename, 'r') as f:\n", + " content = f.read()\n", + " \n", + " # Mientras exista una directiva !include en el contenido, sigue reemplazándola\n", + " while \"!include\" in content:\n", + " # Obtén la posición de la directiva\n", + " start_idx = content.find('!include')\n", + " # Encuentra el inicio y el final de las comillas que contienen el nombre del archivo\n", + " start_quote_idx = content.find('\"', start_idx) + 1\n", + " end_quote_idx = content.find('\"', start_quote_idx)\n", + " \n", + " # Extrae el nombre del archivo\n", + " include_filename = content[start_quote_idx:end_quote_idx]\n", + " \n", + " # Lee el archivo incluido\n", + " with open(path+include_filename, 'r') as include_file:\n", + " included_content = include_file.read()\n", + " \n", + " # Reemplaza la directiva por el contenido del archivo incluido\n", + " content = content[:start_idx] + included_content + content[end_quote_idx+1:]\n", + " \n", + " return content" + ] + }, + { + "cell_type": "markdown", + "id": "701cbf35-6b6e-4da7-9fe3-42725c0eb40e", + "metadata": {}, + "source": [ + "## Project specific yaml reading functions\n", + "> Basic configuration is read from `../config/base.yml`\n", + "> It contains the following information:\n", + "> - User Preferences: `user_preferences`\n", + "> - General configuration: `user_preferences`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |-------------|---------------------------------------------------------|---------------|\n", + "> | `use_wandb` | Indicates whether to use wandb for experiment tracking. | `true` |\n", + "> \n", + "> - Wandb Configuration: `user_preferences.wdb`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |----------------------------|---------------------------------------------------|-------------------------|\n", + "> | `user` | The user name for wandb. | `mi-santamaria` |\n", + "> | `project_name` | The project name in wandb. | `deepvats` |\n", + "> | `version` | Specifies the version for wandb. | `'latest'` |\n", + "> | `mode` | Sets the mode for wandb. | `'offline'` |\n", + "> | `artifacts_path` | The path for storing wandb artifacts. | `'./data/wandb_artifacts'`|\n", + "> \n", + "> - Data configuration: `user_preferences.data`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |--------------------------|---------------------------------------------------|-------------------------|\n", + "> | `folder` | The folder where the data is stored. | `'~/data/'` |\n", + "> | `fname` | The file name of the dataset used. | `'solar_4_seconds_dataset'` |\n", + "> | `ftype` | The file type of the dataset. | `'.tsf'` |\n", + "> | `cols` | The columns to be used from the dataset. | `[]` (all columns) |\n", + "> | `freq` | The frequency of the dataset. | `'4s'` |\n", + "> \n", + "> - Artifact Configuration: `user_preferences.artifact`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |--------------------------|---------------------------------------------------|-------------------------|\n", + "> | `alias` | Alias for the resulting artifact of the run. | `'solar_4_seconds'` |\n", + "> | `algorithm` | The algorithm used in the process. | `'mvp'` |\n", + "> \n", + "> - Directory Configuration: `user_preferences.directories`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |--------------------------|---------------------------------------------------|-------------------------|\n", + "> | `tmp` | Folder for storing temporary files. | `'tmp'` |\n", + "> | `data` | Path for the data. | Combination of *path, *fname, *ftype |\n", + ">\n", + "> - Data Specifications: `data`\n", + "> - Data Details: `data`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |--------------------------|---------------------------------------------------|-------------------------|\n", + "> | `name` | The name of the data file. | Refers to *fname |\n", + "> | `path` | The path of the data file. | Refers to *data_path |\n", + "> | `alias` | The alias of the data artifact. | Refers to *alias |\n", + "> | `cols` | Columns of interest from the dataset. | Refers to *cols |\n", + "> | `csv_config` | Configuration for CSV files, if applicable. | `{}` (if not required) |\n", + "> | `date_offset` | Offset used for setting the date index. | `null` (if not used) |\n", + "> | `date_format` | Default date format for .tsf files. | `'%Y-%m-%d %H:%M:%S'` |\n", + "> | `freq` | Frequency of the data. | Refers to *freq |\n", + "> | `joining_train_test` | Linking training and testing data. | `false` |\n", + "> | `missing_values` | Handling missing values. | `technique: null, constant: null` |\n", + "> | `normalize_training` | Indicates whether to normalize training data. | `false` |\n", + "> | `range_training` | Specifies training ranges. | `null` |\n", + "> | `range_testing` | Specifies testing ranges. | `null` |\n", + "> | `resampling_freq` | Frequency for resampling the data. | `null` |\n", + "> | `start_date` | Starting date for the dataset. | `null` |\n", + "> | `test_split` | Ratio of the test set. | `null` |\n", + "> | `time_col` | Column containing the timestamp. | `null` |\n", + "> \n", + "> - Wandb Specifications: `wandb`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |--------------------------|---------------------------------------------------|-------------------------|\n", + "> | `user` | User name in wandb specifications. | Refers to *wdb_user |\n", + "> | `dir` | Directory for wandb. | Combination of '~/', *wdb_project |\n", + "> | `enabled` | Indicates if wandb is enabled. | `False` |\n", + "> | `group` | Group for the wandb runs. | `null` |\n", + "> | `log_learner` | Whether to log the learner to wandb. | `False` |\n", + "> | `mode` | Mode for wandb operation. | Refers to *wdb_mode |\n", + "> | `project` | The wandb project name. | Refers to *wdb_project |\n", + "> | `version` | Version for the wandb. | Refers to *wdb_version |\n", + "> | `artifacts_path` | Path for storing the TSArtifact in wandb. | Refers to *artifacts_path |\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "73c2aac7-13cb-4395-9b61-c18a94d789a8", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "\n", + "def substitute_env_variables_in_leaves(\n", + " config : AttrDict, \n", + " print_flag : bool = False\n", + "): \n", + " if print_flag: print(\"Config: \", config)\n", + " for key, value in config.items():\n", + " if isinstance(value, str):\n", + " original_value = value\n", + " matches = re.findall(r'\\$\\{([^}]+)\\}', value)\n", + " for match in matches:\n", + " env_value = os.environ.get(match)\n", + " if env_value is not None:\n", + " value = value.replace('${' + match + '}', env_value)\n", + " config[key] = value\n", + " if print_flag and original_value != value:\n", + " print(f\"Changed {key}: from {original_value} to {config[key]}\")\n", + " else:\n", + " if isinstance(value, AttrDict):\n", + " substitute_env_variables_in_leaves(value, print_flag) # Recursividad para diccionarios anidados\n", + " return config" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "36c82c69-859c-40a0-9cc2-3e4144c362b2", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def get_config(\n", + " print_flag: bool = False, \n", + " filename: str = config_base_filename,\n", + " path = config_path\n", + ") -> AttrDict:\n", + " \"\"\"\n", + " This function \n", + " - Reads the content in '../config/base.yml' and \n", + " - Returns its content as AttrDict.\n", + " print_flag option can be changed to True for displaying debugging messages. \n", + " \"\"\"\n", + " # Build file path\n", + " if (path[-1] != '/'):\n", + " path += '/'\n", + " filename = filename+\".yaml\"\n", + " \n", + " # Debug messages\n", + " if (print_flag):\n", + " current_directory = os.getcwd()\n", + " print(\"Current: \" + current_directory)\n", + " print(\"yml: \"+ path + filename)\n", + " \n", + " # Add join constructor\n", + " yaml.add_constructor('!join', join_constructor)\n", + " \n", + " if (print_flag): \n", + " print(\"Getting content\"+ path + filename)\n", + " \n", + " # Get file content\n", + " full_content = replace_includes_with_content(filename, path, print_flag)\n", + " \n", + " if (print_flag):\n", + " print(\"Load content\"+ path + filename)\n", + " \n", + " # Load content \n", + " config = yaml.load(full_content, Loader=yaml.FullLoader)\n", + " \n", + " config_attrdict = recursive_attrdict(config)\n", + "\n", + " substitute_env_variables_in_leaves(\n", + " config = config_attrdict, \n", + " print_flag = print_flag\n", + " ) \n", + " return config_attrdict" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "47c61d83-cb3c-4144-aee1-0c076eac71f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "```json\n", + "{ 'data': { 'alias': 'Monash-Australian_electricity_demand',\n", + " 'cols': [5],\n", + " 'csv_config': {},\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'date_offset': None,\n", + " 'freq': '10h',\n", + " 'joining_train_test': False,\n", + " 'missing_values': {'constant': None, 'technique': None},\n", + " 'name': 'Semantic_Segmentation_TiltABP',\n", + " 'normalize_training': False,\n", + " 'path': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'range_testing': None,\n", + " 'range_training': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None},\n", + " 'user_preferences': { 'artifact': { 'algorithm': 'mvp-SWV',\n", + " 'alias': 'Monash-Australian_electricity_demand'},\n", + " 'data': { 'cols': [5],\n", + " 'fname': 'Semantic_Segmentation_TiltABP',\n", + " 'folder': '~/data/',\n", + " 'freq': '10h',\n", + " 'ftype': '.csv'},\n", + " 'directories': { 'data': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'tmp': 'tmp'},\n", + " 'use_wandb': True,\n", + " 'wdb': { 'artifacts_path': './data/wandb_artifacts',\n", + " 'mode': 'online',\n", + " 'project_name': 'ream',\n", + " 'user': 'angel-gutierrez',\n", + " 'version': 'latest'}},\n", + " 'wandb': { 'artifacts_path': './data/wandb_artifacts',\n", + " 'dir': '~/ream',\n", + " 'enabled': False,\n", + " 'group': None,\n", + " 'log_learner': False,\n", + " 'mode': 'online',\n", + " 'project': 'ream',\n", + " 'user': 'angel-gutierrez',\n", + " 'version': 'latest'}}\n", + "```" + ], + "text/plain": [ + "{'user_preferences': {'use_wandb': True,\n", + " 'wdb': {'user': 'angel-gutierrez',\n", + " 'project_name': 'ream',\n", + " 'version': 'latest',\n", + " 'mode': 'online',\n", + " 'artifacts_path': './data/wandb_artifacts'},\n", + " 'data': {'folder': '~/data/',\n", + " 'fname': 'Semantic_Segmentation_TiltABP',\n", + " 'ftype': '.csv',\n", + " 'cols': [5],\n", + " 'freq': '10h'},\n", + " 'artifact': {'alias': 'Monash-Australian_electricity_demand',\n", + " 'algorithm': 'mvp-SWV'},\n", + " 'directories': {'tmp': 'tmp',\n", + " 'data': '~/data/Semantic_Segmentation_TiltABP.csv'}},\n", + " 'data': {'name': 'Semantic_Segmentation_TiltABP',\n", + " 'path': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'alias': 'Monash-Australian_electricity_demand',\n", + " 'cols': [5],\n", + " 'csv_config': {},\n", + " 'date_offset': None,\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'freq': '10h',\n", + " 'joining_train_test': False,\n", + " 'missing_values': {'technique': None, 'constant': None},\n", + " 'normalize_training': False,\n", + " 'range_training': None,\n", + " 'range_testing': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None},\n", + " 'wandb': {'user': 'angel-gutierrez',\n", + " 'dir': '~/ream',\n", + " 'enabled': False,\n", + " 'group': None,\n", + " 'log_learner': False,\n", + " 'mode': 'online',\n", + " 'project': 'ream',\n", + " 'version': 'latest',\n", + " 'artifacts_path': './data/wandb_artifacts'}}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| hide \n", + "get_config(False)" + ] + }, + { + "cell_type": "markdown", + "id": "5ee136ea-0fe1-4b4d-a1ef-e8b9414a1766", + "metadata": {}, + "source": [ + "### Build the encoder artifact from already read functions\n", + "> The encoder artifact is needed for training the model.\n", + "> \n", + "> To automatize its selection, its W&B name is built up from its latest version\n", + "> \n", + "> TODO: Change for selecting specific version. Version should be selected in .yml file too ." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "44b7dc69-c76c-4056-b2bb-bf732e1e0530", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def build_enc_artifact(\n", + " config: AttrDict, \n", + " print_flag: bool = False\n", + ") -> str:\n", + " \"\"\"\n", + " Build enc_artifact name from config data.\n", + " \"\"\"\n", + " version = config.user_preferences.wdb.version\n", + " enc_artifact = config.configuration.encoder.artifacts.train.enc_prefix\n", + " if (version == 'latest'):\n", + " enc_artifact+=\":latest\"\n", + " else:\n", + " enc_artifact=enc_artifact+\":v\"+version\n", + " if (print_flag):\n", + " print(\"enc_artifact: \"+enc_artifact)\n", + " return enc_artifact\n" + ] + }, + { + "cell_type": "markdown", + "id": "410f96e8-9a6a-464c-ab66-6137bb9a85c3", + "metadata": {}, + "source": [ + "### Build project data\n", + "> Read user, project, version and dataset artifact name form already got information" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9435b302-cc11-4e75-8d9b-d746456b1b87", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def get_project_data(\n", + " print_flag: bool = False,\n", + " filename=config_base_filename,\n", + " path = config_path\n", + ") -> [str, str, str, str]:\n", + " \"\"\"\n", + " Retrieves project data including user, project name, version, and data name. \n", + " It accesses configuration settings, processes them, and optionally prints the project configuration. \n", + " Returns a tuple containing user, project, version, and data information.\n", + " \"\"\"\n", + " config = get_config(print_flag = print_flag, filename = filename, path = path)\n", + " project = config.wandb.project\n", + " user = config.wandb.user\n", + " version = config.wandb.version\n", + " data_name = config.data.alias\n", + " if (version != \"latest\"):\n", + " version = 'v' +version\n", + " data = data_name +\":\"+version\n", + " if print_flag:\n", + " dashes = '-----------' \n", + " print(dashes+\"Project configuration\"+dashes)\n", + " print(\"user: \" + user)\n", + " print(\"project: \" + project)\n", + " print(\"version: \" + version)\n", + " print(\"data: \"+ data)\n", + " print(dashes+\"Project configuration\"+dashes)\n", + " return user, project, version, data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "40535592-81dd-4b7f-9407-d79b73e6e81f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('angel-gutierrez',\n", + " 'ream',\n", + " 'latest',\n", + " 'Monash-Australian_electricity_demand:latest')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| hide \n", + "get_project_data(False)" + ] + }, + { + "cell_type": "markdown", + "id": "065856d6-a217-490c-821c-dd4abf205344", + "metadata": {}, + "source": [ + "### Get training artifact name" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fd10beae-87e1-4738-bd01-4c1162f3bd96", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def get_train_artifact(user: str, project: str, data: str) -> str:\n", + " \"\"\"\n", + " Constructs the train artifact string by combining user, project, and data information. \n", + " The format of the return string is 'entity/project/name:version'.\n", + " \"\"\"\n", + " # entity/project/name:version\n", + " train_artifact=user+'/'+project+'/'+data \n", + " return train_artifact" + ] + }, + { + "cell_type": "markdown", + "id": "ce19aa52-7a5b-430a-9a18-9ca60ffddbca", + "metadata": {}, + "source": [ + "## Build dataset artifact\n", + "> Configuration file: `base`. Described above." + ] + }, + { + "cell_type": "markdown", + "id": "7a3a0a20-c0e9-4495-9957-606ce4b97d57", + "metadata": {}, + "source": [ + "### Auxiliary functions" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5fcd5273-0a72-44dd-9910-8b5bb5585459", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "##############################\n", + "# 01 - DATAFRAME TO ARTIFACT #\n", + "##############################\n", + "def get_artifact_config_sd2a_get_auxiliar_variables(\n", + " print_flag: bool, \n", + " filename = config_base_filename, \n", + " path = config_path\n", + ") -> Tuple[str, str, str, AttrDict, bool, str]:\n", + " \"\"\"\n", + " Retrieves auxiliary variables necessary for the dataset artifact configuration. \n", + " Gathers user, project, version, and data details, along with preferences for using wandb and the wandb artifacts path.\n", + " Returns a tuple containing these elements.\n", + " \"\"\"\n", + " user, project, version, data = get_project_data(print_flag, filename = filename, path = path)\n", + " config = get_config(print_flag, filename = filename, path = path)\n", + " data = config.data\n", + " use_wandb = config.user_preferences.use_wandb\n", + " wandb_path = config.wandb.artifacts_path\n", + " return user, project, version, data, use_wandb, wandb_path" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1175362a-59ef-422f-8fd7-8022d41c37ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('angel-gutierrez',\n", + " 'ream',\n", + " 'latest',\n", + " {'name': 'Semantic_Segmentation_TiltABP',\n", + " 'path': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'alias': 'Monash-Australian_electricity_demand',\n", + " 'cols': [5],\n", + " 'csv_config': {},\n", + " 'date_offset': None,\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'freq': '10h',\n", + " 'joining_train_test': False,\n", + " 'missing_values': {'technique': None, 'constant': None},\n", + " 'normalize_training': False,\n", + " 'range_training': None,\n", + " 'range_testing': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None},\n", + " True,\n", + " './data/wandb_artifacts')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| hide\n", + "get_artifact_config_sd2a_get_auxiliar_variables(False)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c4a83901", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def check_project_and_entity(user: str, project: str):\n", + " \"\"\"\n", + " Checks user and project are correctly setted up comparing to environment variables\n", + " \"\"\"\n", + " os_entity = os.environ['WANDB_ENTITY']\n", + " os_project = os.environ['WANDB_PROJECT']\n", + " if (os_entity != user):\n", + " custom_error(\"Please check .env and base.yml: entity != user os \" + os_entity + \" yaml \" + user)\n", + " if (os_project != project):\n", + " custom_error(\"Please check .env and base.yml: project differs os \" + os_project + \" yaml \" + project)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cb4590b0-3a46-4bab-ba97-9ecc4ae179e6", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def get_artifact_config_sd2a_check_errors(use_wandb: str, artifact_config: AttrDict, user: str, project: str):\n", + " \"\"\"\n", + " Checks for configuration errors in the dataset artifact settings. \n", + " Verifies the project and entity, and ensures appropriate settings are made for offline mode and missing values handling.\n", + " \"\"\"\n", + " check_project_and_entity(user, project)\n", + " if (\n", + " use_wandb == \"offline\" \n", + " and artifact_config.joining_train_test == True\n", + " ):\n", + " custom_error(\"If you're using deepvats in offline mode, set joining_train_test to False\")\n", + " if (\n", + " artifact_config.missing_values_constant is not None \n", + " and artifact_config.missing_values_technique is None\n", + " ):\n", + " custom_error(\"Missing values constant must be setted up only if missing_values_technique is not None. Please check base.yaml\")" + ] + }, + { + "cell_type": "markdown", + "id": "7b570b1c-aea2-48c2-aa7b-53910bd501cd", + "metadata": {}, + "source": [ + "### Main function" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "73afe0d8-eb7f-4431-894e-f58d8f5a7bd2", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def get_artifact_config_sd2a(\n", + " print_flag: bool = False,\n", + " base_filename = config_base_filename,\n", + " path = config_path\n", + ") -> AttrDict:\n", + " \"\"\"\n", + " Constructs the configuration for the dataset artifact by retrieving auxiliary variables and setting up the artifact configuration.\n", + " Validates the configuration to ensure it meets specific criteria and returns the final artifact configuration as an AttrDict.\n", + " \"\"\"\n", + " user, project, version, data, use_wandb, wandb_path = get_artifact_config_sd2a_get_auxiliar_variables(\n", + " print_flag = print_flag,\n", + " filename = base_filename,\n", + " path = path\n", + " )\n", + " artifact_config = AttrDict(\n", + " artifact_name = data.alias,\n", + " csv_config = data.csv_config,\n", + " data_cols = data.cols,\n", + " data_fpath = data.path,\n", + " date_format = data.date_format,\n", + " date_offset = data.date_offset,\n", + " freq = data.freq,\n", + " joining_train_test = data.joining_train_test,\n", + " missing_values_technique= data.missing_values.technique,\n", + " missing_values_constant = data.missing_values.constant,\n", + " normalize_training = data.normalize_training,\n", + " range_training = data.range_training,\n", + " range_testing = data.range_testing,\n", + " resampling_freq = data.resampling_freq,\n", + " start_date = data.start_date,\n", + " test_split = data.test_split,\n", + " time_col = data.time_col,\n", + " use_wandb = use_wandb,\n", + " wandb_artifacts_path = wandb_path\n", + " )\n", + " get_artifact_config_sd2a_check_errors(use_wandb, artifact_config, user, project) \n", + " return artifact_config" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "becad522-6943-4a4c-ab7d-6cdbdd84f369", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "```json\n", + "{ 'artifact_name': 'Monash-Australian_electricity_demand',\n", + " 'csv_config': {},\n", + " 'data_cols': [5],\n", + " 'data_fpath': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'date_offset': None,\n", + " 'freq': '10h',\n", + " 'joining_train_test': False,\n", + " 'missing_values_constant': None,\n", + " 'missing_values_technique': None,\n", + " 'normalize_training': False,\n", + " 'range_testing': None,\n", + " 'range_training': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None,\n", + " 'use_wandb': True,\n", + " 'wandb_artifacts_path': './data/wandb_artifacts'}\n", + "```" + ], + "text/plain": [ + "{'artifact_name': 'Monash-Australian_electricity_demand',\n", + " 'csv_config': {},\n", + " 'data_cols': [5],\n", + " 'data_fpath': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'date_offset': None,\n", + " 'freq': '10h',\n", + " 'joining_train_test': False,\n", + " 'missing_values_technique': None,\n", + " 'missing_values_constant': None,\n", + " 'normalize_training': False,\n", + " 'range_training': None,\n", + " 'range_testing': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None,\n", + " 'use_wandb': True,\n", + " 'wandb_artifacts_path': './data/wandb_artifacts'}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| hide\n", + "get_artifact_config_sd2a(print_flag = False)" + ] + }, + { + "cell_type": "markdown", + "id": "765b90c4-ac6e-42d3-a091-f6e52e35bea8", + "metadata": {}, + "source": [ + "## Build MVP Encoder artifacts\n", + "> Includes also the versions for sliding window view version\n", + "\n", + "The configuration files `02b-encoder_mvp` contains the following information:\n", + "### Configuration for Encoder\n", + "> - General Configuration: `configuration`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |------------|-------------------------------------------------------------|----------------|\n", + "> | `job_type` | The type of job being configured. | `'encoder_MVP'`|\n", + "> | `alias` | Alias for the configuration, refers to the alias in base. | Refers to *alias|\n", + ">\n", + "> - Wandb Configuraconfiguration.tion: `wandb`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |------------|-------------------------------------------|-----------------|\n", + "> | `mode` | The mode for wandb, inherited from base. | Refers to *wdb_mode |\n", + "> | `group` | Specifies if the run is part of a group. | `null` |\n", + ">\n", + "> - Specifications Coconfiguration.nfiguration: `specifications`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |------------------------------------------|------------------------------------------|-----------------|\n", + "> | `batch_size` | The batch size for processing. | `1024` |\n", + "> | `n_epoch` | Number of epochs for training. | `100` |\n", + "> | `mask.future` | Whether to mask future samples. | `false` |\n", + "> | `mask.stateful` | Dictates if masking is stateful or not. | `true` |\n", + "> | `mask.sync` | Mask all variables at once in series. | `false` |\n", + "> | `mvp.ws1` | Min window size for MVP adaptable training. | `15` |\n", + "> | `mvp.ws2` | Max window size for MVP adaptable training. | `30` |\n", + "> | `mvp.r` | Probability of masking in MVP. | `0.710` |\n", + "> | `mvp.valid_size` | Size of the validation set proportion. | `0.2` |\n", + "> | `mvp.normalize.by_sample` | Normalization by sample indicator. | `false` |\n", + "> | `mvp.normalize.use_single_batch` | Use single batch for normalization. | `false` |\n", + "> | `sliding_windows.stride` | Data points the window moves ahead. | `15` |\n", + "> | `sliding_windows.size` | Window size for the sliding wiow. | `30` |\n", + " g window. | `30` |\n" + ] + }, + { + "cell_type": "markdown", + "id": "3b15520a-2fe0-44ae-b184-b1268dd903f2", + "metadata": {}, + "source": [ + "### Auxiliary functions for retrieving the data" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8dcb5baa-504c-4e7c-9e57-99260a11af31", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "######################\n", + "# 02b - ENCODER MVP #\n", + "######################\n", + "def get_artifact_config_MVP_auxiliar_variables(\n", + " print_flag : bool,\n", + " base_filename : str = config_base_filename,\n", + " path : str = config_path\n", + ") -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]:\n", + " \"\"\"\n", + " Retrieves and assembles various configuration parameters and auxiliary variables for an MVP artifact. \n", + " Extracts user, project, version, and data details, fetches configuration settings, and constructs training artifact strings. \n", + " Returns a tuple containing these elements along with MVP workspace specifications and user preferences.\n", + " \"\"\"\n", + " #Get neccesary variables\n", + " user, project, version, data = get_project_data(\n", + " print_flag = print_flag, \n", + " filename = base_filename,\n", + " path = path\n", + " )\n", + " config = get_config(\n", + " print_flag, \"02b-encoder_mvp\"\n", + " )\n", + " user_preferences = config.user_preferences\n", + " config = config.configuration\n", + " train_artifact_ = get_train_artifact(user,project,data) \n", + " mvp_ws1 = config.specifications.mvp.ws1\n", + " mvp_ws2 = config.specifications.mvp.ws2\n", + " mvp_ws = (mvp_ws1,mvp_ws2)\n", + " return user, project, version, data, config, train_artifact_, mvp_ws, user_preferences" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "77d62ca2-14da-4ab1-bbc7-3b5182dd0840", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def get_artifact_config_MVP_auxiliar_variables_SWV(\n", + " print_flag : bool,\n", + " base_filename : str = config_base_filename,\n", + " path : str = config_path\n", + ") -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]: \n", + " \"\"\"\n", + " Retrieves and assembles various configuration parameters and auxiliary variables for an MVP artifact. \n", + " Extracts user, project, version, and data details, fetches configuration settings, and constructs training artifact strings. \n", + " Returns a tuple containing these elements along with MVP (sliding window view) workspace specifications and user preferences.\n", + " \"\"\"\n", + " #Get neccesary variables\n", + " user, project, version, data = get_project_data(print_flag, base_filename, path)\n", + " config = get_config(print_flag, \"02c-encoder_mvp-sliding_window_view\", path)\n", + " user_preferences = config.user_preferences\n", + " config = config.configuration\n", + " train_artifact_ = get_train_artifact(user,project,data) \n", + " mvp_ws1 = config.specifications.mvp.ws1\n", + " mvp_ws2 = config.specifications.mvp.ws2\n", + " mvp_ws = (mvp_ws1,mvp_ws2)\n", + " return user, project, version, data, config, train_artifact_, mvp_ws, user_preferences" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "bd1e4e0d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('angel-gutierrez',\n", + " 'ream',\n", + " 'latest',\n", + " 'Monash-Australian_electricity_demand:latest',\n", + " {'job_type': 'encoder_MVP-SWV',\n", + " 'alias': 'Monash-Australian_electricity_demand',\n", + " 'wandb': {'mode': 'online', 'group': None},\n", + " 'specifications': {'batch_size': 512,\n", + " 'n_epoch': 100,\n", + " 'mask': {'future': False, 'stateful': True, 'sync': False},\n", + " 'mvp': {'ws1': 10,\n", + " 'ws2': 30,\n", + " 'r': 0.71,\n", + " 'valid_size': 0.2,\n", + " 'normalize': {'by_sample': False, 'use_single_batch': False}},\n", + " 'sliding_windows': {'stride': 15, 'size': 30}}},\n", + " 'angel-gutierrez/ream/Monash-Australian_electricity_demand:latest',\n", + " (10, 30),\n", + " {'use_wandb': True,\n", + " 'wdb': {'user': 'angel-gutierrez',\n", + " 'project_name': 'ream',\n", + " 'version': 'latest',\n", + " 'mode': 'online',\n", + " 'artifacts_path': './data/wandb_artifacts'},\n", + " 'data': {'folder': '~/data/',\n", + " 'fname': 'Semantic_Segmentation_TiltABP',\n", + " 'ftype': '.csv',\n", + " 'cols': [5],\n", + " 'freq': '10h'},\n", + " 'artifact': {'alias': 'Monash-Australian_electricity_demand',\n", + " 'algorithm': 'mvp-SWV'},\n", + " 'directories': {'tmp': 'tmp',\n", + " 'data': '~/data/Semantic_Segmentation_TiltABP.csv'}})" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_artifact_config_MVP_auxiliar_variables_SWV(False)" + ] + }, + { + "cell_type": "markdown", + "id": "54ff99b2-ec50-4e45-80c2-5d9c9132728f", + "metadata": {}, + "source": [ + "### Auxiliary functions for error handling" + ] + }, + { + "cell_type": "markdown", + "id": "02aa20ce-ce6b-4d0c-89c6-a1845bd695ec", + "metadata": {}, + "source": [ + "> TODO: Should we allow diferent project from environment ones? Should we just avoid yml configuration for this parameters and directly get dockers environment WANDB_ENTITIY and WANDB_PROJECT environment variables? ." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "f378edb0-797d-43f1-a29c-ada58e9f7446", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def get_artifact_config_MVP_check_errors(\n", + " artifact_config: AttrDict, \n", + " user: str, \n", + " project: str \n", + "):\n", + " \"\"\"\n", + " Avoids incompatible wandb modes configurations.\n", + " Sets project = 'work-nbs' if wandb is not used for tracking.\n", + " \"\"\"\n", + " check_project_and_entity(user, project)\n", + " \n", + " if artifact_config.use_wandb:\n", + " if (artifact_config.analysis_mode != 'online'):\n", + " print(\"Changing to online analysis mode - use_wandb=true\")\n", + " artifact_config.analysis_mode = 'online'\n", + " else:\n", + " project = 'work-nbs'" + ] + }, + { + "cell_type": "markdown", + "id": "658ac00f-2383-40ce-885e-82503c0a5562", + "metadata": {}, + "source": [ + "### Main configuration functions" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "3c77a575-19a9-47e7-a13a-8e81a00affe6", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def get_artifact_config_MVP(print_flag: bool = False) -> Tuple[str, str, str, str, AttrDict, str]:\n", + " \"\"\"\n", + " Gathers and structures the MVP artifact configuration, including user, project, version, data details, and various configuration settings.\n", + " Returns a tuple comprising user, project, version, data, the structured artifact configuration as an AttrDict, and the job type.\n", + " \"\"\"\n", + " user, project, version, data, config, train_artifact_, mvp_ws, user_preferences = get_artifact_config_MVP_auxiliar_variables(print_flag)\n", + "\n", + " artifact_config = AttrDict(\n", + " alias = config.alias,\n", + " analysis_mode = config.wandb.mode, \n", + " batch_size = config.specifications.batch_size,\n", + " epochs = config.specifications.n_epoch,\n", + " mask_future = config.specifications.mask.future,\n", + " mask_stateful = config.specifications.mask.stateful,\n", + " mask_sync = config.specifications.mask.sync,\n", + " mvp_ws = mvp_ws, \n", + " norm_by_sample = config.specifications.mvp.normalize.by_sample,\n", + " norm_use_single_batch = config.specifications.mvp.normalize.use_single_batch,\n", + " r = config.specifications.mvp.r,\n", + " stride = config.specifications.sliding_windows.stride, \n", + " train_artifact = train_artifact_, \n", + " valid_artifact = None, \n", + " use_wandb = user_preferences.use_wandb, \n", + " valid_size = config.specifications.mvp.valid_size,\n", + " w = config.specifications.sliding_windows.size, \n", + " wandb_group = config.wandb.group\n", + " )\n", + " get_artifact_config_MVP_check_errors(artifact_config, user, project)\n", + " return user, project, version, data, artifact_config, config.job_type" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f7b01a75-f9fa-4069-b99b-5ea6703887c5", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def get_artifact_config_MVP_SWV(\n", + " print_flag : bool = False,\n", + " base_filename : str = config_base_filename,\n", + " path : str = config_path\n", + ") -> Tuple[str, str, str, str, AttrDict, str]:\n", + " \"\"\"\n", + " Gathers and structures the MVP_SWV artifact configuration, including user, project, version, data details, and various configuration settings.\n", + " Returns a tuple comprising user, project, version, data, the structured artifact configuration as an AttrDict, and the job type.\n", + " \"\"\"\n", + " user, project, version, data, config, train_artifact_, mvp_ws, user_preferences = get_artifact_config_MVP_auxiliar_variables_SWV(\n", + " print_flag = print_flag,\n", + " base_filename = base_filename,\n", + " path = path\n", + " )\n", + "\n", + " artifact_config = AttrDict(\n", + " alias = config.alias,\n", + " analysis_mode = config.wandb.mode, \n", + " batch_size = config.specifications.batch_size,\n", + " epochs = config.specifications.n_epoch,\n", + " mask_future = config.specifications.mask.future,\n", + " mask_stateful = config.specifications.mask.stateful,\n", + " mask_sync = config.specifications.mask.sync,\n", + " mvp_ws = mvp_ws, \n", + " norm_by_sample = config.specifications.mvp.normalize.by_sample,\n", + " norm_use_single_batch = config.specifications.mvp.normalize.use_single_batch,\n", + " r = config.specifications.mvp.r,\n", + " stride = config.specifications.sliding_windows.stride, \n", + " train_artifact = train_artifact_, \n", + " valid_artifact = None, \n", + " use_wandb = user_preferences.use_wandb, \n", + " valid_size = config.specifications.mvp.valid_size,\n", + " w = config.specifications.sliding_windows.size, \n", + " wandb_group = config.wandb.group\n", + " )\n", + " get_artifact_config_MVP_check_errors(artifact_config, user, project)\n", + " return user, project, version, data, artifact_config, config.job_type" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "87c5650c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('angel-gutierrez',\n", + " 'ream',\n", + " 'latest',\n", + " 'Monash-Australian_electricity_demand:latest',\n", + " {'alias': 'Monash-Australian_electricity_demand',\n", + " 'analysis_mode': 'online',\n", + " 'batch_size': 512,\n", + " 'epochs': 100,\n", + " 'mask_future': False,\n", + " 'mask_stateful': True,\n", + " 'mask_sync': False,\n", + " 'mvp_ws': (10, 30),\n", + " 'norm_by_sample': False,\n", + " 'norm_use_single_batch': False,\n", + " 'r': 0.71,\n", + " 'stride': 15,\n", + " 'train_artifact': 'angel-gutierrez/ream/Monash-Australian_electricity_demand:latest',\n", + " 'valid_artifact': None,\n", + " 'use_wandb': True,\n", + " 'valid_size': 0.2,\n", + " 'w': 30,\n", + " 'wandb_group': None},\n", + " 'encoder_MVP-SWV')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| hide\n", + "get_artifact_config_MVP_SWV(False)" + ] + }, + { + "cell_type": "markdown", + "id": "dc7aa51f-8bbb-4445-b06a-1564e63fc32f", + "metadata": {}, + "source": [ + "## Encoder DCAE configuration\n", + "> The configuration file `02a-encoder_dcae` contains the following information:\n", + "\n", + "> - General Configurat: `configuration`ion\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |------------|--------------------------------------------------|-----------------|\n", + "> | `job_type` | Type of the job for this configuration. | `'encoder_DCAE'`|\n", + "> | `alias` | Alias for the run, referring to the base config. | Refers to *alias|\n", + ">\n", + "> - Wandb Configconfiguration.uration: `wandb`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |-----------|-------------------------------------------------------------|-----------------------|\n", + "> | `use` | Indicates whether to use wandb. | Refers to *use_wandb |\n", + "> | `entity` | Entity (user) for wandb configuration. | Refers to *wdb_user |\n", + "> | `group` | Specifies if this run should be grouped in a wandb group. | `null` |\n", + "> | `project` | Project name for wandb configuration. | Refers to *wdb_project|\n", + ">\n", + "> - Artifacconfiguration.ts Configuration: `artifacts`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |-----------------|--------------------------------------------------------------------------|-------------------------------------------------------|\n", + "> | `train_prefix` | Complete training artifact path in wandb. | Combination of *wdb_user, *wdb_project, and *alg |\n", + "> | `valid.data` | Complete name for the validation dataset artifact (null for random). | `null` |\n", + "> | `valid.size` | Percentage of random items to go to validation set if `valid.data` is null. | `0.1` |\n", + ">\n", + "> - Specifications Configuration: `specifications`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |----------------|---------------------------------------------------|-----------------|\n", + "> | `batch_size` | Batch size for processing. | `64` |\n", + "> | `n_epoch` | Number of epochs to train for. | `200` |\n", + "> | `pool_szs` | Sizes of the pooling layers in the autoencoder. | `[2,2,4]` |\n", + "> | `top_k` | Number of elements to analyse for the top losses. | `3` |\n", + ">\n", + "> - Sliding Windows Configuration: `sliding_windows`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |-----------|-----------------------------------------|---------------|\n", + "> | `stride` | The stride for the sliding window. | `1` |\n", + "> | `size` | Window size for the sliding window. | `32` configuration. |\n", + ">\n", + "> - Autoencoder Configuration: `autoencoder`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |---------------------|----------------------------------------------------------------|----------------|\n", + "> | `delta` | Size of the autoencoder bottleneck layer. | `60` |\n", + "> | `filters.nfs` | Number of filters in each convolutional layer of the autoencoder. | `[64,32,16]` |\n", + "> | `filters.kss` | Kernel sizes for each convolutional layer in the autoencoder. | `[10,5,5]` |\n", + "> | `filters.output_size` | The output size of the autoencoder's final layer. | `10` |\n", + "inal layer. | `10` |\n", + " | `10` |\n", + " |\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "e7b33aed-ba0d-4cf3-b131-c21d7b5519d9", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "######################\n", + "# 02a - ENCODER DCAE #\n", + "######################\n", + "\n", + "def get_artifact_config_DCAE(\n", + " print_flag: bool = False,\n", + " base_filename = config_base_filename,\n", + " path = config_path\n", + ") -> Tuple[AttrDict, str]:\n", + " \"\"\"\n", + " Constructs the configuration for the DCAE (Deep Convolutional AutoEncoder).\n", + " It fetchs the relevant settings and assembles the artifact configuration.\n", + " Validates the configuration to ensure correct project and entity setup and \n", + " returns the artifact configuration as an AttrDict along with the job type.\n", + " \"\"\"\n", + " user, project, version, data = get_project_data(print_flag, base_filename, path)\n", + " config = get_config(print_flag, \"02a-encoder_dcae\", path)\n", + " if print_flag: print(\"Antes de leer configuration \" + str(config))\n", + " config = config.configuration\n", + " \n", + " artifact_config = AttrDict(\n", + " alias = config.alias,\n", + " use_wandb = config.wandb.use,\n", + " wandb_group = config.wandb.group,\n", + " wandb_entity = config.wandb.entity,\n", + " wandb_project = config.wandb.project,\n", + " train_artifact = get_train_artifact(user,project,data),\n", + " valid_artifact = config.artifacts.valid.data,\n", + " valid_size = config.artifacts.valid.size,\n", + " w = config.specifications.sliding_windows.size,\n", + " stride = config.specifications.sliding_windows.stride,\n", + " delta = config.specifications.autoencoder.delta,\n", + " nfs = config.specifications.autoencoder.filters.nfs,\n", + " kss = config.specifications.autoencoder.filters.kss,\n", + " output_filter_size = config.specifications.autoencoder.filters.output_size,\n", + " pool_szs = config.specifications.pool_szs,\n", + " batch_size = config.specifications.batch_size, \n", + " epochs = config.specifications.n_epoch,\n", + " top_k = config.specifications.pool_szs\n", + " )\n", + " check_project_and_entity(\n", + " artifact_config.wandb_entity,\n", + " artifact_config.wandb_project\n", + " )\n", + " return artifact_config, config.job_type" + ] + }, + { + "cell_type": "markdown", + "id": "4b283a86-21a6-4de5-b73f-089b4ff92628", + "metadata": {}, + "source": [ + "## Embeddings configuration\n", + "The specific configurations are in the file `03-embeddings`\n", + "It has the following information:\n", + "\n", + "> - Job Type: `job_type`\n", + "> \n", + "> | Key | Value |\n", + "> |-----------|---------------|\n", + "> | `job_type`| `'embeddings'`|\n", + ">\n", + "> - Configuration: `configuration`\n", + "> - Wandb Configuration: `configuration.wandb`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |---------|------------------------------------------------------|--------------------|\n", + "> | `group` | Whether to group this run in a wandb group. | Refers to *emb |\n", + "> | `use` | Indicates whether to use wandb. | Refers to *use_wandb |\n", + "> | `entity`| Entity (user) for wandb configuration. | Refers to *wdb_user |\n", + "> | `project`| Project name for wandb configuration. | Refers to *wdb_project |\n", + ">\n", + "> - Encoder Configuration: `configuration.encoder`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |------------------------------|----------------------------------------------------|-------------------------------------------------|\n", + "> | `artifacts.train.enc_prefix` | Prefix for the training artifacts. | Combination of *wdb_user, *wdb_project, and *alg |\n", + "> | `artifacts.valid` | If none, the validation set used to train enc is used. | `null` |\n", + ">\n", + "> - Specifications Configuration: `configuration.specifications`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |------------|----------------------------------------------|-----------------|\n", + "> | `input_ar` | Input array, if applicable. | `null` |\n", + "> | `cpu` | Whether to use CPU instead of GPU. | `false` |\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "db2bbe87-33e0-4cc4-9d89-ba6cf1f585c2", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "######################\n", + "# 03 - EMBEDDINGS #\n", + "######################\n", + "def get_artifact_config_embeddings(\n", + " print_flag : bool = False,\n", + " config_path : str = config_path\n", + ") -> Tuple[AttrDict, str]:\n", + " \"\"\"\n", + " Constructs the configuration for embeddings by fetching relevant settings and building the encoder artifact configuration.\n", + " Validates the project and entity settings and returns the artifact configuration as an AttrDict, along with the job type.\n", + " \"\"\"\n", + "\n", + " config = get_config(print_flag, \"03a-embeddings\", config_path)\n", + " job_type=config.job_type\n", + " version = config.user_preferences.wdb.version\n", + " enc_artifact = build_enc_artifact(config, print_flag)\n", + " config = config.configuration\n", + " artifact_config = AttrDict(\n", + " use_wandb = config.wandb.use,\n", + " wandb_group = config.wandb.group,\n", + " wandb_entity = config.wandb.entity,\n", + " wandb_project = config.wandb.project,\n", + " enc_artifact = enc_artifact,\n", + " input_ar = config.specifications.input_ar,\n", + " cpu = config.specifications.cpu\n", + " )\n", + " check_project_and_entity(artifact_config.wandb_entity, artifact_config.wandb_project)\n", + " return artifact_config, job_type" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e1d34641-83c2-4b68-b058-1145eba72c5f", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def get_artifact_config_embeddings_SWV(\n", + " print_flag : bool = False,\n", + " config_path : str = config_path\n", + ") -> Tuple[AttrDict, str]:\n", + " \"\"\"\n", + " Constructs the configuration for embeddings (sliding window view) by fetching relevant settings and building the encoder artifact configuration.\n", + " Validates the project and entity settings and returns the artifact configuration as an AttrDict, along with the job type.\n", + " \"\"\"\n", + " config = get_config(print_flag, \"03b-embeddings-sliding_window_view\", config_path)\n", + " job_type=config.job_type\n", + " version = config.user_preferences.wdb.version\n", + " enc_artifact = build_enc_artifact(config, print_flag)\n", + " config = config.configuration\n", + " artifact_config = AttrDict(\n", + " use_wandb = config.wandb.use,\n", + " wandb_group = config.wandb.group,\n", + " wandb_entity = config.wandb.entity,\n", + " wandb_project = config.wandb.project,\n", + " enc_artifact = enc_artifact,\n", + " input_ar = config.specifications.input_ar,\n", + " cpu = config.specifications.cpu\n", + " )\n", + " check_project_and_entity(artifact_config.wandb_entity, artifact_config.wandb_project)\n", + " return artifact_config, job_type\n" + ] + }, + { + "cell_type": "markdown", + "id": "897f5750-189c-4f6e-9652-c1747600b9bf", + "metadata": {}, + "source": [ + "## Dimensionality Reduction Configuration\n", + "The specific configurations are in the file `04-dimensionality_reduction`\n", + "It contains the following information:\n", + "\n", + "> - Job Type: `job_type`\n", + "> \n", + "> | Key | Value |\n", + "> |------------|----------------------------|\n", + "> | `job_type` | `'dimensionality_reduction'`|\n", + ">\n", + "> - Configuration: `configuration`\n", + "> - Wandb Configuration: `configuration.wandb`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |---------|------------------------------------------------------|--------------------|\n", + "> | `use` | Whether to use wandb for experiment tracking. | Refers to *use_wandb |\n", + "> | `group` | Specifies whether to group this run in a wandb group. | `null` |\n", + "> | `entity`| The entity to use for wandb. | Refers to *wdb_user |\n", + "> | `project`| The project to use for wandb. | Refers to *wdb_project |\n", + ">\n", + "> - Encoder Configuration: `configuration.encoder`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |------------------------------|-----------------------------------------------------|-------------------------------------------------|\n", + "> | `artifacts.train.enc_prefix` | Prefix for the training artifacts. | Combination of *wdb_user, *wdb_project, and *alg |\n", + "> | `artifacts.valid` | If none, the validation set used to train enc is used. | `null` |\n", + ">\n", + "> - UMAP Configuration: `configuration.encoder.umap`\n", + "> \n", + "> | Key | Description | Example Value |\n", + "> |-----------------|-----------------------------|---------------|\n", + "> | `n_neighbors` | Number of neighbors for UMAP. | `15` |\n", + "> | `min_dist` | Minimum distance for UMAP. | `0.1` |\n", + "> | `random_state` | Random state for UMAP. | `1234` |\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "13facd5a-bfda-4973-9db2-0333c2b72a42", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "###################################\n", + "# 04 - DIMENSIONALITY REDUCTION #\n", + "###################################\n", + "def get_artifact_config_dimensionality_reduction(\n", + " print_flag : bool = False,\n", + " config_path : str = config_path\n", + ") -> Tuple[AttrDict, str]:\n", + " \"\"\"\n", + " Constructs the configuration for dimensionality reduction tasks by fetching relevant settings, including building the encoder artifact.\n", + " Returns the artifact configuration as an AttrDict, along with the job type.\n", + " \"\"\"\n", + "\n", + " config = get_config(print_flag, \"04-dimensionality_reduction\", config_path)\n", + " job_type = config.job_type\n", + " enc_artifact = build_enc_artifact(config, print_flag)\n", + " config = config.configuration\n", + " artifact_config = AttrDict(\n", + " use_wandb = config.wandb.use, \n", + " wandb_group = config.wandb.group,\n", + " wandb_entity = config.wandb.entity,\n", + " wandb_project = config.wandb.project,\n", + " valid_artifact = config.encoder.artifacts.valid, \n", + " train_artifact = enc_artifact,\n", + " n_neighbors = config.encoder.umap.n_neighbors,\n", + " min_dist = config.encoder.umap.min_dist,\n", + " random_state = config.encoder.umap.random_state\n", + " )\n", + " return artifact_config, job_type\n" + ] + }, + { + "cell_type": "markdown", + "id": "bccd944c-7e4a-46cd-824b-4a4c5ceae716", + "metadata": {}, + "source": [ + "## XAI - shap reduction configuration\n", + "> TODO: Not yet implemented or configured... In progress.. \n", + "\n", + "The specific configurations are in the file `05-xai_shap`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "f8d335f8-a63b-4dae-a2b3-db80e9ef92b6", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "##################\n", + "# 05 - XAI-SHAP #\n", + "##################\n", + "def get_artifact_config_xai_shap(\n", + " print_flag : bool = False,\n", + " config_path : str = config_path\n", + ") -> Tuple[AttrDict, str]:\n", + " \"\"\"\n", + " Constructs the configuration for the XAI SHAP (Explainable Artificial Intelligence using SHAP values) analysis. \n", + " This includes fetching relevant settings, building the encoder artifact, and assembling the artifact configuration.\n", + " Returns the artifact configuration as an AttrDict, along with the job type.\n", + " \"\"\"\n", + " config = get_config(print_flag, \"05-xai_shap\", config_path)\n", + " job_type = config.job_type\n", + " enc_artifact = build_enc_artifact(config, print_flag)\n", + " config = config.configuration\n", + " artifact_config = AttrDict(\n", + " use_wandb = config.wandb.use, \n", + " wandb_group = config.wandb.group,\n", + " wandb_entity = config.wandb.entity,\n", + " wandb_project = config.wandb.project,\n", + " valid_artifact = config.encoder.artifacts.valid, \n", + " train_artifact = enc_artifact,\n", + " n_neighbors = config.encoder.umap.n_neighbors,\n", + " min_dist = config.encoder.umap.min_dist,\n", + " random_state = config.encoder.umap.random_state\n", + " )\n", + " return artifact_config, job_type\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "1156b20b", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide -> Not yet implemented\n", + "#get_artifact_config_xai_shap(False)" + ] + }, + { + "cell_type": "markdown", + "id": "1186ec85-e47e-432b-992f-e8e143df9a8e", + "metadata": {}, + "source": [ + "## Use a tested configuration" + ] + }, + { + "cell_type": "markdown", + "id": "19b5631a-e992-4bfc-80d1-038e7f73cf03", + "metadata": {}, + "source": [ + "[Monash benchmark](https://huggingface.co/datasets/monash_tsf) datasets used configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "07468ed3-5783-468b-9ebd-92ec8b9465ea", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "# Monash australian electricity demand\n", + "monash_australian_electricity_demand_0 = AttrDict(\n", + " alias =\"Monash-Australian_electricity_demand\",\n", + " fname =\"australian_electricity_demand_dataset\",\n", + " #5 univariate timeseries. 0-4 can be used 1 each time\n", + " cols =[0],\n", + " ftype ='.tsf',\n", + " freq ='30min', \n", + " time_col = None,\n", + " mvp = AttrDict(\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " ws = [2,336], #1h-1week | TODO: Check to ensure freq sense\n", + " stride = 48 #Day2Day TODO: Check\n", + " ),\n", + " dcae = AttrDict(#TODO: Check\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " stride = 48,\n", + " w = 224,\n", + " delta = 60,\n", + " nfs = [64, 32, 16],\n", + " kss = [10, 5, 5],\n", + " output_filter_size = 10,\n", + " top_k = [2,2,4],\n", + " pool_szs = [2,2,4]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "ec8732bc-27e0-43dd-9c27-40d6348c3dd7", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "# Monash sunspot:\n", + "monash_sunspot_0 = AttrDict(\n", + " alias = \"sunspot\",\n", + " fname = \"sunspot_dataset_with_missing_values\",\n", + " ftype = \".tsf\",\n", + " cols = [],\n", + " freq ='1d',\n", + " time_col = None,\n", + " mvp = AttrDict(\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " ws = [7,365], #1week-1year\n", + " stride = 30 #1 month TODO: Check\n", + " ),\n", + " dcae = AttrDict(#TODO: Check\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " stride = 48,\n", + " w = 224,\n", + " delta = 60,\n", + " nfs = [64, 32, 16],\n", + " kss = [10, 5, 5],\n", + " output_filter_size = 10,\n", + " top_k = [2,2,4],\n", + " pool_szs = [2,2,4]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "63b24736-ba1a-4161-99fc-a328529d4203", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "monash_solar_4_seconds_0 = AttrDict(\n", + " alias = 'solar_4_seconds',\n", + " fname = 'solar_4_seconds_dataset',\n", + " ftype = '.tsf',\n", + " freq = '4s',\n", + " cols = [],\n", + " time_col= None,\n", + " mvp = AttrDict(\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " #1 4 seconds\n", + " #15 1 min\n", + " #450 30 min - Too small for G4-0\n", + " #900 1h\n", + " ws = [450,900], #1 min - 30 min (15*60=900 = 1hora intervalos 4 secs)\n", + " stride = 450\n", + " #stride = 10800 #1 min TODO: Check\n", + " ),\n", + " dcae = AttrDict(#TODO: Check\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " stride = 48,\n", + " w = 224,\n", + " delta = 60,\n", + " nfs = [64, 32, 16],\n", + " kss = [10, 5, 5],\n", + " output_filter_size = 10,\n", + " top_k = [2,2,4],\n", + " pool_szs = [2,2,4]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "21735905-a078-4ee5-919d-f8da73f0dff0", + "metadata": {}, + "source": [ + "[Wikipedia web traffic dataset](https://zenodo.org/records/3898474)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "39701d48-8d7d-4928-a528-2ecc5a71ff5e", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "wikipedia_0 = AttrDict(\n", + " alias=\"Wikipedia\",\n", + " fname=\"kaggle_web_traffic_dataset_with_missing_values\",\n", + " cols=[0, 1, 2, 3, 4],\n", + " ftype=\".tsf\",\n", + " freq = '1d',\n", + " time_col=None,\n", + " mvp = AttrDict(\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " ws = [1,365], #1d-1año\n", + " stride = 1 #TODO: Check\n", + " ),\n", + " dcae = AttrDict(#TODO: Check\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " stride = 48,\n", + " w = 224,\n", + " delta = 60,\n", + " nfs = [64, 32, 16],\n", + " kss = [10, 5, 5],\n", + " output_filter_size = 10,\n", + " top_k = [2,2,4],\n", + " pool_szs = [2,2,4]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a14f195f-14d3-4f07-8180-6aaa313bee6b", + "metadata": {}, + "source": [ + "[Traffic San Francisco](https://zenodo.org/records/3898445)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "a200c2b3-daf0-441b-913b-eee9c0e8d326", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "traffic_san_francisco_0 = AttrDict(\n", + " alias=\"Traffic_SF\",\n", + " fname=\"traffic_hourly_dataset\",\n", + " ftype=\".tsf\",\n", + " cols=[],\n", + " time_col=None,\n", + " freq='1h',\n", + " mvp = AttrDict(\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " ws = [1,720], #1h-1week TODO: Check\n", + " stride = 24 #1 day TODO: Check\n", + " ),\n", + " dcae = AttrDict(#TODO: Check\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " stride = 48,\n", + " w = 224,\n", + " delta = 60,\n", + " nfs = [64, 32, 16],\n", + " kss = [10, 5, 5],\n", + " output_filter_size = 10,\n", + " top_k = [2,2,4],\n", + " pool_szs = [2,2,4]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "1db1d924-90eb-4f33-b389-d14968d883e1", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "# Monash solar 10 minutes dataset\n", + "# Multi-dimensional\n", + "monash_solar_10_minutes_0 = AttrDict(\n", + " alias =\"Monash-Solar_10_minutes\",\n", + " fname =\"solar_10_minutes_dataset\",\n", + " #5 univariate timeseries. 0-4 can be used 1 each time\n", + " #137 columns (each one 1 different timeserie)\n", + " cols =[0],\n", + " ftype ='.tsf',\n", + " freq ='10min',\n", + " time_col = None,\n", + " mvp = AttrDict(\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " #1 month 4320 -> Too big for G4 (GPU-0)\n", + " #1 day 144 -> Ok\n", + " #1 week 1008 -> Ok\n", + " #15 days 2160 -> Ok\n", + " ws = [1,2160],\n", + " stride = 144 #1d by 1d\n", + " ),\n", + " dcae = AttrDict(#TODO: Check\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " stride = 48,\n", + " w = 224,\n", + " delta = 60,\n", + " nfs = [64, 32, 16],\n", + " kss = [10, 5, 5],\n", + " output_filter_size = 10,\n", + " top_k = [2,2,4],\n", + " pool_szs = [2,2,4]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d5484725-82ac-4d55-b58d-ded17db5c577", + "metadata": {}, + "source": [ + "### Other public datasets" + ] + }, + { + "cell_type": "markdown", + "id": "a4f223b4-6523-4ff3-bea9-ca765b012547", + "metadata": {}, + "source": [ + "#### Electricity Transformer Temperature\n", + "[ETDataset](https://paperswithcode.com/dataset/ett)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "cbb020bd-6e7d-4668-a24b-b529f93e2f14", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "etth1_0 = AttrDict(\n", + " alias=\"ETTh1\",\n", + " fname=\"ETTh1\",\n", + " ftype=\".csv\",\n", + " cols=[],\n", + " time_col=0,\n", + " freq='1h',\n", + " mvp = AttrDict(\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " ws = [1,720], #1 h - 1 month TODO:Check\n", + " stride = 24 #1 day TODO: Check\n", + " ),\n", + " dcae = AttrDict(#TODO: Check\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " stride = 48,\n", + " w = 224,\n", + " delta = 60,\n", + " nfs = [64, 32, 16],\n", + " kss = [10, 5, 5],\n", + " output_filter_size = 10,\n", + " top_k = [2,2,4],\n", + " pool_szs = [2,2,4]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5630ad06-9eaf-4198-a9dc-f0e71f3a020d", + "metadata": {}, + "source": [ + "#### Stumpy\n", + "##### [Semantic segmentation TitlABP](https://stumpy.readthedocs.io/en/latest/Tutorial_Semantic_Segmentation.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "f3b0e373-a1b7-4fb1-96ca-1992815305e6", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "stumpy_abp_0 = AttrDict(\n", + " alias=\"TitlABP\",\n", + " fname=\"Semantic_Segmentation_TiltABP\",\n", + " ftype=\".csv\",\n", + " cols=[],\n", + " freq=\"1s\",\n", + " time_col=0, \n", + " mvp = AttrDict(\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " ws = [60,3600], #1min-1h TODO:Check\n", + " stride = 60 #1 min TODO: Check\n", + " ),\n", + " dcae = AttrDict(#TODO: Check\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " stride = 48,\n", + " w = 224,\n", + " delta = 60,\n", + " nfs = [64, 32, 16],\n", + " kss = [10, 5, 5],\n", + " output_filter_size = 10,\n", + " top_k = [2,2,4],\n", + " pool_szs = [2,2,4]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c9ebbe03-d4af-44dd-bd38-d56d1e8dec90", + "metadata": {}, + "source": [ + "##### [Multi-dimensional toy data](https://zenodo.org/records/4294932)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "9ec755c5-2013-4802-bb54-24b2642d01b9", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "stumpy_toy_0 = AttrDict(\n", + " alias=\"toy\",\n", + " fname=\"toy\",\n", + " ftype=\".csv\",\n", + " cols=[],\n", + " freq=\"1s\",\n", + " time_col=None,\n", + " mvp = AttrDict(\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " ws = [10,30], \n", + " stride = 1\n", + " ),\n", + " dcae = AttrDict(#TODO: Check\n", + " batch_size = 512,\n", + " n_epoch = 100,\n", + " stride = 48,\n", + " w = 224,\n", + " delta = 60,\n", + " nfs = [64, 32, 16],\n", + " kss = [10, 5, 5],\n", + " output_filter_size = 10,\n", + " top_k = [2,2,4],\n", + " pool_szs = [2,2,4]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c58f4fa2-dfbf-499d-a2c1-b588dc64940c", + "metadata": {}, + "source": [ + "### Get tested configuration function" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "4dee0968-1f1c-4a67-90a0-fa998ab854b3", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "tested_configs = {\n", + " 'monash_australian_electricity_demand_0': monash_australian_electricity_demand_0,\n", + " 'monash_solar_4_seconds_0': monash_solar_4_seconds_0,\n", + " 'wikipedia_0': wikipedia_0,\n", + " 'traffic_san_francisco_0': traffic_san_francisco_0,\n", + " 'monash_solar_10_minutes_0': monash_solar_10_minutes_0,\n", + " 'etth1_0': etth1_0,\n", + " 'stumpy_abp_0': stumpy_abp_0,\n", + " 'stumpy_toy_0': stumpy_toy_0\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "7ff14373-c56f-4a5a-a787-12191196b091", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def show_attrdict(dict: AttrDict):\n", + " for key, value in dict.items():\n", + " print(f\"{key}: {value}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "daadf0fe-5d0b-4068-86dd-1f17cbf30d48", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def show_available_configs():\n", + " print(\"Available datasets: \")\n", + " i = 0\n", + " for key, val in tested_configs.items():\n", + " print(f\"{i} - {key}\")\n", + " i+=1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "eb13402c-46e1-4960-9836-daf47839d3ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available datasets: \n", + "0 - monash_australian_electricity_demand_0\n", + "1 - monash_solar_4_seconds_0\n", + "2 - wikipedia_0\n", + "3 - traffic_san_francisco_0\n", + "4 - monash_solar_10_minutes_0\n", + "5 - etth1_0\n", + "6 - stumpy_abp_0\n", + "7 - stumpy_toy_0\n" + ] + } + ], + "source": [ + "#| hide \n", + "show_available_configs()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "887c90c2-1704-40e2-bd94-1d811e3c1a31", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'traffic_san_francisco_0'" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| hide \n", + "list(tested_configs.items())[3][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "668cc6c8-bb89-4b2e-a598-0ee0f3f788a9", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def show_config(id: int = 0):\n", + " show_attrdict(list(tested_configs.items())[id][1])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "154878de-f491-44e6-86ea-d8bfa8fa4eab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alias: Traffic_SF\n", + "fname: traffic_hourly_dataset\n", + "ftype: .tsf\n", + "cols: []\n", + "time_col: None\n", + "freq: 1h\n", + "mvp: {'batch_size': 512, 'n_epoch': 100, 'ws': [1, 720], 'stride': 24}\n", + "dcae: {'batch_size': 512, 'n_epoch': 100, 'stride': 48, 'w': 224, 'delta': 60, 'nfs': [64, 32, 16], 'kss': [10, 5, 5], 'output_filter_size': 10, 'top_k': [2, 2, 4], 'pool_szs': [2, 2, 4]}\n" + ] + } + ], + "source": [ + "#| hide\n", + "show_config(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "9e12be39-14e7-41bf-8459-6ed47c708792", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def get_tested_config(\n", + " id: int = 0,\n", + " print_flag=False\n", + "):\n", + " if print_flag: show_config(id)\n", + " return list(tested_configs.items())[id][1]\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "3cfac15b-b9e7-4781-8397-5b363c800a63", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alias: Monash-Australian_electricity_demand\n", + "fname: australian_electricity_demand_dataset\n", + "cols: [0]\n", + "ftype: .tsf\n", + "freq: 30min\n", + "time_col: None\n", + "mvp: {'batch_size': 512, 'n_epoch': 100, 'ws': [2, 336], 'stride': 48}\n", + "dcae: {'batch_size': 512, 'n_epoch': 100, 'stride': 48, 'w': 224, 'delta': 60, 'nfs': [64, 32, 16], 'kss': [10, 5, 5], 'output_filter_size': 10, 'top_k': [2, 2, 4], 'pool_szs': [2, 2, 4]}\n" + ] + } + ], + "source": [ + "#| hide\n", + "show_attrdict(get_tested_config(0))" + ] + }, + { + "cell_type": "markdown", + "id": "585097af-8862-45f4-a259-986a3a10d963", + "metadata": {}, + "source": [ + "### Force tested configuration functions\n", + "#### 01 - Dataset Artifact" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "0d400a03-8f9b-4308-90cc-735dc92c1124", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def print_colored(\n", + " key, \n", + " modified_val, \n", + " modified, \n", + " both:bool=False, \n", + " original_val=0,\n", + " missing_in_modified=False,\n", + " missing_in_original=False\n", + "):\n", + " if missing_in_modified:\n", + " color = \"\\033[91m\\033[1m\" # Red and bold\n", + " elif missing_in_original:\n", + " color = \"\\033[93m\\033[1m\" # Orange and bold\n", + " else:\n", + " color = \"\\033[94m\" if modified else \"\"\n", + " reset = \"\\033[0m\"\n", + " \n", + " if modified and both:\n", + " print(f\"{color}{key}: {original_val}{reset} -> {modified_val}{reset}\")\n", + " elif missing_in_modified:\n", + " print(f\"{color}{key} is missing in modified dict | {original_val} {reset}\")\n", + " elif missing_in_original:\n", + " print(f\"{color}{key} is missing in original dict | {modified_val} {reset}\")\n", + " else:\n", + " print(f\"{color}{key}: {modified_val}{reset}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "eeb494df-c5ee-4cde-a635-c6aea6521fb3", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "eb2e52a0-0492-4529-b050-ef5384534f79", + "metadata": {}, + "outputs": [], + "source": [ + "#| export \n", + "def get_resampling_frequency(\n", + " freq: str,\n", + " frequency_factor:int = 1,\n", + " print_flag = False\n", + "):\n", + " if print_flag:\n", + " print(\"--> Frequency factor resampling frequency\")\n", + " print(\"Freq factor: \", frequency_factor)\n", + " freq_new = pd.to_timedelta(freq)\n", + " freq_new = freq_new*frequency_factor\n", + " if print_flag:\n", + " print(\"freq_original: \", freq)\n", + " print(\"freq_new: \", freq_new)\n", + " suffix = \"-\"\n", + " resampling_freq=\"\"\n", + " if freq_new.days > 0 and freq_new.seconds == pd.to_timedelta(0,'s'):\n", + " suffix = str(freq_new.days)+'d'\n", + " resampling_freq = str(freq_new.days)+'D'\n", + " elif freq_new.seconds % 3600 == 0:\n", + " hours = (freq_new.seconds // 3600) + freq_new.days*24\n", + " suffix = str(hours)+'h'\n", + " resampling_freq = str(hours) + 'H'\n", + " elif freq_new.seconds % 60 == 0: \n", + " minutes = (freq_new.seconds // 60) + (freq_new.days*24*60)\n", + " suffix = str(minutes)+'m'\n", + " resampling_freq = str(minutes)+'T'\n", + " else: \n", + " seconds = freq_new.seconds + freq_new.days*24*60*60 \n", + " suffix = str(seconds)+'s'\n", + " resampling_freq = str(seconds)+'S'\n", + " if print_flag:\n", + " print(\"suffix: \", suffix)\n", + " print(\"resampling_freq: \", resampling_freq)\n", + " print(\"Frequency factor resampling frequency -->\")\n", + " return (suffix, resampling_freq)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "84e8fb00-673e-4955-a0c0-9933b2ae3750", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def frequency_factor_config(\n", + " config: AttrDict, \n", + " frequency_factor:int = 1,\n", + " frequency_factor_change_alias: bool = True,\n", + " print_flag = False\n", + "):\n", + " if print_flag:\n", + " print(\"--> Frequency factor config\")\n", + " print(\"Freq factor: \", frequency_factor)\n", + " print(\"frequency_factor_change_alias: \", frequency_factor_change_alias)\n", + " suffix, config.resampling_freq = get_resampling_frequency(config.freq, frequency_factor, print_flag)\n", + "\n", + " if frequency_factor_change_alias:\n", + " #filename = config.data_fpath.split(\".tsf\", 1)[0]\n", + " config.artifact_name = config.artifact_name+\"-\"+suffix\n", + " #config.data_fpath = filename+\"-\"+suffix+\".tsf\"\n", + "\n", + " if print_flag: \n", + " print(\"resampling_freq: \", config.freq)\n", + " print(\"name: \", config.artifact_name)\n", + " print(\"path: \", config.data_fpath) \n", + " print(\"Frequency factor config -->\")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "4baad8bd-bd77-42c9-a6c7-d6bfc9d9cf6d", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def diff_attrdict(\n", + " dict_original: AttrDict, \n", + " dict_modified: AttrDict,\n", + " both: bool = False\n", + "):\n", + " all_keys = set(dict_original.keys()) | set(dict_modified.keys())\n", + " for key in all_keys:\n", + " in_original = key in dict_original\n", + " in_modified = key in dict_modified\n", + " \n", + " if in_original and in_modified:\n", + " modified = dict_original[key] != dict_modified[key]\n", + " print_colored(\n", + " key, \n", + " modified_val = dict_modified[key], \n", + " modified = modified, \n", + " both = both, \n", + " original_val=dict_original[key]\n", + " )\n", + " elif in_original:\n", + " # Key is missing in dict_modified\n", + " print_colored(key, modified_val=None, modified=True, missing_in_modified=True)\n", + " else:\n", + " # Key is missing in dict_original\n", + " print_colored(key, modified_val=dict_modified[key], modified=True, missing_in_original=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "afda84a7-9ea0-4166-8cb7-c36376d4619e", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "from copy import deepcopy\n", + "def force_artifact_config_sd2a(\n", + " config: AttrDict,\n", + " id:int = 0, \n", + " print_flag = False,\n", + " both = False,\n", + " frequency_factor = 1, \n", + " frequency_factor_change_alias = True\n", + "):\n", + " to_set = get_tested_config(id)\n", + " if print_flag: \n", + " config_before = deepcopy(config)\n", + " print(\"Selecting \", list(tested_configs.items())[id][0])\n", + " config.artifact_name = to_set.alias\n", + " config.data_cols = to_set.cols\n", + " config.data_fpath= \"~/data/\"+to_set.fname+to_set.ftype\n", + " config.freq=to_set.freq\n", + " config.time_col = to_set.time_col\n", + " config.csv_config = {}\n", + " joining_train_test= False,\n", + " missing_values_constant= None,\n", + " missing_values_technique= None,\n", + " normalize_training= False,\n", + " range_testing= None,\n", + " range_training= None,\n", + " resampling_freq= None,\n", + " start_date= None,\n", + " test_split= None,\n", + " if frequency_factor > 1: \n", + " frequency_factor_config(config, frequency_factor, frequency_factor_change_alias, print_flag)\n", + " if print_flag: \n", + " diff_attrdict(\n", + " dict_original=config_before, \n", + " dict_modified=config, \n", + " both = both)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "ec3910fc-3d6d-4b94-8d3d-5c18edb3029c", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "sd2a_config = AttrDict(\n", + " artifact_name= \"Monash-Australian_electricity\",\n", + " csv_config= {},\n", + " data_cols= [0],\n", + " data_fpath= '~/data/australian_electricity_demand_dataset.csv',\n", + " date_format= '%Y-%m-%d %H:%M:%S',\n", + " date_offset= None,\n", + " freq= '1s',\n", + " joining_train_test= False,\n", + " missing_values_constant= None,\n", + " missing_values_technique= None,\n", + " normalize_training= False,\n", + " range_testing= None,\n", + " range_training= None,\n", + " resampling_freq= None,\n", + " start_date= None,\n", + " test_split= None,\n", + " time_col= None,\n", + " use_wandb= True,\n", + " wandb_artifacts_path='./data/wandb_artifacts'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "4d1c4265-1294-406f-978a-d3e9bd2a4547", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selecting stumpy_abp_0\n", + "--> Frequency factor config\n", + "Freq factor: 2\n", + "frequency_factor_change_alias: True\n", + "--> Frequency factor resampling frequency\n", + "Freq factor: 2\n", + "freq_original: 1s\n", + "freq_new: 0 days 00:00:02\n", + "suffix: 2s\n", + "resampling_freq: 2S\n", + "Frequency factor resampling frequency -->\n", + "resampling_freq: 1s\n", + "name: TitlABP-2s\n", + "path: ~/data/Semantic_Segmentation_TiltABP.csv\n", + "Frequency factor config -->\n", + "date_offset: None\u001b[0m\n", + "\u001b[94mdata_cols: [0]\u001b[0m -> []\u001b[0m\n", + "csv_config: {}\u001b[0m\n", + "missing_values_technique: None\u001b[0m\n", + "\u001b[94mdata_fpath: ~/data/australian_electricity_demand_dataset.csv\u001b[0m -> ~/data/Semantic_Segmentation_TiltABP.csv\u001b[0m\n", + "start_date: None\u001b[0m\n", + "\u001b[94mtime_col: None\u001b[0m -> 0\u001b[0m\n", + "freq: 1s\u001b[0m\n", + "wandb_artifacts_path: ./data/wandb_artifacts\u001b[0m\n", + "\u001b[94mresampling_freq: None\u001b[0m -> 2S\u001b[0m\n", + "\u001b[94martifact_name: Monash-Australian_electricity\u001b[0m -> TitlABP-2s\u001b[0m\n", + "date_format: %Y-%m-%d %H:%M:%S\u001b[0m\n", + "range_training: None\u001b[0m\n", + "missing_values_constant: None\u001b[0m\n", + "joining_train_test: False\u001b[0m\n", + "use_wandb: True\u001b[0m\n", + "test_split: None\u001b[0m\n", + "normalize_training: False\u001b[0m\n", + "range_testing: None\u001b[0m\n" + ] + } + ], + "source": [ + "#| hide\n", + "force_artifact_config_sd2a(\n", + " config = sd2a_config, \n", + " id = 6, \n", + " print_flag=True, \n", + " both=True, \n", + " frequency_factor = 2, \n", + " frequency_factor_change_alias = True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8a5964ed-1cf6-469b-bced-2cbe262b20bd", + "metadata": {}, + "source": [ + "#### 02(bc) Encoder MVP" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "4d483893-35a9-4be2-8324-9e099af4bfc5", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def split_artifact_string(s:string) -> tuple[string, string, string]:\n", + " # Divide la cadena en dos partes usando ':'\n", + " path, version = s.split(':')\n", + "\n", + " # Divide la parte del path en sus componentes\n", + " parts = path.rsplit('/', 1)\n", + "\n", + " # Retorna los componentes separados\n", + " return parts[0] + '/', parts[1], version" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "52395a2d-0dbd-4226-82b0-7853ff555572", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('mi-santamaria/deepvats/', 'Monash-Australian_electricity_demand', 'latest')\n" + ] + } + ], + "source": [ + "#| hide\n", + "result = split_artifact_string(\"mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest\")\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "cb828063-26b2-43fb-ab92-e60a0ee0db72", + "metadata": {}, + "outputs": [], + "source": [ + "#| export \n", + "def force_artifact_config_mvp(\n", + " config: AttrDict,\n", + " id:int = 0, \n", + " print_flag = False,\n", + " both = False,\n", + " frequency_factor = 1,\n", + " frequency_factor_change_alias = False,\n", + "):\n", + " to_set = get_tested_config(id)\n", + " if print_flag: \n", + " config_before = deepcopy(config)\n", + " \n", + " force_artifact_config_sd2a(\n", + " config = config, \n", + " id = id, \n", + " print_flag = False, \n", + " both = False, \n", + " frequency_factor = frequency_factor, \n", + " frequency_factor_change_alias = frequency_factor_change_alias\n", + " )\n", + "\n", + " config.alias = to_set.alias\n", + " config.batch_size = to_set.mvp.batch_size\n", + " config.epochs = to_set.mvp.n_epoch\n", + " \n", + " config.mask_future = False \n", + " config.mask_stateful = True\n", + " config.mask_sync = False\n", + " config.norm_by_sample = False\n", + " config.norm_use_by_single_batch = False,\n", + " config.r = 0.71\n", + " \n", + " config.stride=to_set.mvp.stride\n", + " path,_,version = split_artifact_string(config.train_artifact)\n", + " config.train_artifact=path+config.artifact_name+\":\"+version\n", + "\n", + " config.valid_size = 0.2\n", + " \n", + " config.mvp_ws= to_set.mvp.ws\n", + " config.w = config.mvp_ws[1]\n", + " \n", + " if print_flag: \n", + " diff_attrdict(\n", + " dict_original=config_before, \n", + " dict_modified=config, \n", + " both = both\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "f50ac55c-f49e-4b4f-ad56-1acce64907ad", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "mvp_config = AttrDict(\n", + " alias = \"Monash-Australian_electric\",\n", + " artifact_name=\"Monash-Australian_electricity\",\n", + " analysis_mode=\"online\",\n", + " batch_size=2,\n", + " epochs=1,\n", + " mask_future=False,\n", + " mask_stateful=True,\n", + " mask_sync=False,\n", + " mvp_ws=(150, 33),\n", + " norm_by_sample=False,\n", + " norm_use_single_batch=False,\n", + " r=0.71,\n", + " stride=13,\n", + " train_artifact=\"mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest\",\n", + " valid_artifact=None,\n", + " use_wandb=True,\n", + " valid_size=0.2,\n", + " w=30,\n", + " wandb_group=None,\n", + " data_cols= [],\n", + " data_fpath= \"~/data/kaggle_web_traffic_dataset_with_missing_values.tsf\",\n", + " freq= '1d',\n", + " time_col= 'None'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "1c71f660-eae7-4d0a-bdb7-7733c3ba8fab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r: 0.71\u001b[0m\n", + "data_cols: []\u001b[0m\n", + "valid_size: 0.2\u001b[0m\n", + "\u001b[93m\u001b[1mcsv_config is missing in original dict | {} \u001b[0m\n", + "analysis_mode: online\u001b[0m\n", + "\u001b[94mbatch_size: 2\u001b[0m -> 512\u001b[0m\n", + "\u001b[94mdata_fpath: ~/data/kaggle_web_traffic_dataset_with_missing_values.tsf\u001b[0m -> ~/data/solar_4_seconds_dataset.tsf\u001b[0m\n", + "\u001b[94mw: 30\u001b[0m -> 900\u001b[0m\n", + "\u001b[94mtrain_artifact: mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest\u001b[0m -> mi-santamaria/deepvats/solar_4_seconds-20s:latest\u001b[0m\n", + "\u001b[94mtime_col: None\u001b[0m -> None\u001b[0m\n", + "\u001b[94mfreq: 1d\u001b[0m -> 4s\u001b[0m\n", + "\u001b[93m\u001b[1mnorm_use_by_single_batch is missing in original dict | (False,) \u001b[0m\n", + "valid_artifact: None\u001b[0m\n", + "norm_use_single_batch: False\u001b[0m\n", + "\u001b[93m\u001b[1mresampling_freq is missing in original dict | 20S \u001b[0m\n", + "\u001b[94martifact_name: Monash-Australian_electricity\u001b[0m -> solar_4_seconds-20s\u001b[0m\n", + "mask_future: False\u001b[0m\n", + "\u001b[94mmvp_ws: (150, 33)\u001b[0m -> [450, 900]\u001b[0m\n", + "\u001b[94mepochs: 1\u001b[0m -> 100\u001b[0m\n", + "use_wandb: True\u001b[0m\n", + "mask_sync: False\u001b[0m\n", + "\u001b[94malias: Monash-Australian_electric\u001b[0m -> solar_4_seconds\u001b[0m\n", + "mask_stateful: True\u001b[0m\n", + "\u001b[94mstride: 13\u001b[0m -> 450\u001b[0m\n", + "wandb_group: None\u001b[0m\n", + "norm_by_sample: False\u001b[0m\n" + ] + } + ], + "source": [ + "#| hide \n", + "force_artifact_config_mvp(mvp_config, 1, True, True, 5, True)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "11c0fb05-4e42-4750-a554-a1f923076c2e", + "metadata": {}, + "outputs": [], + "source": [ + "#| export \n", + "def force_artifact_config_dcae(\n", + " config: AttrDict,\n", + " id:int = 0, \n", + " print_flag = False,\n", + " both = False,\n", + " frequency_factor = 1,\n", + " frequency_factor_change_alias = False,\n", + "):\n", + " to_set = get_tested_config(id)\n", + " if print_flag: \n", + " config_before = deepcopy(config)\n", + " \n", + " force_artifact_config_sd2a(\n", + " config = config, \n", + " id = id, \n", + " print_flag = False, \n", + " both = False, \n", + " frequency_factor = frequency_factor, \n", + " frequency_factor_change_alias = frequency_factor_change_alias\n", + " )\n", + "\n", + " config.alias = to_set.alias\n", + " \n", + " config.batch_size = to_set.dcae.batch_size\n", + " config.epochs = to_set.dcae.n_epoch \n", + " config.r = 0.71\n", + " config.stride=to_set.dcae.stride\n", + " path,_,version = split_artifact_string(config.train_artifact)\n", + " config.train_artifact=path+config.artifact_name+\":\"+version\n", + "\n", + " config.valid_size = 0.2\n", + " config.w = to_set.dcae.w\n", + " config.delta = to_set.dcae.delta\n", + " config.nfs = to_set.dcae.nfs\n", + " config.kss = to_set.dcae.kss\n", + " config.output_filter_size = to_set.dcae.kss\n", + " config.top_k = to_set.dcae.top_k\n", + " config.pool_szs = to_set.dcae.pool_szs\n", + " \n", + " if print_flag: \n", + " diff_attrdict(\n", + " dict_original=config_before, \n", + " dict_modified=config, \n", + " both = both\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "e8e45d01-2db6-4f6a-9b70-9b97929f92b9", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "dcae_config = AttrDict(\n", + " alias = \"Monash-Australian_electric\",\n", + " artifact_name=\"Monash-Australian_electricity\",\n", + " analysis_mode=\"online\",\n", + " batch_size=2,\n", + " epochs=1,\n", + " r=0,\n", + " stride=1,\n", + " train_artifact=\"mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest\",\n", + " valid_artifact=None,\n", + " use_wandb=True,\n", + " valid_size=0.2,\n", + " w=30,\n", + " nfs = [],\n", + " kss = [], \n", + " output_filter_size = 1,\n", + " top_k = [],\n", + " delta = 6,\n", + " wandb_group=None,\n", + " data_cols= [],\n", + " data_fpath= \"~/data/kaggle_web_traffic_dataset_with_missing_values.tsf\",\n", + " freq= '1d',\n", + " time_col= 'None'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "34ad57e1-cc30-460d-966e-81d10e7ab2e1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[94mr: 0\u001b[0m -> 0.71\u001b[0m\n", + "\u001b[94mnfs: []\u001b[0m -> [64, 32, 16]\u001b[0m\n", + "valid_size: 0.2\u001b[0m\n", + "data_cols: []\u001b[0m\n", + "analysis_mode: online\u001b[0m\n", + "\u001b[94mbatch_size: 2\u001b[0m -> 512\u001b[0m\n", + "\u001b[93m\u001b[1mcsv_config is missing in original dict | {} \u001b[0m\n", + "\u001b[94mtop_k: []\u001b[0m -> [2, 2, 4]\u001b[0m\n", + "\u001b[94moutput_filter_size: 1\u001b[0m -> [10, 5, 5]\u001b[0m\n", + "\u001b[94mdata_fpath: ~/data/kaggle_web_traffic_dataset_with_missing_values.tsf\u001b[0m -> ~/data/solar_4_seconds_dataset.tsf\u001b[0m\n", + "\u001b[94mw: 30\u001b[0m -> 224\u001b[0m\n", + "\u001b[93m\u001b[1mpool_szs is missing in original dict | [2, 2, 4] \u001b[0m\n", + "\u001b[94mtrain_artifact: mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest\u001b[0m -> mi-santamaria/deepvats/solar_4_seconds-20s:latest\u001b[0m\n", + "\u001b[94mkss: []\u001b[0m -> [10, 5, 5]\u001b[0m\n", + "\u001b[94mtime_col: None\u001b[0m -> None\u001b[0m\n", + "\u001b[94mfreq: 1d\u001b[0m -> 4s\u001b[0m\n", + "valid_artifact: None\u001b[0m\n", + "\u001b[93m\u001b[1mresampling_freq is missing in original dict | 20S \u001b[0m\n", + "\u001b[94martifact_name: Monash-Australian_electricity\u001b[0m -> solar_4_seconds-20s\u001b[0m\n", + "\u001b[94mepochs: 1\u001b[0m -> 100\u001b[0m\n", + "\u001b[94mdelta: 6\u001b[0m -> 60\u001b[0m\n", + "use_wandb: True\u001b[0m\n", + "\u001b[94malias: Monash-Australian_electric\u001b[0m -> solar_4_seconds\u001b[0m\n", + "\u001b[94mstride: 1\u001b[0m -> 48\u001b[0m\n", + "wandb_group: None\u001b[0m\n" + ] + } + ], + "source": [ + "#| hide \n", + "force_artifact_config_dcae(dcae_config, 1, True, True, 5, True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dvats/config.py b/dvats/config.py new file mode 100644 index 00000000..8b9aa18b --- /dev/null +++ b/dvats/config.py @@ -0,0 +1,1909 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[ ]: + + +#| default_exp config + + +# In[ ]: + + +#| hide +get_ipython().run_line_magic('load_ext', 'autoreload') +get_ipython().run_line_magic('autoreload', '2') + + +# # Configuration functions +# > This notebook introduces configuration functions designed to leverage proposed .yaml files, providing a more transparent and automated approach to artefact definition. While this method offers enhanced clarity, the option for direct definition within each notebook is also fully supported and remains practical. +# > +# > TODO: Check for possible simplifications. + +# First, we import +# - os and sys for basic access to files +# - yaml for .yaml reading +# - tsai.basics for using tsai artifacts + +# ## Basic configuration and definitions + +# In[ ]: + + +#| export +import os +import yaml +import sys +from tsai.basics import * + + +# As the yml are saved in ../nbs_pipeline/config, we will need to add '..' path + +# In[ ]: + + +#| export +sys.path.append(os.path.abspath('../nbs_pipeline')) + + +# In[ ]: + + +#| export +config_path = os.path.expanduser('~/work/nbs_pipeline/config/') +config_base_filename = 'base' + + +# An custom_error(message) function is added just for basic error handling + +# In[ ]: + + +#| export +def custom_error(message: str): + """ + This message raises an exception ensuring red-coloured error message is displayed + """ + # Change to red color ANSI code + red_start = "\033[91m" + # Back to original color ANSI code + reset = "\033[0m" + raise Exception(red_start + message + reset) + + +# ## Yaml reading auxiliar functions +# > Defining basics functions to read yml in order to build Attrdict's +# +# In the version of YAML we are utilising, the '!join' constructor is not present. Therefore, it becomes necessary to define it. + +# In[ ]: + + +#| export +def join_constructor(loader: yaml.Loader, node: yaml.Node) -> str: + """ + This function adds the '!join' constructor to the YAML parsing process. + It constructs a sequence from the provided node and then joins the elements of the sequence into a single string. + """ + seq = loader.construct_sequence(node) + return ''.join(seq) + + +# In[ ]: + + +#| export +def recursive_attrdict(d: dict) -> AttrDict: + """ Recursively converts a dictionary into an AttrDict, including all nested dictionaries. """ + if isinstance(d, dict): + return AttrDict({k: recursive_attrdict(v) for k, v in d.items()}) + return d + + +# In[ ]: + + +#| export +def replace_includes_with_content( + filename: str, + path: str = "./", + print_flag: bool = False +) -> str: + """ + This function processes a YAML file. + It replaces '!include' directives with the content of the specified files. + It takes a filename and a path, reads the file, and iteratively replaces any '!include' directives with the content of the included files. + The final processed content, with all '!include' directives substituted is returned as a string. + """ + if (print_flag): + print("... About to replace includes with content") + with open(path+filename, 'r') as f: + content = f.read() + + # Mientras exista una directiva !include en el contenido, sigue reemplazándola + while "!include" in content: + # Obtén la posición de la directiva + start_idx = content.find('!include') + # Encuentra el inicio y el final de las comillas que contienen el nombre del archivo + start_quote_idx = content.find('"', start_idx) + 1 + end_quote_idx = content.find('"', start_quote_idx) + + # Extrae el nombre del archivo + include_filename = content[start_quote_idx:end_quote_idx] + + # Lee el archivo incluido + with open(path+include_filename, 'r') as include_file: + included_content = include_file.read() + + # Reemplaza la directiva por el contenido del archivo incluido + content = content[:start_idx] + included_content + content[end_quote_idx+1:] + + return content + + +# ## Project specific yaml reading functions +# > Basic configuration is read from `../config/base.yml` +# > It contains the following information: +# > - User Preferences: `user_preferences` +# > - General configuration: `user_preferences` +# > +# > | Key | Description | Example Value | +# > |-------------|---------------------------------------------------------|---------------| +# > | `use_wandb` | Indicates whether to use wandb for experiment tracking. | `true` | +# > +# > - Wandb Configuration: `user_preferences.wdb` +# > +# > | Key | Description | Example Value | +# > |----------------------------|---------------------------------------------------|-------------------------| +# > | `user` | The user name for wandb. | `mi-santamaria` | +# > | `project_name` | The project name in wandb. | `deepvats` | +# > | `version` | Specifies the version for wandb. | `'latest'` | +# > | `mode` | Sets the mode for wandb. | `'offline'` | +# > | `artifacts_path` | The path for storing wandb artifacts. | `'./data/wandb_artifacts'`| +# > +# > - Data configuration: `user_preferences.data` +# > +# > | Key | Description | Example Value | +# > |--------------------------|---------------------------------------------------|-------------------------| +# > | `folder` | The folder where the data is stored. | `'~/data/'` | +# > | `fname` | The file name of the dataset used. | `'solar_4_seconds_dataset'` | +# > | `ftype` | The file type of the dataset. | `'.tsf'` | +# > | `cols` | The columns to be used from the dataset. | `[]` (all columns) | +# > | `freq` | The frequency of the dataset. | `'4s'` | +# > +# > - Artifact Configuration: `user_preferences.artifact` +# > +# > | Key | Description | Example Value | +# > |--------------------------|---------------------------------------------------|-------------------------| +# > | `alias` | Alias for the resulting artifact of the run. | `'solar_4_seconds'` | +# > | `algorithm` | The algorithm used in the process. | `'mvp'` | +# > +# > - Directory Configuration: `user_preferences.directories` +# > +# > | Key | Description | Example Value | +# > |--------------------------|---------------------------------------------------|-------------------------| +# > | `tmp` | Folder for storing temporary files. | `'tmp'` | +# > | `data` | Path for the data. | Combination of *path, *fname, *ftype | +# > +# > - Data Specifications: `data` +# > - Data Details: `data` +# > +# > | Key | Description | Example Value | +# > |--------------------------|---------------------------------------------------|-------------------------| +# > | `name` | The name of the data file. | Refers to *fname | +# > | `path` | The path of the data file. | Refers to *data_path | +# > | `alias` | The alias of the data artifact. | Refers to *alias | +# > | `cols` | Columns of interest from the dataset. | Refers to *cols | +# > | `csv_config` | Configuration for CSV files, if applicable. | `{}` (if not required) | +# > | `date_offset` | Offset used for setting the date index. | `null` (if not used) | +# > | `date_format` | Default date format for .tsf files. | `'%Y-%m-%d %H:%M:%S'` | +# > | `freq` | Frequency of the data. | Refers to *freq | +# > | `joining_train_test` | Linking training and testing data. | `false` | +# > | `missing_values` | Handling missing values. | `technique: null, constant: null` | +# > | `normalize_training` | Indicates whether to normalize training data. | `false` | +# > | `range_training` | Specifies training ranges. | `null` | +# > | `range_testing` | Specifies testing ranges. | `null` | +# > | `resampling_freq` | Frequency for resampling the data. | `null` | +# > | `start_date` | Starting date for the dataset. | `null` | +# > | `test_split` | Ratio of the test set. | `null` | +# > | `time_col` | Column containing the timestamp. | `null` | +# > +# > - Wandb Specifications: `wandb` +# > +# > | Key | Description | Example Value | +# > |--------------------------|---------------------------------------------------|-------------------------| +# > | `user` | User name in wandb specifications. | Refers to *wdb_user | +# > | `dir` | Directory for wandb. | Combination of '~/', *wdb_project | +# > | `enabled` | Indicates if wandb is enabled. | `False` | +# > | `group` | Group for the wandb runs. | `null` | +# > | `log_learner` | Whether to log the learner to wandb. | `False` | +# > | `mode` | Mode for wandb operation. | Refers to *wdb_mode | +# > | `project` | The wandb project name. | Refers to *wdb_project | +# > | `version` | Version for the wandb. | Refers to *wdb_version | +# > | `artifacts_path` | Path for storing the TSArtifact in wandb. | Refers to *artifacts_path | +# + +# In[ ]: + + +#| export + +def substitute_env_variables_in_leaves( + config : AttrDict, + print_flag : bool = False +): + if print_flag: print("Config: ", config) + for key, value in config.items(): + if isinstance(value, str): + original_value = value + matches = re.findall(r'\$\{([^}]+)\}', value) + for match in matches: + env_value = os.environ.get(match) + if env_value is not None: + value = value.replace('${' + match + '}', env_value) + config[key] = value + if print_flag and original_value != value: + print(f"Changed {key}: from {original_value} to {config[key]}") + else: + if isinstance(value, AttrDict): + substitute_env_variables_in_leaves(value, print_flag) # Recursividad para diccionarios anidados + return config + + +# In[ ]: + + +#| export +def get_config( + print_flag: bool = False, + filename: str = config_base_filename, + path = config_path +) -> AttrDict: + """ + This function + - Reads the content in '../config/base.yml' and + - Returns its content as AttrDict. + print_flag option can be changed to True for displaying debugging messages. + """ + # Build file path + if (path[-1] != '/'): + path += '/' + filename = filename+".yaml" + + # Debug messages + if (print_flag): + current_directory = os.getcwd() + print("Current: " + current_directory) + print("yml: "+ path + filename) + + # Add join constructor + yaml.add_constructor('!join', join_constructor) + + if (print_flag): + print("Getting content"+ path + filename) + + # Get file content + full_content = replace_includes_with_content(filename, path, print_flag) + + if (print_flag): + print("Load content"+ path + filename) + + # Load content + config = yaml.load(full_content, Loader=yaml.FullLoader) + + config_attrdict = recursive_attrdict(config) + + substitute_env_variables_in_leaves( + config = config_attrdict, + print_flag = print_flag + ) + return config_attrdict + + +# In[ ]: + + +#| hide +get_config(False) + + +# ### Build the encoder artifact from already read functions +# > The encoder artifact is needed for training the model. +# > +# > To automatize its selection, its W&B name is built up from its latest version +# > +# > TODO: Change for selecting specific version. Version should be selected in .yml file too . + +# In[ ]: + + +#| export +def build_enc_artifact( + config: AttrDict, + print_flag: bool = False +) -> str: + """ + Build enc_artifact name from config data. + """ + version = config.user_preferences.wdb.version + enc_artifact = config.configuration.encoder.artifacts.train.enc_prefix + if (version == 'latest'): + enc_artifact+=":latest" + else: + enc_artifact=enc_artifact+":v"+version + if (print_flag): + print("enc_artifact: "+enc_artifact) + return enc_artifact + + +# ### Build project data +# > Read user, project, version and dataset artifact name form already got information + +# In[ ]: + + +#| export +def get_project_data( + print_flag: bool = False, + filename=config_base_filename, + path = config_path +) -> [str, str, str, str]: + """ + Retrieves project data including user, project name, version, and data name. + It accesses configuration settings, processes them, and optionally prints the project configuration. + Returns a tuple containing user, project, version, and data information. + """ + config = get_config(print_flag = print_flag, filename = filename, path = path) + project = config.wandb.project + user = config.wandb.user + version = config.wandb.version + data_name = config.data.alias + if (version != "latest"): + version = 'v' +version + data = data_name +":"+version + if print_flag: + dashes = '-----------' + print(dashes+"Project configuration"+dashes) + print("user: " + user) + print("project: " + project) + print("version: " + version) + print("data: "+ data) + print(dashes+"Project configuration"+dashes) + return user, project, version, data + + +# In[ ]: + + +#| hide +get_project_data(False) + + +# ### Get training artifact name + +# In[ ]: + + +#| export +def get_train_artifact(user: str, project: str, data: str) -> str: + """ + Constructs the train artifact string by combining user, project, and data information. + The format of the return string is 'entity/project/name:version'. + """ + # entity/project/name:version + train_artifact=user+'/'+project+'/'+data + return train_artifact + + +# ## Build dataset artifact +# > Configuration file: `base`. Described above. + +# ### Auxiliary functions + +# In[ ]: + + +#| export +############################## +# 01 - DATAFRAME TO ARTIFACT # +############################## +def get_artifact_config_sd2a_get_auxiliar_variables( + print_flag: bool, + filename = config_base_filename, + path = config_path +) -> Tuple[str, str, str, AttrDict, bool, str]: + """ + Retrieves auxiliary variables necessary for the dataset artifact configuration. + Gathers user, project, version, and data details, along with preferences for using wandb and the wandb artifacts path. + Returns a tuple containing these elements. + """ + user, project, version, data = get_project_data(print_flag, filename = filename, path = path) + config = get_config(print_flag, filename = filename, path = path) + data = config.data + use_wandb = config.user_preferences.use_wandb + wandb_path = config.wandb.artifacts_path + return user, project, version, data, use_wandb, wandb_path + + +# In[ ]: + + +#| hide +get_artifact_config_sd2a_get_auxiliar_variables(False) + + +# In[ ]: + + +#| export +def check_project_and_entity(user: str, project: str): + """ + Checks user and project are correctly setted up comparing to environment variables + """ + os_entity = os.environ['WANDB_ENTITY'] + os_project = os.environ['WANDB_PROJECT'] + if (os_entity != user): + custom_error("Please check .env and base.yml: entity != user os " + os_entity + " yaml " + user) + if (os_project != project): + custom_error("Please check .env and base.yml: project differs os " + os_project + " yaml " + project) + + +# In[ ]: + + +#| export +def get_artifact_config_sd2a_check_errors(use_wandb: str, artifact_config: AttrDict, user: str, project: str): + """ + Checks for configuration errors in the dataset artifact settings. + Verifies the project and entity, and ensures appropriate settings are made for offline mode and missing values handling. + """ + check_project_and_entity(user, project) + if ( + use_wandb == "offline" + and artifact_config.joining_train_test == True + ): + custom_error("If you're using deepvats in offline mode, set joining_train_test to False") + if ( + artifact_config.missing_values_constant is not None + and artifact_config.missing_values_technique is None + ): + custom_error("Missing values constant must be setted up only if missing_values_technique is not None. Please check base.yaml") + + +# ### Main function + +# In[ ]: + + +#| export +def get_artifact_config_sd2a( + print_flag: bool = False, + base_filename = config_base_filename, + path = config_path +) -> AttrDict: + """ + Constructs the configuration for the dataset artifact by retrieving auxiliary variables and setting up the artifact configuration. + Validates the configuration to ensure it meets specific criteria and returns the final artifact configuration as an AttrDict. + """ + user, project, version, data, use_wandb, wandb_path = get_artifact_config_sd2a_get_auxiliar_variables( + print_flag = print_flag, + filename = base_filename, + path = path + ) + artifact_config = AttrDict( + artifact_name = data.alias, + csv_config = data.csv_config, + data_cols = data.cols, + data_fpath = data.path, + date_format = data.date_format, + date_offset = data.date_offset, + freq = data.freq, + joining_train_test = data.joining_train_test, + missing_values_technique= data.missing_values.technique, + missing_values_constant = data.missing_values.constant, + normalize_training = data.normalize_training, + range_training = data.range_training, + range_testing = data.range_testing, + resampling_freq = data.resampling_freq, + start_date = data.start_date, + test_split = data.test_split, + time_col = data.time_col, + use_wandb = use_wandb, + wandb_artifacts_path = wandb_path + ) + get_artifact_config_sd2a_check_errors(use_wandb, artifact_config, user, project) + return artifact_config + + +# In[ ]: + + +#| hide +get_artifact_config_sd2a(print_flag = False) + + +# ## Build MVP Encoder artifacts +# > Includes also the versions for sliding window view version +# +# The configuration files `02b-encoder_mvp` contains the following information: +# ### Configuration for Encoder +# > - General Configuration: `configuration` +# > +# > | Key | Description | Example Value | +# > |------------|-------------------------------------------------------------|----------------| +# > | `job_type` | The type of job being configured. | `'encoder_MVP'`| +# > | `alias` | Alias for the configuration, refers to the alias in base. | Refers to *alias| +# > +# > - Wandb Configuraconfiguration.tion: `wandb` +# > +# > | Key | Description | Example Value | +# > |------------|-------------------------------------------|-----------------| +# > | `mode` | The mode for wandb, inherited from base. | Refers to *wdb_mode | +# > | `group` | Specifies if the run is part of a group. | `null` | +# > +# > - Specifications Coconfiguration.nfiguration: `specifications` +# > +# > | Key | Description | Example Value | +# > |------------------------------------------|------------------------------------------|-----------------| +# > | `batch_size` | The batch size for processing. | `1024` | +# > | `n_epoch` | Number of epochs for training. | `100` | +# > | `mask.future` | Whether to mask future samples. | `false` | +# > | `mask.stateful` | Dictates if masking is stateful or not. | `true` | +# > | `mask.sync` | Mask all variables at once in series. | `false` | +# > | `mvp.ws1` | Min window size for MVP adaptable training. | `15` | +# > | `mvp.ws2` | Max window size for MVP adaptable training. | `30` | +# > | `mvp.r` | Probability of masking in MVP. | `0.710` | +# > | `mvp.valid_size` | Size of the validation set proportion. | `0.2` | +# > | `mvp.normalize.by_sample` | Normalization by sample indicator. | `false` | +# > | `mvp.normalize.use_single_batch` | Use single batch for normalization. | `false` | +# > | `sliding_windows.stride` | Data points the window moves ahead. | `15` | +# > | `sliding_windows.size` | Window size for the sliding wiow. | `30` | +# g window. | `30` | +# + +# ### Auxiliary functions for retrieving the data + +# In[ ]: + + +#| export +###################### +# 02b - ENCODER MVP # +###################### +def get_artifact_config_MVP_auxiliar_variables( + print_flag : bool, + base_filename : str = config_base_filename, + path : str = config_path +) -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]: + """ + Retrieves and assembles various configuration parameters and auxiliary variables for an MVP artifact. + Extracts user, project, version, and data details, fetches configuration settings, and constructs training artifact strings. + Returns a tuple containing these elements along with MVP workspace specifications and user preferences. + """ + #Get neccesary variables + user, project, version, data = get_project_data( + print_flag = print_flag, + filename = base_filename, + path = path + ) + config = get_config( + print_flag, "02b-encoder_mvp" + ) + user_preferences = config.user_preferences + config = config.configuration + train_artifact_ = get_train_artifact(user,project,data) + mvp_ws1 = config.specifications.mvp.ws1 + mvp_ws2 = config.specifications.mvp.ws2 + mvp_ws = (mvp_ws1,mvp_ws2) + return user, project, version, data, config, train_artifact_, mvp_ws, user_preferences + + +# In[ ]: + + +#| export +def get_artifact_config_MVP_auxiliar_variables_SWV( + print_flag : bool, + base_filename : str = config_base_filename, + path : str = config_path +) -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]: + """ + Retrieves and assembles various configuration parameters and auxiliary variables for an MVP artifact. + Extracts user, project, version, and data details, fetches configuration settings, and constructs training artifact strings. + Returns a tuple containing these elements along with MVP (sliding window view) workspace specifications and user preferences. + """ + #Get neccesary variables + user, project, version, data = get_project_data(print_flag, base_filename, path) + config = get_config(print_flag, "02c-encoder_mvp-sliding_window_view", path) + user_preferences = config.user_preferences + config = config.configuration + train_artifact_ = get_train_artifact(user,project,data) + mvp_ws1 = config.specifications.mvp.ws1 + mvp_ws2 = config.specifications.mvp.ws2 + mvp_ws = (mvp_ws1,mvp_ws2) + return user, project, version, data, config, train_artifact_, mvp_ws, user_preferences + + +# In[ ]: + + +get_artifact_config_MVP_auxiliar_variables_SWV(False) + + +# ### Auxiliary functions for error handling + +# > TODO: Should we allow diferent project from environment ones? Should we just avoid yml configuration for this parameters and directly get dockers environment WANDB_ENTITIY and WANDB_PROJECT environment variables? . + +# In[ ]: + + +#| export +def get_artifact_config_MVP_check_errors( + artifact_config: AttrDict, + user: str, + project: str +): + """ + Avoids incompatible wandb modes configurations. + Sets project = 'work-nbs' if wandb is not used for tracking. + """ + check_project_and_entity(user, project) + + if artifact_config.use_wandb: + if (artifact_config.analysis_mode != 'online'): + print("Changing to online analysis mode - use_wandb=true") + artifact_config.analysis_mode = 'online' + else: + project = 'work-nbs' + + +# ### Main configuration functions + +# In[ ]: + + +#| export +def get_artifact_config_MVP(print_flag: bool = False) -> Tuple[str, str, str, str, AttrDict, str]: + """ + Gathers and structures the MVP artifact configuration, including user, project, version, data details, and various configuration settings. + Returns a tuple comprising user, project, version, data, the structured artifact configuration as an AttrDict, and the job type. + """ + user, project, version, data, config, train_artifact_, mvp_ws, user_preferences = get_artifact_config_MVP_auxiliar_variables(print_flag) + + artifact_config = AttrDict( + alias = config.alias, + analysis_mode = config.wandb.mode, + batch_size = config.specifications.batch_size, + epochs = config.specifications.n_epoch, + mask_future = config.specifications.mask.future, + mask_stateful = config.specifications.mask.stateful, + mask_sync = config.specifications.mask.sync, + mvp_ws = mvp_ws, + norm_by_sample = config.specifications.mvp.normalize.by_sample, + norm_use_single_batch = config.specifications.mvp.normalize.use_single_batch, + r = config.specifications.mvp.r, + stride = config.specifications.sliding_windows.stride, + train_artifact = train_artifact_, + valid_artifact = None, + use_wandb = user_preferences.use_wandb, + valid_size = config.specifications.mvp.valid_size, + w = config.specifications.sliding_windows.size, + wandb_group = config.wandb.group + ) + get_artifact_config_MVP_check_errors(artifact_config, user, project) + return user, project, version, data, artifact_config, config.job_type + + +# In[ ]: + + +#| export +def get_artifact_config_MVP_SWV( + print_flag : bool = False, + base_filename : str = config_base_filename, + path : str = config_path +) -> Tuple[str, str, str, str, AttrDict, str]: + """ + Gathers and structures the MVP_SWV artifact configuration, including user, project, version, data details, and various configuration settings. + Returns a tuple comprising user, project, version, data, the structured artifact configuration as an AttrDict, and the job type. + """ + user, project, version, data, config, train_artifact_, mvp_ws, user_preferences = get_artifact_config_MVP_auxiliar_variables_SWV( + print_flag = print_flag, + base_filename = base_filename, + path = path + ) + + artifact_config = AttrDict( + alias = config.alias, + analysis_mode = config.wandb.mode, + batch_size = config.specifications.batch_size, + epochs = config.specifications.n_epoch, + mask_future = config.specifications.mask.future, + mask_stateful = config.specifications.mask.stateful, + mask_sync = config.specifications.mask.sync, + mvp_ws = mvp_ws, + norm_by_sample = config.specifications.mvp.normalize.by_sample, + norm_use_single_batch = config.specifications.mvp.normalize.use_single_batch, + r = config.specifications.mvp.r, + stride = config.specifications.sliding_windows.stride, + train_artifact = train_artifact_, + valid_artifact = None, + use_wandb = user_preferences.use_wandb, + valid_size = config.specifications.mvp.valid_size, + w = config.specifications.sliding_windows.size, + wandb_group = config.wandb.group + ) + get_artifact_config_MVP_check_errors(artifact_config, user, project) + return user, project, version, data, artifact_config, config.job_type + + +# In[ ]: + + +#| hide +get_artifact_config_MVP_SWV(False) + + +# ## Encoder DCAE configuration +# > The configuration file `02a-encoder_dcae` contains the following information: +# +# > - General Configurat: `configuration`ion +# > +# > | Key | Description | Example Value | +# > |------------|--------------------------------------------------|-----------------| +# > | `job_type` | Type of the job for this configuration. | `'encoder_DCAE'`| +# > | `alias` | Alias for the run, referring to the base config. | Refers to *alias| +# > +# > - Wandb Configconfiguration.uration: `wandb` +# > +# > | Key | Description | Example Value | +# > |-----------|-------------------------------------------------------------|-----------------------| +# > | `use` | Indicates whether to use wandb. | Refers to *use_wandb | +# > | `entity` | Entity (user) for wandb configuration. | Refers to *wdb_user | +# > | `group` | Specifies if this run should be grouped in a wandb group. | `null` | +# > | `project` | Project name for wandb configuration. | Refers to *wdb_project| +# > +# > - Artifacconfiguration.ts Configuration: `artifacts` +# > +# > | Key | Description | Example Value | +# > |-----------------|--------------------------------------------------------------------------|-------------------------------------------------------| +# > | `train_prefix` | Complete training artifact path in wandb. | Combination of *wdb_user, *wdb_project, and *alg | +# > | `valid.data` | Complete name for the validation dataset artifact (null for random). | `null` | +# > | `valid.size` | Percentage of random items to go to validation set if `valid.data` is null. | `0.1` | +# > +# > - Specifications Configuration: `specifications` +# > +# > | Key | Description | Example Value | +# > |----------------|---------------------------------------------------|-----------------| +# > | `batch_size` | Batch size for processing. | `64` | +# > | `n_epoch` | Number of epochs to train for. | `200` | +# > | `pool_szs` | Sizes of the pooling layers in the autoencoder. | `[2,2,4]` | +# > | `top_k` | Number of elements to analyse for the top losses. | `3` | +# > +# > - Sliding Windows Configuration: `sliding_windows` +# > +# > | Key | Description | Example Value | +# > |-----------|-----------------------------------------|---------------| +# > | `stride` | The stride for the sliding window. | `1` | +# > | `size` | Window size for the sliding window. | `32` configuration. | +# > +# > - Autoencoder Configuration: `autoencoder` +# > +# > | Key | Description | Example Value | +# > |---------------------|----------------------------------------------------------------|----------------| +# > | `delta` | Size of the autoencoder bottleneck layer. | `60` | +# > | `filters.nfs` | Number of filters in each convolutional layer of the autoencoder. | `[64,32,16]` | +# > | `filters.kss` | Kernel sizes for each convolutional layer in the autoencoder. | `[10,5,5]` | +# > | `filters.output_size` | The output size of the autoencoder's final layer. | `10` | +# inal layer. | `10` | +# | `10` | +# | +# +# +# + +# In[ ]: + + +#| export +###################### +# 02a - ENCODER DCAE # +###################### + +def get_artifact_config_DCAE( + print_flag: bool = False, + base_filename = config_base_filename, + path = config_path +) -> Tuple[AttrDict, str]: + """ + Constructs the configuration for the DCAE (Deep Convolutional AutoEncoder). + It fetchs the relevant settings and assembles the artifact configuration. + Validates the configuration to ensure correct project and entity setup and + returns the artifact configuration as an AttrDict along with the job type. + """ + user, project, version, data = get_project_data(print_flag, base_filename, path) + config = get_config(print_flag, "02a-encoder_dcae", path) + if print_flag: print("Antes de leer configuration " + str(config)) + config = config.configuration + + artifact_config = AttrDict( + alias = config.alias, + use_wandb = config.wandb.use, + wandb_group = config.wandb.group, + wandb_entity = config.wandb.entity, + wandb_project = config.wandb.project, + train_artifact = get_train_artifact(user,project,data), + valid_artifact = config.artifacts.valid.data, + valid_size = config.artifacts.valid.size, + w = config.specifications.sliding_windows.size, + stride = config.specifications.sliding_windows.stride, + delta = config.specifications.autoencoder.delta, + nfs = config.specifications.autoencoder.filters.nfs, + kss = config.specifications.autoencoder.filters.kss, + output_filter_size = config.specifications.autoencoder.filters.output_size, + pool_szs = config.specifications.pool_szs, + batch_size = config.specifications.batch_size, + epochs = config.specifications.n_epoch, + top_k = config.specifications.pool_szs + ) + check_project_and_entity( + artifact_config.wandb_entity, + artifact_config.wandb_project + ) + return artifact_config, config.job_type + + +# ## Embeddings configuration +# The specific configurations are in the file `03-embeddings` +# It has the following information: +# +# > - Job Type: `job_type` +# > +# > | Key | Value | +# > |-----------|---------------| +# > | `job_type`| `'embeddings'`| +# > +# > - Configuration: `configuration` +# > - Wandb Configuration: `configuration.wandb` +# > +# > | Key | Description | Example Value | +# > |---------|------------------------------------------------------|--------------------| +# > | `group` | Whether to group this run in a wandb group. | Refers to *emb | +# > | `use` | Indicates whether to use wandb. | Refers to *use_wandb | +# > | `entity`| Entity (user) for wandb configuration. | Refers to *wdb_user | +# > | `project`| Project name for wandb configuration. | Refers to *wdb_project | +# > +# > - Encoder Configuration: `configuration.encoder` +# > +# > | Key | Description | Example Value | +# > |------------------------------|----------------------------------------------------|-------------------------------------------------| +# > | `artifacts.train.enc_prefix` | Prefix for the training artifacts. | Combination of *wdb_user, *wdb_project, and *alg | +# > | `artifacts.valid` | If none, the validation set used to train enc is used. | `null` | +# > +# > - Specifications Configuration: `configuration.specifications` +# > +# > | Key | Description | Example Value | +# > |------------|----------------------------------------------|-----------------| +# > | `input_ar` | Input array, if applicable. | `null` | +# > | `cpu` | Whether to use CPU instead of GPU. | `false` | +# + +# In[ ]: + + +#| export +###################### +# 03 - EMBEDDINGS # +###################### +def get_artifact_config_embeddings( + print_flag : bool = False, + config_path : str = config_path +) -> Tuple[AttrDict, str]: + """ + Constructs the configuration for embeddings by fetching relevant settings and building the encoder artifact configuration. + Validates the project and entity settings and returns the artifact configuration as an AttrDict, along with the job type. + """ + + config = get_config(print_flag, "03a-embeddings", config_path) + job_type=config.job_type + version = config.user_preferences.wdb.version + enc_artifact = build_enc_artifact(config, print_flag) + config = config.configuration + artifact_config = AttrDict( + use_wandb = config.wandb.use, + wandb_group = config.wandb.group, + wandb_entity = config.wandb.entity, + wandb_project = config.wandb.project, + enc_artifact = enc_artifact, + input_ar = config.specifications.input_ar, + cpu = config.specifications.cpu + ) + check_project_and_entity(artifact_config.wandb_entity, artifact_config.wandb_project) + return artifact_config, job_type + + +# In[ ]: + + +#| export +def get_artifact_config_embeddings_SWV( + print_flag : bool = False, + config_path : str = config_path +) -> Tuple[AttrDict, str]: + """ + Constructs the configuration for embeddings (sliding window view) by fetching relevant settings and building the encoder artifact configuration. + Validates the project and entity settings and returns the artifact configuration as an AttrDict, along with the job type. + """ + config = get_config(print_flag, "03b-embeddings-sliding_window_view", config_path) + job_type=config.job_type + version = config.user_preferences.wdb.version + enc_artifact = build_enc_artifact(config, print_flag) + config = config.configuration + artifact_config = AttrDict( + use_wandb = config.wandb.use, + wandb_group = config.wandb.group, + wandb_entity = config.wandb.entity, + wandb_project = config.wandb.project, + enc_artifact = enc_artifact, + input_ar = config.specifications.input_ar, + cpu = config.specifications.cpu + ) + check_project_and_entity(artifact_config.wandb_entity, artifact_config.wandb_project) + return artifact_config, job_type + + +# ## Dimensionality Reduction Configuration +# The specific configurations are in the file `04-dimensionality_reduction` +# It contains the following information: +# +# > - Job Type: `job_type` +# > +# > | Key | Value | +# > |------------|----------------------------| +# > | `job_type` | `'dimensionality_reduction'`| +# > +# > - Configuration: `configuration` +# > - Wandb Configuration: `configuration.wandb` +# > +# > | Key | Description | Example Value | +# > |---------|------------------------------------------------------|--------------------| +# > | `use` | Whether to use wandb for experiment tracking. | Refers to *use_wandb | +# > | `group` | Specifies whether to group this run in a wandb group. | `null` | +# > | `entity`| The entity to use for wandb. | Refers to *wdb_user | +# > | `project`| The project to use for wandb. | Refers to *wdb_project | +# > +# > - Encoder Configuration: `configuration.encoder` +# > +# > | Key | Description | Example Value | +# > |------------------------------|-----------------------------------------------------|-------------------------------------------------| +# > | `artifacts.train.enc_prefix` | Prefix for the training artifacts. | Combination of *wdb_user, *wdb_project, and *alg | +# > | `artifacts.valid` | If none, the validation set used to train enc is used. | `null` | +# > +# > - UMAP Configuration: `configuration.encoder.umap` +# > +# > | Key | Description | Example Value | +# > |-----------------|-----------------------------|---------------| +# > | `n_neighbors` | Number of neighbors for UMAP. | `15` | +# > | `min_dist` | Minimum distance for UMAP. | `0.1` | +# > | `random_state` | Random state for UMAP. | `1234` | +# +# + +# In[ ]: + + +#| export +################################### +# 04 - DIMENSIONALITY REDUCTION # +################################### +def get_artifact_config_dimensionality_reduction( + print_flag : bool = False, + config_path : str = config_path +) -> Tuple[AttrDict, str]: + """ + Constructs the configuration for dimensionality reduction tasks by fetching relevant settings, including building the encoder artifact. + Returns the artifact configuration as an AttrDict, along with the job type. + """ + + config = get_config(print_flag, "04-dimensionality_reduction", config_path) + job_type = config.job_type + enc_artifact = build_enc_artifact(config, print_flag) + config = config.configuration + artifact_config = AttrDict( + use_wandb = config.wandb.use, + wandb_group = config.wandb.group, + wandb_entity = config.wandb.entity, + wandb_project = config.wandb.project, + valid_artifact = config.encoder.artifacts.valid, + train_artifact = enc_artifact, + n_neighbors = config.encoder.umap.n_neighbors, + min_dist = config.encoder.umap.min_dist, + random_state = config.encoder.umap.random_state + ) + return artifact_config, job_type + + +# ## XAI - shap reduction configuration +# > TODO: Not yet implemented or configured... In progress.. +# +# The specific configurations are in the file `05-xai_shap` +# + +# In[ ]: + + +#| export +################## +# 05 - XAI-SHAP # +################## +def get_artifact_config_xai_shap( + print_flag : bool = False, + config_path : str = config_path +) -> Tuple[AttrDict, str]: + """ + Constructs the configuration for the XAI SHAP (Explainable Artificial Intelligence using SHAP values) analysis. + This includes fetching relevant settings, building the encoder artifact, and assembling the artifact configuration. + Returns the artifact configuration as an AttrDict, along with the job type. + """ + config = get_config(print_flag, "05-xai_shap", config_path) + job_type = config.job_type + enc_artifact = build_enc_artifact(config, print_flag) + config = config.configuration + artifact_config = AttrDict( + use_wandb = config.wandb.use, + wandb_group = config.wandb.group, + wandb_entity = config.wandb.entity, + wandb_project = config.wandb.project, + valid_artifact = config.encoder.artifacts.valid, + train_artifact = enc_artifact, + n_neighbors = config.encoder.umap.n_neighbors, + min_dist = config.encoder.umap.min_dist, + random_state = config.encoder.umap.random_state + ) + return artifact_config, job_type + + +# In[ ]: + + +#| hide -> Not yet implemented +#get_artifact_config_xai_shap(False) + + +# ## Use a tested configuration + +# [Monash benchmark](https://huggingface.co/datasets/monash_tsf) datasets used configurations + +# In[ ]: + + +#| export +# Monash australian electricity demand +monash_australian_electricity_demand_0 = AttrDict( + alias ="Monash-Australian_electricity_demand", + fname ="australian_electricity_demand_dataset", + #5 univariate timeseries. 0-4 can be used 1 each time + cols =[0], + ftype ='.tsf', + freq ='30min', + time_col = None, + mvp = AttrDict( + batch_size = 512, + n_epoch = 100, + ws = [2,336], #1h-1week | TODO: Check to ensure freq sense + stride = 48 #Day2Day TODO: Check + ), + dcae = AttrDict(#TODO: Check + batch_size = 512, + n_epoch = 100, + stride = 48, + w = 224, + delta = 60, + nfs = [64, 32, 16], + kss = [10, 5, 5], + output_filter_size = 10, + top_k = [2,2,4], + pool_szs = [2,2,4] + ) +) + + +# In[ ]: + + +#| export +# Monash sunspot: +monash_sunspot_0 = AttrDict( + alias = "sunspot", + fname = "sunspot_dataset_with_missing_values", + ftype = ".tsf", + cols = [], + freq ='1d', + time_col = None, + mvp = AttrDict( + batch_size = 512, + n_epoch = 100, + ws = [7,365], #1week-1year + stride = 30 #1 month TODO: Check + ), + dcae = AttrDict(#TODO: Check + batch_size = 512, + n_epoch = 100, + stride = 48, + w = 224, + delta = 60, + nfs = [64, 32, 16], + kss = [10, 5, 5], + output_filter_size = 10, + top_k = [2,2,4], + pool_szs = [2,2,4] + ) +) + + +# In[ ]: + + +#| export +monash_solar_4_seconds_0 = AttrDict( + alias = 'solar_4_seconds', + fname = 'solar_4_seconds_dataset', + ftype = '.tsf', + freq = '4s', + cols = [], + time_col= None, + mvp = AttrDict( + batch_size = 512, + n_epoch = 100, + #1 4 seconds + #15 1 min + #450 30 min - Too small for G4-0 + #900 1h + ws = [450,900], #1 min - 30 min (15*60=900 = 1hora intervalos 4 secs) + stride = 450 + #stride = 10800 #1 min TODO: Check + ), + dcae = AttrDict(#TODO: Check + batch_size = 512, + n_epoch = 100, + stride = 48, + w = 224, + delta = 60, + nfs = [64, 32, 16], + kss = [10, 5, 5], + output_filter_size = 10, + top_k = [2,2,4], + pool_szs = [2,2,4] + ) +) + + +# [Wikipedia web traffic dataset](https://zenodo.org/records/3898474) + +# In[ ]: + + +#| export +wikipedia_0 = AttrDict( + alias="Wikipedia", + fname="kaggle_web_traffic_dataset_with_missing_values", + cols=[0, 1, 2, 3, 4], + ftype=".tsf", + freq = '1d', + time_col=None, + mvp = AttrDict( + batch_size = 512, + n_epoch = 100, + ws = [1,365], #1d-1año + stride = 1 #TODO: Check + ), + dcae = AttrDict(#TODO: Check + batch_size = 512, + n_epoch = 100, + stride = 48, + w = 224, + delta = 60, + nfs = [64, 32, 16], + kss = [10, 5, 5], + output_filter_size = 10, + top_k = [2,2,4], + pool_szs = [2,2,4] + ) +) + + +# [Traffic San Francisco](https://zenodo.org/records/3898445) + +# In[ ]: + + +#| export +traffic_san_francisco_0 = AttrDict( + alias="Traffic_SF", + fname="traffic_hourly_dataset", + ftype=".tsf", + cols=[], + time_col=None, + freq='1h', + mvp = AttrDict( + batch_size = 512, + n_epoch = 100, + ws = [1,720], #1h-1week TODO: Check + stride = 24 #1 day TODO: Check + ), + dcae = AttrDict(#TODO: Check + batch_size = 512, + n_epoch = 100, + stride = 48, + w = 224, + delta = 60, + nfs = [64, 32, 16], + kss = [10, 5, 5], + output_filter_size = 10, + top_k = [2,2,4], + pool_szs = [2,2,4] + ) +) + + +# In[ ]: + + +#| export +# Monash solar 10 minutes dataset +# Multi-dimensional +monash_solar_10_minutes_0 = AttrDict( + alias ="Monash-Solar_10_minutes", + fname ="solar_10_minutes_dataset", + #5 univariate timeseries. 0-4 can be used 1 each time + #137 columns (each one 1 different timeserie) + cols =[0], + ftype ='.tsf', + freq ='10min', + time_col = None, + mvp = AttrDict( + batch_size = 512, + n_epoch = 100, + #1 month 4320 -> Too big for G4 (GPU-0) + #1 day 144 -> Ok + #1 week 1008 -> Ok + #15 days 2160 -> Ok + ws = [1,2160], + stride = 144 #1d by 1d + ), + dcae = AttrDict(#TODO: Check + batch_size = 512, + n_epoch = 100, + stride = 48, + w = 224, + delta = 60, + nfs = [64, 32, 16], + kss = [10, 5, 5], + output_filter_size = 10, + top_k = [2,2,4], + pool_szs = [2,2,4] + ) +) + + +# ### Other public datasets + +# #### Electricity Transformer Temperature +# [ETDataset](https://paperswithcode.com/dataset/ett) + +# In[ ]: + + +#| export +etth1_0 = AttrDict( + alias="ETTh1", + fname="ETTh1", + ftype=".csv", + cols=[], + time_col=0, + freq='1h', + mvp = AttrDict( + batch_size = 512, + n_epoch = 100, + ws = [1,720], #1 h - 1 month TODO:Check + stride = 24 #1 day TODO: Check + ), + dcae = AttrDict(#TODO: Check + batch_size = 512, + n_epoch = 100, + stride = 48, + w = 224, + delta = 60, + nfs = [64, 32, 16], + kss = [10, 5, 5], + output_filter_size = 10, + top_k = [2,2,4], + pool_szs = [2,2,4] + ) +) + + +# #### Stumpy +# ##### [Semantic segmentation TitlABP](https://stumpy.readthedocs.io/en/latest/Tutorial_Semantic_Segmentation.html) + +# In[ ]: + + +#| export +stumpy_abp_0 = AttrDict( + alias="TitlABP", + fname="Semantic_Segmentation_TiltABP", + ftype=".csv", + cols=[], + freq="1s", + time_col=0, + mvp = AttrDict( + batch_size = 512, + n_epoch = 100, + ws = [60,3600], #1min-1h TODO:Check + stride = 60 #1 min TODO: Check + ), + dcae = AttrDict(#TODO: Check + batch_size = 512, + n_epoch = 100, + stride = 48, + w = 224, + delta = 60, + nfs = [64, 32, 16], + kss = [10, 5, 5], + output_filter_size = 10, + top_k = [2,2,4], + pool_szs = [2,2,4] + ) +) + + +# ##### [Multi-dimensional toy data](https://zenodo.org/records/4294932) + +# In[ ]: + + +#| export +stumpy_toy_0 = AttrDict( + alias="toy", + fname="toy", + ftype=".csv", + cols=[], + freq="1s", + time_col=None, + mvp = AttrDict( + batch_size = 512, + n_epoch = 100, + ws = [10,30], + stride = 1 + ), + dcae = AttrDict(#TODO: Check + batch_size = 512, + n_epoch = 100, + stride = 48, + w = 224, + delta = 60, + nfs = [64, 32, 16], + kss = [10, 5, 5], + output_filter_size = 10, + top_k = [2,2,4], + pool_szs = [2,2,4] + ) +) + + +# ### Get tested configuration function + +# In[ ]: + + +#| export +tested_configs = { + 'monash_australian_electricity_demand_0': monash_australian_electricity_demand_0, + 'monash_solar_4_seconds_0': monash_solar_4_seconds_0, + 'wikipedia_0': wikipedia_0, + 'traffic_san_francisco_0': traffic_san_francisco_0, + 'monash_solar_10_minutes_0': monash_solar_10_minutes_0, + 'etth1_0': etth1_0, + 'stumpy_abp_0': stumpy_abp_0, + 'stumpy_toy_0': stumpy_toy_0 +} + + +# In[ ]: + + +#| export +def show_attrdict(dict: AttrDict): + for key, value in dict.items(): + print(f"{key}: {value}") + + +# In[ ]: + + +#| export +def show_available_configs(): + print("Available datasets: ") + i = 0 + for key, val in tested_configs.items(): + print(f"{i} - {key}") + i+=1 + + + +# In[ ]: + + +#| hide +show_available_configs() + + +# In[ ]: + + +#| hide +list(tested_configs.items())[3][0] + + +# In[ ]: + + +#| export +def show_config(id: int = 0): + show_attrdict(list(tested_configs.items())[id][1]) + + +# In[ ]: + + +#| hide +show_config(3) + + +# In[ ]: + + +#| export +def get_tested_config( + id: int = 0, + print_flag=False +): + if print_flag: show_config(id) + return list(tested_configs.items())[id][1] + + + +# In[ ]: + + +#| hide +show_attrdict(get_tested_config(0)) + + +# ### Force tested configuration functions +# #### 01 - Dataset Artifact + +# In[ ]: + + +#| export +def print_colored( + key, + modified_val, + modified, + both:bool=False, + original_val=0, + missing_in_modified=False, + missing_in_original=False +): + if missing_in_modified: + color = "\033[91m\033[1m" # Red and bold + elif missing_in_original: + color = "\033[93m\033[1m" # Orange and bold + else: + color = "\033[94m" if modified else "" + reset = "\033[0m" + + if modified and both: + print(f"{color}{key}: {original_val}{reset} -> {modified_val}{reset}") + elif missing_in_modified: + print(f"{color}{key} is missing in modified dict | {original_val} {reset}") + elif missing_in_original: + print(f"{color}{key} is missing in original dict | {modified_val} {reset}") + else: + print(f"{color}{key}: {modified_val}{reset}") + + +# In[ ]: + + +#| export +import pandas as pd + + +# In[ ]: + + +#| export +def get_resampling_frequency( + freq: str, + frequency_factor:int = 1, + print_flag = False +): + if print_flag: + print("--> Frequency factor resampling frequency") + print("Freq factor: ", frequency_factor) + freq_new = pd.to_timedelta(freq) + freq_new = freq_new*frequency_factor + if print_flag: + print("freq_original: ", freq) + print("freq_new: ", freq_new) + suffix = "-" + resampling_freq="" + if freq_new.days > 0 and freq_new.seconds == pd.to_timedelta(0,'s'): + suffix = str(freq_new.days)+'d' + resampling_freq = str(freq_new.days)+'D' + elif freq_new.seconds % 3600 == 0: + hours = (freq_new.seconds // 3600) + freq_new.days*24 + suffix = str(hours)+'h' + resampling_freq = str(hours) + 'H' + elif freq_new.seconds % 60 == 0: + minutes = (freq_new.seconds // 60) + (freq_new.days*24*60) + suffix = str(minutes)+'m' + resampling_freq = str(minutes)+'T' + else: + seconds = freq_new.seconds + freq_new.days*24*60*60 + suffix = str(seconds)+'s' + resampling_freq = str(seconds)+'S' + if print_flag: + print("suffix: ", suffix) + print("resampling_freq: ", resampling_freq) + print("Frequency factor resampling frequency -->") + return (suffix, resampling_freq) + + +# In[ ]: + + +#| export +def frequency_factor_config( + config: AttrDict, + frequency_factor:int = 1, + frequency_factor_change_alias: bool = True, + print_flag = False +): + if print_flag: + print("--> Frequency factor config") + print("Freq factor: ", frequency_factor) + print("frequency_factor_change_alias: ", frequency_factor_change_alias) + suffix, config.resampling_freq = get_resampling_frequency(config.freq, frequency_factor, print_flag) + + if frequency_factor_change_alias: + #filename = config.data_fpath.split(".tsf", 1)[0] + config.artifact_name = config.artifact_name+"-"+suffix + #config.data_fpath = filename+"-"+suffix+".tsf" + + if print_flag: + print("resampling_freq: ", config.freq) + print("name: ", config.artifact_name) + print("path: ", config.data_fpath) + print("Frequency factor config -->") + + +# In[ ]: + + +#| export +def diff_attrdict( + dict_original: AttrDict, + dict_modified: AttrDict, + both: bool = False +): + all_keys = set(dict_original.keys()) | set(dict_modified.keys()) + for key in all_keys: + in_original = key in dict_original + in_modified = key in dict_modified + + if in_original and in_modified: + modified = dict_original[key] != dict_modified[key] + print_colored( + key, + modified_val = dict_modified[key], + modified = modified, + both = both, + original_val=dict_original[key] + ) + elif in_original: + # Key is missing in dict_modified + print_colored(key, modified_val=None, modified=True, missing_in_modified=True) + else: + # Key is missing in dict_original + print_colored(key, modified_val=dict_modified[key], modified=True, missing_in_original=True) + + +# In[ ]: + + +#| export +from copy import deepcopy +def force_artifact_config_sd2a( + config: AttrDict, + id:int = 0, + print_flag = False, + both = False, + frequency_factor = 1, + frequency_factor_change_alias = True +): + to_set = get_tested_config(id) + if print_flag: + config_before = deepcopy(config) + print("Selecting ", list(tested_configs.items())[id][0]) + config.artifact_name = to_set.alias + config.data_cols = to_set.cols + config.data_fpath= "~/data/"+to_set.fname+to_set.ftype + config.freq=to_set.freq + config.time_col = to_set.time_col + config.csv_config = {} + joining_train_test= False, + missing_values_constant= None, + missing_values_technique= None, + normalize_training= False, + range_testing= None, + range_training= None, + resampling_freq= None, + start_date= None, + test_split= None, + if frequency_factor > 1: + frequency_factor_config(config, frequency_factor, frequency_factor_change_alias, print_flag) + if print_flag: + diff_attrdict( + dict_original=config_before, + dict_modified=config, + both = both) + + +# In[ ]: + + +#| hide +sd2a_config = AttrDict( + artifact_name= "Monash-Australian_electricity", + csv_config= {}, + data_cols= [0], + data_fpath= '~/data/australian_electricity_demand_dataset.csv', + date_format= '%Y-%m-%d %H:%M:%S', + date_offset= None, + freq= '1s', + joining_train_test= False, + missing_values_constant= None, + missing_values_technique= None, + normalize_training= False, + range_testing= None, + range_training= None, + resampling_freq= None, + start_date= None, + test_split= None, + time_col= None, + use_wandb= True, + wandb_artifacts_path='./data/wandb_artifacts' +) + + +# In[ ]: + + +#| hide +force_artifact_config_sd2a( + config = sd2a_config, + id = 6, + print_flag=True, + both=True, + frequency_factor = 2, + frequency_factor_change_alias = True +) + + +# #### 02(bc) Encoder MVP + +# In[ ]: + + +#| export +def split_artifact_string(s:string) -> tuple[string, string, string]: + # Divide la cadena en dos partes usando ':' + path, version = s.split(':') + + # Divide la parte del path en sus componentes + parts = path.rsplit('/', 1) + + # Retorna los componentes separados + return parts[0] + '/', parts[1], version + + +# In[ ]: + + +#| hide +result = split_artifact_string("mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest") +print(result) + + +# In[ ]: + + +#| export +def force_artifact_config_mvp( + config: AttrDict, + id:int = 0, + print_flag = False, + both = False, + frequency_factor = 1, + frequency_factor_change_alias = False, +): + to_set = get_tested_config(id) + if print_flag: + config_before = deepcopy(config) + + force_artifact_config_sd2a( + config = config, + id = id, + print_flag = False, + both = False, + frequency_factor = frequency_factor, + frequency_factor_change_alias = frequency_factor_change_alias + ) + + config.alias = to_set.alias + config.batch_size = to_set.mvp.batch_size + config.epochs = to_set.mvp.n_epoch + + config.mask_future = False + config.mask_stateful = True + config.mask_sync = False + config.norm_by_sample = False + config.norm_use_by_single_batch = False, + config.r = 0.71 + + config.stride=to_set.mvp.stride + path,_,version = split_artifact_string(config.train_artifact) + config.train_artifact=path+config.artifact_name+":"+version + + config.valid_size = 0.2 + + config.mvp_ws= to_set.mvp.ws + config.w = config.mvp_ws[1] + + if print_flag: + diff_attrdict( + dict_original=config_before, + dict_modified=config, + both = both + ) + + +# In[ ]: + + +#| hide +mvp_config = AttrDict( + alias = "Monash-Australian_electric", + artifact_name="Monash-Australian_electricity", + analysis_mode="online", + batch_size=2, + epochs=1, + mask_future=False, + mask_stateful=True, + mask_sync=False, + mvp_ws=(150, 33), + norm_by_sample=False, + norm_use_single_batch=False, + r=0.71, + stride=13, + train_artifact="mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest", + valid_artifact=None, + use_wandb=True, + valid_size=0.2, + w=30, + wandb_group=None, + data_cols= [], + data_fpath= "~/data/kaggle_web_traffic_dataset_with_missing_values.tsf", + freq= '1d', + time_col= 'None' +) + + +# In[ ]: + + +#| hide +force_artifact_config_mvp(mvp_config, 1, True, True, 5, True) + + +# In[ ]: + + +#| export +def force_artifact_config_dcae( + config: AttrDict, + id:int = 0, + print_flag = False, + both = False, + frequency_factor = 1, + frequency_factor_change_alias = False, +): + to_set = get_tested_config(id) + if print_flag: + config_before = deepcopy(config) + + force_artifact_config_sd2a( + config = config, + id = id, + print_flag = False, + both = False, + frequency_factor = frequency_factor, + frequency_factor_change_alias = frequency_factor_change_alias + ) + + config.alias = to_set.alias + + config.batch_size = to_set.dcae.batch_size + config.epochs = to_set.dcae.n_epoch + config.r = 0.71 + config.stride=to_set.dcae.stride + path,_,version = split_artifact_string(config.train_artifact) + config.train_artifact=path+config.artifact_name+":"+version + + config.valid_size = 0.2 + config.w = to_set.dcae.w + config.delta = to_set.dcae.delta + config.nfs = to_set.dcae.nfs + config.kss = to_set.dcae.kss + config.output_filter_size = to_set.dcae.kss + config.top_k = to_set.dcae.top_k + config.pool_szs = to_set.dcae.pool_szs + + if print_flag: + diff_attrdict( + dict_original=config_before, + dict_modified=config, + both = both + ) + + +# In[ ]: + + +#| hide +dcae_config = AttrDict( + alias = "Monash-Australian_electric", + artifact_name="Monash-Australian_electricity", + analysis_mode="online", + batch_size=2, + epochs=1, + r=0, + stride=1, + train_artifact="mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest", + valid_artifact=None, + use_wandb=True, + valid_size=0.2, + w=30, + nfs = [], + kss = [], + output_filter_size = 1, + top_k = [], + delta = 6, + wandb_group=None, + data_cols= [], + data_fpath= "~/data/kaggle_web_traffic_dataset_with_missing_values.tsf", + freq= '1d', + time_col= 'None' +) + + +# In[ ]: + + +#| hide +force_artifact_config_dcae(dcae_config, 1, True, True, 5, True) + diff --git a/nbs_pipeline/01_dataset_artifact.ipynb b/nbs_pipeline/01_dataset_artifact.ipynb index c9e87388..aa6c908e 100644 --- a/nbs_pipeline/01_dataset_artifact.ipynb +++ b/nbs_pipeline/01_dataset_artifact.ipynb @@ -21,14 +21,22 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:45.387900Z", + "iopub.status.busy": "2024-06-04T18:16:45.377200Z", + "iopub.status.idle": "2024-06-04T18:16:48.224797Z", + "shell.execute_reply": "2024-06-04T18:16:48.224672Z" + } + }, "outputs": [], "source": [ + "#| export\n", "import sys\n", + "import dvats.utils as ut\n", "if '--vscode' in sys.argv:\n", " print(\"Executing inside vscode\")\n", - " import nbs_pipeline.utils.vscode as vs\n", - " vs.DisplayHandle.update = vs.update_patch" + " ut.DisplayHandle.update = ut.update_patch" ] }, { @@ -43,9 +51,34 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:48.230575Z", + "iopub.status.busy": "2024-06-04T18:16:48.230507Z", + "iopub.status.idle": "2024-06-04T18:16:48.231526Z", + "shell.execute_reply": "2024-06-04T18:16:48.231389Z" + } + }, + "outputs": [], + "source": [ + "#| export\n", + "print_flag = True" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:48.238876Z", + "iopub.status.busy": "2024-06-04T18:16:48.238824Z", + "iopub.status.idle": "2024-06-04T18:16:48.265392Z", + "shell.execute_reply": "2024-06-04T18:16:48.265299Z" + } + }, "outputs": [], "source": [ + "#| hide\n", "print_flag = True\n", "reset_kernel=False" ] @@ -63,188 +96,2529 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:48.271016Z", + "iopub.status.busy": "2024-06-04T18:16:48.270976Z", + "iopub.status.idle": "2024-06-04T18:16:49.388160Z", + "shell.execute_reply": "2024-06-04T18:16:49.387170Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Available datasets: \n", - "0 - monash_australian_electricity_demand_0\n", - "1 - monash_solar_4_seconds_0\n", - "2 - wikipedia_0\n", - "3 - traffic_san_francisco_0\n", - "4 - monash_solar_10_minutes_0\n", - "5 - etth1_0\n", - "6 - stumpy_abp_0\n", - "7 - stumpy_toy_0\n" + "Available datasets: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 - monash_australian_electricity_demand_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 - monash_solar_4_seconds_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 - wikipedia_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 - traffic_san_francisco_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 - monash_solar_10_minutes_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 - etth1_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6 - stumpy_abp_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7 - stumpy_toy_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alias: Traffic_SF" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fname: traffic_hourly_dataset" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ftype: .tsf" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cols: []" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time_col: None" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq: 1h" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mvp: {'batch_size': 512, 'n_epoch': 100, 'ws': [1, 720], 'stride': 24}" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dcae: {'batch_size': 512, 'n_epoch': 100, 'stride': 48, 'w': 224, 'delta': 60, 'nfs': [64, 32, 16], 'kss': [10, 5, 5], 'output_filter_size': 10, 'top_k': [2, 2, 4], 'pool_szs': [2, 2, 4]}" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alias: Monash-Australian_electricity_demand" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fname: australian_electricity_demand_dataset" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cols: [0]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ftype: .tsf" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq: 30min" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time_col: None" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mvp: {'batch_size': 512, 'n_epoch': 100, 'ws': [2, 336], 'stride': 48}" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dcae: {'batch_size': 512, 'n_epoch': 100, 'stride': 48, 'w': 224, 'delta': 60, 'nfs': [64, 32, 16], 'kss': [10, 5, 5], 'output_filter_size': 10, 'top_k': [2, 2, 4], 'pool_szs': [2, 2, 4]}" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selecting " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stumpy_abp_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--> Frequency factor config" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Freq factor: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "frequency_factor_change_alias: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--> Frequency factor resampling frequency" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Freq factor: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq_original: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1s" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq_new: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 days 00:00:02" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "suffix: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2s" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "resampling_freq: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2S" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Frequency factor resampling frequency -->" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "resampling_freq: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1s" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TitlABP-2s" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "path: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "~/data/Semantic_Segmentation_TiltABP.csv" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", 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"cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:49.304405Z", + "iopub.status.busy": "2024-06-04T18:16:49.304259Z", + "iopub.status.idle": "2024-06-04T18:16:49.355911Z", + "shell.execute_reply": "2024-06-04T18:16:49.355825Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available datasets: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 - monash_australian_electricity_demand_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 - monash_solar_4_seconds_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 - wikipedia_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 - traffic_san_francisco_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 - monash_solar_10_minutes_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 - etth1_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6 - stumpy_abp_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7 - stumpy_toy_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "#| hide\n", + "cfg_.show_available_configs()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:49.389214Z", + "iopub.status.busy": "2024-06-04T18:16:49.389138Z", + "iopub.status.idle": "2024-06-04T18:16:49.431420Z", + "shell.execute_reply": "2024-06-04T18:16:49.431306Z" + } + }, + "outputs": [], + "source": [ + "#| export \n", + "# This cell is for instrumental code. Use the next cell for real final configuration for this notebook\n", + "pre_configured_case = False\n", + "case_id = None\n", + "frequency_factor = 1\n", + "frequency_factor_change_alias = True" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:49.437480Z", + "iopub.status.busy": "2024-06-04T18:16:49.437407Z", + "iopub.status.idle": "2024-06-04T18:16:49.474704Z", + "shell.execute_reply": "2024-06-04T18:16:49.474595Z" + } + }, + "outputs": [], + "source": [ + "#| hide\n", + "pre_configured_case = True\n", + "case_id = 7\n", + "frequency_factor = 1\n", + "frequency_factor_change_alias = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Main code\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:49.480849Z", + "iopub.status.busy": "2024-06-04T18:16:49.480783Z", + "iopub.status.idle": "2024-06-04T18:16:49.520346Z", + "shell.execute_reply": "2024-06-04T18:16:49.520243Z" + } + }, + "outputs": [], + "source": [ + "#| export\n", + "import pandas as pd\n", + "import numpy as np\n", + "from fastcore.all import *\n", + "import wandb\n", + "from dvats.load import TSArtifact, infer_or_inject_freq\n", + "import pickle\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from tsai.data.external import convert_tsf_to_dataframe\n", + "from tsai.utils import stack_pad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Path and Artiffact configurattions\n", + "This notebook gets configuration from `config\\base.yaml` and `config\\01-dataset_artifact.yaml`" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:49.526377Z", + "iopub.status.busy": "2024-06-04T18:16:49.526328Z", + "iopub.status.idle": "2024-06-04T18:16:49.563129Z", + "shell.execute_reply": "2024-06-04T18:16:49.563023Z" + } + }, + "outputs": [], + "source": [ + "#| export\n", + "base_path = Path.home()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:49.569306Z", + "iopub.status.busy": "2024-06-04T18:16:49.569157Z", + "iopub.status.idle": "2024-06-04T18:16:49.640130Z", + "shell.execute_reply": "2024-06-04T18:16:49.640069Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selecting " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stumpy_toy_0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "csv_config: {}\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "missing_values_constant: None\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time_col: None\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "missing_values_technique: None\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "range_testing: None\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "start_date: None\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "use_wandb: True\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[94mdata_fpath: ~/data/australian_electricity_demand_dataset.tsf\u001b[0m -> ~/data/toy.csv\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "resampling_freq: None\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wandb_artifacts_path: ./data/wandb_artifacts\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "date_format: %Y-%m-%d %H:%M:%S\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "date_offset: None\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[94martifact_name: Monash-Australian_electricity_demand\u001b[0m -> toy\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[94mdata_cols: [0]\u001b[0m -> []\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "normalize_training: False\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "range_training: None\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test_split: None\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[94mfreq: 1h\u001b[0m -> 1s\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "joining_train_test: False\u001b[0m" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "#| export\n", + "config = cfg_.get_artifact_config_sd2a(print_flag = False)\n", + "if pre_configured_case: \n", + " cfg_.force_artifact_config_sd2a(\n", + " config = config, \n", + " id = case_id, \n", + " print_flag = print_flag, \n", + " both = print_flag, \n", + " frequency_factor = frequency_factor, \n", + " frequency_factor_change_alias = frequency_factor_change_alias\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Extraction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data is assumed to come as a dataframe, either as a binarized picke file or\n", + "as a csv file. It can also come as a `.tsf` file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Check file content (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:49.692941Z", + "iopub.status.busy": "2024-06-04T18:16:49.692796Z", + "iopub.status.idle": "2024-06-04T18:16:49.755926Z", + "shell.execute_reply": "2024-06-04T18:16:49.755865Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/agutierrez/data/toy.csv" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T3,T2,T1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7418217407048813,0.6371797479205732,0.5651174160530583\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7397310623320076,0.6294149853417309,0.49351340038822233\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7187567854120941,0.5392199104076484,0.46934998330275307\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7301690144996238,0.5776701391250909,0.4440996674413469\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7524056920505833,0.5701801791118106,0.37300780215582824\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7173686807875511,0.5439381598630367,0.4250306471976907\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6828549014634744,0.5968434716370429,0.35396080886927506\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7013592638393263,0.5281288533936315,0.2995819396437861\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6411104193217257,0.5656793700127485,0.30444732370665484\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6089596256371969,0.576548578046155,0.3270511350770369\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6034718811428907,0.6077293874759166,0.35334613064970627\n" ] - } - ], - "source": [ - "import utils.config as cfg_\n", - "cfg_.show_available_configs()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "pre_configured_case = True\n", - "case_id = 7\n", - "frequency_factor = 1\n", - "frequency_factor_change_alias = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Main code\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from fastcore.all import *\n", - "import wandb\n", - "from dvats.load import TSArtifact, infer_or_inject_freq\n", - "import pickle\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "from tsai.data.external import convert_tsf_to_dataframe\n", - "from tsai.utils import stack_pad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Path and Artiffact configurattions\n", - "This notebook gets configuration from `config\\base.yaml` and `config\\01-dataset_artifact.yaml`" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "base_path = Path.home()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Selecting stumpy_toy_0\n", - "start_date: None\u001b[0m\n", - "\u001b[94mfreq: 1h\u001b[0m -> 1s\u001b[0m\n", - "use_wandb: True\u001b[0m\n", - "range_testing: None\u001b[0m\n", - "csv_config: {}\u001b[0m\n", - "missing_values_constant: None\u001b[0m\n", - "test_split: None\u001b[0m\n", - "time_col: None\u001b[0m\n", - "resampling_freq: None\u001b[0m\n", - "data_fpath: ~/data/australian_electricity_demand_dataset.tsf\u001b[0m\n", - "wandb_artifacts_path: ./data/wandb_artifacts\u001b[0m\n", - "normalize_training: False\u001b[0m\n", - "joining_train_test: False\u001b[0m\n", - "date_format: %Y-%m-%d %H:%M:%S\u001b[0m\n", - "range_training: None\u001b[0m\n", - "date_offset: None\u001b[0m\n", - "\u001b[94mdata_cols: [0]\u001b[0m -> []\u001b[0m\n", - "missing_values_technique: None\u001b[0m\n", - "\u001b[94martifact_name: Monash-Australian_electricity_demand\u001b[0m -> toy\u001b[0m\n" + "0.6556284395994489,0.7144243525282192,0.30579381196757144\n" ] - } - ], - "source": [ - "config = cfg_.get_artifact_config_sd2a(print_flag = False)\n", - "if pre_configured_case: \n", - " cfg_.force_artifact_config_sd2a(\n", - " config = config, \n", - " id = case_id, \n", - " print_flag = print_flag, \n", - " both = print_flag, \n", - " frequency_factor = frequency_factor, \n", - " frequency_factor_change_alias = frequency_factor_change_alias\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "config.data_fpath = '~/data/toy.csv'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Extraction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data is assumed to come as a dataframe, either as a binarized picke file or\n", - "as a csv file. It can also come as a `.tsf` file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Check file content (if wanted)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + }, + { + "name": "stdout", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error while converting file. Maybe not a tsf: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing attribute section. Attribute section must come before data." + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "/home/agutierrez/data/toy.csv\n", - "T3,T2,T1\n", - "0.7418217407048813,0.6371797479205732,0.5651174160530583\n", - "0.7397310623320076,0.6294149853417309,0.49351340038822233\n", - "0.7187567854120941,0.5392199104076484,0.46934998330275307\n", - "0.7301690144996238,0.5776701391250909,0.4440996674413469\n", - "0.7524056920505833,0.5701801791118106,0.37300780215582824\n", - "0.7173686807875511,0.5439381598630367,0.4250306471976907\n", - "0.6828549014634744,0.5968434716370429,0.35396080886927506\n", - "0.7013592638393263,0.5281288533936315,0.2995819396437861\n", - "0.6411104193217257,0.5656793700127485,0.30444732370665484\n", - "0.6089596256371969,0.576548578046155,0.3270511350770369\n", - "0.6034718811428907,0.6077293874759166,0.35334613064970627\n", - "0.6556284395994489,0.7144243525282192,0.30579381196757144\n", - "Error while converting file. Maybe not a tsf: Missing attribute section. Attribute section must come before data.\n" + "\n" ] } ], "source": [ + "#| hide\n", "if print_flag:\n", " fpath=os.path.expanduser(config.data_fpath)\n", " print(fpath)\n", @@ -268,10 +2642,18 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:49.790128Z", + "iopub.status.busy": "2024-06-04T18:16:49.790034Z", + "iopub.status.idle": "2024-06-04T18:16:49.859377Z", + "shell.execute_reply": "2024-06-04T18:16:49.859276Z" + } + }, "outputs": [], "source": [ + "#| export\n", "ext = str(config.data_fpath).split('.')[-1]\n", "\n", "if ext == 'pickle':\n", @@ -292,15 +2674,42 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:49.865012Z", + "iopub.status.busy": "2024-06-04T18:16:49.864957Z", + "iopub.status.idle": "2024-06-04T18:16:49.907658Z", + "shell.execute_reply": "2024-06-04T18:16:49.907587Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "File loaded successfully\n", - "(550, 3)\n" + "File loaded successfully" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(550, 3)" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] }, { @@ -378,6 +2787,7 @@ } ], "source": [ + "#| hide\n", "if print_flag:\n", " print(f'File loaded successfully')\n", " print(df.shape)\n", @@ -393,8 +2803,15 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:49.917597Z", + "iopub.status.busy": "2024-06-04T18:16:49.917556Z", + "iopub.status.idle": "2024-06-04T18:16:49.955594Z", + "shell.execute_reply": "2024-06-04T18:16:49.955519Z" + } + }, "outputs": [ { "data": { @@ -471,6 +2888,7 @@ } ], "source": [ + "#| export\n", "if config.time_col is not None:\n", " if print_flag: print(\"time_col: \"+str(config.time_col))\n", " \n", @@ -508,18 +2926,33 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:49.962418Z", + "iopub.status.busy": "2024-06-04T18:16:49.962358Z", + "iopub.status.idle": "2024-06-04T18:16:49.996631Z", + "shell.execute_reply": "2024-06-04T18:16:49.996578Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n" + "" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] } ], "source": [ + "#| export\n", "df = infer_or_inject_freq(\n", " df, \n", " injected_freq=config.freq, \n", @@ -538,48 +2971,119 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.335600Z", + "iopub.status.busy": "2024-06-04T18:16:50.330300Z", + "iopub.status.idle": "2024-06-04T18:16:50.342290Z", + "shell.execute_reply": "2024-06-04T18:16:50.341760Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num. variables: 3" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "#| export\n", + "# Subset of variables\n", + "if config.data_cols:\n", + " if print_flag: print(\"data_cols: \", config.data_cols)\n", + " df = df.iloc[:, config.data_cols]\n", + "\n", + "if print_flag: print(f'Num. variables: {len(df.columns)}')" + ] + }, + { + "cell_type": "markdown", "metadata": {}, + "source": [ + "#### Ensure data integrity" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.419290Z", + "iopub.status.busy": "2024-06-04T18:16:50.418740Z", + "iopub.status.idle": "2024-06-04T18:16:50.741860Z", + "shell.execute_reply": "2024-06-04T18:16:50.741290Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Num. variables: 3\n" + "df shape before dropping duplicates" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(550, 3)" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "df shape after dropping duplicates" ] - } - ], - "source": [ - "# Subset of variables\n", - "if config.data_cols:\n", - " if print_flag: print(\"data_cols: \", config.data_cols)\n", - " df = df.iloc[:, config.data_cols]\n", - "\n", - "if print_flag: print(f'Num. variables: {len(df.columns)}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Ensure data integrity" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(550, 3)" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "df shape before dropping duplicates (550, 3)\n", - "df shape after dropping duplicates (550, 3)\n" + "\n" ] } ], "source": [ + "#| hide\n", "#Duplicated rows\n", "if print_flag: print(\"df shape before dropping duplicates\", df.shape)\n", "df.drop_duplicates()\n", @@ -591,10 +3095,18 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.876960Z", + "iopub.status.busy": "2024-06-04T18:16:50.876540Z", + "iopub.status.idle": "2024-06-04T18:16:50.119260Z", + "shell.execute_reply": "2024-06-04T18:16:50.119167Z" + } + }, "outputs": [], "source": [ + "#| export\n", "# Replace the default missing values by np.NaN\n", "if config.missing_values_constant:\n", " df.replace(config.missing_values_constant, np.nan, inplace=True)" @@ -609,10 +3121,18 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.124828Z", + "iopub.status.busy": "2024-06-04T18:16:50.124763Z", + "iopub.status.idle": "2024-06-04T18:16:50.155649Z", + "shell.execute_reply": "2024-06-04T18:16:50.155559Z" + } + }, "outputs": [], "source": [ + "#| hide\n", "show_time_serie_flag = False\n", "if show_time_serie_flag:\n", " # Show time series plot\n", @@ -661,10 +3181,18 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.162173Z", + "iopub.status.busy": "2024-06-04T18:16:50.162107Z", + "iopub.status.idle": "2024-06-04T18:16:50.193728Z", + "shell.execute_reply": "2024-06-04T18:16:50.193639Z" + } + }, "outputs": [], "source": [ + "#| export\n", "rg = config.range_training\n", "\n", "if isinstance(rg, list):\n", @@ -691,14 +3219,42 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.199571Z", + "iopub.status.busy": "2024-06-04T18:16:50.199516Z", + "iopub.status.idle": "2024-06-04T18:16:50.253954Z", + "shell.execute_reply": "2024-06-04T18:16:50.253724Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "About to write df to /home/agutierrez/data/wandb_artifacts/2078634713863647172\n" + "About to write df to " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/agutierrez/data/wandb_artifacts/2078634713863647172" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] }, { @@ -721,6 +3277,7 @@ } ], "source": [ + "#| export\n", "training_artifact = TSArtifact.from_df(\n", " df_training, \n", " name=config.artifact_name, \n", @@ -734,10 +3291,18 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.261132Z", + "iopub.status.busy": "2024-06-04T18:16:50.260922Z", + "iopub.status.idle": "2024-06-04T18:16:50.308942Z", + "shell.execute_reply": "2024-06-04T18:16:50.308843Z" + } + }, "outputs": [], "source": [ + "#| export\n", "#Debugging \n", "if df_training.index.duplicated().any():\n", " raise ValueError(\"Duplicated index names\")" @@ -759,18 +3324,33 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.311903Z", + "iopub.status.busy": "2024-06-04T18:16:50.311830Z", + "iopub.status.idle": "2024-06-04T18:16:50.350877Z", + "shell.execute_reply": "2024-06-04T18:16:50.350769Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "rg None | test_split None\n" + "rg None | test_split None" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] } ], "source": [ + "#| export\n", "# Testing data\n", "rg = config.range_testing\n", "\n", @@ -797,11 +3377,11 @@ " path=str(Path.home()/config.wandb_artifacts_path))\n", " display(testing_artifact.metadata)\n", " if df_testing.index.duplicated().any():\n", - " print(\"Hay valores duplicados en el índice del dataframe.\")\n", + " print(\"There exist duplicated value(s) in the index dataframe.\")\n", " else:\n", - " print(\"No hay valores duplicados en el índice del dataframe.\")\n", + " if print_flag: print(\"There is no duplicated value in the index dataframe.\")\n", "else:\n", - " print(\"rg \"+ str(rg) + \" | test_split \"+ str(config.test_split))\n", + " if print_flag: print(\"rg \"+ str(rg) + \" | test_split \"+ str(config.test_split))\n", " testing_artifact = None" ] }, @@ -821,25 +3401,35 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.355144Z", + "iopub.status.busy": "2024-06-04T18:16:50.355068Z", + "iopub.status.idle": "2024-06-04T18:16:50.393732Z", + "shell.execute_reply": "2024-06-04T18:16:50.393621Z" + } + }, "outputs": [], "source": [ + "#| export\n", "# Training + Testing data\n", "if(config.joining_train_test):\n", " print(\"joining_train_test: \"+ str(config.joining_train_test))\n", " df_train_test = pd.concat([df_training, df_testing])\n", - " train_test_artifact = TSArtifact.from_df(df_train_test,\n", - " name=config.artifact_name, \n", - " missing_values_technique=config.missing_values_technique,\n", - " resampling_freq=config.resampling_freq, \n", - " normalize=False,\n", - " path=str(Path.home()/config.wandb_artifacts_path))\n", + " train_test_artifact = TSArtifact.from_df(\n", + " df_train_test,\n", + " name=config.artifact_name, \n", + " missing_values_technique=config.missing_values_technique,\n", + " resampling_freq=config.resampling_freq, \n", + " normalize=False,\n", + " path=str(Path.home()/config.wandb_artifacts_path)\n", + " )\n", " if df_train_test.index.duplicated().any():\n", - " print(\"Hay valores duplicados en el índice del dataframe.\")\n", + " print(\"There exist duplicated value(s) within the dataframe index.\")\n", " else:\n", - " print(\"No hay valores duplicados en el índice del dataframe.\")\n", - " display(train_test_artifact.metadata)\n", + " if print_flag: print(\"There is no duplicated value in the dtaframe index\")\n", + " if print_flag: display(train_test_artifact.metadata)\n", "else:\n", " train_test_artifact = None" ] @@ -865,18 +3455,80 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.396736Z", + "iopub.status.busy": "2024-06-04T18:16:50.396650Z", + "iopub.status.idle": "2024-06-04T18:16:50.434993Z", + "shell.execute_reply": "2024-06-04T18:16:50.434895Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "angel-gutierrez" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ream" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "#| hide\n", + "print(os.environ['WANDB_ENTITY'])\n", + "print(os.environ['WANDB_PROJECT'])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.440919Z", + "iopub.status.busy": "2024-06-04T18:16:50.440848Z", + "iopub.status.idle": "2024-06-04T18:16:50.478761Z", + "shell.execute_reply": "2024-06-04T18:16:50.478663Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "runname: 01_dataset_artifact\n" + "runname: 01_dataset_artifact" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] } ], "source": [ + "#| export\n", "import os\n", "path = os.path.expanduser(\"~/work/nbs_pipeline/\")\n", "name=\"01_dataset_artifact\"\n", @@ -887,20 +3539,109 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, + "execution_count": 27, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.482940Z", + "iopub.status.busy": "2024-06-04T18:16:50.482861Z", + "iopub.status.idle": "2024-06-04T18:16:50.522199Z", + "shell.execute_reply": "2024-06-04T18:16:50.522097Z" + } + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "```json\n", + "{ 'artifact_name': 'toy',\n", + " 'csv_config': {},\n", + " 'data_cols': [],\n", + " 'data_fpath': '~/data/toy.csv',\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'date_offset': None,\n", + " 'freq': '1s',\n", + " 'joining_train_test': False,\n", + " 'missing_values_constant': None,\n", + " 'missing_values_technique': None,\n", + " 'normalize_training': False,\n", + " 'range_testing': None,\n", + " 'range_training': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None,\n", + " 'use_wandb': True,\n", + " 'wandb_artifacts_path': './data/wandb_artifacts'}\n", + "```" + ], + "text/plain": [ + "{'artifact_name': 'toy',\n", + " 'csv_config': {},\n", + " 'data_cols': [],\n", + " 'data_fpath': '~/data/toy.csv',\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'date_offset': None,\n", + " 'freq': '1s',\n", + " 'joining_train_test': False,\n", + " 'missing_values_technique': None,\n", + " 'missing_values_constant': None,\n", + " 'normalize_training': False,\n", + " 'range_training': None,\n", + " 'range_testing': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None,\n", + " 'use_wandb': True,\n", + " 'wandb_artifacts_path': './data/wandb_artifacts'}" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:16:50.526109Z", + "iopub.status.busy": "2024-06-04T18:16:50.526037Z", + "iopub.status.idle": "2024-06-04T18:17:01.471442Z", + "shell.execute_reply": "2024-06-04T18:17:01.471246Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mangel-gutierrez\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" + "wandb: Currently logged in as: angel-gutierrez. Use `wandb login --relogin` to force relogin\n" ] }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "88e7a480ebef44f5a22ebc6ab23d3405", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016669281634191672, max=1.0…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ - "wandb version 0.16.3 is available! To upgrade, please run:\n", + "wandb version 0.17.0 is available! To upgrade, please run:\n", " $ pip install wandb --upgrade" ], "text/plain": [ @@ -925,7 +3666,7 @@ { "data": { "text/html": [ - "Run data is saved locally in /home/agutierrez/work/wandb/run-20240229_191007-bn8e6lvh" + "Run data is saved locally in /home/agutierrez/work/nbs_pipeline/wandb/run-20240604_181652-0x7lnh8b" ], "text/plain": [ "" @@ -937,7 +3678,7 @@ { "data": { "text/html": [ - "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" + "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -961,7 +3702,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/angel-gutierrez/ream/runs/bn8e6lvh" + " View run at https://wandb.ai/angel-gutierrez/ream/runs/0x7lnh8b" ], "text/plain": [ "" @@ -970,6 +3711,48 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'stream.Stream' object attribute 'write' is read-only" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Logging training_artifact" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, { "data": { "text/html": [ @@ -982,10 +3765,24 @@ "metadata": {}, "output_type": "display_data" }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "987b241b2e554810a2d4f55a85711cc1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='0.006 MB of 0.006 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ - " View run 01_dataset_artifact at: https://wandb.ai/angel-gutierrez/ream/runs/bn8e6lvh
Synced 5 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)" + " View run 01_dataset_artifact at: https://wandb.ai/angel-gutierrez/ream/runs/0x7lnh8b
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" @@ -997,7 +3794,7 @@ { "data": { "text/html": [ - "Find logs at: /home/agutierrez/work/wandb/run-20240229_191007-bn8e6lvh/logs" + "Find logs at: ./wandb/run-20240604_181652-0x7lnh8b/logs" ], "text/plain": [ "" @@ -1008,6 +3805,7 @@ } ], "source": [ + "#| export\n", "mode = 'online' if config.use_wandb else 'disabled'\n", "\n", "# Make the run that will produce the artifact\n", @@ -1020,28 +3818,51 @@ " run.log_artifact(train_test_artifact, aliases=['all'])\n", " \n", " else:\n", + " if print_flag: print(\"Logging training_artifact\", training_artifact)\n", " run.log_artifact(training_artifact, aliases=['all'])" ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, + "execution_count": 29, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:17:01.477000Z", + "iopub.status.busy": "2024-06-04T18:17:01.476864Z", + "iopub.status.idle": "2024-06-04T18:17:01.527712Z", + "shell.execute_reply": "2024-06-04T18:17:01.527616Z" + } + }, "outputs": [], "source": [ + "#| export\n", "run.finish()" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, + "execution_count": 30, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:17:01.533092Z", + "iopub.status.busy": "2024-06-04T18:17:01.533028Z", + "iopub.status.idle": "2024-06-04T18:17:02.576926Z", + "shell.execute_reply": "2024-06-04T18:17:02.576733Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Execution ended\n" + "Execution ended" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] }, { @@ -1063,6 +3884,7 @@ } ], "source": [ + "#| export\n", "from dvats.imports import beep\n", "print(\"Execution ended\")\n", "beep(1)" @@ -1070,40 +3892,583 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, + "execution_count": 31, + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-04T18:17:02.583970Z", + "iopub.status.busy": "2024-06-04T18:17:02.583861Z", + "iopub.status.idle": "2024-06-04T18:17:02.640046Z", + "shell.execute_reply": "2024-06-04T18:17:02.639877Z" + } + }, "outputs": [], "source": [ + "#| hide\n", "if reset_kernel:\n", " import os\n", " os._exit(00)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.10 (XPython)", "language": "python", - "name": "python3" + "name": "xpython" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.12" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + 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"stream", "text": [ - "\u001b[94mw: 30\u001b[0m -> 900\u001b[0m\n", - "\u001b[94mmvp_ws: (15, 30)\u001b[0m -> [450, 900]\u001b[0m\n", - "batch_size: 512\u001b[0m\n", - "\u001b[94mstride: 15\u001b[0m -> 450\u001b[0m\n", - "\u001b[93m\u001b[1mcsv_config is missing in original dict | {} \u001b[0m\n", - "r: 0.71\u001b[0m\n", - "analysis_mode: online\u001b[0m\n", - "valid_size: 0.2\u001b[0m\n", - "mask_stateful: True\u001b[0m\n", - "epochs: 100\u001b[0m\n", - "\u001b[93m\u001b[1mnorm_use_by_single_batch is missing in original dict | (False,) \u001b[0m\n", + "wandb_group: None\u001b[0m\n", "\u001b[94malias: Monash-Australian_electricity_demand\u001b[0m -> solar_4_seconds\u001b[0m\n", + "\u001b[93m\u001b[1mnorm_use_by_single_batch is missing in original dict | (False,) \u001b[0m\n", + "\u001b[93m\u001b[1martifact_name is missing in original dict | solar_4_seconds-20s \u001b[0m\n", "\u001b[93m\u001b[1mfreq is missing in original dict | 4s \u001b[0m\n", + "batch_size: 512\u001b[0m\n", + "\u001b[93m\u001b[1mcsv_config is missing in original dict | {} \u001b[0m\n", "use_wandb: True\u001b[0m\n", - "\u001b[94mtrain_artifact: mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest\u001b[0m -> mi-santamaria/deepvats/solar_4_seconds-20s:latest\u001b[0m\n", - "wandb_group: None\u001b[0m\n", - "\u001b[93m\u001b[1mresampling_freq is missing in original dict | 20S \u001b[0m\n", - "\u001b[93m\u001b[1martifact_name is missing in original dict | solar_4_seconds-20s \u001b[0m\n", + "\u001b[94mmvp_ws: (15, 30)\u001b[0m -> [450, 900]\u001b[0m\n", "mask_sync: False\u001b[0m\n", - "norm_use_single_batch: False\u001b[0m\n", + "mask_stateful: True\u001b[0m\n", "\u001b[93m\u001b[1mtime_col is missing in original dict | None \u001b[0m\n", - "norm_by_sample: False\u001b[0m\n", - "\u001b[93m\u001b[1mdata_cols is missing in original dict | [] \u001b[0m\n", + "valid_artifact: None\u001b[0m\n", + "\u001b[94mw: 30\u001b[0m -> 900\u001b[0m\n", + "norm_use_single_batch: False\u001b[0m\n", + "r: 0.71\u001b[0m\n", "mask_future: False\u001b[0m\n", - "valid_artifact: None\u001b[0m\n" + "valid_size: 0.2\u001b[0m\n", + "epochs: 100\u001b[0m\n", + "\u001b[94mtrain_artifact: angel-gutierrez/ream/Monash-Australian_electricity_demand:latest\u001b[0m -> angel-gutierrez/ream/solar_4_seconds-20s:latest\u001b[0m\n", + "\u001b[93m\u001b[1mdata_cols is missing in original dict | [] \u001b[0m\n", + "norm_by_sample: False\u001b[0m\n", + "analysis_mode: online\u001b[0m\n", + "\u001b[93m\u001b[1mresampling_freq is missing in original dict | 20S \u001b[0m\n", + "\u001b[94mstride: 15\u001b[0m -> 450\u001b[0m\n" ] } ], @@ -299,7 +299,7 @@ "norm_use_single_batch: False\n", "r: 0.71\n", "stride: 450\n", - "train_artifact: mi-santamaria/deepvats/solar_4_seconds-20s:latest\n", + "train_artifact: angel-gutierrez/ream/solar_4_seconds-20s:latest\n", "valid_artifact: None\n", "use_wandb: True\n", "valid_size: 0.2\n", @@ -336,14 +336,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "wandb: WARNING WANDB_NOTEBOOK_NAME should be a path to a notebook file, couldn't find /home/macu/work/nbs_pipeline/02a_encoder_MVP.ipynb.\n", - "wandb: Currently logged in as: mi-santamaria. Use `wandb login --relogin` to force relogin\n" + "wandb: WARNING WANDB_NOTEBOOK_NAME should be a path to a notebook file, couldn't find /home/agutierrez/work/nbs_pipeline/02a_encoder_MVP.ipynb.\n", + "wandb: Currently logged in as: angel-gutierrez. Use `wandb login --relogin` to force relogin\n" ] }, { "data": { "text/html": [ - "wandb version 0.16.1 is available! To upgrade, please run:\n", + "wandb version 0.16.6 is available! To upgrade, please run:\n", " $ pip install wandb --upgrade" ], "text/plain": [ @@ -368,7 +368,7 @@ { "data": { "text/html": [ - "Run data is saved locally in /home/macu/work/wandb/run-20240105_141032-g77p6ktg" + "Run data is saved locally in /home/agutierrez/work/wandb/run-20240419_123811-pbdqrq15" ], "text/plain": [ "" @@ -380,7 +380,7 @@ { "data": { "text/html": [ - "Syncing run 02a_encoder_MVP to Weights & Biases (docs)
" + "Syncing run 02a_encoder_MVP to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -392,7 +392,7 @@ { "data": { "text/html": [ - " View project at https://wandb.ai/mi-santamaria/deepvats" + " View project at https://wandb.ai/angel-gutierrez/ream" ], "text/plain": [ "" @@ -404,7 +404,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/mi-santamaria/deepvats/runs/g77p6ktg" + " View run at https://wandb.ai/angel-gutierrez/ream/runs/pbdqrq15" ], "text/plain": [ "" @@ -482,7 +482,7 @@ "norm_use_single_batch: False\n", "r: 0.71\n", "stride: 450\n", - "train_artifact: mi-santamaria/deepvats/solar_4_seconds-20s:latest\n", + "train_artifact: angel-gutierrez/ream/solar_4_seconds-20s:latest\n", "valid_artifact: None\n", "use_wandb: True\n", "valid_size: 0.2\n", @@ -496,6 +496,32 @@ "resampling_freq: 20S\n", "norm_use_by_single_batch: [False]\n" ] + }, + { + "ename": "", + "evalue": "Project angel-gutierrez/ream does not contain artifact: \"solar_4_seconds-20s:latest\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/apis/normalize.py:41\u001b[0m, in \u001b[0;36mnormalize_exceptions..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mHTTPError \u001b[38;5;28;01mas\u001b[39;00m error:\n", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/apis/public.py:957\u001b[0m, in \u001b[0;36mApi.artifact\u001b[0;34m(self, name, type)\u001b[0m\n\u001b[1;32m 956\u001b[0m entity, project, artifact_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parse_artifact_path(name)\n\u001b[0;32m--> 957\u001b[0m artifact \u001b[38;5;241m=\u001b[39m \u001b[43mArtifact\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mentity\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproject\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 958\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m artifact\u001b[38;5;241m.\u001b[39mtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mtype\u001b[39m:\n", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/apis/public.py:4355\u001b[0m, in \u001b[0;36mArtifact.__init__\u001b[0;34m(self, client, entity, project, name, attrs)\u001b[0m\n\u001b[1;32m 4354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attrs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4355\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4357\u001b[0m \u001b[38;5;66;03m# The entity and project above are taken from the passed-in artifact version path\u001b[39;00m\n\u001b[1;32m 4358\u001b[0m \u001b[38;5;66;03m# so if the user is pulling an artifact version from an artifact portfolio, the entity/project\u001b[39;00m\n\u001b[1;32m 4359\u001b[0m \u001b[38;5;66;03m# of that portfolio may be different than the birth entity/project of the artifact version.\u001b[39;00m\n", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/apis/public.py:5082\u001b[0m, in \u001b[0;36mArtifact._load\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 5077\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 5078\u001b[0m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 5079\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m response\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 5080\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124martifact\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 5081\u001b[0m ):\n\u001b[0;32m-> 5082\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 5083\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProject \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m does not contain artifact: \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 5084\u001b[0m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mentity, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mproject, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_artifact_name)\n\u001b[1;32m 5085\u001b[0m )\n\u001b[1;32m 5086\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attrs \u001b[38;5;241m=\u001b[39m response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124martifact\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "\u001b[0;31mValueError\u001b[0m: Project angel-gutierrez/ream does not contain artifact: \"solar_4_seconds-20s:latest\"", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mCommError\u001b[0mTraceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m print_flag: cfg_\u001b[38;5;241m.\u001b[39mshow_attrdict(config)\n\u001b[1;32m 3\u001b[0m artifacts_gettr \u001b[38;5;241m=\u001b[39m run\u001b[38;5;241m.\u001b[39muse_artifact \u001b[38;5;28;01mif\u001b[39;00m config\u001b[38;5;241m.\u001b[39muse_wandb \u001b[38;5;28;01melse\u001b[39;00m wandb_api\u001b[38;5;241m.\u001b[39martifact\n\u001b[0;32m----> 4\u001b[0m train_artifact \u001b[38;5;241m=\u001b[39m artifacts_gettr(config\u001b[38;5;241m.\u001b[39mtrain_artifact)\n", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:344\u001b[0m, in \u001b[0;36m_run_decorator._noop_on_finish..decorator_fn..wrapper_fn\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper_fn\u001b[39m(\u001b[38;5;28mself\u001b[39m: Type[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRun\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_is_finished\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m--> 344\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 346\u001b[0m default_message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 347\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRun (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) is finished. The call to `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` will be ignored. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease make sure that you are using an active run.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 349\u001b[0m )\n\u001b[1;32m 350\u001b[0m resolved_message \u001b[38;5;241m=\u001b[39m message \u001b[38;5;129;01mor\u001b[39;00m default_message\n", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:334\u001b[0m, in \u001b[0;36m_run_decorator._attach..wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 332\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 333\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_is_attaching \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 334\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/sdk/wandb_run.py:2675\u001b[0m, in \u001b[0;36mRun.use_artifact\u001b[0;34m(self, artifact_or_name, type, aliases, use_as)\u001b[0m\n\u001b[1;32m 2673\u001b[0m name \u001b[38;5;241m=\u001b[39m artifact_or_name\n\u001b[1;32m 2674\u001b[0m public_api \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_public_api()\n\u001b[0;32m-> 2675\u001b[0m artifact \u001b[38;5;241m=\u001b[39m \u001b[43mpublic_api\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43martifact\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m!=\u001b[39m artifact\u001b[38;5;241m.\u001b[39mtype:\n\u001b[1;32m 2677\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 2678\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSupplied type \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m does not match type \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m of artifact \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 2679\u001b[0m \u001b[38;5;28mtype\u001b[39m, artifact\u001b[38;5;241m.\u001b[39mtype, artifact\u001b[38;5;241m.\u001b[39mname\n\u001b[1;32m 2680\u001b[0m )\n\u001b[1;32m 2681\u001b[0m )\n", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/apis/normalize.py:87\u001b[0m, in \u001b[0;36mnormalize_exceptions..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 87\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CommError(message, err)\u001b[38;5;241m.\u001b[39mwith_traceback(sys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m])\n", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/apis/normalize.py:41\u001b[0m, in \u001b[0;36mnormalize_exceptions..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 39\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhoa, you found a bug.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mHTTPError \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[1;32m 43\u001b[0m errors \u001b[38;5;241m=\u001b[39m parse_backend_error_messages(error\u001b[38;5;241m.\u001b[39mresponse)\n", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/apis/public.py:957\u001b[0m, in \u001b[0;36mApi.artifact\u001b[0;34m(self, name, type)\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou must specify name= to fetch an artifact.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 956\u001b[0m entity, project, artifact_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parse_artifact_path(name)\n\u001b[0;32m--> 957\u001b[0m artifact \u001b[38;5;241m=\u001b[39m \u001b[43mArtifact\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mentity\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproject\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 958\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m artifact\u001b[38;5;241m.\u001b[39mtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mtype\u001b[39m:\n\u001b[1;32m 959\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 960\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m specified but this artifact is of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00martifact\u001b[38;5;241m.\u001b[39mtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 961\u001b[0m )\n", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/apis/public.py:4355\u001b[0m, in \u001b[0;36mArtifact.__init__\u001b[0;34m(self, client, entity, project, name, attrs)\u001b[0m\n\u001b[1;32m 4353\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attrs \u001b[38;5;241m=\u001b[39m attrs\n\u001b[1;32m 4354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attrs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4355\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4357\u001b[0m \u001b[38;5;66;03m# The entity and project above are taken from the passed-in artifact version path\u001b[39;00m\n\u001b[1;32m 4358\u001b[0m \u001b[38;5;66;03m# so if the user is pulling an artifact version from an artifact portfolio, the entity/project\u001b[39;00m\n\u001b[1;32m 4359\u001b[0m \u001b[38;5;66;03m# of that portfolio may be different than the birth entity/project of the artifact version.\u001b[39;00m\n\u001b[1;32m 4360\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_birth_project \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 4361\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attrs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124martifactType\u001b[39m\u001b[38;5;124m\"\u001b[39m, {})\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m, {})\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4362\u001b[0m )\n", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/wandb/apis/public.py:5082\u001b[0m, in \u001b[0;36mArtifact._load\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 5074\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 5075\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAttempted to fetch artifact without alias (e.g. \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m:v3\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m:latest\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 5076\u001b[0m )\n\u001b[1;32m 5077\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 5078\u001b[0m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 5079\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m response\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 5080\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124martifact\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 5081\u001b[0m ):\n\u001b[0;32m-> 5082\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 5083\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProject \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m does not contain artifact: \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 5084\u001b[0m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mentity, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mproject, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_artifact_name)\n\u001b[1;32m 5085\u001b[0m )\n\u001b[1;32m 5086\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attrs \u001b[38;5;241m=\u001b[39m response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124martifact\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 5087\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_attrs\n", + "\u001b[0;31mCommError\u001b[0m: Project angel-gutierrez/ream does not contain artifact: \"solar_4_seconds-20s:latest\"" + ] } ], "source": [ @@ -507,28 +533,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "8acf714f-0fe1-4aed-8b57-23ccd31b22fe", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "wandb: 1 of 1 files downloaded. \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1479445, 1)\n", - "df_train ~ (1479445, 1)\n", - "window_sizes = [450, 900]\n", - "wlen = 900\n" - ] - } - ], + "outputs": [], "source": [ "df_train = train_artifact.to_df()\n", "print(df_train.shape)\n", @@ -551,18 +559,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "8d25ab5a-b1e6-4eb7-8542-e4dc87f2a883", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(3286, 1, 900)\n" - ] - } - ], + "outputs": [], "source": [ "sw = SlidingWindow(window_len=config.w, stride=config.stride, get_y=[])\n", "X_train, _ = sw(df_train)\n", @@ -579,39 +579,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "bb1e270e-c6a2-4dc0-a54d-8f6fdc7565b1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(3286, 1, 900)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "((#2629) [0,1,2,3,4,5,6,7,8,9...],\n", - " (#657) [2629,2630,2631,2632,2633,2634,2635,2636,2637,2638...])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "assert config.analysis_mode in ['offline','online'], 'Invalid analysis mode'\n", "\n", @@ -647,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "c7c3cd99", "metadata": {}, "outputs": [], @@ -665,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "b97038fe-116f-4d6c-8569-e9a9c015a434", "metadata": {}, "outputs": [], @@ -685,21 +656,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "668e7b5a", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dls = get_ts_dls(X, splits=splits, tfms=tfms, bs=config.batch_size, batch_tfms=batch_tfms)\n", "dls.show_at(0)" @@ -715,7 +675,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "f5f2b562-c1d8-4b01-aa69-6aa974ee959b", "metadata": {}, "outputs": [], @@ -751,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "cb9dd304-6691-49b7-9ffc-f004757f3cd8", "metadata": {}, "outputs": [], @@ -772,21 +732,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "563efbd4-bc93-4969-b7e8-bed4b191291e", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(20, 2))\n", "plt.pcolormesh(example_mask[0], cmap=colors.ListedColormap(['whitesmoke', 'orchid']))\n", @@ -813,19 +762,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "ab1d5cc8-fac5-4f9d-bbd9-4083c2c60d8a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "w 900 mvp_ws [450, 900]\n", - "expected [450, 900]\n" - ] - } - ], + "outputs": [], "source": [ "expected_window_size = config.mvp_ws\n", "print(\"w\", config.w, \"mvp_ws\", config.mvp_ws)\n", @@ -842,18 +782,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "385e5d4c-84de-440c-9572-8688951c2873", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "obtained [450, 900]\n" - ] - } - ], + "outputs": [], "source": [ "mvp_cb = learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0] # Encuentra el callback MVP\n", "obtained_window_size=mvp_cb.window_size\n", @@ -862,18 +794,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "ce3a3112-ba6b-4756-9ce1-e15e4c2af1fd", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Obtained window size tuple is the expected one. Continue!\n" - ] - } - ], + "outputs": [], "source": [ "if (expected_window_size != obtained_window_size):\n", " raise ValueError(\"Obtained window_size for MVP training different from expected window size. Check size, ws1 & ws2 parameters in '02b-encoder_MVP.yaml'\")\n", @@ -891,18 +815,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "9272a132-9026-419c-81b0-d16d03894f70", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[156 188]\n" - ] - } - ], + "outputs": [], "source": [ "if (obtained_window_size[1] < obtained_window_size[0]):\n", " raise ValueError(\"Ws2 must be greater than Ws1 as they are the maximun and minimum window size respectively. Please ensure w2 > w1\")\n", @@ -921,24 +837,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "437a49d0-7ee1-4519-a871-b4f82f94c1a4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x (TSTensor(samples:512, vars:1, len:900, device=cuda:0, dtype=torch.float32),)\n", - "Data shape: torch.Size([512, 1, 900])\n", - "Time serie len: 900\n", - "diff time serie len - ws 744\n", - "ws 156 diff 744 result 132\n", - "diff time serie len - ws 712\n", - "ws 188 diff 712 result 665\n" - ] - } - ], + "outputs": [], "source": [ "#Get data batch\n", "x = next(iter(dls.train))\n", @@ -965,109 +867,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "610dd97f-1cb2-4364-9748-db882ac6162a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "epoch train_loss valid_loss time \n", - "0 0.669158 0.748629 00:00 \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 0.486056 0.744538 00:00 \n", - "2 0.373436 0.664465 00:00 \n", - "3 0.310263 0.485209 00:00 \n", - "4 0.265954 0.291867 00:00 \n", - "5 0.229860 0.175626 00:00 \n", - "6 0.201852 0.081892 00:00 \n", - "7 0.178501 0.067111 00:00 \n", - "8 0.158712 0.030105 00:00 \n", - "9 0.142073 0.019322 00:00 \n", - "10 0.127762 0.016648 00:00 \n", - "11 0.115639 0.013029 00:00 \n", - "12 0.105083 0.011033 00:00 \n", - "13 0.095871 0.018097 00:00 \n", - "14 0.087551 0.008384 00:00 \n", - "15 0.080240 0.009521 00:00 \n", - "16 0.073913 0.006233 00:00 \n", - "17 0.068623 0.008005 00:00 \n", - "18 0.063604 0.013670 00:00 \n", - "19 0.059013 0.004887 00:00 \n", - "20 0.055152 0.011128 00:00 \n", - "21 0.051889 0.013473 00:00 \n", - "22 0.048776 0.009863 00:00 \n", - "23 0.045698 0.007194 00:00 \n", - "24 0.042956 0.005565 00:00 \n", - "25 0.040160 0.006314 00:00 \n", - "26 0.037758 0.006352 00:00 \n", - "27 0.035712 0.004502 00:00 \n", - "28 0.033731 0.004269 00:00 \n", - "29 0.031870 0.020114 00:00 \n", - "30 0.030324 0.006156 00:00 \n", - "31 0.028654 0.003554 00:00 \n", - "32 0.027170 0.004361 00:00 \n", - "33 0.025713 0.005218 00:00 \n", - "34 0.024367 0.003865 00:00 \n", - "35 0.023163 0.003188 00:00 \n", - "36 0.022420 0.007980 00:00 \n", - "37 0.021670 0.011922 00:00 \n", - "38 0.020918 0.005214 00:00 \n", - "39 0.020080 0.004016 00:00 \n", - "40 0.019426 0.005706 00:00 \n", - "41 0.018637 0.008125 00:00 \n", - "42 0.017959 0.003073 00:00 \n", - "43 0.017304 0.002604 00:00 \n", - "44 0.016585 0.002638 00:00 \n", - "45 0.016272 0.003158 00:00 \n", - "46 0.016052 0.005475 00:00 \n", - "47 0.015696 0.003908 00:00 \n", - "48 0.015181 0.004634 00:00 \n", - "49 0.014624 0.003909 00:00 \n", - "50 0.014179 0.005249 00:00 \n", - "51 0.013764 0.003559 00:00 \n", - "52 0.013476 0.003963 00:00 \n", - "53 0.013086 0.003623 00:00 \n", - "No improvement since epoch 43: early stopping\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "lr_valley, lr_steep = learn.lr_find(suggest_funcs=(valley, steep))\n", "learn.fit_one_cycle(n_epoch=config.epochs, lr_max=lr_valley, cbs=[EarlyStoppingCallback(monitor='valid_loss', min_delta=0.000001, patience=10)])" @@ -1083,28 +886,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "b546f8d3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "not enough values to plot a chart\n" - ] - }, - { - "data": { - "text/plain": [ - "(#1) [0.50827556848526]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "learn.validate()" ] @@ -1119,23 +904,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "bfdf3667-a698-451a-9900-0ac1d6fa0cf5", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "learn.MVP.show_preds(sharey=True, nrows=2) # error with nwors=1 or ncols=1" ] @@ -1151,21 +925,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "bf3bf350-4f52-4a18-ad19-9d260bd060d1", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Remove the ShowGraphCallback2 callback to avoid errors in the frontend (TODO)\n", "learn.remove_cb(sgc)" @@ -1173,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "9cc0de64-aced-433e-9c10-28fbcf4f9a91", "metadata": {}, "outputs": [], @@ -1202,80 +965,12 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "ddfe12cd-6e5f-40eb-8f84-12d2db7a355b", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "Waiting for W&B process to finish... (success)." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "746e75c2a7d544dd860713706cf19e4f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Label(value='0.006 MB of 0.006 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "

Run summary:


epoch54
eps_01e-05
eps_11e-05
lr_00.00118
lr_10.00118
mom_00.88218
mom_10.88218
raw_loss0.00944
sqr_mom_00.99
sqr_mom_10.99
train_loss0.01309
train_samples_per_sec5180.97373
valid_loss0.00362
wd_00.01
wd_10.01

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " View run 02a_encoder_MVP at: https://wandb.ai/mi-santamaria/deepvats/runs/g77p6ktg
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Find logs at: /home/macu/work/wandb/run-20240105_141032-g77p6ktg/logs" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "run.finish()" ] @@ -1292,6 +987,14 @@ " import os\n", " os._exit(00)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7759ac9-ed1f-49dd-9a5b-65e4090b9aec", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/nbs_pipeline/base.yaml b/nbs_pipeline/base.yaml new file mode 100644 index 00000000..da36f6e0 --- /dev/null +++ b/nbs_pipeline/base.yaml @@ -0,0 +1,96 @@ +######################################################### +########### NBS - PIPELINE CONFIGURATION FILE ########### +######################################################### +###### Author: Maria Inmaculada Santamaria-Valenzuela # +###### Date: 08-2023 # +######################################################### + +########################## +# Basic user preferences # +########################## +user_preferences: + #Weights & biases + # Whether to use or not wandb for experiment tracking + use_wandb: &use_wandb true + wdb: + user: &wdb_user angel-gutierrez + project_name: &wdb_project ream + version: &wdb_version 'latest' #'6' + # 'online' if the model will be tested on future data, or 'offline' if not + mode: &wdb_mode 'online' + artifacts_path: &artifacts_path './data/wandb_artifacts' + data: + folder: &path '~/data/' + fname: &fname "toy" + ftype: &ftype '.csv' + cols: &cols [5] + freq: &freq '10h' + artifact: + # Alias of the artifact resulting of this run. null will create one automatically + alias: &alias 'Monash-Australian_electricity_demand' + algorithm: &alg 'mvp-SWV' + directories: + # Folder to store temporary files + tmp: &tmp 'tmp' + data: &data_path !join [ *path, *fname, *ftype ] + +####################### +# Data specifications # +####################### +data: + name: *fname + # Name of the data file. Must be pickle, csv or tsf file + path: *data_path + # Name of the artifact to be created + alias: *alias + # Put here the idxs of the columns of interest ([] for all) + cols: *cols + # CSV file config ({} if this setting is not required) + csv_config: {} + # Frequency offset used to set the date index (null to omit it) [Example pd.offsets.MonthEnd(0)] + date_offset: null + # Default date format (only for .tsf files) + date_format: '%Y-%m-%d %H:%M:%S' + # Frequency of the data (cannot be null). It can be used to force a sampling freq to data without an index + # See offset aliases in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases + freq: *freq + # To create an artifact linking training and testing data (False if it doesn't) + # If you're using deepvats in "offline" mode, set this to False + joining_train_test: false + missing_values: + # Handle missing values technique (null for no handling) + technique: null + # Default value used for missing values (only if missing_values_technique is not null) + constant: null + # To normalize the data + normalize_training: false + # Training and Testing ranges. They can be lists with values to be included + # (of the same type as the index) or dictionaries that include the keys 'start', 'end' and 'freq'. + range_training: null + range_testing: null + # Resampling frequency (null for no resampling) + resampling_freq: null + # Starting date (in format yyyy-mm-dd) (set to null for default start date) + start_date: null + # Ratio of test set, only if range_training and range_testing are null + # Set to null for no test set (offline mode, all training) + test_split: null + # List or int with the idx(s) of the column containing the timestamp + # (null if there is no timestamp column) + time_col: null + +######################## +# Wandb specifications # +######################## +wandb: + user: *wdb_user + dir: !join [ '~/', *wdb_project] # TODO: Make relative to this file? (null for using the OS default) + enabled: False # To use it, the environment variable WANDB_API_KEY must be set + group: null # Useful to group runs that belong to the same optuna study + log_learner: False # Log learner to wandb + mode: *wdb_mode + project: *wdb_project + version: *wdb_version + # Output path where the resulting TSArtifact will be stored + artifacts_path: *artifacts_path + diff --git a/nbs_pipeline/utils/config.py b/nbs_pipeline/utils/config.py index ee75d943..8b9aa18b 100644 --- a/nbs_pipeline/utils/config.py +++ b/nbs_pipeline/utils/config.py @@ -1,19 +1,65 @@ -# -*- coding: utf-8 -*- -"""config.ipynb +#!/usr/bin/env python +# coding: utf-8 -Automatically generated. +# In[ ]: -Original file is located at: - /home/macu/work/nbs_pipeline/utils_nbs/config.ipynb -""" +#| default_exp config + + +# In[ ]: + + +#| hide +get_ipython().run_line_magic('load_ext', 'autoreload') +get_ipython().run_line_magic('autoreload', '2') + + +# # Configuration functions +# > This notebook introduces configuration functions designed to leverage proposed .yaml files, providing a more transparent and automated approach to artefact definition. While this method offers enhanced clarity, the option for direct definition within each notebook is also fully supported and remains practical. +# > +# > TODO: Check for possible simplifications. + +# First, we import +# - os and sys for basic access to files +# - yaml for .yaml reading +# - tsai.basics for using tsai artifacts + +# ## Basic configuration and definitions + +# In[ ]: + + +#| export import os import yaml import sys from tsai.basics import * -sys.path.append(os.path.abspath('..')) +# As the yml are saved in ../nbs_pipeline/config, we will need to add '..' path + +# In[ ]: + + +#| export +sys.path.append(os.path.abspath('../nbs_pipeline')) + + +# In[ ]: + + +#| export +config_path = os.path.expanduser('~/work/nbs_pipeline/config/') +config_base_filename = 'base' + + +# An custom_error(message) function is added just for basic error handling + +# In[ ]: + + +#| export def custom_error(message: str): """ This message raises an exception ensuring red-coloured error message is displayed @@ -24,6 +70,16 @@ def custom_error(message: str): reset = "\033[0m" raise Exception(red_start + message + reset) + +# ## Yaml reading auxiliar functions +# > Defining basics functions to read yml in order to build Attrdict's +# +# In the version of YAML we are utilising, the '!join' constructor is not present. Therefore, it becomes necessary to define it. + +# In[ ]: + + +#| export def join_constructor(loader: yaml.Loader, node: yaml.Node) -> str: """ This function adds the '!join' constructor to the YAML parsing process. @@ -32,12 +88,22 @@ def join_constructor(loader: yaml.Loader, node: yaml.Node) -> str: seq = loader.construct_sequence(node) return ''.join(seq) + +# In[ ]: + + +#| export def recursive_attrdict(d: dict) -> AttrDict: """ Recursively converts a dictionary into an AttrDict, including all nested dictionaries. """ if isinstance(d, dict): return AttrDict({k: recursive_attrdict(v) for k, v in d.items()}) return d + +# In[ ]: + + +#| export def replace_includes_with_content( filename: str, path: str = "./", @@ -53,7 +119,7 @@ def replace_includes_with_content( print("... About to replace includes with content") with open(path+filename, 'r') as f: content = f.read() - + # Mientras exista una directiva !include en el contenido, sigue reemplazándola while "!include" in content: # Obtén la posición de la directiva @@ -61,22 +127,137 @@ def replace_includes_with_content( # Encuentra el inicio y el final de las comillas que contienen el nombre del archivo start_quote_idx = content.find('"', start_idx) + 1 end_quote_idx = content.find('"', start_quote_idx) - + # Extrae el nombre del archivo include_filename = content[start_quote_idx:end_quote_idx] - + # Lee el archivo incluido with open(path+include_filename, 'r') as include_file: included_content = include_file.read() - + # Reemplaza la directiva por el contenido del archivo incluido content = content[:start_idx] + included_content + content[end_quote_idx+1:] - + return content + +# ## Project specific yaml reading functions +# > Basic configuration is read from `../config/base.yml` +# > It contains the following information: +# > - User Preferences: `user_preferences` +# > - General configuration: `user_preferences` +# > +# > | Key | Description | Example Value | +# > |-------------|---------------------------------------------------------|---------------| +# > | `use_wandb` | Indicates whether to use wandb for experiment tracking. | `true` | +# > +# > - Wandb Configuration: `user_preferences.wdb` +# > +# > | Key | Description | Example Value | +# > |----------------------------|---------------------------------------------------|-------------------------| +# > | `user` | The user name for wandb. | `mi-santamaria` | +# > | `project_name` | The project name in wandb. | `deepvats` | +# > | `version` | Specifies the version for wandb. | `'latest'` | +# > | `mode` | Sets the mode for wandb. | `'offline'` | +# > | `artifacts_path` | The path for storing wandb artifacts. | `'./data/wandb_artifacts'`| +# > +# > - Data configuration: `user_preferences.data` +# > +# > | Key | Description | Example Value | +# > |--------------------------|---------------------------------------------------|-------------------------| +# > | `folder` | The folder where the data is stored. | `'~/data/'` | +# > | `fname` | The file name of the dataset used. | `'solar_4_seconds_dataset'` | +# > | `ftype` | The file type of the dataset. | `'.tsf'` | +# > | `cols` | The columns to be used from the dataset. | `[]` (all columns) | +# > | `freq` | The frequency of the dataset. | `'4s'` | +# > +# > - Artifact Configuration: `user_preferences.artifact` +# > +# > | Key | Description | Example Value | +# > |--------------------------|---------------------------------------------------|-------------------------| +# > | `alias` | Alias for the resulting artifact of the run. | `'solar_4_seconds'` | +# > | `algorithm` | The algorithm used in the process. | `'mvp'` | +# > +# > - Directory Configuration: `user_preferences.directories` +# > +# > | Key | Description | Example Value | +# > |--------------------------|---------------------------------------------------|-------------------------| +# > | `tmp` | Folder for storing temporary files. | `'tmp'` | +# > | `data` | Path for the data. | Combination of *path, *fname, *ftype | +# > +# > - Data Specifications: `data` +# > - Data Details: `data` +# > +# > | Key | Description | Example Value | +# > |--------------------------|---------------------------------------------------|-------------------------| +# > | `name` | The name of the data file. | Refers to *fname | +# > | `path` | The path of the data file. | Refers to *data_path | +# > | `alias` | The alias of the data artifact. | Refers to *alias | +# > | `cols` | Columns of interest from the dataset. | Refers to *cols | +# > | `csv_config` | Configuration for CSV files, if applicable. | `{}` (if not required) | +# > | `date_offset` | Offset used for setting the date index. | `null` (if not used) | +# > | `date_format` | Default date format for .tsf files. | `'%Y-%m-%d %H:%M:%S'` | +# > | `freq` | Frequency of the data. | Refers to *freq | +# > | `joining_train_test` | Linking training and testing data. | `false` | +# > | `missing_values` | Handling missing values. | `technique: null, constant: null` | +# > | `normalize_training` | Indicates whether to normalize training data. | `false` | +# > | `range_training` | Specifies training ranges. | `null` | +# > | `range_testing` | Specifies testing ranges. | `null` | +# > | `resampling_freq` | Frequency for resampling the data. | `null` | +# > | `start_date` | Starting date for the dataset. | `null` | +# > | `test_split` | Ratio of the test set. | `null` | +# > | `time_col` | Column containing the timestamp. | `null` | +# > +# > - Wandb Specifications: `wandb` +# > +# > | Key | Description | Example Value | +# > |--------------------------|---------------------------------------------------|-------------------------| +# > | `user` | User name in wandb specifications. | Refers to *wdb_user | +# > | `dir` | Directory for wandb. | Combination of '~/', *wdb_project | +# > | `enabled` | Indicates if wandb is enabled. | `False` | +# > | `group` | Group for the wandb runs. | `null` | +# > | `log_learner` | Whether to log the learner to wandb. | `False` | +# > | `mode` | Mode for wandb operation. | Refers to *wdb_mode | +# > | `project` | The wandb project name. | Refers to *wdb_project | +# > | `version` | Version for the wandb. | Refers to *wdb_version | +# > | `artifacts_path` | Path for storing the TSArtifact in wandb. | Refers to *artifacts_path | +# + +# In[ ]: + + +#| export + +def substitute_env_variables_in_leaves( + config : AttrDict, + print_flag : bool = False +): + if print_flag: print("Config: ", config) + for key, value in config.items(): + if isinstance(value, str): + original_value = value + matches = re.findall(r'\$\{([^}]+)\}', value) + for match in matches: + env_value = os.environ.get(match) + if env_value is not None: + value = value.replace('${' + match + '}', env_value) + config[key] = value + if print_flag and original_value != value: + print(f"Changed {key}: from {original_value} to {config[key]}") + else: + if isinstance(value, AttrDict): + substitute_env_variables_in_leaves(value, print_flag) # Recursividad para diccionarios anidados + return config + + +# In[ ]: + + +#| export def get_config( print_flag: bool = False, - filename: str = "base" + filename: str = config_base_filename, + path = config_path ) -> AttrDict: """ This function @@ -85,32 +266,58 @@ def get_config( print_flag option can be changed to True for displaying debugging messages. """ # Build file path + if (path[-1] != '/'): + path += '/' filename = filename+".yaml" - path = "./config/" + # Debug messages if (print_flag): current_directory = os.getcwd() print("Current: " + current_directory) print("yml: "+ path + filename) - + # Add join constructor yaml.add_constructor('!join', join_constructor) - + if (print_flag): print("Getting content"+ path + filename) - + # Get file content full_content = replace_includes_with_content(filename, path, print_flag) - + if (print_flag): print("Load content"+ path + filename) - + # Load content config = yaml.load(full_content, Loader=yaml.FullLoader) + + config_attrdict = recursive_attrdict(config) + + substitute_env_variables_in_leaves( + config = config_attrdict, + print_flag = print_flag + ) + return config_attrdict + - # Return content it as AttrDict - return recursive_attrdict(config) +# In[ ]: + +#| hide +get_config(False) + + +# ### Build the encoder artifact from already read functions +# > The encoder artifact is needed for training the model. +# > +# > To automatize its selection, its W&B name is built up from its latest version +# > +# > TODO: Change for selecting specific version. Version should be selected in .yml file too . + +# In[ ]: + + +#| export def build_enc_artifact( config: AttrDict, print_flag: bool = False @@ -128,13 +335,25 @@ def build_enc_artifact( print("enc_artifact: "+enc_artifact) return enc_artifact -def get_project_data(print_flag: bool = False) -> [str, str, str, str]: + +# ### Build project data +# > Read user, project, version and dataset artifact name form already got information + +# In[ ]: + + +#| export +def get_project_data( + print_flag: bool = False, + filename=config_base_filename, + path = config_path +) -> [str, str, str, str]: """ Retrieves project data including user, project name, version, and data name. It accesses configuration settings, processes them, and optionally prints the project configuration. Returns a tuple containing user, project, version, and data information. """ - config = get_config() + config = get_config(print_flag = print_flag, filename = filename, path = path) project = config.wandb.project user = config.wandb.user version = config.wandb.version @@ -152,6 +371,20 @@ def get_project_data(print_flag: bool = False) -> [str, str, str, str]: print(dashes+"Project configuration"+dashes) return user, project, version, data + +# In[ ]: + + +#| hide +get_project_data(False) + + +# ### Get training artifact name + +# In[ ]: + + +#| export def get_train_artifact(user: str, project: str, data: str) -> str: """ Constructs the train artifact string by combining user, project, and data information. @@ -161,22 +394,64 @@ def get_train_artifact(user: str, project: str, data: str) -> str: train_artifact=user+'/'+project+'/'+data return train_artifact + +# ## Build dataset artifact +# > Configuration file: `base`. Described above. + +# ### Auxiliary functions + +# In[ ]: + + +#| export ############################## # 01 - DATAFRAME TO ARTIFACT # ############################## -def get_artifact_config_sd2a_get_auxiliar_variables(print_flag: bool) -> Tuple[str, str, str, AttrDict, bool, str]: +def get_artifact_config_sd2a_get_auxiliar_variables( + print_flag: bool, + filename = config_base_filename, + path = config_path +) -> Tuple[str, str, str, AttrDict, bool, str]: """ Retrieves auxiliary variables necessary for the dataset artifact configuration. Gathers user, project, version, and data details, along with preferences for using wandb and the wandb artifacts path. Returns a tuple containing these elements. """ - user, project, version, data = get_project_data(print_flag) - config = get_config(print_flag) + user, project, version, data = get_project_data(print_flag, filename = filename, path = path) + config = get_config(print_flag, filename = filename, path = path) data = config.data use_wandb = config.user_preferences.use_wandb wandb_path = config.wandb.artifacts_path return user, project, version, data, use_wandb, wandb_path + +# In[ ]: + + +#| hide +get_artifact_config_sd2a_get_auxiliar_variables(False) + + +# In[ ]: + + +#| export +def check_project_and_entity(user: str, project: str): + """ + Checks user and project are correctly setted up comparing to environment variables + """ + os_entity = os.environ['WANDB_ENTITY'] + os_project = os.environ['WANDB_PROJECT'] + if (os_entity != user): + custom_error("Please check .env and base.yml: entity != user os " + os_entity + " yaml " + user) + if (os_project != project): + custom_error("Please check .env and base.yml: project differs os " + os_project + " yaml " + project) + + +# In[ ]: + + +#| export def get_artifact_config_sd2a_check_errors(use_wandb: str, artifact_config: AttrDict, user: str, project: str): """ Checks for configuration errors in the dataset artifact settings. @@ -194,12 +469,27 @@ def get_artifact_config_sd2a_check_errors(use_wandb: str, artifact_config: AttrD ): custom_error("Missing values constant must be setted up only if missing_values_technique is not None. Please check base.yaml") -def get_artifact_config_sd2a(print_flag: bool = False) -> AttrDict: + +# ### Main function + +# In[ ]: + + +#| export +def get_artifact_config_sd2a( + print_flag: bool = False, + base_filename = config_base_filename, + path = config_path +) -> AttrDict: """ Constructs the configuration for the dataset artifact by retrieving auxiliary variables and setting up the artifact configuration. Validates the configuration to ensure it meets specific criteria and returns the final artifact configuration as an AttrDict. """ - user, project, version, data, use_wandb, wandb_path = get_artifact_config_sd2a_get_auxiliar_variables(print_flag) + user, project, version, data, use_wandb, wandb_path = get_artifact_config_sd2a_get_auxiliar_variables( + print_flag = print_flag, + filename = base_filename, + path = path + ) artifact_config = AttrDict( artifact_name = data.alias, csv_config = data.csv_config, @@ -224,19 +514,81 @@ def get_artifact_config_sd2a(print_flag: bool = False) -> AttrDict: get_artifact_config_sd2a_check_errors(use_wandb, artifact_config, user, project) return artifact_config + +# In[ ]: + + +#| hide +get_artifact_config_sd2a(print_flag = False) + + +# ## Build MVP Encoder artifacts +# > Includes also the versions for sliding window view version +# +# The configuration files `02b-encoder_mvp` contains the following information: +# ### Configuration for Encoder +# > - General Configuration: `configuration` +# > +# > | Key | Description | Example Value | +# > |------------|-------------------------------------------------------------|----------------| +# > | `job_type` | The type of job being configured. | `'encoder_MVP'`| +# > | `alias` | Alias for the configuration, refers to the alias in base. | Refers to *alias| +# > +# > - Wandb Configuraconfiguration.tion: `wandb` +# > +# > | Key | Description | Example Value | +# > |------------|-------------------------------------------|-----------------| +# > | `mode` | The mode for wandb, inherited from base. | Refers to *wdb_mode | +# > | `group` | Specifies if the run is part of a group. | `null` | +# > +# > - Specifications Coconfiguration.nfiguration: `specifications` +# > +# > | Key | Description | Example Value | +# > |------------------------------------------|------------------------------------------|-----------------| +# > | `batch_size` | The batch size for processing. | `1024` | +# > | `n_epoch` | Number of epochs for training. | `100` | +# > | `mask.future` | Whether to mask future samples. | `false` | +# > | `mask.stateful` | Dictates if masking is stateful or not. | `true` | +# > | `mask.sync` | Mask all variables at once in series. | `false` | +# > | `mvp.ws1` | Min window size for MVP adaptable training. | `15` | +# > | `mvp.ws2` | Max window size for MVP adaptable training. | `30` | +# > | `mvp.r` | Probability of masking in MVP. | `0.710` | +# > | `mvp.valid_size` | Size of the validation set proportion. | `0.2` | +# > | `mvp.normalize.by_sample` | Normalization by sample indicator. | `false` | +# > | `mvp.normalize.use_single_batch` | Use single batch for normalization. | `false` | +# > | `sliding_windows.stride` | Data points the window moves ahead. | `15` | +# > | `sliding_windows.size` | Window size for the sliding wiow. | `30` | +# g window. | `30` | +# + +# ### Auxiliary functions for retrieving the data + +# In[ ]: + + +#| export ###################### # 02b - ENCODER MVP # ###################### -def get_artifact_config_MVP_auxiliar_variables(print_flag: bool) -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]: +def get_artifact_config_MVP_auxiliar_variables( + print_flag : bool, + base_filename : str = config_base_filename, + path : str = config_path +) -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]: """ Retrieves and assembles various configuration parameters and auxiliary variables for an MVP artifact. Extracts user, project, version, and data details, fetches configuration settings, and constructs training artifact strings. Returns a tuple containing these elements along with MVP workspace specifications and user preferences. """ - #Get neccesary variables - user, project, version, data = get_project_data(print_flag) - config = get_config(print_flag, "02b-encoder_mvp") + user, project, version, data = get_project_data( + print_flag = print_flag, + filename = base_filename, + path = path + ) + config = get_config( + print_flag, "02b-encoder_mvp" + ) user_preferences = config.user_preferences config = config.configuration train_artifact_ = get_train_artifact(user,project,data) @@ -245,15 +597,24 @@ def get_artifact_config_MVP_auxiliar_variables(print_flag: bool) -> Tuple[str, s mvp_ws = (mvp_ws1,mvp_ws2) return user, project, version, data, config, train_artifact_, mvp_ws, user_preferences -def get_artifact_config_MVP_auxiliar_variables_SWV(print_flag: bool) -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]: + +# In[ ]: + + +#| export +def get_artifact_config_MVP_auxiliar_variables_SWV( + print_flag : bool, + base_filename : str = config_base_filename, + path : str = config_path +) -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]: """ Retrieves and assembles various configuration parameters and auxiliary variables for an MVP artifact. Extracts user, project, version, and data details, fetches configuration settings, and constructs training artifact strings. Returns a tuple containing these elements along with MVP (sliding window view) workspace specifications and user preferences. """ #Get neccesary variables - user, project, version, data = get_project_data(print_flag) - config = get_config(print_flag, "02c-encoder_mvp-sliding_window_view") + user, project, version, data = get_project_data(print_flag, base_filename, path) + config = get_config(print_flag, "02c-encoder_mvp-sliding_window_view", path) user_preferences = config.user_preferences config = config.configuration train_artifact_ = get_train_artifact(user,project,data) @@ -262,17 +623,21 @@ def get_artifact_config_MVP_auxiliar_variables_SWV(print_flag: bool) -> Tuple[st mvp_ws = (mvp_ws1,mvp_ws2) return user, project, version, data, config, train_artifact_, mvp_ws, user_preferences -def check_project_and_entity(user: str, project: str): - """ - Checks user and project are correctly setted up comparing to environment variables - """ - os_entity = os.environ['WANDB_ENTITY'] - os_project = os.environ['WANDB_PROJECT'] - if (os_entity != user): - custom_error("Please check .env and base.yml: entity != user os " + os_entity + " yaml " + user) - if (os_project != project): - custom_error("Please check .env and base.yml: project differs os " + os_project + " yaml " + project) +# In[ ]: + + +get_artifact_config_MVP_auxiliar_variables_SWV(False) + + +# ### Auxiliary functions for error handling + +# > TODO: Should we allow diferent project from environment ones? Should we just avoid yml configuration for this parameters and directly get dockers environment WANDB_ENTITIY and WANDB_PROJECT environment variables? . + +# In[ ]: + + +#| export def get_artifact_config_MVP_check_errors( artifact_config: AttrDict, user: str, @@ -283,7 +648,7 @@ def get_artifact_config_MVP_check_errors( Sets project = 'work-nbs' if wandb is not used for tracking. """ check_project_and_entity(user, project) - + if artifact_config.use_wandb: if (artifact_config.analysis_mode != 'online'): print("Changing to online analysis mode - use_wandb=true") @@ -291,6 +656,13 @@ def get_artifact_config_MVP_check_errors( else: project = 'work-nbs' + +# ### Main configuration functions + +# In[ ]: + + +#| export def get_artifact_config_MVP(print_flag: bool = False) -> Tuple[str, str, str, str, AttrDict, str]: """ Gathers and structures the MVP artifact configuration, including user, project, version, data details, and various configuration settings. @@ -321,12 +693,25 @@ def get_artifact_config_MVP(print_flag: bool = False) -> Tuple[str, str, str, st get_artifact_config_MVP_check_errors(artifact_config, user, project) return user, project, version, data, artifact_config, config.job_type -def get_artifact_config_MVP_SWV(print_flag: bool = False) -> Tuple[str, str, str, str, AttrDict, str]: + +# In[ ]: + + +#| export +def get_artifact_config_MVP_SWV( + print_flag : bool = False, + base_filename : str = config_base_filename, + path : str = config_path +) -> Tuple[str, str, str, str, AttrDict, str]: """ Gathers and structures the MVP_SWV artifact configuration, including user, project, version, data details, and various configuration settings. Returns a tuple comprising user, project, version, data, the structured artifact configuration as an AttrDict, and the job type. """ - user, project, version, data, config, train_artifact_, mvp_ws, user_preferences = get_artifact_config_MVP_auxiliar_variables_SWV(print_flag) + user, project, version, data, config, train_artifact_, mvp_ws, user_preferences = get_artifact_config_MVP_auxiliar_variables_SWV( + print_flag = print_flag, + base_filename = base_filename, + path = path + ) artifact_config = AttrDict( alias = config.alias, @@ -351,22 +736,96 @@ def get_artifact_config_MVP_SWV(print_flag: bool = False) -> Tuple[str, str, str get_artifact_config_MVP_check_errors(artifact_config, user, project) return user, project, version, data, artifact_config, config.job_type + +# In[ ]: + + +#| hide +get_artifact_config_MVP_SWV(False) + + +# ## Encoder DCAE configuration +# > The configuration file `02a-encoder_dcae` contains the following information: +# +# > - General Configurat: `configuration`ion +# > +# > | Key | Description | Example Value | +# > |------------|--------------------------------------------------|-----------------| +# > | `job_type` | Type of the job for this configuration. | `'encoder_DCAE'`| +# > | `alias` | Alias for the run, referring to the base config. | Refers to *alias| +# > +# > - Wandb Configconfiguration.uration: `wandb` +# > +# > | Key | Description | Example Value | +# > |-----------|-------------------------------------------------------------|-----------------------| +# > | `use` | Indicates whether to use wandb. | Refers to *use_wandb | +# > | `entity` | Entity (user) for wandb configuration. | Refers to *wdb_user | +# > | `group` | Specifies if this run should be grouped in a wandb group. | `null` | +# > | `project` | Project name for wandb configuration. | Refers to *wdb_project| +# > +# > - Artifacconfiguration.ts Configuration: `artifacts` +# > +# > | Key | Description | Example Value | +# > |-----------------|--------------------------------------------------------------------------|-------------------------------------------------------| +# > | `train_prefix` | Complete training artifact path in wandb. | Combination of *wdb_user, *wdb_project, and *alg | +# > | `valid.data` | Complete name for the validation dataset artifact (null for random). | `null` | +# > | `valid.size` | Percentage of random items to go to validation set if `valid.data` is null. | `0.1` | +# > +# > - Specifications Configuration: `specifications` +# > +# > | Key | Description | Example Value | +# > |----------------|---------------------------------------------------|-----------------| +# > | `batch_size` | Batch size for processing. | `64` | +# > | `n_epoch` | Number of epochs to train for. | `200` | +# > | `pool_szs` | Sizes of the pooling layers in the autoencoder. | `[2,2,4]` | +# > | `top_k` | Number of elements to analyse for the top losses. | `3` | +# > +# > - Sliding Windows Configuration: `sliding_windows` +# > +# > | Key | Description | Example Value | +# > |-----------|-----------------------------------------|---------------| +# > | `stride` | The stride for the sliding window. | `1` | +# > | `size` | Window size for the sliding window. | `32` configuration. | +# > +# > - Autoencoder Configuration: `autoencoder` +# > +# > | Key | Description | Example Value | +# > |---------------------|----------------------------------------------------------------|----------------| +# > | `delta` | Size of the autoencoder bottleneck layer. | `60` | +# > | `filters.nfs` | Number of filters in each convolutional layer of the autoencoder. | `[64,32,16]` | +# > | `filters.kss` | Kernel sizes for each convolutional layer in the autoencoder. | `[10,5,5]` | +# > | `filters.output_size` | The output size of the autoencoder's final layer. | `10` | +# inal layer. | `10` | +# | `10` | +# | +# +# +# + +# In[ ]: + + +#| export ###################### # 02a - ENCODER DCAE # ###################### -def get_artifact_config_DCAE(print_flag: bool = False) -> Tuple[AttrDict, str]: +def get_artifact_config_DCAE( + print_flag: bool = False, + base_filename = config_base_filename, + path = config_path +) -> Tuple[AttrDict, str]: """ Constructs the configuration for the DCAE (Deep Convolutional AutoEncoder). It fetchs the relevant settings and assembles the artifact configuration. Validates the configuration to ensure correct project and entity setup and returns the artifact configuration as an AttrDict along with the job type. """ - user, project, version, data = get_project_data(print_flag) - config = get_config(print_flag, "02a-encoder_dcae") + user, project, version, data = get_project_data(print_flag, base_filename, path) + config = get_config(print_flag, "02a-encoder_dcae", path) if print_flag: print("Antes de leer configuration " + str(config)) config = config.configuration - + artifact_config = AttrDict( alias = config.alias, use_wandb = config.wandb.use, @@ -388,20 +847,64 @@ def get_artifact_config_DCAE(print_flag: bool = False) -> Tuple[AttrDict, str]: top_k = config.specifications.pool_szs ) check_project_and_entity( - artifact_config.wandb_entity, artifact_config.wandb_project + artifact_config.wandb_entity, + artifact_config.wandb_project ) return artifact_config, config.job_type + +# ## Embeddings configuration +# The specific configurations are in the file `03-embeddings` +# It has the following information: +# +# > - Job Type: `job_type` +# > +# > | Key | Value | +# > |-----------|---------------| +# > | `job_type`| `'embeddings'`| +# > +# > - Configuration: `configuration` +# > - Wandb Configuration: `configuration.wandb` +# > +# > | Key | Description | Example Value | +# > |---------|------------------------------------------------------|--------------------| +# > | `group` | Whether to group this run in a wandb group. | Refers to *emb | +# > | `use` | Indicates whether to use wandb. | Refers to *use_wandb | +# > | `entity`| Entity (user) for wandb configuration. | Refers to *wdb_user | +# > | `project`| Project name for wandb configuration. | Refers to *wdb_project | +# > +# > - Encoder Configuration: `configuration.encoder` +# > +# > | Key | Description | Example Value | +# > |------------------------------|----------------------------------------------------|-------------------------------------------------| +# > | `artifacts.train.enc_prefix` | Prefix for the training artifacts. | Combination of *wdb_user, *wdb_project, and *alg | +# > | `artifacts.valid` | If none, the validation set used to train enc is used. | `null` | +# > +# > - Specifications Configuration: `configuration.specifications` +# > +# > | Key | Description | Example Value | +# > |------------|----------------------------------------------|-----------------| +# > | `input_ar` | Input array, if applicable. | `null` | +# > | `cpu` | Whether to use CPU instead of GPU. | `false` | +# + +# In[ ]: + + +#| export ###################### # 03 - EMBEDDINGS # ###################### -def get_artifact_config_embeddings(print_flag: bool = False) -> Tuple[AttrDict, str]: +def get_artifact_config_embeddings( + print_flag : bool = False, + config_path : str = config_path +) -> Tuple[AttrDict, str]: """ Constructs the configuration for embeddings by fetching relevant settings and building the encoder artifact configuration. Validates the project and entity settings and returns the artifact configuration as an AttrDict, along with the job type. """ - config = get_config(print_flag, "03a-embeddings") + config = get_config(print_flag, "03a-embeddings", config_path) job_type=config.job_type version = config.user_preferences.wdb.version enc_artifact = build_enc_artifact(config, print_flag) @@ -418,12 +921,20 @@ def get_artifact_config_embeddings(print_flag: bool = False) -> Tuple[AttrDict, check_project_and_entity(artifact_config.wandb_entity, artifact_config.wandb_project) return artifact_config, job_type -def get_artifact_config_embeddings_SWV(print_flag: bool = False) -> Tuple[AttrDict, str]: + +# In[ ]: + + +#| export +def get_artifact_config_embeddings_SWV( + print_flag : bool = False, + config_path : str = config_path +) -> Tuple[AttrDict, str]: """ Constructs the configuration for embeddings (sliding window view) by fetching relevant settings and building the encoder artifact configuration. Validates the project and entity settings and returns the artifact configuration as an AttrDict, along with the job type. """ - config = get_config(print_flag, "03b-embeddings-sliding_window_view") + config = get_config(print_flag, "03b-embeddings-sliding_window_view", config_path) job_type=config.job_type version = config.user_preferences.wdb.version enc_artifact = build_enc_artifact(config, print_flag) @@ -440,16 +951,61 @@ def get_artifact_config_embeddings_SWV(print_flag: bool = False) -> Tuple[AttrDi check_project_and_entity(artifact_config.wandb_entity, artifact_config.wandb_project) return artifact_config, job_type + +# ## Dimensionality Reduction Configuration +# The specific configurations are in the file `04-dimensionality_reduction` +# It contains the following information: +# +# > - Job Type: `job_type` +# > +# > | Key | Value | +# > |------------|----------------------------| +# > | `job_type` | `'dimensionality_reduction'`| +# > +# > - Configuration: `configuration` +# > - Wandb Configuration: `configuration.wandb` +# > +# > | Key | Description | Example Value | +# > |---------|------------------------------------------------------|--------------------| +# > | `use` | Whether to use wandb for experiment tracking. | Refers to *use_wandb | +# > | `group` | Specifies whether to group this run in a wandb group. | `null` | +# > | `entity`| The entity to use for wandb. | Refers to *wdb_user | +# > | `project`| The project to use for wandb. | Refers to *wdb_project | +# > +# > - Encoder Configuration: `configuration.encoder` +# > +# > | Key | Description | Example Value | +# > |------------------------------|-----------------------------------------------------|-------------------------------------------------| +# > | `artifacts.train.enc_prefix` | Prefix for the training artifacts. | Combination of *wdb_user, *wdb_project, and *alg | +# > | `artifacts.valid` | If none, the validation set used to train enc is used. | `null` | +# > +# > - UMAP Configuration: `configuration.encoder.umap` +# > +# > | Key | Description | Example Value | +# > |-----------------|-----------------------------|---------------| +# > | `n_neighbors` | Number of neighbors for UMAP. | `15` | +# > | `min_dist` | Minimum distance for UMAP. | `0.1` | +# > | `random_state` | Random state for UMAP. | `1234` | +# +# + +# In[ ]: + + +#| export ################################### # 04 - DIMENSIONALITY REDUCTION # ################################### -def get_artifact_config_dimensionality_reduction(print_flag: bool = False) -> Tuple[AttrDict, str]: +def get_artifact_config_dimensionality_reduction( + print_flag : bool = False, + config_path : str = config_path +) -> Tuple[AttrDict, str]: """ Constructs the configuration for dimensionality reduction tasks by fetching relevant settings, including building the encoder artifact. Returns the artifact configuration as an AttrDict, along with the job type. """ - config = get_config(print_flag, "04-dimensionality_reduction") + config = get_config(print_flag, "04-dimensionality_reduction", config_path) job_type = config.job_type enc_artifact = build_enc_artifact(config, print_flag) config = config.configuration @@ -466,16 +1022,30 @@ def get_artifact_config_dimensionality_reduction(print_flag: bool = False) -> Tu ) return artifact_config, job_type + +# ## XAI - shap reduction configuration +# > TODO: Not yet implemented or configured... In progress.. +# +# The specific configurations are in the file `05-xai_shap` +# + +# In[ ]: + + +#| export ################## # 05 - XAI-SHAP # ################## -def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, str]: +def get_artifact_config_xai_shap( + print_flag : bool = False, + config_path : str = config_path +) -> Tuple[AttrDict, str]: """ Constructs the configuration for the XAI SHAP (Explainable Artificial Intelligence using SHAP values) analysis. This includes fetching relevant settings, building the encoder artifact, and assembling the artifact configuration. Returns the artifact configuration as an AttrDict, along with the job type. """ - config = get_config(print_flag, "05-xai_shap") + config = get_config(print_flag, "05-xai_shap", config_path) job_type = config.job_type enc_artifact = build_enc_artifact(config, print_flag) config = config.configuration @@ -492,6 +1062,21 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st ) return artifact_config, job_type + +# In[ ]: + + +#| hide -> Not yet implemented +#get_artifact_config_xai_shap(False) + + +# ## Use a tested configuration + +# [Monash benchmark](https://huggingface.co/datasets/monash_tsf) datasets used configurations + +# In[ ]: + + #| export # Monash australian electricity demand monash_australian_electricity_demand_0 = AttrDict( @@ -522,6 +1107,10 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st ) ) + +# In[ ]: + + #| export # Monash sunspot: monash_sunspot_0 = AttrDict( @@ -551,6 +1140,10 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st ) ) + +# In[ ]: + + #| export monash_solar_4_seconds_0 = AttrDict( alias = 'solar_4_seconds', @@ -584,6 +1177,12 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st ) ) + +# [Wikipedia web traffic dataset](https://zenodo.org/records/3898474) + +# In[ ]: + + #| export wikipedia_0 = AttrDict( alias="Wikipedia", @@ -612,6 +1211,12 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st ) ) + +# [Traffic San Francisco](https://zenodo.org/records/3898445) + +# In[ ]: + + #| export traffic_san_francisco_0 = AttrDict( alias="Traffic_SF", @@ -640,6 +1245,10 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st ) ) + +# In[ ]: + + #| export # Monash solar 10 minutes dataset # Multi-dimensional @@ -676,6 +1285,15 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st ) ) + +# ### Other public datasets + +# #### Electricity Transformer Temperature +# [ETDataset](https://paperswithcode.com/dataset/ett) + +# In[ ]: + + #| export etth1_0 = AttrDict( alias="ETTh1", @@ -704,6 +1322,13 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st ) ) + +# #### Stumpy +# ##### [Semantic segmentation TitlABP](https://stumpy.readthedocs.io/en/latest/Tutorial_Semantic_Segmentation.html) + +# In[ ]: + + #| export stumpy_abp_0 = AttrDict( alias="TitlABP", @@ -732,6 +1357,12 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st ) ) + +# ##### [Multi-dimensional toy data](https://zenodo.org/records/4294932) + +# In[ ]: + + #| export stumpy_toy_0 = AttrDict( alias="toy", @@ -743,7 +1374,7 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st mvp = AttrDict( batch_size = 512, n_epoch = 100, - ws = [10,1008], + ws = [10,30], stride = 1 ), dcae = AttrDict(#TODO: Check @@ -760,6 +1391,13 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st ) ) + +# ### Get tested configuration function + +# In[ ]: + + +#| export tested_configs = { 'monash_australian_electricity_demand_0': monash_australian_electricity_demand_0, 'monash_solar_4_seconds_0': monash_solar_4_seconds_0, @@ -771,11 +1409,19 @@ def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, st 'stumpy_toy_0': stumpy_toy_0 } + +# In[ ]: + + #| export def show_attrdict(dict: AttrDict): for key, value in dict.items(): print(f"{key}: {value}") + +# In[ ]: + + #| export def show_available_configs(): print("Available datasets: ") @@ -783,12 +1429,41 @@ def show_available_configs(): for key, val in tested_configs.items(): print(f"{i} - {key}") i+=1 + + + +# In[ ]: + + +#| hide +show_available_configs() + + +# In[ ]: + + +#| hide +list(tested_configs.items())[3][0] + + +# In[ ]: #| export def show_config(id: int = 0): show_attrdict(list(tested_configs.items())[id][1]) + +# In[ ]: + + +#| hide +show_config(3) + + +# In[ ]: + + #| export def get_tested_config( id: int = 0, @@ -796,6 +1471,20 @@ def get_tested_config( ): if print_flag: show_config(id) return list(tested_configs.items())[id][1] + + + +# In[ ]: + + +#| hide +show_attrdict(get_tested_config(0)) + + +# ### Force tested configuration functions +# #### 01 - Dataset Artifact + +# In[ ]: #| export @@ -815,7 +1504,7 @@ def print_colored( else: color = "\033[94m" if modified else "" reset = "\033[0m" - + if modified and both: print(f"{color}{key}: {original_val}{reset} -> {modified_val}{reset}") elif missing_in_modified: @@ -825,8 +1514,17 @@ def print_colored( else: print(f"{color}{key}: {modified_val}{reset}") + +# In[ ]: + + +#| export import pandas as pd + +# In[ ]: + + #| export def get_resampling_frequency( freq: str, @@ -864,6 +1562,10 @@ def get_resampling_frequency( print("Frequency factor resampling frequency -->") return (suffix, resampling_freq) + +# In[ ]: + + #| export def frequency_factor_config( config: AttrDict, @@ -888,6 +1590,10 @@ def frequency_factor_config( print("path: ", config.data_fpath) print("Frequency factor config -->") + +# In[ ]: + + #| export def diff_attrdict( dict_original: AttrDict, @@ -898,7 +1604,7 @@ def diff_attrdict( for key in all_keys: in_original = key in dict_original in_modified = key in dict_modified - + if in_original and in_modified: modified = dict_original[key] != dict_modified[key] print_colored( @@ -915,6 +1621,10 @@ def diff_attrdict( # Key is missing in dict_original print_colored(key, modified_val=dict_modified[key], modified=True, missing_in_original=True) + +# In[ ]: + + #| export from copy import deepcopy def force_artifact_config_sd2a( @@ -931,7 +1641,7 @@ def force_artifact_config_sd2a( print("Selecting ", list(tested_configs.items())[id][0]) config.artifact_name = to_set.alias config.data_cols = to_set.cols - #config.data_fpath= "~/data/"+to_set.fname+to_set.ftype + config.data_fpath= "~/data/"+to_set.fname+to_set.ftype config.freq=to_set.freq config.time_col = to_set.time_col config.csv_config = {} @@ -952,6 +1662,53 @@ def force_artifact_config_sd2a( dict_modified=config, both = both) + +# In[ ]: + + +#| hide +sd2a_config = AttrDict( + artifact_name= "Monash-Australian_electricity", + csv_config= {}, + data_cols= [0], + data_fpath= '~/data/australian_electricity_demand_dataset.csv', + date_format= '%Y-%m-%d %H:%M:%S', + date_offset= None, + freq= '1s', + joining_train_test= False, + missing_values_constant= None, + missing_values_technique= None, + normalize_training= False, + range_testing= None, + range_training= None, + resampling_freq= None, + start_date= None, + test_split= None, + time_col= None, + use_wandb= True, + wandb_artifacts_path='./data/wandb_artifacts' +) + + +# In[ ]: + + +#| hide +force_artifact_config_sd2a( + config = sd2a_config, + id = 6, + print_flag=True, + both=True, + frequency_factor = 2, + frequency_factor_change_alias = True +) + + +# #### 02(bc) Encoder MVP + +# In[ ]: + + #| export def split_artifact_string(s:string) -> tuple[string, string, string]: # Divide la cadena en dos partes usando ':' @@ -963,7 +1720,19 @@ def split_artifact_string(s:string) -> tuple[string, string, string]: # Retorna los componentes separados return parts[0] + '/', parts[1], version -#| Export + +# In[ ]: + + +#| hide +result = split_artifact_string("mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest") +print(result) + + +# In[ ]: + + +#| export def force_artifact_config_mvp( config: AttrDict, id:int = 0, @@ -975,7 +1744,7 @@ def force_artifact_config_mvp( to_set = get_tested_config(id) if print_flag: config_before = deepcopy(config) - + force_artifact_config_sd2a( config = config, id = id, @@ -988,23 +1757,23 @@ def force_artifact_config_mvp( config.alias = to_set.alias config.batch_size = to_set.mvp.batch_size config.epochs = to_set.mvp.n_epoch - + config.mask_future = False config.mask_stateful = True config.mask_sync = False config.norm_by_sample = False config.norm_use_by_single_batch = False, config.r = 0.71 - + config.stride=to_set.mvp.stride path,_,version = split_artifact_string(config.train_artifact) config.train_artifact=path+config.artifact_name+":"+version config.valid_size = 0.2 - + config.mvp_ws= to_set.mvp.ws config.w = config.mvp_ws[1] - + if print_flag: diff_attrdict( dict_original=config_before, @@ -1012,7 +1781,49 @@ def force_artifact_config_mvp( both = both ) -#| Export + +# In[ ]: + + +#| hide +mvp_config = AttrDict( + alias = "Monash-Australian_electric", + artifact_name="Monash-Australian_electricity", + analysis_mode="online", + batch_size=2, + epochs=1, + mask_future=False, + mask_stateful=True, + mask_sync=False, + mvp_ws=(150, 33), + norm_by_sample=False, + norm_use_single_batch=False, + r=0.71, + stride=13, + train_artifact="mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest", + valid_artifact=None, + use_wandb=True, + valid_size=0.2, + w=30, + wandb_group=None, + data_cols= [], + data_fpath= "~/data/kaggle_web_traffic_dataset_with_missing_values.tsf", + freq= '1d', + time_col= 'None' +) + + +# In[ ]: + + +#| hide +force_artifact_config_mvp(mvp_config, 1, True, True, 5, True) + + +# In[ ]: + + +#| export def force_artifact_config_dcae( config: AttrDict, id:int = 0, @@ -1024,7 +1835,7 @@ def force_artifact_config_dcae( to_set = get_tested_config(id) if print_flag: config_before = deepcopy(config) - + force_artifact_config_sd2a( config = config, id = id, @@ -1035,7 +1846,7 @@ def force_artifact_config_dcae( ) config.alias = to_set.alias - + config.batch_size = to_set.dcae.batch_size config.epochs = to_set.dcae.n_epoch config.r = 0.71 @@ -1051,10 +1862,48 @@ def force_artifact_config_dcae( config.output_filter_size = to_set.dcae.kss config.top_k = to_set.dcae.top_k config.pool_szs = to_set.dcae.pool_szs - + if print_flag: diff_attrdict( dict_original=config_before, dict_modified=config, both = both - ) \ No newline at end of file + ) + + +# In[ ]: + + +#| hide +dcae_config = AttrDict( + alias = "Monash-Australian_electric", + artifact_name="Monash-Australian_electricity", + analysis_mode="online", + batch_size=2, + epochs=1, + r=0, + stride=1, + train_artifact="mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest", + valid_artifact=None, + use_wandb=True, + valid_size=0.2, + w=30, + nfs = [], + kss = [], + output_filter_size = 1, + top_k = [], + delta = 6, + wandb_group=None, + data_cols= [], + data_fpath= "~/data/kaggle_web_traffic_dataset_with_missing_values.tsf", + freq= '1d', + time_col= 'None' +) + + +# In[ ]: + + +#| hide +force_artifact_config_dcae(dcae_config, 1, True, True, 5, True) + diff --git a/nbs_pipeline/utils_nbs/config.ipynb b/nbs_pipeline/utils_nbs/config.ipynb index 6d825f99..1383ab40 100644 --- a/nbs_pipeline/utils_nbs/config.ipynb +++ b/nbs_pipeline/utils_nbs/config.ipynb @@ -1,5 +1,27 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "9c2ed463-8d9f-40cd-be11-d8d1d510fcef", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp config" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "34d5df84-5213-4795-bb13-2bb0b7f922f4", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, { "cell_type": "markdown", "id": "f49d5247-f6be-4126-8480-b6b307798212", @@ -22,14 +44,6 @@ "- tsai.basics for using tsai artifacts" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "c01a544c-ab2b-48f9-8948-1b8899b9f7ae", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "7e6faaa9-abda-444c-ad5c-b1345ac9306b", @@ -40,11 +54,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "97cc1c68-f710-46eb-9fc8-e57f7f020f83", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "import os\n", "import yaml\n", "import sys\n", @@ -56,17 +71,30 @@ "id": "f64aea40-28b4-44cb-ada2-d77927836eed", "metadata": {}, "source": [ - "As the yml are saved in ../config, we will need to add '..' path" + "As the yml are saved in ../nbs_pipeline/config, we will need to add '..' path" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "af853d2e-758c-4691-9127-9239ab46544c", "metadata": {}, "outputs": [], "source": [ - "sys.path.append(os.path.abspath('..'))" + "#| export\n", + "sys.path.append(os.path.abspath('../nbs_pipeline'))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9a9ca90e-bc12-4d4b-823e-f9b2694f3b27", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "config_path = os.path.expanduser('~/work/nbs_pipeline/config/')\n", + "config_base_filename = 'base'" ] }, { @@ -79,11 +107,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "a3a27344-8e4b-4738-badb-73c1cbe59199", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "def custom_error(message: str):\n", " \"\"\"\n", " This message raises an exception ensuring red-coloured error message is displayed\n", @@ -108,11 +137,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "9c06dfbe-9eb0-47e2-8b02-06f977fa5267", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "def join_constructor(loader: yaml.Loader, node: yaml.Node) -> str:\n", " \"\"\"\n", " This function adds the '!join' constructor to the YAML parsing process. \n", @@ -124,11 +154,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "7efaffe9-b8a0-47f1-9082-9480fdf166f3", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "def recursive_attrdict(d: dict) -> AttrDict:\n", " \"\"\" Recursively converts a dictionary into an AttrDict, including all nested dictionaries. \"\"\"\n", " if isinstance(d, dict):\n", @@ -138,11 +169,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "e338cefb-6a7b-42b7-be07-aebc0a89ee62", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "def replace_includes_with_content(\n", " filename: str, \n", " path: str = \"./\", \n", @@ -269,14 +301,47 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, + "id": "73c2aac7-13cb-4395-9b61-c18a94d789a8", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "\n", + "def substitute_env_variables_in_leaves(\n", + " config : AttrDict, \n", + " print_flag : bool = False\n", + "): \n", + " if print_flag: print(\"Config: \", config)\n", + " for key, value in config.items():\n", + " if isinstance(value, str):\n", + " original_value = value\n", + " matches = re.findall(r'\\$\\{([^}]+)\\}', value)\n", + " for match in matches:\n", + " env_value = os.environ.get(match)\n", + " if env_value is not None:\n", + " value = value.replace('${' + match + '}', env_value)\n", + " config[key] = value\n", + " if print_flag and original_value != value:\n", + " print(f\"Changed {key}: from {original_value} to {config[key]}\")\n", + " else:\n", + " if isinstance(value, AttrDict):\n", + " substitute_env_variables_in_leaves(value, print_flag) # Recursividad para diccionarios anidados\n", + " return config" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "id": "36c82c69-859c-40a0-9cc2-3e4144c362b2", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "def get_config(\n", " print_flag: bool = False, \n", - " filename: str = \"base\"\n", + " filename: str = config_base_filename,\n", + " path = config_path\n", ") -> AttrDict:\n", " \"\"\"\n", " This function \n", @@ -285,8 +350,10 @@ " print_flag option can be changed to True for displaying debugging messages. \n", " \"\"\"\n", " # Build file path\n", + " if (path[-1] != '/'):\n", + " path += '/'\n", " filename = filename+\".yaml\"\n", - " path = \"./config/\"\n", + " \n", " # Debug messages\n", " if (print_flag):\n", " current_directory = os.getcwd()\n", @@ -308,8 +375,120 @@ " # Load content \n", " config = yaml.load(full_content, Loader=yaml.FullLoader)\n", " \n", - " # Return content it as AttrDict\n", - " return recursive_attrdict(config)" + " config_attrdict = recursive_attrdict(config)\n", + "\n", + " substitute_env_variables_in_leaves(\n", + " config = config_attrdict, \n", + " print_flag = print_flag\n", + " ) \n", + " return config_attrdict" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "47c61d83-cb3c-4144-aee1-0c076eac71f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "```json\n", + "{ 'data': { 'alias': 'Monash-Australian_electricity_demand',\n", + " 'cols': [5],\n", + " 'csv_config': {},\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'date_offset': None,\n", + " 'freq': '10h',\n", + " 'joining_train_test': False,\n", + " 'missing_values': {'constant': None, 'technique': None},\n", + " 'name': 'Semantic_Segmentation_TiltABP',\n", + " 'normalize_training': False,\n", + " 'path': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'range_testing': None,\n", + " 'range_training': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None},\n", + " 'user_preferences': { 'artifact': { 'algorithm': 'mvp-SWV',\n", + " 'alias': 'Monash-Australian_electricity_demand'},\n", + " 'data': { 'cols': [5],\n", + " 'fname': 'Semantic_Segmentation_TiltABP',\n", + " 'folder': '~/data/',\n", + " 'freq': '10h',\n", + " 'ftype': '.csv'},\n", + " 'directories': { 'data': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'tmp': 'tmp'},\n", + " 'use_wandb': True,\n", + " 'wdb': { 'artifacts_path': './data/wandb_artifacts',\n", + " 'mode': 'online',\n", + " 'project_name': 'ream',\n", + " 'user': 'angel-gutierrez',\n", + " 'version': 'latest'}},\n", + " 'wandb': { 'artifacts_path': './data/wandb_artifacts',\n", + " 'dir': '~/ream',\n", + " 'enabled': False,\n", + " 'group': None,\n", + " 'log_learner': False,\n", + " 'mode': 'online',\n", + " 'project': 'ream',\n", + " 'user': 'angel-gutierrez',\n", + " 'version': 'latest'}}\n", + "```" + ], + "text/plain": [ + "{'user_preferences': {'use_wandb': True,\n", + " 'wdb': {'user': 'angel-gutierrez',\n", + " 'project_name': 'ream',\n", + " 'version': 'latest',\n", + " 'mode': 'online',\n", + " 'artifacts_path': './data/wandb_artifacts'},\n", + " 'data': {'folder': '~/data/',\n", + " 'fname': 'Semantic_Segmentation_TiltABP',\n", + " 'ftype': '.csv',\n", + " 'cols': [5],\n", + " 'freq': '10h'},\n", + " 'artifact': {'alias': 'Monash-Australian_electricity_demand',\n", + " 'algorithm': 'mvp-SWV'},\n", + " 'directories': {'tmp': 'tmp',\n", + " 'data': '~/data/Semantic_Segmentation_TiltABP.csv'}},\n", + " 'data': {'name': 'Semantic_Segmentation_TiltABP',\n", + " 'path': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'alias': 'Monash-Australian_electricity_demand',\n", + " 'cols': [5],\n", + " 'csv_config': {},\n", + " 'date_offset': None,\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'freq': '10h',\n", + " 'joining_train_test': False,\n", + " 'missing_values': {'technique': None, 'constant': None},\n", + " 'normalize_training': False,\n", + " 'range_training': None,\n", + " 'range_testing': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None},\n", + " 'wandb': {'user': 'angel-gutierrez',\n", + " 'dir': '~/ream',\n", + " 'enabled': False,\n", + " 'group': None,\n", + " 'log_learner': False,\n", + " 'mode': 'online',\n", + " 'project': 'ream',\n", + " 'version': 'latest',\n", + " 'artifacts_path': './data/wandb_artifacts'}}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| hide \n", + "get_config(False)" ] }, { @@ -327,11 +506,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "id": "44b7dc69-c76c-4056-b2bb-bf732e1e0530", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "def build_enc_artifact(\n", " config: AttrDict, \n", " print_flag: bool = False\n", @@ -361,18 +541,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "id": "9435b302-cc11-4e75-8d9b-d746456b1b87", "metadata": {}, "outputs": [], "source": [ - "def get_project_data(print_flag: bool = False) -> [str, str, str, str]:\n", + "#| export\n", + "def get_project_data(\n", + " print_flag: bool = False,\n", + " filename=config_base_filename,\n", + " path = config_path\n", + ") -> [str, str, str, str]:\n", " \"\"\"\n", " Retrieves project data including user, project name, version, and data name. \n", " It accesses configuration settings, processes them, and optionally prints the project configuration. \n", " Returns a tuple containing user, project, version, and data information.\n", " \"\"\"\n", - " config = get_config()\n", + " config = get_config(print_flag = print_flag, filename = filename, path = path)\n", " project = config.wandb.project\n", " user = config.wandb.user\n", " version = config.wandb.version\n", @@ -391,6 +576,31 @@ " return user, project, version, data" ] }, + { + "cell_type": "code", + "execution_count": 15, + "id": "40535592-81dd-4b7f-9407-d79b73e6e81f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('angel-gutierrez',\n", + " 'ream',\n", + " 'latest',\n", + " 'Monash-Australian_electricity_demand:latest')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| hide \n", + "get_project_data(False)" + ] + }, { "cell_type": "markdown", "id": "065856d6-a217-490c-821c-dd4abf205344", @@ -401,11 +611,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "id": "fd10beae-87e1-4738-bd01-4c1162f3bd96", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "def get_train_artifact(user: str, project: str, data: str) -> str:\n", " \"\"\"\n", " Constructs the train artifact string by combining user, project, and data information. \n", @@ -435,22 +646,27 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "id": "5fcd5273-0a72-44dd-9910-8b5bb5585459", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "##############################\n", "# 01 - DATAFRAME TO ARTIFACT #\n", "##############################\n", - "def get_artifact_config_sd2a_get_auxiliar_variables(print_flag: bool) -> Tuple[str, str, str, AttrDict, bool, str]:\n", + "def get_artifact_config_sd2a_get_auxiliar_variables(\n", + " print_flag: bool, \n", + " filename = config_base_filename, \n", + " path = config_path\n", + ") -> Tuple[str, str, str, AttrDict, bool, str]:\n", " \"\"\"\n", " Retrieves auxiliary variables necessary for the dataset artifact configuration. \n", " Gathers user, project, version, and data details, along with preferences for using wandb and the wandb artifacts path.\n", " Returns a tuple containing these elements.\n", " \"\"\"\n", - " user, project, version, data = get_project_data(print_flag)\n", - " config = get_config(print_flag)\n", + " user, project, version, data = get_project_data(print_flag, filename = filename, path = path)\n", + " config = get_config(print_flag, filename = filename, path = path)\n", " data = config.data\n", " use_wandb = config.user_preferences.use_wandb\n", " wandb_path = config.wandb.artifacts_path\n", @@ -459,11 +675,75 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, + "id": "1175362a-59ef-422f-8fd7-8022d41c37ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('angel-gutierrez',\n", + " 'ream',\n", + " 'latest',\n", + " {'name': 'Semantic_Segmentation_TiltABP',\n", + " 'path': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'alias': 'Monash-Australian_electricity_demand',\n", + " 'cols': [5],\n", + " 'csv_config': {},\n", + " 'date_offset': None,\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'freq': '10h',\n", + " 'joining_train_test': False,\n", + " 'missing_values': {'technique': None, 'constant': None},\n", + " 'normalize_training': False,\n", + " 'range_training': None,\n", + " 'range_testing': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None},\n", + " True,\n", + " './data/wandb_artifacts')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| hide\n", + "get_artifact_config_sd2a_get_auxiliar_variables(False)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c4a83901", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def check_project_and_entity(user: str, project: str):\n", + " \"\"\"\n", + " Checks user and project are correctly setted up comparing to environment variables\n", + " \"\"\"\n", + " os_entity = os.environ['WANDB_ENTITY']\n", + " os_project = os.environ['WANDB_PROJECT']\n", + " if (os_entity != user):\n", + " custom_error(\"Please check .env and base.yml: entity != user os \" + os_entity + \" yaml \" + user)\n", + " if (os_project != project):\n", + " custom_error(\"Please check .env and base.yml: project differs os \" + os_project + \" yaml \" + project)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "id": "cb4590b0-3a46-4bab-ba97-9ecc4ae179e6", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "def get_artifact_config_sd2a_check_errors(use_wandb: str, artifact_config: AttrDict, user: str, project: str):\n", " \"\"\"\n", " Checks for configuration errors in the dataset artifact settings. \n", @@ -492,17 +772,26 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 21, "id": "73afe0d8-eb7f-4431-894e-f58d8f5a7bd2", "metadata": {}, "outputs": [], "source": [ - "def get_artifact_config_sd2a(print_flag: bool = False) -> AttrDict:\n", + "#| export\n", + "def get_artifact_config_sd2a(\n", + " print_flag: bool = False,\n", + " base_filename = config_base_filename,\n", + " path = config_path\n", + ") -> AttrDict:\n", " \"\"\"\n", " Constructs the configuration for the dataset artifact by retrieving auxiliary variables and setting up the artifact configuration.\n", " Validates the configuration to ensure it meets specific criteria and returns the final artifact configuration as an AttrDict.\n", " \"\"\"\n", - " user, project, version, data, use_wandb, wandb_path = get_artifact_config_sd2a_get_auxiliar_variables(print_flag)\n", + " user, project, version, data, use_wandb, wandb_path = get_artifact_config_sd2a_get_auxiliar_variables(\n", + " print_flag = print_flag,\n", + " filename = base_filename,\n", + " path = path\n", + " )\n", " artifact_config = AttrDict(\n", " artifact_name = data.alias,\n", " csv_config = data.csv_config,\n", @@ -528,6 +817,69 @@ " return artifact_config" ] }, + { + "cell_type": "code", + "execution_count": 22, + "id": "becad522-6943-4a4c-ab7d-6cdbdd84f369", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "```json\n", + "{ 'artifact_name': 'Monash-Australian_electricity_demand',\n", + " 'csv_config': {},\n", + " 'data_cols': [5],\n", + " 'data_fpath': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'date_offset': None,\n", + " 'freq': '10h',\n", + " 'joining_train_test': False,\n", + " 'missing_values_constant': None,\n", + " 'missing_values_technique': None,\n", + " 'normalize_training': False,\n", + " 'range_testing': None,\n", + " 'range_training': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None,\n", + " 'use_wandb': True,\n", + " 'wandb_artifacts_path': './data/wandb_artifacts'}\n", + "```" + ], + "text/plain": [ + "{'artifact_name': 'Monash-Australian_electricity_demand',\n", + " 'csv_config': {},\n", + " 'data_cols': [5],\n", + " 'data_fpath': '~/data/Semantic_Segmentation_TiltABP.csv',\n", + " 'date_format': '%Y-%m-%d %H:%M:%S',\n", + " 'date_offset': None,\n", + " 'freq': '10h',\n", + " 'joining_train_test': False,\n", + " 'missing_values_technique': None,\n", + " 'missing_values_constant': None,\n", + " 'normalize_training': False,\n", + " 'range_training': None,\n", + " 'range_testing': None,\n", + " 'resampling_freq': None,\n", + " 'start_date': None,\n", + " 'test_split': None,\n", + " 'time_col': None,\n", + " 'use_wandb': True,\n", + " 'wandb_artifacts_path': './data/wandb_artifacts'}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| hide\n", + "get_artifact_config_sd2a(print_flag = False)" + ] + }, { "cell_type": "markdown", "id": "765b90c4-ac6e-42d3-a091-f6e52e35bea8", @@ -582,24 +934,34 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 23, "id": "8dcb5baa-504c-4e7c-9e57-99260a11af31", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "######################\n", "# 02b - ENCODER MVP #\n", "######################\n", - "def get_artifact_config_MVP_auxiliar_variables(print_flag: bool) -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]:\n", + "def get_artifact_config_MVP_auxiliar_variables(\n", + " print_flag : bool,\n", + " base_filename : str = config_base_filename,\n", + " path : str = config_path\n", + ") -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]:\n", " \"\"\"\n", " Retrieves and assembles various configuration parameters and auxiliary variables for an MVP artifact. \n", " Extracts user, project, version, and data details, fetches configuration settings, and constructs training artifact strings. \n", " Returns a tuple containing these elements along with MVP workspace specifications and user preferences.\n", " \"\"\"\n", - "\n", " #Get neccesary variables\n", - " user, project, version, data = get_project_data(print_flag)\n", - " config = get_config(print_flag, \"02b-encoder_mvp\")\n", + " user, project, version, data = get_project_data(\n", + " print_flag = print_flag, \n", + " filename = base_filename,\n", + " path = path\n", + " )\n", + " config = get_config(\n", + " print_flag, \"02b-encoder_mvp\"\n", + " )\n", " user_preferences = config.user_preferences\n", " config = config.configuration\n", " train_artifact_ = get_train_artifact(user,project,data) \n", @@ -611,20 +973,25 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 24, "id": "77d62ca2-14da-4ab1-bbc7-3b5182dd0840", "metadata": {}, "outputs": [], "source": [ - "def get_artifact_config_MVP_auxiliar_variables_SWV(print_flag: bool) -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]: \n", + "#| export\n", + "def get_artifact_config_MVP_auxiliar_variables_SWV(\n", + " print_flag : bool,\n", + " base_filename : str = config_base_filename,\n", + " path : str = config_path\n", + ") -> Tuple[str, str, str, str, AttrDict, str, Tuple[float, float], AttrDict]: \n", " \"\"\"\n", " Retrieves and assembles various configuration parameters and auxiliary variables for an MVP artifact. \n", " Extracts user, project, version, and data details, fetches configuration settings, and constructs training artifact strings. \n", " Returns a tuple containing these elements along with MVP (sliding window view) workspace specifications and user preferences.\n", " \"\"\"\n", " #Get neccesary variables\n", - " user, project, version, data = get_project_data(print_flag)\n", - " config = get_config(print_flag, \"02c-encoder_mvp-sliding_window_view\")\n", + " user, project, version, data = get_project_data(print_flag, base_filename, path)\n", + " config = get_config(print_flag, \"02c-encoder_mvp-sliding_window_view\", path)\n", " user_preferences = config.user_preferences\n", " config = config.configuration\n", " train_artifact_ = get_train_artifact(user,project,data) \n", @@ -635,47 +1002,82 @@ ] }, { - "cell_type": "markdown", - "id": "54ff99b2-ec50-4e45-80c2-5d9c9132728f", + "cell_type": "code", + "execution_count": 25, + "id": "bd1e4e0d", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('angel-gutierrez',\n", + " 'ream',\n", + " 'latest',\n", + " 'Monash-Australian_electricity_demand:latest',\n", + " {'job_type': 'encoder_MVP-SWV',\n", + " 'alias': 'Monash-Australian_electricity_demand',\n", + " 'wandb': {'mode': 'online', 'group': None},\n", + " 'specifications': {'batch_size': 512,\n", + " 'n_epoch': 100,\n", + " 'mask': {'future': False, 'stateful': True, 'sync': False},\n", + " 'mvp': {'ws1': 10,\n", + " 'ws2': 30,\n", + " 'r': 0.71,\n", + " 'valid_size': 0.2,\n", + " 'normalize': {'by_sample': False, 'use_single_batch': False}},\n", + " 'sliding_windows': {'stride': 15, 'size': 30}}},\n", + " 'angel-gutierrez/ream/Monash-Australian_electricity_demand:latest',\n", + " (10, 30),\n", + " {'use_wandb': True,\n", + " 'wdb': {'user': 'angel-gutierrez',\n", + " 'project_name': 'ream',\n", + " 'version': 'latest',\n", + " 'mode': 'online',\n", + " 'artifacts_path': './data/wandb_artifacts'},\n", + " 'data': {'folder': '~/data/',\n", + " 'fname': 'Semantic_Segmentation_TiltABP',\n", + " 'ftype': '.csv',\n", + " 'cols': [5],\n", + " 'freq': '10h'},\n", + " 'artifact': {'alias': 'Monash-Australian_electricity_demand',\n", + " 'algorithm': 'mvp-SWV'},\n", + " 'directories': {'tmp': 'tmp',\n", + " 'data': '~/data/Semantic_Segmentation_TiltABP.csv'}})" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### Auxiliary functions for error handling" + "get_artifact_config_MVP_auxiliar_variables_SWV(False)" ] }, { "cell_type": "markdown", - "id": "02aa20ce-ce6b-4d0c-89c6-a1845bd695ec", + "id": "54ff99b2-ec50-4e45-80c2-5d9c9132728f", "metadata": {}, "source": [ - "> TODO: Should we allow diferent project from environment ones? Should we just avoid yml configuration for this parameters and directly get dockers environment WANDB_ENTITIY and WANDB_PROJECT environment variables? ." + "### Auxiliary functions for error handling" ] }, { - "cell_type": "code", - "execution_count": 16, - "id": "829c322d-a42c-4655-9e67-3b3cc2e3a9e0", + "cell_type": "markdown", + "id": "02aa20ce-ce6b-4d0c-89c6-a1845bd695ec", "metadata": {}, - "outputs": [], "source": [ - "def check_project_and_entity(user: str, project: str):\n", - " \"\"\"\n", - " Checks user and project are correctly setted up comparing to environment variables\n", - " \"\"\"\n", - " os_entity = os.environ['WANDB_ENTITY']\n", - " os_project = os.environ['WANDB_PROJECT']\n", - " if (os_entity != user):\n", - " custom_error(\"Please check .env and base.yml: entity != user os \" + os_entity + \" yaml \" + user)\n", - " if (os_project != project):\n", - " custom_error(\"Please check .env and base.yml: project differs os \" + os_project + \" yaml \" + project)\n" + "> TODO: Should we allow diferent project from environment ones? Should we just avoid yml configuration for this parameters and directly get dockers environment WANDB_ENTITIY and WANDB_PROJECT environment variables? ." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 26, "id": "f378edb0-797d-43f1-a29c-ada58e9f7446", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "def get_artifact_config_MVP_check_errors(\n", " artifact_config: AttrDict, \n", " user: str, \n", @@ -705,11 +1107,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 27, "id": "3c77a575-19a9-47e7-a13a-8e81a00affe6", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "def get_artifact_config_MVP(print_flag: bool = False) -> Tuple[str, str, str, str, AttrDict, str]:\n", " \"\"\"\n", " Gathers and structures the MVP artifact configuration, including user, project, version, data details, and various configuration settings.\n", @@ -743,17 +1146,26 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 28, "id": "f7b01a75-f9fa-4069-b99b-5ea6703887c5", "metadata": {}, "outputs": [], "source": [ - "def get_artifact_config_MVP_SWV(print_flag: bool = False) -> Tuple[str, str, str, str, AttrDict, str]:\n", + "#| export\n", + "def get_artifact_config_MVP_SWV(\n", + " print_flag : bool = False,\n", + " base_filename : str = config_base_filename,\n", + " path : str = config_path\n", + ") -> Tuple[str, str, str, str, AttrDict, str]:\n", " \"\"\"\n", " Gathers and structures the MVP_SWV artifact configuration, including user, project, version, data details, and various configuration settings.\n", " Returns a tuple comprising user, project, version, data, the structured artifact configuration as an AttrDict, and the job type.\n", " \"\"\"\n", - " user, project, version, data, config, train_artifact_, mvp_ws, user_preferences = get_artifact_config_MVP_auxiliar_variables_SWV(print_flag)\n", + " user, project, version, data, config, train_artifact_, mvp_ws, user_preferences = get_artifact_config_MVP_auxiliar_variables_SWV(\n", + " print_flag = print_flag,\n", + " base_filename = base_filename,\n", + " path = path\n", + " )\n", "\n", " artifact_config = AttrDict(\n", " alias = config.alias,\n", @@ -779,6 +1191,50 @@ " return user, project, version, data, artifact_config, config.job_type" ] }, + { + "cell_type": "code", + "execution_count": 29, + "id": "87c5650c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('angel-gutierrez',\n", + " 'ream',\n", + " 'latest',\n", + " 'Monash-Australian_electricity_demand:latest',\n", + " {'alias': 'Monash-Australian_electricity_demand',\n", + " 'analysis_mode': 'online',\n", + " 'batch_size': 512,\n", + " 'epochs': 100,\n", + " 'mask_future': False,\n", + " 'mask_stateful': True,\n", + " 'mask_sync': False,\n", + " 'mvp_ws': (10, 30),\n", + " 'norm_by_sample': False,\n", + " 'norm_use_single_batch': False,\n", + " 'r': 0.71,\n", + " 'stride': 15,\n", + " 'train_artifact': 'angel-gutierrez/ream/Monash-Australian_electricity_demand:latest',\n", + " 'valid_artifact': None,\n", + " 'use_wandb': True,\n", + " 'valid_size': 0.2,\n", + " 'w': 30,\n", + " 'wandb_group': None},\n", + " 'encoder_MVP-SWV')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| hide\n", + "get_artifact_config_MVP_SWV(False)" + ] + }, { "cell_type": "markdown", "id": "dc7aa51f-8bbb-4445-b06a-1564e63fc32f", @@ -844,24 +1300,29 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 30, "id": "e7b33aed-ba0d-4cf3-b131-c21d7b5519d9", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "######################\n", "# 02a - ENCODER DCAE #\n", "######################\n", "\n", - "def get_artifact_config_DCAE(print_flag: bool = False) -> Tuple[AttrDict, str]:\n", + "def get_artifact_config_DCAE(\n", + " print_flag: bool = False,\n", + " base_filename = config_base_filename,\n", + " path = config_path\n", + ") -> Tuple[AttrDict, str]:\n", " \"\"\"\n", " Constructs the configuration for the DCAE (Deep Convolutional AutoEncoder).\n", " It fetchs the relevant settings and assembles the artifact configuration.\n", " Validates the configuration to ensure correct project and entity setup and \n", " returns the artifact configuration as an AttrDict along with the job type.\n", " \"\"\"\n", - " user, project, version, data = get_project_data(print_flag)\n", - " config = get_config(print_flag, \"02a-encoder_dcae\")\n", + " user, project, version, data = get_project_data(print_flag, base_filename, path)\n", + " config = get_config(print_flag, \"02a-encoder_dcae\", path)\n", " if print_flag: print(\"Antes de leer configuration \" + str(config))\n", " config = config.configuration\n", " \n", @@ -886,7 +1347,8 @@ " top_k = config.specifications.pool_szs\n", " )\n", " check_project_and_entity(\n", - " artifact_config.wandb_entity, artifact_config.wandb_project\n", + " artifact_config.wandb_entity,\n", + " artifact_config.wandb_project\n", " )\n", " return artifact_config, config.job_type" ] @@ -933,21 +1395,25 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 31, "id": "db2bbe87-33e0-4cc4-9d89-ba6cf1f585c2", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "######################\n", "# 03 - EMBEDDINGS #\n", "######################\n", - "def get_artifact_config_embeddings(print_flag: bool = False) -> Tuple[AttrDict, str]:\n", + "def get_artifact_config_embeddings(\n", + " print_flag : bool = False,\n", + " config_path : str = config_path\n", + ") -> Tuple[AttrDict, str]:\n", " \"\"\"\n", " Constructs the configuration for embeddings by fetching relevant settings and building the encoder artifact configuration.\n", " Validates the project and entity settings and returns the artifact configuration as an AttrDict, along with the job type.\n", " \"\"\"\n", "\n", - " config = get_config(print_flag, \"03a-embeddings\")\n", + " config = get_config(print_flag, \"03a-embeddings\", config_path)\n", " job_type=config.job_type\n", " version = config.user_preferences.wdb.version\n", " enc_artifact = build_enc_artifact(config, print_flag)\n", @@ -967,17 +1433,21 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 32, "id": "e1d34641-83c2-4b68-b058-1145eba72c5f", "metadata": {}, "outputs": [], "source": [ - "def get_artifact_config_embeddings_SWV(print_flag: bool = False) -> Tuple[AttrDict, str]:\n", + "#| export\n", + "def get_artifact_config_embeddings_SWV(\n", + " print_flag : bool = False,\n", + " config_path : str = config_path\n", + ") -> Tuple[AttrDict, str]:\n", " \"\"\"\n", " Constructs the configuration for embeddings (sliding window view) by fetching relevant settings and building the encoder artifact configuration.\n", " Validates the project and entity settings and returns the artifact configuration as an AttrDict, along with the job type.\n", " \"\"\"\n", - " config = get_config(print_flag, \"03b-embeddings-sliding_window_view\")\n", + " config = get_config(print_flag, \"03b-embeddings-sliding_window_view\", config_path)\n", " job_type=config.job_type\n", " version = config.user_preferences.wdb.version\n", " enc_artifact = build_enc_artifact(config, print_flag)\n", @@ -1039,21 +1509,25 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 33, "id": "13facd5a-bfda-4973-9db2-0333c2b72a42", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "###################################\n", "# 04 - DIMENSIONALITY REDUCTION #\n", "###################################\n", - "def get_artifact_config_dimensionality_reduction(print_flag: bool = False) -> Tuple[AttrDict, str]:\n", + "def get_artifact_config_dimensionality_reduction(\n", + " print_flag : bool = False,\n", + " config_path : str = config_path\n", + ") -> Tuple[AttrDict, str]:\n", " \"\"\"\n", " Constructs the configuration for dimensionality reduction tasks by fetching relevant settings, including building the encoder artifact.\n", " Returns the artifact configuration as an AttrDict, along with the job type.\n", " \"\"\"\n", "\n", - " config = get_config(print_flag, \"04-dimensionality_reduction\")\n", + " config = get_config(print_flag, \"04-dimensionality_reduction\", config_path)\n", " job_type = config.job_type\n", " enc_artifact = build_enc_artifact(config, print_flag)\n", " config = config.configuration\n", @@ -1084,21 +1558,25 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 34, "id": "f8d335f8-a63b-4dae-a2b3-db80e9ef92b6", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "##################\n", "# 05 - XAI-SHAP #\n", "##################\n", - "def get_artifact_config_xai_shap(print_flag: bool = False) -> Tuple[AttrDict, str]:\n", + "def get_artifact_config_xai_shap(\n", + " print_flag : bool = False,\n", + " config_path : str = config_path\n", + ") -> Tuple[AttrDict, str]:\n", " \"\"\"\n", " Constructs the configuration for the XAI SHAP (Explainable Artificial Intelligence using SHAP values) analysis. \n", " This includes fetching relevant settings, building the encoder artifact, and assembling the artifact configuration.\n", " Returns the artifact configuration as an AttrDict, along with the job type.\n", " \"\"\"\n", - " config = get_config(print_flag, \"05-xai_shap\")\n", + " config = get_config(print_flag, \"05-xai_shap\", config_path)\n", " job_type = config.job_type\n", " enc_artifact = build_enc_artifact(config, print_flag)\n", " config = config.configuration\n", @@ -1116,6 +1594,17 @@ " return artifact_config, job_type\n" ] }, + { + "cell_type": "code", + "execution_count": 35, + "id": "1156b20b", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide -> Not yet implemented\n", + "#get_artifact_config_xai_shap(False)" + ] + }, { "cell_type": "markdown", "id": "1186ec85-e47e-432b-992f-e8e143df9a8e", @@ -1134,7 +1623,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 36, "id": "07468ed3-5783-468b-9ebd-92ec8b9465ea", "metadata": {}, "outputs": [], @@ -1172,7 +1661,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 37, "id": "ec8732bc-27e0-43dd-9c27-40d6348c3dd7", "metadata": {}, "outputs": [], @@ -1209,7 +1698,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 38, "id": "63b24736-ba1a-4161-99fc-a328529d4203", "metadata": {}, "outputs": [], @@ -1258,7 +1747,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 39, "id": "39701d48-8d7d-4928-a528-2ecc5a71ff5e", "metadata": {}, "outputs": [], @@ -1302,7 +1791,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 40, "id": "a200c2b3-daf0-441b-913b-eee9c0e8d326", "metadata": {}, "outputs": [], @@ -1338,7 +1827,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 41, "id": "1db1d924-90eb-4f33-b389-d14968d883e1", "metadata": {}, "outputs": [], @@ -1399,7 +1888,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 42, "id": "cbb020bd-6e7d-4668-a24b-b529f93e2f14", "metadata": {}, "outputs": [], @@ -1444,7 +1933,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 43, "id": "f3b0e373-a1b7-4fb1-96ca-1992815305e6", "metadata": {}, "outputs": [], @@ -1488,7 +1977,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 44, "id": "9ec755c5-2013-4802-bb54-24b2642d01b9", "metadata": {}, "outputs": [], @@ -1504,7 +1993,7 @@ " mvp = AttrDict(\n", " batch_size = 512,\n", " n_epoch = 100,\n", - " ws = [10,1008], \n", + " ws = [10,30], \n", " stride = 1\n", " ),\n", " dcae = AttrDict(#TODO: Check\n", @@ -1532,11 +2021,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 45, "id": "4dee0968-1f1c-4a67-90a0-fa998ab854b3", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "tested_configs = {\n", " 'monash_australian_electricity_demand_0': monash_australian_electricity_demand_0,\n", " 'monash_solar_4_seconds_0': monash_solar_4_seconds_0,\n", @@ -1551,7 +2041,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 46, "id": "7ff14373-c56f-4a5a-a787-12191196b091", "metadata": {}, "outputs": [], @@ -1564,7 +2054,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 47, "id": "daadf0fe-5d0b-4068-86dd-1f17cbf30d48", "metadata": {}, "outputs": [], @@ -1581,7 +2071,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 48, "id": "eb13402c-46e1-4960-9836-daf47839d3ce", "metadata": {}, "outputs": [ @@ -1608,7 +2098,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 49, "id": "887c90c2-1704-40e2-bd94-1d811e3c1a31", "metadata": {}, "outputs": [ @@ -1618,7 +2108,7 @@ "'traffic_san_francisco_0'" ] }, - "execution_count": 38, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1630,7 +2120,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 50, "id": "668cc6c8-bb89-4b2e-a598-0ee0f3f788a9", "metadata": {}, "outputs": [], @@ -1642,7 +2132,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 51, "id": "154878de-f491-44e6-86ea-d8bfa8fa4eab", "metadata": {}, "outputs": [ @@ -1657,7 +2147,7 @@ "time_col: None\n", "freq: 1h\n", "mvp: {'batch_size': 512, 'n_epoch': 100, 'ws': [1, 720], 'stride': 24}\n", - "dcae: {'batch_size': 512, 'n_epoch': 100, 'stride': 48, 'w': 236, 'delta': 60, 'nfs': [64, 32, 16], 'kss': [10, 5, 5], 'output_filter_size': 10, 'top_k': [2, 2, 4]}\n" + "dcae: {'batch_size': 512, 'n_epoch': 100, 'stride': 48, 'w': 224, 'delta': 60, 'nfs': [64, 32, 16], 'kss': [10, 5, 5], 'output_filter_size': 10, 'top_k': [2, 2, 4], 'pool_szs': [2, 2, 4]}\n" ] } ], @@ -1668,7 +2158,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 52, "id": "9e12be39-14e7-41bf-8459-6ed47c708792", "metadata": {}, "outputs": [], @@ -1685,7 +2175,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 53, "id": "3cfac15b-b9e7-4781-8397-5b363c800a63", "metadata": {}, "outputs": [ @@ -1700,7 +2190,7 @@ "freq: 30min\n", "time_col: None\n", "mvp: {'batch_size': 512, 'n_epoch': 100, 'ws': [2, 336], 'stride': 48}\n", - "dcae: {'batch_size': 512, 'n_epoch': 100, 'stride': 48, 'w': 236, 'delta': 60, 'nfs': [64, 32, 16], 'kss': [10, 5, 5], 'output_filter_size': 10, 'top_k': [2, 2, 4]}\n" + "dcae: {'batch_size': 512, 'n_epoch': 100, 'stride': 48, 'w': 224, 'delta': 60, 'nfs': [64, 32, 16], 'kss': [10, 5, 5], 'output_filter_size': 10, 'top_k': [2, 2, 4], 'pool_szs': [2, 2, 4]}\n" ] } ], @@ -1720,7 +2210,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 54, "id": "0d400a03-8f9b-4308-90cc-735dc92c1124", "metadata": {}, "outputs": [], @@ -1755,17 +2245,18 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 55, "id": "eeb494df-c5ee-4cde-a635-c6aea6521fb3", "metadata": {}, "outputs": [], "source": [ + "#| export\n", "import pandas as pd" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 56, "id": "eb2e52a0-0492-4529-b050-ef5384534f79", "metadata": {}, "outputs": [], @@ -1810,7 +2301,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 57, "id": "84e8fb00-673e-4955-a0c0-9933b2ae3750", "metadata": {}, "outputs": [], @@ -1842,7 +2333,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 58, "id": "4baad8bd-bd77-42c9-a6c7-d6bfc9d9cf6d", "metadata": {}, "outputs": [], @@ -1877,7 +2368,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 59, "id": "afda84a7-9ea0-4166-8cb7-c36376d4619e", "metadata": {}, "outputs": [], @@ -1898,7 +2389,7 @@ " print(\"Selecting \", list(tested_configs.items())[id][0])\n", " config.artifact_name = to_set.alias\n", " config.data_cols = to_set.cols\n", - " #config.data_fpath= \"~/data/\"+to_set.fname+to_set.ftype\n", + " config.data_fpath= \"~/data/\"+to_set.fname+to_set.ftype\n", " config.freq=to_set.freq\n", " config.time_col = to_set.time_col\n", " config.csv_config = {}\n", @@ -1922,7 +2413,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 60, "id": "ec3910fc-3d6d-4b94-8d3d-5c18edb3029c", "metadata": {}, "outputs": [], @@ -1953,7 +2444,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 61, "id": "4d1c4265-1294-406f-978a-d3e9bd2a4547", "metadata": {}, "outputs": [ @@ -1974,27 +2465,27 @@ "Frequency factor resampling frequency -->\n", "resampling_freq: 1s\n", "name: TitlABP-2s\n", - "path: ~/data/australian_electricity_demand_dataset.csv\n", + "path: ~/data/Semantic_Segmentation_TiltABP.csv\n", "Frequency factor config -->\n", - "freq: 1s\u001b[0m\n", - "use_wandb: True\u001b[0m\n", - "\u001b[94martifact_name: Monash-Australian_electricity\u001b[0m -> TitlABP-2s\u001b[0m\n", - "joining_train_test: False\u001b[0m\n", - "range_testing: None\u001b[0m\n", - "\u001b[94mtime_col: None\u001b[0m -> 0\u001b[0m\n", - "csv_config: {}\u001b[0m\n", "date_offset: None\u001b[0m\n", - "test_split: None\u001b[0m\n", - "\u001b[94mresampling_freq: None\u001b[0m -> 2S\u001b[0m\n", - "data_fpath: ~/data/australian_electricity_demand_dataset.csv\u001b[0m\n", - "range_training: None\u001b[0m\n", "\u001b[94mdata_cols: [0]\u001b[0m -> []\u001b[0m\n", - "wandb_artifacts_path: ./data/wandb_artifacts\u001b[0m\n", + "csv_config: {}\u001b[0m\n", "missing_values_technique: None\u001b[0m\n", - "date_format: %Y-%m-%d %H:%M:%S\u001b[0m\n", + "\u001b[94mdata_fpath: ~/data/australian_electricity_demand_dataset.csv\u001b[0m -> ~/data/Semantic_Segmentation_TiltABP.csv\u001b[0m\n", "start_date: None\u001b[0m\n", + "\u001b[94mtime_col: None\u001b[0m -> 0\u001b[0m\n", + "freq: 1s\u001b[0m\n", + "wandb_artifacts_path: ./data/wandb_artifacts\u001b[0m\n", + "\u001b[94mresampling_freq: None\u001b[0m -> 2S\u001b[0m\n", + "\u001b[94martifact_name: Monash-Australian_electricity\u001b[0m -> TitlABP-2s\u001b[0m\n", + "date_format: %Y-%m-%d %H:%M:%S\u001b[0m\n", + "range_training: None\u001b[0m\n", "missing_values_constant: None\u001b[0m\n", - "normalize_training: False\u001b[0m\n" + "joining_train_test: False\u001b[0m\n", + "use_wandb: True\u001b[0m\n", + "test_split: None\u001b[0m\n", + "normalize_training: False\u001b[0m\n", + "range_testing: None\u001b[0m\n" ] } ], @@ -2020,7 +2511,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 62, "id": "4d483893-35a9-4be2-8324-9e099af4bfc5", "metadata": {}, "outputs": [], @@ -2039,7 +2530,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 63, "id": "52395a2d-0dbd-4226-82b0-7853ff555572", "metadata": {}, "outputs": [ @@ -2059,12 +2550,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 64, "id": "cb828063-26b2-43fb-ab92-e60a0ee0db72", "metadata": {}, "outputs": [], "source": [ - "#| Export \n", + "#| export \n", "def force_artifact_config_mvp(\n", " config: AttrDict,\n", " id:int = 0, \n", @@ -2116,7 +2607,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 65, "id": "f50ac55c-f49e-4b4f-ad56-1acce64907ad", "metadata": {}, "outputs": [], @@ -2151,7 +2642,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 66, "id": "1c71f660-eae7-4d0a-bdb7-7733c3ba8fab", "metadata": {}, "outputs": [ @@ -2159,32 +2650,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[94mfreq: 1d\u001b[0m -> 4s\u001b[0m\n", - "\u001b[94mepochs: 1\u001b[0m -> 100\u001b[0m\n", - "use_wandb: True\u001b[0m\n", - "wandb_group: None\u001b[0m\n", - "\u001b[93m\u001b[1mnorm_use_by_single_batch is missing in original dict | (False,) \u001b[0m\n", - "\u001b[94mbatch_size: 2\u001b[0m -> 512\u001b[0m\n", - "\u001b[94mstride: 13\u001b[0m -> 450\u001b[0m\n", - "\u001b[94malias: Monash-Australian_electric\u001b[0m -> solar_4_seconds\u001b[0m\n", - "\u001b[94mmvp_ws: (150, 33)\u001b[0m -> [450, 900]\u001b[0m\n", - "valid_size: 0.2\u001b[0m\n", - "\u001b[94martifact_name: Monash-Australian_electricity\u001b[0m -> solar_4_seconds-20s\u001b[0m\n", - "\u001b[94mtrain_artifact: mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest\u001b[0m -> mi-santamaria/deepvats/solar_4_seconds-20s:latest\u001b[0m\n", "r: 0.71\u001b[0m\n", + "data_cols: []\u001b[0m\n", + "valid_size: 0.2\u001b[0m\n", + "\u001b[93m\u001b[1mcsv_config is missing in original dict | {} \u001b[0m\n", + "analysis_mode: online\u001b[0m\n", + "\u001b[94mbatch_size: 2\u001b[0m -> 512\u001b[0m\n", + "\u001b[94mdata_fpath: ~/data/kaggle_web_traffic_dataset_with_missing_values.tsf\u001b[0m -> ~/data/solar_4_seconds_dataset.tsf\u001b[0m\n", "\u001b[94mw: 30\u001b[0m -> 900\u001b[0m\n", + "\u001b[94mtrain_artifact: mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest\u001b[0m -> mi-santamaria/deepvats/solar_4_seconds-20s:latest\u001b[0m\n", "\u001b[94mtime_col: None\u001b[0m -> None\u001b[0m\n", - "\u001b[93m\u001b[1mcsv_config is missing in original dict | {} \u001b[0m\n", - "mask_stateful: True\u001b[0m\n", + "\u001b[94mfreq: 1d\u001b[0m -> 4s\u001b[0m\n", + "\u001b[93m\u001b[1mnorm_use_by_single_batch is missing in original dict | (False,) \u001b[0m\n", + "valid_artifact: None\u001b[0m\n", "norm_use_single_batch: False\u001b[0m\n", "\u001b[93m\u001b[1mresampling_freq is missing in original dict | 20S \u001b[0m\n", - "data_fpath: ~/data/kaggle_web_traffic_dataset_with_missing_values.tsf\u001b[0m\n", - "data_cols: []\u001b[0m\n", + "\u001b[94martifact_name: Monash-Australian_electricity\u001b[0m -> solar_4_seconds-20s\u001b[0m\n", + "mask_future: False\u001b[0m\n", + "\u001b[94mmvp_ws: (150, 33)\u001b[0m -> [450, 900]\u001b[0m\n", + "\u001b[94mepochs: 1\u001b[0m -> 100\u001b[0m\n", + "use_wandb: True\u001b[0m\n", "mask_sync: False\u001b[0m\n", - "valid_artifact: None\u001b[0m\n", - "analysis_mode: online\u001b[0m\n", - "norm_by_sample: False\u001b[0m\n", - "mask_future: False\u001b[0m\n" + "\u001b[94malias: Monash-Australian_electric\u001b[0m -> solar_4_seconds\u001b[0m\n", + "mask_stateful: True\u001b[0m\n", + "\u001b[94mstride: 13\u001b[0m -> 450\u001b[0m\n", + "wandb_group: None\u001b[0m\n", + "norm_by_sample: False\u001b[0m\n" ] } ], @@ -2195,12 +2686,12 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 67, "id": "11c0fb05-4e42-4750-a554-a1f923076c2e", "metadata": {}, "outputs": [], "source": [ - "#| Export \n", + "#| export \n", "def force_artifact_config_dcae(\n", " config: AttrDict,\n", " id:int = 0, \n", @@ -2250,7 +2741,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 68, "id": "e8e45d01-2db6-4f6a-9b70-9b97929f92b9", "metadata": {}, "outputs": [], @@ -2284,7 +2775,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 69, "id": "34ad57e1-cc30-460d-966e-81d10e7ab2e1", "metadata": {}, "outputs": [ @@ -2292,30 +2783,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[94mfreq: 1d\u001b[0m -> 4s\u001b[0m\n", - "\u001b[94moutput_filter_size: 1\u001b[0m -> [10, 5, 5]\u001b[0m\n", - "\u001b[94mepochs: 1\u001b[0m -> 100\u001b[0m\n", - "use_wandb: True\u001b[0m\n", - "wandb_group: None\u001b[0m\n", - "\u001b[94mbatch_size: 2\u001b[0m -> 512\u001b[0m\n", + "\u001b[94mr: 0\u001b[0m -> 0.71\u001b[0m\n", "\u001b[94mnfs: []\u001b[0m -> [64, 32, 16]\u001b[0m\n", - "\u001b[94mstride: 1\u001b[0m -> 48\u001b[0m\n", - "\u001b[94malias: Monash-Australian_electric\u001b[0m -> solar_4_seconds\u001b[0m\n", "valid_size: 0.2\u001b[0m\n", - "\u001b[94martifact_name: Monash-Australian_electricity\u001b[0m -> solar_4_seconds-20s\u001b[0m\n", + "data_cols: []\u001b[0m\n", + "analysis_mode: online\u001b[0m\n", + "\u001b[94mbatch_size: 2\u001b[0m -> 512\u001b[0m\n", + "\u001b[93m\u001b[1mcsv_config is missing in original dict | {} \u001b[0m\n", + "\u001b[94mtop_k: []\u001b[0m -> [2, 2, 4]\u001b[0m\n", + "\u001b[94moutput_filter_size: 1\u001b[0m -> [10, 5, 5]\u001b[0m\n", + "\u001b[94mdata_fpath: ~/data/kaggle_web_traffic_dataset_with_missing_values.tsf\u001b[0m -> ~/data/solar_4_seconds_dataset.tsf\u001b[0m\n", + "\u001b[94mw: 30\u001b[0m -> 224\u001b[0m\n", + "\u001b[93m\u001b[1mpool_szs is missing in original dict | [2, 2, 4] \u001b[0m\n", "\u001b[94mtrain_artifact: mi-santamaria/deepvats/Monash-Australian_electricity_demand:latest\u001b[0m -> mi-santamaria/deepvats/solar_4_seconds-20s:latest\u001b[0m\n", - "\u001b[94mr: 0\u001b[0m -> 0.71\u001b[0m\n", - "\u001b[94mw: 30\u001b[0m -> 236\u001b[0m\n", + "\u001b[94mkss: []\u001b[0m -> [10, 5, 5]\u001b[0m\n", "\u001b[94mtime_col: None\u001b[0m -> None\u001b[0m\n", - "\u001b[93m\u001b[1mcsv_config is missing in original dict | {} \u001b[0m\n", + "\u001b[94mfreq: 1d\u001b[0m -> 4s\u001b[0m\n", + "valid_artifact: None\u001b[0m\n", "\u001b[93m\u001b[1mresampling_freq is missing in original dict | 20S \u001b[0m\n", + "\u001b[94martifact_name: Monash-Australian_electricity\u001b[0m -> solar_4_seconds-20s\u001b[0m\n", + "\u001b[94mepochs: 1\u001b[0m -> 100\u001b[0m\n", "\u001b[94mdelta: 6\u001b[0m -> 60\u001b[0m\n", - "data_fpath: ~/data/kaggle_web_traffic_dataset_with_missing_values.tsf\u001b[0m\n", - "data_cols: []\u001b[0m\n", - "\u001b[94mkss: []\u001b[0m -> [10, 5, 5]\u001b[0m\n", - "valid_artifact: None\u001b[0m\n", - "analysis_mode: online\u001b[0m\n", - "\u001b[94mtop_k: []\u001b[0m -> [2, 2, 4]\u001b[0m\n" + "use_wandb: True\u001b[0m\n", + "\u001b[94malias: Monash-Australian_electric\u001b[0m -> solar_4_seconds\u001b[0m\n", + "\u001b[94mstride: 1\u001b[0m -> 48\u001b[0m\n", + "wandb_group: None\u001b[0m\n" ] } ], diff --git a/r_shiny_app/global.R b/r_shiny_app/global.R index 28e36155..a684f8bf 100644 --- a/r_shiny_app/global.R +++ b/r_shiny_app/global.R @@ -19,6 +19,8 @@ library(stringr) #options(rgl.useNULL = TRUE) #library(rgl) library(plotly) +library(shinyFiles) +library(yaml) ##################QUITAR CUANDO YA TIRE library(reactlog) library(feather) diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index 6e115c72..13564216 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -1098,18 +1098,17 @@ shinyServer(function(input, output, session) { encoder_name <- basename(input$encoder) get_ts_plot_name(dataset_name, encoder_name) }) - - #---------------------GRAFICA 3D---------------------------------------------------- + embedding_3d <- reactive({ - # Obtener la proyección de embedding en tres dimensiones + prj <- req(projections()) print("------------------------------") str(embedding_ids) print("------------------------------") - # Suponiendo que las coordenadas de la proyección en tres dimensiones están en las primeras tres columnas - prj_3d <- prj[, 1:3] # Seleccionar las primeras tres columnas - colnames(prj_3d) <- c("xcoord", "ycoord", "zcoord") # Asignar nombres a las columnas + + prj_3d <- prj[, 1:3] + colnames(prj_3d) <- c("xcoord", "ycoord", "zcoord") prj_3d }) @@ -1117,7 +1116,6 @@ shinyServer(function(input, output, session) { prj_3d <- embedding_3d() - # Crear un gráfico 3D con Plotly plot <- plot_ly( data = prj_3d, x = ~xcoord, y = ~ycoord, z = ~zcoord, @@ -1127,11 +1125,126 @@ shinyServer(function(input, output, session) { ) }) - # Inicializar el valor del botón toggle_graph observe({ if (is.null(input$toggle_graph)) { updateNumericInput(session, "toggle_graph", value = 0) } }) + shinyFileChoose(input, "file", roots = c(wd = "~")) + + observeEvent(input$load_dataset, { + showModal(modalDialog( + title = "Upload Dataset", + shinyFilesButton("file", "Select a file", title = "Please select a file:", multiple = FALSE), + textOutput("file_path_text"), + tags$hr(), + h4("Configuration"), + numericInput("cols_input", "Cols:", value = 5, min = 1), + numericInput("freq_input", "Freq:", value = 10, min = 1), + numericInput("n_epoch_input", "n_epoch:", value = 100, min = 1), + numericInput("ws1_input", "ws1:", value = 10, min = 1), + tags$hr(), + footer = tagList( + actionButton("load_file", "Load"), + modalButton("Cancel") + ) + )) + }) + + observe({ + req(input$file) + output$file_path_text <- renderText({ + parseFilePaths(roots = c(wd = "~"), input$file)$datapath + }) + }) + + observeEvent(input$load_file, { + filepath <- isolate(parseFilePaths(roots = c(wd = "~"), input$file)$datapath) + if (length(filepath) > 0) { + showModal(modalDialog( + title = "Loading...", + tagList( + div(style = "display: flex; align-items: center; flex-direction: column; height: 300px;", + addSpinner(div(style = "margin-bottom: 200px;", textOutput("progress_text")), spin = "circle", color = "#007bff"), + div(style = "width: 80%; margin-top: 60px;", progressBar(id = "progress_bar", value = 0)) + ) + ), + footer = NULL, + size = "l" + )) + mod_file_base(filepath) + mod_file_02encodermvp(filepath) + updateProgressBar(session, "progress_bar", value = 33, total = 100, status = "info") + ejecucion_notebooks() + + } + removeModal() + }) + + + ejecucion_notebooks <- function() { + print("-------------Inicio------------------------") + ruta_a_primer_nb <- "~/work/nbs_pipeline/01_dataset_artifact.ipynb" + print("Se ha inicializado Primer notebook") + system(paste("jupyter nbconvert --execute --to notebook --inplace", ruta_a_primer_nb)) + + print("El primer notebook ha finalizado") + updateProgressBar(session, "progress_bar", value = 66, total = 100, status = "info") + + ruta_a_segundo_nb <- "~/work/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb" + print("Se ha inicializado Segundo notebook") + system(paste("jupyter nbconvert --execute --to notebook --inplace", ruta_a_segundo_nb)) + + print("El segundo notebook ha finalizado.\n") + updateProgressBar(session, "progress_bar", value = 100, total = 100, status = "info") + + print("Ambos notebooks han finalizado completamente.\n") + + } + + mod_file_base <- function(filepath){ + yaml_content <- readLines("~/work/nbs_pipeline/config/base.yaml") + + file_extension <- tools::file_ext(filepath) + + filename <- basename(filepath) + filename <- tools::file_path_sans_ext(filename) + + new_fname <- paste("fname: &fname \"", filename, "\"", sep = "") + yaml_content <- gsub("fname: &fname \".*\"", new_fname, yaml_content) + + new_ftype <- paste("ftype: &ftype \'.", file_extension, "\'", sep = "") + yaml_content <- gsub("ftype: &ftype \'.*\'", new_ftype, yaml_content) + + new_cols <- paste("cols: &cols [", input$cols_input, "]", sep = "") + new_freq <- paste("freq: &freq '", input$freq_input, "h'", sep = "") + + yaml_content <- gsub("cols: &cols \\[.*\\]", new_cols, yaml_content) + yaml_content <- gsub("freq: &freq '.*'", new_freq, yaml_content) + + writeLines(yaml_content, "~/work/nbs_pipeline/base.yaml") + + print("Se han anadido las modificaciones al fichero base", sep = "\n") + print(yaml_content, sep = "\n") + } + + mod_file_02encodermvp <- function(filepath){ + + yaml_content <- readLines("~/work/nbs_pipeline/config/02c-encoder_mvp-sliding_window_view.yaml") + + new_n_epoch <- paste(" n_epoch:", input$n_epoch_input) + new_ws1 <- paste(" ws1:", input$ws1_input) + + yaml_content <- gsub(" n_epoch: .*", new_n_epoch, yaml_content) + yaml_content <- gsub(" ws1: .*", new_ws1, yaml_content) + + writeLines(yaml_content,"~/work/nbs_pipeline/config/02c-encoder_mvp-sliding_window_view.yaml") + + print(yaml_content, sep = "\n") + + print("Se han anadido las modificaciones a ficheros de configuracion") + } + + }) From e1ab5b5a2e3f4412ddbdf75a1b2711b32161ecac Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Wed, 5 Jun 2024 20:34:57 +0000 Subject: [PATCH 05/37] Plot zoom projections_plot_ui --- r_shiny_app/server.R | 154 +++++++++++++++++-------------------------- 1 file changed, 62 insertions(+), 92 deletions(-) diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index 13564216..260bbdd6 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -718,11 +718,21 @@ shinyServer(function(input, output, session) { }) + # Reactive to store selected points + selected_points <- reactive({ + event_data("plotly_selected", source = "projections_plot") + }) + + # Handle plotly brush event embedding_ids <- reactive({ print("--> embedding idx") on.exit(print("embedding idx -->")) - bp = brushedPoints(prj_object(), input$projections_brush, allRows = TRUE) #%>% debounce(miliseconds) #Wait 1 seconds: 1000 - bp %>% rownames_to_column("index") %>% dplyr::filter(selected_ == TRUE) %>% pull(index) %>% as.integer + bp <- selected_points() + if (is.null(bp)) { + return(integer(0)) + } else { + bp$pointNumber + 1 # Adjusting for 0-based index + } }) window_list <- reactive({ @@ -761,90 +771,59 @@ shinyServer(function(input, output, session) { reduced_window_list }) - # Generate timeseries data for dygraph dygraph + # Reactive expression to generate ts_plot ts_plot <- reactive({ print("--> ts_plot | Before req 1") on.exit({print("ts_plot -->"); flush.console()}) req(tsdf(), ts_variables, input$wlen != 0, input$stride) - ts_plt = ts_plot_base() + ts_plt <- ts_plot_base() print("ts_plot | bp") #miliseconds <- ifelse(nrow(tsdf()) > 1000000, 2000, 1000) #if (!is.data.frame(bp)) {bp = bp_} print("ts_plot | embedings idxs ") - embedding_idxs = embedding_ids() + embedding_idxs <- embedding_ids() # Calculate windows if conditions are met (if embedding_idxs is !=0, that means at least 1 point is selected) print("ts_plot | Before if") - if ((length(embedding_idxs)!=0) & isTRUE(input$plot_windows)) { - reduced_window_list = req(window_list()) + if ((length(embedding_idxs) != 0) & isTRUE(input$plot_windows)) { + reduced_window_list <- req(window_list()) print(paste0("ts_plot | reduced_window_list[1] = ", reduced_window_list[1])) - start_indices = min(sapply(reduced_window_list, function(x) x[1])) - end_indices = max(sapply(reduced_window_list, function(x) x[2])) + start_indices <- min(sapply(reduced_window_list, function(x) x[1])) + end_indices <- max(sapply(reduced_window_list, function(x) x[2])) - view_size = end_indices-start_indices+1 - max_size = 10000 + view_size <- end_indices - start_indices + 1 + max_size <- 10000 - start_date = isolate(tsdf())$timeindex[start_indices] - end_date = isolate(tsdf())$timeindex[end_indices] + start_date <- isolate(tsdf())$timeindex[start_indices] + end_date <- isolate(tsdf())$timeindex[end_indices] - print(paste0("ts_plot | reuced_window_list (", start_date, end_date, ")", "view size ", view_size, "max size ", max_size)) + print(paste0("ts_plot | reduced_window_list (", start_date, end_date, ")", "view size ", view_size, "max size ", max_size)) if (view_size > max_size) { - end_date = isolate(tsdf())$timeindex[start_indices + max_size - 1] + end_date <- isolate(tsdf())$timeindex[start_indices + max_size - 1] #range_color = "#FF0000" # Red } - range_color = "#CCEBD6" # Original - + range_color <- "#CCEBD6" # Original - # # Plot the windows - count = 0 - for(ts_idxs in reduced_window_list) { - count = count + 1 - start_event_date = isolate(tsdf())$timeindex[head(ts_idxs, 1)] - end_event_date = isolate(tsdf())$timeindex[tail(ts_idxs, 1)] + # Plot the windows + count <- 0 + for (ts_idxs in reduced_window_list) { + count <- count + 1 + start_event_date <- isolate(tsdf())$timeindex[head(ts_idxs, 1)] + end_event_date <- isolate(tsdf())$timeindex[tail(ts_idxs, 1)] ts_plt <- ts_plt %>% dyShading( from = start_event_date, to = end_event_date, color = range_color ) ts_plt <- ts_plt %>% dyRangeSelector(c(start_date, end_date)) - #%>% dyEvent( - # start_event_date, - # label = paste0("SW-", count), - # labelLoc="bottom" , - # strokePattern = "solid", - # color = range_color - # ) %>% dyEvent( - # end_event_date, - # label = paste0("SW-",paste0("SW-", count), - # labelLoc="bottom", - # strokePattern = "solid"), - # color = range_color - # ) - } ts_plt <- ts_plt - # NOTE: This code block allows you to plot shadyng at once. - # The traditional method has to plot the dygraph n times - # (n being the number of rectangles to plot). With the adjacent - # code it is possible to plot the dygraph only once. Currently - # it does not work well because there are inconsistencies in the - # timezones of the time series and shiny (there is a two-hour shift[the current plot method works well]), - # which does not allow this method to be used correctly. If that - # were fixed in the future everything would work fine. - # num_rects <- length(reduced_window_list) - # rects_ini <- vector(mode = "list", length = num_rects) - # rects_fin <- vector(mode = "list", length = num_rects) - # for(i in 1:num_rects) { - # rects_ini[[i]] <- head(reduced_window_list[[i]],1) - # rects_fin[[i]] <- tail(reduced_window_list[[i]],1) - # } - # ts_plt <- vec_dyShading(ts_plt,rects_ini, rects_fin,"red", rownames(tsdf())) } ts_plt @@ -971,75 +950,63 @@ shinyServer(function(input, output, session) { ts_ar_config() %>% enframe() }) - - - + ranges <- reactiveValues(x = NULL, y = NULL) # Generate projections plot - output$projections_plot <- renderPlot({ + output$projections_plot <- renderPlotly({ req(input$dataset, input$encoder, input$wlen != 0, input$stride != 0) print("--> Projections_plot") prjs_ <- req(projections()) print("projections_plot | Prepare column highlights") + # Prepare the column highlight to color data if (!is.null(input$ts_plot_dygraph_click)) { - selected_ts_idx = which(ts_plot()$x$data[[1]] == input$ts_plot_dygraph_click$x_closest_point) - projections_idxs = tsidxs_per_embedding_idx() %>% map_lgl(~ selected_ts_idx %in% .) - prjs_$highlight = projections_idxs + selected_ts_idx <- which(ts_plot()$x$data[[1]] == input$ts_plot_dygraph_click$x_closest_point) + projections_idxs <- tsidxs_per_embedding_idx() %>% map_lgl(~ selected_ts_idx %in% .) + prjs_$highlight <- projections_idxs } else { - prjs_$highlight = FALSE + prjs_$highlight <- FALSE } + # Prepare the column highlight to color data. If input$generate_cluster has not been clicked # the column cluster will not exist in the dataframe, so we create with the value FALSE if(!("cluster" %in% names(prjs_))) - prjs_$cluster = FALSE + prjs_$cluster <- FALSE + print("projections_plot | GoGo Plot!") plt <- ggplot(data = prjs_) + aes(x = xcoord, y = ycoord, fill = highlight, color = as.factor(cluster)) + scale_colour_manual(name = "clusters", values = req(update_palette())) + - geom_point(shape = 21,alpha = config_style$point_alpha, size = config_style$point_size) + + geom_point(shape = 21, alpha = config_style$point_alpha, size = config_style$point_size) + scale_shape(solid = FALSE) + - #geom_path(size=config_style$path_line_size, colour = "#2F3B65",alpha = config_style$path_alpha) + guides() + - scale_fill_manual(values = c("TRUE" = "green", "FALSE" = "NA"))+ - coord_cartesian(xlim = ranges$x, ylim = ranges$y, expand = TRUE)+ + scale_fill_manual(values = c("TRUE" = "green", "FALSE" = "NA")) + + coord_cartesian(xlim = ranges$x, ylim = ranges$y, expand = TRUE) + theme_void() + theme(legend.position = "none") - if (input$show_lines){ - #plt <- plt + geom_path(size=config_style$path_line_size, colour = "#2F3B65",alpha = config_style$path_alpha) - plt <- plt + geom_path(linewidth=config_style$path_line_size, colour = "#2F3B65",alpha = config_style$path_alpha) + if (input$show_lines) { + plt <- plt + geom_path(linewidth = config_style$path_line_size, colour = "#2F3B65", alpha = config_style$path_alpha) } observeEvent(input$savePlot, { plt <- plt + theme(plot.background = element_rect(fill = "white")) ggsave(filename = prjs_plot_name(), plot = plt, path = "../data/plots/") }) - #observeEvent(c(input$dataset, input$encoder, clustering_options$selected), { - #req(input$dataset, input$encoder) - #print("!-- CUDA?: ", torch$cuda$is_available()) - #prjs_ <- req(projections()) - #filename <- prjs_plot_name() - #print(paste("saving embedding plot to ",filename)) - #ggsave(filename = filename, plot = plt, path="../data/plots/") - #print("Embeding plot saved") - #}) - plt + # Convert ggplot to plotly + ggplotly(plt, source = "projections_plot") %>% + config(scrollZoom = TRUE) %>% + event_register("plotly_selected") }) - - # Render projections plot - output$projections_plot_ui <- renderUI( - { - plotOutput( - "projections_plot", - click = "projections_click", - brush = "projections_brush", - height = input$embedding_plot_height - ) %>% withSpinner() - } - ) + # Render projections plot UI + output$projections_plot_ui <- renderUI({ + plotlyOutput( + "projections_plot", + height = input$embedding_plot_height + ) %>% withSpinner() + }) # Render information about the selected point in the time series graph output$point <- renderText({ @@ -1112,6 +1079,8 @@ shinyServer(function(input, output, session) { prj_3d }) + selected_point <- reactiveVal(NULL) + output$embedding_plot_3d <- renderPlotly({ prj_3d <- embedding_3d() @@ -1121,9 +1090,10 @@ shinyServer(function(input, output, session) { x = ~xcoord, y = ~ycoord, z = ~zcoord, type = "scatter3d", mode = "lines+markers", - marker = list(color = "black", size = 5)# Definir color de los puntos + marker = list(color = "black", size = 5) ) }) + observe({ if (is.null(input$toggle_graph)) { From 9f1c930e1df04fae6e3e12e8ad26d01507c3d4c8 Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Wed, 5 Jun 2024 21:36:57 +0000 Subject: [PATCH 06/37] Selection of a point on the 3d graph and its next --- r_shiny_app/server.R | 59 +++++++++++++++++++++++++++++++++++--------- 1 file changed, 48 insertions(+), 11 deletions(-) diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index 260bbdd6..de12632c 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -1081,20 +1081,57 @@ shinyServer(function(input, output, session) { selected_point <- reactiveVal(NULL) - output$embedding_plot_3d <- renderPlotly({ + points_in_radius <- reactiveVal(NULL) + + output$embedding_plot_3d <- renderPlotly({ + prj_3d <- embedding_3d() + + # Define colores iniciales + initial_colors <- rep("black", nrow(prj_3d)) + + plot <- plot_ly( + data = prj_3d, + x = ~xcoord, y = ~ycoord, z = ~zcoord, + type = "scatter3d", + mode = "markers", + marker = list(color = initial_colors, size = 5), + line = list(color = "blue"), + showlegend = FALSE + ) + + plot %>% + event_register("plotly_click") + }) + + observeEvent(event_data("plotly_click"), { + click_data <- event_data("plotly_click") + if (!is.null(click_data)) { + point_idx <- click_data$pointNumber + 1 + selected_point(point_idx) prj_3d <- embedding_3d() + selected_coords <- prj_3d[point_idx, ] - plot <- plot_ly( - data = prj_3d, - x = ~xcoord, y = ~ycoord, z = ~zcoord, - type = "scatter3d", - mode = "lines+markers", - marker = list(color = "black", size = 5) - ) - }) - - + # Calcula la distancia euclidiana + distances <- sqrt((prj_3d$xcoord - selected_coords$xcoord)^2 + + (prj_3d$ycoord - selected_coords$ycoord)^2 + + (prj_3d$zcoord - selected_coords$zcoord)^2) + + # Define el radio + radius <- 0.5 # ajusta este valor según sea necesario + radius_idxs <- which(distances <= radius) + points_in_radius(radius_idxs) + + # Actualiza los colores de los puntos + new_colors <- rep("black", nrow(prj_3d)) + new_colors[radius_idxs] <- "blue" + new_colors[point_idx] <- "red" + + plotlyProxy("embedding_plot_3d", session) %>% + plotlyProxyInvoke("restyle", list(marker = list(color = new_colors, size = 5)), list(0)) + } + }) + observe({ if (is.null(input$toggle_graph)) { updateNumericInput(session, "toggle_graph", value = 0) From 393b24c2a256da5f172c41dc3b9964f1e57e3ce0 Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Thu, 6 Jun 2024 11:31:11 +0000 Subject: [PATCH 07/37] Delete duplicate entry docker-compose --- docker/docker-compose.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml index 3e5b7b49..5b2e5229 100644 --- a/docker/docker-compose.yml +++ b/docker/docker-compose.yml @@ -27,8 +27,6 @@ services: - ${LOCAL_DATA_PATH}:/home/${USER_NAME}/data/ - conda-env:/usr/local/share/miniconda3/envs/env - miniconda:/usr/local/share/miniconda3 - - conda-env:/usr/local/share/miniconda3/envs/env - - miniconda:/usr/local/share/miniconda3 init: true stdin_open: true tty: true From e5b46e1f907bcdc4c82cc0ba9ee0a4977966dfae Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Thu, 6 Jun 2024 14:18:46 +0000 Subject: [PATCH 08/37] Modified base.yaml variables and added libraries --- docker/DESCRIPTION | 3 +++ nbs_pipeline/base.yaml | 8 ++++---- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/docker/DESCRIPTION b/docker/DESCRIPTION index 679d6d38..3042bb3b 100644 --- a/docker/DESCRIPTION +++ b/docker/DESCRIPTION @@ -13,6 +13,9 @@ Depends: zoo, reticulate, xts + plotly + shinyFiles + yaml Remotes: r-lib/later, apache/arrow/r \ No newline at end of file diff --git a/nbs_pipeline/base.yaml b/nbs_pipeline/base.yaml index da36f6e0..04a797b2 100644 --- a/nbs_pipeline/base.yaml +++ b/nbs_pipeline/base.yaml @@ -13,15 +13,15 @@ user_preferences: # Whether to use or not wandb for experiment tracking use_wandb: &use_wandb true wdb: - user: &wdb_user angel-gutierrez - project_name: &wdb_project ream - version: &wdb_version 'latest' #'6' + user: &wdb_user ${WANDB_ENTITY} #Sustituir por usuario + project_name: &wdb_project ${WANDB_PROJECT} #Sustituir por proyecto + version: &wdb_version 'latest' # 'online' if the model will be tested on future data, or 'offline' if not mode: &wdb_mode 'online' artifacts_path: &artifacts_path './data/wandb_artifacts' data: folder: &path '~/data/' - fname: &fname "toy" + fname: &fname "Semantic_Segmentation_TiltABP" ftype: &ftype '.csv' cols: &cols [5] freq: &freq '10h' From 2af5384cdf68b2f291ed3355330ce5fbc6d0152d Mon Sep 17 00:00:00 2001 From: "mi.santamaria" Date: Thu, 13 Jun 2024 16:30:50 +0200 Subject: [PATCH 09/37] no tiene sentido copiar dvats dentro de nbs_pipeline --- docker/docker-compose.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml index 5b2e5229..9594b719 100644 --- a/docker/docker-compose.yml +++ b/docker/docker-compose.yml @@ -70,7 +70,6 @@ services: - ${LOCAL_DATA_PATH}:/home/${USER_NAME}/data/ - ../dvats:/home/${USER_NAME}/dvats - ../nbs_pipeline:/home/${USER_NAME}/work/nbs_pipeline - - ../dvats:/home/${USER_NAME}/work/nbs_pipeline/dvats - conda-env:/usr/local/share/miniconda3/envs/env - miniconda:/home/user/local/share/miniconda3 deploy: From 45d558d9386232526b702186fd19f2ca07cad33f Mon Sep 17 00:00:00 2001 From: "mi.santamaria" Date: Thu, 11 Jul 2024 22:16:20 +0200 Subject: [PATCH 10/37] Docker rstudio-server service fixed --- docker/DESCRIPTION | 6 +++++- docker/Dockerfile.rstudio | 8 ++++++++ 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/docker/DESCRIPTION b/docker/DESCRIPTION index ef714994..f2e225bb 100644 --- a/docker/DESCRIPTION +++ b/docker/DESCRIPTION @@ -1,3 +1,7 @@ +Package: vdvats +Version: 0.1.0 +Title: DeepVATS +Description: Deep Learning Visual Analytics Tools Depends: R (>= 3.6.0), shiny, @@ -20,4 +24,4 @@ Depends: semantic.dashboard Remotes: r-lib/later, - apache/arrow/r \ No newline at end of file + apache/arrow/r diff --git a/docker/Dockerfile.rstudio b/docker/Dockerfile.rstudio index f235ca7e..8eae7fbd 100644 --- a/docker/Dockerfile.rstudio +++ b/docker/Dockerfile.rstudio @@ -12,6 +12,10 @@ FROM misantamaria/dvats-rstudio:rocker-ml4.2 SHELL [ "/bin/bash", "--login", "-c" ] + + +COPY ./docker/DESCRIPTION /tmp/ + ## Add Rstudio Server user ARG USER=user ARG UID=1000 @@ -41,6 +45,10 @@ RUN chmod u+x /usr/local/bin/entrypoint-rstudio.sh WORKDIR $HOME +RUN R -e "devtools::install_deps('/tmp', dependencies = TRUE)" + # Initialize R-Stusio Server ENTRYPOINT [ "/usr/local/bin/entrypoint-rstudio.sh" ] + + From cbdc435869092632ebaf5c394bdc7c7ab3bdc6e2 Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Fri, 12 Jul 2024 16:01:32 +0000 Subject: [PATCH 11/37] restore branch --- docker/entrypoint.sh | 4 ---- nbs_pipeline/config/base.yaml | 4 ++-- r_shiny_app/global.R | 1 + 3 files changed, 3 insertions(+), 6 deletions(-) diff --git a/docker/entrypoint.sh b/docker/entrypoint.sh index b9ebb516..7f1e70e7 100644 --- a/docker/entrypoint.sh +++ b/docker/entrypoint.sh @@ -57,10 +57,6 @@ fi [ -d "/home/$USER/data/wandb_artifacts" ] || mkdir -p "/home/$USER/data/wandb_artifacts" -if ! grep -Fxq "./path/to/check_yml_changes.sh" $HOME/work/.git/hooks/pre-commit; then \ - sed -i '$i./path/to/check_yml_changes.sh' $HOME/work/.git/hooks/pre-commit; \ - fi - exec "$@" diff --git a/nbs_pipeline/config/base.yaml b/nbs_pipeline/config/base.yaml index 2ab0686d..7ec7f777 100644 --- a/nbs_pipeline/config/base.yaml +++ b/nbs_pipeline/config/base.yaml @@ -13,8 +13,8 @@ user_preferences: # Whether to use or not wandb for experiment tracking use_wandb: &use_wandb true wdb: - user: &wdb_user mi-santamaria - project_name: &wdb_project deepvats + user: &wdb_user angel-gutierrez + project_name: &wdb_project ream version: &wdb_version 'latest' # 'online' if the model will be tested on future data, or 'offline' if not mode: &wdb_mode 'online' diff --git a/r_shiny_app/global.R b/r_shiny_app/global.R index 17393506..ca6a6ede 100644 --- a/r_shiny_app/global.R +++ b/r_shiny_app/global.R @@ -62,6 +62,7 @@ hdbscan = reticulate::import("hdbscan") dvats = reticulate::import_from_path("dvats.all", path=paste0(Sys.getenv("HOME"))) + print("--> py_config ") print(reticulate::py_config()) print("py_config -->") From 701087aa93deda8de73e09964685b186908ee895 Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Fri, 12 Jul 2024 16:06:21 +0000 Subject: [PATCH 12/37] Restore Branch-resolve conflicts --- nbs_pipeline/base.yaml | 4 +- r_shiny_app/server.R | 187 ++++++++++++++++++++++++++++++++++++----- 2 files changed, 170 insertions(+), 21 deletions(-) diff --git a/nbs_pipeline/base.yaml b/nbs_pipeline/base.yaml index 04a797b2..8233e7fa 100644 --- a/nbs_pipeline/base.yaml +++ b/nbs_pipeline/base.yaml @@ -13,8 +13,8 @@ user_preferences: # Whether to use or not wandb for experiment tracking use_wandb: &use_wandb true wdb: - user: &wdb_user ${WANDB_ENTITY} #Sustituir por usuario - project_name: &wdb_project ${WANDB_PROJECT} #Sustituir por proyecto + user: &wdb_user angel-gutierrez + project_name: &wdb_project ream version: &wdb_version 'latest' # 'online' if the model will be tested on future data, or 'offline' if not mode: &wdb_mode 'online' diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index de12632c..00775b25 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -102,6 +102,7 @@ shinyServer(function(input, output, session) { #req(encs_l) print("--> observeEvent input_dataset | update encoder list") print(input$dataset) + print(encs_l) freezeReactiveValue(input, "encoder") print(paste0("observeEvent input_dataset | update encoders for dataset ", input$dataset)) updateSelectizeInput( @@ -526,7 +527,8 @@ shinyServer(function(input, output, session) { print("prj_object | Before switch ") cpu_flag = ifelse(input$cpu_flag == "CPU", TRUE, FALSE) - + #print("Before switch------------------") + #print(embs()) res = switch( input$dr_method, #### Comprobando parametros para saber por qué salen diferentes los embeddings ######### Comprobando los parámetros @@ -543,17 +545,20 @@ shinyServer(function(input, output, session) { TSNE = dvats$get_TSNE_prjs( X = embs, cpu=FALSE, - random_state=as.integer(input$prj_random_state) + random_state=as.integer(input$prj_random_state), + n_components = as.integer(3) ), PCA = dvats$get_PCA_prjs( X = embs, cpu=FALSE, - random_state=as.integer(input$prj_random_state) + random_state=as.integer(input$prj_random_state), + n_components = as.integer(3) ) ) #----------------------------------------------------------------------------------- - #print(paste("---------------------ncol", ncol(res))) - #print(res) + print("-----Aqui estamos-----") + print(paste("---------------------ncol", ncol(res))) + print(res) #----------------------------------------------------------------------------------- res = res %>% as.data.frame # TODO: This should be a matrix for improved efficiency colnames(res) = c("xcoord", "ycoord", "zcoord") @@ -1043,6 +1048,8 @@ shinyServer(function(input, output, session) { input$wlen != 0, input$stride != 0 ) + print("---data---") + print(dataset) #print("Saving time series plot") ts_plot <- req(ts_plot()) #save_path <- file.path("..", "data", "plots", ts_plot_name()) @@ -1138,21 +1145,29 @@ shinyServer(function(input, output, session) { } }) - shinyFileChoose(input, "file", roots = c(wd = "~")) + dataset_path <- reactiveVal(NULL) + dataset_preview <- reactiveVal(NULL) + + volumes <- getVolumes() + shinyFileChoose(input, "file", roots = volumes, session = session) + #shinyFileChoose(input, "file", roots = c(wd = "~")) + + showFirstModal <- function() observeEvent(input$load_dataset, { showModal(modalDialog( title = "Upload Dataset", - shinyFilesButton("file", "Select a file", title = "Please select a file:", multiple = FALSE), + shinyFilesButton("file", "Select file", title = "Please select a file:", multiple = FALSE), textOutput("file_path_text"), tags$hr(), h4("Configuration"), - numericInput("cols_input", "Cols:", value = 5, min = 1), - numericInput("freq_input", "Freq:", value = 10, min = 1), - numericInput("n_epoch_input", "n_epoch:", value = 100, min = 1), - numericInput("ws1_input", "ws1:", value = 10, min = 1), + numericInput("cols_input", "Calls:", value = 5, min = 1), + numericInput("freq_input", "Frequency:", value = 10, min = 1), + numericInput("n_epoch_input", "Epochs:", value = 100, min = 1), + numericInput("ws1_input", "Window:", value = 10, min = 1), tags$hr(), footer = tagList( + actionButton("show_graphic", "Show Graphic"), actionButton("load_file", "Load"), modalButton("Cancel") ) @@ -1162,12 +1177,12 @@ shinyServer(function(input, output, session) { observe({ req(input$file) output$file_path_text <- renderText({ - parseFilePaths(roots = c(wd = "~"), input$file)$datapath + parseFilePaths( roots = volumes, input$file)$datapath }) }) observeEvent(input$load_file, { - filepath <- isolate(parseFilePaths(roots = c(wd = "~"), input$file)$datapath) + filepath <- isolate(parseFilePaths( roots = volumes, input$file)$datapath) if (length(filepath) > 0) { showModal(modalDialog( title = "Loading...", @@ -1185,6 +1200,9 @@ shinyServer(function(input, output, session) { updateProgressBar(session, "progress_bar", value = 33, total = 100, status = "info") ejecucion_notebooks() + encs_l <- actualizar_encs_l(encs_l) + embs() + } removeModal() }) @@ -1203,10 +1221,22 @@ shinyServer(function(input, output, session) { print("Se ha inicializado Segundo notebook") system(paste("jupyter nbconvert --execute --to notebook --inplace", ruta_a_segundo_nb)) - print("El segundo notebook ha finalizado.\n") - updateProgressBar(session, "progress_bar", value = 100, total = 100, status = "info") + result <- get_input_output(2) + input_nb <- result[[1]] + output_nb <- result[[2]] + parameters <- result[[3]] - print("Ambos notebooks han finalizado completamente.\n") + _ <- ploomber::execute_notebook( + input_path = input_nb, + output_path = output_nb, + log_output = FALSE, + progress_bar = TRUE, + parameters = parameters, + remove_tagged_cells = c('skip', 'hide') + ) + print("Second notebook: end -->") + updateProgressBar(session, "progress_bar", value = 100, total = 100, status = "info") + print("------------- END ------------------------") } @@ -1230,9 +1260,9 @@ shinyServer(function(input, output, session) { yaml_content <- gsub("cols: &cols \\[.*\\]", new_cols, yaml_content) yaml_content <- gsub("freq: &freq '.*'", new_freq, yaml_content) - writeLines(yaml_content, "~/work/nbs_pipeline/base.yaml") + writeLines(yaml_content, "~/work/nbs_pipeline/config/base.yaml") - print("Se han anadido las modificaciones al fichero base", sep = "\n") + print("The files has been modified", sep = "\n") print(yaml_content, sep = "\n") } @@ -1250,8 +1280,127 @@ shinyServer(function(input, output, session) { print(yaml_content, sep = "\n") - print("Se han anadido las modificaciones a ficheros de configuracion") + print("all amendments have been added to the files ") } + best_nsizes_sequence_lengths <- function(){ + + print("-----Calculo de ventana-----") + + # Cargar el script de Python + source_python("~/work/nbs_pipeline/mplots.py") + print("------source -------") + # Llamar a la función de Python + print(tsdf()) + df <- subset(tsdf(), select = T1) + print(df) + print("----------------funcion ejecutada---------------") + resultado <- find_dominant_window_sizes_list(df) + print(resultado) + return(resultado) + } + + actualizar_encs_l <- function(encs_l) { + print(encs_l) + api <- wandb$Api() + + print("Querying encoders") + encs_l <- dvats$get_wandb_artifacts(project_path = glue(WANDB_ENTITY, "/", WANDB_PROJECT), + type = "learner", + last_version=F) %>% + discard(~ is_empty(.$aliases) | is_empty(.$metadata$train_artifact)) + encs_l <- encs_l %>% set_names(encs_l %>% map(~ glue(WANDB_ENTITY, "/", WANDB_PROJECT, "/", .$name))) + + # Actualizar encs_l con los nuevos artefactos + print("El nuevo artefacto") + print("------------------------------------") + print(encs_l) + return(encs_l) + } +#------------------------------------------------------------------------------------------------- + + observeEvent(input$show_graphic, { + file_info <- parseFilePaths(volumes, input$file) + if (nrow(file_info) > 0) { + dataset_preview(file_info$datapath) + } + }) + + dataset <- reactive({ + req(dataset_preview()) + data <- read.csv(dataset_preview()) + start_date =isolate(start_date()) + + date_col <- which(str_detect(tolower(colnames(data)), "date|time|fecha")) + + if (length(date_col) == 0) { + # No se encontró ninguna columna de fechas, se agrega una columna de fechas + data$fecha <- seq.POSIXt(from = as.POSIXct("2023-01-01 00:00:00"), by = "min", length.out = nrow(data)) + date_col <- which(colnames(data) == "fecha") + } else { + # Se encontró una columna de fechas, asegurarse de que está en formato POSIXct + for (col in date_col) { + if (!inherits(data[[col]], "POSIXct")) { + # Intentar convertir el contenido a POSIXct + data[[col]] <- tryCatch( + as.POSIXct(data[[col]], format="%Y-%m-%d %H:%M:%S"), + error = function(e) { + # Si falla, usar el origen para conversiones numéricas + as.POSIXct(data[[col]], origin="1970-01-01") + } + ) + } + } + } + + list(data = data, date_col = date_col) + }) + + output$data_preview_plot <- renderDygraph({ + data <- dataset() + print("-----data load------") + print(data) + + data_info <- dataset() + data <- data_info$data + date_col <- data_info$date_col + #print(data) + #print(date_col) + # Transformar los datos en un formato de serie temporal + ts_data <- xts(data[ , -date_col], order.by = data[[date_col]]) + print("-----grafico------") + + # Generar la gráfica con dygraphs + dygraph(ts_data) %>% + dyAxis("x", label = "Fecha") %>% + dyAxis("y", label = "Valores") + }) + + + observeEvent(input$show_graphic,{ + showModal(modalDialog( + title = "Graphic", + #plotOutput("umap_plot"), + dygraphOutput("data_preview_plot"), + #plotOutput("ts_plot_dygraph"), + footer = + tagList( + actionButton("back_to_upload", "Back to Upload"), + modalButton("Close") + ), + size = "l" + + )) + }) + + observeEvent(input$load_dataset, { + showFirstModal() + }) + + observeEvent(input$back_to_upload, { + removeModal() + showFirstModal() + }) + }) From b6f9a0963cf27b5f2461e48bb6926ba144c6a68c Mon Sep 17 00:00:00 2001 From: "mi.santamaria" Date: Thu, 18 Jul 2024 10:51:14 +0200 Subject: [PATCH 13/37] restaurado server.R --- nbs_pipeline/config/base.yaml | 4 +- r_shiny_app/server.R | 187 ++++------------------------------ 2 files changed, 21 insertions(+), 170 deletions(-) diff --git a/nbs_pipeline/config/base.yaml b/nbs_pipeline/config/base.yaml index 7ec7f777..2ab0686d 100644 --- a/nbs_pipeline/config/base.yaml +++ b/nbs_pipeline/config/base.yaml @@ -13,8 +13,8 @@ user_preferences: # Whether to use or not wandb for experiment tracking use_wandb: &use_wandb true wdb: - user: &wdb_user angel-gutierrez - project_name: &wdb_project ream + user: &wdb_user mi-santamaria + project_name: &wdb_project deepvats version: &wdb_version 'latest' # 'online' if the model will be tested on future data, or 'offline' if not mode: &wdb_mode 'online' diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index 00775b25..de12632c 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -102,7 +102,6 @@ shinyServer(function(input, output, session) { #req(encs_l) print("--> observeEvent input_dataset | update encoder list") print(input$dataset) - print(encs_l) freezeReactiveValue(input, "encoder") print(paste0("observeEvent input_dataset | update encoders for dataset ", input$dataset)) updateSelectizeInput( @@ -527,8 +526,7 @@ shinyServer(function(input, output, session) { print("prj_object | Before switch ") cpu_flag = ifelse(input$cpu_flag == "CPU", TRUE, FALSE) - #print("Before switch------------------") - #print(embs()) + res = switch( input$dr_method, #### Comprobando parametros para saber por qué salen diferentes los embeddings ######### Comprobando los parámetros @@ -545,20 +543,17 @@ shinyServer(function(input, output, session) { TSNE = dvats$get_TSNE_prjs( X = embs, cpu=FALSE, - random_state=as.integer(input$prj_random_state), - n_components = as.integer(3) + random_state=as.integer(input$prj_random_state) ), PCA = dvats$get_PCA_prjs( X = embs, cpu=FALSE, - random_state=as.integer(input$prj_random_state), - n_components = as.integer(3) + random_state=as.integer(input$prj_random_state) ) ) #----------------------------------------------------------------------------------- - print("-----Aqui estamos-----") - print(paste("---------------------ncol", ncol(res))) - print(res) + #print(paste("---------------------ncol", ncol(res))) + #print(res) #----------------------------------------------------------------------------------- res = res %>% as.data.frame # TODO: This should be a matrix for improved efficiency colnames(res) = c("xcoord", "ycoord", "zcoord") @@ -1048,8 +1043,6 @@ shinyServer(function(input, output, session) { input$wlen != 0, input$stride != 0 ) - print("---data---") - print(dataset) #print("Saving time series plot") ts_plot <- req(ts_plot()) #save_path <- file.path("..", "data", "plots", ts_plot_name()) @@ -1145,29 +1138,21 @@ shinyServer(function(input, output, session) { } }) - dataset_path <- reactiveVal(NULL) - dataset_preview <- reactiveVal(NULL) - - volumes <- getVolumes() - shinyFileChoose(input, "file", roots = volumes, session = session) - #shinyFileChoose(input, "file", roots = c(wd = "~")) - - showFirstModal <- function() + shinyFileChoose(input, "file", roots = c(wd = "~")) observeEvent(input$load_dataset, { showModal(modalDialog( title = "Upload Dataset", - shinyFilesButton("file", "Select file", title = "Please select a file:", multiple = FALSE), + shinyFilesButton("file", "Select a file", title = "Please select a file:", multiple = FALSE), textOutput("file_path_text"), tags$hr(), h4("Configuration"), - numericInput("cols_input", "Calls:", value = 5, min = 1), - numericInput("freq_input", "Frequency:", value = 10, min = 1), - numericInput("n_epoch_input", "Epochs:", value = 100, min = 1), - numericInput("ws1_input", "Window:", value = 10, min = 1), + numericInput("cols_input", "Cols:", value = 5, min = 1), + numericInput("freq_input", "Freq:", value = 10, min = 1), + numericInput("n_epoch_input", "n_epoch:", value = 100, min = 1), + numericInput("ws1_input", "ws1:", value = 10, min = 1), tags$hr(), footer = tagList( - actionButton("show_graphic", "Show Graphic"), actionButton("load_file", "Load"), modalButton("Cancel") ) @@ -1177,12 +1162,12 @@ shinyServer(function(input, output, session) { observe({ req(input$file) output$file_path_text <- renderText({ - parseFilePaths( roots = volumes, input$file)$datapath + parseFilePaths(roots = c(wd = "~"), input$file)$datapath }) }) observeEvent(input$load_file, { - filepath <- isolate(parseFilePaths( roots = volumes, input$file)$datapath) + filepath <- isolate(parseFilePaths(roots = c(wd = "~"), input$file)$datapath) if (length(filepath) > 0) { showModal(modalDialog( title = "Loading...", @@ -1200,9 +1185,6 @@ shinyServer(function(input, output, session) { updateProgressBar(session, "progress_bar", value = 33, total = 100, status = "info") ejecucion_notebooks() - encs_l <- actualizar_encs_l(encs_l) - embs() - } removeModal() }) @@ -1221,22 +1203,10 @@ shinyServer(function(input, output, session) { print("Se ha inicializado Segundo notebook") system(paste("jupyter nbconvert --execute --to notebook --inplace", ruta_a_segundo_nb)) - result <- get_input_output(2) - input_nb <- result[[1]] - output_nb <- result[[2]] - parameters <- result[[3]] - - _ <- ploomber::execute_notebook( - input_path = input_nb, - output_path = output_nb, - log_output = FALSE, - progress_bar = TRUE, - parameters = parameters, - remove_tagged_cells = c('skip', 'hide') - ) - print("Second notebook: end -->") + print("El segundo notebook ha finalizado.\n") updateProgressBar(session, "progress_bar", value = 100, total = 100, status = "info") - print("------------- END ------------------------") + + print("Ambos notebooks han finalizado completamente.\n") } @@ -1260,9 +1230,9 @@ shinyServer(function(input, output, session) { yaml_content <- gsub("cols: &cols \\[.*\\]", new_cols, yaml_content) yaml_content <- gsub("freq: &freq '.*'", new_freq, yaml_content) - writeLines(yaml_content, "~/work/nbs_pipeline/config/base.yaml") + writeLines(yaml_content, "~/work/nbs_pipeline/base.yaml") - print("The files has been modified", sep = "\n") + print("Se han anadido las modificaciones al fichero base", sep = "\n") print(yaml_content, sep = "\n") } @@ -1280,127 +1250,8 @@ shinyServer(function(input, output, session) { print(yaml_content, sep = "\n") - print("all amendments have been added to the files ") + print("Se han anadido las modificaciones a ficheros de configuracion") } - best_nsizes_sequence_lengths <- function(){ - - print("-----Calculo de ventana-----") - - # Cargar el script de Python - source_python("~/work/nbs_pipeline/mplots.py") - print("------source -------") - # Llamar a la función de Python - print(tsdf()) - df <- subset(tsdf(), select = T1) - print(df) - print("----------------funcion ejecutada---------------") - resultado <- find_dominant_window_sizes_list(df) - print(resultado) - return(resultado) - } - - actualizar_encs_l <- function(encs_l) { - print(encs_l) - api <- wandb$Api() - - print("Querying encoders") - encs_l <- dvats$get_wandb_artifacts(project_path = glue(WANDB_ENTITY, "/", WANDB_PROJECT), - type = "learner", - last_version=F) %>% - discard(~ is_empty(.$aliases) | is_empty(.$metadata$train_artifact)) - encs_l <- encs_l %>% set_names(encs_l %>% map(~ glue(WANDB_ENTITY, "/", WANDB_PROJECT, "/", .$name))) - - # Actualizar encs_l con los nuevos artefactos - print("El nuevo artefacto") - print("------------------------------------") - print(encs_l) - return(encs_l) - } -#------------------------------------------------------------------------------------------------- - - observeEvent(input$show_graphic, { - file_info <- parseFilePaths(volumes, input$file) - if (nrow(file_info) > 0) { - dataset_preview(file_info$datapath) - } - }) - - dataset <- reactive({ - req(dataset_preview()) - data <- read.csv(dataset_preview()) - start_date =isolate(start_date()) - - date_col <- which(str_detect(tolower(colnames(data)), "date|time|fecha")) - - if (length(date_col) == 0) { - # No se encontró ninguna columna de fechas, se agrega una columna de fechas - data$fecha <- seq.POSIXt(from = as.POSIXct("2023-01-01 00:00:00"), by = "min", length.out = nrow(data)) - date_col <- which(colnames(data) == "fecha") - } else { - # Se encontró una columna de fechas, asegurarse de que está en formato POSIXct - for (col in date_col) { - if (!inherits(data[[col]], "POSIXct")) { - # Intentar convertir el contenido a POSIXct - data[[col]] <- tryCatch( - as.POSIXct(data[[col]], format="%Y-%m-%d %H:%M:%S"), - error = function(e) { - # Si falla, usar el origen para conversiones numéricas - as.POSIXct(data[[col]], origin="1970-01-01") - } - ) - } - } - } - - list(data = data, date_col = date_col) - }) - - output$data_preview_plot <- renderDygraph({ - data <- dataset() - print("-----data load------") - print(data) - - data_info <- dataset() - data <- data_info$data - date_col <- data_info$date_col - #print(data) - #print(date_col) - # Transformar los datos en un formato de serie temporal - ts_data <- xts(data[ , -date_col], order.by = data[[date_col]]) - print("-----grafico------") - - # Generar la gráfica con dygraphs - dygraph(ts_data) %>% - dyAxis("x", label = "Fecha") %>% - dyAxis("y", label = "Valores") - }) - - - observeEvent(input$show_graphic,{ - showModal(modalDialog( - title = "Graphic", - #plotOutput("umap_plot"), - dygraphOutput("data_preview_plot"), - #plotOutput("ts_plot_dygraph"), - footer = - tagList( - actionButton("back_to_upload", "Back to Upload"), - modalButton("Close") - ), - size = "l" - - )) - }) - - observeEvent(input$load_dataset, { - showFirstModal() - }) - - observeEvent(input$back_to_upload, { - removeModal() - showFirstModal() - }) - }) From d60a6aeece69aa499a59972a7ecfb67efe42521f Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Thu, 18 Jul 2024 13:12:12 +0000 Subject: [PATCH 14/37] preview plot file data selected --- nbs_pipeline/base.yaml | 96 -------------------- nbs_pipeline/config/base.yaml | 14 +-- r_shiny_app/server.R | 161 +++++++++++++++++++++++++++++++--- 3 files changed, 155 insertions(+), 116 deletions(-) delete mode 100644 nbs_pipeline/base.yaml diff --git a/nbs_pipeline/base.yaml b/nbs_pipeline/base.yaml deleted file mode 100644 index 8233e7fa..00000000 --- a/nbs_pipeline/base.yaml +++ /dev/null @@ -1,96 +0,0 @@ -######################################################### -########### NBS - PIPELINE CONFIGURATION FILE ########### -######################################################### -###### Author: Maria Inmaculada Santamaria-Valenzuela # -###### Date: 08-2023 # -######################################################### - -########################## -# Basic user preferences # -########################## -user_preferences: - #Weights & biases - # Whether to use or not wandb for experiment tracking - use_wandb: &use_wandb true - wdb: - user: &wdb_user angel-gutierrez - project_name: &wdb_project ream - version: &wdb_version 'latest' - # 'online' if the model will be tested on future data, or 'offline' if not - mode: &wdb_mode 'online' - artifacts_path: &artifacts_path './data/wandb_artifacts' - data: - folder: &path '~/data/' - fname: &fname "Semantic_Segmentation_TiltABP" - ftype: &ftype '.csv' - cols: &cols [5] - freq: &freq '10h' - artifact: - # Alias of the artifact resulting of this run. null will create one automatically - alias: &alias 'Monash-Australian_electricity_demand' - algorithm: &alg 'mvp-SWV' - directories: - # Folder to store temporary files - tmp: &tmp 'tmp' - data: &data_path !join [ *path, *fname, *ftype ] - -####################### -# Data specifications # -####################### -data: - name: *fname - # Name of the data file. Must be pickle, csv or tsf file - path: *data_path - # Name of the artifact to be created - alias: *alias - # Put here the idxs of the columns of interest ([] for all) - cols: *cols - # CSV file config ({} if this setting is not required) - csv_config: {} - # Frequency offset used to set the date index (null to omit it) [Example pd.offsets.MonthEnd(0)] - date_offset: null - # Default date format (only for .tsf files) - date_format: '%Y-%m-%d %H:%M:%S' - # Frequency of the data (cannot be null). It can be used to force a sampling freq to data without an index - # See offset aliases in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases - freq: *freq - # To create an artifact linking training and testing data (False if it doesn't) - # If you're using deepvats in "offline" mode, set this to False - joining_train_test: false - missing_values: - # Handle missing values technique (null for no handling) - technique: null - # Default value used for missing values (only if missing_values_technique is not null) - constant: null - # To normalize the data - normalize_training: false - # Training and Testing ranges. They can be lists with values to be included - # (of the same type as the index) or dictionaries that include the keys 'start', 'end' and 'freq'. - range_training: null - range_testing: null - # Resampling frequency (null for no resampling) - resampling_freq: null - # Starting date (in format yyyy-mm-dd) (set to null for default start date) - start_date: null - # Ratio of test set, only if range_training and range_testing are null - # Set to null for no test set (offline mode, all training) - test_split: null - # List or int with the idx(s) of the column containing the timestamp - # (null if there is no timestamp column) - time_col: null - -######################## -# Wandb specifications # -######################## -wandb: - user: *wdb_user - dir: !join [ '~/', *wdb_project] # TODO: Make relative to this file? (null for using the OS default) - enabled: False # To use it, the environment variable WANDB_API_KEY must be set - group: null # Useful to group runs that belong to the same optuna study - log_learner: False # Log learner to wandb - mode: *wdb_mode - project: *wdb_project - version: *wdb_version - # Output path where the resulting TSArtifact will be stored - artifacts_path: *artifacts_path - diff --git a/nbs_pipeline/config/base.yaml b/nbs_pipeline/config/base.yaml index 2ab0686d..b1758bc0 100644 --- a/nbs_pipeline/config/base.yaml +++ b/nbs_pipeline/config/base.yaml @@ -13,18 +13,18 @@ user_preferences: # Whether to use or not wandb for experiment tracking use_wandb: &use_wandb true wdb: - user: &wdb_user mi-santamaria - project_name: &wdb_project deepvats + user: &wdb_user angel-gutierrez + project_name: &wdb_project ream version: &wdb_version 'latest' # 'online' if the model will be tested on future data, or 'offline' if not mode: &wdb_mode 'online' artifacts_path: &artifacts_path './data/wandb_artifacts' data: folder: &path '~/data/' - fname: &fname "australian_electricity_demand_dataset" - ftype: &ftype '.tsf' - cols: &cols [0] - freq: &freq '1h' + fname: &fname "Semantic_Segmentation_TiltABP" + ftype: &ftype '.csv' + cols: &cols [1] + freq: &freq '10h' artifact: # Alias of the artifact resulting of this run. null will create one automatically alias: &alias 'Monash-Australian_electricity_demand' @@ -93,4 +93,4 @@ wandb: version: *wdb_version # Output path where the resulting TSArtifact will be stored artifacts_path: *artifacts_path - \ No newline at end of file + diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index de12632c..97d3c3dd 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -1138,26 +1138,32 @@ shinyServer(function(input, output, session) { } }) - shinyFileChoose(input, "file", roots = c(wd = "~")) + dataset_path <- reactiveVal(NULL) + dataset_preview <- reactiveVal(NULL) - observeEvent(input$load_dataset, { - showModal(modalDialog( + volumes <- c(wd = "~") + shinyFileChoose(input, "file", roots = volumes) + + createUploadModal <- function() { + modalDialog( title = "Upload Dataset", - shinyFilesButton("file", "Select a file", title = "Please select a file:", multiple = FALSE), + shinyFilesButton("file", "Select file", title = "Please select a file:", multiple = FALSE), textOutput("file_path_text"), tags$hr(), h4("Configuration"), - numericInput("cols_input", "Cols:", value = 5, min = 1), - numericInput("freq_input", "Freq:", value = 10, min = 1), - numericInput("n_epoch_input", "n_epoch:", value = 100, min = 1), - numericInput("ws1_input", "ws1:", value = 10, min = 1), + numericInput("cols_input", "Calls:", value = 5, min = 1), + numericInput("freq_input", "Frequency:", value = 10, min = 1), + numericInput("n_epoch_input", "Epochs:", value = 100, min = 1), + numericInput("ws1_input", "Window:", value = 10, min = 1), tags$hr(), footer = tagList( + actionButton("show_graphic", "Show Graphic"), actionButton("load_file", "Load"), modalButton("Cancel") ) - )) - }) + ) + } + observe({ req(input$file) @@ -1185,23 +1191,31 @@ shinyServer(function(input, output, session) { updateProgressBar(session, "progress_bar", value = 33, total = 100, status = "info") ejecucion_notebooks() + encs_l <- actualizar_encs_l(encs_l) + embs() + } removeModal() }) + ejecucion_notebooks <- function() { print("-------------Inicio------------------------") ruta_a_primer_nb <- "~/work/nbs_pipeline/01_dataset_artifact.ipynb" print("Se ha inicializado Primer notebook") - system(paste("jupyter nbconvert --execute --to notebook --inplace", ruta_a_primer_nb)) + system(paste("jupyter nbconvert --execute --to notebook --inplace", + "--TagRemovePreprocessor.enabled=True --TagRemovePreprocessor.remove_cell_tags=hide", + ruta_a_primer_nb)) print("El primer notebook ha finalizado") updateProgressBar(session, "progress_bar", value = 66, total = 100, status = "info") ruta_a_segundo_nb <- "~/work/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb" print("Se ha inicializado Segundo notebook") - system(paste("jupyter nbconvert --execute --to notebook --inplace", ruta_a_segundo_nb)) + system(paste("jupyter nbconvert --execute --to notebook --inplace", + "--TagRemovePreprocessor.enabled=True --TagRemovePreprocessor.remove_cell_tags=hide", + ruta_a_segundo_nb)) print("El segundo notebook ha finalizado.\n") updateProgressBar(session, "progress_bar", value = 100, total = 100, status = "info") @@ -1230,7 +1244,7 @@ shinyServer(function(input, output, session) { yaml_content <- gsub("cols: &cols \\[.*\\]", new_cols, yaml_content) yaml_content <- gsub("freq: &freq '.*'", new_freq, yaml_content) - writeLines(yaml_content, "~/work/nbs_pipeline/base.yaml") + writeLines(yaml_content, "~/work/nbs_pipeline/config/base.yaml") print("Se han anadido las modificaciones al fichero base", sep = "\n") print(yaml_content, sep = "\n") @@ -1253,5 +1267,126 @@ shinyServer(function(input, output, session) { print("Se han anadido las modificaciones a ficheros de configuracion") } + #------------------------------------------------------------------------------------------------- + + best_nsizes_sequence_lengths <- function(){ + + print("-----Calculo de ventana-----") + + # Cargar el script de Python + source_python("~/work/nbs_pipeline/mplots.py") + print("------source -------") + # Llamar a la función de Python + print(tsdf()) + df <- subset(tsdf(), select = T1) + print(df) + print("----------------funcion ejecutada---------------") + resultado <- find_dominant_window_sizes_list(df) + print(resultado) + return(resultado) + } + + actualizar_encs_l <- function(encs_l) { + print(encs_l) + api <- wandb$Api() + + print("Querying encoders") + encs_l <- dvats$get_wandb_artifacts(project_path = glue(WANDB_ENTITY, "/", WANDB_PROJECT), + type = "learner", + last_version=F) %>% + discard(~ is_empty(.$aliases) | is_empty(.$metadata$train_artifact)) + encs_l <- encs_l %>% set_names(encs_l %>% map(~ glue(WANDB_ENTITY, "/", WANDB_PROJECT, "/", .$name))) + + # Actualizar encs_l con los nuevos artefactos + print("El nuevo artefacto") + print("------------------------------------") + print(encs_l) + return(encs_l) + } + #------------------------------------------------------------------------------------------------- + + observeEvent(input$show_graphic, { + file_info <- parseFilePaths(volumes, input$file) + if (nrow(file_info) > 0) { + dataset_preview(file_info$datapath) + } + }) + + dataset <- reactive({ + req(dataset_preview()) + data <- read.csv(dataset_preview()) + start_date =isolate(start_date()) + + date_col <- which(str_detect(tolower(colnames(data)), "date|time|fecha")) + + if (length(date_col) == 0) { + # No se encontró ninguna columna de fechas, se agrega una columna de fechas + data$fecha <- seq.POSIXt(from = as.POSIXct("2023-01-01 00:00:00"), by = "min", length.out = nrow(data)) + date_col <- which(colnames(data) == "fecha") + } else { + # Se encontró una columna de fechas, asegurarse de que está en formato POSIXct + for (col in date_col) { + if (!inherits(data[[col]], "POSIXct")) { + # Intentar convertir el contenido a POSIXct + data[[col]] <- tryCatch( + as.POSIXct(data[[col]], format="%Y-%m-%d %H:%M:%S"), + error = function(e) { + # Si falla, usar el origen para conversiones numéricas + as.POSIXct(data[[col]], origin="1970-01-01") + } + ) + } + } + } + + list(data = data, date_col = date_col) + }) + + output$data_preview_plot <- renderDygraph({ + data <- dataset() + print("-----data load------") + print(data) + + data_info <- dataset() + data <- data_info$data + date_col <- data_info$date_col + #print(data) + #print(date_col) + # Transformar los datos en un formato de serie temporal + ts_data <- xts(data[ , -date_col], order.by = data[[date_col]]) + print("-----grafico------") + + # Generar la gráfica con dygraphs + dygraph(ts_data) %>% + dyAxis("x", label = "Fecha") %>% + dyAxis("y", label = "Valores") + }) + + + observeEvent(input$show_graphic,{ + showModal(modalDialog( + title = "Graphic", + #plotOutput("umap_plot"), + dygraphOutput("data_preview_plot"), + #plotOutput("ts_plot_dygraph"), + footer = + tagList( + actionButton("back_to_upload", "Back to Upload"), + modalButton("Close") + ), + size = "l" + + )) + }) + + observeEvent(input$load_dataset, { + showModal(createUploadModal()) + }) + + observeEvent(input$back_to_upload, { + removeModal() + showModal(createUploadModal()) + }) + }) From c5f256d4d65a101a930a85c38e7c3021ff107c13 Mon Sep 17 00:00:00 2001 From: angelgutierrezgonzalez Date: Thu, 18 Jul 2024 13:14:57 +0000 Subject: [PATCH 15/37] ploomber import added --- r_shiny_app/global.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/r_shiny_app/global.R b/r_shiny_app/global.R index ca6a6ede..d83eb3f9 100644 --- a/r_shiny_app/global.R +++ b/r_shiny_app/global.R @@ -61,7 +61,7 @@ pd = reticulate::import("pandas") hdbscan = reticulate::import("hdbscan") dvats = reticulate::import_from_path("dvats.all", path=paste0(Sys.getenv("HOME"))) - +ploomber = reticulate::import("ploomber_engine") print("--> py_config ") print(reticulate::py_config()) From 50cb86b2aad287fb28f5b61583ee803d54336cb5 Mon Sep 17 00:00:00 2001 From: "mi.santamaria" Date: Tue, 23 Jul 2024 14:50:56 +0200 Subject: [PATCH 16/37] =?UTF-8?q?A=C3=B1adido=20utf-8=20en=20get=5Fartifac?= =?UTF-8?q?t=5Fconfig=5Fsd2a?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- dvats/config.py | 2 +- nbs/config.ipynb | 14 +- nbs/load.ipynb | 1306 ++++++++- nbs_pipeline/01_dataset_artifact.ipynb | 501 +++- .../02c_encoder_MVP-sliding_window_view.ipynb | 496 ++-- nbs_pipeline/_ploomber_engine_example_.ipynb | 2534 +---------------- .../02c-encoder_mvp-sliding_window_view.yaml | 4 +- nbs_pipeline/config/base.yaml | 8 +- .../output/01_dataset_artifact-output.ipynb | 264 +- .../output/02b_encoder_MVP-output.ipynb | 1985 ++++++++++--- r_shiny_app/server.R | 113 +- 11 files changed, 3903 insertions(+), 3324 deletions(-) diff --git a/dvats/config.py b/dvats/config.py index 02f73f4f..258fc7f6 100644 --- a/dvats/config.py +++ b/dvats/config.py @@ -69,7 +69,7 @@ def replace_includes_with_content( """ if (print_flag): print("... About to replace includes with content") - with open(path+filename, 'r') as f: + with open(path+filename, 'r', encoding = 'utf-8') as f: content = f.read() # Mientras exista una directiva !include en el contenido, sigue reemplazándola diff --git a/nbs/config.ipynb b/nbs/config.ipynb index 594ed360..85264a94 100644 --- a/nbs/config.ipynb +++ b/nbs/config.ipynb @@ -197,7 +197,7 @@ " \"\"\"\n", " if (print_flag):\n", " print(\"... About to replace includes with content\")\n", - " with open(path+filename, 'r') as f:\n", + " with open(path+filename, 'r', encoding = 'utf-8') as f:\n", " content = f.read()\n", " \n", " # Mientras exista una directiva !include en el contenido, sigue reemplazándola\n", @@ -2831,6 +2831,18 @@ "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } }, "nbformat": 4, diff --git a/nbs/load.ipynb b/nbs/load.ipynb index 70927539..4b634ab7 100644 --- a/nbs/load.ipynb +++ b/nbs/load.ipynb @@ -1,19 +1,43 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hide_input": true + }, + "outputs": [], + "source": [ + "from nbdev import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#default_exp load" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| default_exp load" + "#hide\n", + "import sys\n", + "sys.path.append(\"..\")\n", + "%load_ext autoreload\n", + "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Load\n", + "# Load data from the longwall\n", "\n", "> Methods for loading data" ] @@ -24,16 +48,519 @@ "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#hide\n", + "from nbdev.showdoc import *\n", + "from fastcore import test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", "import pandas as pd\n", "import numpy as np\n", "from fastcore.all import *\n", "import wandb\n", "from datetime import datetime, timedelta\n", - "from dvats.imports import *\n", - "from dvats.utils import *\n", - "import pickle\n", - "import pyarrow.feather as ft" + "from timecluster_extension.utils import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read data from HMB longwall\n", + "\n", + "We will take data from one day of the shearer. the data is hosted at https://aida.ii.uam.es/2018-01-15.csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# !wget -O /data/input_data.csv https://aida.ii.uam.es/2018-01-06.csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('/home/user/data/PACMEL-2019/2018-01-06.csv', sep=';', skiprows=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " description Status kombajnu Łącznik sterowania \\\n", + "0 2018-01-06 00:00:00 2091 2.0 \n", + "1 2018-01-06 00:00:01 2091 2.0 \n", + "2 2018-01-06 00:00:02 2091 2.0 \n", + "3 2018-01-06 00:00:03 2091 2.0 \n", + "4 2018-01-06 00:00:04 2091 2.0 \n", + "\n", + " Stan ramienia lewego Stan ramienia prawego Stan ciągnika lewego \\\n", + "0 7.0 7.0 7.0 \n", + "1 7.0 7.0 7.0 \n", + "2 7.0 7.0 7.0 \n", + "3 7.0 7.0 7.0 \n", + "4 7.0 7.0 7.0 \n", + "\n", + " Stan ciągnika prawego Stan pompy lewej Stan pompy prawej Stan kruszarki \\\n", + "0 7.0 7.0 7.0 7.0 \n", + "1 7.0 7.0 7.0 7.0 \n", + "2 7.0 7.0 7.0 7.0 \n", + "3 7.0 7.0 7.0 7.0 \n", + "4 7.0 7.0 7.0 7.0 \n", + "\n", + " ... Prąd fazy 3.15 Zabezpieczenie.15 Przeciążenie.15 Zwarcie.21 \\\n", + "0 ... 3.0 False False False \n", + "1 ... 3.0 False False False \n", + "2 ... 3.0 False False False \n", + "3 ... 3.0 False False False \n", + "4 ... 3.0 False False False \n", + "\n", + " Asymetria.21 Ciągłość żyły.15 Doziemienie.15 Temperatura.15 Unnamed: 373 \\\n", + "0 False False False False NaN \n", + "1 False False False False NaN \n", + "2 False False False False NaN \n", + "3 False False False False NaN \n", + "4 False False False False NaN \n", + "\n", + " Unnamed: 374 \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + "[5 rows x 375 columns]" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 86400 entries, 0 to 86399\n", + "Columns: 375 entries, description to Unnamed: 374\n", + "dtypes: float64(181), int64(10), object(184)\n", + "memory usage: 247.2+ MB\n" + ] + } + ], + "source": [ + "data.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The timestamp is given in the column `description`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data['timestamp'] = pd.to_datetime(data['description'])\n", + "data = data.drop('description', axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df1 = data.select_dtypes(exclude='object')\n", + "df2 = data.select_dtypes(include='object').astype('bool')\n", + "data = pd.concat([df2.reset_index(drop = True), df1], axis = 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the dimensionality reduction we might be interested only in the numeric columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_numeric = data.select_dtypes(include=['float', 'datetime'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As detailed in the TimeCluster paper, the data will be normalized into the range $[0, 1]$. Also, NaN columsn will be removed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tmp = data_numeric.select_dtypes(include='float')\n", + "#data_numeric[data_numeric.select_dtypes(include='float')] = (tmp - tmp.min())/(tmp.max()-tmp.min())\n", + "data_numeric[data_numeric.select_dtypes(include='float').columns] = (tmp - tmp.min())/(tmp.max()-tmp.min())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_numeric = data_numeric.dropna(axis=1, how='all').fillna(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we define a function that gathers all this operations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "def fpreprocess_numeric_vars(data, cname_ts=None, normalize=True, nan_replacement=0):\n", + " \"Preprocess a dataframe `data` containing the monitoring data from a mining longwall. \\\n", + " Non-numeric variables will be removed. Each column \\\n", + " is expected to have values of a variable in form of a time series, whose index will be described in the \\\n", + " column named `cname_ts`. If `cname_ts` is None (default), the index of the dataframe is assumed to contain the \\\n", + " timestamps. .NaN values will be \\\n", + " replaced by a constant value `nan_replacement`\"\n", + " if cname_ts is not None:\n", + " data.index = pd.to_datetime(data[cname_ts])\n", + " data = data.drop(cname_ts, axis=1)\n", + " df1 = data.select_dtypes(exclude='object')\n", + " df2 = data.select_dtypes(include='object').astype('bool')\n", + " data = pd.concat([df2, df1], axis = 1)\n", + " data_numeric = data.select_dtypes(include=['float', 'datetime'])\n", + " tmp = data_numeric.select_dtypes(include='float')\n", + " if normalize: data_numeric[data_numeric.select_dtypes(include='float').columns] = (tmp - tmp.min())/(tmp.max()-tmp.min())\n", + " data_numeric = data_numeric.dropna(axis=1, how='all').fillna(nan_replacement)\n", + " return data_numeric" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "path = Path(\"/data/PACMEL-2019/JNK/jnk_before_handling_missing.pickle\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = None\n", + "with open(path, 'rb') as f:\n", + " bin_data = f.read()\n", + " df = pickle.loads(bin_data)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "preprocessed_df = fpreprocess_numeric_vars(data=df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.SM_ShearerLocation.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "preprocessed_df.SM_ShearerLocation.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read multiple monitoring files, given as daily CSVs\n", + "\n", + "Since the mining monitoring data is given a set of CSV files, one per day, it is usefl to have a function to load multiple files in order to analyse data from multiple days" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "def fread_and_concat(paths, **read_args):\n", + " \"Read, from `paths`, a list of mining dataframes and concat them. All dataframes \\\n", + " must have the same columns. \"\n", + " return pd.concat([pd.read_csv(x, **read_args) for x in paths],\n", + " ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "paths = ['/data/PACMEL-2019/343_HMB/2018-01-14.csv', '/data/PACMEL-2019/343_HMB/2018-01-15.csv']\n", + "df1 = pd.read_csv(paths[0], sep=';', skiprows=2, nrows=3)\n", + "df2 = pd.read_csv(paths[1], sep=';', skiprows=2, nrows=3)\n", + "df = fread_and_concat(paths, sep=';', skiprows=2, nrows=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test.equals(df1.shape[0] + df2.shape[0], df.shape[0])\n", + "test.all_equal([df1.shape[1], df2.shape[1], df.shape[1]], np.repeat(df1.shape[1], 3))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "def fread_mining_monitoring_files(paths, **kwargs):\n", + " \"Read monitoring files from the PACMEL mining use case.\"\n", + " df = fread_and_concat(paths,\n", + " sep=';',\n", + " low_memory=False,\n", + " skiprows=2,\n", + " **kwargs)\n", + " # Convert the timestamp column into a proper datetime object\n", + " df['description'] = pd.to_datetime(df['description'])\n", + " return df" ] }, { @@ -42,8 +569,8 @@ "metadata": {}, "outputs": [], "source": [ - "#| hide\n", - "from tsai.imports import beep" + "paths = ['/data/PACMEL-2019/343_HMB/2018-01-14.csv', '/data/PACMEL-2019/343_HMB/2018-01-15.csv']\n", + "df = fread_mining_monitoring_files(paths, nrows=3)" ] }, { @@ -52,8 +579,7 @@ "metadata": {}, "outputs": [], "source": [ - "base_path = Path.home()\n", - "base_path" + "isinstance(df, pd.core.frame.DataFrame)" ] }, { @@ -76,22 +602,20 @@ "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#export\n", "class TSArtifact(wandb.Artifact):\n", - "\n", - " default_storage_path = Path(Path.home()/'data/wandb_artifacts/')\n", + " \n", + " default_storage_path = Path('/home/user/data/PACMEL-2019/wandb_artifacts/')\n", " date_format = '%Y-%m-%d %H:%M:%S' # TODO add milliseconds\n", " handle_missing_values_techniques = {\n", " 'linear_interpolation': lambda df : df.interpolate(method='linear', limit_direction='both'),\n", " 'overall_mean': lambda df : df.fillna(df.mean()),\n", - " 'overall_median': lambda df : df.fillna(df.median()),\n", - " 'backward_fill' : lambda df : df.fillna(method='bfill'),\n", - " 'forward_fill' : lambda df : df.fillna(method='ffill')\n", + " 'overall_median': lambda df : df.fillna(df.median())\n", " }\n", "\n", " \"Class that represents a wandb artifact containing time series data. sd stands for start_date \\\n", " and ed for end_date. Both should be pd.Timestamps\"\n", - "\n", + " \n", " @delegates(wandb.Artifact.__init__)\n", " def __init__(self, name, sd:pd.Timestamp, ed:pd.Timestamp, **kwargs):\n", " super().__init__(type='dataset', name=name, **kwargs)\n", @@ -102,23 +626,23 @@ " self.metadata['TS'] = dict(sd = self.sd.strftime(self.date_format),\n", " ed = self.ed.strftime(self.date_format))\n", "\n", - "\n", + " \n", " @classmethod\n", " def from_daily_csv_files(cls, root_path, fread=pd.read_csv, start_date=None, end_date=None, metadata=None, **kwargs):\n", - "\n", + " \n", " \"Create a wandb artifact of type `dataset`, containing the CSV files from `start_date` \\\n", " to `end_date`. Dates must be pased as `datetime.datetime` objects. If a `wandb_run` is \\\n", " defined, the created artifact will be logged to that run, using the longwall name as \\\n", " artifact name, and the date range as version.\"\n", - "\n", + " \n", " return None\n", "\n", - "\n", + " \n", " @classmethod\n", " @delegates(__init__)\n", " def from_df(cls, df:pd.DataFrame, name:str, path:str=None, sd:pd.Timestamp=None, ed:pd.Timestamp=None,\n", " normalize:bool=False, missing_values_technique:str=None, resampling_freq:str=None, **kwargs):\n", - "\n", + " \n", " \"\"\"\n", " Create a TSArtifact of type `dataset`, using the DataFrame `df` samples from \\\n", " `sd` (start date) to `ed` (end date). Dates must be passed as `datetime.datetime` \\\n", @@ -127,7 +651,7 @@ " be normalized (see `normalize` argument) or transformed using missing values \\\n", " handling techniques (see `missing_values_technique` argument) or resampling (see \\\n", " `resampling_freq` argument).\n", - "\n", + " \n", " Arguments:\n", " df: (DataFrame) The dataframe you want to convert into an artifact.\n", " name: (str) The artifact name.\n", @@ -142,45 +666,44 @@ " None. Default None.\n", " resampling_freq: (str, optional) The offset string or object representing \\\n", " frequency conversion for time series resampling. Default None.\n", - "\n", - " Returns:\n", + " \n", + " Returns: \n", " TSArtifact object.\n", " \"\"\"\n", + " \n", " sd = df.index[0] if sd is None else sd\n", " ed = df.index[-1] if ed is None else ed\n", " obj = cls(name, sd=sd, ed=ed, **kwargs)\n", " df = df.query('@obj.sd <= index <= @obj.ed')\n", " obj.metadata['TS']['created'] = 'from-df'\n", " obj.metadata['TS']['n_vars'] = df.columns.__len__()\n", - "\n", + " \n", " # Handle Missing Values\n", " df = obj.handle_missing_values_techniques[missing_values_technique](df) if missing_values_technique is not None else df\n", " obj.metadata['TS']['handle_missing_values_technique'] = missing_values_technique.__str__()\n", " obj.metadata['TS']['has_missing_values'] = np.any(df.isna().values).__str__()\n", - "\n", - " # Indexing and Resampling\n", - " if resampling_freq: df = df.resample(resampling_freq).mean()\n", + " \n", + " # Resample\n", + " df = df.resample(resampling_freq).mean()\n", " obj.metadata['TS']['n_samples'] = len(df)\n", " obj.metadata['TS']['freq'] = str(df.index.freq)\n", - "\n", + " \n", " # Time Series Variables\n", " obj.metadata['TS']['vars'] = list(df.columns)\n", - "\n", + " \n", " # Normalization - Save the previous means and stds\n", " if normalize:\n", " obj.metadata['TS']['normalization'] = dict(means = df.describe().loc['mean'].to_dict(),\n", " stds = df.describe().loc['std'].to_dict())\n", " df = normalize_columns(df)\n", - "\n", + " \n", " # Hash and save\n", - " hash_code = str(pd.util.hash_pandas_object(df).sum()) # str(hash(df.values.tobytes()))\n", + " hash_code = str(hash(df.values.tobytes()))\n", " path = obj.default_storage_path/f'{hash_code}' if path is None else Path(path)/f'{hash_code}'\n", - " print(\"About to write df to \", path)\n", - " ft.write_feather(df, path, compression = 'lz4')\n", - " #feather.write_dataframe\n", + " df.to_pickle(path)\n", " obj.metadata['TS']['hash'] = hash_code\n", " obj.add_file(str(path))\n", - "\n", + " \n", " return obj" ] }, @@ -188,7 +711,35 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'TS': {'sd': '2021-01-01 00:00:00',\n", + " 'ed': '2021-01-01 00:00:29',\n", + " 'created': 'from-df',\n", + " 'n_vars': 4,\n", + " 'handle_missing_values_technique': 'overall_median',\n", + " 'has_missing_values': 'False',\n", + " 'n_samples': 6,\n", + " 'freq': '<5 * Seconds>',\n", + " 'vars': ['A', 'B', 'C', 'D'],\n", + " 'normalization': {'means': {'A': 0.3837807973595773,\n", + " 'B': -0.1702837787510083,\n", + " 'C': 0.0003015766713802802,\n", + " 'D': -0.18042534224509024},\n", + " 'stds': {'A': 0.28755677338495317,\n", + " 'B': 0.29094157184208197,\n", + " 'C': 0.5287787048279057,\n", + " 'D': 0.5244784339968553}},\n", + " 'hash': '7582266588543823729'}}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# TSArtifact class TEST\n", "\n", @@ -216,37 +767,6 @@ " " ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hash = artifact.metadata['TS']['hash']\n", - "path = \"../../data/wandb_artifacts/\"+hash\n", - "print(path)\n", - "f = ft.read_feather(path)\n", - "print(f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = ft.read_feather(\"/home/macu/data/wandb_artifacts/-2535364569820284064\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(df)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -260,7 +780,7 @@ "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#export\n", "@patch\n", "def to_df(self:wandb.apis.public.Artifact):\n", " \"Download the files of a saved wandb artifact and process them as a single dataframe. The artifact must \\\n", @@ -272,8 +792,7 @@ " dir = Path(self.download())\n", " if self.metadata['TS']['created'] == 'from-df':\n", " # Call read_pickle with the single file from dir\n", - " #return pd.read_pickle(dir.ls()[0])\n", - " return ft.read_feather(dir.ls()[0])\n", + " return pd.read_pickle(dir.ls()[0])\n", " else:\n", " print(\"ERROR: Only from_df method is allowed yet\")" ] @@ -291,7 +810,7 @@ "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#export\n", "@patch\n", "def to_tsartifact(self:wandb.apis.public.Artifact):\n", " \"Cast an artifact as a TS artifact. The artifact must have been created from one of the \\\n", @@ -305,10 +824,105 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Inject or infer frequencies in a dataframe" + "#export\n", + "def create_longwall_data_artifact(root_path, start_date, end_date, longwall_name='Unnamed_longwall', wandb_run=None):\n", + " \"Create a wandb artifact of type `dataset`, containing the CSV files from `start_date` \\\n", + " to `end_date`. Dates must be pased as `datetime.datetime` objects. If a `wandb_run` is \\\n", + " defined, the created artifact will be logged to that run, using the longwall name as \\\n", + " artifact name, and the date range as version.\"\n", + " # Compute the number of variables for the metadata (total and numeric)\n", + " root_path = Path(root_path)\n", + " date_diff = end_date - start_date\n", + " sd_str = start_date.strftime(\"%Y-%m-%d\")\n", + " ed_str = end_date.strftime(\"%Y-%m-%d\")\n", + " mock_data = fread_mining_monitoring_files([f'{root_path/start_date.strftime(\"%Y-%m-%d\")}.csv'],\n", + " nrows=1)\n", + " artifact_name = longwall_name if longwall_name else root_path\n", + " artifact = wandb.Artifact(type='dataset',\n", + " name=artifact_name,\n", + " description='Dataset from the PACMEL mining use case. It contains \\\n", + " monitoring data from a longwall shearer',\n", + " metadata={\n", + " 'longwall': longwall_name,\n", + " 'start_time': datetime.strftime(start_date, format='%Y-%m-%d %H:%M:%S'),\n", + " 'end_time': datetime.strftime(end_date, format='%Y-%m-%d %H:%M:%S'),\n", + " 'n_variables': len(mock_data.columns)-1 # Exclude timestamp\n", + " })\n", + " # ADd files as references (we do not upload files for confidential reasons)\n", + " [artifact.add_reference(f'file://{root_path/x.strftime(\"%Y-%m-%d\")}.csv')\n", + " for x in (start_date + timedelta(days=n) for n in range(date_diff.days + 1))]\n", + "\n", + " if wandb_run:\n", + " artifact_version = f'{sd_str}_{ed_str}'\n", + " wandb_run.log_artifact(artifact,\n", + " aliases=['latest', artifact_version])\n", + " return artifact" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " Logging results to Weights & Biases (Documentation).
\n", + " Project page: https://app.wandb.ai/vrodriguezf/timecluster-extension
\n", + " Run page: https://app.wandb.ai/vrodriguezf/timecluster-extension/runs/1qaeu0v9
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run = wandb.init(job_type='create_dataset', resume=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'create_longwall_data_artifact' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"2018-01-01\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"%Y-%m-%d\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0med\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msd\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtimedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m ar = create_longwall_data_artifact(root_path='/data/PACMEL-2019/343_HMB', \n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mstart_date\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msd\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mend_date\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0med\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'create_longwall_data_artifact' is not defined" + ] + } + ], + "source": [ + "sd = datetime.strptime(\"2018-01-01\", \"%Y-%m-%d\")\n", + "ed = sd + timedelta(hours=3)\n", + "ar = create_longwall_data_artifact(root_path='/data/PACMEL-2019/343_HMB', \n", + " start_date=sd, \n", + " end_date=ed,\n", + " longwall_name='HMB', \n", + " wandb_run=None)\n", + "ar.metadata, ar.manifest.entries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try to use the logged artifact" ] }, { @@ -317,22 +931,469 @@ "metadata": {}, "outputs": [], "source": [ - "#| export\n", - "@delegates(pd.to_datetime)\n", - "def infer_or_inject_freq(df, injected_freq='1s', start_date=None, **kwargs):\n", - " \"\"\"\n", - " Infer index frequency. If there's not a proper time index, create fake timestamps,\n", - " keeping the desired `injected_freq`. If that is None, set a default one of 1 second.\n", - " start_date: the first date of the index (int or string).\n", - " \"\"\"\n", - " inferred_freq = pd.infer_freq(df.index)\n", - " if inferred_freq == 'N':\n", - " timedelta = pd.to_timedelta(injected_freq)\n", - " df.index = pd.to_datetime(ifnone(start_date, 0), **kwargs) + timedelta*df.index\n", - " df.index.freq = pd.infer_freq(df.index)\n", - " else:\n", - " df.index.freq = inferred_freq\n", - " return df" + "ar_recovered = run.use_artifact(name='HMB:2018-01-01_2018-01-04', type='dataset')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dir = Path(ar_recovered.download())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "retrieved_data = fread_mining_monitoring_files(dir.ls(), nrows=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "retrieved_data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load longwall data artifact\n", + "\n", + "This function is quite handy to turn the contents of a longwall artifact, created with the function `create_longwall_data_artifact`. This is specially useful in the case where the monitoring files are given in a daily basis, but you are only interested in analysing a couple of hours of data. In that case, the artifact will link the whole day file, but using the metadata, this function will only read the corresponding " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "def load_longwall_data_artifact(a:wandb.Artifact):\n", + " \"Returns a dataframe with the longwall data, subsetted by the artifact metadata\"\n", + " a_refs = [x.ref for x in a.manifest.entries.values()]\n", + " data = fread_mining_monitoring_files(a_refs)\n", + " sd = datetime.strptime(a.metadata['start_time'], '%Y-%m-%d %H:%M:%S')\n", + " ed = datetime.strptime(a.metadata['end_time'], '%Y-%m-%d %H:%M:%S')\n", + " data = data.query('description >= @sd and description <= @ed')\n", + " return data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO: Tiene que haber un error si el start date pasado es menor que el inicio del primer fichero, y lo mismo con el end date final" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "paths = ['/data/PACMEL-2019/343_HMB/2018-01-01.csv']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = fread_mining_monitoring_files(paths, nrows=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "start_date = df['description'][0]\n", + "end_date = start_date + timedelta(minutes=15)\n", + "start_date, end_date" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = create_longwall_data_artifact(root_path='/data/PACMEL-2019/343_HMB/', \n", + " start_date=start_date, \n", + " end_date=start_date + timedelta(minutes=15), \n", + " wandb_run=None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_subset = load_longwall_data_artifact(a)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test.equals(df.columns, df_subset.columns) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test.equals(df_subset['description'][0], start_date)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test.equals(df_subset['description'][len(df_subset['description']) -1], end_date)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## JNK data\n", + "\n", + "The data from the JNK longwall comes in two formats:\n", + "1. Queryable database\n", + "2. Preprocessed pickle files\n", + "3. Preprocessed CSV files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### JNK pickle files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "base_path_JNK = Path('/data/PACMEL-2019/JNK/') # *\n", + "[path for path in base_path_JNK.ls()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's read the pickle files and show the contents of each of them" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#hide\n", + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#hide\n", + "jnk_files = []\n", + "filepaths_pickle = base_path_JNK.ls(file_exts='.pickle')\n", + "for i, path in enumerate(filepaths_pickle):\n", + " f = open(path, 'rb') \n", + " bin_data = f.read()\n", + " print(f'Loading file {i}...')\n", + " jnk_files.append(pickle.loads(bin_data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first file is a dataframe with information about the *boolean variables*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(filepaths_pickle[0].name)\n", + "df_bool_dict = jnk_files[0]\n", + "df_bool_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second file is a dataframe with information about the *categorical variables*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(filepaths_pickle[1].name)\n", + "df_categorical_dict = jnk_files[1]\n", + "df_categorical_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The third file is a dataframe with the raw data of one month of the longwall (June 2019). It contains missing values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(filepaths_pickle[2].name)\n", + "df_jnk_base = jnk_files[2]\n", + "df_jnk_base" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This dataframe contains all the 95 bool variables described in the first pickle file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(df_jnk_base.columns) - len(set(df_jnk_base.columns) - set(df_bool_dict.bool_variables))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, not all the variables listed in the dataframe for the categorical variables are present in the base data. (TODO: as why!). We can check which categorical variables are in the base data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_jnk_base.filter(items=df_categorical_dict.categorical_variables).columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The 4th file is called `jnk_before_handling_missing`, and it is a list f 4 elements:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(filepaths_pickle[3].name)\n", + "jnk_before_handling_missing = jnk_files[3]\n", + "len(jnk_before_handling_missing)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first element is a dataframe with a subset of 16 columns from the base data (the number of rows is the same)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jnk_before_handling_missing[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second and third elements of the list mark which of the variables of this dataframe are boolean, numerical, and categorical respectively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(jnk_before_handling_missing[1])\n", + "set(jnk_before_handling_missing[1]).issubset(df_bool_dict.bool_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(jnk_before_handling_missing[2])\n", + "set(jnk_before_handling_missing[2]).issubset(df_bool_dict.bool_variables), \\\n", + "set(jnk_before_handling_missing[2]).issubset(df_categorical_dict.categorical_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jnk_before_handling_missing[3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This subset of data may represent the most interesting variables of analysis from an expert perspective (TODO: confirm)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The 5th and 6th pickle files look like an output instead of an input. More specifically, both of them are lists with two items:\n", + "1. A 11 $\\times$ 11 array, containing the description of 11 clusters.\n", + "2. An array of 1363601 elements with 11 different values (from 0 to 10), containing the assignation of each data point to each of the 11 clusters described in the first element of the file.\n", + "\n", + "The input dataset to achieve this result must be a preprocessed input, due to the size of the assignment array is much lower than the size of the base dataframe. Also,the number of columns of this input dataset should be 11, in case the first element of the list represents a multidimensional description of a cluster." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filepaths_pickle[4].name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jnk_files[4][0][0].shape, jnk_files[4][0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(jnk_files[4][1][0]), set(jnk_files[4][1][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(filepaths_pickle[6].name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The 7th file, `jnk_filled_removelong_imputeinterpolate_nothing.pickle`, is a list with two elements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(filepaths_pickle[6].name)\n", + "jnk_filled_removelong_imputeinterpolate_nothing = jnk_files[6]\n", + "len(jnk_filled_removelong_imputeinterpolate_nothing)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first item in the list contains a dataframe with the same number of rows that the clustering results mentioned above. Therefore, this is the preppreprocessed data used for those clustering computations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(jnk_filled_removelong_imputeinterpolate_nothing[0].__len__(), \n", + " jnk_files[4][1][0].shape)\n", + "jnk_filled_removelong_imputeinterpolate_nothing[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The columns of this datafrae are classified in the second element of the list. It¡s the same set of variables than the dataset seen in the file `jnk_before_handling_missing`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jnk_files[6][1]" ] }, { @@ -341,13 +1402,14 @@ "metadata": {}, "outputs": [], "source": [ - "foo = pd.DataFrame([1, 2, 3])\n", - "bar = pd.DataFrame([1, 2, 3])\n", - "foo = infer_or_inject_freq(foo)\n", - "bar = infer_or_inject_freq(bar, injected_freq='2s')\n", - "test_eq(foo.index.freq, '1s')\n", - "test_eq(bar.index.freq, '2s')\n", - "foo, bar" + "set(jnk_files[3][0].columns) == set(jnk_files[6][0].columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check te effect of the removed periods if we compare the evolution of the timestamps between this dataset and the one without preprocessing." ] }, { @@ -356,17 +1418,31 @@ "metadata": {}, "outputs": [], "source": [ - "foo = pd.DataFrame([1, 2, 3])\n", - "bar = infer_or_inject_freq(foo, injected_freq='1W', start_date='01/01/2020')\n", - "baz = infer_or_inject_freq(foo, injected_freq='1W', start_date='2020-01-01', format = '%Y-%m-%d')\n", - "test_eq(bar, baz)\n" + "pd.Series(jnk_files[3][0].index).plot(), pd.Series(jnk_files[6][0].index).plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This must be taken into account when making use of this datasets. If one wants to consider timestamps as a countinuos source of information, even if there is no data, this last dataset, and none of them with removed periods, is an option for that analysis. For example, a forecastig task should take this very carefully." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Files 8th to 13th are the results of processing the data with the library `ts_learn`, and the resulting cluasters found in the data when using that preprocessing. Each file corresponds to the use of a different feature extracted from the package (mean, first element...) but I do not know more details about it. For know, these files can be ignored." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Export -" + "Finally, the last file is a list the type of variables of all the 146 variables found in the base dataframe.\n", + "1. The first element lists the boolean variables\n", + "2. The second element lists the numeric variables\n", + "3. The third element lists the categorical variables" ] }, { @@ -375,10 +1451,16 @@ "metadata": {}, "outputs": [], "source": [ - "#| hide\n", - "#from nbdev.export import *\n", - "#notebook2script()\n", - "beep(1)" + "print(filepaths_pickle[13].name)\n", + "types_variables = jnk_files[13]\n", + "types_variables[0], types_variables[1], types_variables[2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Export notebook" ] }, { @@ -386,12 +1468,16 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#hide\n", + "from nbdev.export import *\n", + "notebook2script()" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" } diff --git a/nbs_pipeline/01_dataset_artifact.ipynb b/nbs_pipeline/01_dataset_artifact.ipynb index f2c0c849..a738d59a 100644 --- a/nbs_pipeline/01_dataset_artifact.ipynb +++ b/nbs_pipeline/01_dataset_artifact.ipynb @@ -146,21 +146,7 @@ }, "tags": [] }, - "outputs": [ - { - "ename": "", - "evalue": "/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so: undefined symbol: iJIT_NotifyEvent", - "output_type": "error", - "traceback": [ - "\u001b[0;31m\u001b[0m", - "\u001b[0;31mImportError\u001b[0mTraceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#| export\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mdvats\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mut\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--vscode\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m sys\u001b[38;5;241m.\u001b[39margv:\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExecuting inside vscode\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/work/dvats/utils.py:16\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m#import tensorflow as tf\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnn\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastai\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbasics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# %% ../nbs/utils.ipynb 5\u001b[39;00m\n", - "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/torch/__init__.py:192\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m USE_GLOBAL_DEPS:\n\u001b[1;32m 191\u001b[0m _load_global_deps()\n\u001b[0;32m--> 192\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_C\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m \u001b[38;5;66;03m# noqa: F403\u001b[39;00m\n\u001b[1;32m 194\u001b[0m \u001b[38;5;66;03m# Appease the type checker; ordinarily this binding is inserted by the\u001b[39;00m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;66;03m# torch._C module initialization code in C\u001b[39;00m\n\u001b[1;32m 196\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m TYPE_CHECKING:\n", - "\u001b[0;31mImportError\u001b[0m: /usr/local/share/miniconda3/envs/env/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so: undefined symbol: iJIT_NotifyEvent" - ] - } - ], + "outputs": [], "source": [ "#| export\n", "import sys\n", @@ -188,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "editable": true, "slideshow": { @@ -225,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -235,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "editable": true, "slideshow": { @@ -243,7 +229,23 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available datasets: \n", + "0 - monash_australian_electricity_demand_0\n", + "1 - monash_solar_4_seconds_0\n", + "2 - wikipedia_0\n", + "3 - traffic_san_francisco_0\n", + "4 - monash_solar_10_minutes_0\n", + "5 - etth1_0\n", + "6 - stumpy_abp_0\n", + "7 - stumpy_toy_0\n" + ] + } + ], "source": [ "#| hide\n", "cfg_.show_available_configs()" @@ -251,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "editable": true, "slideshow": { @@ -286,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "editable": true, "slideshow": { @@ -319,15 +321,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "execution": { - "iopub.execute_input": "2024-06-04T18:16:49.526377Z", - "iopub.status.busy": "2024-06-04T18:16:49.526328Z", - "iopub.status.idle": "2024-06-04T18:16:49.563129Z", - "shell.execute_reply": "2024-06-04T18:16:49.563023Z" - } - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "#| export\n", @@ -336,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "editable": true, "slideshow": { @@ -383,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "editable": true, "slideshow": { @@ -393,7 +388,29 @@ "skip" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/macu/data/penguin_sample.csv\n", + "penguin_sample\n", + "0.253906\n", + "0.259033\n", + "0.269287\n", + "0.27124\n", + "0.265137\n", + "0.26001\n", + "0.246826\n", + "0.239746\n", + "0.231445\n", + "0.230469\n", + "0.243652\n", + "0.248779\n", + "Error while converting file. Maybe not a tsf: Missing attribute section. Attribute section must come before data.\n" + ] + } + ], "source": [ "#| hide\n", "if print_flag:\n", @@ -419,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "editable": true, "slideshow": { @@ -450,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "editable": true, "slideshow": { @@ -460,7 +477,77 @@ "skip" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File loaded successfully\n", + "(109842, 1)\n" + ] + }, + { + "data": { + "text/html": [ + "
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penguin_sample
00.253906
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20.269287
30.271240
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" + ], + "text/plain": [ + " penguin_sample\n", + "0 0.253906\n", + "1 0.259033\n", + "2 0.269287\n", + "3 0.271240\n", + "4 0.265137" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#| hide\n", "if print_flag:\n", @@ -478,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "editable": true, "slideshow": { @@ -486,7 +573,69 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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penguin_sample
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30.271240
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" + ], + "text/plain": [ + " penguin_sample\n", + "0 0.253906\n", + "1 0.259033\n", + "2 0.269287\n", + "3 0.271240\n", + "4 0.265137" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#| export\n", "if config.time_col is not None:\n", @@ -526,7 +675,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "editable": true, "slideshow": { @@ -534,7 +683,15 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<10 * Hours>\n" + ] + } + ], "source": [ "#| export\n", "df = infer_or_inject_freq(\n", @@ -555,9 +712,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data_cols: [0]\n", + "Num. variables: 1\n" + ] + } + ], "source": [ "#| export\n", "# Subset of variables\n", @@ -577,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "editable": true, "slideshow": { @@ -585,7 +751,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "df shape before dropping duplicates (109842, 1)\n", + "df shape after dropping duplicates (109842, 1)\n" + ] + } + ], "source": [ "#| export\n", "#Duplicated rows\n", @@ -599,7 +774,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -618,7 +793,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "editable": true, "slideshow": { @@ -678,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "editable": true, "slideshow": { @@ -715,7 +890,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "editable": true, "slideshow": { @@ -723,7 +898,33 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "About to write df to /home/macu/data/wandb_artifacts/3058220854446460321\n" + ] + }, + { + "data": { + "text/plain": [ + "{'TS': {'sd': '1970-01-01 00:00:00',\n", + " 'ed': '2095-04-22 02:00:00',\n", + " 'created': 'from-df',\n", + " 'n_vars': 1,\n", + " 'handle_missing_values_technique': 'None',\n", + " 'has_missing_values': 'False',\n", + " 'n_samples': 109842,\n", + " 'freq': '<10 * Hours>',\n", + " 'vars': ['penguin_sample'],\n", + " 'hash': '3058220854446460321'}}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#| export\n", "training_artifact = TSArtifact.from_df(\n", @@ -739,7 +940,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "editable": true, "slideshow": { @@ -771,7 +972,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "editable": true, "slideshow": { @@ -779,7 +980,15 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rg None | test_split None\n" + ] + } + ], "source": [ "#| export\n", "# Testing data\n", @@ -832,7 +1041,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "editable": true, "slideshow": { @@ -885,7 +1094,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "editable": true, "slideshow": { @@ -893,7 +1102,15 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "runname: 01_dataset_artifact\n" + ] + } + ], "source": [ "#| export\n", "import os\n", @@ -906,7 +1123,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "editable": true, "slideshow": { @@ -914,7 +1131,146 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "wandb: Currently logged in as: mi-santamaria. Use `wandb login --relogin` to force relogin\n" + ] + }, + { + "data": { + "text/html": [ + "wandb version 0.17.5 is available! To upgrade, please run:\n", + " $ pip install wandb --upgrade" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.14.2" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /home/macu/work/wandb/run-20240723_124135-elsv7taa" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/mi-santamaria/deepvats" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/mi-santamaria/deepvats/runs/elsv7taa" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'stream.Stream' object attribute 'write' is read-only\n", + "Logging training_artifact \n" + ] + }, + { + "data": { + "text/html": [ + "Waiting for W&B process to finish... (success)." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5f9f6a4c430342f9bd262d7b1b50235d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='0.004 MB of 0.004 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/elsv7taa
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Find logs at: /home/macu/work/wandb/run-20240723_124135-elsv7taa/logs" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#| export\n", "mode = 'online' if config.use_wandb else 'disabled'\n", @@ -935,7 +1291,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "editable": true, "slideshow": { @@ -951,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "editable": true, "slideshow": { @@ -959,7 +1315,32 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution ended\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#| export\n", "from dvats.imports import beep\n", diff --git a/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb b/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb index bb3b3ebb..89f3f9b2 100644 --- a/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb +++ b/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "185023c6", "metadata": {}, "outputs": [], @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "f3bed3a6-eaea-4f74-9411-ea4bea5bfa26", "metadata": {}, "outputs": [], @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "da23843a", "metadata": {}, "outputs": [], @@ -95,9 +95,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "fa0e189a-1cc3-4e40-93d9-86d56cee23a0", "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "import dvats.config as cfg_" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e2ba1f8c", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -115,17 +126,6 @@ ] } ], - "source": [ - "#| export\n", - "import dvats.config as cfg_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e2ba1f8c", - "metadata": {}, - "outputs": [], "source": [ "#| hide\n", "cfg_.show_available_configs()" @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "6f53b9f6", "metadata": {}, "outputs": [], @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "59a05e15-80e7-44fd-91b3-637a0d2e7a7f", "metadata": {}, "outputs": [], @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "4a511d12-df7f-420e-b570-f37bc13d1781", "metadata": {}, "outputs": [], @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "be090327-2eda-44be-9a71-89e20242dc9a", "metadata": {}, "outputs": [], @@ -225,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "e773d2a8", "metadata": {}, "outputs": [], @@ -236,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "210b085c", "metadata": {}, "outputs": [], @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "cb164924-13e2-4099-ba35-06e675035d34", "metadata": {}, "outputs": [ @@ -255,8 +255,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "GPU | Used mem: 260\n", - "GPU | Used mem: 24576\n", + "GPU | Used mem: 0\n", + "GPU | Used mem: 12\n", "GPU | Memory Usage: [\u001b[90m--------------------\u001b[0m] \u001b[90m0%\u001b[0m\n" ] } @@ -281,9 +281,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "5b845205-b133-4ee1-baaf-acc2ddd6533b", "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "user, project, version, data, config, job_type = cfg_.get_artifact_config_MVP_SWV(False)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "494fae16", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -295,22 +306,11 @@ "cols: []\n", "freq: 1s\n", "time_col: None\n", - "mvp: {'batch_size': 512, 'n_epoch': 100, 'ws': [10, 1008], 'stride': 1}\n", + "mvp: {'batch_size': 512, 'n_epoch': 100, 'ws': [10, 30], 'stride': 1}\n", "dcae: {'batch_size': 512, 'n_epoch': 100, 'stride': 48, 'w': 224, 'delta': 60, 'nfs': [64, 32, 16], 'kss': [10, 5, 5], 'output_filter_size': 10, 'top_k': [2, 2, 4], 'pool_szs': [2, 2, 4]}\n" ] } ], - "source": [ - "#| export\n", - "user, project, version, data, config, job_type = cfg_.get_artifact_config_MVP_SWV(False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "494fae16", - "metadata": {}, - "outputs": [], "source": [ "#| hide\n", "if pre_configured_case: \n", @@ -333,18 +333,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "f30caa23", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "runname: 02c_encoder_MVP-sliding_window_view\n" - ] - } - ], + "outputs": [], "source": [ "#| export\n", "path = os.path.expanduser(\"~/work/nbs_pipeline/\")\n", @@ -355,10 +347,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "fbc1edf2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "runname: 02c_encoder_MVP-sliding_window_view\n", + "alias: toy\n", + "analysis_mode: online\n", + "batch_size: 512\n", + "epochs: 100\n", + "mask_future: False\n", + "mask_stateful: True\n", + "mask_sync: False\n", + "mvp_ws: [10, 30]\n", + "norm_by_sample: False\n", + "norm_use_single_batch: False\n", + "r: 0.71\n", + "stride: 1\n", + "train_artifact: mi-santamaria/deepvats/toy:latest\n", + "valid_artifact: None\n", + "use_wandb: True\n", + "valid_size: 0.2\n", + "w: 30\n", + "wandb_group: None\n", + "artifact_name: toy\n", + "data_cols: []\n", + "data_fpath: ~/data/toy.csv\n", + "freq: 1s\n", + "time_col: None\n", + "csv_config: {}\n", + "norm_use_by_single_batch: (False,)\n" + ] + } + ], "source": [ "#| hide\n", "if print_flag:\n", @@ -368,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "e4411368-d772-4381-9cc0-5c9b7ea5361a", "metadata": {}, "outputs": [ @@ -376,27 +401,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "wandb: Currently logged in as: angel-gutierrez. Use `wandb login --relogin` to force relogin\n" + "wandb: Currently logged in as: mi-santamaria. Use `wandb login --relogin` to force relogin\n" ] }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1d89b6c246a74fa890d561e6feca4831", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016669257066678256, max=1.0…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ - "wandb version 0.16.3 is available! To upgrade, please run:\n", + "wandb version 0.17.5 is available! To upgrade, please run:\n", " $ pip install wandb --upgrade" ], "text/plain": [ @@ -421,7 +432,7 @@ { "data": { "text/html": [ - "Run data is saved locally in /home/macu/work/wandb/run-20240301_152213-lei5rxhx" + "Run data is saved locally in /home/macu/work/wandb/run-20240723_124854-kl9g41ca" ], "text/plain": [ "" @@ -433,7 +444,7 @@ { "data": { "text/html": [ - "Syncing run 02c_encoder_MVP-sliding_window_view to Weights & Biases (docs)
" + "Syncing run 02c_encoder_MVP-sliding_window_view to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -445,7 +456,7 @@ { "data": { "text/html": [ - " View project at https://wandb.ai/angel-gutierrez/ream" + " View project at https://wandb.ai/mi-santamaria/deepvats" ], "text/plain": [ "" @@ -457,7 +468,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/mi-santamaria/deepvats/runs/lei5rxhx" + " View run at https://wandb.ai/mi-santamaria/deepvats/runs/kl9g41ca" ], "text/plain": [ "" @@ -512,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "fc36de96", "metadata": {}, "outputs": [], @@ -523,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "3ebeba46-eb67-405c-9de7-c1acb8e4085b", "metadata": {}, "outputs": [ @@ -566,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "2cd3daa2-d550-424a-9113-df1d9c7bef14", "metadata": {}, "outputs": [], @@ -577,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "id": "78dced3c-8280-460e-bd11-8188495bf470", "metadata": {}, "outputs": [], @@ -588,10 +599,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "02f7a835", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "wandb: 1 of 1 files downloaded. \n" + ] + } + ], "source": [ "#| export\n", "df_train = train_artifact.to_df()" @@ -599,17 +618,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "8acf714f-0fe1-4aed-8b57-23ccd31b22fe", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "wandb: 1 of 1 files downloaded. \n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -669,19 +681,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "2ca6f7fd-ca88-449a-81db-9ebcce6beb80", "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "name 'print_flag' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m\u001b[0m", - "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mprint_flag\u001b[49m: \n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdf_train ~ \u001b[39m\u001b[38;5;124m\"\u001b[39m, df_train\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwindow_sizes = \u001b[39m\u001b[38;5;124m\"\u001b[39m, config\u001b[38;5;241m.\u001b[39mmvp_ws)\n", - "\u001b[0;31mNameError\u001b[0m: name 'print_flag' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "df_train ~ (550, 3)\n", + "window_sizes = [10, 30]\n", + "wlen = 30\n" ] } ], @@ -695,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "973a9e71-2e00-4cbd-a93b-2824e2f45c4e", "metadata": {}, "outputs": [], @@ -706,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "3deacf2c-348f-414c-8cdd-2de61c49733a", "metadata": {}, "outputs": [ @@ -738,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "9ff39cfd-bab8-4b9e-96c4-7a8600652afe", "metadata": {}, "outputs": [], @@ -760,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "8d25ab5a-b1e6-4eb7-8542-e4dc87f2a883", "metadata": {}, "outputs": [ @@ -809,7 +819,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "854c7680-e590-449a-a454-6b6a7595b0ea", "metadata": {}, "outputs": [], @@ -820,10 +830,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "98bb418e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/plain": [ @@ -894,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "c7c3cd99", "metadata": {}, "outputs": [], @@ -913,7 +924,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "b97038fe-116f-4d6c-8569-e9a9c015a434", "metadata": {}, "outputs": [], @@ -934,10 +945,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "5565e966", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(520, 3, 30)\n" + ] + } + ], "source": [ "#| hide\n", "if print_flag: print(X.shape)" @@ -945,7 +964,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "02951c77", "metadata": {}, "outputs": [], @@ -956,17 +975,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "668e7b5a", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(520, 3, 30)\n" - ] - }, { "data": { "image/png": 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", @@ -1002,7 +1014,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "f9e0746c-7d67-474b-85db-f65e5fe147b8", "metadata": {}, "outputs": [ @@ -1040,7 +1052,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "68800c87-ba6e-4097-bfb0-6862a0fb1a2c", "metadata": {}, "outputs": [], @@ -1067,7 +1079,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "a291d3f9-ab4a-4233-9fe7-2febc19e80f9", "metadata": {}, "outputs": [ @@ -1077,7 +1089,7 @@ "[0.3333333333333333, 30]" ] }, - "execution_count": null, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1101,7 +1113,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "f5f2b562-c1d8-4b01-aa69-6aa974ee959b", "metadata": {}, "outputs": [], @@ -1132,7 +1144,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "3deaf33e-2d3d-4fac-a10f-21704a2cca60", "metadata": {}, "outputs": [ @@ -1172,7 +1184,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "cb9dd304-6691-49b7-9ffc-f004757f3cd8", "metadata": {}, "outputs": [], @@ -1194,13 +1206,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "563efbd4-bc93-4969-b7e8-bed4b191291e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -1236,26 +1248,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "ab1d5cc8-fac5-4f9d-bbd9-4083c2c60d8a", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "w 30 mvp_ws [10, 30]\n", - "expected [10, 30]\n", - "10 30\n" - ] - }, { "data": { "text/plain": [ - "20" + "19" ] }, - "execution_count": null, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1268,10 +1271,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "ce0cfbe7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w 30 mvp_ws [10, 30]\n", + "expected [10, 30]\n", + "10 30\n" + ] + } + ], "source": [ "#| hide\n", "if print_flag:\n", @@ -1282,7 +1295,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "99425d9b", "metadata": {}, "outputs": [], @@ -1293,10 +1306,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "8bf85abb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "obtained [0.3333333333333333, 30]\n" + ] + } + ], "source": [ "#| hide\n", "if print_flag: print(\"obtained \", obtained_window_size)" @@ -1304,7 +1325,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "c4a428d5", "metadata": {}, "outputs": [], @@ -1315,7 +1336,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "385e5d4c-84de-440c-9572-8688951c2873", "metadata": {}, "outputs": [ @@ -1323,7 +1344,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "obtained [0.3333333333333333, 30]\n", "obtained [10, 30]\n" ] } @@ -1335,7 +1355,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "ce3a3112-ba6b-4756-9ce1-e15e4c2af1fd", "metadata": {}, "outputs": [ @@ -1365,7 +1385,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "9272a132-9026-419c-81b0-d16d03894f70", "metadata": {}, "outputs": [ @@ -1374,7 +1394,7 @@ "output_type": "stream", "text": [ "[10, 30]\n", - "11\n" + "20\n" ] } ], @@ -1398,7 +1418,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "ca10238a-f7f2-4fa2-9497-1b59f83cc6b2", "metadata": {}, "outputs": [ @@ -1418,7 +1438,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "f4402b8e-a0e3-40ac-b208-8172d9ba0457", "metadata": {}, "outputs": [ @@ -1426,8 +1446,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "GPU | Used mem: 3\n", - "GPU | Used mem: 24576\n", + "GPU | Used mem: 0\n", + "GPU | Used mem: 12\n", "GPU | Memory Usage: [\u001b[90m--------------------\u001b[0m] \u001b[90m0%\u001b[0m\n" ] } @@ -1439,7 +1459,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "d4870d10-dcd3-4cfc-937e-3db61ed57f3a", "metadata": {}, "outputs": [ @@ -1447,12 +1467,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 99/101 : \r" + "Epoch 89/101 : \r" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1468,18 +1488,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "fc5f53b3-5c75-4d9b-94c9-0385f65dd22f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "obtained [10, 30]\n" - ] - } - ], + "outputs": [], "source": [ "#| export\n", "obtained_window_size = deepcopy(learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size)" @@ -1487,10 +1499,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "de465ad5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "obtained [10, 30]\n" + ] + } + ], "source": [ "#| hide\n", "if print_flag: print(\"obtained \", obtained_window_size)" @@ -1498,7 +1518,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "id": "bd3a6e2e-6b3b-44f4-9f8a-7585931b2c5b", "metadata": {}, "outputs": [ @@ -1523,7 +1543,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "id": "c26833f1-ba03-470e-b209-b20ddebc7294", "metadata": {}, "outputs": [ @@ -1531,8 +1551,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "GPU | Used mem: 3\n", - "GPU | Used mem: 24576\n", + "GPU | Used mem: 0\n", + "GPU | Used mem: 12\n", "GPU | Memory Usage: [\u001b[90m--------------------\u001b[0m] \u001b[90m0%\u001b[0m\n" ] } @@ -1544,7 +1564,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "id": "610dd97f-1cb2-4364-9748-db882ac6162a", "metadata": {}, "outputs": [ @@ -1553,12 +1573,12 @@ "output_type": "stream", "text": [ "epoch train_loss valid_loss time \n", - "0 1.335125 1.106112 00:00 \n" + "0 1.327078 1.263283 00:00 \n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1570,43 +1590,45 @@ "name": "stdout", "output_type": "stream", "text": [ - "1 1.307635 1.128473 00:00 \n", - "2 1.254325 1.088959 00:00 \n", - "3 1.209721 1.115432 00:00 \n", - "4 1.143754 1.149175 00:00 \n", - "5 1.078671 1.071289 00:00 \n", - "6 1.013750 1.138755 00:00 \n", - "7 0.956994 1.119565 00:00 \n", - "8 0.906724 1.047401 00:00 \n", - "9 0.869582 1.080126 00:00 \n", - "10 0.829422 1.034296 00:00 \n", - "11 0.790754 1.013882 00:00 \n", - "12 0.753778 0.981945 00:00 \n", - "13 0.720004 1.014672 00:00 \n", - "14 0.687163 0.920570 00:00 \n", - "15 0.657708 0.836749 00:00 \n", - "16 0.629757 0.760024 00:00 \n", - "17 0.603699 0.810107 00:00 \n", - "18 0.579462 0.608843 00:00 \n", - "19 0.556975 0.567801 00:00 \n", - "20 0.536297 0.519852 00:00 \n", - "21 0.516913 0.486550 00:00 \n", - "22 0.498764 0.585782 00:00 \n", - "23 0.481989 0.547926 00:00 \n", - "24 0.466115 0.561339 00:00 \n", - "25 0.451770 0.540102 00:00 \n", - "26 0.438615 0.516013 00:00 \n", - "27 0.426683 0.594002 00:00 \n", - "28 0.414670 0.600242 00:00 \n", - "29 0.403325 0.502061 00:00 \n", - "30 0.394009 0.643128 00:00 \n", - "31 0.383844 0.676720 00:00 \n", - "No improvement since epoch 21: early stopping\n" + "1 1.258904 1.288188 00:00 \n", + "2 1.222621 1.236291 00:00 \n", + "3 1.178792 1.363012 00:00 \n", + "4 1.123909 1.289420 00:00 \n", + "5 1.063149 1.306458 00:00 \n", + "6 1.004390 1.320256 00:00 \n", + "7 0.942477 1.293941 00:00 \n", + "8 0.891052 1.270941 00:00 \n", + "9 0.842252 1.261686 00:00 \n", + "10 0.799672 1.299559 00:00 \n", + "11 0.767707 1.344131 00:00 \n", + "12 0.734807 1.235493 00:00 \n", + "13 0.703934 1.214709 00:00 \n", + "14 0.674135 1.193509 00:00 \n", + "15 0.647674 1.275057 00:00 \n", + "16 0.622848 1.198747 00:00 \n", + "17 0.600453 1.144523 00:00 \n", + "18 0.578717 0.927796 00:00 \n", + "19 0.557322 0.937904 00:00 \n", + "20 0.537918 0.809693 00:00 \n", + "21 0.520362 0.899546 00:00 \n", + "22 0.503035 0.658950 00:00 \n", + "23 0.487807 0.611871 00:00 \n", + "24 0.473315 0.651909 00:00 \n", + "25 0.459258 0.709203 00:00 \n", + "26 0.446166 0.862378 00:00 \n", + "27 0.434540 0.833218 00:00 \n", + "28 0.422974 0.769998 00:00 \n", + "29 0.411980 0.670565 00:00 \n", + "30 0.403069 0.699046 00:00 \n", + "31 0.393108 0.660544 00:00 \n", + "32 0.385020 0.634809 00:00 \n", + "33 0.378577 0.632318 00:00 \n", + "No improvement since epoch 23: early stopping\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1622,7 +1644,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "id": "e92b920f-09d3-4bdc-9dbe-0d0ecf673138", "metadata": {}, "outputs": [ @@ -1630,8 +1652,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "GPU | Used mem: 3\n", - "GPU | Used mem: 24576\n", + "GPU | Used mem: 0\n", + "GPU | Used mem: 12\n", "GPU | Memory Usage: [\u001b[90m--------------------\u001b[0m] \u001b[90m0%\u001b[0m\n" ] } @@ -1651,7 +1673,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "id": "f2ab4815-050b-475e-b0e7-7278fbae4215", "metadata": {}, "outputs": [ @@ -1676,7 +1698,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "id": "b546f8d3", "metadata": {}, "outputs": [ @@ -1690,10 +1712,10 @@ { "data": { "text/plain": [ - "(#1) [1.2937777042388916]" + "(#1) [0.9758278727531433]" ] }, - "execution_count": null, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -1713,13 +1735,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "id": "bfdf3667-a698-451a-9900-0ac1d6fa0cf5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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R/9hMZqbto634z6kv0Kd3A/IFZT6hIsQqdnrdU8QvsTpFVerP78goAq4Aev29YGD0Xo3N59hsjUTkzT6LhUWUaqWWj00ks9lZ1NSann4A1N8vqfAnFr6DLxaIwe1w7/h5fDfgfG4eVaXakrGR3UmSBMYYVKbSa4sQ0lKKqujR8MYUBCP9jFiK+yQWozJVL1gb7/l53Cc1+t6ICn+kKVSm4quvfxV/+dpfoqJUzB4OIaSN7DbZ7fX3wuv0oqyUsZhfbPXwhKLvINko9gH1CdRcbg5lpWzGsAjZt4pSwXxuHsCNhb+QO4RuXzcYGHW2EktayC+golTgdXr1Xdn8fbvdO1sZY9ue78eF3WF0ejvBGGv7XVHGmE9eIOXnx6XKKaRKKfMGRzbRz/fbIeaTi3qiCLlDUJmKudxcK4ZG9mg8Mg4AuJa6ZvJICCHtZLGwiIpSgc/pQ6+/d9vP4U2Rc9k5VFVqGiHWsVxYRrFWhNvhRiwQ0z/O7/+XCpSGshUV/khTzOfmsVZaQ66ao/NHCCEtxRdId5rsypKsL6S08/tTRanoi0TGQknUG0XUE9UWSalIQizCeP5Z1Bu94d95QaCdX/PEuoxnscqSrP9vQNvF1c47fVLlFNLlNGRJ3jESUe9sb+MuYJWpm3aNch6HB4NB7RxEen8UQ02t6fMzvji7E0mS9Dltuxe2RTMa0d6v10vrWC+tmz0cQkib4HOd4dDwDSkIXJe3C0FXEApT9MZJQqyA3xONhEfgkB36xwOuAHr8Wtw/zWc3o8IfaYrr6ev6/6YuN0JIK+mT3fDOk12+CNjOhS0edRrxRNDh7dj0b/pOEpo0EYu4Wbyv8eOJbAKKqrRmUIQ0AGOsXvgzFGw6vZ2IeCJQmNLWC/78mj8YHNwxEtF4zW/XiG9jh/RAYGDTv/FzUCmSUAwL+fqZlTyS9Wb0c/4yM2CMNXt4ZI88Do/ejHA9dX2XzyZke4wxStAi+2JcC9mJJEnUFEUsaS9H+rR7GspWVPgjDacyddPkdiYzQ5MVQkjL7FYAAIB4KA5JkrBeWkemkmnNwATDf058wc+IF/6mM9NUJCHCU5l606g/QIv49Tv9WiRonjpbiXXwBA2n7EQ8FNc/LkmS/v7dzje4/Fq202sfAPoD/fA4PCgrZSzll1o0MrHwot5waHhThzRgOAeRzkEVQiKjne8XD8d3bGAzGgwOQpZkZCoZpMvpZg+P7MORyBEAm5uiCdmPH8//GJ+/8Hks5BbMHgqxgEwlg2QpCQnSpjnjdnhhcDozTU0jxBLS5bT2/JakbQvbfA1rNjdLEbYGVPgjDbeQX0ChVoDH4dE7kWnXCCGkFVKleuTXzSa7XqdXzwSfTrdfl5uiKnqhxLiDhOv198Ln9FGRhFjCcmEZpVrphqx/I+MNAnW2EivhBZt4KA6X7Nr0b/z9eyoz1ZY72cpKGQt5bTH0ZoU/WZL113+73pNsF/PJhd1h/RxUen80H9/Bu9v5fpzx2tfOu39FNBYZgyRJWC2uUlGW7FtVreLS2iUwxnBh7YLZwyEWwNOM+gP98Dq9N/3coeAQZElGtpJFspxsxfAIORR+TzQYHITH4bnh33mEbU2tYTY72+rhCYsKf6TheLTnWGQMR6NHN32MEEKaiS9sDQQHdoz84tr5zK/FwiLKShlepxf9gf4b/l2W5PrPp413khBr4K/h7XayGPFdwNTZSqzEeL7fVnxhp6yU2/KMlkQmAcYYop4oIp7ITT+3nSOtUqWU3iG9U4GU4pHEkK1ksV5a39NuDSM97pMKf0LxOr0YCmpxn7QeQvYrkUnou1am0lO0g4XsarcEFCOXw6Wf8dvOx58Q69gt5UOSpHpTJM1ndVT4Iw1ljPk8Gj2KI1Et3oLiPgkhrcAXPPYy2eWfM5+bR1Vprxsp46HIsrT9VMC4k4SKJERkfNfubq/7eCgOWZKRLqeRKqdaMDJCDidTyWC1uAoJ2xdsZEmuF2zasIlF37l+k2hvbjg0DAntGfHNnxs7dUgD9Wt+IptATa21aGRkq0RWi/nsC/TtulvDiBcJ53Pz9PsTzHh0HABwLU2FP7I/E6kJ/X9X1SoVZ8hNVdUq5nJzAG5+vp8RpaEQqyjVSnrKx83m/fwYBGr0raPCH2koY8znYHAQXd4uivskhLRERanok92R0O6Fvw5PB8LuMBSmYDbXPlEAjDG9A2q78/24eCgOh+RAtpLFWmmtRaMjZH8ylQzWSmuQsH3Wv5Gxs5XmJMQK+Ht1f6Affpd/28/hBZvJ9GRb3eCqTMV0dqPoH9n9mm/c4d5ui6c32zXK9fh6EHAFNi0cktbjDWz72e0HaPFWAVcANbVGZ4EJZiw8BgkSVgorbdd0QA6uqlT1Ygx/PzAWAgnZaiG3gJpaQ8AVQJe3a09fw+cF8/l52qhBhDaTnQFjDJ3ezpumfMSCMbgdbhRqBSwXlls4QnFR4Y80lDHm0yE7IEmSvuuP7wQkhJBmSGS1yK+IJ4KoN7rr5xsjr9qpy43vdnBIjpsuLLlkl/7vFJVARMV3+/UH+uFz+nb9fIqzI1bCCzb8sPrtxENxOGVn2zVpGM/27PffGFm9nXaM+C7WiljMLwLY/nw/TpIk/XlG74/mUFRFP5Nmr+f7cZIk6V9DcZ9i8bv8GAgOAKD1ELJ3k5lJ1NQaIp4Ifin2SwC0OS8VZ8hOjDGIkiTt6WsingginggYY3QmGhEan5vebC4LAE7ZWV/DaqP5/s1Q4Y80jDHmkxf7AOjn/E1nptsuTo8Q0jr8wr6XyC/OWPhrl50S/Oc0FBqCy+G66efqO0kyk00eFSEHo7/ud7kJ4PjOoMXCIkq1UpNGRcjhlWolzOe1c/tuVvgzNmnwQmE7MO6EuNnZnkb8mj+XnWubs5JmMjNgYOj2dSPsDt/0c42xse0yJxLJUmEJFaUCr9OLXn/vvr+eItvExddG6Jw/sldXk1cBaGtp3b5uPUWrna7zZO8YY3qawV5jPrl2bIoi1qKoit7UtJe1Pmr03YwKf6RhjDGf/BBrABT3SQhpOuNkdy/n+3EDwQG4ZBfy1Xzb7JTYa7cUoE2aeDxRrpJr7sAI2aeqUo+k2+vrPuwOo8vbpb1n0K4IIjDekLJbpA1Qv8FtpwXB/Zzvx3V6OxFyh6AwBXPZ9oiz5I07e/k5GedEFI/UesaYz73u1jAaCg1BkiSkyimKlBTMWESL+1wqLCFbyZo9HCK4slLW3w+ORo9CkiQcix4DQMVjsj3+vi9LMuLB/UVF8yNSZjIz1PRDhMSjaH1OH/r8fbt+/nBYO9d7rbRG8yFQ4Y800NaYT84Y90kTFUJIMywXllGsFeF2uBELxPb8dU7ZiaGQ1qjQDh1B+WoeS4UlAHtbBPS7/HrXOXWQE9EkcgmoTEXYHUaHp2PPX8d3/bXDa55Yl16w2UOTxkh4BBIkrBZX22JROVfJYbW4CgnSvs5Ca7eI75paQyKTALC355FTdiIepngks+i7NfYZ88l5HB499pb/3okYAq6Afsbo9TTFfZKbm0xPQmUqOr2d6PJpZ7Xx9bSZ7AwlVpAb8DnNYHBw10SfrWLBGJyyE4VaAavF1WYMj5BD0RvXw6N7aozyOX36NZfu96nwRxpkp5hPjuI+CSHNxCe7Q6GhPUd+ce20CMgX8nr9vQi4Anv6Gh4xR3GfRDTG3av72R3Bi94z2RkoqtKEkRFyOMaCzVh455hPzu/yt9UN7nRWu173Bfrgd/n39bXGSCu7d7bP5bRI04ArgB5fz56+hj/f2uF5JJJCtb7gup9i9lb8a2lHu3j4Ggmd80d2M5GcAFBfQwOALl8XOr2dUJnaVrv7yd7w9/z9xnwCG43QG4lt7bAeQqyFMbbvoz2A+hoWNbJR4Y80CI/5dDvcm2I+OYr7JIQ000HO9+P4IuByYRmFaqGBoxKPsVtqr/gEazY7S40bRBgHjfcFtMK31+lFRalgsbDYjOERcijGgs1ez/pqpyaN6bS2MHWQnVEDwQE4ZWdbRHzvt0Ma2Ng9KmnxSOlyuomjI0aJrFbo7/H37LuYbcQXfWezs9TYIpjxyDgAYDG/iHw1b/JoiKiKtSJmc7MANhf+jP99NXW15eMi4qooFczntDOheWznfvE0FGoaIaJZK60hW8nCITn0pK694Otdc7k5lJVyk0ZnDVT4Iw2xU8wnR3GfhJBmyVfzeuTXQRYBA64Aun3dYLD3mV9VpYrZrHYjyReI96LD04GIJwKVqbb++RBrWS4s6w1HA4GBfX2tLMn1nb5p6mwl4uHd/Psp2PD3dbvf4FbVqr4oup/OX84lu/QmRTvvajN2SO/nmu91evXIdGrWbB19t8YBYz65Hl8PfE4fqmqVGlsEE3QH0efvAwOjHVtkR9dT18EYQ7evG1FvdNO/8cLfbG7W9s2qZO9ms7NQmYqIJ3LDc2av+LVnKb+EYq3YwNERcjh8rj4UGoJL3nuMbdQbRdQT3dQs3K6o8EcOzRjzubUryehIRCv8UdwnIaSReCRFr7/3wF3SvCPIzvEWiVwCClMQdofR6e3c89dJkqT/fOy8SEqs5TDxvkD9Nd8Ou6OItTDG9Pfa/RRsIp4IOr2dYIzZ+lo2n5tHTa0h4Aqgy9t1oO/RDp3tK8UV5Kt5uGQXBoL7a46ga35rqUzVd/wdtvAnSfUmuHZf6BIRNUKT3fDdfMc6jt3wb1FvVGtWZVQ8JnV8zneY60fIHUKXt0trhKZrBxEIf34fpNmPf42d74v2ggp/5NB2i/nkun3dFPdJCGk4fbJ7gEx7ju/+SWQTto1G0neQ7PM8NP41gNb9rzK10UMjZN/0nSx7OP9sO/FQHLIkI11OI1VKNW5ghBzSUmGpvpt1vwWbjfdqOy8I6gsA+9gNuRWPwlrKL9l21wR/DsTDcThl576+lhec5/PzKNVKDR8b2Wy5sIxSrQS3w42+QN+hv188vHHOHy3eCmc8qsV9zufmbfveQw4uX83rkY28SLwVLwhOpCZaNi4iLsbqiUX7PfpgK76WYuemKGIthWoBS4UlAAc70kc/tzozZds1vr2gwh85tN1iPjmK+ySENFpNrenxlQeZDHC9/l74nD5UlAoW8gsNGp04VKZuWizdr1ggBo/Dg7JSxmKeoqOIuXKVnB7vGw/FD/Q9jEUVakYiItELNqEDFGw2bnBnMjOoqbWGj81sxt2Mh1ngCrqDesQ332llN/qu0QM0Rxh3j9ICYPPx5+BQaAiydPjlmaHgECRo5zTSWXJiCbvD6PH3aHGflDhAtriWugYGhv5AP8Lu8Lafw9fTFnIL9Pom+vu8U3buu1lsKz6vmsnMUKMvEQK/R+/19yLgCuz76/sCffA6vagolbaOP6fCHzmUvcZ8chT3SQhppPncPKpqVT+n76AkSdK73OwYBbCUX9K7yfnZPfshSzJFfxFh8NdoX6DvwPG+QP0G146veWJdBzmXjeM3xlW1qu8asJP10jqylSwckgODocFDfS/++rdj4T9TyWCttLZpbrNf+k5/uuY3Hd+Zd9iYT87v8qPH37PpexNx8PUQvoZCCMdjPnfa7QdoxeP+QD8YmP75pH3pRx8Eh/bdLLZVf6AfbocbZaWM5cJyI4ZHyKHwOehBG/xlSdZTPtp5PkuFP3Ioi/nFPcV8chT3SQhpJGPn/0Ejvzg7n/PHu4pHw6MHOg8NMETIZSbBGGvU0AjZNz5/OGykDX/Nz+fnUVbKhxwVIYeXKqWQLCUhSdKBnt/GM1ntGPfJr8+DoUG4ZNehvpedI7754kYsEIPP6TvQ9+DPo5nsjO1+PiIp1or6AmujCn/G70U7NsXDizqzuVkUa0WTR0NEka1ksZhfhARp14Z6/u+UokV4c8dh74kArUjCrx12XA8h1lJVq5jNbSR7HeB8P854ZE27rmFR4Y8cCu8y2i3mk5MkCeMRLdv+WpomKmR3/7jwj/ju9Hdp0YHcwBj5dZjz/Th+5leqnEK6nD709xPJYbulgM1noiXLycYMjJB9qqrVhsT7AlqcXYe3Q4uzo10RRAC8SWMwOAiPw3Og78F3CtrxBvcwkdVb9fp7bRv/w5sjDvNz6vP3we/0o6JUbLl7VBSz2VkwMHR5uxB0Bxv2ffm8OJFNUGSbYCKeiBY1zFhb70Agm/EiXiwY2zXS7kj0CCRIWMwvIlPJtGJ4REDFWlE/gqMRayEApaEQccxmZ1FTawi5Q+jydh34+9AaFhX+yCGoTNW7ifcS88nxz53JzFDcJ7mpZCmJF5ZewJXkFVxPUxwK2SxZTiJTycAhORAPHuycLyO7nvmVLCWRKqe0Lr5D3BS4HW4MBrVoNVqoIGaZzc5CYQpC7hA6vZ2H/n50g0tEcphz2biB4ADcDjfy1bytoppKtVJDF7iM8T/Tafu8/stKGXO5OQAHi4vljLtO6Syy5uE78uLhw89jjXr9vfA4PKgoFVu9D9gFNUKTrSZSEwCAY9Fju35uwBVALKgd3UC7/tpXIpsAA0OntxMhd6gh3zMeikOChNXiKp0hSUzF74kOm+xFa1hU+COHsJhfRL6a33PMJ8fjPmtqzVaL66TxLq5d3PZ/EwLUF+oHggNwOQ4X+cXZMd6Cv88OBgfhdrgP9b34QgW9dxOzNDLeF9gc8Uu7IoiZCtWCXtg6TKSNU3YiHtKKCHYq2MxkZ/QFrrA73JDvacdz/qYz02CMocPbgYgncqjvpe8eTdtv96gIGGNIZBIAGhvzCWiFbf4+QDvaxaPHfWZnKWqcIF1OY6WwAkmS9tywwQuEE8mJZg6NCEw/H7ZBu/2AzWfE2mk9hFiLMdnrMM2QnDENpR1R4Y8c2H5jPjmK+yR7UVWruLx+Wf/v+dw81kvrJo6IiKaRkV+cfuZXbh4VpdKw72umRsR8cnyRdCm/hEK1cOjvR8h+GG8CGvW67w/0w+PwoKyUsZRfasj3JOQgpjJTYGDo9nUfunOb3+Da6Zw/Y9G/UeLhOCRJslXEdyOv+YOhQThlJ3LVHNZKa4f+fmSz1eIqCrUCXLILsUCs4d+fLwZPZ2nxVjQd3g50ejuhMrVtdyCQOl68GwoOwe/y7+lrxqPjkCRtZ1aqlGri6IiIVKbqO8YbuRYC1OdZ1DRCzLJcWEahVtiUyHUY7b6G1dTC33PPPYd3v/vdGBgYgCRJ+PrXv77r1zz77LO4/fbb4fV6MT4+jscff7yZQyQHxBjTFxOORI7s++sp7pPs5mryKipKBWF3WJ/MXFylXX9EU6qVsJBfANDYLreoN4qIJwKVqfo5YlbWqB0kXNAdRI+/BwysbTumiHlWiivIV/Nwya6G3AQAG3F/FGdHBKDHfB4inpHjO2KTpaQtCloqU/UFqEYucHkcHr3gYofOdkVV9IXARjyPXLKrvnvURkVkUfDf1WBwcF9NtHvFf3crhZW2XOgSHd/1R1GNhD8H9nN8js/p01O3eEM+aR/LhWWUaiW4HW70+fsa+r35DvTZ3CwUVWno9yZkL/g6UzwUb8j8KOQOaWfrgulzr3bS1MJfPp/Hrbfeis9+9rN7+vzJyUm8853vxJ133omXX34Zv/u7v4vf/u3fxle/+tVmDpMcwEJ+QY/55DcV+9Ht60bYHUZNrdniRps0Ho/2PNN9Brd03wIAuJK8gqpKhWKykWnfoCirrfiioh0KWzwarRE7SDj950MdyqTF+HwhHorDKTsb9n3pnD9itqpSRSKrRf41omDjcXj08yzsULBZzC+irJThcXjQF2jsApcx7tfq5vNaWoHP6WvYQiD/+djheSSaZsS0GQVcAXT7ugFAf38h4uAJSIlswjYpI2T/1kvrWCutQZbkfV//eaGQCn/th18/GlUYMer198Ln9KGiVPRGa0JaqZHpFZwxvr7dNLXwd9999+ETn/gE3vve9+7p8x9//HEMDw/j3LlzOHXqFD784Q/jN37jN/CpT32qmcMkB8C7ksbC+4v55CRJ0rvcrqZpokI2WymsYLmwDFmScaLjBOKhOMLuMCpKBVeT9HwhzYn84ozxFlY/04Yv1DViIZnj32s2N0uFeNJSvBjf6Nf9cHjYVrujiPUksgkoTEHIHUKXt6sh35OfiWGHgo3xmi9Ljb195e8nc7k5yy++89/1aHi0IWegAhu7R6HFyWUr2YZ8TwKUlTIWC1oiw0GaaPeKf28q/Imn09uJiCcChSm2aDwgB8PXNoZDw/A6vfv62vHoOGRJ1oqHRYpjbifNuicCtLVa3pBCcZ+k1TKVDNZKa5AkqaHPb15EnMnOoKbWGvZ9rUCoM/6ef/55vOMd79j0sXvuuQcvvPACqtXtFxfL5TIymcymP6S5GGO4nr4OoB5RcRAU90l2wnf7HYkegd/lhyRJONN9ZtO/kfbVzEx7AIgFYnA73CjUClguLDf8+7dKVa3vIGnkz6nL24WQO4SaWrNFHKod2XFulK/msVJYgYTG3gQA2u6ogYAWHUqLb8QMPGZ2LDLWsIINj3dezC9aPuaPvy6bsTMq6rFHxDdjrN4h3YBob87v8qM/0A+A3h8baS47B8aY/vxrFn3xNmv9ZrbDEHFeZGyEvpamuM92xBjDREo73+8g62oeh0ePZaRdf+0jX81jtbgKoB7L2WgjIe1eyw4JSMRa+Fw2FojtuxniZrp93Qi4AqipNczl5hr2fa1AqMLf4uIi+vo2x5L09fWhVqthdXV126/55Cc/iUgkov+Jx5vXMUc0h4355Cjuk2ynrJTxevJ1AMCZrjP6x090nIAsyVguLFu6GEMOz5hpzxejGskhOzAU0s5MsHIG+Fx2DjW1hqArqEc9NYIkSRT3KTg7zo34jWevvxd+l7/h358XE+kGl7SaylR9HtzI3dnG8yysPM/OVDJYL61rHehNWOAydhRb+ee0VlpDrpqDU3bqc5hG4YVEO+weFQWfXzYr5pPr9/fD7XCjVCthpbjS1McSmajzoiMRrdhDjdDtabW4inQ5DYfkOPD1/2hHPe6znYv77YTvwuvx9zTlnggA4uE4JElCqpyiNBTSUvxevNEN/u28hiVU4Q/ADV2u/OK1U/fro48+inQ6rf9JJCjGotkOG/PJUdwn2c7r66+jptbQ6e1ELBDTP+53+fXny8VV2vXXzvhkYDg03PDIL84O5/zpk6ZI4yK/OL4IOJWZgsrUhn5vcnh2nBtNp5sX7wvUX/PzuXnLx/0Ra1nIL6BUK8Hj8Gya9zSCfp6Fha9l/LXf7+9vaOevEe9sn85MW3bhlBfl4qE4XLKrod+bvz/aIQ5VBIzVi/HN2q3BOWQHhoIbzWxtHNkm6rzI2Aht5WZDcjB8l95IZARuh/tA32MsPAan7ES6nNZ3gRF7m85q149mJB9xHocH/X6twbqdrx2ktcpKWd+N14znt3ENy6rz/YMQqvDX39+PxcXFTR9bXl6G0+lEV9f25114PB6Ew+FNf0jzNCrmk+Pfg7rcCKA9v3iU55muMzcUK/gOwInUBMpKueXjI2JodgEA0BZiJEhYKawgX8037XGaxRj5xc95aqSBwADcDjeKtSLtwBWQ3eZGVbWK2ZwWwdfICDujqLce90dnIZFWMh5g3+hmFl74S2QTlj2TVV/gatJrHwBiwRhcsguFWsGyu6Ka1SENAB3eDv39kRYAD2+9tI58NQ+H5EAs2Nhi/3biYW13WzsXlkSdF0mShPHoOIB6czVpD4wxvfB3LHrswN/H5XDpO4d5bCixL0VV9FjyZjeO6GkIWeumIRBrmcnM6DHoUW+04d9/MDgIl+zSjhCx6Hz/IIQq/L3lLW/B008/velj3/nOd3DHHXfA5Wps5yI5mMX8oh7z2YgYmR5fD8V9Et1ifhHrpXU4ZSeOdx6/4d9jgRg6vZ2oqTW8vv66CSMkZstWstphv5CaGo/kd/nR4+8BYM3or6XCEgq1AtwOd1MWlRyyQ7/ZoOgv0mzzuXk9trbLu30jWCPwIrmVd0cRa2GM6e+hjYz55IxnslqxoF1VqpjLap2/zVzgcspO/fgCK17zc5Vc085A5ej9sXH4a3EgONDw3Znb4a+dpfwSSrVS0x+P7A+P+5zOTFu2QYPs31JhCdlKFi7Zdeh7Wl44vJqkuE+7WywsoqJU4HP60Ovvbepj8fnEXHaO3ptIS/A5eLOa/aw+3z+ophb+crkcXnnlFbzyyisAgMnJSbzyyiuYmdG6zR599FF86EMf0j//gQcewPT0NB555BG89tpr+MIXvoDPf/7z+J3f+Z1mDpPsA+9K4pECh0WHWhOjC2sXAGiTV4/Dc8O/S5Kk7/q7sHaBJrZtiHea9wf64XP6mvpYfLJrxe52vjAXD8Ub8l69HTtEyBFr0HdENSG21mgkUo/7owhb0gprpTVkKhk4JMehzs3eiSRJ9fdqC55nMZubhcIUhNwhdHo7m/pYVj7nj1+H+wJ9TTvvx3jNV1SlKY/RLvjOu2YmVxiF3CF0eDvAwPSdIkQcvf5eBF1BVNWqJRs0yMHwdbXRyOihGwCGw8NwyS7kqjksFZYaMTwiKJ58NBwabuo9EQB0ejsRdAWhMAXzufmmPhYhiqro89lmJFZx7XhudVMLfy+88AJuu+023HbbbQCARx55BLfddhv+4A/+AACwsLCgFwEBYGxsDN/85jfxzDPP4I1vfCP+83/+z/jTP/1T3H///c0cJtmjRsd8cvx7TWemKe6zjRWqBT3i5Ez3mR0/73jncThlJ5KlJBbyC60aHhGEfr5fE3f7cTwuK5FNoKbWmv54jWSMjmuW4bB2w5EsJZEqpZr2OKS9Mcb0132zF0n7/f1wO9wo1UoUYUtagr9XD4WG4HI0Z+eP8cxaqxW0eRFuJDzS9AUu/v6yXFhGoVpo6mM1Gl+8aOY1vy/QB6/Ti4pSwWJhcfcvINuqKlV9AbXZMW1G/LHaOe5TVMZG6Oup6yaPhrQCY0xf9zgaPXro7+eSXXpzxkSS4j7tjMdutmItRJIkSzdFEWvhu1m9Ti/6An1Nexx+pM9qcRXZSrZpjyOSphb+7rrrLjDGbvjzxS9+EQDwxS9+Ec8888ymr3n729+Ol156CeVyGZOTk3jggQeaOUSyD42O+eQo7pMAwJXkFahMRa+/96axBR6HR4+z4OcBkvZQVat6p3IruqS7fd0IuAKoqlUs5KxTZE6X01gvrW+arDeDx+HBYHAQAO36I82zVlpDvpqHU3ZiIDjQ1McyRtjSc5q0wmSmeTGf3EBwAB6HB6VaCYt56xRsGGP1yJ8mFrQ4q0Z8V5QK5nJaHGozn0eyJOtzinbqkm60udwcVKYi7A4j4om07HH5tS2RTVBiioB44W8qM2W5ZkOyfwv5BX1drVG7/XkB8Xr6uuWafMjeZCoZJEtJSJLUlJSI7fAC43Rmmq4dpKl4M+RIeKThZ54b+V1+9Af6AVhrvn8YQp3xR8TW6JhPbtOh1hT32ZYYY7i4qhXxeJTnzfAdgddS1yzXlU0Obi47B4UpTT/ni5MkyZJFAD7WgcAAvE5vUx+LL8bSIiBpFn1HVHCoJWch8fgPHqVDSLMYz2VrZmFLluT6rj8LxX22sujP8Z+TlRYCEtkEVKYi4okg6ok29bH0c/7SU7QAeEB8xx1PTWiVWDAGp+xEvprHWmmtZY9L9qbP34eAK4CKUqE41jbA19XGI+MNW1eLh+JwO9zIV/OUimRT+pEn/v6m3+NzQ8EhyJKMbCWLZDnZksck7ceY8NPMmE+u3RrZqPBH9sQY88mLdI3EO5ToUOv2lMgmkKlk4Ha4cbRj97gLvitQZSour19uwQiJCPhiSbPP+TIynvlllUUuPfKrSYciG/HHWMwvolgrNv3xSPvRYz4jrTkLiZ+Zwc9eI6RZWnEuG6efZ5GZtMy1zFj0b9ZZtVvxhQArRXzza/5YeKzpc6N4KA6H5ECmksF6ab2pj2VHjDF94bZVuzU4p+zUUxqseHa13UmShPEINUK3A5WpDY355ByyQ38OXU1ebdj3JeIwxp+3isvhomsHabpkOYl0OQ1ZklsyP+L3RXO5OVSUStMfz2xU+CN7Yoz5bMYL0Rj3SReU9sMjO092ntzzjg6+M/DS2iXLLGKRg2OMbdr+3yrxYH2RK1VOtexxD6pUK+ldnq2IRgu7w+j2dYOB0Xs3abhCtaCftdeK5zMAeJ1e9Ps34j9o1x9pIr1g08R4Rm44NAyH5NCjoK1AX+BqUdEf0O5H/E6/FvFtgR0TKlPrcagtaPZxOVz6cQ9WSkIQRbqcRqaSgSzJGAo27tiMvaJz/sSmx32mp6CoismjIc0yl51DsVaE1+nVCyqNwo9DuZa+RnGfNlNVq3qsdyvO9zOic/5Is/F1vsHgYNPOPDfq8HQg4olAZSoS2UTTH89sVPgje9KsmE/OGPfJH4u0h1wlp7/Rn+46veevO9pxFG6HG5lKpi3erNvdWmkNuWqupZFfgLbIxR/PCpNdvjOx09vZsrNj9LjPTHtEJZDW4a+5Hn8PAq5Ayx6XL6DTwjZplrJS1hdwWlHUtlrBxlj0Hwm1rvAnSdKm82xEt5BfQFkpaw0LG+eVNJv+/mih2FhR8IJbLBBrycLWVvy5vZBfaIsOd6vpD/TD5/ShrJQxn5s3ezht6fz0edz/5P24/Uu34/4n78f56fMNfwy+1nUkcgQO2dHQ7z0YGoTX6UWpVsJcdq6h35uYaz43j5paQ8AVaMmRJ0a88Defn6drRwu14v1IFHrMZwuaIQFtvm/FYxAOigp/ZFfNjvnkKO6zPV1auwQGhoHgADq9nXv+OpfswsnOkwCAC6sXmjU8Igi+ADcYHGzJOV9G+qTAAoulrZ40AfVFwETGOtFoxBr0nSwt2u3H8ceby82hqtB8hDTeTGYGKlMR9UTR4e1oyWPqcZ8WOM8ikU2AgaHb142gO9jSxzae8yd6ooQxCUGWWnNbz38+S4Ul5Kv5ljymXfDCXyuTK4wingginggYY5jN0TlyopElmeI+TXR++jwefuZhTCQnUFErmEhO4OFnHm7oYruiKvrvdi/Hm+yXLMk4EtF2jk6kJhr+/Yl5jDGfrTwfFthy7aAzSFuiFe9HoihUC1jKLwFo7T0/vy+azk7bfoc0Ff7Irpod88n1+HoQcoco7rONKKqC19ZfA1CP7twP/jXTmWlkK9mGjo2IxawCALC5Q7qslFv++HtVU2v67tdW/px6fNpuLGMECSGHZXw+t3qRNOqJtlX8B2m9VsZ8cmPhMUiQsFxYFr5go5/taUKBZCg0BFmSkS6nhY74ZozVm33CrXseBVwB9Pp7AVijIUoUVbWq7+Jq9fl+RvyxExm6tomIN1lfT1+3/UKkaB579TFIkMCgNXwwMEiQ8PirjzfsMRLZBCpKBQFXALFArGHf14gXFK+nr1NDpk0Yz4dtdcwnx+djdN1vjVa8H4liJjtjSrNfv78fHocHpVpJLzzaFRX+yK54V9JoeLQpMZ+cJEl6tj3FfbaH6cw08tU8fE6f3uG4Hx3eDgwEB8DA8Nraa00YIRFBsVbUL8ZmTHYjngg6vB1gjAm9UDKfm9dvJvmiXCu0W1QCaY353DyqahUBVwA9vp6WPrYkSXSDS5pGURV9508rC39+lx99gT4AYr9XK6piWtEfANwOt37ukshxn+uldaTLaTgkR8sLSXTN37/F3KIe07afhJNGM57zJ/qO1nY0GKxHNVLcZ2tNpaf0RXaOgTV0lzxf4xqPjDdtl3YsEEPAFUBFqVDzmk2kyin9fNh40JzGER67PpOha0crtOL9SBR8LtnKeyIAcMgO/T7D7kfWUOGP3BRjDNdSWuGPF+WaiUcTUNxne7i4dhEAcKrr1IEz7m/pugUA8Nr6a3QQuk3NZLQuoC5vF0LukCljsMKh1sYdEq2OAOETtanMFN0MkIYw8/nMHxegG1zSeLxJw+/0o8/f19LH5gUbHuEvIn7+mM/pa/nPh7PCOX/8PXIwNNjy8+L4NX82O0txyHvEi/3DoWFTrmncYHAQDsmBbCUr9I7WdiVLsr6DV+T3aTsajYxCwubXpgSpYYvRVbWqL9of6zjWkO+5HVmSqZneZjYdeWLC+bAAEAvG4JJdKNQKWC2umjKGdtLs9yNR1NSaPj8yI9mrXRrZqPBHboqf39DsmE+u199LcZ9tIlVKIZFNQIKE012nD/x9xiJj8Dv9yFfztDPDpvRM+4g5Z6IAhjN/BM0AZ6zeAWbGpGkgOACX7EK+msdKcaXlj0/shTG26SwLMwwEBuB2uFGoFbBcWDZlDMSeeFfpaGTUtCaNudwcKkqlpY+9V8Y4K7MKJPw6Op+fFzbiW++QbmHMJ9fp7UTYHYbCFCRytKNkL/g1zayYNs7lcGEgOABA7MJ2O+NFm+spivtspQdvfVCP0wOgx+w9eOuDDfn+iUwCVbWKoCvY9KaWo1Et7nMqPUXN9DagN46YeP1wyk4MhYYA0LWjFZr9fiSKudycnobQ7etu+ePHw3FIkoRUOYVUKdXyx28VKvyRm+JdQs2O+eQo7rN9XFq7BEB7sw27wwf+Pg7ZgZNdJwHUdxAS+1CZamoXENcf6Ifb4UapVhKyCLBaXEW+mt80KW8lp+xEPKw1h9gxgoK01lppDdlKFg7JYcrzGdCuLbzhiZpKSKOY3aTR4e1A1BPVrq2CNtjpu31D5jX7RDwRRD1RYSO+C9V6Q8JoZLTljy9Jkv64dM3fXaaSQaqcgiRJpl3TjPRz/igGUEiDwUG98Wgxv2j2cNrG2ZGz+PRdn8bxjuNwy24c7ziOc3edw90jdzfk+0+kJgBoZ/A1u6mlz9+HkDuEqloV9lpP9qaiVPTYXzPnRUC98MjXZkjzNPv9SBTGmE8zmv08Do8e72/n+30q/JEdtTrmk6O4T/urqTVcTl4GUI/qPIzTXachQcJsdtbWnRrtaDG/iIpSgdfpbem5dVvJkqyfiyLipICPaTg03JImje3wXQd2j0ogzcc7SYdCQ3DJ5kTaAIadvtTZShpkpbiCfDUPl+wyrQCgF2wEPM8iXU7rBRLeTGIWkV//Uxnt7Jdefy8CroApYzD+fGhX0s3x4nG/vx8eh8fk0dQXb/lZukQsDtmh787mazGkNc6OnMUT73kCL37wRTzxnicatsheVar6teRYtHkxn5yxmZ4XHIk1zWZnoTJVa0jyRk0dC18LWcovoVgrmjqWdtCs9yNRMMY2He1hFj6ftXMjGxX+yI5aHfPJUdyn/V1LXUOpVkLQFWxIZEHYHda/D99JSOzBWNBq1iHoe2U880s0+g4SEzr/OR7LtlZaQ7qcNm0cxPr44oiZu3yBjec0JKwWV5GtZE0dC7EH/l4dD8dNa9IYj4wD0F5nop2NzK/5A4EB0wskxs520QpbZu4a5WKBmJ6EsJRfMm0cViBCTJtRh6cDQVcQClP0nSRELLwR+nr6Op0zbAOTmUnU1BoinkjL4ux4gXE6PS1stDfZnR4THTL/+hFyh9Dl7QIDE3I9hFiLsRmS77ozA18/W8wv2ragTYU/sqNWx3xykiTpk13qcrMnHsl5uut0w4o5Z7rOAABeW3+NuldtxOxzvoxELQJkK1msFlchQTL1psDn9CEWiAEQc4cEsYZCtaAvIpv9uvc5fegP9AMQs+BPrIcXbMw4l43r8/fB7/Rr8VF5sRb9+evM7Nc+UC9sFWtFoSK+q0oVs9lZAOY2+zhkR71LWsDdo6JQVEX/fYmwcAto99p6YZuubUIaCg3B7XAjX81jqUCFdau7mtTW1Y5Gmx/zyXX7uhHxRKAwxdY7WeyMMSbEkSdGFPdJGoWvF8VD5jVDAtomErsXtKnwR7ZlVswnxx+T4j7tZ7W4isX8IiRJwumu0w37vsPhYQRdQZSVMq6nrjfs+xLzpMtpJEvJTQsUZhK1CMCjNfsD/fC7/KaOhd+UiBiHSqxhJjsDBoZuXzeC7qDZw9ELELSwTQ4rXU5jvbQOSZJMLWwZH1+kxcCKUsFcbg6AGAtcxnM+RWpmSeQSUJiid96bSb/mU8T3jhYLi6iqVficvpbt9NkLXoSkxVsxOWWn/vqiRmhrKytl/XV2NHq0ZY8rSZK+64+eQ9a0VlpDvpqHU3YiFoyZPRwAmxOQREtDINYiQmIVx8dg1zUsKvyRbZkV88nxuE86kNh+eBTneGS8oUUKWZL1QiLfUUisjS+0xQIx0yO/OF6AFGlSwMfCzwMxEx/DXG4OZaVs8miIFfHnswgL/0D9BncuO4eqQo1I5OD4De5AYABep9fUsfD36qn0lDAxciKdY8OJeM4fL7KNRcZatnNkJ8NhLYY9VU4hWUqaOhZRGWPazP59GQ2FhiBJEtLlNMWzC2o8qsUyX0tdE+Z9muzfZHoSKlPR6e1El6+1zRq8mX4mO4NSrdTSxyaHp595HhwydUeUUX+gH26HG2WlLFQaArEWURKrOD7fT2QTqKk1cwfTBFT4I9syK+aTo7hPe6ooFVxZvwKgHs3ZSKe7TkOSJCzmF7FaXG349yetJco5X0Z6ESA3J8Ru5LJSFmqHRMQTQYe3A4wxoRZKiTUYI9FEiPoDgE5vJ0LuEBSmYDY3a/ZwiIWJ1Nk6FBqCS3YhV80JM1/iRX9RXvuAFj/EI75zlZzZw4HKVKHmRm6HWz+XRaSGKJEkMgkA2rmeInE73Ho8OzXZiikeiuvv07TAbl0TyQkA5qRodfm60OnthMpUoXb4k70R6cgTTpZkvVBD9/rkoPhzR4TEKkDbeMSPQVjILZg9nIajwh+5gTHmk3eamYHiPu1nIjmBqlpF1BNtygGufpcf4xHtOXtxlXb9WVlVqeoFLRFiPrkubxeCriBqag3zOfPPRuIxG1FPVLgdEhT9RfZrPj+PilKB3+lHr7/X7OEA0BqRRNz1Q6ylWCtiMb8IQIzd2U7ZqSd6iLAYyBgT6nw/zu+qvxdNZ81//S/ll1CsFTcVbcxG1/yd5at5rJXWIEEyJUFnNxT3KTaX7NLvga6n6RgLKyrWinrTGI/dbDUeL8ob+4k1FGtF/cxzkdZCgPo8je6LyEGJ1AwJbNzvb4zFjsd7UOGP3MAY82nmtltj3CfvliTWxRjTIzjPdJ9pWtwN30n4evJ1VJRKUx6DNF8il4DKVITdYXR4Oswejs44KRBhsqvHIgoyaQLqi9oz2RkoqmLyaIiV8IXj4bBYkWjGwh/FbZGDmM5M62dXht1hs4cDoP5eLULhb6W4gkKtAJfswkBgwOzhbKIvcKXNv+bzxYiR8AgcssPk0Wj4/GMxv4hCtWDuYATDi9k9/h74nD6TR3Mjvpg8l5uzZbSVHfAEpOvp6zT/sCD+e+v2dZvWoMkLf7O5WXqPtpBENgEGpiePiMSYhpCv5s0eDrEY0c705vh8X6RjEBqFCn/kBny3n1kxn5wx7pM6lKxvqbCE1eIqHJIDJzpONO1xBoODiHqiqKpVPVqDWA9fYBsJjwhVAADqHdJmTwoUVdGLj2Nh83eQcH3+Pj0qYT5v/q5IYg3GeFiRCtkAEAvG4JJdyFfzWCmumD0cYkG8qC3aDa4kSVgrrZl+xhd/7cdDcWEKWhx/P5rNzZqeQCLi8yjkDqHb1w0GRjvHtuA/D5F2sRp1ebvgd/pRU2tYyNsv2soORsIjcMpOpMtprJXWzB4O2aerSW0N61iHObv9ACDqjaLH3wPGmBCNPmRveOOIaLv9AC0NocffA0CMRmhiLYlson6mtydq9nB0QyHtLM1cNWe76y0V/sgmosR8chT3aR88evNox1F4nd6mPY4kSTjTre36u7B6wXbdGu2AsfrikUiLW9xgaFCIScFCfgEVpQKf04e+QJ9p49hKkiR9kYtuMMleJctJZCoZyJKMeFCsSDRjLCLd4JL9qqpV/ZomQswn53V69bhIs89nE/F8P67L24WAK2B6xHeqlEKqnNLO1xFsIVCk3aOiUJmKRHbjfD8BYz4Bbb7Gzx6kdB0xuRwuveGQr9EQa8hX8/o1w4zz/Yz4rr+JFDVFW4HKVKHXQoD6fI3OiCX7ZWxiE6nB3yW7MBQcAmC/+SwV/sgmS4Ul5Ko502M+OYr7tIdSraTv2uRRnM10ouMEHJIDa6U1LBWWmv54pLF4bIRLdiEWFOMMGyOX7NLPqDRzsmtcKJUlsS7nfIeE2bsiiXXwgtpgcBAuh8vk0dxIf06bXCAh1jObnUVNrSHoCqLb1232cDYRoWCTr+axUtB20opY+DM2s5hZ+OfvPYPBQXgcHtPGsR2+MJnIJqhRc8NyYRkVpQKPwyPMmbXbGQltLN7Sbk1h8Wbsq6mrNKe2kGupa2Bg6PP3mR7xzQuPC7kFima0gOXCMkq1EtwON/r84jT3GvF50Wxulo72IHumMlU/M1u0hB8AQh3p00hirRQS0/FOMh4rYTaK+7SHy+uXoTAF3b7ulkxevE4vjnZonW18pyGxDr64xbfbi8h45pcZjHEtIk6a7ByVQJpDxAg7o+HQMCRIWCms0KIJ2Rf9uR0Rq7MVqBf+FvILKNaKpoyBN9D0+nvhd/lNGcNujIU/sxbe9Wu+gO+R3b5uBF1B1NQa5rJzZg9HCMb4WtGas4yGQkOQIGG9tI5sJWv2cMg2RsOjkCUZ6XIa66V1s4dD9oivXfE1CTOF3WH0B/rBwGhNzQJEjj/nenza2bUVpUJR0WTPlvJLKNVK8Dg8euqISPgce7mwbKv7fXFnoaTljDGfZscRGPEuN4r7tCbGGC6uacW3M91nWrbodUvXLQC0SbdZi1nkYPhkV8TOf47HbC3mF015fq2V1pCtZOGQHMLFIgLarkgebWW3qATSeMVaEYv5RQDASETM173f5dd3bdCuP7JXKlP154tIMZ9c2B1Gl7dLi9g2aQe7Fa75Q8EhOCQHspWsKQvvhWpBf48UsdlHkiTaFb0F30EnWizrVl6nV4+Lp11/YjImMV1PXzd5NGQvspUsFvOLkCDpMZtm4+OgyFjxWWFeJEmSfn2juE+yV5MZbV1IxMQqQLvf5xtV7DSfFe8nTUzDYz6NC7Yi6PP3IegKUtynRc3mZpEup+F2uHE8erxlj9vr70W3rxsKU3Bl/UrLHpccTqFaEDryiwu5Q9piKcxZLOU7SIZCQ0LGIgL1jik7TZpIc8xkZsDA0OXtMj0O6Wb0XT9pe8V/kOZZyi+hWCvC7XBjIDBg9nC2ZWbcZ02t6eegiXzNdzlcGAxpEd9m7PSfyWrvkd2+boTcoZY//l6MhbXnEUV8b57LinRPvRM+RrrPFhdvyqaijTXw31MsGEPAFTB5NJoj0SOQIGExv4hMJWP2cMgO8tU8VourACDE0Us3w6Oi6V6f7JXoCT/A5iNr7IIKf0THJyijkVG4ZHEWkiWp3il1LU2TXavhu/2OdxxvaYFCkiSc6T6jj6HdFyGsgi9u9fh7hLlR2gnfmWRGh7TIO0i4kfCIHo2Yq+TMHg4RmN7ZKuhuP47fCMzmZimBgOyJsbNV1Lgmfh0x43y2hdwCqmoVAVcAPb6elj72fpkZ8c0XH0S+5g8EB+B2uFGoFdr+fG1ezO72dQs/lwXqi8t0VpO4+O6I9dI6kqWk2cMhu5hITQAAjkWPmTySuoArgFhQi9ajArK4eENxj79H2PhzLh6OQ5IkpMoppMtps4dDBJcqpZAqpyBLMuJhcZui+Hx/NjuLqmKP+30q/BEA4sZ8cjzucyo9RYttFpKv5vUO8tNdp1v++Mejx+F2uJEupzGbm23545P94wUt3kEmMuMioMrUlj1uvprHcmF50xhE5Hf59fgo6gQkO1FURS+ei/x8BoAubxedY0X2zHgWK98NJSJ+PltVrbb8ec2vDcOhYeHOP9zKGPFdqpVa9rg1tWaJ90iH7NB3jrX7NZ8X/kSP+eR6/b3wOr2oKJW2L9qKyuv0Yig0BICKNqJLl9NYKaxAkiThmjV4IXIiOWHySMhOprNac5HI13vO4/Cg398PgOI+ye743HAgOACPw2PuYG6i09uJkDsEhSlI5OyRhECFPwJA3JhPjuI+rem1tdfAGEMsEEO3r7vlj+9yuHC8Q4sX5TsPibgUVcFsVivQihz5xRkXSvjZO63AO//7/H3CdwLymxY654/sZCG/gIpSgc/p08/QE5XxHCszdv002vnp87j/yftx+5dux/1P3o/z0+fNHpKtJMtJpMtpyJIsdAHA+Lxu5Xs1Y8wyu30B7TzETm+nFvHdwp3+s9lZ1NQaAq6AKXPp/dAjvm0Uj7RfjNWfH6LHtHGSJOn3/3TOn7iORLTmbDrnT2y8qDYUHBLuPm08Og5JkrBaXEWqlDJ7OGQL41qIVa4f+n1R1vr3RaS5+D2G6EVtSZJsN5+lwh8BIG7MJydJUj3bnuI+LUFlKi6tXQJgzm4/7kyXFvc5mZ5Evpo3bRxkd/P5eVSUCvxOv/AFAADaYu7GpLyV3e08Oo5PtEXGO13ncnOoKBWTR0NExBf+h8PDQh7yvRVvSpjKWPscq/PT5/HwMw9jIjmBilrBRHICDz/zMBX/GojfLA4GB+F2uM0dzC74e/VUZqplO9hT5RQylYwW+RMUr+lwO/o5ny0s/BujvUXfFTkSHoEkSVgvrbdt7NdKcQWlWgluhxt9/j6zh7NnfD5LuzbENRoZ1Ys27fr6sgK+rsaPqhGJz+nDUFDbOXo1ddXk0ZCtrNQMyfFrx1x2jpLZyI6KtaLeKG+FNSxjo6+V7/c58VdYSNNtivmMiBfzyfHCH8V9WsN0Zhq5ag5ep9fU+NguXxdigRgYY3ohkoiJLzYMh8WP/OL4ImCrFkqqSj2KTfRuKQCIeqKIeCJQmapHXxFixBe1rfB8BrQijlN2Il/NY620ZvZwDuyxVx+DBAkM2s0MA4MECY+/+rjJI7MP3tk6Hhk3eSS7Gwho57MVa0U9SrrZePFsMDjY0jOgD4O/T81kZlpSIDXuirTCe6TX6cVAYABA+8Z98t/XUHBI2HM9t8N3/K0WV1GoFkweDdmOz+nDYHAQAO36E9V6aR1rpTXIkixczCfHC5JU+BOPvhZigfhzrtPbiaArCIUpmM/Nmz0cIqiZzAwYGLq8XQi7w2YPZ1fG+yI7RKBT4Y9sjvkU+JDNTXGftIAsPB6tebLzJJyy09Sx8B2Hl9YutfQsNrI/+vl+Foj55HiRslXd7YlsAgpTEPFE0OntbPrjHZYkSfrZVhT3SbZKlupRiCLGjG/HKTv1sVr5OT2VntKLfhwDs/T/J5Hkq3n9RtEKna0O2aFfe1u1oGzFa35foA8ehwdlpYylfPMXApYLy8hX83A73BgIDjT98RrBjNhYkfB7VJHvqbfjd/nR4+8BQHGfIuNN2nTOn5iuJrViWjwUh9fpNXk02xuPjkOWZK1IWbRuA5sd8bhMkePht5IkyZQ0BGIteqOvBe6JAO2+yIxkr2ZpeuHvc5/7HMbGxuD1enH77bfjBz/4wY6f+8wzz0CSpBv+XL58udnDbGuix3xym+I+abIrtHQ5rZ/FyKM2zXQkegRepxf5at4Wb9x2lCqlLFcAALRDrWOBGIDW7PrTYz7Do5bpBDRGJVDhnRgZd/yIHoVoZIcb3NHIKCRsfg+RIAnboW41fK7R6+9FwBUwdzB7xJs0WnGeRVkpYyG/AMBahT/jeY2tmE/yx4iH4qY30e0V35m4kF9AqVYydzAtVqqV9IKwVc5nMqK4T/GNRcYgQcJyYRmZSsbs4RADxhgmUtr5fiLGfHIeh0d/rdOuP3FkKhkkS8lNZ65aBZ8X2SUWkTRWTa3pTVFWSK/g+BqWHc75a2rh76//+q/x0Y9+FL/3e7+Hl19+GXfeeSfuu+8+zMzcfDJ55coVLCws6H+OHTvWzGG2NavEfHIU92kNl9YugYEhHooj4omYPRw4ZSdOdp4EAFxcvWjyaMh2+OLWQHDAUgUAYPOZX82kMrUe+WWRbikA6A/0w+v0blroJQSw5o4foD7e5cKyZSPRHrz1QT3eE4Ae+/ngrQ+aPDJ74LudrFRI5edspsopJEvJpj5WIpMAY0yPg7aSVhb++fPISgslPJHAGFPaLmazs2Bg6PR2IuQOmT2cfePFgEQ2QY1agvK7/IgFtYbD6ymK+xTJWmkN6XIaDskh/LX/aEc97pMKNWLgDRf9/n5hd4vuZCg4BIfkQLaSRbLc3PkjsZ6FnHZ2pd/pt8zZlUA9ctcO51Y3tfD3J3/yJ/jN3/xNfPjDH8apU6dw7tw5xONxPPbYYzf9ut7eXvT39+t/HA7r5ONbjVViPjmK+xRfTa3h8rq2S/dMt/m7/Ti+83A2O2v5N2474rFCVisAAPUxz+fmUVWa15CwmF9EqVbatMvQCmRJrhdHbdAxRRqjVCtZcscPAARcAT0SzaoL22dHzuLTd30axzuOwy27cbzjOM7ddQ53j9xt9tAsr6JUMJudBWCtgo3b4dbPj2p2TKOVzq3bajg0DAnaQkAzd9yky2msl9Y3xWhZBV/0breUDT6XteJuP0CLsnU73Cgr5Zad9Un2jzdr0zl/YplIarv9RiIjwjexjoXH4JSdSJfTWC2umj0cgvq8yGrXewBwOVx6HDntGCdb6YlVEeskVgH2Ore6aYW/SqWCF198Ee94xzs2ffwd73gHfvzjH9/0a2+77TbEYjHcfffd+P73v9+sIRLUO8VGwiNCx3xyFPcpvsn0JIq1IgKugFALOhFPBPFQHAwMl9YumT0cYlBRKpjLzQEARkLWm+x2eDoQdoehMAWzudmmPQ4vmo2ERyBL1jqiV4+Qy0xRZykBoC2QMsbQ4e2w3I4foF6wsPKNwNmRs3jiPU/gxQ++iCfe8wQV/RqE75axylmsRuORcQDNfV6rTNXPsRmJWO+a73V60R/oB9DcBS7+O4gFYpbr/ufvj4lsAjW1Zu5gWoQxZtnz/ThZkjEUGgIAarAV2HhUe59ezC8iV8mZPBoCaK9/Hpt5LCp+WpnL4dLjGXk8KTFPVa3qayFWOt/PyA7HIJDGY4xtWsOyGrs0rzdt5XB1dRWKoqCvr2/Tx/v6+rC4uLjt18RiMfz5n/85vvrVr+JrX/saTpw4gbvvvhvPPffcjo9TLpeRyWQ2/SF7wxjDtbRWPBM5h3wrXvibzky3zc2klVxYvQAAON11WrjiBN+B+Nr6a/TcEUgiq0V+RTwRRL1Rs4ezb6041JoxpndLiR4fs514KA6H5NB3MJDmssLcyMo7foD6uGezs3Q9IZvoMZ/hMUt1tgL1GOml/FLTYmyXC8so1UpwO9zo9/c35TGarRUR33yRwYrXfH62ZUWpYD43b/ZwWmKttIZ8NQ+n7LRUKsNWvAHPbrs2rDAv2quAK6A/x2jXnxiWCkvIVrJwyS7LFG54gfJqkuI+G+2pCwu499xzOPH738K9557DUxduftTFfG4eNbWGgCuALm9Xi0bZWHoCUn4eFaWy+xdcehJ47K3AJ3q1vy892eQREjOsldaQq+bglJ16Y5GV8Dn4fH7e0udWN31VfusNL2Nsx5vgEydO4CMf+Qje9KY34S1veQs+97nP4V3vehc+9alP7fj9P/nJTyISieh/4nFrdtiZYbmwrE9QrNSZyOM+K0pFj1QhYlgvrWMhvwBJknCq85TZw7nBaHgUAVcApVqJbpQEwhfOrFoAAOpjb9ah1qlyCulyGrIkW+7Ab0DrLB0MaRFyVt4hZRWiz41UpuqLilZ93Xf7uhFwBVBVq22zsE12p6hK/ZpmobNYuYArgF5/LxjqzSaNxov+w6FhOGRrHufAF7jmsnNNifgu1UqYz2vvK1Z8jzQ2RLXLNZ9f0waDg3DKTpNHc3B8jrlcWEaxVjR5NI0j+rxov/iuP0pAEgPf7TcaGbVEihag7SxzyS7kqjksFZbMHo5tPHVhAQ98+SVcWcyiXFNxZTGLB7780k2Lf8aYT6s1jHERTwQRTwSMMT3ufkeXngS+8kFg6RJQK2t/f+WDVPyzId4MORQcssx7o1HEE0GHtwOMMUvXHppW+Ovu7obD4bhhd9/y8vINuwBv5pd+6ZcwMbHz9vNHH30U6XRa/5NIUCzFXvGJolViPjmK+xTXxdWLALQu96A7aPJobiRLMk53nQZQHysxF2NMXyyx4vZ/LhaMwSW7kK/mm3JWAp80DQYHhT83Yif6mT8Wj0qwAtHnRov5RZSVMjwOD/oCe58TiqQdF7bJ7hby2gH2xjhIq2n2e7WVz7HhOr2dCLlDTYv45k1End5OS0YhA/Xn0WR6si12k1j9fD8u6A6iy9sFBmaruE/R50X7xWOZF/OLTdudTfaGMaavSVkpRcslu/T3aV64JId37vwEJAD8qscASBLwme9uv6ZtXAuxym7Rnez5vujZPwZu/CkBz/7X5g2OmEJP+LFgMyRnbPC3qqYV/txuN26//XY8/fTTmz7+9NNP461vfeuev8/LL7+MWGznuAyPx4NwOLzpD9mdVWM+OYr7FE9VqeJK8goA4EzXGZNHs7NTnacgSRIW8gtYK66ZPZy2xzuK3Q63paORjPEFzZgU8Am0FSO/OD5pWio0L0KOaESfG1n5vEoj/Zy/NJ1dSTS8SWM0PGrZ57YxxrbRu9lylRxWi6uQIFl6gctY+G9GJKKVd41yfOdbsxqiRFJRKljIa7s5rPy85ngSUCJj7eKYkejzov0KuUPo8/eBgVGKjckW8gvIV/NwO9yWS2Xh64DXUtegMtXk0djD5GoeW+8IGAOur+S3/fxUOYVMJaOl+gSt9fzZyhgVfdP7orWrwI0/JWCNzpu0k3w1j+XCMgBrpldwfP1tOjMNRVVMHs3BNPWO9JFHHsF/+2//DV/4whfw2muv4eGHH8bMzAweeOABAFrn1Yc+9CH988+dO4evf/3rmJiYwMWLF/Hoo4/iq1/9Kn7rt36rmcNsS1aN+eT6/H362RFW3nJrJxOpCVSUCiKeiND5zUF3EGNh7c374hrt+jMbL5INhYYsG/nFNeucv0K1gKW8FsFi5UkTj5ADaIdUu7NDvC8ADIYG4ZAcyFVzWCtRI0m7Y4zVC38WLtjwXWYKUxo+x+bXx75AH3xOX0O/d6sZO9sbWfhXVEXfaWXl90in7NR3v9n9mj+bm9XPqrbqDk0j/nvjZ3ATMVECkhj4brmxyJjlYn7joTjcDjfy1bzevEAOZ6w7gK1hnZIEjPcEtv18Pi8aDA7C5bBOCtt2eAJSoVa4ecNP11Hgxp8S0HWsmcMjLcbnfn3+PvhdfnMHcwi9/l74nL5NTV5W09TC3/vf/36cO3cOf/RHf4Q3vvGNeO655/DNb34TIyPajdLCwgJmZuo3lJVKBb/zO7+DN7zhDbjzzjvxwx/+EN/4xjfw3ve+t5nDbEtWjfnkjHGf11PU5SYCXkQ73XVa+GxyviPx9eTrTTmbheydXQoAQH0RcLmw3NAdbdOZaTAw9Ph7hIzQ3Q/+e+aL46T9pMtppMopSJJkycYjI5fsaupOX2ItxgPsrdb1byRJkt4g1ei4TzvEfHLGHW2NLPzP5+ZRUSrwO/3o81szCpnjBXC7X/P5zjirx3xyscAeF2+JqfguhPncPCVpmERlqr6udixqvaKFQ3bosbFXkxT32QgfPXtMj/fExt+MAQ/dfXzbz9fPPbbBbvE9JyC9/ePQ4z0B6LGfd328ySMkrcTvIazcDAlox0VZ/XiPpmfQ/Pt//+8xNTWFcrmMF198EW9729v0f/viF7+IZ555Rv/vj33sY7h69SqKxSLW19fxgx/8AO985zubPcS2Y/WYT46PfSozRXGfJlsuLGOlsAJZknGy86TZw9nVUGgIEU8EFaWCiRRFCpiFxz9JkGyxWBJwBdDt6wZDYw//tVNxlE/8mhEhR6yBP58HAgPwODzmDqYB7JD7L4rz0+dx/5P34/Yv3Y77n7wf56fPmz2kfeHFjXgobsmmOiP+Xj2VmWpY/FdVrern4dmh8OeUnRgKagtcjSyQTmbqu0ZFb6TbzUh4BBIkrBZXka1kzR5OUzBWn/PZYeEW0IoBfPGWknXEFfFE9PsO/r5BWmsuO4dirQiv04vB4KDZwzkQXrC8lqa4z0a495YYHv/Am3CyPwSPU8bJ/hAe/8DtuPeWG899Nu4g4jGZVsevgze9dpx+D/C+LwF9ZwCnR/v7/V8GTr27RaMkzVZVqpjNanN+O61hWfV4D2sePkEOxeoxnxzFfYrj4qq22+9o9KglopskSdJ3/VHcp3n4Qnmvv9fS2/+N9DO/GtQNVFWreuSXlc/347q8XQi7w1CYgkTOPmfHkL0znu9nB/z/x1Kezq48jPPT5/HwMw9jIjmBilrBRHICDz/zsKWKf8bz/ayuP9APr9OLslLGfG6+Id9zPjePmlpDwBVAl7erId/TbCORxkZ8M8bqHdI2eB75nD70B7TFzkbvHhVFspxEtpKFQ3JgIDhg9nAahjfkNeMMS9I4lIBkLh7zeSRyxLJHVgyGBuF1elGqlTCXnTN7OLZw7y0xfOuht+HKJ+7Dtx5627ZFP0BrhFWZiogngqg32tpBNgkvYC7ll1CsFXf+xNPvAR78EfD7y9rfVPSzlUQuAYUpCLlD6PR2mj2cQ4sH43BIDmQqGayX1s0ezr5R4a8NWT3mk7N63KfVO9u5slLWd83xYpoVnOg8AYfkwEphRT8/jbSWnaItOF4EmM3ONuTw39nsLGpqDUFX0BYLpZIkbeqYIu2lrJQxn9eKCHYoZAPaubHN2Onbbh579TFIkMCgdVEyMEiQ8Pirj5s8sr3JVDL6DnY7FLVlSW54Iwu/5o+Grb+TjeMLXI2K+F4trupxsYMha+4e2Yq/11s1Hmk3vDA2EByw9H31VnzH32JhEWWlbPJoyE6ORLS1kNncLEq1ksmjaS+KqtRTtDqsm6IlS7L+PNpLEtJTFxZw77nncOL3v4V7zz2Hpy5Y88wrEehrITZIPuKCbm3NgoFR40gbMzax2WHO73K49Hm5FeezVPhrM8aYT140szKrxn3aobOdu7J+BTW1hk5vp97VawU+p09/DdCuv9arqTVbbf/nGn34rzEb3Q6TJmBzNCJFyrSXRCYBxhiiniginojZw2mYRhdI2tFUekov+nEMzDJng/H36v5Av312sBvOZztsrA1jzFbn+3HGwj/fnX8Y/D3EDnGxHH9/nMvN2bKAxH/vVj7XczsRTwRRTxSMMX2+TsQT9Ua1RXbGaA7SYolsQj+PNRaImT2cQ+GFy+vp6zddU3vqwgIe+PJLuLKYRbmm4spiFg98+SUq/h2AMSbaTmshwB7jPoltGef8Vj/fz6hZ55+3AhX+2gyP+XTKTlvssjHGfTbihrtVrN7ZzjHG9KLZLd23WK4wcUv3LQC0mA7qkmyt+dw8qmpVPxfPLiRJ0t9bDxv9ZZw02WV3FADEAjG4HW4Ua0XabdtmjDt+7IQXMhKZREN2+raj0cgoJGyeQ0iQLPPexwuUVhnvXsRDcThlJ7KVLNZKa4f6XuuldT0O0S472Tj++m/Eorsdn0dRbxRRTxQqU23X/V9Vq3oUrh3uq7fSF29t9nuzm/HoOIB6qhNpDT3mM3oEsmTtZdVYILanNbVz5ycgAXqbFgMgScBnvrv7TkGy2VppDflqHk7ZiVjQ2oXjrfh93kxmhpp8bWSviXVLBS3m1e1wYyBgnwh0Pt9vVMpHK1n7CkX2je/2Gw2P2qKT1Bj3udtkV6RoTat3tnPz+XkkS0m4ZBeOdxw3ezj71ufvQ5e3CzW1hivJK2YPp60YO/+tVjDeTaN2/ywVllCoFWw3aXLIDn3iNJmx1nseOTiVqZjObrzuI/bZ8QNsnFPq9G9aBN5KpDmIiB689UG9CQqA3hz14K0Pmjyy3ZVqJdtF2ALQzgLf2MV0s/npXmK/+DV/MDRoi/sPI73wn9258L+Xn1G2ktXjYu0U+wXUO77ttiNpPjevn2HT4ekwezgNp5/zl53ZdtcvXdfEwNdCEtmELXfViqiqVvXr4rGOYyaP5vBkSdafR7yguZ3J1Ty2vhMwBlxfyTdxdPbEdw0NBYfglJ3mDqbB+gJ9cDvcKCtlLBeWzR4OaYD9JNbxud5waNiyZ59uJ+gOosffAwbWsLO9W4UKf22EMaYXx+wQ88ntJe5TtGhNq3e2cxdXtd1+xzqOwe1wmzya/ZMkCWe6tXMJL65ePHSUFdkb4042O3ZIx0NxyJKMdDmNVCl14O/DbyjtNmkCrB2VQA5mKb+EUq0Et8Nt+UikrSRJuumuH9HmICI6O3IWn77r0zjecRxu2Y3jHcdx7q5zuHvkbrOHtqvpzDQYY+j0dtoqwhaoN7LsVPjba+yXXXf7Alrh3+v0oqJUsFhYvOHf9/ozsmNcLDce0XYkTWembbUrmu+EGw4N266JDQBiwRgckgP5ah7rpfVN/0bXNXF0ejvR4e2AylSaV7dIIpNAVa0i6Aqiz99n9nAaQl9TS0+hqla3/Zyx7gC2vtNJEjDeE2jy6OyHx2DaKf6ckyVZbxyxWoGEbG8/iXXGo2rsxqrHe1Dhr43YLeaT20vcp2jRmlbubOcK1QKup68DAM50nTF5NAd3vOM4XLILqXJK79gnh7NbB3CynESmkoFDciAetNeZKAC0HXpBbYce3+F0EHxCYcdJUzysFUdT5RSSpaTZwyEtwJ/PI+ERy0cibYfvYuRFICPR5iCiOjtyFk+85wm8+MEX8cR7nrBE0Q+o71y243v1SHgEEiSsFleRrWRv+Pe9xH6VaiUs5rWCmJ3uPzhZkjES2nj9p2+85u81Gs3O1/xGn38sCr5wGw/bby4LaLt+eTTv1ntsuq6J5UhEa+rm9+akuSZS2vv30Y6jtin69/n7EHKHUFWrO8b7fvTsMf0aho2/GQMeutvE5KdLTwKPvRX4RK/296UnzRvLHhmPu7DjvAioFzSp8GcPe02sS5fTWC+ta8ff2Cy9AqgnuySyiR0bJERkv5UXsiMe8zkSHrFVzM5e4j5Fi9a0cmc7d3n9MlSmos/fhx5/j9nDOTC3w63HlPIdjOTg9tIBzCeAA8EBuBz2eS8yOuxkN11OI1lKbtpJZCcehweDQW0xiW4I2oMx3teO4sE4HJIDmUoGyfLmYrZocxDSODW1hkRGWxDnO5ntxO/yoz/QD2D7Hdp7if2ayc6AgaHL24WwO9zE0ZrnZjt+9/IzqigVzOXmANhzV6QsyQ09C1EE6XIa6XIakiRhKDhk9nCaxhj3aUTXNbHwc/5mMjOoKBWTR2NvVaWqz2n5Ljk7MK6p8cLmVvfeEsPjH3gTTvaH4HHKONkfwuMfuB333tLfyqHWXXoS+MoHoS5dAmpl7e+vfFD44l8imwCDlhQRcofMHk5TxENxvXEsX6UoWKvba2Idn+MNBAbgdXpbNbyW6fJ2IegKoqbWMJedM3s4e0aFvzZhjPm00wSF411uO8V9ihitadXOdkA7q+nimlYk41GZVsb/P1xPX7fcQa2i2UsHsL7934aLWxzv/p/PzR/oBpwvnAwGB+FxeBo6NlHsFiFH7MPu3X8A4HLUd0ZsLZCIOAchjTGXm0NVrSLgCqDX32v2cJqCP0+3O5N1L7Ffdo725uLhOCRJQqqcQrqc3vRve/kZzWRmoDIVUU8UHV77nRUH1HcyTqYnLRGtv1t6Bd8REwvELHncwV7xa/Z8bh5Vpd7dTtc1sXR5uxDxRKAwhRrqmmwyM4maWkPEE0GPz7rNz9s5FtXOK5xOT+94/3rvLTF866G34con7sO3HnqbeUU/AJlvfwIqkyBvrDvIYFCZhMy3/4tpY9oLfv2wazMkoDWO8c0B9J5kfXtNrOP3wHZ9bkuSZMlzq6nw1yZWiiu2jPnk+gP9N437tEO0pkgS2QSylSw8Do8tzovs9nWjz98Hlal4bf01s4djabt1AJdqJf0MHDu+F3FRbxQRTwQqUzGbnd331+uRXzYujvJJ02J+kQruNsdv+GKBmC27/zj+et16g0tzEPvi17bR8Kht4r624gv5c7k5lJXypn/bLfZLZaq+wGXn65nH4cFAYCPie8vrfy/RaHaO+eT4ruhsJYu10prZw7mpvaRX8B1wdm1m4SKeCMLusDafzdXns3RdE8umBKT09glIpDGMzfR2u+53+7r1ArIVFrW96euQpc3rDrLE4BX4NaAy1dbn+xnx/387RccS69hLYl2pVtKPTrJzE5Dxft8KjWwAFf7axtXUVQD2i/nkjJPd66kbs+3tEK0pEh6JebLzpG2eT3zX36W1S1CZavJo9u6pCwu499xzOPH738K9557DUxfMPTtltw7gRDYBxhg6vB2IeCJmDLFlDnr4b7FWxEJO+z3aeREw5A6h29cNBnZDhJSIduv+JzvjC+F2XvgH6je4i/lFFGtF/eM0B7Enxpje2WrnG9yIJ4JObycYYzcUtXaL/VrML6KslOFxeNAX6DNj+C2zU8T3bj8jRa0vsNoxLpZzOVyIh7Sz8LaLjRXJbukVNbWmR7PauYkN0O6x+f9HY3MtXdfEMx6px30ad2eKyorz6rJStmXMJydJkr7r72ryqsmj2d11FoPKNq87qEzCNTZg0oh2t1xYRqlWgtvhRp+/PeZFs7lZKKpi8mjIYe2WWDeTnWmLdb6B4ABcsgv5ah7LhWWzh7MnTrMHQJrP7jGf3JHIEfxs5Wd6/IJT3vz0PjtyFmdHzpo0OvvIVrL6hPd012mTR9M4R6JH8KO5HyFbySKRTViiA+upCwt44MsvQQLAAFxZzOKBL7+Exz/wJtx7S8yUMT1464N4+JmH9QWTrR3Adj/ny2gkPIJXV17Vu4H22hU6k7H/eUjcWGQMq8VVTKWncLLzpNnD2RHv/ufPZ979/+m7Pk3XlV0Yz66y++s+5A6hy9uFtdIaZjIzONF5Qv83moPYz1JhCYVaAW6HGwNBcReZGmE0Mor10jom05P6ucjcvbfEdpxzGK/5smTvftOR8Ah+PP9jzOXmUFEqm+Ifb/YzWsgvoKJU4HV6bV8cHY2MYiozhanMFO7ov8Ps4exot/SKhfwCamoNAVcAXd4uM4bYUvFQHBdWL2jzU8N8lq5rYunx9SDkDiFbyWImOyN0Ko9V59WT6UmoTEWHtwNdPnu+9o9Ej+CFpRcwk51BqVYSOqnjbwL/Gv+h8Ekt7lNi+t9PBP41/oPZg9sBnxfFQ3E4ZIfJo2muHl8P/E4/CrUCFvILGArZ9zxc0j6Nvk7ZiXg4juup65jOTFti7m7vOzACwP4xn9xucZ+kMS6tXQIDw2Bw0FbnkLhkl154uLB6weTR7M258xN60Q8bf0sS8Jnvbn8gdyvcrAPYGG1h9wkBUD/3pVgr7qsbiJ+jZOfdfhx/HsxkZ7Y9n1UUezm7kmxvNjsLlamIeCKIeqJmD6fprJj7Tw6GFwHiofgNzWZ2w3eizWT2917dDuf7cVHPwSK++XtFOxRHR8NaKsRyYRn5at7s4exo1/SKjHafGQ/FbRf1t52h4BBkSUamkrnhDEsiDkmScCSyEfeZEjfqELDuvHoiqd1j27mZvsvXhU5vJ1SmCn8O+5vf+b/j31U+isssjhJz4TKL499VHsab7/vfzR7ajtqpCVqSJMTD2k5/ivu0N0Wtny9r5xQUjt8XWeV+3953FwSA/WM+OUmS9IiL7eI+yeEpqoLX1rQz8M50nTF5NI3HdzDOZGaQqWRMHs3uJlfz2JoqzRhwfcXcxZSdYgCM0Rb9AfMOAm8Vh+zQO9v2eqh1Ta3pC0rtMGnq9nUj4AqgptYOdBZiq+zW/U92pheybXwGmhG/kU9kExRrY3N6PGMbvFf3+nsRcAVQVauYz83v6WsylQzWS+taVKDNz0EDtPsQ/vrf60JAu8TFcn6XH73+XgBix33udn5du5zvx7kcLn1XsxWi2dvZeFRbC5nOTAvdUGfFeXWxVtTPueRxmHbFC5t8HVFU994Sw6/9+gP4v7r+DLcqX8L/1fVn+LVff0CP0hZNvprHanEVQPtcP0ZC+5sXEWvi6RU+p0+f59nZcHgYEiSsFlc3rRuLGmFNhT+bY4zpRTDeAWZnfJLC4z5JY01lplCoFeB3+m25QNHh7cBgcBAMTC9wbiXSm/lYdwBbl9ElCRjvCZgynt3wCd9waNj2Xe2cfvhvdm+Fv/ncPKpqFQFXAD2+niaOTAySVO+g33pDINJrbbfuf7I9xpje4dkOna0A0Ofvg8/pQ0WpYCFv7pmrpHlSpRSSpeSmYo+dSZKkX8/2ujA7ndaue/3+fqGjwhqJL3DxSMTdrJfWkalk4JAciAfjzR6eEPiuaN4UIqKbpVdkK1mtoA2prWLL+CI1Ff7E1ufvQ9AVRFWtCp2AZMV59fX0dTDG0O3rRtQbNXs4TcXX1GZzsyhUCyaP5ubuvSWGbz30Nlz5xH341kNvE7boB9R3vfX6e+F3+U0eTWvEw9rO+FQ5RTvGbayd0isAwOf06RsZ+P0Oj7CeSE6golb0CGsRin/2/420uZXiCjKVDJyysy0WJoxxnyLvHrGqi2sXAQCnuk7ZNpP8lu5bAACvrb12w24N0d7MP3r2mB7viY2/GQMeuvv4Tb/OLPyi2A7vRdxwSOsGWims7CnWii+otsvuKMAQlZCe0hdKRXut7db9T7a3VFhCsVaE2+FGLGDOuaOtJkmSHmu4152+xHp40WIwOAiPw2PyaFrD2KSxl6IWb3hph9hqLhaMwSW7UKgVsFJc2fXz+TV/KDQEl8O+qSxG/Hk0l51DVamaPJqd7ZRewQtffYG+tiloA1qsKbDxe1PF/b21O0mS9F1/IicgWXFefTWp7X471mHv3X4AEPVG0ePvAWNi78K0Gj4vaqe1EI/Do98DUtynPRnTK9ppzr+1eV3kCGsq/Nkcz3cfCY+0xQ2lMe5T9Gx7q0mVUpjNzkKCpEdi2tFoeFQ/hHjrRFe0N/N7b4nh8Q+8CSf7Q/A4ZZzsD+HxD9wuZKdbtpLFWmkNEqS2OOuH87v86PFrO/d2KwIwxvSJQztNmmJB7SzEQq2gn4Uo2mvtZt3/ZGftdIC9kTH3fy8FEmI9ejxjWNzdCY02EByA2+FGvprf9dzaqlLFXHYOQPvEWQGAU3bqBZK9FP7b8Zrf4elAxBOBwhShdyTtxHi+Xzvp9HYi4ApAYQoWcrSbXWQ85UnkBCSrzavz1bwec30kav8ULaC+628iNWHySOxBURV9Y0I7zYuAeqFzrwlIxFraMb0CqCd7zeXmUFEqQkdY2/sk+jbHGNOLX+0Q88kdjR7Fz1d/rk92nTI9zRuB7/YbDg8j5A6ZPJrmccgOnOo6hReXXsTFtYs42lE/vFvEN/N7b4nh3lvE30nDO7z6A/3wOX0mj6a1RsIjWC4sYzozfdOi+UpR2xXokl0YDA62cITm4gul11LXMJWZQl+gT8jX2tmRszg7cta0x7civftvY2LcLoZCQ5AlGelyGqlyCh3eDrOHRBqoUC1gMb8IoL0KNsb36snMJPoCfTt+7mxuFgpTEHKH0OntbOEozTcSHsH19HVMZabw5v437/h5xgJqO71H8tjYV1dexWRmUt+dZAWKquhnfLXTjg0A+lmdr62/hpnsTFs18VkNT0DKV/OYy80J+1y10rz6WuoaGBj6/H0Iu8NmD6cljkSP4Pn557GQW0C+mkfAJeZRIlbRbmegGQ2HhvE8ntd3jLtk+29IaSe8iW0wNNgWm424qDeKiCeCdDmNmcwMRiOjmEhObFrDEiXCmnb82Vi7xXxyFPfZeFW1isvrlwHUozDt7HTXaUiQMJebQ7KU1D9uxfMIRGHM/W43fEFvNjt7085bXiSJh+Nt17Cw9ewoeq1ZX7vu8gUAt8OtF+8p7tN+pjJaY0K3r9vWjVDb4e/BuzVh8Of9SHikbWKrOT7PWSms3PRsJD4v6vP3td2CKi+YT2emoTLV3MHsw1JhCRWlAq/T2xbnMG/Fr+UU1yY24/nZlIDUGFdTWsynsSHY7sLuMPoD/WBg+v9/cnD8fXM4NNx286JObyeCriAUpug7Z4Vz6UngsbcCn+jV/r70pNkjsox2TEHhjCk/IkdYU+HvAM5Pn8f9T96P2790O+5/8n4hDmvcDp/oDYeH26ryTnGfjXctdQ1lpYyQO9QW0TYhd0hfuOE7HQFrnkcggqparUdbtFkBAAC6fd0IuAKoqtV6PNI2k0u+CNiOkya+OLxeWke6nKbXmg3whf923OUL1Bf/+etaRE9dWMC9557Did//Fu499xyeukDxbXuh3+C2YSMCf69OlpJIl9Pbfg5jTH/9t9NONm6vEd/teB4KFwvE4HF4UKqV9N2zVsDP92vHhVtA280uSRJS5dSOr38iBh5HOZmevOHMerI/2UoWi/lFSJDaKkULqMd90pra4fGYy3ZcC5EkqR73KWJD5KUnga98EFi6BNTK2t9f+SAV//agUK0f1dKWDf6GRrZfGf4VYSOsqfC3T+enz+PhZx7GRHICFbWCieQEHn7mYeGKf8aYz6OR9ulM4vgkReRseyu5uKoVv053nYYstcfbxpnuMwCAy+uX9UPsrXYegSjmsnNQmIKgK4gub5fZw2k542R3KjO17eQy88SHsDrzo7bcHQUAXqcXA4EBANrPiF5r1tfOu3yB+v/vhfwCSrWSyaO50VMXFvDAl1/ClcUsyjUVVxazeODLL5la/LNCIbKqVPVzydqx8OdxePTdrDvt+lsrrSFfzcMpOzEQHGjl8ITBC547LXBVlXpDVDsWR2VJ1v9/8wKoFfAdG+3QBLkdj8ODfr92jjil6ogtFojB5/ShrJTF3WFjEXxNLRaMIegOmjya1joSPQIJEhbzi8hUMmYPx7IylQySpSQkSWrb6wdf35jOTIt3/vmzfwxAAvSIRqb997P/1bwxWcR0ZhoMDD3+nrZ7fwS0Bmev04uyUsZifhFnR87iifc8gRc/+CKeeM8TwqxdtccKfgM99upj+s4DAPqOhMdffdzUcW3dhfi1ia+1ZcwnR3GfB7PdbtbV4iqWCkuQJRmnOk+ZPcSWiYfiCLlDqCiVTV1uor6Zi0zv/I+MtmWHNLBlsvvsJ7F1cjnlcgPTP2zb3VFAvWOKLwLSa826qkoVc9k5AO25mwUAIp4IOr2dYIzpu0REcu78xI23uBLwme9OmDIeEQuR20lkE/rZde3YyALUd6XvVPjj7+FDwaG2i63m+L1XIpvYtgGRP4/C7nDbnYHI8WvDZGZSvEXAbRSqBawWVyGhfRduAUPcp4DXNVInS3I9ASlNu7UOYyKlzYt4Y3k7CbgCiAVjAGjX32HwppF+v1YkaEdDwSE4JAeylSyS5eTuX9BKa1cBbJ2HMGDNnHsiK+GNvu3YxAZo19qR0EaDv8CNbFT426ep9NSmwxoBrfi321kXzbTdLsQ/fP4PcT11ve1iPjmK+9y/nXaz/sWlvwAAjEfG4Xf5TR5l68iSjDNd2q4/vuOR7J8x8qsdmxC4eDAOh+TQOv6S17F1cjnlcgKF9bbcQcLxCeN8fl7IHVJk7xK5+qJ2h6fD7OGYRuRYm8nV/I23uAy4vpI3ZTyiFSJ3MpnR5vtjkbG2bWThBZvF/OK2Z9jp1/xI+17ze3w98Dv9myO+DfjzqJ0bouKhOGRJRrqcRqqcMns4u+KFrm5/d1vdD201HNIKf7PZWYqQFBxfC5lMT1rqLE2RpMtprBRWNq0ttZtj0WMAgImkWPMxK6G1EMDlcOkpEMKdE9t1FMDWuZgEdB0zYzSWUVXbOwWFMzayiYoKf/s0GhnVzxziJEimPtG324UIAC8svdCWMZ+cnm1PcZ97stNu1q9f/TqAevRlOznZeRKyJGOpsISVworZw7GktdIactVcW0d+AVsmu12jME4uyxIw53QC/s627ZYCNu+QErFQQvZuOl2/wW3XRW1gc9yfaItuY92BG29xJWC8J2DKeEQrRG5HZWpbn+/HhdwhdPu6wXDje/Wmsz5C7bvAJUn12G5+rg+nMlX/ubXz88jtcO8aGysSvlDJC1/tqtvXXS9q58XakU02GwgOwOPwoFgrUtznAV1NXQWg7VZq14L/eHQckiRhtbiKVCm1bUIU2cz4M3rv370Xz84+C6A9z/czErYh8u0fhx7vCUBPZrrr4+aNyQLmsnOoqbW2Pc6HMzayJUuC7WbdQIW/fXrw1gf1gggAvVDy4K0Pmjam7XYhAkCqnGrrrpJYIEZxn/uw027WZDmJqCeqn7/VTvwuv97dd3GNdv0dBJ/YDQYH4ZLbb/exkX6ezan7YJxczjjdYAA6jt6DqDdq0ujEoMd9bsRGEOsxFm7buZANAH2BPnidXlSUinALpB89e0zfVYeNvxkDHrr7uCnjEa0QuZ2F/ALKShkehwexQMzs4ZiKF6y2FmwS2QQYGLp93W151oeRsfBvjLJcyi+hVCvR8wj155Ho13yVqXpXezvfWwPYdEYVxX2KzSE79NfY9fR1k0djTVeTWuGvHWM+OZ/Th6HgEADgf175n9smRJlZ/BOtELk1Retq6iq+NfktzOXm2ro4AtSvn/P5eTw1+ZQ4v7fT7wHe9yWg7wzg9Gh/v//LwKl3mzcmC9BjPts4vQLY3Mgm6nyWCn/7dHbkLD5916dxvOM43LIbxzuO49xd50w9e2i7XYgA0OvvbcuYT06S6jsxKdt+dzs9j6KeKM50n2nbN3O+03EiOYGKUjF5NNZDBYA63uW3EOlD+V9+QZ9cTvaMAWd+DaPHaXLJz47a6VwkIr7lwjIKtQJcsqutd/kCm3P/+S5IUdx7SwyPf+BNONkfgscp42R/CI9/4Hbce0u/KeMRrRC5Hb7bbzQ8Cllq71soPr+ezc2iqlb1j/Mb3nYvjgDAUGho2yhLXiwdCY+0/fOIzw2X8kv4xvVviLMIuMVyYRllpQy3w41ef6/ZwzFdPKwV/hKZhMkjIbvhCUjXU9ctcZamSNZL61grrUGW5LbenQ3UC59/c+Vvtk2IevzVx00Z105H1Zh5/dgpje3FpRfbdj2Ni3giiHgiuJa8hv/7uf9bqN8bTr8HePBHwO8va39T0e+mqNF3M72RTdBz/tr7buOAzo6cxRPveQIvfvBFPPGeJ0wt+gE37kLkPnT6QyaNSBx8kjKZnqRzCHax3W5WAPjF2C/iRMcJM4dmqoHAADq8HaiqVbyefN3s4dxAtC43o2KtiKX8EgCKtgC0yW6HtwOMMcwMvgF48EdQfncBM2/5P4CeE21/UwloDSt+p1/bIbXNuUhETMb3oX/z1L/B9dR1xMNxOGSH2UMzHS+AiNgBeO8tMXzrobfhyifuw7ceeptpRT8+FpEKkVsxVj/Pm96rgS5vF0LuEGpqTd8JpagK7YoyMHYAG2Ot9PP9aKEEQXcQPf4eXEtdw8d/8HGxFgEN+POaxzm1u3goDgmSFudfyZk9HHITQ8EhuB1uFGoFLOYXzR7OJk9dWMC9557Did//Fu499xyeuiDWvJ/v9ouH4vA6vSaPxlzj0XHIkoy10tq2CVFmxTXvdFSNWYVIYOc0Nh6D3u5GwiN4YekFABDq90b2Z7mwjHw1T42+G/h9z2J+EcVa0eTR3IhmrjZg3IXokl3o8nbhnWPvxPtPvN/soZluU9xnjuI+b2brbtZYIIZ7Ru/BPaP3tPVkV5IknOnSdv1dWL0gVLekiF1uRjOZGTAwfYGQ1CcF/KyY+fw8KkoFPqePusihvd6scEAyqdv6PjSbm8W3p7+tF/3bXTwchyRJSJVTSJVSZg9HaCIVIrdaK60hU8nAITn0mLt2ZkzV4At+C/kF/XrW5+8zc3jC0M/52yj8JUtJpMtpyJJMDVEbRsOjwi8C8t9fu5/vxxnnrLwoSsTkkB16moZICUhPXVjAA19+CVcWsyjXVFxZzOKBL78kTPGPMYaJ1ASA9o755DwOD4ZDw4h6ojdsNpAgmdYQtdNRNWaeG7tTihY1jWlGwiObUhA4s39vZH/4vCgejsMpO00ejfmM55/zdT6RUOHPJvguxD+7+8/wvhPvwy8P/3Jbx3xyxoUJfjgz2Rl/Hv3wX/0Q7z32XoxHxvWiVzs70XkCTtmJ9dK6UN2SIna5GfEJwUiEOv85/cyf7DRUplJ03Db0sxDTU0IV2sn2tr4PcX939e9MGpFYPA6P8Ln/ZHf8vXooNETz6w3GM+xUpuo3usPh4baPs+L4z2g+P4+yUtbfAwaDg3A73OYNTCBjkTGhFwGLtSJWCisAKL3CiP8s6Jw/8Y1HtfPqr6WuCTOvPnd+AhKgzxx51Pdnvjth4qjq1kprSJfTcEgOKthsONpxFHf03XFDQhQDw4O3PmjKmLYrsplZiAR2TmP797f+e5NGJJZYIIaoJ3rDx83+vZH94fNZ3lhC6nN+EZvXaZXRRhhj+sHNRyJHTB6NOCjuc/+urF+BwhT0+HvQF6CubY/Doz+PLq5dNHk0dSJ2uXEqU/XFAIqzqusP9MPtcOPS6iX86t/9Kv7P7/2f+MqVr2A+P2/20IQxGBqEU3YiV81htbhq9nDILnaKtDFG27U7vtOXfibWw2NsP/ydD+MrV75CUU0GA8EBeBwelGolLOYX9UUAuubXRTwRRD1RMMaQyCT0AjItbtV1ebvQ6e284eOiLAImsgk9vSLgCpg9HGHwnc+JbAIqU00eDbmZeCgOl+xCvprHUkGMNIbJ1fwNM0fGgOsreVPGs9VEUitAjkRGqEljw1h4DMc7j+OekXswHhmHW3bjeMdxnLvrnGlHH213VI2ZhUhgI0XryK/juCLByRi6VAkPDb7D9OOhROGUnXjn+DsBQKjfG9m7TCWD1eIqJEjUEGXAU6sSmQRqas3cwWzR9MLf5z73OYyNjcHr9eL222/HD37wg5t+/rPPPovbb78dXq8X4+PjePxxMXauWMFqcVXvTKKb7rr+QL9+ZhTFfe6OMaYXt2i3X90t3bcA0HaOipLbLGKXG7eYX0RFqcDr9FKEpYEsyUiVUvj29Le1ZgSmYK20hj/+xz8WJqLVbC7ZpcVprVzB1JfeBXyiF3jsrcClJ80eGtmGyO9Doti664dYgzHGlr9X/8mLf0Lv1RtkSdaf26+uvIpUOQVJkjAUGjJ3YILhP6PLyct6agTdp9VJkoRfPfqr2v8WcBEwkdk43y9MEb9Gvf5eeBweVJQKRXsLzik79QXJ66nr5g5mw1h34IYwREkCxnvML64zxvSkqKMRivnkXA4XhsPDGI+O42P/5GN48YMv4on3PGFqQWvrUTVmFyIBAJeexNnzf4y/nJnGv0ul8b50Cv/yx1+g+1iDd42/C/eM3INef68wvzfjefX3P3k/zfVvYjqtNbL2B/rhc/pMHo04enw9CLgCqKpVzOfEaupvauHvr//6r/HRj34Uv/d7v4eXX34Zd955J+677z7MzGwfCTE5OYl3vvOduPPOO/Hyyy/jd3/3d/Hbv/3b+OpXv9rMYdrCUxcW8P/6wt/is9+bwF/8sIDvvka7JDhZkvWIC4r73N1sdhbpchpuhxvHosfMHo4wev296PH3QGUqLq9fNns4AMTscuN45/9IaIQiLLd4JvHMDR8TKaJVBKNrU8DFv8Vkbg6olYGlS8BXPkg3TQLaLtJGlPchUWzd9UOsYbsYW3qv3kw/k3UjaWAgoO0CJHU87pyfe9zt60bQHTR5VGL5tWO/hntG7kG3r1uYRUBAKwDw9Aq+c5toZEnWd/1R3Kf4eBrUtbQYcZ8fPXtMj/fExt+MAQ/dfdzUcQHAUmEJ2UoWLtlFx1VscSx6DFi5gqvf+G0wQRoz+VE1IhQiAQDP/jEACbMuB1RIiKgKoioDnv2v5o5LICOhEYxHx3H/sfvxw3/1Q9N/b1vPq59ITuDhZx6m4t9Wl54EHnur1pj9wucxtk6baowkSaofWSPY8R5NXY39kz/5E/zmb/4mPvzhD+PUqVM4d+4c4vE4HnvssW0///HHH8fw8DDOnTuHU6dO4cMf/jB+4zd+A5/61KeaOcz923jCi7ILQTsc+UXM56dQUxnmlsNCHY4sgqPRo8DKFUx++2NQBPm9iYrv9jvecZzOsdmC74C8tHZJiJsmIbvcNvBIO9r+f6OF/I3vzaJEtIpi+Kf/DyQAqw4HshI/BUSimyYB8feh0fAoHJIDPb4efPquTwvxPiQSXiAR7UaA7EzkOG1RDIeG4ZAc+n9TceRG/f7+TVFxtBv6RgOBAZzsOol/efxf4hvv/Ybpi4DcanEVxVoRLtmFfn+/2cMRDp3zZx3xcBxO2YlsJYuV4orZw8G9t8Tw+AfehJP9IXicMk72h/D4B27HvbeY/zrjjeKjkVG4ZFoLMRqe/RlcF76GXH4ZS6xGjZnbWbsKgGHapT13hqs1AAxYE+P8ShEE3UF0ebvAwPTzoc20tdGPN7RSo5/BpSeBr3wQlaVLmJMZkFvByLf/gF77W4yuXANe+Dwmn/gQ2GNvEebn42zWN65UKnjxxRfx8Y9/fNPH3/GOd+DHP/7xtl/z/PPP4x3veMemj91zzz34/Oc/j2q1CpdLgAvvpSfBvvJB/MzjgeRgYKmrkP7uN7Fy5YModN1iypD+508TOBEooOhcgMRkRCtpyFIOf/e/ZjFWMnfHVkfAjd6Q19QxAED/9efg//lXUZBl/MQlIZS6CvzdbwK5PwTG7jRtXGu5CorVGvojXjhl83dFqUoVk3M/AdQazoRPArMvmDselWE5W4YkAb0hDyRpayhIax1Ta/hxbgXpzDyer/wPBJ3mP7d7ATw08KtY6/Oi5o8hmQGe+PmPTB1TjdVwKTkPGTLWUyG8nE2aOh4AcBbX4MvPwueS4XU74HM54HU6IMutf06N+nvx+pbYYTOiERljKNdU5Mo15Ms15MsKJAkY7PAh7DX3eutfu4Z+rxMLTif+0edFj7JxPmvmOnDxr00ZU01lSBUqWM9X8I473oVwOGbKOER0duQsVqYnMOG5jmO+YcRXZFxZ+Z7Zw4IsSRjo8CHgbtp0d89Gixm8kpnHdDmHnwkSf6yqDIlkEeWqWOcf+9wODEZ9prw/G/UF+jCbnb1hx5/ZhZu1XBmJZFGIBiQAqFU6sLr+cyguPzKhKF4umX/NF41S6sDK2kuQ1AqU4gCuzGbNHhIAQHUFUIoeq2+7MZFS7sJC/jq+dukH6PUOmD0cBBaeR3XiL8Fq6+h1dWE6FUR69F6zhwVJkhCLeNEXNv8eZDikFf5WCisoVAvwu/wmj0gsa4nLmLk+gV9489vg9EdMHYtLdmEkPIJrqWt4YfEFISKZB/qA//gvHFheyqPiDyHHruGJn18ze1i4kv4ZSkoRo95evDxj/vVMrubhTV0FBDhLc+TZ/4hRpYYJtwsveD0YrtXAIKH69O9jYZUSxwAgFuqDq7CEaad27zFa1X5GxfA4rpj8fHI7ZZzsD8Nh8vwa0NIQ1pJXcenqN1AOvGLqWCZT17Zt9Luevo6frfzMpFEJ5gf/BfB4kJIlfSdrh8qA838IhM2fswlh8gcY/O4fwhEJIy9JWFm9gt6vfBB435eA0+8xdWhNWwlZXV2Foijo6+vb9PG+vj4sLi5u+zWLi4vbfn6tVsPq6ipisRsX2crlMsrl+nkpmUymAaO/iWf/GCok/MhXn2wzANXZb2DJpMx0h1xEX0B7o4pWfRh3/BwAIOUlrL9qbmfJOoBalx8DEXOzf+UXPo9xtYoLHg9e9RgiiH72BcBtzoVvNVfG1eUcGAMcsoTOgBvdQTciPpd5Ba70HLB+HTFnAF3KP5gyBAaGXLmG1VwFa7kyqor23F71OjHWHYDfxAVcF4ATxVX8vLyKV9bMvzFRGEO6WEW+oi3aLgVOo+oQJ2c75OzBjytNfk/eC6biTfN/CbdS2PRhCYDLIcPtlOFySBt/y3BvfMztkOFyynDJUkNfkw96x/CwofDX6IhWXtDjxbxcuYZ8pYZcuYZCWUG+XNOLfTV1+4XjqN+FeIcf8U4/hjp8CHha/LrrOorR1FUsOJ244nbjCv+4OwC8/vWWDIEBKNdUlKsKyjUVFUXVbwmOx2/BGwQu/LV6bsQYw8T0D1Gs5eFXc1jHdFMfbz9SsoTjfSFEfOYWs/uXL8Oz+g8o14r44at/CYy9Heg5Ydp4qoqK15eyyBTFOnycC3gcGOsOIGRiE8LpztNIZOvRrGbHaZdrCp6/toZXE2moghT9AKCSyaGU+Tmcjg68qFQAmL+bRDTVbBal9EX4mBPqgor1G063Ms9E991Y8x8xexhYrwYxVSxgCpcBmBup31mcxInV8zxrAEdSMxj/3gP4+5P/FVe7fsXUsXHxTj/ePNqB4U6/afeNfpcf3b5urBZXkcgmcKLTvGvablo9L1JVhtd++CQchWVMdvbh2C/8k6Y+3l4ciRzBtdQ1TGWmhEgfYIxh9sqLQHEda74jKLqiZg9J55BciJbduCKZfz07vvIddBanzB4GAOBYdhbHnBIm3C7MuLQ/AMBYGrPXxNjZYjafrxPdLA0GwMUY+moKJDCc7/23uHbF/OdTuljFW490mz0MjLgieGnpIhYYwwJ+aupYwpITa+zGJsiwO4wfzv3QhBEJqLwMGGogYxs7WVlyCsqVbwuxicVMFUWF9MIX4AQwXKth0uXCtMuJXkXVEqvsWvjjtk5EGWM3nZxu9/nbfZz75Cc/if/0n/7TIUe5D2tXIYPhaKWy6cNMUuAbOtW6cRgsJteQKymQIKFQ7MIV1QMJ2m47z8CgKWOaXM3jxekk0sUagnMOvP14FPf9Qsy829xiCrezGhRJQtX48fQyEG39oc2JZAFryTSizk64HBKqCkOtBCyWgJRLRizixVCHH2Gfc9O5SU2XnIfD3YFbu38BCLZ2UTtXrmEuVcRcqohCeePC69E6kxRVxZoKTCWB8Z4gjvYG4TKpU+l25QiU7HVUVPMWSxm0wvFcqggHgB45gw5nGUM+P7IRMc5llCFjOHgKYVfI7KHAl5/FQLKGqhTAmjuOck1BqaYCW9dNaxt/tpAkwOOU4XE64HHJ8Lm0v71OBzwuB7yujX9zynt6tZ7FKfx/Q334zMy3sKwUMRY9ggff+P/eNdZqa0EvX6kZinj1gl6hUtML5nvhdTkQ9DjgdztRVVQsZcpIFapIFdL4+VwaANAddGOo0494h1YI9Locu3zXQ3r7x3HmKx9EVpZQkiRoS28MGDsLdI435SFVAIVKDdmS9idXqulPEY9j449TRsjrRMBrbuf2blo9N/rmhTlMLfUhrawBtX7cMdyFse5Ayx5/J9lSDWulGpYywKlACOPdQXPmIbMvQH7t6/gVlxMTLhdQXgJe/mvgrb8NDN3R8uGki1W8MLUORy2EHq+E3qAHwtQhGLCy0fQztwwMd/pxsj8Et7P1N5RHo0fRv3IVT2cnsCJLGFclPHji11seQcgYw+tLOTz3+gpyZe0iFe/0w+9u8vvwHvVVFEyXe9Ev+5HvqKHq6TB7SMLprSqYKfWiwzsEry9u9nAAAK5qBt7iEvzydcz1v9Hs4UBlp9CVLaG0pUnLDKd+9nV4K1VIYPAxhhOVChgkvH3hv8Nx5l+YOraqomJqtYDEuvanN+zBm0c7cbQnaMou6WMdx9Dh7UDIbf58/2ZaPS+SZQn98TGsXFnG1NRVHL3lzaYn14xHx/HG4huRq+RMHQe3li1DKf4UDhbEsD+EdNT88/24ft8oer1Rs4cBMIbx9DocDjfywVGoJkePllb7EC/O4/ZSCemNhX4GCRVPB5xRc9ZCReTPDaBz9UUcK6ZQDh/Fz0//NpyD/xxmtkZUFRXXV/J4cSqJWwYjpqf79JdLeLOnF0lZBvydpo7lf/NF8P8s3ZiYdd/ou7QjowhUdzekzCwkMLgZ8MZSGQwSMu5e/GilEz1hL4aiXvSGvELsKG2FqqpiMV3CXLKI1XwF95VTWrNYRas4dCsKRIn5lViTcmIqlQr8fj/+5m/+Br/2a7+mf/yhhx7CK6+8gmefffaGr3nb296G2267DZ/5zGf0j/3t3/4t3ve+96FQKGwb9bld91Y8Hkc6nUY4HG7w/ytoZ8MtXcLmFWMJ6DsDPGhOvJ52xt9L+qHI/G+zctL18WDzT+mhu4/ho2ePmTPpFej39moihe9dXgYA/MJgBL9yshcLmRKuLGZwZTGHkiFyq8PvwslYGCf7Q4j63Tt9y8aoloAffUaLkfilBwBf8xdu8uUarixlcWUxi8V0Sf+4yyHhSE8QJ2NhDHf6kSvX8OzrK7i2rN2ohLxO3HWiB0d6gqbfRLXaUqaE711e1n9evWEP3tG1gp7E00CgG/gnHzF5hAJ6/TvA3ItA7A3AyXcB0DpxC1VDsYzvjDPsjsuXayhUFOz1KilLEgIeBwIeJwIeJ4IeBwJup/7fAY8DQY8TPpdDe96+8AUguwR27B2o9L9xU/GuXsQzjKu8v4KexyUj6HHqYwh6nPBvjCHgcSLo1v7b5di8oF6uKZhLFpFIFpFYL2AlW97071r0rhfxTh/iHX4MRH2NX5QvZYC/fwiY+iFQSgPdx4C7Pg6cenfDHoIxhpVsGYlkAYl1remgUtscoxP0OBHv9GFoY/djq3aNZTIZRCKRA89lWjk32nrN538//oE34d5bzN0VWVNUfO/yMi7Oa539J/tDOHu674bnfNMJNAd5bSGD85eWUFMZOvwuvPvWAXQFPbt/YQsVKjX8YGIVlzZ+bz63A//saDfODIRbe82/8LfAE//G8IGNZ3cLI1vW8xV8//IyZta1YkjU78KvnOzFSJf5hXUA2tzxx/8/gDdDjf5TYOxt5o5JNLUy8KM/1X5Gd/xbIGT+GVYAtOvsTz6n3TT+k49oc0ii+USv9nvbyukBfn+59ePZIlOq4qXpJC7MpfV5YdTvwh0jnTgVC8HZ6mtcC1hpXsRVZl/GK0//JZKuPhy95wEc6Qk25XGs6ps/uYCOi19Ef9iDsbGj2vsj2Sy/BvzjnwOyE7jzEUA2ueFn45wvbJ31v//LDb1Hs4WXvgSkZ7W1h9gbzB4NGGN44sVZzCaLONkfwn2/YHJyzWv/C1j8OTDyVmD87aYORVEZ/sPTf4kfr/4FMrU59LMATkZ/E7fHzuK9tw3BJ0ijnVnWcmW89NT/wD+/8DuGV72Wg/LMbZ/Gy/5/pn+uxyXjaE8Qp2JhIY5taDRFZZhczePKYhbXV3Kb0rP+7c/+NSLZCUgtut/fz7yoaTv+3G43br/9djz99NObCn9PP/00/sW/2L5T7i1veQv+/u//ftPHvvOd7+COO+7Y8Xw/j8cDj6eFCxZv//j2F7u7Pn7zr2sifjjyZ747gesreYz3BPDQ3cdNOxz53PmJG4p+APAX/zCNNw5HcdfxntYXawT5vb0wtY4fTGj557cNR/H2jZ/FYNSHwagPbz/ei6k17Y3k2nIOyUIVz19bw/PX1hCLeHEyFsbxvmBz4i6TU1rRz9/Z1KJfpabi6nIOV5YymFkr6nFVsiRhpMuPE/0hHOkJbiokRHwuvOfWAVxfyeH7V1aQKVbx968uYKw7gF8+0YuIX4DzP5usVFXw42ur+NlsGoxpOyHfeqQLtw5FISt9wNz3gPyqdoMQ6DJ7uOJgDFjdCIrsrvfZybKE4EYxrG+HLwVuLBDutMOuUFGgMqbvFrsZXiAcznejZ/ky1hefxc+6994pzQt6fvdGcdFTL+zdrKC35+/vdGC8J4jxjQWKYkXBbLKgF8jW8xUsZUpYypTwwlQS8sZ5M0MbhcBYxHv4RaeVy0D3ceDILwNv+tDhvtcGxhjW8xW9oDmbLG5qtAC0AsNQh0+POe3wmxi9fAitnBudOz+Be+V/xEPOr2FMWsAki+Ezynvxme+GTC/8OR0y/vnpPvSGvXj2ygouL2axXqjgf3vDQGujP9eu4sZZUWs7AFWV4QdXV/HStHa+yHhPAPec6W/+7t0D8LuduOdMP84MhPH9y8tYzVXw9KUlXJxP45dP9rbu7Ojv/5ctH9iYP7YgsqWqqPjp5DpemE5CURmcsoQ3j3XijpEOsRb1V1/H+dwkHkv/HFPVDEYXv40Hpd/D2dF/bvbIxLH6ulb083cBwZvNOFrMGwa6jgKrE8D8K8Cxs2aPSBydR4DlS1s+KAFdYqRqhL0u3HWiF7841oVXEim8kkghVaji/GtL+Mn1NbxpJIpbBiPwOMV7fzdLy9eMALg74ugPe1FNr+LFyTUq/BmsZMtYW5xCJ4BYxAfklgGlCjjsf0+/L5k57e9Qv/lFP0Cb+7zvS9o8aG1Ce09scGOmbUQGtcJfelaIwp8kSXj78R785T/O4PJiFm8cjmqvPbOkN449iZh/3ugriRR65Dfj1+O/hH/r+R4qqUV8szaA2UwZT7yYwHvfNNT6Y08EsZwp4Wsvz6EYeTt6x/8P/MLS30IqpiD1nADu+jjuOvVunMmWcWUxi8uLGWRLNVycz+DifAZBjxMn+kM4GQuhJ+ix5JoKoK0hzaWKuLKYxetLmzfqdAbcONkfwsn+MCKD/0GImsN2mvrsfeSRR/DBD34Qd9xxB97ylrfgz//8zzEzM4MHHngAAPDoo49ibm4O/+N//A8AwAMPPIDPfvazeOSRR/CRj3wEzz//PD7/+c/jr/7qr5o5zP0R9GJ37y0x0xfYuMnV/A3LWwCQLFTxykwKNYXh7pO9ra3+89/b+T/UClyhfuC+/9qy3xtjDD+5vo6fXF8DAPziWCfecqTrhjc/h6ztdDvSE0S5puDach6XFzOYWS9gIV3CQrqEZ6+sYKTLj5MxLbasYTtt1jfOqOxs/DkfisowzQuaK7lNu5ZiES9O9Idwoj+0a0FzvCeIeKdfXwybXM0jsT4l5mJYgzDG8NpCFj+YWEFh4yy/U7EQ7jzWU5+AyF6gYxRYu6YVTAL/1LwBiyYzB5RzgNOt/Yz2aa8FQkVlKFQMZ+rpuwc3Fw2NBcIJNoRQlcFfnYe7lgO84XrxbqOgpxX36rsF/W5nyyPvfG4HjvWFcKxPK07myjU9YiqRLCJTrOoxvf+AdThlCQNRH+KdfsQ7fegLeff/fs8X3HrPHGrs6UJ1o2CpFS7z5c2FPrdTxlAH39Hns/Sk1CxH176Pz7rPQWUSZInhBBJ43HUOv7UqAzB/548kSXhjPIqugBvf/PkCljNl/NU/zuBdvxBDvNPfmkF0Hd1+x1+LFpGLFQXf+PkCEhu7xnaag4hmqMOPX//FEbySSOIn19cxnyrhr/4hgVvjEbzlSFfzF7WTU9t8sPkF22srOTyz0eQEQOgmp/Ov/y0eXv2Bfos7UVnDw88+gk9Ln8bZESokAdh47QPoO61tmRfJwG1a4W/xZ1rHPS26a97wPu2ecVMrKwP+2SPmjWkbPrcDbznShdtHOvDzuTRenkkiW6rhuddX8Q+T63jjUBRvHI6aej56W/N1oq8zjIX0EpIr85hP9WIgKs5Z7GZ6cTqJYHkFnQG31oDEVCC7CETFiEIWRmZe+ztizvE92zr9HtPPq7KESBzAP9SLtwLoDXtxOhbGxfkMnr2ygve/OW7OvUA5BxST2pwobO5zu1hR8A+T2hrtW490w1U+DldhFfd2ZfE/C06s5ir4mxcSeO/tQ6bHo7baQrqIv315DuWqir6QB6f8Y5Djvwmc+TWg96T+eT0hD3pCHvzTo12YTW4UyJazyJVreHE6iRenk+gKunGyP4wT/aHWNt8ewmqOFzSz+j0ZoCVCHe8P4VR/CD0hw9qRoLUioMmFv/e///1YW1vDH/3RH2FhYQG33HILvvnNb2JkZAQAsLCwgJmZGf3zx8bG8M1vfhMPP/ww/uzP/gwDAwP40z/9U9x///3NHOb+0cXupsa6A7iymN28vCVh4/Bx4MJcGjVFxTvO9Lc2//f0e4CxO4GfPA5IMnC0Nd3IjDH8YGIVL2502f/To934J2O751h7nA6cHgjj9EAYuXINry9lcXkhi6VMCZOreUyu5uF2yjjSE8DJfi0S88DFVMYMhb+xg32PG74lw0K6hCuLWVxZyqJY2RxheqJfizDtCOwvwtTlkPHWo904FQvjexvxV89fW8NrCxn88olejApwrlSjrObK+N7lZcwliwCArqAbv3yid/vF6u7j9cLfKBX+dCuXtb+7jgGO5l3yHLKEkNeF0C4TQkVl+tl8hYqCjiun4CvM445jZbjGrJEhH/Q4cSoWxqmYFimwXXFtZr2gR9Ptu7hWWAcyC9qFo2d/pyFsV5Q0OlRR8tKTwLN/rO3e6jqq7SSnuQAecX0NqqIV/QBAlhhUJuFh998C+I/mDs4g3unHv/rFYfyvVxewlCnhay/N4c7j3bgtHm3+Te8NqQPQ/n77/6e5jwtgOVvC37+6gEyxCrdTxj1n+nC0V+yzmIwcsoTbRzpxvC+E515fxetLWbw8k8LEUg5vO96D431NivxmTEtAyC1t+YfmFWzTxSqeubKM6yt5ADzWvBdHegJiFmkreTw2d37rsxoSJDz+6uNU+AO0xS1eQO49bepQttU5DngjWqT28mtC7EowHWOAJwSc/jVg8VVt0dbXBQz/IhAdNnt023I7Zdw+0oE3xqN4bSGDF6eTWM9X8A+T63hpJokzAxG8aaTDMotttiHL8HQMonslhWBlBS9MJ/EeKvwhU/r/s/fnwY3d95U3fC5WglgJguBONsluNntTb9ptS/KadmzJsWPFcWKN/b4Tz9ipTNmZSs3jPPEkTlUyTlKTRKlJlTWTed5HjpVkYjtRIseWHG+SHG1Wq7vVanU3e2E39wUAiZXEft8/vvjhgt3cAF4A9wLfTxULaDYBXILAxb2/8z3nZDC+EMOh9CJ6Omx0fpbL0nuNhb/1RAuuqDqLI0wFiL9ZIgikVwFLjYYNt+H+vT5cXYrTOt1iDGNd1Yk63hIhhtp9gLlGKR6b8MpEEKlMHh1OKw52u4D4PmDyZThXp/DoiQ/jH84tYmU1g2+dnsEvnuitfv2SRpheXsUzb84hnc2j12PDI6NWWM5EyHm8yXqxJEmFNZZWPLS/AzdDq7i8EMWNQAKheBovXQvipWtB9HpsGOt2Yp/fqbkY1Vjh8+nyQmxd3Y3FZMBevwMHulzoa9siwlSjWlHVx79+/dd/Hb/+67++4f89+eSTt33vwQcfxJkzZ6q8VbvjuQvzePyHV3EjmMCQz44vvm+fZtx2WuCL79u3Yefg/3VqDMMddjz71gIuL8SQycv4+cNdtXVp2dpoIWd1mU7ESyYVqoEsy/jJ+BLenI4AAB7c34ETA+XHaDqsJpwYaMOJgTYsJ9K4vBDF5fkYImsZXJqP4dJ8DK0WI0a7nBjrcqLL1VLeIlEiAKRidODtGSx7+0pZTqRxeT6Kywu0fYJWi5Gs3l0udLp276pps1vwsRO9uLoUxwvjAYRXM3j67Cz2dTrw4GjHtgKMlkln83h1IoSzU2HkZRlmo4R7httxYqBtc7HcNwpc+T5Fpawu17UkWTP7SFkGAoWYz47qvtd3itEgwdViVibG0seAK4tA6BIwdF9dt61S3K1muFvdONzr3jROcyKQKC5mt5iNxX7ADeM0ly7RZdsewLp1LNJGMaSlqBZDemunxeJF+ncNu760yiDmiqKfwCDJ2CNrZ8JV4Gox49E7+/CjS4u4NB/DC+MBLEVTeO8Bf3V7/0onAINXAZuHOi16T1TvMQGML8Twg4sLyORkeAp9fj6N9fntFGeLGR+6oxuHQzT0E17N4HtvzePCbCvePeaHt8whom2JzgID9wMXn0a1BdtsLo8zU2H87EYImZwMgyTh5GAb7h7y1tzhXRaBy7iZiW4QYivjRuRGXTZJcwQuk5PF1V3X47JNkSSg5xgw8QIwd5aFP4De+/EAdbJ89GuA2QbMnqFj7LkzQN+d2nNuFjAaJBzudeNQjwvXA3G8fnMFC5Ekzk2HcX4mgv1dDty5x6vbzwFd4uxGj2cCjpUlXF+KYzmRVv/zSmecnQpDzmfQbYrBYXUAXXfQe0xDzihNkE2RaAQArp76bgtTPpZWErYSQXpt+7QRFe2wmnDXHi9euhbEv10NYqTDUfvu88g0XbrrK/QH4ymcnyms0452kJjj7Kb1h1QcntQ8Hr1zAP/4xkxR/PvYiV7NdaOrzc1gAt95cw7ZvIwBbysePtoDy9zP6D89g9R3vA0mIwlle/0OJDM5XFuK4/JCDDMrq8WkqOcLSXYHul0Y8tlr/zoscOv2FZqoYDRI2OOzY6zLWdftUwPOfSiT5y7Mk6gFWgIYX4jhc0+dwROfOsHiX4HtOgdNBgnfPT+P60txfOf8HD58R09t30TeERJGlieqKvzl8zJ+cGkRF+eikCTgvWOdONLnLv5/peKI127B/SM+3DfcjoVoEpfnyVG3ms7h3FQY56bC8LSaMVaOoy50nS49gxW5ouKpLDn7FsiRKCBHogNjXc7dORI3QZIkjHY6MdjeilcnlnGu4AKYDK3i3mEvjvVvIZRpEFmWcW0pjheuBIo9cXv9Djy4v2PzaIFSF1KrD+i7m/pkBu6t4ZYraGofGZsHklGKrlLJyao6HWPA1R8AscWG6GeUJAntDivaHVYc6/dAlmUEYqmiMDcbJiHw6mIcVxfjAOgEpN9bcAS22eAuxnweuO3+U9kcZlfWisJi6SQWPT7gd7YUhcUej02dhfMX/gi3Lf7XqOtL6xg79kFevLiuyFqGBGPHaB23avPPWLPRgJ871AW/qwU/vRLEpfkolhNpfPhod3UjXEonAC8+Ayy+TaJAFbot8nkZL10P4vRNShrY42vFBw93a7LPr1wG2+147N5BnJ5cwes3ljG1vIqnXp0sCmWqHU8Gr5Dj+P7/BFz/SUGwdQOD7wD67lLnMQBMhVbxk/Gl4tBCX5sN7xnz62NRYekS9phduJqJQC55/0sAhhzsTgBA73Ng17HVVaXrDuDGTylSLrYIODXUQ1gP5s7Spf8AiX4A0HkYmPiJMjiq1WPKApIkYa+fOtNnVtbw+s1lTIZWi8Oiwx123LXHy7GTtcDVA5vZiCFLBBOgiMv3H2ze91gyk8OF2Qjs6RB6XRYSR/wHCsLfnDI1ztB5rCyTK9uqn6QGpgR3Hwl/kWnNCH8AcHzAg/MzYcSSFMV473CN1x800O8nyzJevBKALNN6WzFRS5Io2WfuHBC6Blf7CB69sx//eGYGwXga335jBh890Vu7vvEac20pju+9NY9cXsZwhx0fOtJNQ9PBQs2Br/yEqhazEYd7aUg8lszgyiIdiwRiqeJw+I4ddSqRzeVxM5TA5YUYbgQSyOaV85jeNhvGupwY7XQ2xLkzwMJf2Tz+w6u3L/1JwF/86CoLfyVs1Tk43OHAR4714pk3Z3EzuIp/OjuLR4711K6A3DsEzLxOwl+VDi5zeRnPXVjAlcUYDJKEDxzqLMbiAeqII5IkodttQ7fbhgdGOzC1vIrL81FcD8QRXs3g1YkQXp0IocvdUtxxbVpKW0G/XypLkxHjCzFMLSuTEQZJwh5fK/Z3qdxBuAVWkxEPjnbgYLcLP768iLlwEi9eCeLiXBTvHvOjr03FaIUqxf2FV9P4yfgSbgYpHtFtM+Oh/R0Y3qoI/lYXUnSO3AmtbXUT/jS1jxRuv/YR7fbWWFppnxS6Tr12Q++q9xapiiRJ8Lta4He14OQg7RuXYklML5NwNxdeQzyVVZzL6SDuDl6F025DLteNrrXMuijRxWgKeVle9xg+hwV93lb0t7Wir81WnQO00DVgA19Ltbu+dMGDX4J0S5G1VOci6+0+YyVJwomBNnQ4rPjuWxT9+XevTeFDd3Sr+3mxGR1jBeFvHBh5r6rHIclMDt97ax6TIfosuWuPF/ePtNe2V7nKmIwG3DvcjgNdLvxkfAk3ggn87MYyLi/E8ND+Doxs9bm5E2RZOcE99qvAB/6Arr/9T+RIXnqbHFy7IJ7K4sUrAYwvxAAAdqsRD4x2YH+nU5uxnreSjADhaXzefaTQ8SdBhly8/LyfI8extkLHZZK04SBLPbl1MOKrY34cty6Q6LX/VL03r35k1oClQkR8z3Hl+yYL0HkEmH2DXH8aF/4EpbFbi9EkXr+5jGtL8eJCW2+bDXft8WJPe6s+9jt6xEmfFXtsqzDkM7g0H8V9I+1wbHZO3uCcn4kgnc1j2BSG22oGnD30HEkGikZORUnoYpR+P3b76Rd3HwlIQujSCGajAe/a14HvvTWP0zeXcajHVbu0rGyahoyAugp/N0OrmAytwmiQ8K59vvX/2b6vKPxB/gDsVhM+frIfT5+dxWI0SeLf8V50uxtreObyQhTfv7CIvCxjtNOJU4cL1VzphOLI3mXdgbPFjJODXpwc9N7WoXdxLoqLc9HNO/R2iSzLmFlZw+WFGK4uxZDK5Iv/53NYsF9nHYTl0JxHHLvgRjBx+9KfjGKEGbMzBtpb8dETffins7OYWVnD02dm8QvHe2ujqAtXWypGEZcOv6p3n83l8d235jERSMBokPDzR7pu69NRWxwxGiQM+ewY8tmRzuZxPRDH5YUopkJrWIgksRAhIWyg3YaxLhdGOkoEuWxKORjZ5kQ2l5dxM5TA+EIM15fi6yYjejwtGOtyYV+no24l8h1OK37pzn68PRfFv10LFsp4Z3Cg24UHRn27364qxP1lcnmcvrmC0zeXkc3LMBok3LmnDXft2YFzYUMXEoBL/wK89/coTq7GaGYfqcGYz03xH1SEvz3vbOhJV6NBGVi4e8iLbC6P+UgS0yurmFleg2lyAslsHnPpDly5FAYQvu0+PK3mYkxoX5tt84EGNWnfS+/39e21Vev60hUaLLLe6Wdsv7cVn7x7AN95cw6BWAr/8MYsHtrfgTv63NVdBPUO0TBCMkoLO2513FGBWArfeXMOkbUMzEYJHzjUhdHOxp0Sd7ea8ZFjPbgeSOD58SVE1zJ45twchjvseGi/v/ITt9UQOXsMRupAE/gPFoS/yxULtvm8jHMzYbxyPYR0Ng9JAo72e3DfcLu+pkoL4sj7eu7Hnx/+GJ548wnciNzAkKMXn7f04b15E5DLaHfophYsFtzrO4itriUbDUb8p0XgyWMx7DVeAEbevaMYp4Zk4QKQz9K54a2L7b0nSPgLXqV9d0sdepF2QaerBR++owcriTROT67g0nwUsytrmF2Zhc9pxV172jDqdzbUkIgmaHEBVidciGHEEMfVdBvOTYXxzlsXmpuAbC6Ps1OURHDEuQopLdEQjdFM77nYAhCZZeFPECkstHO/n34Rf7vYAvVYVpCsVS1GOx04N92CuXASL10LFdPZqk5sjiLQrc66vddzeXL7AcCxfs/tvX1thfXiZJRqdJydsFmM+NiJXvzzuVnMhakr/iPHemozMFoDLsxG8MNLi5Bl4EC3Cx842KkcD4Su09qas1PVYx+fwwrfXivuH2nHXCSJy/NRXFmMI57K4szkCs5MrsBrt2CsUBflbq3snCIQS+HyQhTjC7FiqhpAXer7u5zY3+VEh0M9gVGLaGfPoxOGfHaML8TWL/1JwHCHvW7bpFd6PTZ8/GQf/vHMLOYjNDnxsRO91ReNRI9d6Do53VQU/tLZPP7l/BwmQ6swGSR8+GgPhny3vzaqKY5YTAYc6HbhQLcLiVQWVxZpimIhksTN4CpuBldhNkoY6XBgf5cTg/kZGOU8dY9s0D8iy/K6HXEykyv+nxo7YrWRJOq32Ot34KVrQbw1G8Gl+SgmgnG8Y8SHI73uyk9qVY77uxFM4CeXl4pdiIPtrXj3fv/O4lmBTVxIoEXL4BWg/+6yt2m3aGYfGV+iaXuDqSwna13w7aPtXF0G4ouAs0YH3hrAZDQUp9ExLCObjiBuc+K6/zgi+RYsxZJwWE0UA+q1od/bWt04xs148EvrRX9xWUdXm6bQWJF1OZ+xbpsZn7irHz+4uIjxhRh+fHkJS7EU3r2/o3odxEYzve8XLxbiPne/qHN1MYZ/vbiIdDYPl82Mh492N2wMTSkUaefAgLcVP7uxjDcmVzARSGB6+SbuHmrHiQFP+X/H4BW6bNuzXgDxDtO/UzGKbfIMlHW3c+E1/PjyUjGiuMvdgveO+eF36fDvVBLJ/L7ILN43OweEpoF2K9DdSxPdoetV79LWLLJcEvN5sL7bcgsbDUbMogM/uHkRe/0Z+tuWut2aBVlWYj57jt8u7Nt99J4PTwHz54ChB2q+iWrQZrfg/Qc7ce+wF2enwnhrNoJgLIVn31rAy7YQTg624WCPS9ddNprD1Q0EYjjWtoqri214cyaMu4baapd2pBEuzkexms7B2WJCj5EEQOGIhKuXxJHoHNCprX1mXZBlxWHDjj/9YmsDLHZyTMXmAU99O+1KkSQJD4768Xc/m8Kl+SiOD3jQWYvjUSFo19Htd34mjOVEGq0WI+4e2qB/2WgG2oZo0Cd0tRiB3mI24qPH+/DMm3OYXqbkuoeP9mCwXd9awNmpFTw/TkLoHX1uvGfMv14EEwlHVRp4liQJvR4bej02PLTfTxGc8zFMBKgX9+XrIbx8PVQ0mox2OmGzbP35GU1myE04H0Uwni5+32o2YJ/fibEuJ3o91Y8U1Qos/JXJF9+3j6YkJSUlUpaBL7y3vl02eqXT1VIQ/2YQiKUK4l9f9eMvvCO0IBG6rlokYiqbwz+fncNseA0WkwGPHO1RsqJvoVbiiN1qwvGBNhwfaMNKIo3LCzGML0SxsprB5YKteiz6MsbyCbj2HoH34j9DeuGPgdA1ZNtGcPXAb+Bly/2IFoQpuk8j9hf6A/0qWq83otIeRIA+mN97oBMHe1y0kBtN4ceXl/D2XBTvGfOjy13BgY1KcX/RZAYvjAdwbYk6zpwtJjww2oF9fkd5z+dmLqTWdnK71UH408w+MlCIamofpogmLWOyUl760mVaKGwi4W8d0VmY0lF4nHacPHE3ThrNyOVlGCTUfwJLg642ZnPK/Yw1Gw344OEudLqs+OnVIC7MRhCKp/ChO7qrF33TMVYQ/saBkfdU7PTN52W8MhHCz24sAwAGvK34+SPd254QNRoWkwHv3OfDgW4nfnx5CTMra3jpWhAX5yJ4z1gnBtrLmMgVwt+tfSxGE/X+zZ+nv90Ohb+1dA7/do1eVwAdn7xzrw+He13137dVwuoyLdBKBiA0Afzjr2FdEsLi28DBj1IkarMKf/ElGsIy0Gvmh5M/xNfe/BpuRm5ij3sPPn/083jf4PvqsmkbD0ZIeD7Wh89jlcSv7mMNnT6wIeEp+psZzUDnJp2MvSfo5+bOUd+nQb/7WWeLGQ+MduDuIS/enA7j7HQYkbUMfnx5Ca9OhHB8oA139Ln15UTWKs4eIHAFvYYw2h1DCMXTeGsmgjv3bLDg3KDk8zLemCSx72SvDYbC9aKo5eohR60Qu5qdtRWKHjaYmve8sBGQJBK4AuOUsKUh4Q+gAbQD3S5cmo/ihfEAHr2zr/rHpcV+v/o8F8lMDq9O0DnTfSNbpG349pHwF7xKiUwFLCYDPnKsB989P48bwQT++dwcfv5IN/b6tZPsUA6v31zGv10NAgBODrbhXft8618DuSywfIOu16Cn0mggg8pIh6NYLXV5PobplVXMhZOYCyfx/HigWC010uEoDiolMzlcXYzj0gIlGpTe55DPjrEuJ4Z89uoN9mqY5vuNd8mpw9144lMnMNblhNVkwFiXE0986mTtrNENSIfTikfv7IezxYRQPI1vnZ4uOqCqhoi0jMxQ1OUuSWZy+Mczs5gNr8FqNuCjx3s3Ff0AEkdE9BhQG3GkzW7BfSPt+PT9e/DJuwdwbMCDVrMBrbFJLESTOHf+TUjf/HeQFy8C2RQMgUs48OKvwz/zfVhMBhzsceFjJ3rxa+8cxoOjHeh0tVRd9PvcU2cwvhBDKpsvdjQ9d2G+rPvpdtvwybsG8O4xP6xmAxajSfyf16fw48uL69yLO6J9L2hxq5Sdx/3l8jJO31zGX798E9eW4jBIEk4OtuGx+wYxWkmvz4NfguI+KmwLZGDPO+i1nYqVd38qoJl9pIj59O2v7eNWinAELF0C5FuX5JoEEYvmGy3GwxkNknYWxg8+Anz+JeDLS3TJop9mqeQzVpIknBz04qOF2PH5SBJ/97MpzIXXNr3NrvAOF+I+IzQFXAHJTA7PvDlXFP1ODrbho8d7m070K6XdYcXHT/bh1OEu2K1GrKxm8A9nZvC9t+YRT2W3v4NkFIjO04tmo8920dUWuAzktz6GkGUZb81E8OTLN4ui36EeFz59/yCOVDtOtposlURYvvTn2DAJYfIlGq7LJOuyiXVn8QJdto/gh7M/xW8+/5u4unwF6XwaV5ev4Def/038cPKHddm0IZ/99iNZCUj5DtAic2yx4n2SrhFuv87Dm0ed+kYV94YYENA5LWYj7hlux79/5xDePeaHs8WE1XQOL10L4v/5txv4t6tBJHay72Q2p9AJK8XmcXKwDQBwdiqMbC6/1a0aiuuBOMKrGbSYjTjkKKQv2NoAc6EjSyQfxBdpobnZEf1+zk5dDxgwUAQujfX8Cd6xtx1mo4TZ8BquFobSq0Y+D0SF8Fcfx98rEyEkMzn4HBYc7tkiarR9Lx0cxRbo3KAEs9GAh4/2YF+nA7m8jO+eny92dusFWZbx8rVgUfS7Z9h7u+gHAOFJiu63OgFHZ0230Woy4lCPG794sg+/9q5hPDDaAb/LirwsYyKQwLNvLeB/vTiB5y4s4Jk35/C/XpzADy8tFkW/vjYb3n+wE//hgeHC38vZlKIfwI6/ijh1uLuiHjZmc7x2Cx492Y9/ODOD8GoG3zo9jV880bfzyMNyEbGWq8vAyk2a4K6Q1XQW/3BmFsFYirKfj/duG9skxJG/+NFVTAQSGO6w4wvvHa2JOCJJErrcLehyt+DBHiCaMGBptRX7bvwj8pBgKCzeGCBDhoT3B74O46P/oeaRL2r2IBoMEo71e7DP78BPrwZwaT6GN6cjuLoYxzv3+XCwe4dT97uI+5teXsVPxpcQKljNez02vHvMjw7nLnpUNnMhra1QjELgCtB3svL7r5C67yMTwcKkvbEg1uoA78iuIuR0Tz4PBC7R9c0m7Rlmh+zmM3aw3Y5P3t2P75yfR7CQRPDu/X4c6VO5h8Jopv3T0iUSkcqMcgrFqc9vZTUDk0HC+w91YqxLX51T1UKSJBzodmHIZ8crEyG8OR3G+EIMN4IJ3DfSjmN9ns2jXYSD39WzcS+bZw9gaQXSq3T82L5xlPRSNIkfX17CfISEL5/TiveO+dHjse3+F6wnsqwMafgPbJ6EsLZMwmjgMtBzrMYbWWdkmd7XANB5CF/7yW9AkmXIheNMWQIkWcYTr/1xXVx/myUzfO59dwDGFPXczZ1trni5UiFvq9erwUj/f/MlYPaMMgjQAJiNBhzr9+BIrxvjCzG8MbmMYDyN128u4+zUCg72uHBysO32LiRme5zd9EZLRjDmNeBlqwnxVBaXF2I43Nv4fXayLON0weF3tM8Ny2ohlaV0H9PiUT5b4wt1jQHUBBzz2TiI13J0RvnQ1RDOFjNODnrx6kQIP70axHA1HVGJAEXBmyyAvaM6j7EFy4k0zk/TIN4Dox3rzgU2TBpz9dCaWugaOf5LMBok/PzhbvyrYRGX5qN49sI8Mrm8LvbpsizjxatBnCnsl9+1z7e5Az1YOC/y7avra9dhNeHkYBtODrZhOZHG5fkoLi/EEFnL4NK8Isz6nFYc6HJitMtZn3oYjcLCH6MZ3K1mPHondf4tJ9L41hvT+NiJPvgcVSqY9w6T8Lc8UbHwF0tmittrtxrL2t66iyMADCsT8LRa4OkfgXz5CUi3LN5IkNESuQ7UYTKiGj2IdqsJpw5341CPuyjC/evbi8X4z23/dhXE/SVS2aLYCACtFmN5YuN2bNStNfVaQfi7XBfhr+6ImM+2IcCsk+6kCiPkGobwTTrZN9vIRcIwu2Q3n7GeVgs+cWc//vXiAq4uxvHDS4tYiiXx0H4/jGp2AXSMkUCwdBkYfveOT6iuLcXx/bcXkM7m4Wwx4ZGjPfrsiasyLWYj3r3fj0PdLvzo8hIWIkm8MB7A23PRzUW44gnuJu5QgwHoOECRZEsXbxP+kplcUWyUZYoE2lZs1BOlEZa+0c0jx8Vn2NLF5hP+wlM0xGOyAt4R3FxdKIp+AlmScGN1oS6bt+VgRPgYCX9LF4GR9+rnGGq3zJ8nodrVXYzV27RuoPsYMPkK/Z3jAcBR+8XLamI0SDjY48KBbicmggmcvrmMuXAS52cieGs2gtFOJ+7c09YUHbKqYbJSDUMiCGN8AScG2/HilSDOTK3gUI9OI5/LYGZlDQuRJEwGCccGPMDlgqO4VNSSJOr5C14ltxsLf3Tp2n0HNFNnHH46z88kaThZg58Zd+5pw9tzEUTXMjgzFd64904NhOvR1UvH0zXmp1cDyMsyhjvs63r5RNKYGO0XSWP/55QX92Jj4Q8gc8HPHeqE2Sjh/EwEP7i4iGxexrF+T81+p3KRZRk/vryE8zMkgL57zL/59sollUYaGqb32i24f68P9420Yz6SxNWlOEwGCaOdzt2ZKhqY5vQ5MprF2ULin89pRSKVw7ffmMFStEoxQd5hugxdryhaL7KawbdOz2A5kYazxYRHT/ZXT6SsFssTdOkdhuTbXYyl2mwWRaRGD2K/txW/es8g3rXPR9EGK2v4m1en8OKVAFLZbeI/dxj3l8/LODcdxtdfuYlL8zFIEnC0341P378Hh3qqHPElhOzINE0xNxtC+NuFk7culBEh13AIB0nHGEfaMJrAYjLgQ0e68Y69PkgScH4mgm+/Mb2zuMid0j5CiwHJCEXJbIMsy3j5ehDfeXMO6Wwe/d5W/Mo9Ayz6bYPf1YJfvqsf7zvQiRazEcFYCn//+jT+9e0FrKVL9rWZNWBlkq5vJvwByr46eIXib0B/m0vzUfz1KzdxbopEv/1dTnz6/j04MdDWGKIfoMR8tg+TKLRZ5PiD/xf9U4hgzcRSyeeZ0YQ9mQykW84zJFnGUKbKtQZbcOpwN579wgMY/4MP4tkvPKC4od19gN1HUXsirrRGPHdhHqcefxH7v/wsTj3+YtnR/hUjy8D8Obrec7y4LZvWDbS4qJcZUOJBGxBJop6dX7qzH4/e2Ychnx2yTAuif/PqFJ4+O4Pp5VXIzRpPXy7OwiBSdA6He92wmAwIxdOYCDb+eZro9jvU60Kr2QjERIzlLcNZQghs9p6/bJqGCgAW/hoBg1H5O0am67stm2A2GvCOvT4A1PlWtXhn8fvXQdifDCUwEUjAIEl417714utmSWOPny18Z+UmvS83QJIkvGfMjxOFGOefXF7C6ZvLVfkddks+L+P7by/i/EwEkgS8/2Dn1iJlbAFIxSmlxjNYs+3cKZIkocdjw4OjHXjHXh+LflvAwh+jOVotJjx6sg9d7haspXP49pmZ6nTseAZoYjkVo+mbMhCOxMhaBp5WMx69s796saTVIptSpm68w5sv3uwgxrIaVLsH0WiQcOceL/7d/Xuw1+9AXqbS8W+8Mokri7FdncjOR9bwd69P4SeXl5DK5NHpasEv3zWA94x1bl4grCY2D00sy3LDdJDsmNVlOlmSDDUpIFYVESGXWaMDzGYhlwWChU7GzoP13RaGKUGSJNw95MVHjvXCajZgLpzE3702hfmISsckIu4TUAYWNkH0+b1WKKQ/PuDBR4/3otXC4R07QZIkHOlz49P3D+JQD0Wivj0XxZMv38RbMxH6zA9dB+Q8CR+tW0w7u/to8T+bBkLXEYpTJOxzFxaQSOXgtVvwiyf68PNHuuGwNtDfpzTCUvTSiiSEzkPkauk8BHziKeDoL9PzVHqbZiCXVd7Lhc+zz8tOyJJUFP9E7OfnZQ1G80oS0FOYap87W7POYbV6vStieQJYC9Prt/C63qpuAIDyHC2+pUpXvJaRJAl9ba34heO9+NV7BzDW5YQkATeDq/j2GzP4+9encW0pzgLgdghRKzYPq8mIo30eAMAbN1fqt001IBBL4UYwAUkCTgy00aBTepXEkFv7ooriSJMLf7F5OhaxOulYg9E/xbhP7b62x7qc6HK3IJ3N4+Xroeo8iPj9ayz85fMyXrxCYvrRfje8t6zbbpY0djZkpC7SfA5YubHp/UuShAf2+XBPwSn506tBvHw9qKnPxVxexvcuzOPSfBQGScIHD3dvH0sq3H7eYRpUZXQLC3+MJmkxG/GxE73obbMhlcnj6bOzmF5eVfdBjGYlimj5+o5vFoil8K3T04gls2h3WPDonf1w23SYH7wySR9itjZa4Nps8WaLGMtqIqKIxrqcsJoMGOty4olPnVS9B9HVYsbDR3vwC8d74baZEUtm8d3z83j67CxWEhtP9mxGMpPDjy4t4u9fn8ZSNAWr2YD3jPnxy3f1o8tdY0dGxxhdBsZr+7j1Rvy+bYNKYbxeEBFyALD4dn23pZYsX6cFdKtTKUBnGA0x5LPjk3cNoN1hQTyVxbdOz+DCbESdOy/uqy9vusi+nEjj71+fxkQgAZNBws8d6lI/drRJaLWY8IFDXfjEXf3wOa1IZnL4YeFzOzJd2O9uNzQiSYD/AHJ5GZfPv4anXp3CzMoazEYJ79jrw6/eM4CB9tbq/zK1JjpLi7algjWweRKCGORops+zlRsU52V1AG46x3jfO/8r/nwxgNF0Bpa8jNF0Bo8vBvDed/3XOm/sJnQeogWeRLBm7oRthbZqIlx7XUfotY0d1A207aHoxmy6qV7ffmcLPnikG5+5fw+O9rthMkiYjyTxnTfn8OqENh0OmqHoZpsDZBnHBjwwGiTMhteqM+CsEYTbb5/fSf2Q0YLbz95x+0Ky6EJMxYBkFE2LeI64369xEEKXGLrXIJIk4cFRcsK9PRdRP3UtGaH3tWQAnLV9bb81G0EwnkaL2Yh7h9tv+//Nk8YcisM/uPXxiCRJuH+vD+/cR87J1yaW8dOr2hD/srk8/uX8HK4uxmE0SPjQHd3Y3+Xc/oal/X6MrmHhj9EsVpMRv3CsF4PtrUhn8/ins7O4oXYchuhmEZGX27AYTeLbb8xgNZ1Dh9OKj5/s0+80txA7ReQpsOMYy1qxaRRRFRjy2fHYfYO4d7gdJoOEydAqvvHqJF6+HkQml9/ytrIs48JsBE++fBPnZyKQZeBAtwufvm8PjvbXqddHxFyuTJKDrFnQa8ynQCyUlkTINTxi0cx/QHOF5wwjaLNb8Im7+jHidyCXl/GDi4v4yeUl5PK7PKHzFuI+18JAfPG2/74eiOPvfjZVjBX/pbv6cbCHJ8B3S4/Hhl+9ewAP7u+AxWTAQjiOCxfO4UYogaR7ZMvbyrKMG4ZBvDkTRmjqIqRcCsMddjx23x7cPeSFqQ69yDVBOPd8o0WBZEs6DtACT2wBSFRpelxriHhM/wGlv+bgI3jfh/8Xvp3z4Y3ZJXw758N7H/6ruh9jb4q5BfAfous1irKsRq/3jkhGyekLFGM+gR3UDUiS8vNzZ2rmjNQKnlYL3jPWif/vO4dw1x4vWsxGHOzmz6UtsXdQ0lA2BaytwGE14UDhOTs92Ziuv2gyg/EFino+WYjBK8Z8unqAi88AX7sf+AM/XV55jp4nQBG/mpE6uaKYKuLqpc+NtbCm4897PDaMdTkhy8ALVwLqilZC9HR2AqbaJaWJ7m0AuG+kfcP0rS2TxkTtUegakN96TRAA7trjxUP7aT/2xuQKfnx5qa7iXzqbxz+dm8NEIAGzUcIjR3uw1+/Y/obJCPV6SxKdqzK6pkHPTJuQWw+cLj5T7y1SBYvJgEeO9mC4w45sXsZ33pzDtSUVPyyF6BWZ2TaqZTa8hm+/MYNkJodudws+frJPvxFbsqyIne28IxeYjQbcN9KOT907iD2+VuTyMl6bWMY3XpncVHQmB+gMfnBxEWvpHHwOCx69sw+nDnfBXk9RuNVL5dFyftsJpYZhLUwLjJK0dT+TlnH1Ai1uEv1C1+q9NdUnm1IW3ToP1XdbGGYbrCYjHr6jG/eN0LTouekw/uHMDFbTu+jCMFmUY5GSuE9ZlvHqRAjPnKM+v942Gz559wA6uc9PNQwGCScG2vDp+/fguCMMQz6DqbgRf30hhUvz0Q1P1COrGTzz5hz+6WoWYckFm1HGRwcS+MixXn2mP+yUfP72mM/tsLQC3iG6vtQErqhsCggWPrf9t3yeaWywbluEqBUYp1i+KlPNXu8tWThPx8mefor4LbCjuoGuIzS0EQ9o2sVRTexWE965z4fPvmsI7tYG3v+pgcFIC95AUdgRYthEII7lMlNm9MDZqTDysox+b6uSfBMtxPcGrwLffIw6vrMpuvzmY4ropeFIxKoiy+z4a0RMVsDhp+sa/7x4xz4fTAYJMytruB6Iq3fH4veusaD9sxvLWEvn0O6w4Mgm0ZZbJo25+2ggKrO24/3S8YE2vP9gZ7Ej/vtvLyK/20HRCkhmcsU+XovJgF843os9vh0eV4njWXcfHc8zuoaFv0bg4jMbHzg1iPhnMhrw4Tt6sL/LiVxexnfPL+DSvErxD63ektzmyU1/bCq0iqfPzCCdzaOvzYaPnuitTVdbtUgEacrVYFLiTpkibXYLfuFYLz58RzecLSZE1jL4p7Oz+M6bc4gmyYWVyubwwpUA/va1KcyG12AxGfDAqA+/cs8g+to08uHYbHGf4vf0DAAW5aDmuQvzOPX4i9j/5Wdx6vEXa9MZUymFCDkAwNLF+m5LLQheAfLZglDduf3PM0ydkSQJ9w6345FjPbCYDJhdWcPfvjaFxd1E4oiI3yWK+0xlc/iX8/N4pdCxcazfg1880VffYZIGxmE14UFvGAe7XUh7RpBI5/HchQV8+40ZhOI0FJbN5fHqRAh//cpNTAQSMBoN8A8fw9F+D/ozmx8/NgzhSSCdoMUPIebtBDHQsXix8V1Rxc+zdupZ1jOubvod8jlg4a2qP1y1e703JJ8H5s7R9RK3H7DDuoF1zsgz1dtOHdCwLme1EfF2BfHLa7dgxO+ALCuRmI1CMpMrRqLfKdx++RwQX6DrZ78B3B7wC1z8Z/pnswp/yTB91hqMgKNLX+ewzNa4tB/3CVANjhhKePFKENltkq92jIgOd9VO+FtJpHFuOgwAeNe+ji0rEjZNGjMYFcdbaOfD9Id73Th1uAsGScKl+SievbCw+5SYMlhL5/CPZ2YxF07CajbgYyd6y1ujFL9rO8d8NgJ8lNYIvPBH2PDA6YU/rt82qYzRIOHUoS4c7HEhL8v4/tsLeGtGpX4dMWm/SdznRCCOfz43i0xOxh4flZtbTToW/QDld/UM7CyuqQmRJAn7Op147L5BnBxsg0GScG0pjr9++SaeH1/CX788iTOTK8jLMvZ1Ogo/59VW55IQ/kTnTKMj3DI+JebzuQvz+NxTZzC+EEMqm8f4Qgyfe+qMtk+cxEJpaKLx/26lDhKO+WR0xEiHA5+8ewBeuwWxZBbffH0aF+cqHEpqH6FBnLUVhJdm8PevT+PaEvUwvP9gJ949xn1+VSWfB0JX4baZ8e53vhPv2KtMOz/16hR+fHkRT706iVeuh5DNyxjwtuJT9w7i8PH7YJQkYPlGTVxRdUXsqzvGaBFkp7TvK0TZrgAxDX/uqsFiYVins0E+z4pRlmerLtrWqtd7HcvXKXLNbFt33Fi6TdvWDfSeoMvAOJBS0RnBNCaubrqMKTGWQhS7NB9FPLWL9ACNcX4mgnQ2D5/TikHReZsIArksuZ+WbwC3B/wC4YI4EFskobDZEG4/hx/PXQro7xyW2Zxiz19tunN3w517vHBYafhdCGe7IpOk9z9QU8ffT68FkcvTGu7QTp1uGyE67oLlpTGNdbnwoTu6YTRIuLIYw7+cn1NPSN2CRCqLb78xjcVoEjaLER8/2Ydut23nd5BNAeEpul7S78eDCPqFhb9GIHQNGx44lTGRoAcMBgkfONiJo/1uyDLww0uLODOlwnRcsefv+m0ntlcWY/jOm/PI5mWM+B14+I4emBthqlEIf6X9fsyGWE1GPDDagV+9dwC9bTZkcjLOToURT2XhaTXjo8d78eE7euBq0aCAavfRVz7X+LGRySidLEkS0KFMiD/+w6u3j0VIwF/8SMP7R3tH4e+WBYIN7NZMJwon/th5dBzDaAhvofdPxJF//+0FPD9eQe+fyQp4h7CymsYLL/8UoXgaDqsJj97Zh8ObxNIwKhKdJeHOZIXJO4i7h7z4d/ftwXCHHXlZxpvTEaysZuCwmvDzR7rxsRO98NotgL2dotvk/LqY1oYjl1V+P+FI3ykmixK9vdjALvZUHFi5Sdcb5fPMf5D+fmsryu9WRWrZ6w1A6S8UkZ2V4OyiOL58jmJDGWYrRHRjfIn2q6BOrV6PDbm8jHNT4fptm4pkc3mcLazR3DnYBkkMQgjB09kN+PYCtwf80iKz2UbnQBv0Hjc8xZjPPn2ewzKbIwSv+NK2FUP1xmIy4P69VGvw2o3l3VUaAHScLcuUtGbdQb+cCkwvr+L6UhwGScID+zp2d2dtQzT0thoCVpfLuulevwOPHO2BySBhIpDAPxcqHKpFNJnBt05PIyjOJU/2we8ssyZieYKOa1rbKZUJOh2mZ4o0gILBoH2TA6cGtOVKkoR37/fjzj00HffCeAA/u1Hezvc2PAM0aZ+M0s68wMW5KL731jzysoyxLic+dKS7MaJMsmll0oj7/XaMz2HFoyf78HOHutDlbsG9w+147N7Bnedk1wux4NbIi5IARWwB1JFndRa/fSOYuH0sQgYmAht3NmoCSVIWDoXLohEJXKYFc2cnLaAzjA5pMRvxyNEe3DNMJ0Znp8J4+uxsWSfJsizjrUwPxhdicEWvo8dtxSfvGShvOpOpHPH50b636GZzt5rxkWO9eORYD7rdLTgx2IZ/d/8g9nc5lUVMoDn21Ss3aJHK6gDcFcTDizjEpYvkrmxEAuP0eebqLi6S6B6TBeg8QteFSNYorIWVIchbYj7LRrj+5s427uubUYcWT0HUygGJpeK3TxbWNd6cCSOV1b/L7dJ8DKvpHJwtJox2KudkiqjVDTz4JRRTqgAU06se+hKdy5X+fDMhIk5dPfo8h2U2p8UFtLjX9zhqmIPdLvhdVqSz+WL1QMXUuN8vn5fx/JUAAOCOPjfaHdbd3aG5RalHCpYvvO/x2fELx3thMRkwtbyKfzo7i2RG/X19ZDWDb52ewcpqBi6bGY/e2VfZ7y4MA769xW/xIIK+aQAVg9nywKkBkSQJ79zrw73DtFD80rUgXroWhFxpDI3RrOzIQ9cBAOdnwvj+2wuQZcpn/rlDXY0TsxWepBMOm4embpgdI0kSDva48Mm7B3DfSLs+hGAR97l8g0TfRkUIm+L3LTDks98+FiEBwx0aF2yFq2LlZuPGRxVjPg/VdzsYZpdIkoT7R3x4+Gg3LCYDppdX8Xc/m8bSDnr/0tk8vvvWPH4ScCEnGTHYmsTHx1rg4D6/2iDLykm87/Y+sZEOB3757gE8ONqxccy72FdHpmmArBERfbMdBwBDBcc93iFa7E4n6Bi0EVm8QJeN9nnWc4wug1cpFrNRmD9H7/22PbsXajsO0Os7GaX0GIbZDElSXH9RxSUx7LOj3WFBOpsv9uLplXxexhuTNJR9YrBt/fqJEDucPcDBR4Bf+gbVG5isdPmJp4ADDyvPkca70FQnl6GIUwBw9ej3HJbZHLc+ev4AOrd5cJSccm/NRhCI7cKlWGPh7+J8FMFYClazobhmvGuEqabCVL1+bys+dqIXVrMBs+E1/OMZdcW/5UQa3zw9jehaBm2tJPp5Wi3l31E+rwh/7Yrwx4MI+kYHq9bMtmx14NSgSJKE+0ba8cCoDwDwsxvLeOFKoHLxr6Tn743JFfzoEk3hHRvw4H0H/DA0iugHFMVNeEcao4OE2RqHnwTefLZxFyRSceWAsmP9wu0X37evOJGEwqUsA1947+0LvJqi1UsTsbJMToJGIxlRejz8Y1v/LMPohL1+Jz5xVz88rWZE1zL45ulpXF7YXAyKrGbw96encXUxDpis6B85iGGfA8ZQA77ntUoiSFGGBlNl8ectblrIkOXGdNZn04ojstyYT4HBqAzlLL6tznZpibUVJWq80udIqzj8gLuX3IzzDRJlmc8pv8tu3X4AxYR230HXZ8/s/v6YxsZ5e8+fJEk4MUDDuGcmw+XHhWuIiWAcK6sZtJiNONxTElWeTSnJSqLr8OAjwOdfAr68RJdi7aoojmrfFaUqsQXa11rsQItbv+ewzOboSPgDgL62VuzrdECWgRcrXWvN55T9nbtf3Q3cgFQ2h5euUZ/gvcPtsFnK6KXeCpGUFpmpuNe7223Dx0/0wWYxYjGaxLfemEFChW7XpVgS3zo9jXgqC5/Dgkfv7K+8hig6Q52MZhvgUoRaHkTQNyz8NQqbHTg1OCcHvXjPmB8AxWv96NIS8pUcLHuHIUPG9M1x/Ntl+mC6e8iLh0Y71kc66R1Z5n6/ZkOSgI79dL0RFyUBWpSUZTqRbFnfh3XqcDee+NQJjHU5YTUZMNblxBOfOln97hg1KMajNeBC6VLhtejpv+1vxjB6xuew4pN3D2DIZ0cmJ+PZtxbw4pXAbccmk6EE/vZnUwjGUrBbjfjFk30Y2H+S/rMRxX6tIkSttj0UbVgJxbjPBuywC12jLiqbR1mMrYTOwudZcJxcDY2E6C5s21Oz7pqaIsSx+XONEWUZvELuU4ud+sTUoOc4HW8vT5Td/8M0GZuIWmNdTjisJsRT2S0HhrSMLMt4/SZ1+x3tc8NiKllqjC3QuVqLa10lw4a4euj9lIw0burJRojXhLsXkCR9n8MyGyOEv+isbj5P37W3A0aDhKnlVUwEK3B3xRboONJsq0kU+us3VrCazqGt1YyjfR717tjmoWEoWd7VML3f1YJHT/bBYTUhGEvhW6enEUtWfly8EEniH96YxWo6B7/Lio+f7Id9N6kxIgWlfWRdygcPIugbFv4Y3XO034MPHOqEJJEN/V8vLpQt/sm2NtyImzETisOdnMX9I+14x15fY4l+AE3aJSM02d42WO+tYWqFEP5C1xtvwQ1QFsk7NnaOnTrcjWe/8ADG/+CDePYLD+jnhMk/RkdVkVnqo2kkhJgpFsx1wHMX5nHq8Rex/8vP4tTjL3KZNbMpovfv7iE6wX1jcgVPn53FWjoHWaYYrKcL/Q7d7hZ88u4B9HpsSsdcIgjEA3X+LZoEIfztRgAQ++rofOMt+gsx039gdykR7j5a8M2mleSJRkCWS54j/XyelUXHGPXbJKPK8KCemTtHl91Hi52eu8bWpgxUNlofIqMuwvG3ugxk1orfNhkNOD7gAUDHDBWnGNWR2fAaFiJJmAwSjhV+lyKxwjGz+P23wmQF7JTq1FSuv2jBBSY6DqHjc1hmY+wd9PrOZdb1fGoZd6u56Ej+6ZVA+Y7k0pjPKq+tRlYzODNFwwfvGu1Qv6pJRF9W0PO37m4cVjx6Zx+cLSasrGbwzdMziKyWv0Y3s7KKfzgzg2Qmhx5PC36x4CasmNL6g/b150U8iKBvWPhjGoJDPW78/JFuGCQJl+Zj+O5b8zv+UJJlGc9fDeL8KuU/v7M9invUyoLWGuKE3dNP3YZMc+DspgW3XIa6/hqJ9CoQnqLrQuBsFKxOpX9U9OE1AokQdVhIhk3FWq3x3IV5fO6pMxhfiCGVzWN8IYbPPXWGxT9mUwwGCe/Y68OH7uiG2UiTsn/3syn8y/l5vHglCFkGDvW48PGTfXCKOBZzC9A2RNcb1aGtJZIRmkSWpN0JfxY7ub2AxtpXZ9aU48bddtdJUokzsrou9poOacSXSKg3mDbsiGwIjGag6whdnz9X103ZNavL1J0sSST8qUnPCbpcON+YQ3aMOlhaSSgGFDGswOFecsmF4unKnDV15nTB7Xewx4VWyy2OEyHg7dQ5LsSvqD4iEXeNLJf/HDH6Q5J0F/cJAHcNtaHVYsTKagZvzoTLu3G0dv1+P71GwuSAtxXDvipEUIpzheUJcjHuAk+rBb9UUg/xrTemsZxI7/j2k6EE/unsLNLZPPq9rfjo8T60mHc5zLS6XKg/MFI/9y3wIIJ+YeGPaRhGO5348NFuGA0Sri3F8Z0355DJbW2hz+dl/ODiIs5NhRFu7cewz469xiU6+GpEiv1+HPPZVDRy3GfwCvUhODuVE+lGokYLpTVFuCO8Q7QAogMe/+FVSECx1FpEXfzFj3Y38cc0PqOdTnzirgG4bWZE1jK4thSHQZLw7jE/3n+wEybjLYfiovOy0fbVWkRMtbp6SbzbDaLbrZHiPoNXqJvF7gMcHbu/PxH3GbpO/SFVoOZDGuKzuX2EhPtGpbsQ9xm6RoK5XhFuPO8IxXapiXeYosszycYaAGDUR3TcRdfvl1rMxmI03RsFEU0vBGIp3AgmIEnAycENzsfKcfwBzdfzl4pSrKlk2PlzxOiTovA3Xd/tKAOryYh37CUX7qsTIaylczu7oSyvd/xVkZmVVVxdjEOSgAeqVdfk7KZI91wGCE/u+u5cLWY8emc/2h0WxJJZfOv0NAKx1La3ux6I45/PzSGTkzHks+Mjx3rWRytXSqhwXuQZJGcq0zCw8Mc0FCMdDnzkWA/MRgk3ggn887k5pLMbi3+5vIzvv72At+eikCTgnuPH0emx0wmtKJ9uJLJp5QDDO1LfbWFqj3BWha7uekJJU4iYT1+Duf0EHftp6ioeIFeB3lkXi3agvttSBjeCCdw6DiLLwERAfxPZTO3pcFrxK/cMYMTvgKfVjF882Ytj/Z6NT0rb9ylxn43wntcyxZhPFZxavv0lMa36iG/alkWVIywdfhIR8znq+qsCNR3SkGXlOercpSNS69jbqSJAloH5N+u9NZWRy5IbD1B6C9XEYFDud+6M+vfPNA7OgqgVu30g4diAB0aDhNnwGubCa7f9v1Z5Y5KEyr1+Bzytt/TlpmIUFSxJZQh/BZEgNk+fGY2OEDgdfk5lanRKHX86Mhsc7Hahw2lFKpPHqxM7XCtdW6F0JoMJcFTPHSbLMl64QhUJR3rd6HBWSbSSJCXuM3RNlbt0WE149GQ//C4rVtM5fPuNGSxENh+OG1+I4V/epHS7fZ0OPHy0B+Zbh0grRQxE+vaqc39NiFarYVj4YxqOwXY7fuF4LywmA6aXV/H0Wco9LiWby+O7b83j8kIMRoOEDx3pxoE+nxKr1wgdFrcSnqQD5xZ3TYp1GY3h6qUJpWyaYo4agcyaMm2lk8jIsjHbFIfuYgO4/uKLFCOhs1i0IZ8dt0o0kgQMd1QhRoRpSETv3//nHUPoa9vC6WpuUWIj2fVXPTJrQLgwDLWbmE+BuUXZVzeC6y8VVz5f1RzSEAJZlT7PajqkEZ6iBW2TtTkG6oSoNf+mPhfiA5fJjdfiql7ySfcdNAAQnW8epxJTPkXH39xtC/8OqwkHul0AgNOT+nD9RZMZjC/EAAB3Dm6wxiCcjXYfYLLc/v8b0eotdKFlG2eYZisis3RZ0u/HNCjObvqcSMWBZLjeW7NjDAYJD45S+sP5mQhC8e2daUXTgasbMJq2/tldcHE+iqVoChaTAfeNVLmySXTfBa+qJtzaLEb84ok+9HhakMzk8A9nZjCzsnrbz709F8GzF+aRl2Uc6Hbi5w93q9djmE4A0cJ+qF2F86ImRMvVMCz8MQ1JX1srfvEE5RzPhZP4hzMzRUt6JpfHd87P4fpSHCaDhIeP9mBfp5NuKE4ERSRmIyHEzPaRqhfrqsbFZ4Cv3Q/8gZ8uLz5T7y3SL5KkuOIaZTE5dE2JIbM3aC8nsD5CTkeTgRsiFnt9e3UVIfHF9+0rOkdQuJRl4Avv1Y94yeiIDo77rDqhaxQT7ehQbxiqGM18Sf/76sA4/Q6ubnWHxcRzJEQzlanpkIYQeDvGqrqgpRl8oxTPnYqrNuleU0TMZ/dRcudVA4td2X+Lx2OYW3F0UqRjOrFhdK6IypwIxMvqfKoXZ6fCyMsy+tps6HJvEHkcK4jgzjK66ySppOevCUR0seDO/X6Nj9FM+wBAVz1/ANDvbcWI34G8LOPFq4HtbyB+vyoK2ulsHi9do4SUe4a8t/eLqk3bIB3zpWI00KwSLWYjPnq8D31tNqSzefzT2VlMhpShtTenw/jXtxchy+Rq/LlDXTCoJfoBtP4ty1Sf0+JS736bCC1Xw7DwxzQsXe4WfPxkH1otRixFU/j2G9NYSaTx9NlZ3AyuwmIy4BeO92KotPhVTOxGpskZ1SjIsv76/S4+A3zzMYpRyqbo8puPsfi3G0TPX+iqPqe1b0XEfDaq20/Qvo8OMNfCG8YC6QZZVnpv1IqOqxGnDnfjiU+dwFiXE1aTAWNdTjzxqZNcas1UB98+WhSMB4BEA0aPawE1Yz4Fvn20oLMW1v9CpeiuU3tfbfNQzFXp54GK1GxII59ThPlOfX2eVYzBSKIZoD9RKx6gBUjJoPwO1aL3BF0uXiRnMcPcitGs9KZucFzvtVsw4ndAlpUITa2SzORwYZbEy7v2bDIkIhx/rjK764o9f7MVbp1OyGUVAcHNjr+moBj3qb/X9gP7fDAaJNwMruJGcJs0BfH7ufurtj2nby4jkcrBbTPjWL+nao9TxGgG2oboelBdQad0fTqTk/HP5+ZwPRDH6ZvL+PFlcj4fH/DgvQf86ncYin4/dvtVjJarYVj4YxqaDqcVj97ZD4fVhGA8ja+/chOzK2uwmAz46PFe9Htvidtq9VIUZj5H08iNwuoyTRQajFTWqgde+CPg9pkJ4IU/rt826R13P01rZ5KqFBLXlWwKWL5B1zsatN9PYLIoi9OLOo6Qi0wXYtEsuoxFO3W4G89+4QGM/8EH8ewXHmDRj6keZhvHfVaTXEZJQVBT+DOaldjQKohaQI26I9bCtFgjSdXpYhVCWRXiPms2pLE8QcdSVgfgHlD3vrVM91F6XSzfoHMLvSCESt9ewOqs7mO5eqmnK58FFt6q7mMx+mUbN9udBdffpfko4intdrOfn4kgnc3D57RisH2DGHNZrszxBygimN4HabYjvkhrT5ZWoMVT761haoEQwkQUpo7wtFqKAttPrwaQy2+ScJFOAKuF4cUqCdqRtUxxOOKBUR9ManXdbYc41g+p7+QyGw348B3d2Ot3IJeX8Z035/DTq4qj8cHRDvVFv1y25LyIhb9K0XI1DAt/TMPjtVvwS3f2w2UzQ5bJRv3xk33o8dhu/2FJoihMAFhuoLhPsSN39+88W7/ehK4Bt89MVOUDtmkwGEriPsfruy27JXSdFlVavYC9o95bU32E6yJwCcjn67stlSJES9/+5ohFY5jd4Oe4z6qxcpNOcltcStySWvgLHXZLF1XfV9esO0K85tz91RFJOg6Q8yq2UBVHa02GNIRo6T9QvdhILWJrUybd58/VdVN2TDYNLBYEONFTWE0kSXH9zZ7Rf+wvUx2cBffbJkkePR4bej025PIyzk2Fa7ddZZDN5XFumhbd7xxs23gxenWZ3oNGU/nna0IoXFshEaEG1GS45laKMZ+9+qljYXaHEMISQV06w+8e8sJmMSIUT+Ot2dvjigEobj+7jwYaq8BL14LI5ilmeKTDUZXH2JD2vfRejS0Cyajqd28yGvChI9040O0sHkK8Y68P9+/1qS/6AWQIyGXpmF/t86Jqo6FqKC1XwzTRmQrTzLhbzfjEXf24b6Qdn7irH52uDfLnBSIKc3micU7WhIjZriOXTfte4PaZCbaf75aOwgdP8Ip+BSRAWZjs2N8cJ0neYcDcQt06ER26kZsxFo1hdkO7iPtc0pezRg+Uxnyq/fnhHaJ9dTqh+r66Zt0RpaJWNbC00vMEKD15eiKbUobQhNDbTAjxbP48LRRpncAlEh5sHkW0rDb+QzRoubYCrNyozWMy+kLEWMbmNz0fO7mHXH9vzoSRymqvouHSfAyJVA7OFhNGOzcZEhGilqOr/CEJcwuJBkBNXH81G665Fe73az4sdqC1na7rMO6zxWzEfcO0/a9cDyGZ2WD/JNyMVYr5nAuvYXwhBklCdVxwW2GxK+/XKnUeGwwSPnCwC+8e8+Pho924e0jFvu1bEZGlvn36WlfTWDWUlqthqir8rays4LHHHoPb7Ybb7cZjjz2GcDi85W0+85nPQJKkdV/33ntvNTeTaRIcVhPuHW6H176N480zSJGYa+HGWGzLZYBw4YNXL/1+APDgl1CM9wRQjP186Ev126ZGwDNYWJRc1WW8BABawBFidqP3+wkMRsWtqce4z5WbNNFoaQU8e+q9NQyjfSytVCAPsOtPTfL59Se4alO6r1Y57rMm3RGJEInNkqG6n6/+krhPvQ3ZBa+Q4NXqBZz1P5mviN1MSLcX4jIza0BQB+kRIuaz53jtFrRMFqDrDro+e6Y2j8noC5uXXie5LLAa3PBHhn12tDssSGfzxR49rZDPy3hjktZJTgy2wWjY5L0Vq7DfT1DDnr+aDdfcihA1Xdzv11QI159O12OO9Lrhc1iQzOTw6sQG6Q2RGbqsQsynLMt44UoAAHCoxw3/VqaOaiHMCFUS/gAS/471e7DXX8WIcrkkUa19b/UepxposBpKq9UwVRX+fuVXfgXnzp3Dc889h+eeew7nzp3DY489tu3tTp06hfn5+eLX9773vWpuJsOsx2RRJlNERKaeWZmkSMQWtzJZpAcOPgL80jeAzkOAyUqXn3gKOPBwvbdM3xiMSqeRXuM+lyfoRNnm0V8cwW4QTrngODno9IRwkHQ0WSwaw+yGDo77VJ3oDAkW5pbqdbOJfXXgsqr76pp0RywV9tXeIRKfq4VvlKLf1lY2jbrTLGL4pvOQviajBbudkDYYqOsPAObOVW0zVSE6T18GI9B1pLaP3VOI+wxdo551hinFYFDiPjcRtSRJwokBcv2dmQxv3qVVByaCcaysZtBiNuJwj3vzH9ytqLVNF6Ka1GS45laSUfqSJOX1wDQH7j66FAKZzjAYJDwwSvG9b05HsJxIK/+Zy1B3JaD8nipyeSGGhUgSFpMB94/UaX1TDA+u3KShdL0SW6BEKaOZDAJ6gquhdkzVVt8uXbqE5557Dv/7f/9v3HfffbjvvvvwV3/1V/iXf/kXjI9vvdhstVrR1dVV/PJ6q2hrZZiNKMZ9NkDPnxAvvcP6W6A4+Ajw+ZeALy/RJYt+6iAWk4Pj+pu0B5RF8GrEtGkZ9wBgdQCZpL6GEnIZJVqPYz4ZZuf4RgtdaIuNkUCgBcS+qH1v9YYQ3AMUA5RJAsvqxfxVvTtClhWXor/K+2qTRRlC0pOLPRWnRR6g+s9RtVBjQrr7KL0Aw1PUUaRVRA9hx356T9YSezu5tmVZ+wIpUx+Kwt/mww9jXU44rCbEU1lcXlC/S6oSZFnG6zep2+9onxsW0yafpbkskCBXTsWiVqnwV+WKipoM19yKEDTtHfS5yDQPwmgQW9BHbPYGDLbbMdxhR16W8dOrAeU/YvM0+GZ1AC0eVR8znc3jpWt03HHXHi/sVpOq979jWtup9zif03ektxDJvMM0kKcnuBpqx1RN+HvllVfgdrtxzz33FL937733wu124+WXX97yts8//zz8fj9GR0fx2c9+FktLS9XaTIbZGNGFF56mRWu9Isv67PdjqotnkE4uUnH9TZnlskqkQrPEfAoMBnLMAfrqRQpdo/1oi5tjbBimHCytgKfgStOrQ1tLyHJJzGcVi9YNBqUfT8V9ddW7I+IFgdlgqk4M6q2Ifryli/rpHA6MA3KeYutadTqYqsaEdItLiYTSqqiVTSlpA6KXsNYI19/8Of0lNTDVp9jzt7mbzWQ04PiABwDwxuQKZA0MbM6G17AQScJkkHC037P5D8YX6XVvaaVzgEqw+wqRqBlFRKwSVR+u2Yhivx+fHzUdtjZ6b+SzQHyh3ltTMe/a1wGDJGEikMBkqOCOLcZ89qk+pP3G5ApiySxcNjNOFPaNdUGSAF/hOCioY4dZNesPqg1XQ+2Yqgl/CwsL8Pv9t33f7/djYWHzHdsHP/hB/M3f/A1+/OMf40//9E/x+uuv4z3veQ9SqdSGP59KpRCNRtd9McyuaW2nk9p8lqIy9craCnUVGozK4iHDGE3KJIzeFpNXbtDJn9XZnCXoxbjPK/qJlRAL3/4DzeXQrCN8bNRA+DnuUzXiS4VjIhPQNlTdx/KX7KtVHCCraneEEEnaRyhivdp4hwCzDUgngLBOjrUXL9ClEC31iFoT0kJMWzivzSHJxQu0XXaf4qyoNb595HhIr+rveLvB0ORxkTiPSQS3PKY/3EuuulA8jRvBKsZO7pA3Jsntd7DHtbXbRsQ4O3sqP/6XJLo9UPWev6oP12yEcPxVoQeN0TiSpPu4TwDw2i042k/C/otXAsjn5RLhT93P3lgyU+wWfdc+H0zGOteHlPb86WWArZRkpNDrLQFeHZpEuBpqx5T9TvnKV74CSZK2/Dp9+jQAyiW/FVmWN/y+4BOf+AQ+9KEP4fDhw3j44Yfx7LPP4sqVK/jud7+74c9/9atfhdvtLn7199fpwJ5pLEp3fnqK1LuVUMHt5+6rzSIOox/0GvcpFr87xppTRHJ204RgLquP/PJMUtkPdep4oVRn8LFRAyEijWMLNMzDVI6I+fQOVT9Sy9VDDodcRnGpaxlZVj5fa7WvNhiVYxE9uNjXVmiRVpIUQV6PqDUh7R2m13g2pUTEagVZBubO0vXuY/U7XjQY6fEBYO5MfbaBAaDR4yKrk75keUvHT4vZiDv6aGH99M36HgcE4ylMBBKQJODkYNvWP1x0s+2yu06IYlUW/oAqD9fcSj5Hx3YAO/6aFSGM6Vj4A4B7h9vRYjYiGE/jwmx4veNPRV66FkImJ6PXY8M+v0PV+64Idx91hmfWarJ/Up1g4fzE3VfdXu9qwtVQO6Js4e83fuM3cOnSpS2/Dh8+jK6uLiwuLt52+0AggM7Ozh0/Xnd3NwYHB3H16sYLnL/927+NSCRS/Jqeni73V2KYjSnt+dOTMFJKsd9PhxMcTHXxDlGJbzKqTGRqnXxOiSPoKCN25eIzwNfuB/7AT5cXn6nO9tUCSSqJkNPYQttGBMfp72b3UX8FUxP42KiBsNg57lMthPBXizib0n21cNJpmcgMHQ+YLLU9ZhQu9sBl7XfciC5CzyAt1usVtSakJQnoOUbXhcimFaKzQDxA7t6uw/Xdlp5j1NUanqbJeqYuaPa4yLV9zx8AHB9og9EgYTa8hrnwWg02bGOE22+v3wFP6zYDNOJ3qrTfT1Da89dIxBcpXcrcQkOdTPMhXtuRGf2uN4KGE+4dpvjzs5evIpteo3Um++0JgJWyEEni0jw5tR/c37GlmahmGIzKMbMeBrJvRWwzd+I1PGW3N/p8Pvh8vm1/7r777kMkEsHPfvYz3H333QCA1157DZFIBPfff/+OHy8UCmF6ehrd3RsfMFitVlit7GRiqkDbIO3M18I05au3Lo9cBghP0XUhYjKMwGimuKelS7TgpofYzJWbNFlusQOuHU6QXXwG+OZjKE60L16kf//SN2jxS490HgImXyZhP7NGUWlaRYiT/oPN6dCsE3xs1GB0jFHseOAyMHBvvbdGn6yFlTgb0U1WbToPAVOvFvbVSVrc0yrCcecbpTjwWuHup2j9ZJSckVp10smy8hw1gnv94CPqHAN13QHc+CktyMcWAefOh3urihAi/Qfqf4xkddKwQWCctmv05+q7PU2KZo+LnD1A4Mq2bhGH1YSxLifenovi9OQKHvHU/nUdS2ZweT4GALhzcJt1kcyaklKw23NMcfvVZYrN1asz5VaEkOnq5XOkZsXZRcdcmTV6fdvb671FFXNHnwfnZyIwL8xiNr+Gwb3D1HmtArIs44UrNDhzoNuFTpeGjqd9+2jAL3gNGHlPvbdm52RTylqxHvv9mLKoWijugQMHcOrUKXz2s5/Fq6++ildffRWf/exn8eEPfxj79+8v/tzY2BiefvppAEA8Hsdv/dZv4ZVXXsHNmzfx/PPP4+GHH4bP58NHP/rRam0qw2yMyarY00VUnZ4IT9EUWYuL3DYMcysdhX1xQCdxn8Lt0rF/5weSL/wRiqIfgGK81Qt/rP721Qq7D3B0kJNOyw6gVJzEWkBxvjAMUz4i7jM6TwIWUz7CLe7up+GRWmDvoP11Pqe4DbVIPq/EfIpuwlohScpjLmnYGRlfoh4ug4nejwxhdSgJDFpx/WXWgKXC61n0ENab3hN0ufAWLbYxjEA4/naQvnLnHhLbJgJxLCdq3/N9ZiqMvCyjr82GLvc2C+9C1Gr17l58N9uA1oIgopeUmp1QjELdIuazkVJrmNsxGJUOy4hGXMgVYjRIeGC0A870IhaiScRb1IvJvbIYx1w4CbNRwjv2akwcbRuiv+NqCEiE6r01O2d5gs5PWtv1Z3BhyqaqbZh/8zd/gyNHjuADH/gAPvCBD+COO+7AN77xjXU/Mz4+jkgkAgAwGo1466238JGPfASjo6P49Kc/jdHRUbzyyitwOnUcqcLoFz33/BVjPod5iozZGO8ITZkJJ4SWyeeVhdOO/Vv/bCmha1BEP4GszziGUvwFx4GWe5ECl0lQdnXzASXD7AarQ+kB0bLYr2WKMZ81FG3WiVoa3leHb5KLwmwD2vbU/vGFgy50nZyRWkSIku0j2nZu1gMhri1e0IaotXCBBh8dfu2kWXgGaXEtl6HniWEEzm76rEhGaWBuC7x2C0b8DsiyErlZK5KZHC7M0pqdECC3JKZSzKdAvJd13oW2jogQ/jbZT4nUmsWLtG8VqTUs/jUWwmjQAK/tPe2tGDQtIy8Dry2r48zN5PL46dUAANr3OFvMqtyvaphblEoGPXR6C8RApK9GKShMXamq8Of1evHUU08hGo0iGo3iqaeegsfjWfczsizjM5/5DADAZrPh+9//PpaWlpBOpzE5OYknn3xSG+XLTHMiIjLDU3SypieES5H7/ZjNMFmU17iY9tcq4Ukl1tI9sPPbte8FOf5KkfSfZS4cdOEpIBWr77Zshljo9jdALBrD1JuOQgSi1vfVWiS9qkxS1/oEV+yrVyaBdKK2j71TRHed/wBNLdcah7/EGalBYVuWleeoEWI+1cYzSMM9uUz9BW5ZVpyHPce1M/goSYrrb/aMPlI2mNpgspblZrtzkLrgLs1HEU/Vrhf1/EwE6WwePocFe9p3sKAv+v3UEt/F/TRKz18qDiQjtG/Y7DlqxNQa5nYaSPiTUjHsdeYAyYC3Y3ZML6/u+j7PTK4glszC2WLCyUGNdmGKdSW9DJbn88ByYa1Y72tizI6oqvDHNDGNEktg91FUZj6rZCDrgdVlytU3GKmrkGE2o7iYrPG4z1K3Rjl58Q9+CcUTJQDFE6iHvqTu9tUamwdw9xZ6hzQoBKyFaZJVkrTb2cQweqJjfyHuc44Wi5idE7pG+0qHH7DVeNGg1UsdLnJem6JtLquIbfWMZBaC2qIGnZFiwMZk5WG6jZAkxfU3d7a+x5LhKYrbMpq1J9J2HqaUjURQ95FujMqUIWr1eGzo9diQy8s4NxWu7nYVyObyODdNDsOTg15I2wnqsqzEWKrm+CvEYcbmaNFa74i/td1Hny0b0aipNcx6RMfj2sq2rl/NE5lBq8WENn8vcgYLXrgSQD5f+TFBPJXF6YK7+Z37fDAbNSpftBeODSMzNGyodSLThe5x29ZRw0zDoNF3DqNrGimWQJIUR5Se4j7Ftrr7Nj+YZBiAHHHFXPJgvbdmY/L59f1+5XDwEeCXvkELQCYrXX7iKeDAw+pvZ60pxn1qsBdp6RJdegYAK0d1M8yusTqUqWCO+yyPesR8lqJlUWv5OpBN037aXceEFRGJGp7UnotduNg69pNww9xO52HqP4wt1reDS7j9Og9r7/zH3ELbBZDrj2lonrswj1OPv4j9X34Wpx5/Ec9d2OJ94dx5zx8AnNxDAyznZ8NIZXO73dRtuTQfQyKVg7PFhP1dOzimT4YppcVgBByd6myEvYME/Wyazln1TrTg7tpq0b1RU2uY9ZhbSAAG9O/6K2z/0Mh+WM0GBGIpXJyPVnx3L18LIp3No9vdgv2dGl5PsHlouFCWFSedlhGRpO0j5Q3UM7qF/8qM+jRaLIEQ/kI62IkLSvv9GGYrTFYqJQa06UYA6OQonShs657yb3/wEeDzLwFfXqLLRhD9gBIH0Dy5fLWEECPr6SBhmEaD4z7LJ5cBVm7Q9XoJfx1jtK+OzGjPrblUEvNZz1hEm4eEbVlWBke0QD6nvN+05iDTEpZWxd0vxLdak04oIn/Psfpsw3b0FOI+A+P6d3Ywm/LchXl87qkzGF+IIZXNY3whhs89dWZz8a/U8bcDx+ywzw6v3YJUJl/s3asW+byMNybpHOP4QBuMhh18ToiYT4dfvWEJg6HkedK5OAIojr+tolAbNbWGuR0xeKX313bBzW5tH8Q9QxRh/PL1YEUDCkvRZFE0fHB/x/ZO43rjKwjyQY07cmVZ2UYeImgaWPhj1KfRYgna9gCSgez3Wltc34hchrpkAI4kYnaGcNFpsVsHUNwtvtH69A9pFatDEUK1tFAaD9CXwagIFQzD7B4h9kdmgWTlE7RNxfINirNscdMiZD1ocSluTS1FM2fTytSvcNzVk87CNixqyMW+PEFxSFZHef3CzYiI+1y6SM9ZrZk/T0Ktq5vidbWIs7MQ054H5t8s++ZluciYuvH4D6/ePgItAX/xo03WQuwd5JjNpna01iBJUrHr6sxkGLldROltx0QwjpXVDFrMRhzpde/sRrGCqOVUqd9P0Cg9f/mc4u7cyvHXyKk1zHoaoecvmwISAbru7sOxfg/aWs1IpHI4fXOlrLuSZRnPXwlAloGxLie63bYqbLDKCBFteYLOO7RKaSWUd6jeW8PUCBb+GPVptFgCk1X5MNZD3Gd4ijoJrU4lNoBhtsK3j8TteEB74rYsVx7z2QwIR93SRe10NAoHiXeYsuMZhlEHq1NZJOK4z51RGvNZz2lhIaxpKZo5eIUWJ2xt2hBKOsboWCS2oJ1jkcUS9zrHIW2NqxdwdNBravFCbR9bloH5c3RdCJBaRbj+5s6W1VVWtouMqRs3gonbR6BlYCKQ2PgGBiOJwoAimm3DWJcTDqsJ8VQWlxeqMwgky3Jxwf5onxsW0w73gcLx51Kp308gjn/0LvwlArSfNFmB1vatf7ZRU2uY9Yi1xtgiDWXpkegs7ehsHsDqhNEg4V2jHQCAM5MriKxmdnxX15bimF1Zg9ko4R37dLKe6eyiIbFchmLrtYow43gGtReJzlQNPoNh1KcRYwlEYasehD+xje0j9V3kYvSD2Qa0DdJ1rUXIReeo78dkUSJJGQXffpoSTgSVKbt6Isvro+MYhlEXjvvcOfm8coLrq/PwWVHUWtSOqCWc4p0HtXG8aLEr08dacP1lU8rrx88xn9siSUB3QXSbO1vbYaSVG8BamBaxtOBe3YqOMTruTsUUx+0OKNtFxtSNIZ/99hFoCRjusG9+o6KbbWdCrslowPEBDwDgjckVyFV4v82G1zAfScJkkHC037OzG+VzQHyBrlfL8ZcI1sdVrBaRWbp09Wrjs5epPy1uSoeQ8zsW/zWHcCsKERMUSzzgbUU2L+PfrgV3dDfZXB4/vUo/e2KwDa4Ws+qbWhWkEqNLGZ/tNUfEfPr21nc7mJrCwh+jPo0YSyC68lYmaYpDy3C/H1MJwk2ntcVksT3te9XriWgkzC1Ae+G9roWF0tg8Lb4ZTfp1eTOMlhH76sgMx31uR2SKFgfNNqU/pV5YWrUlamXWlO5DLQklRWekBlzswhHZ6tWGI1IPdB6iz/9EsNj1UxNEr2DXEcCo8UVCownoPkrX587s+GZlu8iYuvHF9+0rCrMoXMoy8IX3btEzK0SyMhb9D/eSCy8UT+NGUP3XwRuT5PY72OOC3brDc7BEsMTN5lV3gyx2cqgD5C7SK2Lbt+r3Y5oP4WjVa9yn2O6S+FpJkvDAaAckCbiyGMNseG3buzk3HUZkLQOH1YQ7B1Xeh1Sb0p6/eh/DbkQ6oex/eJ2mqWDhj6kOjRZLYO+giK18lqI0tcrqMn1JBqX7i2F2gohBiy1S7rcWWBfzyV1xm1JcKL1U/4PMxYLbr30fuTQZhlGXFhd1RAFKjCWzMcXy+r3aiGksRjNrYF8duEzODEeHtmLhfaMkjKwuKx1I9UJ8nvk14ojUA+YWxR0pxLhqk4wCwcJ0ffex2jzmbuk5Rq+p5Rs7dgBX5CJj6sKpw9144lMnMNblhNVkwFiXE0986iROHd5igEDEYsaXdtwP1WI24o4+6t07PanuuVswnsJEIAFJAk4MtO38hmJR2dldnf1mI/T8iW1n4Y8pRQyo6VH4y+eU9/4tg3YdTisO99B+6oXxwJbu5EQqi9du0GfiO/b6dh4vrBU8g3QMm4oB8cV6b83thK7T+Yezk84nmaZBZ+8khqkTkqQ46LQc97lcmN5293FmM1MeFjvgGaDrAY0sJscWgGSEDqDYwbo57Xtpwj0Zqe8EbD6vxHx2ciwaw1QNjvvcHlle3++nBXyjFM28GqLF3XoiYj61FmFpsih/LyG81YN0Ali5Sdf586w8RMdeYBxIr1b/8RbOUzyap5+EbD1gawO8hRqJHbr+KnKRMXXj1OFuPPuFBzD+Bx/Es194YGvRDwBaPOROz+eAxM4/H44PtMFokDC7soa5Hbhpdopw++31O9BmL2OQL1alfj+BW+c9f+mEMmBb4oximGJEZnS2rP7XnfDchXmcevxF7P/yszj1+Ivqd8PGF2lgwdyy4TDZfSPtsJgMWIwmcXF+87SSV66HkM7m0elqwYFup7rbWAuMJqWaJqjBGG4RX89uv6aDhT+G2Sl66Plbvk6XLJIwlaC1uM9gwe3nHdF+dFM9MZq1sVAamaITWnML74MYppqUxn2mYvXdFq0SXyQnkNGkRGzWG5NVOZZcqmPcZyqmpFdosYtViJFLF1Vf/NoxS5dJTHJ1qx9X1+i4uikaNZ8DFt6q7mPl88DcObouBEe90HuCLufP76hGoiIXGaMfJKkiN5vDasJYFy2Qq+X6iyUzuDxPxxYnB8tw+wElbrYqiVrifqOz9XfOV4J4fuw+Ol9iGIG9g4afsmkgEVDtbp+7MI/PPXUG4wsxpLJ5jC/E8Lmnzqgr/hV7K/s2dPrarSbcM0THUi9fI3HvVpZiSVyYiwAAHtzfAUmvSQsi7jOkMeEvl1XWsevde87UHBb+GGantO2hCE0Rp6k1clkgPEnXxcISw5SDiPuMzpF7rJ7IMi28ARzzuROEIyFwqX4LpUJ09O0HDMb6bAPDNAMtbloglGXtOLS1hnD7eYe1NTiihWjmpcv02O5ewOapzzZshXeIFkTTCeW4ttYIYVZrjki9IES4ubPVfZ0vXych22yjYw890TZE779sSklL2IayXWSMvnAWXHJlutmEODcRiGM5kd71ZpydCiMvy+hrs6Hbbdv5DbMpcrQD1etFtftpoKf0sfQE9/sxm2EwkHAGqBr3+fgPr0ICih2xwjn+Fz9SUZgSnb7CtbgBx/o9cNvMiKeyOD25fi1VlmW8eCUIWQZGO53o9ZSx39Ea7XuV+hwtdbGHJ2m92OoEHJ313hqmxrDwxzA7xWRVPsxEpKaWiEwpO3O7TqJuGG1hdSqTlPVeTE4EKArFYGIheye07aGFr/QqEL5Z+8fP5xSHZufB2j8+wzQbHPe5NVqL+RS0j9BEdzJav2jmpZLuOi1iMAIdog+xDi72tRWaXpckwM+DRxXhP0iv87UVJTK1Gogewa4jJAboCYNBEUhn39Cne4lRFyEGldlv2u6wYrjDDllWIjorJZnJ4a1ZGv68c0+ZbufYAr2OW1x0TlkNDIYSgbSO9QaVUm1HJKNvxFqjENJU4EYwgVs/XWQZmAgk1HkAWVaEyi2EP5PRgHftoxjQN26uIJpUnO7XAwlML6/CZJDwzn0a6p2uBItd2ZdryfUnokd9+7i3uglh4Y9hykHLPX+hwjZ5h3lnzlSOWEwWIk69EIvZ3iHuq9wJBqPytxPdTbVkeQLIJAGrA3AP1P7xGeZWLj4DfO1+4A/8dHnxmXpvkboU4z6ngVS8vtuiNdZWgHiAUhq8GhscqXc089oKLTxKkrbd9GKAJHCZhtpqifi7eAart3jd6JgsQOcRui7EObVZCyvnY3qL+RR03UEDbrHFssUepgERgtbqMpApr69PiHSX5qOIpyrfZ741G0E6m4fPYcGe9tbybixew84q9fsJXDrt+cvnWfhjtkZ0WKro+Bvy2XHryqAkAcMddnUeYG2FEhoMxm3f+3v9DvS22ZDNy3jpahAAkMvL+OlVijY9MdgGt01DKR2VIjr0gtfqux0CWS7p99tb321h6gILfwxTDkL4C9+s/ULEdiyXCH8MUykdhQXJyEx9F5MDBeGxQ2fRTfWkrgulhVi0jgM0jcsw9eTiM8A3H6MF/GyKLr/5WGOJfzYPdWnJcv0HNbSGmGr19AOWMhcua4Fw2tUjmlkMhngGaVBDq7j7yTWSTQOhGi6cyLLiMmT3+u7oOUaXwavV6SKdP0d/r7Y9+u1htLQqrtLZM/XdFqb+WFoBW6FTr0whuNdjQ4+nBbm8jHNT4YoePpvL4+wUOQZPDnrL79iqVYylS31xpCYkAtTnabJQxx/D3Iqzh4bWUjHVale++L59xXhPFC5lGfjCe1VKxBDvQ2fXts57SZLw0GgHJAm4vBDDfGQN56bDCK9mYLcaceeeMjtFtYro0AtP0nlovYkt0Lqe0UzH/0zTwatzDFMODj8tlOSyFK2pFdZWKOdeMtAJMMNUSou7/ovJiSB9GYzKxBSzPe5+cidkf49bGwABAABJREFU09R7UyuyaWWKjBdKGS3wwh8BtzdaAC/8cf22qRoU4z5Z+FuHVmM+BfWMZi7GfB6o7eOWiySV9CG+XbvHjS8Vjj9M+uuM0xoOP7kX5Dwwf17d+87nlPvUq9tP0HOCLpcu0T6BaW5cIsayfAeocP2dnw0jlc2VffvLCzEkUjk4W0zY31WB2zlaK8dfQVhcDWljUX2nCGHU2cPpTMzGmCyAs9C/ppKwfepwN5741AmMdTlhNRkw1uXEE586qV5HrHhdbxHzWYrf1YKD3S4AwI8vL+G1G9TVef+ID1aTUZ1tqjet7TTEkc9poyJKrNN4h/UXi86oAgt/DFMOkqQ46kIaivsUbj93L2Buqe+2MPqn3ovJ4nHb9vDruRwkSVnMrWUvUugqDUPY2qp/ss8wOyF0Dbi90UJbXQtqIBzR4SmK2WHoeRCLJVqNs6lXNHM8QF8Goz7c9J2H6DJUiJKuBUJkbB/h4w81EKLc/Dl13a3Bq/Ret9iVyXq94uqhhd58Flh4q95bw9QbZ0HUqiDGcthnh9duQSqTx4XZ8txC+byM0zeXAQDHB9pgNJQpTKVi9CVJ1T8XsDpoUFWW9RX3KbbVzTGfzBYUe/7Uc7SeOtyNZ7/wAMb/4IN49gsPqCf6ASX9fv07vsn9e32wmAxYiqaQyuTR4bQWxcCGQJIAX+EcpJapFZtR2u/HNCUs/DFMuYi+GC31/BX7/TTWZcPok+Ji8nR9FpNFv58eFia1hlgoDV6r3RSsWLj2H+AJVkYbtO8Fbm+0aDwHsa2NonVkmV1/gtA1ej6cnRSHqlXEkEZgvHbRzGIgxDtMjkOtY++gOLR8tjYJBLKs9PuJz1Jmd3SMkYCajKp73iR6A7uPkpCtZyRJcf3NnaHXIdO8CMdfbK7s14IkSTg5SFF5Z6fCyOV3fvuJYBwrqxlYzQYc7q1gAV64/ew+ci1VG7cOe/6KUags/DFbIAS0yHR9t2MnpFcpJQEo63XtsJpw1x4lovvB0Q4Yyh020DrinDN0rfax/qUkI5RmIUm8VtzEsPDHMOXStociNVdDFLFZb3JZJSqK+/0YNbC10aKpnFcmhGrF6nLh4MTQeIv0tcDRSV03+Wxt/naZNWUxjxdKGa3w4JdQjPcEUIz9fOhL9dumalFvh7bWKE61ajTmU+AZINdCNlWbQTJZXj+koQckSflcWayBiz08RY4Vk5UXR9TCaAa6jtB1IdbtltVlYOUmvT66j6pzn/XGf5Bed2thbQ2WMrXH0UnnQOnVijq+xrqccFhNiCWzuLwQ3dFtZFnG6Zu0pnG0z1NZ3F6sIMA5q9zvJxAigxDTtE56lfZdAKejMFsjXtuJYO3SDipFvP/svrI7tY8PeDDW5cQ9w170ezXYx71b3P00+JRZq+9+KlhwHLr7tNl7ztQEFv4YplzMLcqUmRZOziJTJP5ZHdSnwTBqUK/FZNHN5Bngg5NKWNeLtPOF0ucuzOPU4y9i/5efxanHX8RzF3bYLRK4TPn1jg4uqme0w8FHgF/6BokGJitdfuIp4MDD9d4y9VkX99nk/VDZtNKloXXhr9bRzLF5GlYzmvQ1VCOeo/AkiXLVRPwdOvZzB4qadBfiPpevVyRk3IYQEL0j2nb1loPJAnTdQdfVEkgZfWI0K+fzsfJ7/kxGA44PeAAAb0yuQN6Ba3A2vIb5SBImg4Rj/Z6yHxOA4vhz1UjUEj1/0Vl9uGTF37LVy+e3zNZYHTSELcvaF7aFK7ECF6vZaMAHj3Tj/pEGXT8wGJQhsnpWTYjH1tOxP6M6LPwxTCVoqedPiI/eYY7ZY9TDV1hMXrlJk0q1gmM+d48Q/pZv7EgIeO7CPD731BmML8SQyuYxvhDD5546szPxr+ggYbcfozEOPgJ8/iXgy0t02YiiH0CLSEWHdpO7/pYnyO1s81BMpNYR++rQVRItq4kQtdr31SaGTS1sbTRsV+pYrAb5nHL8If4ujDrY24G2Qfobzr+5u/vKlfTgif7ARkH8PqFr5PzTC6k4cOOn9Y0yazRclff8AcDhXjcsJgNC8TRuBLevbHhjktx+B7pdsFsrGHqQ5do7/hydgMFEjigtJDBtB8d8MuVQhZ6/qlDs9+ur73ZoFdGpF6xTz182RYOhpdvCNCUs/DFMJYjpjfDN2nWzbEaoRPhjGLWwt5ODS87XrpR4LUwTo5KkfbeGlrG3K0KAWMjcgsd/eFUEIQKFS0kC/uJH20ynpWLKwaReouMYphHhuE9COMZ9+/QxCOXsJmErl63uNPC6mE8dilrFuM+3q/cYyxO0gGx1AJ7B6j1OsyJErfk3SWStlMBlGkZrcTXeeY+9neokZBmYP1fvrdmebBq4+W/Aa0/Q5eJb9d6ixkFEQVbg+AOAFrMRd/S5AQCnJ7cWxYLxFCYCCUgSiv2AZbMaoteD0VS7oRuDkTqOAe2LIwAQEcJfjYRRRt/oQfjLZYHYAl1n4W9jvMO0r1oNAYlQ7R9/eYKOuVrbaVCUaVpY+GOYSnD4aXEgl6WozXqxFqYPEskAtA3VbzuYxqTWi8li0dbdT+8vpnKEA28HEXI3ggncGpIjy8BEYJsp4aXL9IPu3saJ22IYPSL21SuTzRv3mc8pQyp6GRxZF/dZRTdbeIpcOSarPsWSjjE6zo0tKB1JaiNERf8Bimdi1MU3SvF2qfjuhslEDGb30cb8O/WeoMv5N+s/WLoZ+Twwdw742f8kp18uQwKMjRcVVUOIQ7H5ip2Ux/o9MBokzK6sYS68eXKLcPvt9TvQZq/QDS6cic7u2r4vd+mMrBn5vOKIZMcfsxPc/XQZndvdsEw1ic3TtlnsNMTG3I7JSvU1QO0G6Usp9p7vrf1jM5qiAY+YGaYGSJKyeFLPnj/x2K4e6h5kGDURi8nLExQVUG045lM9/IW/XWRm206dIZ8dt3pjJAkY7rBv/RhLYqGUYz4Zpq60emkgSc4rAxTNRniKPqcsrYBLR5PHxWjmierFagtRUa/ddRY74C0Mt1XD9ZdNKY5L/jyrDgYjiXVA5R128QAd00gG5b4ajfZ9NPiWXtVedLMsA6HrwOn/Bxh/lkTcFjdw8CPAyc8Anv56b2Hj0NpOkcy5LJAIVHQXzhYzxrqcABRx71ZiyQzGF6g7tWK3H6A4E5016vcTCBFN6z1oRUekWR8x5Ez9afUCZhvF1wtXndYojfnUQ8pGvRDderXu+cvnqVu5dBuYpoWFP4aplKLwd6N+2yCEv/aR+m0D07jYfXTgWeqkqBbJqBKDohe3hpZpcdMijCyTM28Lvvi+fcV4TxQuZRn4wnu3+DusLiuxrCzUMkz9afa4z2BJeb2enECODvrK56rzt1vXXafjSGYhkC5dpA8oNQleoQX2Vq8SHceoT/dROmZYvlGZc1PEX/r2AlanqpumGQwGJRZ19kx9t6WU2ALw5t8B578JJII0bLr3vcDd/wHoPMiLvmojSSVxn5W72YSYdz0Qx3Li9h7Zs1Nh5PIy+tps6HbbKn6couOu1jGW7oLwlwhUvyd3N0QLAomrR1/HJ0z9kCTtx30WhT8e+tgSsU4bmaltKktkmiLszTZ2GjMs/DFMxbTtoQ/lRLA+Jey5LLByk67rMbqJ0T6los4OuuJ2hVi0dfdSdwuze4oRcls7JE4d7sYTnzqBsS4nrCYDxrqceOJTJ3Hq8BYLoMJB0raHY1kZRgsU4z5vVs85plVkuaTfT4eDI0VRqwpxn+L1YGkFPHvUv/9a4Rslt+LqsvrT74uFSGw/CxhVxdam1BKU22GXTQMLhQ45IYw1Kt1HydUYmQFii/XdlmQEuPgMcPr/pShpgxHovxu453N0qUcHsV4oxlhW1vMHAO0OK4Y77JDl211/yUwOb81SIside3YR05rLAvElul5rx5/VSeeMsrwrgbTq1EsYZfRNUfibru92bIQsK4K2m0WlLbF5CqkssuLAqwXCYdi+lwcOGBb+GKZiSqcn6hH3GZmmXgWLHXB01v7xmeZgXdxnFacpizGfY9V7jGaj2Iu0uO10/anD3Xj2Cw9g/A8+iGe/8MDWop8sK92BenaQMEwjYW8n55icVwYpmoXYApCKUYxW22C9t6Z8xH40PEnxeWoi9tUdOu+uM1mUqCI14z7TCWWIrpNjPquOEO3mz5fXYRe4RJGsNk/jd5pbnUBHYYCh0ljU3ZJJAtd/DLz2v5T3W+dB4O7PktPPvAt3GLMznKLnb3eClhD1Ls1HEU8p77m3ZiNIZ/PwOSzY095a+QPEF+i4w9JKaSO1Rg89f1Hu92MqQAh/0Vn1kw52SyJInxNGE69D7gRf4fi1VudnsgwERe85x3wyLPwxzO6oZ8+feEzvME8oM9XD0UkLLbls9V7n6YQyzaZHt4ZWqVYvUiJAB/wGI+DjmE+G0QzFuM8qO7S1hnD7eYdJ/NMbtjZavJRldf92uYwSH9oIQxqdh+ly6SJ1l6jB0mVatHZ1U9QnU13aCzGdmbXyOuyEANZzvDnOeXpO0OXiBVpcrRX5HDD9OvDaE8DUa9Qv5RmgDr+DH6F9FVMbXAX3XCK4q8HLXo8NPZ4W5PIyzk2FAQDZXB5np8gBeHLQC2k37ynhSHT21Oe9Wez506jwl0nS3xBgxx9THo4uwGCieMi1jXs660YxvraX1gOYrRGDa8sT5Q09VcpqiF4zBmPjD0sxO4KFP4bZDSKzeeVmbXbipXC/H1MLJEkR46q1mBy8Qguezi4SGRn1KMZ9XlJvWlCIiO0j1PPCMIw2WBf3WcPF4nqj55hPQWmHnVqErpP41+JSJsf1jHeIPnPSCXJHqoGIwvaz268mGAwUZQkAc+d2dpvYAokLBiPQdaRqm6YpPAPUs53LqDu4tRmyTMeJP/sr4NoPSZi1+4AjjwLHfkURoZjaYXXSlyyTq24XnBykoYbzs2GksjlcXoghkcrB2WLC/q5d9mXG6hxjWRT+NOiKAmi7ABLNLfb6bgujL4wmZd+rtbjPYr9fAxxb1gJnF1Wj5DLqHb9uhXAWegYpMYNpelj4Y5jd4Oikg7hcprYfyMkITY9JEnVsMUw1EYvJoWvVEbiFI4FjPtXHN0rTgqshIK5CV4xYHAKUhWqGYbSB3Udf+ZzS7dDorC4XjocM+h6E8o/RMV1kVr3e6NJI5kZwSRmMFFkKqCOQrq3Q8y1J9PwztUF02IWnFCfMVgi3X8f+5lk4lyTF9Td3prqCRngaOPPXwNv/RO8Jix3Yfwq4898Dvr2Nse/QK2LRf5dutpEOO7x2C1KZPN6aieD0TYr/Pz7QBqNhl39f4firlzjs6KTPBi26ogDu92N2R7Hnb6a+23ErLPyVhyQprr/Qteo/nngM397qPxajC1j4Y5jdIEn1ifsMFYphXb3cs8BUH1cPTZ3mMsDKDXXvO70KrBQmnzo4NlJ1TFZlMVyNqfHoLA0eGM0U2cUwjLYQAxRLTRL3WZxqHdD38ZDVSb8DoAxX7IZsSjlWbCQ3W2dh4CRwefeDSIsF8dAzSM8/UxtaXMpxyXauv2xKOXbpPlbNrdIenYfoWCsRJJFUbRIh4K1vA2efInHCaAL2vBO453MUqarnTtBGwalOf50kSTg5SDGtL18PYWU1A6vZgMO9rt1tX6nY5qyT8FfaMabFuE/u92N2g0uDwl8qRgNqksSv63Io7fmr5jBPOqE4jdu5348hqnpE94d/+Ie4//770draCo/Hs6PbyLKMr3zlK+jp6YHNZsNDDz2Et9+uQcQFw1RKPYS/0n4/hqk2klS97qjQVerXcfi5X6dadBYWfdWI+xQLpb5RfXZpMUyjU4z7vNEccZ+NEPMp8KvoZgteoX6u1nb6fG0U3P0kHGXTwPL1yu9HlpXnuZPd6zWn5zhdLpynobLNWLxA/9/argjjzYK5Rem1nDuj3v2mE8CV7wOv/29agJQkoOcYCX5D7+JYMC0hXGKx+V3f1ViXEw6rCbk8nQcc7fPAatplN5fYrlZvfQdvtNrzJ8vKArybBRKmAsTrZnWZ9t1aQIiQ9g4aMGZ2hmeQBhVSMXVSmDYjdL1QodNJx8sMgyoLf+l0Go8++ig+//nP7/g2f/Inf4I/+7M/w1/+5V/i9ddfR1dXF97//vcjFotVcUsZZhd4h+ikKREkJ0y1yeeovwdg4Y+pHcKNF7xKr0G1KMZ8stuvanhHaCEnFdtdJHE+DwQKTpTOBnKQMEwj4egoifusQZxMPUnFlUW1Roiz6RijyLL40s4iELeiGMncIDGfAklSBNLFC5Xfj3iODSbAx8cfNcc7DLS4ydG3mcNVlpWYz57jjfU63ilCIA1coWO43ZDLADdfAl57Apg9Q0N37Xsp0nP/B9n1qkWcXfS6T0bp824XmIwGHB/w0HWDhGP9nt1vnxD+6uX2E7hLev60xGqI9nFGE4kkDFMuZhsd0wMUTa4FijGf/fXdDr1hNClrt8Eq1jGIqgd2+zElVFX4+/3f/3385m/+Jo4c2VkRtyzLePzxx/E7v/M7+NjHPobDhw/j61//OlZXV/G3f/u31dxUhqkcs02ZyAvtYvp4p0Sm6eTN0konBAxTC9x91PuRTSnC827JJJX74n6/6mEsWdjcTYRc+CbF+pht3C3KMFpGDFKo7dDWGqFCXI6zi0QEvWO2AW1DdH03rr/0KrBciOVuxC5W4YIKTVTual0qpMm0j5CziqktkqSIWkLcu5XoHBAPkDjbdbh226YlnJ10/C3ngfk3K7uPfOG2r/1P4MaL5JZ1dgHHPgnc8SgNizDaxGQltyugiuvvjj4PDnQ78dB+P+xW067vTzP9deLx40tbO4hrjRAind001MMwlSAEtt0M76oJ9/tVTrHnr0rCXy6rJMP5WPhjFDQV3n7jxg0sLCzgAx/4QPF7VqsVDz74IF5++eU6bhnDbIO30FVRi7hPIS56h5tz+pWpD5Kk/mJy6Bq5Uuw+ZZqNqQ4iymzpUuWOTRHzKVwpDMNoEzFIsXyDhjUaFTEx2wgxn4Kim+1i5dHMgcskFDg7AXu7etumFezC1ZoFguPl316Wlc8zdq/Xj64jgGQg8SC2QeyVEAT9B/Td37lbek/Q5dzZ8o/fQteBN/5/wOXvkWOwxQ0cfAQ4+Rke4NILLnV6/gDAYjLg1OFuHOlTYVBGlpVtqrfjz+oCrA763FNBIFUNrQijjL5xa6jnL5tSYipZ+Cuf9hFaU4stkpNbbcKTJP5ZnUr3KcNAY8LfwsICAKCzc/2LtLOzs/h/t5JKpRCNRtd9MUzNEbbtlZvqxiBuRLHfb6S6j8Mwt1KM+7yizutcCIgc81l9PHvIJZxZq8yxmStZYOU+JM3Dx0ZNjr2DXAL5bOPGfZa6zxtJ+PONkkt7bQWIbXzusy3FmM8G3VdLkiLYLVbgjAxPkQhisvKxdD2xOoCOwnv3VtdfZk15HQtnYLPi20/Hb6n4zvfnsUXgzf8DnP8muSZNVmDkPcDd/4HeO004OFrz46KLzwBfux/4Az9dXnymsvsRoprW+uuSYXqfGoz1X2CWJG32/AmhxsUCCbMLhMAWX6y/ozU6R6J/i5v74yrBYlf2VdVw/RUHIvc15ec8szllC39f+cpXIEnSll+nT5/e1UZJt7xIZVm+7XuCr371q3C73cWv/n7OGmbqgLOLTspymera8JMR6iSRJOoWZJha4h6gqetMkhbOdkM2rUSRcb9O9TEYgI6Ck6SSCLnl6/Q3szo5018H8LFRk1Pq0N5NvK+WWZ6gAZRWb2M5xk0W6t0CKttXJ6PKcahwDzYi4ncLT5bffSae1479JLIy9UOIeosX1ruTFy7Q4ILDz24ZownoPkrXZ89s/bPJCHDpO8Ab/y8dYxuMQP9dwL2fBwbuaerXe02Piy4+A3zzMRpMyKbo8puPVSb+idd/bL5yF3g1iBacdQ6/Nl5XYjFdC64ogP7uqyG63uz7MGZ3tLhpUCafq7+wXYz57K3vdugZcYwfVHkwU5ZL+v0aoPecUZWyhb/f+I3fwKVLl7b8Ony4shz+ri7qK7vV3be0tHSbC1Dw27/924hEIsWv6WmNZB8zzYUkKa6/asZ9ivt29TR37A1THwwGxVkRqCBeq5Tl67SoY2ujk0am+ginXmC8/IlBsVDqP8ATZDqAj42Yho/7DF6hy0acavUX3GxLl8pf6A1cptu4+xqj93AzbG208CTLwFIZ8eP5nJI20KiOSD3hGSTxPpdRjjNkWXEA9hxvvPd3JXQfo+dh5SaQCN3+/5kkcP0nwGv/i0RTWabjtbs/C+x9H58zosbHRS/8EQAJgNh/y/TvF/64/Puyd1DPZTYFrC6rt427JSZiPjUiapVGompBIC11Rlkd9d4aRs9IkuL6E72R9YL7/XaP6N4LT6p7fhZboGQAo5mOrRimhLLHc3w+H3y+6kzWDg0NoaurCz/4wQ9w/DhNAKbTabzwwgv44z/e+EDJarXCarVWZXsYpiy8w3SyFbpOcSrVoBjzOVyd+2eY7ejYD8y/SbGP+z5AYmAllMZ88qJObXD10gloMkL7Kf/Yzm6XTSlTadyHpAv42IiBw08L6qvL9H5vpIjefE7pO26kmE+Bd5icf6kYufc8Azu/7eLbdNkMopb/EBCZJbdY/107u83yBIkkVgcvjGgBSSJx79qPSOzrPkaJEqshWrziYw7C5qEJ/uBVep72vY++n8/Rv2/+G8UuAoCnn85D2WW0jpoeF4WuQRH9BHJl0W4GIyULRWZIbNNKb6tw/Lnq3O8ncHZRZ2g6Qec5Nk99t4f7/Rg1cffTkFM9Ha35vCI8cvpP5bS20/Da2goNZ+50PWY7xOeLd1gbLmxGU1S1429qagrnzp3D1NQUcrkczp07h3PnziEejxd/ZmxsDE8//TQAivj84he/iP/23/4bnn76aVy4cAGf+cxn0Nrail/5lV+p5qYyzO5pG6IT2ESQDjjVJp9T+my4k4SpF217qCskvQpEKzz4zGWURdsOlQ52mO2RJCUebentnd8ueIXcma3e+vd4MAyzM0rjPgMNFvcppmQtdu24DdTEaFIisMuJal1dpolfyaDeQoKW8Y/R7xpb2LkTpiiMHqh8cIlRl64j5GiKLVKcoXD7dR6i402GELGoC+cpen3pMvCzvwKu/oBEv9Z24MjHgWO/ymJDvWnfC3L8lSIB7fsquz8hrgmxrd7kc0oHrVY+g41mwFk4R6l3HCJQIpCwM4pRAfE6iszUz9GaWKI1HJOVnMhMZUgS4CtEcarZ81fa78cwt1DVM57f/d3fxfHjx/F7v/d7iMfjOH78OI4fP76uA3B8fByRiCKS/Jf/8l/wxS9+Eb/+67+OO++8E7Ozs/jXf/1XOJ3Oam4qw+weS6tSwF2NuM/IDJ3oWVppqo1h6oHBuPu4z+UJOnBscfNrudYIF0io4HrYCWLh2X+Q3ZkMoyfEYEVogo4fGoXSk9tGFW+EQ3PpEi2y7gSxr24bJFG00bHYlb7rxR0Ms2RTyiKLn51kmsFsU4Tqmy8pMb5C6GII7zC5mLIp4PW/At5+mhwDFjsw+nPAXb/WmNHHeuTBL6EY7wmgGPv50Jcquz8hrsU0IGgBQCJAA4EmKw0FagXR81fvOERZVraBRXhGDex+ErezKXr/1YPSmE/+nNkdYggkdJ2clLtlLQzElwr1U2wQYW6nqmfLTz75JGRZvu3roYceKv6MLMv4zGc+U/y3JEn4yle+gvn5eSSTSbzwwgsVdwYyTM1pL+xoqyH8LRccUt5h/rBl6kvRRTJe2dSZEAw7Rvm1XGscfsDuoxN2sbi2FelViqEAmiM6jmEaCUcnxcnks8oxhN6R5ZJ+vwaM+RR49tCgV2ZNSXvYClku6WJton21+F2XLm5/PBK8AuQK7nUeOtIWQuQLXSOh29XNf6NbkSSg5wRdT0bJGbznHcA9/xHoPdG4QxB65OAjwC99Q3Gtdh4CPvEUcODhyu5POP7iS7QPqzelMZZaOo8r9vzVWfhbW6HhSoOJk1IYdTAYSlx/deptF4/LLtbd4+4HzC10jF9pglYpIknL3UfnDgxzC3yEyDBqIrr3Vm7ufEJ7p3C/H6MV2oaU/qFyT65yWWXinmM+a48krV8o3Y7AZUDOU3yOVnpFGIbZGaVxn+VERmqZ2HxzlNcbDMpn5E7+dokARc2XuvKbAd8oCSAi5nQrFkuEUS0tVjPk1HGURIex229jeo7R0FzvCeCezwFDD3AcqlY5+Ajw+ZeALy/RZaWiHwC0eMgZm88B8UXVNrFiYoXIUadG+v0EQvirt0Aqzo2dXfSZzDBqIByt9ej5k2XlccV2MJVjMCjOvKAKcZ9iba3SOGmm4WHhj2HUxNlNUxbZtLofyskoEA/QQkXbkHr3yzCVYDQV+itAwlA5rNyk94fVwQeO9UL0/K1M0gL6VhQdJByLxjC6RIhHy9cbI+5TuP3aRxq/vF4MaQTHt1/EFPtq7zBNETcLJouy0LFVd206oTgnO/nzTHNIkiL2mazN5VotB5MVOPyLFO1p5RqUpkGSFFErpoGev1LHn5Zo8VD0bT4HxLcZBKkmEY75ZKpA0fFXB0drMkxrBgYjv67VwlcS97kbsikgPLX+PhnmFlj4Yxg1KRXm1IzVEm4/ISwyTL0Ri8mBK+XFfQqhsGOMJ+7rRauXYoPk/NY9jckIEC7EevjZnckwusTZRb1QuWx1YshrTbHfrwlcbe4+oMVFgu1Wx5SyrLgCm1HUEr/z0qXNu1KWhHu9S1udVIxC11Gg705g7EPk6GUYRqEYY1nnnr9sClgN0XWtOf5KBdJ6iCOCYr8fD7gyKuLqBSQDnZ8nI7V9bPF+cnTy57NaeIdJSF0NAYlQ5fezPEHDDq3tfHzLbAoLfwyjNtXo+Svt92MYLeAdJrdFMrJ9vJYgn1OiCJph0VbLFOM+t3BILBVEWk8/0OKu/jYxDKM+pXGfAZ3HfSZCSpxlM5TXS5Li0F7cYl8dnQPWwrQYI9z4zYRwOabiQHhy458Rn3Wd3BuvWYwmYN/7lf0VwzAKQmSrt+MvNk/DJi0uSm/RGkJsq1fPXzZN0dsAO6MYdTFZAIefrtda2BZJZtzvpx4mK+AZoOuha5XfT3EgsgmP/5kdw8Ifw6hN2xAt1sQDFNG5W/I5JZ6ovQkWuhh9YDQrC687jfsMT1LZuaWVSo2Z+uE/QPupyCwtGG+EWCgVC88Mw+gT4dAOXQdymfpuy24QMZ+egeaJsxRDGqHr5LTYCOH28+1rzklsgxHoKHxObdRdu7ZCn3WSxO51hmH0iRD+VpeBzFr9tiOq0X4/gVsIf3VyRsbmFGG0xVWfbWAaF7F+Uuuev8j0+sdn1EFE1Ycq7PnL5xWDCPf7MVvAwh/DqI2lVTkYVsP1F5mh6TGzTbsH2UxzUoz7HN9Z3KeIlfTtp1Jjpn5YncqU2dIGLqBECIgtUqRIBwt/DKNrnN3k2s1l9B33KYS/ZuqwcHRSdE8+q0z1lpLPcxcrAHQWBNLA5dv7EBcLz49nkHvRGIbRJ5ZWwNZG1+vp+ouJfj+Nxlg6uujcJRVTZwC7XLTaf8g0BsWev+naPWZmjdI2AEVYZ9RBuPQiM0B6tfzbR6ZpqN5s0+4+mdEEvPLKMNVARHKqscAm7sM7zJ1ojLZoHwEMJpqmF7Emm5HPK4u2HOOkDYSTb6O4T7GQ7B3iXlGG0TulcZ8bCf16IBVTFtSaaapVkkqimTdws0WmgHSCHJDeodpum5Zw92/chyjLyvMmxEGGYRg94ioMANez5084/lwaHUY2WQBHB12vR9xnVOPCKKNvhPCWCGyeAqE2Ila01QtY7LV5zGahxU3xrbK8dZf3ZginYPteHqpntoRfHQxTDYTwt3KDojp3Q6nwxzBawmRVFhq3i/uMTNEkk9mmOM2Y+tIxRhFp8YAyyQesXyjlmE+GaQyKcZ/X9Bn3Kdxurp7mi88Swt/yjdsngoWbTezPm5XN+hDjS4VeSBOlDTAMw+gVZ8FFFq2T4y8ZpSEcSSJnnVapV8+fLCuPyY4/phpYnYDNs/61Vm2KMZ/c71cVRIrJRqkeWyHLQPDa+vtgmE1g4Y9hqoGzmwSObHp3H8qpGC1aSBILf4w2ES4SEeO5GYGSiLZmXpzUEmYbdZICtyyULlKHiMEE+Ebrs20Mw6iLEMxyGRKQ9EaxvL4J90n29sJEcB4IlnzW5nPKv3lIQ4k6DV2n6CNAcbS3DzdPLyTDMI2JEJNEj1ytiS3Qpd1Hzjqt4qpTz9/aCg3nGIzaFkYZfVOM+6xRz59Yy2ThrzqIFJPliduj6rdiNUT7HINRWc9hmE1g4Y9hqoHBoDihQhXYtgXC7efs4rg9Rpu0F4S8RHC9a6wUWVYcgcJ1wmgDEX22dElZRBAiYPsIuToZhtE/pXGfAZ3FfWZTQHiSrjej8AcAnQVRa7Ek7nP5BglcVgfgZic9HH5akM5nSRCVZeX56jxc321jGIbZLY5O6q9LrwLJSO0fX/T7OTXuZisKpAvlLaTvFiE0OjoBo6l2j8s0F7UU/nJZxWHs7q/+4zUjzi46js9llHOdnSAGItv2aHsQg9EELPwxTLXwjtDlbnr+hGgo7othtIa5hQ44gM3jPiMz1EFksio/y2iD9n10crq2AsTmCzGfBVFALDQzDNMYrIv7rOFi2G4JXSd3W2s7ud+aEfG3i0xT3BqguNk6DnC3B0DidqlAGpmm5AyThY+jGYbRP0YTDTgAdMxea4r9dRrt9xPY2ijVJJ+jFJNawf1+TC0QAlx0dveVQtsRX6BhKksrva8Y9ZEkxfUXurbz25X2+zHMNvBZIsNUC+8Q7cjjS7TwUC75PLBys3BfHPPJaJjt4j7F9znmU3uYLMrBJi+UMkxj4+qlfpBsmjqI9UKwJCq6WbF5AHdvwUE/TpPBYtqXYz4VxHMRngSmf0bXO8bYfcEwTGMg3Gy1jrGUZUVs1LqwJUn1ifvkfj+mFrS20+B1Llt9YVu4Cl299L5iqkNpz99OYpzTCWXfxsIfswNY+GOYamGxk3UbqMz1F52heCuzjToDGUartO+j6Jn4EnXDlSLLSgeRb3/tt43ZHuGQCFxSYj59+3mhlGEaDUlSnGObObS1Ri4LLBfSD5o15lMgOuyW3i64NjMkCPIio4KtTRFIi8LowfpuE8MwjFqINYFaC3+rIRoaMpqAVl9tH7sS3EL4m63N4+UydB5c+tgMUw0kCXCJuM8qv76F8Mcxn9XFM0j71lRsZ2Ju6Bod5zo7qb+dYbaBhT+GqSbCqVdJz58QC71DHOHEaBtLK+Ap9Avd6vqLzVMsmdGs9F4y2qJtiGJYU3Fg/jx9r5MXShmmIREO7eAVfcR9hidpsdHqYIGrYz8t+ETngalX6Hv+AzyFfSv+kphqi50WVBiGYRoB8TkYX6B0oFohhEZntz7WJYrOyBoJf7F5QM7TsYqVF+KZKlPs+Zuu3mPIconw11e9x2FI9BPrxmJobSvEz7Q3cRIKUxY6+NRmGB0jovJWbpZ/cM79foyeKMZ93uIiEf9u30viH6M9jCbFBSTnC0LunrpuEsMwVcLdRwtT2bQSJ65lSk9um13gsjqUntxYYSKY3Wy34x+jFAKAnh89LFIzDMPshNZ2iuPPZYFEoHaPK2I+9ZJC5OymY4ZktLLKlXIp9vv18LEKU32Kwt/MzqIhK2F1GcisAQaTkmLGVI9iz982wl8uq9Q1NHMFAlMWfCbEMNXE2U1RndkURXfulFRMiYtglxSjB3yjdKITWwDWwvQ90UUEKMISo01KHX4dB3ihlGEaFT3Ffcoy9/vdSmmfn90H2Dvqty1axWIHug7TsFHPsXpvDcMwjHpIEuAsuNliteyvKxG29IDJSp+RQG1iUYtdaOyMYmqAsxswGKnrbW2lOo8h3ISuHnosprq0jxTW0hZpYGEzwpMk/lmdgKOzdtvH6Bpe2WOYamIwKMJdOT1/4medXbSAwTBax+pQ8t/FQm18kUTA0vgCRpu4B5SM+M5DW/8swzD6pjTuM5+r77ZsRXSWFjVMFsXp1uz49isLMP6D7CzYjP0/D7zji8rCL8MwTKPgEj1/87V5vNL+Or04/gBFhKt23Kcs608YZfSN0aS8FyNlmAvKgWM+a4vFDrgK/aBbuf5EEoqPk1CYncPCH8NUm0p6/oTw184xn4yOuDXuU7j9vMO0cMtoF4MBOPpJ4Ogvcyk9wzQ6rj46wcymtB33KYZIvCM8bSwwtwB9d5Gg1X1HvbdGu0gSLYwxDMM0GrV2/MUXlSqAFndtHlMNhAgXqbLwl4zQkJJk4EhEpnaI83UW/hoHkW4SvLbx/8uyIgq2763NNjENAQt/DFNt2gqOv/jSzjLm83lguZDbzC4pRk/4RukyMksRBUIA5JhPfdDq5WhhhmkGDAbtx33KcslU62h9t0VrjLwbuPuzFPPDMAzDNBfC8ZcIUl9vtRHOQlevvhwmwj0TW6huuoFw+zn83GfP1A6RtFQNR2sqThGikqS8j5jqI3r+wpM0nHkrsQX62xjNgGewttvG6BoW/him2lgdyvSXEPS2IjpLO3pzizLRxzB6oMWlTJ9Nvkyl0AYjTyQxDMNoDa3Hfa6GSj5DOP2AYRiGYQDQ0IfVSQMysRrEfQpnoZ5iPgEaaDS3APmsElVaDYTwws4oppYIQS4RBNKr6t63cPvZffQeYmpDq5e+8rmN142F2887zKkWTFmw8McwtUA495Z3EPcpYj7bhmgqn2H0hHCRzJ2lS+8wFawzDMMw2sHdT3GfmaQ24z5FzGfbHv4MYRiGYZhShOuvFsJf0fGnM+Gv1K1UzZ4/cd/c78fUEkur0mOs9us7KmI++9W9X2ZrJEkZmN+o56+0349hyoBVBYapBWJaffkGRXluhRAHecKd0SO3RrJxRBvDMIz2MBgU19+l7wBv/j1w7UfA/HmKrapFfNhWCOGPT24ZhmEYZj1FQavKPX/pVYr8A/Tn+AMUMa5awl+uxE3Iwh9Ta4TLNDKt7v1yv1/9EOc9oevr143XwrSvkSTqPmeYMmB/KMPUAmcP2eQzSTrw9GwyPZOKA7FFut7GXVuMDrF5KNo2tkAl57xoyzAMo0267iChL7NGaQMicUBg8wCtPpootnfQZWt79TtsklFyGEiS0nfBMAzDMAzhrJHjT9x/qxcw26r7WNWgKPxVSSCNF/oDLa1Ai6c6j8Ewm+HuA+bOKUKdGmTTynokC3+1x9VXWDdeI+elZ4C+HyqYQ9x9tL9hmDJg4Y9haoHBQELe0iVaWNtM+BOLbs4u6gZkGD3iP0DCn3dInyeJDMMwzYCrG7j/PwGJAH2thpTr6VWaLl0LA6Frym0kCbC1kQBo71gvCBqM6myXiLdx9fCxEMMwDMPcirOLPo+TUSAVo86/ahDVab+fwNlDz9NamAas1T6miIiYz156HIapJcL5G1sg96kavW+xOUDO0z6lxb37+2PKw2AgR9/i2xTtWRT+CudGPBDJVAALfwxTK7zDBeHvOjD84MY/I4Q/0QnIMHqk7y5yhPCBCcMwjLYxt9Aw0q0DSekEkAjS12pQEQQzSWB1mb6CJf0TkoEcAXZfwSVYEAVtbeX3FRc7LDgqmmEYhmFuw2SlgZtEkBb9qyX8CcefEBj0hrlFeZ6ic0CHyscV0RLhj2Fqja2N+rrTCXqvbmYuKAeO+aw/vn0k/IWuAXvfS+de4Snl/ximTFj4Y5haIcS82OLGE2f5PLByg65zvx+jZwxGoPdkvbeCYRiGqRSLnb7aBpXvyXJBEAzcLghm04pQWIrBWBAEO0oEQR9FYm0kCGaSwMokXWfhj2EYhmE2xtVTELRmq7MYLMuK48+lU8cfQKKceJ5UF/7E88P9fkwdkCQS6ALjJNipKvypcF9MZXiH6fxpdRlIhIDEEkUKt7bTORXDlAkLfwxTK6wOwNlJwt/yBNB9x/r/j83Rgpe5hWIpGIZhGIZhtIIk0bGM1UFRzgJZpqgxIQgmAgVRMAjkMkA8QF+lGEyAvf12QTAyQxFDdh+f3DIMwzDMZji7qac3WqWev2SYeqYMRsDur85j1AJXDzD/pvo9f8kIHftIBv1GoTL6x92vCH+7JZ9XXKzs+KsfJitFfC7foIjP+BJ937e3vtvF6BYW/himlnhHNhf+RGFr21D5sVgMwzAMwzD1QJKAFhd9lSYWyDItHCZKugNXg/TvfJaOh2KLG98nR9kwDMMwzOYU+73m6fNW7Y45ISg6/Op0h9ULIWDE5kjYUGudRQiJjg7AZFHnPhmmXMTrOzqz+/2ASPAwWWggj6kf7ftI+AuMA2vLyvcYpgJ0/AnOMDrEOwxMvkyRnrceeHK/H8MwDMMwjYIkUf+IrW39lGo+XxAEg+sFwdVlirKRDID/YN02m2EYhmE0j91Hglw2RZ+f9nZ1718IW3pPImptJwdNNkWRec4ude6X+/0YLSCE+UySjqsduxDshGvQ1ctGhHrj2wtc/VdlP2y28b6GqRgW/himlrh66cAzk6SpMzGhk4pTMTfAwh/DMAzDMI2LwUAxnq3e9X07+RywtkLCH8d8MgzDMMzmGIyAo4sW62Nz6gt/sQbo9wNoCMnVQ+6Z6KyKwh/3+zEawGCkNcaVSXL97Ur4m6ZLjvmsPy1uEnVFzGf7XhZjmYqp6ivnD//wD3H//fejtbUVHo9nR7f5zGc+A0mS1n3de++91dxMhqkdBoPSiyMcfgA5AAHqALQ6ar9dDMMwDMMw9cRg5G4/hmEYhtkpQpRTu+cvn1OiuBvBZSJ+B7V6/nJZZWi7EZ4fRt8IoW63PX/i9iz8aYPS2gOuQGB2QVWFv3Q6jUcffRSf//zny7rdqVOnMD8/X/z63ve+V6UtZJg6IBx9otMP4JhPhmEYhmEYhmEYhmF2hojhFLGTapEIUBevyUpx3XpHuPLUEv7iiySOmm2N8fww+kYN4S8ZAVIxSt3Qe7xvo+ArpKIYTEDbUH23hdE1VY36/P3f/30AwJNPPlnW7axWK7q6VLLgM4zWEOJebAFIJwCTrUT4G6nfdjEMwzAMwzAMwzAMo32E4y8RIBeaUaXlvdIYS0lS5z7riRD+VpeB9Cpgad3d/RWfn97GeH4YfSNeh2thEu+szvLvQ4iGzk7AZFF185gKcXYB+z8IWBz8N2F2hSZDYp9//nn4/X6Mjo7is5/9LJaWluq9SQyjHlYn5TUDJPjF5qnzz2TlqAiGYRiGYRiGYRiGYbamxUMiVj5HLjS1iBWiQ5067/cTmG1Aa6EDUQ3Xn3BYcr8fowVMVmV9sVLXH8d8apOeY4Bvb723gtE5VXX8VcIHP/hBPProoxgcHMSNGzfwX//rf8V73vMevPHGG7Barbf9fCqVQiqVKv47Go3WcnMZpjLaR6iodXkCsK3Q97xDXNjKMAzD7Bo+NmIYhmEYhiEa9rhIkiiWL3SNxDq3SkPEpY6/RsHdC6yGSLTb7UK6EP7Uer4ZZre4+qiXMzID+A+Uf/vItHI/DMM0FGWrDF/5ylcgSdKWX6dPn654gz7xiU/gQx/6EA4fPoyHH34Yzz77LK5cuYLvfve7G/78V7/6Vbjd7uJXf39/xY/NMDVDxH0uTyhdf9zvxzAMw6gAHxsxDMMwDMMQDX1cJOI+1eqvy6ZIIAMax/EHlPT87bIPMRUDktGC6NpAzw+jb3bT85dJAong+vthGKZhKNvx9xu/8Rv45V/+5S1/Zs+ePZVuz210d3djcHAQV69e3fD/f/u3fxv/+T//5+K/o9FoYx3IMY2Jq48s+ZkkkFmg77HwxzAMw6gAHxsxDMMwDMMQDX1cJMQnEc+5W2LzgCwDLS7A6lDnPrWAqFSJzgH5fOVJS0JgtftoPYdhtIAQ7OKLJN6X89qMztJ73tbWWO95hmEAVCD8+Xw++Hy+amzLhoRCIUxPT6O7e+NpGqvVumEEKMNoGoMBaNsDBMbp3w5/ZSW8DMMwDHMLfGzEMAzDMAxDNPRxkXCyrS4DmTXqs9sN0fn199sotPoAkwXIpoHVoNKJVi7Ffj+O+WQ0RIsLaHEDyQiJ096hnd+W+/0YpqGpaqHY1NQUzp07h6mpKeRyOZw7dw7nzp1DPB4v/szY2BiefvppAEA8Hsdv/dZv4ZVXXsHNmzfx/PPP4+GHH4bP58NHP/rRam4qw9Se9pGNrzMMwzAMwzAMwzAMw2yF2UZOHUCduM9Y4T6cDSb8GQzK77SbuM8IC3+MRqk07pOFP4ZpaKoq/P3u7/4ujh8/jt/7vd9DPB7H8ePHcfz48XUdgOPj44hEIgAAo9GIt956Cx/5yEcwOjqKT3/60xgdHcUrr7wCp5PdUEyDURrtyTGfDMMwDMMwDMMwDMOUg0vFuM+i468B++uEizFSofCXzwGxQk0LC3+M1qhE+MvnlIEBd4PEHzMMs46yoz7L4cknn8STTz655c/Isly8brPZ8P3vf7+am8Qw2sHqBPa8A0gnqPOPYRiGYRiGYRiGYRhmp7h6gcWLimhXKckokIoBkgQ4utTZNi1R2vNXCfElIJ8FzC1Aq1e97WIYNRDCX3R25z2WsYXCa9rGr2mGaVCqKvwxDLMNQw/UewsYhmEYhmEYhmEYhtEjTuH4mwNkmYS7ShCOQXuhD6/RKPYhhirrQyzt96v0OWaYamHvAExWIJsCEkuAcwfifWnMJ7+mGaYhqWrUJ8MwDMMwDMMwDMMwDMMwVcDRCRiMQHoVSEYqv59og/b7CSytiqupEtdfUfhr0OeH0TeSVH7cZ2SaLrnfj2EaFhb+GIZhGIZhGIZhGIZhGEZvGE3k9gF21/MnbtvIwpb43aIV9PwJsbCRnx9G3xSFv+ntf1aWlfcBC38M07Cw8McwDMMwDMMwDMMwDMMwemQ3ghZAnWBNJfyV6fhLxYG1MLmqGtURyeifUsefLG/9s2sr5BI2mBqz05NhGAAs/DEMwzAMwzAMwzAMwzCMPhE9f9EKHX9ry0A2Te7BVp9626U1XAVhJDq3vTBSihAKW9sBc4v628UwauDsptjfVBxIhrf+WeEKdHXT+55hmIaEhT+GYRiGYRiGYRiGYRiG0SOuXrqML5B7r1yK/X7dgKGBlwntHYDRDGRTQCK489sV+/16q7NdDKMGRjN1fgJAZBv3r+gB5Nc0wzQ0DfyJzjAMwzAMwzAMwzAMwzANTKsXMFmAXBZIBMq/fanw18gYDCXuyDJiUbnfj9ELpXGfWyH+391f3e1hGKausPDHMAzDMAzDMAzDMAzDMHqktHsuVmZ/XeltmkHYKrfnL58veX7YHcVoHCHkiSjPjUgngNXlws/za5phGhkW/hiGYRiGYRiGYRiGYRhGr7gq7PnLZYB4wSXYDMKfcETt1PGXWCInpckC2Bu4/5BpDISQlwgCmbWNf0bEgNp9gNlWm+1iGKYusPDHMAzDMAzDMAzDMAzDMHpFOP7KibAEgPgiIOcBix2wutTfLq0hxM3VEJBJbv/zpf1+klS97WIYNbDYgdZ2ur5Zz59wA3LMJ8M0PCz8MQzDMAzDMAzDMAzDMIxeEY6/1RCQTe38dsIh6OppDmHLYgdsHkCWdxaLyv1+jN4Qrr/N4j6L/X4c88kwjQ4LfwzDMAzDMAzDMAzDMAyjV6xOoMVVELQWdn47IX45u6uzXVqknJ6/SInjj2H0wFZxtrkMuXxLf45hmIaFhT+GYRiGYRiGYRiGYRiG0TNCvIuV0fNXdLQ1k/AnhJFthL/0KrC2UrgNO/4YnSAiPKPz1E9ZSnQOyOcAqwNo8dR80xiGqS0s/DEMwzAMwzAMwzAMwzCMninHyQYUhK0wXXc2kbDlKulDlOXNf048j63tgNlW/e1iGDWwtQGWViCfBeK3uH+FC9Dd1xzRvgzT5LDwxzAMwzAMwzAMwzAMwzB6plzHn/i51nbA3FKdbdIiDj9gNAGZJLC6vPnPCZGE3X6MnpAkJcZT9PkJiv1+/bXdJoZh6gILfwzDMAzDMAzDMAzDMAyjZ5zdtOifjAKp2PY/34wxnwBgMCoi6UY9aIKiO4r7/RidIYS9UuFPlkuEP+73Y5hmgIU/hmEYhmEYhmEYhmEYhtEzJgu59wDq99oO4fhrpphPwXaxqPl8iTDKwh+jM8RrNjKjxNkmAkA2BRjNgN1fv21jGKZmsPDHMAzDMAzDMAzDMAzDMHpHLPjHtun5k+XmdfwByvMUndn4/1eDQC5DIkmrr3bbxTBq4OwqxNmuKXG2kWm6dPUCBpYDGKYZ4Hc6wzAMwzAMwzAMwzAMw+gdIeJt5/hLhkkUMBib0/0jHH+JILmgbqW0349FEkZvGIyKk1cIfhzzyTBNB396MQzDMAzDMAzDMAzDMIzeEYv9sTkl4m8jhNvP4SdnULNhdQItbnqOYhuIpBEh/HHMJ6NThMAnBD/xmmbhj2GaBhb+GIZhGIZhGIZhGIZhGEbv2DtIyMumlYi/jYg2cb+fQLj+hCBSCvf7MXqnVPhLRuhLMiive4ZhGh4W/hiGYRiGYRiGYRiGYRhG7xgMgKOLrm/V8yf+r5lFgGLP3y3PU2YNWA0VfqaJnx9G37h6AUkC1laAwBX6nsMPmKz13S6GYWoGC38MwzAMwzAMwzAMwzAM0wgUe/42Ef7yOSC2WPjZJha2xO8enV0fiyqeN1sbYGmt/XYxjBqYWwC7j67P/IwuOeaTYZoKFv4YhmEYhmEYhmEYhmEYphEQ8Z2bCX+JAJDPkvPH1la77dIazi7AYCKH39qK8v2o6ELjmE9G57j76TIZLfybhT+GaSZY+GMYhmEYhmEYhmEYhmGYRkA42RIBIJe9/f+jJTGfklS77dIaBiPg7KTr0ZKevyjHoDINwq1CHwt/DNNUsPDHMAzDMAzDMAzDMAzDMI1Ai5siKvM5IL54+/8LYcvZXdvt0iKuW9yRsqyIgC52/DE6p1Tos3kAq7Num8IwTO1h4Y9hGIZhGIZhGIZhGIZhGgFJUuI+Y/O3/7/4HjvaFHFPiH2JIJBNA0YTYPfXb7sYRg1a3ECLi66z249hmg4W/hiGYRiGYRiGYRiGYRimUXAV3Hy39vxlU8BqiK6z408R/uIBEvyEAOjsAQy8ZMo0AN6R9ZcMwzQNpnpvAMMwDMMwDMMwDMMwDMMwKuHcRPiLzVOcZYsbsDpqv11ao8VF8YepGD033O/HNBoj7wE6D7Hjj2GaEB5fYRiGYRiGYRiGYRiGYZhGQQhXaytAZk35flTEfLLbr0hpzx/3+zGNhskCePopAphhmKaiasLfzZs38e///b/H0NAQbDYbRkZG8Hu/93tIp9Nb3k6WZXzlK19BT08PbDYbHnroIbz99tvV2kyGYRiGYRiGYRiGYRiGaRzMNqDVS9dLXX+xwnUnO9qKCCfU8oQSg8qOP4ZhGEbnVE34u3z5MvL5PP7n//yfePvtt/Hnf/7neOKJJ/B//9//95a3+5M/+RP82Z/9Gf7yL/8Sr7/+Orq6uvD+978fsVisWpvKMAzDMAzDMAzDMAzDMI2DiPuMzSvfK0ZZsuOviBD5wlMUg2rzcAwqwzAMo3uq1vF36tQpnDp1qvjv4eFhjI+P42tf+xr++3//7xveRpZlPP744/id3/kdfOxjHwMAfP3rX0dnZyf+9m//Fv/xP/7Ham0uwzAMwzAMwzAMwzAMwzQGrh5g8W0l3jMZBVJxQDIAjq76bpuWcHQBBiOQz9G/2e3HMAzDNAA17fiLRCLwer2b/v+NGzewsLCAD3zgA8XvWa1WPPjgg3j55ZdrsYkMwzAMwzAMwzAMwzAMo2+Kjr85crIJ55/dR71fDGE0AQ6/8m/u92MYhmEagKo5/m7l+vXr+B//43/gT//0Tzf9mYWFBQBAZ2fnuu93dnZicnJyw9ukUimkUqniv6PRqApbyzAMwzAMo0/42IhhGIZhGIZo6uMiRyc52dKrQDJcEvPJjrbbcPUpzkgW/hiGYZgGoGzH31e+8hVIkrTl1+nTp9fdZm5uDqdOncKjjz6KX/u1X9v2MSRJWvdvWZZv+57gq1/9Ktxud/Grv7+/3F+JYRiGYRimYeBjI4ZhGIZhGKKpj4uMJsDeQdej84rjz8n9frchxFDDLe4/hmEYhtEpkizLcjk3CAaDCAaDW/7Mnj170NLSAoBEv3e/+92455578OSTT8Jg2FxrnJiYwMjICM6cOYPjx48Xv/+Rj3wEHo8HX//612+7zUbTW/39/YhEInC5XOX8agzDMAzD/P/Z+/Potu7zTvx/X+wEQIALuO+kRGqzJVmy4yWOZUt27NiWk2radNJJp52037H767ffyL98c5K2ZzpdZtKeydRup1N7Ms0yibsksRNX3mKbtiXvsbVaEkWK+74BBLHvuN8/ru6VKFEiKQK4F8D7dQ4PRRIEHoIQ+MHn+TzPQ6rz+/1wOp3XvZbh2oiIiIgKBddF63T+NWDyGNB4MzBzCkjGgZu/wuTW5RJR4JMfA2XNQMfdakdDRES0rLWsi9bc6tPlcsHlcq3qspOTk7j77ruxa9cufP/7379m0g8A2traUFtbi9dff11J/MXjcRw5cgR/9Vd/tez3mM1mmM3mtf0QRERERAWKayMiIiIiSdGvixx1wCSAuR4p6ac3AtbV7ekVFaMF2PXv1Y6CiIgoY9bc6nO1pqamsGfPHjQ1NeHb3/425ufnMTMzo8zxk23atAk///nPAUgtPr/61a/iv/7X/4qf//znOHPmDH7rt34LVqsVX/rSl7IVKhERERERERERUWEpvdDCMh668HEtsMKhfCIiIsp/a674W63XXnsNAwMDGBgYQGNj45KvXdpdtK+vDz6fT/n461//OiKRCH7v934PXq8Xn/rUp/Daa6+htLQ0W6ESEREREREREREVFmsFYDADyQvtTuVZdkRERFTQ1jzjT+vW2/+diIiISE2ZXstwbURERET5iuuiDDj5z4B3RPr31i8A1ZtUDYeIiIiuz1rWMazvJyIiIiIiIiIiKkSOuuX/TURERAWLiT8iIiIiIiIiIqJC5GiQ3ptsgLlIqhyJiIiKXNZm/BEREREREREREZGKKjqAplsAZxMgCGpHQ0RERDnAxB8REREREREREVEh0umADXvVjoKIiIhyiK0+iYiIiIiIiIiIiIiIiAoAE39EREREREREREREREREBYCJPyIiIiIiIiIiIiIiIqICwMQfERERERERERERERERUQFg4o+IiIiIiIiIiIiIiIioADDxR0RERERERERERERERFQAmPgjIiIiIiIiIiIiIiIiKgBM/BEREREREREREREREREVACb+iIiIiIiIiIiIiIiIiAoAE39EREREREREREREREREBYCJPyIiIiIiIiIiIiIiIqICwMQfERERERERERERERERUQFg4o+IiIiIiIiIiIiIiIioADDxR0RERERERERERERERFQAmPgjIiIiIiIiIiIiIiIiKgBM/BEREREREREREREREREVACb+iIiIiIiIiIiIiIiIiAoAE39EREREREREREREREREBYCJPyIiIiIiIiIiIiIiIqICwMQfERERERERERERERERUQFg4o+IiIiIiIiIiIiIiIioADDxR0RERERERERERERERFQAmPgjIiIiIiIiIiIiIiIiKgBM/BEREREREREREREREREVACb+iIiIiIiIiIiIiIiIiAoAE39EREREREREREREREREBYCJPyIiIiIiIiIiIiIiIqICkLXE38jICL7yla+gra0NJSUl6OjowJ/8yZ8gHo9f8/t+67d+C4IgLHm79dZbsxUmERERERERERERERERUUEwZOuKe3t7kU6n8b/+1//Chg0bcObMGfzu7/4uQqEQvv3tb1/ze++//358//vfVz42mUzZCpOIiIiIiIiIiIiIiIioIGQt8Xf//ffj/vvvVz5ub29HX18fnnrqqRUTf2azGbW1tdkKjYiIiIiIiIiIiIiIiKjgZC3xtxyfz4eKiooVL3f48GFUV1ejrKwMd911F/7Lf/kvqK6uXvaysVgMsVhM+djv92csXiIiIqJ8w7URERERkYTrIiIiIipGWZvxd7nBwUH8j//xP/Doo49e83IPPPAA/vEf/xFvvvkm/vt//+/4+OOPcc899yxZqF3qW9/6FpxOp/LW1NSUjfCJiIiI8gLXRkREREQSrouIiIioGAmiKIpr+Yb//J//M/70T//0mpf5+OOPsXv3buXjqakp3HXXXbjrrrvwD//wD2sKcHp6Gi0tLfiXf/kX/Mqv/MoVX1/u9FZTUxN8Ph8cDseabouIiIhIbX6/H06n87rXMlwbERERUaHguoiIiIhIspZ10Zpbff7+7/8+fv3Xf/2al2ltbVX+PTU1hbvvvhu33XYbvvOd76z15lBXV4eWlhb09/cv+3Wz2Qyz2bzm6yUiIiIqRFwbEREREUm4LiIiIqJitObEn8vlgsvlWtVlJycncffdd2PXrl34/ve/D51u7Z1FPR4PxsfHUVdXt+bvJSIiIiIiIiIiIiIiIioWWZvxNzU1hT179qCpqQnf/va3MT8/j5mZGczMzCy53KZNm/Dzn/8cABAMBvG1r30NH3zwAUZGRnD48GE8/PDDcLlc+MIXvpCtUImIiIiIiIiIiIiIiIjy3por/lbrtddew8DAAAYGBtDY2Ljka5eOFezr64PP5wMA6PV6nD59Gj/84Q+xuLiIuro63H333fjxj3+M0tLSVd2ufN1+vz9DPwkRERFR7shrmDWOYb4qro2IiIgoX3FdRERERCRZy7pIEDO1etKIiYkJNDU1qR0GERER0bqMj49fcXjqenBtRERERPmO6yIiIiIiyWrWRQWX+Eun05iamkJpaSkEQcja7fj9fjQ1NWF8fBwOhyNrt5PPeB+tjPfRyngfrYz30cp4H62M99HKcnUfiaKIQCCA+vr665qPfLlcrI34+FkZ76OV8T5aGe+jlfE+Whnvo5XxPloZ10VXx8fPyngfrYz30cp4H62M99HKeB+tjPfRyrS4Lspaq0+16HS6jJwCWy2Hw8EH/Ap4H62M99HKeB+tjPfRyngfrYz30cpycR85nc6MXVcu10Z8/KyM99HKeB+tjPfRyngfrYz30cp4H62M66Kr4+NnZbyPVsb7aGW8j1bG+2hlvI9WxvtoZVpaF63/uBQRERERERERERERERERqY6JPyIiIiIiIiIiIiIiIqICwMTfdTKbzfiTP/kTmM1mtUPRLN5HK+N9tDLeRyvjfbQy3kcr4320Mt5HV8f7ZmW8j1bG+2hlvI9WxvtoZbyPVsb7aGW8j66O983KeB+tjPfRyngfrYz30cp4H62M99HKtHgfCaIoimoHQURERERERERERERERETrw4o/IiIiIiIiIiIiIiIiogLAxB8RERERERERERERERFRAWDij4iIiIiIiIiIiIiIiKgAMPFHREREREREREREREREVACY+CMiIiIiIiIiIiIiIiIqAEz8ERERERERERERERERERUAJv6IiIiIiIiIiIiIiIiICgATf0REREREREREREREREQFgIk/IiIiIiIiIiIiIiIiogLAxB8RERERERERERERERFRAWDij4iIiIiIiIiIiIiIiKgAMPFHREREREREREREREREVAAMageQael0GlNTUygtLYUgCGqHQ0RERLQmoigiEAigvr4eOt36z2hxbURERET5iusiIiIiIsla1kUFl/ibmppCU1OT2mEQERERrcv4+DgaGxvXfT1cGxEREVG+47qIiIiISLKadVHBJf5KS0sBSD+8w+FQORoiIiKitfH7/WhqalLWNOvFtRERERHlK66LiIiIiCRrWRcVXOJPbtXgcDi4iCMiIqK8lan2U1wbERERUb7juoiIiIhIspp10fobpBMRERERERERERERERGR6pj4IyIiIiIiIiIiIiIiIioATPwRERERERERERERERERFQAm/oiIiIiIiIiIiIiIiIgKABN/RERERERERERERERERAWAiT8iIiIiIiIiIiIiIiKiAsDEHxEREREREREREREREVEBYOKPiIiIiIiIiIiIiIiIqAAw8UdERERERERERERERERUAJj4IyIiIiIiIiIiIiIiIioATPwRERERERERERERERERFQAm/oiIiIiIiIiIiIiIiIgKABN/RERERERERERERERERAWAiT8iIiIiIiIiIiIiIiKiAsDEHxEREREREREREREREVEBYOKPiIiIiIiIiIiIiIiIqAAw8UdERERERERERERERERUAJj4IyIiIiIiIiIiIiIiIioATPwRERERERERERERERERFQAm/oiIiIiIiIiIiIiIiIgKABN/RERERERERERERERERAUgq4m/t99+Gw8//DDq6+shCAKef/75a17+8OHDEAThirfe3t5shklERERERERERERERESU9wzZvPJQKITt27fjt3/7t3HgwIFVf19fXx8cDofycVVVVTbCIyIiIiIiIiIiIiIiIioYWU38PfDAA3jggQfW/H3V1dUoKyvLfEBEREREREREREREREREBSqrib/rtXPnTkSjUWzZsgV//Md/jLvvvvuql43FYojFYsrHfr8/FyESERERaRLXRkREREQSrouIiIioGGV1xt9a1dXV4Tvf+Q6ee+45/OxnP0NXVxf27t2Lt99++6rf861vfQtOp1N5a2pqymHERERERNrCtRERERGRhOsiIiKi9QklQjg5dxKJdELtUGgNBFEUxZzckCDg5z//OT7/+c+v6fsefvhhCIKAQ4cOLfv15U5vNTU1wefzLZkTSERERJQP/H4/nE7nda9luDYiIiKiQsF1ERERkbpeHXkVg4uDuLn2Ztxce7Pa4RS1tayLNNnq81K33nornnnmmat+3Ww2w2w25zAiIiIiIu3i2oiIiIhIwnURERHR9UumkxjzjwEAhn3DTPzlEU21+lzOiRMnUFdXp3YYRERERERERERERERERWE6OK20+HRH3AjGgypHRKuV1Yq/YDCIgYEB5ePh4WGcPHkSFRUVaG5uxje/+U1MTk7ihz/8IQDgySefRGtrK7Zu3Yp4PI5nnnkGzz33HJ577rlshklERERERERERERFKJVO4f2p92HWm7G9ejvMelaJEhEBwIh/ZMnHo/5RbHVtVScYWpOsJv6OHj2Ku+++W/n48ccfBwD8+3//7/GDH/wA09PTGBsbU74ej8fxta99DZOTkygpKcHWrVvx0ksv4XOf+1w2wyQiIiIiIiIiIqIiNOwbxmn3aQDAafdp7KrZhW2ubTDoND8hiYgoa0RRxKh/FABQY63BbHgWI/4RJv7yRFb/gu3ZsweiKF716z/4wQ+WfPz1r38dX//617MZEhEREREREREREREAYNg/DAAw6AyIpWJ4f+p9fDL/CT5V9yl0lndCEASVIyQiyr2F6AL8cT/0gh53NNyBn/X/DBOBCSTSCRh1RrXDoxVofsYfERERERERERERUaal0imlouWh9odwd9PdsBltCCaCeGPsDfz0/E8x5h+7ZmEDEVEhkp8b6+31qLHWoNRUipSYwkRgQuXIaDWY+CMiIiIiIiIiIqKiMxWaQjwVR4mhBLW2Wmyu3Iwvbf4Sbq27FSa9Ce6IGy8OvYhDg4cwF55TO1wiopyRE39tzjYIgoAWR8uSz5O2MfFHRERERERERERERWfENwIAaHG0QCdI26RGnRE31dyE39j8G9hetR06QYfJ4CSePf8sXht5Db6YT8WIiYiyL5KMYCY0AwBodjQDAFodrQCkxB+roLWPiT8iIiIiIiIiIiIqKqIoYtgnzfdrc7Zd8fUSQwnuaLgDX9r8JWnWHwQMLA7gn3v/Ge9MvINwIpzrkImIcmLMPwYRIiotlXCYHACklp9GnRGhRAjuiFvlCGklTPwRERER0bosRBdwav4UT/0RERERUd7wRD0IJoIw6AxoLG286uUcJgf2tezDr3b9KppKm5AW0zjtPo1/6v0nHJ05ikQqkcOoiYiyb8Q/AgBocbYonzPoDGgqbVryddIuJv6IiIiI6LolUgn8tO+neG/yPXiiHrXDISIiIiJaFbnar6m0CUadccXLu0pceLjjYTzc8TBcJS7EU3F8NPMR/qn3n3DWcxZpMZ3tkImIsi6VTmE8MA7gYntPmTznj4k/7WPij4iIiIium1FvVHr+Dy4OqhwNEREREWlZ92g3Dhw6gF0/2oUDhw6ge7RbtVjkxN/lG9sraSptwq92/ir2teyDw+RAKBHCkfEj+Jfef8GQb4hdMIgor02HphFPxVFiKEG1tXrJ11ocLRAgYD48j1AipFKEtBpM/BERERHRurQ72wFIiT9udBARERHRcrpHu3Hw8EH0e/sRT8fR7+3HwcMHVUn+BeIBuCNuCBCUCpa1EAQBneWd+PVNv447Gu6AxWDBYmwRvxj+BZ4feB4zoZksRE1ElH2j/lEAQLOjGTphafrIarQqyUD5cqRNTPwRERER0bq0OluhE3RYjC1iIbqgdjhEREREpEFPnXoKAgSIkA6KiRAhQMDTp57OeSwjvhEAQK2tFlaj9bqvx6AzYHvVdvzG5t/ATTU3waAzYDo0jZ/1/wyvDL8Cb9SboYiJiLJPFEWljefVqqGVdp8XnkdJm5j4IyIiIqJ1MevNypDvId+QytEQERERkRaN+EaUpJ9MhKi03MxpLBc2ttucbRm5PrPejFvrbsWXNn0Jmys2Q4CAYd8w/qXvX3B4/DBb4hFRXliMLcIX80En6JTX+JdrdbYCACaCE0ikEzmMjtaCiT8iIiIiWrcNZRsAcM4fERERES2v1dkKAcKSzwkQMpZ8W61YKobJ4KQU0xrn+63EbrLj7ua78cVNX0Sbsw2iKKLH04N/PPeP+OX0LxFPxTN6e0REmSS372ywN8CkNy17mUpLJexGO5LpJKaCU7kMj9aAiT8iIiIiWrcWRwt0gg4L0QW2+yQiIiKiKzy2/TGlvScApe3nY9sfy2kcY/4xpMU0yi3lKLOUZeU2KiwVeKDtAXxhwxdQa6tFMp3EsdljeObcM/hk/hOk0qms3C4R0XrI1dDXmn0qCIJS9cc5f9rFxB8RERERrZvFYEFjaSMAYGiR7T6JiIiIaKl9LfvwxJ4n0FneCZPOhM7yTjy550nsbdmb0zjk1qKZrvZbTp29Dl/Y8AXc33o/ysxliCajeHfyXfxz7z+j39sPURRXvhIiohyIJqOYDk0DuHbi79Kvj/hG+DymUQa1AyAiIiKiwtDh7MCYfwyDi4PYXbtb7XCIiIiISGP2tezDvpZ9qt1+Kp3CWGAMQObm+61EEAS0l7Wj1dmKc55z+HjmY/jjfrw++jpOzp/ErXW3XnWWFhFRrowFxiCKIiosFXCande8bIO9AQadAcFEEJ6oB64SV46ipNVixR8RERERZUSbsw2CIMAT9WAxuqh2OERERERES0wFpxBPxWE1WFFjrcnpbesEHba6tuI3Nv8Gbq69GUadEfPhebww+AJeGHwB7og7p/EQEV1Kbtu5UrUfABh0BjTapY4/I76RbIZF14mJPyIiIiLKCIvBoiz+B32DKkdDRERERLTUsP9Cm09nKwRBUCUGo96Im2tvxm9s/g3c4LoBgiBgPDCOn/b9FG+MvgF/3K9KXERUvNJiWkn8rbYNMuf8aRsTf0RERESUMe1l7QCAwUUm/oiIiIhIO0RRVCpTcjHfbyVWoxV3Nt6JL236EjaUbYAIEX3ePvzTuX/C+5PvI5qMqh0iERWJ6dA04qk4LAYLamyrq4aWKwPnwnMIJ8LZDI+uAxN/RERERJQxbQ6p3ac74oYv5lM7HCIiIiIiAIA74kYwEYRRZ0RjaaPa4SicZifua70PBzYeQIO9AWkxjZPzJ/GP5/4R3qhX7fCIqAiM+i60+SxtgU5YXcrIZrShyloFESKr/jSIiT8iIiIiyhir0YoGewMAYMg3pHI0RERERESSYZ/U5rOptAkGnUHlaK5UY6vB/o79eKj9IZSZyxBLxfDJ/Cdqh0VERWDEPwJgdfP9LiVXTzPxpz1M/BERERFRRnU4OwCw3ScRERERaYe8sd3mbFM3kGsQBAHNjmZ8pvEzAID+xX4k00mVoyKiQrYYXcRibBGCIKDJ0bSm75UTf+OBcT5XaQwTf0RERESUUW3ONggQMBeegz/uVzscIiIiIipy/rgf7ogbAqTEmtY12BtgN9oRT8WVuYRERNkgH4qot9XDrDev6XtdJS7YjDYk0glMBaeyEB1dLyb+iIiIiCijrEYr6ux1AIChRbb7JCIiIiJ1ycmzWlstSgwl6gazCoIgoLOiEwDQ6+1VORpajVQ6hcPjh9melfKO3Kaz1dm65u8VBEFpDyonEEkbmPgjIiIiooxju08iIiIi0gp5vp+W23xerqu8C4DUQi+cCKscDa1k0DeIHk8P3pt6j78vyhuxVAxTIalST27buVZy4m/UPwpRFDMVGq0TE39ERERElHHtZe0QIGA2PItAPKB2OERERERUpKLJqLKxnU+Jv3JLOWqsNRBFEee959UOh1bQuyBVZoqiiD5vn8rREK3OuH8coiiizFwGp9l5XdfRWNoIvaBHIB7AQnQhwxHS9WLij4iIiIgyzma0odZWCwAY8rHdJxERERGpQ65CqbBUXPfGtlo2VWwCAPQtMJGkZb6YDxOBCeXjHk8PK58oL8jtOa+32g8AjDojGksbl1wfqY+JPyIiIiLKivaydgCc80dERERE6lE2tq9jfpXaOso6oBN08EQ9cEfcaodDVyEnZutsdTDqjPDFfJgJzagcFdG1pcX0uub7XUpOHMrXR+pj4o+IiIiIskKe8zcTmkEoEVI5GpLFU3EMeAfwzsQ73JAgIiKigpZMJzEeGAcAtDnyp82nzGKwKBvycitJ0pa0mMa5hXMAgG2ubdhQtgEA0LPQo2ZYRCuaDc0ilorBrDcr3XqulzznbzY0yxmXGsHEHxERERFlhd1kR62tFiJEVv2pzB/34/T8abww+AK+d+Z7eG30NZx2n8YbY2+wDREREREVrKngFOKpOGxGG6qt1WqHc102lUvtPvu9/UilUypHQ5ebCEwglAjBrDejzdmGzZWbAQCDi4OIp+IqR0d0dXI1dLOjGTphfWkiu8kOV4kLIkTlsAWpi4k/IiIiIsqadqfU7nPQN6hyJMVFFEXMhefw0fRH+EnfT/BMzzN4Z/IdjAfGkRbTKDOXKW2ILp1HQkRERFRILp1fJQiCusFcp6bSJpQYShBJRrihrkFyZV9neScMOgNqrDUot5QjmU6if7Ff5eiIrk5+fpSr9dZLbvc57B/OyPXR+jDxR0RERERZI8/5mw5Os+VHliXTSYz6R3Fk/Ah+2PNDPHv+WRydPQp3xA0BAupsdbi9/nb8203/Fl/a/CV0VXQBAM56zqocOREREVHmiaKIYZ+0AZ2P8/1kep0eneWdAIBeL9t9akk4EcaIbwQAlEo/QRCwuUL6d6+Hvy/SJl/MB2/UC0EQ0FzanJHrlBOIE4EJVidrQFYTf2+//TYefvhh1NfXQxAEPP/88yt+z5EjR7Br1y5YLBa0t7fj6aefzmaIRER5rXu0GwcOHcCuH+3CgUMH0D3arXZIRERLOEwOVFurpXafPrb7zLRwIoxznnN4ZfgVfO/M9/DS0Es46zmLUCIEo86I9rJ27G3ei9/a9lv4wsYvYEf1DpRbygEAWyu3ApBOZAbjQTV/DCIiIqKMm4/MK2uiBnuD2uGsi3xga8Q3gmgymvPb597D8s57zyMtplFlrYKrxKV8vrO8E4IgYDY8C0/Eo2KERMsb9Y8CAOpsdbAYLBm5zmprNawGK+KpOKZCUxm5Trp+WU38hUIhbN++HX/3d3+3qssPDw/jc5/7HO68806cOHECf/iHf4g/+IM/wHPPPZfNMImI8lL3aDcOHj6Ifm8/4uk4+r39OHj4IBfgRKQ5HWUdAKQ5F7Q+oihiIbqA47PH8bP+n+H/nP0/eGv8LQz7hpFMJ2Ez2rDNtQ0PtT+E397227i/9X50VXShxFByxXVVllSizlYHURRxbuGcCj8NERERUfbI1X5NjiYYdAaVo1kfV4kLlZZKpMU0BhYHcnrb3HtYniiK6F2QKvrkCj+Z1WhFm6MNAJTLEGmJnPiT23NmgiAIStWffP2knqz+1XvggQfwwAMPrPryTz/9NJqbm/Hkk08CADZv3oyjR4/i29/+Ng4cOJClKImI8tNTp56CAAEiRACACBECBDx96mnsa9mncnRERBe1O9vxwdQHmApOIZwIw2q0qh1SXkmLaUyHpjHiG8GIfwS+mG/J110lLrQ529DqaIWrxLWm+TVbXVsxHZpGj6cHu2p2rXuoOxEREZFWyC0Y5QRMvuuq6ML7U++jb6EP21zbcna73HtY3mx4FgvRBegFPTaWb7zi65sqNmHIN4Q+bx9urbsVep1ehSiJrhRPxTEZnASQufl+shZnC84tnMOIbwR31N+Rt7NVC4Gmjrt88MEHuO+++5Z87rOf/Sy++93vIpFIwGg0qhQZEZH2jPhGlIW3TMTFGQZERFrhNDvhKnHBHXFj2D+stJikq4un4hjzj2HEP4JR/yhiqZjyNZ2gQ2NpI1odrWh1tMJusl/37bQ721FiKEEoEcKIfwTtzvZMhE9ERESkKl/MB0/Us6QCJd91lnfig+kPMBuehTfqVdq3Zxv3HpYnV/J1lHXArDdf8fVmRzNsRpuyzpa7oBCpbTwwjrSYhtPszPjzSJO9CXpBD3/cD2/MiwpLRUavn1ZPU4m/mZkZ1NTULPlcTU0Nkskk3G436urqrvieWCyGWOziRojf7896nEREWtDqbEW/t3/JAlyAgDZnYZxmJKLro9W1UUdZB9wRN4YWh5j4uwp/3I9R3yhG/COYDE4iLaaVr1kMFrQ4WtDmaENjaSNMelNGbtOgM2BTxSacmDuBs+6zTPwREVFB0eq6iLJvxD8CAKi31WdsfpXarEYrmkubMeofVarIcoF7D1dKpBJKy9XNlZuXvYxO0KGrogvHZ4+jx9PDxB9phvz8mMk2nzKj3oh6ez3GA+MY9Y8y8acizfXyubz8UxTFZT8v+9a3vgWn06m8NTU1ZT1GIiIteGz7Y0qLDQBK643Htj+mcmREpCatro06nNIL3YngBCLJiMrRaIMoipgNzeKX07/ET/p+gmd6nsE7k+8oJzDLzGXYUb0DX9jwBfzW1t/C3ua9aC9rz1jST7alcgsECBgPjF/RRpSIiCifaXVdRNknV6O1OlvVDSTDusq7AADnF84re6bZxr2HKw36BhFPxeE0O1Fvq7/q5eTZfxOBCQTigVyFR3RVaTGNMf8YgMy3+ZTJCUW53XKmdY9248ChA9j1o104cOhA0c8bvRpNJf5qa2sxMzOz5HNzc3MwGAyorKxc9nu++c1vwufzKW/j4+O5CJWISHX7WvbhiT1PoLO8EyadCZ3lnXhyz5PY27JX7dCISEVaXRuVWcrgKnFBFMWsvQDIB4l0AiO+ERweP4wf9vwQz/U/h2Ozx+COuCFAQJ2tDrfX345/u+nf4kubv4Tb629Hnb0uq7P3nGYnGksbAQBnPWezdjtERES5ptV1EWVXNBnFdGgaAAquKq3V2QqT3oRgIoiJ4ERObpN7D1c65zkHQJrjd60ZZk6zE/X2eogQldagRGqaC88hkozApDehznZld8VMaHFKCcWZ0EzGD/12j3bj4OGD6Pf2I56Oo9/bj4OHDzL5twxNtfq87bbb8MILLyz53GuvvYbdu3dfdb6f2WyG2XxlH2UiomKwr2VfUQ/TJqIraXlt1O5shzvixqBv8KotcQrVXHgOx2aPYTwwjmQ6qXzeqDOiydGENkcbmh3NKDGUqBLfNtc2jAfG0bvQi1tqb4FBp6mXCURERNdFy+siyp5R/yhEUUSlpRIOk0PtcDLKoDNgY9lGnPWcxfmF82gqzU0VK/ceLvJGvZgOTUOAoFRgXsvmis2YCk6hd6EXu2t2XzNRSJRtcpvP5tJm6HX6rNyGw+RApaUSnqgHY/4xdFWs/P9ktZ469ZRSdQxAqUZ++tTTfI66TFYr/oLBIE6ePImTJ08CAIaHh3Hy5EmMjUnlpN/85jfxm7/5m8rlH330UYyOjuLxxx/HuXPn8L3vfQ/f/e538bWvfS2bYRIRERFRDshzLSYCE4gmoypHkzuJVAIvDr2IYd8wkukkbEYbtrm24aH2h/Db234b97fej66KLtWSfoDU5sVmtCGajGJwcVC1OIiIiIjWa9gvtfkstGo/mbyJPuQbQiKVUDma4nNuQar2a3Y0w26yr3h5uVV/IB7IWZUm0dWM+kYBZK/Np0yu+hv1j2b0ekd8I0vmjQJS8k9u70wXZTXxd/ToUezcuRM7d+4EADz++OPYuXMn/tN/+k8AgOnpaSUJCABtbW14+eWXcfjwYezYsQN//ud/jr/927/FgQMHshkmEREREeVAuaUcFZYKpMW0ctKwGPQs9CCajMJhcuBXO38Vv7nlN/GZxs+g2dGsmco6naDDlsotAIAeT4/K0RARERFdn2Q6iXG/1NK10Ob7yWqsNXCanUikExj08cBWLqXSKZxfOA/g4vy+lRh1RnSWdwIA232SqvxxPzxRDwQIaHY0Z/W25Dl/Y4ExpNKpzF2vs1WZNyoTIBTsQY/1yGrib8+ePRBF8Yq3H/zgBwCAH/zgBzh8+PCS77nrrrtw/PhxxGIxDA8P49FHH81miERERESUQ3LVX7FUlaXSKZyaOwUA2FmzE1XWKs2299lSuQWCIGA6NA13xK12OERERERrNhmcRCKdgM1oQ1VJldrhZIUgXGwx2bfQp3I0xWU0MIpwMowSQ8maKqY2VWwCAAwtDhVV5xPSFrnar9ZWm/VuM9XWapQYShBPxZWZq5nw2PbHlPaeAJS2n49tfyxjt1Eospr4IyIiIiK6VLuzHQAwHhhHLBVTOZrsG1gcQDARhNVgXdUMEDXZjDblpCSr/oiIiCgfye3e2pxtmj1slQmdFVIF2WRwEv64X+VoikevR6rY6yrvWtN8tKqSKrhKXEiJKfR7+7MVHtE1yV13clENrRN0SlVhJtt97mvZhyf2PIHO8k6YdCZ0lnfiyT1PYm/L3ozdRqFg4o+IiAree5Pv4dDgIcRTcbVDISp6FZYKlJnLpHafvhG1w8kqURRxfO44AODGqhs109bzWrZWbgUgnR7ncyYRERHlE1EUlfWl3GauUDlMDjTYGwBAaT1J2RVKhDAakBIYmytX1+ZTJgiCUvUnzwgkyqVEKoHJ4CSA7M/3k8nPw5ke87GvZR+e3f8sjn35GJ7d/yyTflfBxB8RERW02dAsTs2fwkRgAqfmT6kdDlHREwRBafc55BtSOZrsGvGPwBv1wqQ3Yatrq9rhrEqjvVGZGcPTyERERGvTPdqNA4cOYNePduHAoQPoHu1WO6SiMhueRTgZhklvQr29Xu1wsq6r4kK7T28fRFFUOZrC17cg3c+1tlqUW8rX/P2d5Z3QC3q4I27Mh+ezECHR1Y0Hx5EW03CYHCg3r/3xez2aSpugE3TwxXxYjC7m5DbpIib+iIiooH08+7Hy70/mP2EFC5EGyIm/Mf9YQf+fPDF3AoBURWfWm1WOZnUEQVCq/s56znITiYiIaJW6R7tx8PBB9Hv7EU/H0e/tx8HDB5n8yyG5qqSptCkvOi2sV4ezAwadAb6YD7PhWbXDKWiiKCqVepsr1lbtJ7MYLEpbfVb9Ua7J1dAtjpactUE26U1KZXKmq/5oZUz8ERFRwZoNzWLMPwYBAuxGO2KpGD6Z/0TtsIiKXqWlEk6zEykxldF+/1oyHZzGTGgGekGPG6tuVDucNdlUsUk5jcxNJCIiotV56tRTECBAhHRoRoQIAQKePvW0ypEVD3ljW06uFDqj3ogOp3Sgrm+hT+VoCtt0aBq+mA9GnREbyjZc9/XILULPe88jkU5kKjyiaxJFEWP+MQC5me93KbmtKBN/ucfEHxERFayjs0cBSC01PlX3KQDAqflTBV1hRJQPLm33OegbVDma7JBn+3VVdMFmtKkczdpYDBZsKJc2NM66z6ocDRERUX4Y8Y0oST+ZCBHDvmGVIiouvpgPC9EFCIKQs/lVWtBZ0QkA6F/sRzKdVDmawnXOI1XobSjbAKPeeN3X02hvRKmpFPFUHEOLhT32gLRjLjx3sQ2yLbdtkOXn4+nQNKLJaE5vu9gx8UdEmsX5CLQec+E5jPpHIUDArppd2Fi+EU6zE7FUDKfdp9UOj6joyaeTx/xjSKQK67SrO+JWnn92Vu9UO5zrIrf7HFgcQCQZUTkaIiIi7Wt1tkLA0vZpAoSiqT5Tm5xgbbA35E2L9UxotDfCbrQjnoorFY+UWbFUTDmsKFfsXS9BELCpYhMAoHehd92xEa2G3GWnsbQRep0+p7ftNDtRbimHKIoYD4zn9LaLHRN/RKRJnI9A63V0Rqr221i+EWWWMugEHXbX7AYgVf0VWqKBKN+4SlxwmBxIppMF1+7z5NxJAEB7WTucZqe6wVynGmsNXCUupMQUW0cRERGtwmPbH1PaewJQ2n4+tv0xlSMrDnLir9XRqm4gOSYIglL11+flmi0bBrwDSKaTKLeUo8Zas+7r21SxCQIETAYn4Yv5MhAh0bXJbTbVen6Ub5ftPnOLiT8i0iTOR6D1mA/PY8Q/olT7yeSqv2gyyqo/IpUJgoD2snYAhdXu0x/3o3+xHwDyttoPkH4/W11S1d9Zz1mIorjCdxARERW3fS378MSeJ9BZ3gmTzoTO8k48uedJ7G3Zq3ZoBS+SjGAmNAMg9/OrtKCrvAsAMBYYQzgRVjmawnNuQWrzubliMwRBWOHSKys1laKxtBEAq/4o+4LxINwRNwQIaC5tViUGOfE35h9DWkyrEkMxYuKPiDSJ8xFoPeTZfhvKN6DcUq58XifolETgyfmTrPojUtmSdp8FMtz+1NwpiKKIxtJGVFur1Q5nXTrLOmHSm+CL+TARnFA7HCIiIs3b17IPz+5/Fse+fAzP7n+WSb8cGfWPQoSodJQoNnIlmiiKOO89r3Y4BcUdcWMuPCdVVpZ3Zux6N1dILUN7F3qZCKGskrvr1NhqYDVaVYmhxlYDs96MWCqmHNKg7GPij4g0ifMR6Hq5I24M+4avqPaTdZZ3KlV/ZzxnVIiQiGTV1mrYjXYk0gmM+/O/3384EVZOBOdztZ/MqDcqGxxnPWdVjoaIiIhoefJsu2LeL+iqkKr+2KI9s+SKvDZHW0aTJq3OVlgMFoQSIc49o6yS22u2OFpUi0En6JTbZ7vP3GHij4g0ifMR6HrJs/06yjpQYam44utLqv7mWPVHpCZBENBRJlX9FUK7zzPuM0imk6iyVqHR3qh2OBmxtVJq9znsG0YoEVI5GiIiIqKlEukExgJjAIpvvt+lNpRtgE7QwRP1wB1xqx1OQUimk0oidXPl5oxet0FnUA7YnfOcy+h1E8kS6QQmAlLnFjUTf8Alc/4uHNSg7GPij4g0ifMR6Hq4I24M+YYgQMDu2t1XvVxneSccJgciyQirWIhUJif+RnwjSKaTKkdz/RKphDI79KbqmzIy/0MLKksqUWergyiK6PH0qB0OERER0RKTgUkk00nYjXa4Slxqh6Mai8GizDdk1V9mjPhGEEvFYDPa0FTalPHrl9t9DvuHOZuRsmIiMIGUmEKpqRSVlkpVY2lyNEEQBCzGFuGL+VSNpVgw8UdEmsX5CLRW8my/9rL2Zav9ZJdW/Z2YO1Ews8WI8lGNtQY2o01q95nHbW56FnoQS8XgNDsLrs3UlsotAIAeTw9nkBAREZGmDPuGAVwYF1IgB6+u16byTQCA897zSKVTKkeT/+QW/psqNkEnZH4LvbKkEtXWas5mpKyR5/u1OFpUf340682ot9UDYLvPXGHij4iICoI74sbQ4hAAYHfN1av9ZEuq/tys+iNSy6XtPuX/w/kmlU7h5NxJAMCO6h1Z2RhQU0dZhzKDhC/SiIiISCtEUVQ2tgvt4NX1aCptQomhBJFkJK8P1GmBP+5XWiRuqtiUtduRW4ieWzgHURSzdjtUfC59ftRKG2S53agcF2VXYe1KEBFR0To2ewyAVO1XWbJyCwO9To+bam4CcGHWH6v+iFTT4ZQSf8P+4bxs99m/2I9QIgSb0Yau8i61w8k4g86gbHjwoAQRERFpxWx4FuFkGCa9SakkKWZ6nR4byzcCAHq9vSpHk9/6FvogQkSDvQFOszNrt7OxbCMMOgO8US9mw7NZux0qPvOReYQSIRh1RtTbtfH8KB/QmAxOIpaKqRxN4WPij4iI8p4n4llTtZ+sq7wLpaZShJNhzq4iUlGtrRY2ow3xVFw5WZsvRFHEibkTAIAbq26EQWdQOaLs2Fq5FQAwHhjnTAYiIiLSBLnNZ3NpM/Q6vcrRaIN8WGvEN4JoMqpyNPlJFEX0LkiJ02xW+wGASW9Sup+c85zL6m1RcZGr6ppKmzTzGtVpdqLMXAZRFDHuZ1VytjHxR0REee/Y7DGIENHubF/TQPdLq/5OzOZu1l/3aDcOHDqAXT/ahQOHDqB7tDsnt0ukVYIgKKf/Bn2DKkezNsP+YXijXpj0JiU5VoicZieaSpsAAGc9rPojIiIi9cmJP7b5vMhV4kKlpRJpMY3BxfxaV2vFRGACgXgAJr0J7WXtWb+9zRVSu8+BxQEkUuxERJkhj2iQ22tqhdx2lO0+s4+JPyIiymsL0QXlBc3u2tVX+8k2lW+C3WhHOBnOyQm77tFuHDx8EP3efsTTcfR7+3Hw8EEm/6joySddh33DSKVTKkezOqIo4sSsVO23tXIrTHqTyhFl11aXlNjsXejNy5asREREVDgWo4tYjC1CJ+jQ7GhWOxxN6aqQWs/LVWu0NucWpH2BzvJOGHXGrN9ena0OTrMTiXQCA4sDWb89KnyhRAjz4XkA2kv8tTgvzPkLjCItplWOprAx8UdERHlNrvZrc7atqdpPptfpsatmFwDgxNyJrG9mP3XqKQgQIEIa3C1ChAABT596Oqu3S6R1dbY6WA1WxFNxTAYn1Q5nVaZD05gNz0Iv6LG9arva4WRdq6MVNqMN0WSUJ8iJiIhIVcN+qdqvwd4As96scjTa0lneCUEQMBuehTfqVTucvBJJRjDkk8aIyJV42SYIgnJbctKRaD3kar9qazWsRqu6wVymzlYHk96EaDKK2RDnWmYTE39ERJS3vFEvBrzSibi1zPa7XFdFF2xGG0KJUNar/kZ8I0rSTyZCVNrUEBUrnaBTWunkS1Lp+NxxANLsD629oMoGnaDDlsotAMC5qERERHmiUMcMjPhGAFxsG0cXWY1WpUV7n7dP5WjyS7+3H2kxDVeJC1XWqpzd7qaKTRAEATOhGSxEF3J2u1SYRn1SG00tPj/qBJ1ShSgnKCk7mPgjIqK8JVf7tTpa17UoN+gMStXf8bnjWa36a3W2QoCw5HMCBM6lIALQ7pQSf0O+Ic23+3RH3Bjzj0GAgB3VO9QOJ2e2VG6BIAiYDk3DHXGrHQ4RERFdQ6GOGQgnwpgJzQCQXl/RlTaVbwIAnF84D1EUV7g0AVIbf/kgcK6q/WRWoxUtpVIypNfDFq10/RLpBCaCEwC0+/woJ/445y+7mPgjIqK8tBhdRL+3H8D1zfa73KaKTUrVXzZnITy2/TGlvScApe3nY9sfy9ptEuWLens9LAYLYqkYpkJTaodzTSfnTgKQZhM6zU51g8khm9GGNod0UKHQq/4KtUKCiIiKR6GOGRj1j0KECFeJC6WmUrXD0aRWZytMehOCiWDetNFX23xkHp6oB3pBj43lG3N++5srpWRjn7dP84cgSbumglNIppOwGW2otFSqHc6ymkubIQgCFqIL8MV8aodTsJj4IyKivHRptV+1tXrd12fQGXBT9U3KdWer6m9fyz48secJdJZ3wqQzobO8E0/ueRJ7W/Zm5faI8olO0ClVf1pu9+mL+dC/KB082Fm9U+Vocm+raysA4Lz3PBKphMrRZEehVkhQ7kWSEbw68mrWW4kTES2nUMcMyO3h2DXl6gw6AzaWScmrvgW2+1wN+W91e1k7LAZLzm+/xdECq8GKSDLCSii6bpe2QRYE4doXVonFYEGdrQ4Aq/6yiYk/IiLKO4vRRZz3ngeQmWo/2ebKzUrVXzZfHO1r2Ydn9z+LY18+hmf3P8ukH9ElOpwdAIBh3zDSYlrlaJZ3av4URFFEU2lTTmd/aEWjvRFOsxPxVBznF8+rHU5WFGqFBOWWKIp4a+wtDC4O4vDEYaUtHRFRrhTimIFEOoHxwDgAJv5W0lXRBUBqo1+oh7UyJZFOKAf7NlVsUiUGnaBTfmfnFnhgiNZOFEXlYIRW23zK5PmDTPxlDxN/VDS01q5Ja/EQ5ZNjc1K1X4ujJSPVfjKDzqBU7xybPcb2GkQqqLfXw6w3I5KMYCqovXaf4URYOQ1cjNV+ACAIArZWSlV/Z91nC3JuTKFWSFBu9Xh6lM0XURTRPdqNeCqublBEVFQKcczARGACyXQSpaZSzbax04oaaw2cZicS6QQGfdrtpqEFQ4tDiKficJgcaLQ3qhaHPFtwzD+GYDyoWhyUnzxRD0KJEAw6A+rt9WqHc03ynL/J4CTXx1nCxB8VBa21a9JaPET5xBfzXaz2q8lctZ9sS+UW2Iw2BBNB9Ho5VJso1/Q6vXJ6e8g3pHI0VzrtPo2UmEK1tRoN9ga1w1FNV0UX9IIe7ogbs+FZtcPJuEKskKDc8ka9eG/qPQDAzbU3o9RUCn/cj3cm31E5MiIqJoU4ZkA+hKPlNnZaIQgCusqlCjK2+7w2ucKuq6JL1cdVmaUMdbY6iBDR5+XvjNZGbvPZaG+EUWdUN5gVlJnL4DQ7kRbTmAhMqB1OQWLij4qC1to1aS0eonxybPYYRFFEs6MZNbaajF+/QWfAjuodAIDjs8dZ9acSVkUXt44yqd3n0OKQptp9xlNxnHGfAQDcVH1TUW82lRhKsKFsAwDgrOesytFkXiFWSBQDrfztSKVTeH30dSTTSTSWNmJ3zW7sa94HAQL6Fvow4B1QJS4iKk6FNGYgLaaVtnA8jLM6nRWdAKSqGn/cr3I02uSL+TAVnIIAQbU2n5faXClV/fUu9BZkZw3KHrnTRIuzRd1AVkEQBKXd57CfXVWygYm/AqGVF7lapbV2TVqLhyhf+GI+5dRbNqr9ZFsqt8BqsCIQD/CUnQpYFU2N9kaY9CaEk2FNzcTq8fQglorBaXZqfmZCLmx1Se0+B7wDiCajKkeTWYVYIVHotPS346OZj+COuGHWm7G3eS8EQUCdvQ431dwEADg8cRiBeCDncRER5bvZ0CwiyQhMehPqbHVqh5MXHCaH0qXi/EJhzmZeL7nar7G0EaWmUpWjkWaem/QmKSEZ0t7oA9KmcCKMufAcgIvz87RObvc55h9jkjsLmPgrAFp6katVWmvXpLV4iPKFXO3XVNqEWltt1m7HqDMqVX+c9Zd7rIqmS9t9Di5qYx5JKp3CqflTAKTZfjqBy+gaaw1cJS6kxBR6FwqvNXIhVUgUA6387ZgMTuLk3EkAwN1Nd8NmtClf212zG9XWasRTcbwx9oamKpqJiPKBXBXS6miFXqdXOZr80VVxod2nt4+b65dJi2mlDao8X09tRr1R6awhzxYnWolcDV1lrVqy/tSyOlsdTHoTIslIQY6PUFvWdyz+/u//Hm1tbbBYLNi1axfeeefqMw0OHz4MQRCueOvtLbyNhEzSyotcLdNauyatxUOUDy6t9ru59uas395W11ZW/amEVdEESCddAWnOnxY2KM57zyOUCMFmtKGzvFPtcDRBEARsrZSq/s56zmri90TFSwt/O6LJKLpHuyFCxJbKLWgva1/ydb1Oj3tb7oVRZ8RUcEpJEBIR0epcOt+PVq/d2Q6DzgBfzFc4m+s9h4Cnbgf+olp633Pouq5mzD+GUCIEi8GiqY4ecsvRwcVBxFIxlaOhfCAn/vLp+VGv06OptAnAxfgpc7Ka+Pvxj3+Mr371q/ijP/ojnDhxAnfeeSceeOABjI2NXfP7+vr6MD09rbxt3Lgxm2HmPS28yL2c1lqPaq1dk9biodXT2mO7mByfPZ6Taj/ZpVV/nPWXW6yKJkBqtWPSmxBKhFTfoBBFESfmTgAAtldth0FnUDUeLdlYvlFpRTQR5FB2Uo/afztEUcSRiSMIJUJwmp24o/6OZS/nNDvx6YZPA5BagsotmYiI6Nq8US98MR90gg7Njma1w8krJr1JOVQnV7fltZ5DwE++DMz2AMmY9P4nX76u5J/c5rOzvFNTa/waaw0qLBVIiSn0e/vVDoc0LplOYjwwDuBi+8x8IScqmfjLvKwm/v76r/8aX/nKV/A7v/M72Lx5M5588kk0NTXhqaeeuub3VVdXo7a2VnnT61m+fy1qv8i9nFZbj2qtXZPW4qGVafWxXQz8cT96vVL1dzZn+11ua+VWlBhK4I/7cd7LeQi5osWqaCb9c8+gMygvAgYWB1SNZdg/jMXYIkx6E7ZUblE1Fq0x6U1KBeRZz1mVo6Fipvbfjj5vHwYXByEIAvY174NRb7zqZTdVbEJ7WTvSYhrdo91IpBI5iZGIKJ/Jh9sb7A0w6U0qR5N/Oiuk9Vr/Yj+S6aTK0azTkb8EIABKEYQofXzkr9Z0NeFEGCP+EQDaafMpEwRBqfqTk5NEVzMVnEIinYDNaENVSZXa4axJs6MZAgS4I27OwM6wrCX+4vE4jh07hvvuu2/J5++77z68//771/zenTt3oq6uDnv37sVbb72VrRALhtovci/H1qP5ixvb18bHtnrkar/G0kbU2XM3xN2ov6Tqb+44Z/HkiNaqopn0V4/cJm9wcVC1NpKiKOLErFTtt821jRtNy5CTocO+YYQSIZWjoWKl5t8OX8yHdyakkRa31N6CGlvNNS8vCAL2NO6BzWjDYmwR709d+/Ux5RZfExFpk5ygaXe2X/uCtKxGeyPsRjviqThGfCNqh7M+ngEAl782EAHP2irjznvPQxRFVFurUVlSmbHwMqWrogs6QYf58DzcEbfa4ZCGyc+PLY4WCIJw7QtrTImhROnqxaq/zMpa4s/tdiOVSqGmZumLnpqaGszMzCz7PXV1dfjOd76D5557Dj/72c/Q1dWFvXv34u23377q7cRiMfj9/iVvxUZrG6RabD1KK+PG9sr42F6dTG+WBOIB9C7kvtpPtq1yGywGC3wxH6v+ckhLVdH5lvQvpLVRU2kTjDqjqu0+p0JTmA3PQi/ocaPrRlVi0DpXiQt1tjqIoogeT4/a4VARU+Nvh1K1l06gzlaHndU7V/V9FoMFe5v3QoCAs56zGPINZTlSWg2+Jio8hbQuKmbhRBizIWktmG9t7LRCEASl6i/vZ9hXbgBweXJDACpXPyrq0nXr5kptVfvJSgwlytzBcx5W/dHyRFFUEmb5+vwoxy0nMCkzstrqE8AVWWZRFK+aee7q6sLv/u7v4qabbsJtt92Gv//7v8eDDz6Ib3/721e9/m9961twOp3KW1NTU0bjzxda2iDVWutRWp1829hWAx/bK8vGZsnxWanSrsHegHp7fQajXR2j3ogdVTsAAMdmjxVk1R9Ptl9bviX9C2ltZNQZlRcBQ4vqbIofnz0OQNoQsBqtqsSQD+Sqvx5PT0E+TxJdzbHZY5gNz8KkN2Ffyz7ohNW/xG4sbcT2qu0AgMPjh1kxqwF8TVR4CmldVMxG/NJ6vMpaBbvJrnY4eaurvAsAMBYYQzgRVjmadbjrG1DaewJQ2n7u+caqr2I2PIvF2CIMOgM2lq0+YZhrWyqkNfZ57/n8b9FKq9NzCHjqduAvqqX3K8yuXIguIBAPQC/o0VjamKMgM0tOcE8GJtkCP4OylvhzuVzQ6/VXVPfNzc1dUQV4Lbfeeiv6+69eqv3Nb34TPp9PeRsfH7/umCkztNZ6lFYn3za21cDH9soyvVkSiAeUfva7a3Nf7Se7wXWDUvVXaIO1ebJ9ZfmW9C+0tVFHWQcAYNCX+3af7ogb44FxCBCUzXlaXkdZBywGC0KJEFu0UNGYCc3g6MxRAMBdjXeh1FS65uu4pe4WuEpciCajeHPsTdXaGpNEq6+J3BE3/qX3X/Dm2Js8XLFGhbYuKlby/8E2hzbX3/mi3FKOGmsNRFHM7242W/YDv/YjoGYrYDBL77/4DLD54VVfhVzt11HWoelW/o2lUovWWCqm+t8iyoGeQ8BPvgzM9gDJmPT+J1++ZvJPrpJrLG2EUXf1GdNaVm4uh8PkQEpMYTzIv9OZkrXEn8lkwq5du/D6668v+fzrr7+O22+/fdXXc+LECdTVXX2ek9lshsPhWPJG6tJa61FanXzb2FYDH9sry/RmyYm5E0iLadTb69Fgb8hEiNfl0qq/o7NHC2rDhSfbV5ZvSf9CWxs1O5ph0BkQiAcwH5nP6W2fmJNm+3WUdcBpdub0tvONQWfApopNAIAz7jMqR0OUffFUHK+Pvg4RIrrKu7Cx/PqqBQw6A/a17INe0GM8MI5P3J9kOFJaCy2+JlqMLuLFwRexEF1A70IvE8RrVGjromKUSCUwEZgAcLEqhK5fV4VU9ZfXiT9ASv499h7wx3PS+zUk/eKpOAYXBwEAmyu02eZTphN0yhpbHoFCBezIX0KpYAWgVLYe+aurfot86LLV0Zrl4LJHEASl08+oj4dIMyWrrT4ff/xx/MM//AO+973v4dy5czh48CDGxsbw6KOPApBOXv3mb/6mcvknn3wSzz//PPr7+3H27Fl885vfxHPPPYff//3fz2aY12e2B5g+pXYUmqWl1qO0Ovm2sa0WPravLZObJcF4UDmFd3PtzRmJbz22ubbBrDcXXNWfVk+2awmT/uq6tN2n/AI9F3wxHwa8AwCAm2puytnt5rOtlVsBABOBCfhiPpWjKUwL0QWc954vqAMo+eqdiXcQiAfgMDlwZ+Od67quCksF7mi4AwDw4dSHcEfcmQiRroPWXhMF4gEcGjyEcDIMp9kJQRBw3nseb0+8zeQfFY3xwDhSYgoOkwOVlkq1w8l7G8o2QCfo4I64i/bvzcDiABLpBJxmJ+psVy820Qo5WTsRmIA/zjmlBc0zAODyv+8i4Fl+D6qQ5p/KictR/yjXOBmS1cTfF7/4RTz55JP4sz/7M+zYsQNvv/02Xn75ZbS0SA/E6elpjI2NKZePx+P42te+hhtvvBF33nkn3n33Xbz00kv4lV/5lWyGuXaBGeDcC0Dvy8DUSbWjIcoIbmxTJmRys+T4nDTbr85Wp2q1n8ykN2FH9Q4AhTXrT4sn27WISX91dTildp9DvqGcvQg4NX8KIkQ0lTbBVeLKyW3mO6fZiabSJogQlYMblDmTwUk8e/5ZdI92s92fyvq9/ejz9kGAgL3NezPSImxr5Va0OFqQElPoHu3mHB+VaOk1UTgRxqHBQwgmgnCanfjChi9gb/NeCBBw1nMWH0x/wI0xKgrDfulAYquzFYIgrHBpWonFYFEqJ/sW+tQNRiVy5dzmis158Zhymp1oLG2ECLFof2dFo3IDgMsfkwJQuXxnifHAOESIcJW48n7+ab29HkadEeFkOOedfgpVVhN/APB7v/d7GBkZQSwWw7Fjx/CZz3xG+doPfvADHD58WPn461//OgYGBhCJRLCwsIB33nkHn/vc57Id4trZa4CGCye/+14BJo+rGw9RhnBjm9YrU5sloUQI5zzSbD8tVPvJbnDdALPejMXYIgYWB9QOJyO0drKdaDktjhboBT18MV9OTiaHE2HlOYjVfmuz1SVV/Z1bOMfERQZNBCbw0tBLyn163nseb4y9weSfCgLxAI5MHAEA7KrZhTp7ZioFBEHA3U13o8RQgoXoAj6c/jAj13u57tFuHDh0ALt+tAsHDh3gTN9laOE1UTQZxQuDL8AX88FutGN/x35YjVZ0lnfiM03SnsrJuZM4Nnss57EBfBxR7qTFtNLGjgcTM6er/GK7z1Q6pXI0ubUQXcBMaAaCICgtNPOBHOs5zzmu/wrZXd+A0t4TgNL2c883lr24cjAij9t8yvQ6PZocTQCkzlS0fllP/BUkQQA27AOaLmxGn38VmFBnwU1EpDWZ2Cw5PnscKTGlmWo/mUlvwvaq7QAKp+pPSyfbia7GqL+k3acv++0+T7tPIyWmUG2tRr2tPuu3V0haHa2wGW2IJqMY8g2pHU5BGA+M4+Xhl5FMJ9HsaMa9LfdCEAT0e/vRPdpdEH+L8kVaTKN7tBvxVBw11hrsrt2d0eu3Gq24p/keAMAn859gzD+2wnesTfdoNw4ePoh+bz/i6Tj6vf04ePggkzYaE0/F8dLQS/BEPbAarHhkwyMoNZUqX99auRW3198OAPho5iN8Mp/buZB8HFEuzYRmEE1GYdab86IlY75oLm1GiaEEkWQE44FxtcPJqV6PVO3XUtoCq9GqcjSr1+5sh1lvRjARxGRgUu1wKFu27Ad+7UdAzVbAYJbef/GZZWdYptIpZf5pvrf5lMkJzBH/iKpxFAom/q6XIAAde4GmW6SP+18DJo6qGxMRUQEIJUJKi7jdtbs113rjhqobYNKb4I16czpvLJu0cLKdaCXtZe0ApDl/2WxtFk/Fcdp9GgBwU/VNmnsO0jqdoMOWyi0AgLPusypHk//G/eN4eUhK+rU4WnB/6/3YWL4R97feD52gw8DiAF4ffb3oTuur5eTcSUyHpmHUGbGvZR90QuZfTrc4WrDNtQ0A8ObYmwgnwhm77qdOPaVU9gNQKv6fPvV0xm6D1ieZTuKV4VcwG56FWW/Gwx0Pw2l2XnG5HdU7lK4Y706+q1Sp5wIfR5RLctVHq6M1K8+5xUqv02NjudQ6sM9bPK0jU+mU8vNurtyscjRrY9AZlN9ZzwJb6he0LfuBx94D/nhOer9M0g8ApkJTiKfisBqsqLZW5zjI7GgubYYAAe6IG8F4UO1w8h7/aq6HIAAd9wDNt0of978OjH+sbkxERHnuxNwJpMQUam21aLQ3qh3OFcx6s1L1d3T2aGHMVuk5BDx1O/AX1dL7nkNqR0R0hVZHq9Lu0xP1ZO12ejw9iKfiKDOXsaXUdZLnpUyHpuGJZO93VejG/GN4efhlpMQUWh2t+GzrZ2HQGQBI7c7k5N/g4iBeH2PyL9vmwnP45cwvAQB3Nt65bDImU26rvw3llnKEk2EcHj+csbXGiG9ESdbIRIgY9g1n5PppfVLpFF4deRWTwUkYdUY81P4QKksqr3r53TW7lTXp4fHDGPDmpg09H0eUK6IoLpnvR5klt44c9g0jmoyqHE1ujPpHEUlGYDVY87JCSj5cN+wbRiQZUTkaUpvcBrnZ0Vwwh1WtRitqbDUAgNHAqMrR5D8m/q7Dkn72L/wbdOuTQMtt0hcHuoGxX6obIBFRngolQkqFyM01N2t28XJj1Y2FU/XXcwj4yZeB2R4gGZPe/+TLTP6R5pj0JjSVSj3/hxaz00IymU7i1PwpAMDO6p2afQ7SOrvJjjaHlDQ962HV3/UY9Y8qSb82Z9uSpJ+s1dmKB9oegE7QYWhxiJV/WZRIJfD66OsQRREdZR3KbKRsMeqMuLflXugEHUb8I0onhPVqdbYqM31lAgTVDzlwXpzURvaNsTcw6h+FXtDjc+2fUza+rkYQBNxefzs2V2yGCBHdY93KJmA2afVxRIXHG/PCF/NBL+jRXNqsdjgFp9JSiUpLJdJiOv9f066SXCnXVdGVlxWkrhIXXCUupMU0znvPqx0OqUgUxYsV0QV2MEJOynPO3/rl37OcypbtZ3/kcXTrEkDrHdKFBt8ExrIzjJ2IqJCdnDuJlJhCjbUGjaXaq/aTFVTV35G/hDIwGoAySPrIX6kXE9FVdJR1AMjenL9+bz9CiRBsRpvSSoeuz9bKrQCA897zSKQSKkeTX0Z8I3hl+BWkxTTane24r+U+6HX6ZS/b4mjB59o+B72gx5BvCK+NvsbkXxa8N/UefDEfbEYb7mq8KyeHAlwlLtxad6ty+96od93X+dj2x5S2jACUdo2PbX9s3dd9vTgvTtq8OzJ+BAOLA9AJOtzfdv+qZ1wLgoC7mu7ChrINSItp/GL4F5gMZnf2kxYfR1SY5CrShtIGGPVGlaMpPIIgoKtCOsjSu9CrcjTZF4wHMe6X5hlursivNp+Xkqv+ej29+b0PQevijXnhj/uhE3RosjepHU5GyYm/icAEEmm+jlwPJv7W6Kr97D/5X0DbZ4DWT0sXHHwLGH1fxUiJiPJLOBFWKkNurtVutZ/sBpc0628huoAhX3aqj3LCMwDg8hcMIuDpVyMaomtqdUrzXbxRLxaiCxm9blEUcWLuBABge9X2K6qraG0aSxvhNDsRT8VxfpEnkldr2DeMX4z8Qkr6lbXj3pZ7r5r0kzU7mpXkn/z9yXQyRxEXviHfEHo8PRAgYG/zXlgMlpzd9vaq7WgsbUQyncxIRee+ln14Ys8T6CzvhElnQmd5J57c86Sqs32LfV6cKIp4f+p9nFs4BwEC9jXvW3P7OZ2gw97mvWhxtCAlpvDy0MuYDc1mKWJtPo6oMMnVHqwmzZ7O8k4IgoDZ8GxGDphoWe9CL0SIqLPVocxSpnY4121j+UboBT08UQ/mwnNqh0MqkSv8G+yFdzCi0lIJu9GOlJjCZCC7h5kKHRN/a7RiP/u2O6UEIAAMHQFG3s1xhERE+enk3Ekk00lUW6uVdn5aZjFYcKPrRgDA0Zk8rvqr3ADg8iSrAFSy2om0x6w3K88PmW5JNOwbxmJsESa9STlJS9dPEASl6u+s+2z+Pkfm0JBvCK+OvIq0mEZHWQfubV456SdrcjThc+1S8m/UP8rkX4aEEiEcHj8MANhevT3n3QgEQUo2mvVmuCNuZcbgeuxr2Ydn9z+LY18+hmf3P6t6sqbY58UdnT2qtJje07QHG8o3XNf16HV63Nd6HxrsDUikE3hx6EW4I+5MhrqE1h5HVHhCiRBmw1ICu9XRqm4wBcxqtCpr60JuHSmKolLVuLkyf6v9AOn1kNwFpRgqNWl5SpvPAnx+FARBaV+aixbmhYyJvzVaVT/71juA9rukfw+/I70REdFVhRNhnPGcAZAf1X4yedafJ+rJ3w2qu74Bpb0nAKXt555vqBcT0TXIL3QzOedPFEUcnzsO4GI1L61fV0UX9IIe7oh7xRPJxT7ja2jxYtJvQ9mGVVX6Xa6ptAkPtj8Ig86AMf8YXhl+JevJv0L+vYmiiDfH3kQ0GYWrxIVP1X5KlThsRhvubrobAHBq7hQmAhOqxJEtxTwv7uTcSXw88zEA4NMNn173ZrRRZ8QDbQ+gxlqDWCqGFwdfxGJ0MQOREuXeiH8EAFBjrYHNaFM3mAK3qXwTAKBvoa9gD2pNBifhj/th0pvQ4exQO5x121Qh/c76F/vZCrEIRZIRzIRmAAAtzrV1CcgXckJzxD+SF89LWn1NxMTfGq26n33L7UCH9AINI+8Cw28DefBAJSJSw8n5i9V++TS43WKw4AbXDQDyeNbflv3Ar/0IqNkKGMzS+y8+A2x+WO3IiJbV6miFIAjwRD0Za0k0FZrCXHgOekGv/J+m9SsxlGBDmVS9Ih/uWE6xz/gaXBzEq6OvQhRFbCzfiH0t+6ATru9lWmNpo5L8Gw+M45XhV7K2IVTov7dT86cwHhiHQWe4rkRsJrWXtWNL5RaIEPHG2BuIJqOqxZJpxTov7qznLN6fkkaD3FJ7C26sujEj12vSm/Bg+4OotFQinAzj0OAhBOKBjFw3US7Jhyrlqg/KnlZnK0x6E4KJYNZnhKpFrozbULahINoiNtgb4DA5EE/FM3oYkvLDmH8MIkRUWirhMDnUDicr6u31MOqMCCVCWe1gkAlafk3ExN8aramfffOtwIYLnx95Dxg+wuQfEdFlwokwzrilDeHdNbvzptpPtr1qO0x6E9wRN4b9eVr1t2U/8Nh7wB/PSe+Z9CMNsxgsaLRL7fYy1e7z+KxU7be5cjOsRmtGrpMkW11Su88B78BVkxXFPONrwDuA10ZfgyiK6Crvwt7mvded9JM12BvwUPtDMOqMWU3+FfLvzR1x48PpDwEAd9TfgXJLucoRSXE4zU6p/ejE4fw8bLSMYpwX1+/tx9vjbwMAdlTvwK6aXRm9fovBgoc7HobT7EQwEcShwUMIJ8IZvQ30HAKeuh34i2rpfc+hzF4/FbVEKqHMdSqG6l+1GXQG5aBW30KfytFkXiwVU14zbK7I7zafMkEQlKq/Hk+PytFQrsntLwu12g+QnpfkFvtyBbhWafk1ERN/12FN/eybbgE27JP+PfoBMPQWk39ERJc4NX8KyXQSVdYqtDjyb+FiMViwzbUNQJ7P+iPKI0q7T9/6T7jOh+cxHhiHIAjYXrV93de3rCLeIK2x1qDSUomUmEKfd/nNpGKd8dXv7cfrY69LSb+KLtzdfPe6k36yens9Hmx/EEadEROBCbw89HLGk3+F+ntLpBPoHu1GWkyj1dGqmZmfRr0R+5r3QRAEDC0OXfX/Uz4qpnlxw75hdI91Q4SIrZVbcVvdbVk59GY1WrG/Yz/sRjt8MR9eGHwhc5WiPYeAn3wZmO0BkjHp/U++XFR/2yi7xgJjSIkpOM1OlJvVP3hRDOQk0pBvCIlUYbWO7Pf2IyWmUGGpQLW1Wu1wMmZTxSYIEDAdmmZb5yKSSqcwFhgDUJjz/S4l7w9qfc6fll8TMfGXC003Axvvlf499ktg8E0m/4iIIPUmz+dqP9n2qu0w6oxwR9yaP41EVAjanG0QBAHuiBu+mG9d13Vi7gQAqfWP0+zMRHhLFfkGqSAIyuGIs+6zyx6OKMYZX30Lfege7YYoithUsQl3N2Uu6Sert9fjofaHYNKbMBmcxEtDL2V0M69Qf28fTn2IhegCrAYr9jTt0dTapMZWg1tqbwEAvDPxzrqf/yi3JgITeG1EqvDtLO/EZxo/k9XHV6mpFPs79sNqsMIT9eCloZcQT8XXf8VH/hLKTGgAyqzoI3+1/usmgrSJClxs707ZV2OtgdPsRCKdwKAvMx01tEKuiNtcubmgHk92kx1NjiYAwLmFcypHQ7kyHZpGPBVHiaGkoBLZy5ETm3PhOYQSIXWDuQYtvyZi4i9XGncDnfdJ/x7/CBh4g8k/Iip6p+ZPIZFOwFXiyuvTSiWGEmVj++OZj1n1R5RlJYYSNNgbAKyv3acv5lO+f2f1zozEdgVukGJj+UYYdUYsxhaXnR1TbDO++hb68ObYmxAhYnPF5qwk/WR19jol+TcVnMKLQy9mLPlXiL+3Uf8oTrtPAwDuab5Hk61/d1bvRJ2tTqlMTKVTaodEqzATmsErw68gJabQ5mzDPc335GQDusxShoc6HoJZb8ZseBavDL+CZDq5viv1DAC4fK0rAp7+9V0vEYC0mFYOUmph07RYCIKArvIuAIXV7tMdccMdcUMn6NBZ3ql2OBm3pULqSnDeex5pMa1yNJQLcvVbs6M5a68ftMJqtCrJTS1X/Wn5NVFhP0K0pmEX0HW/9O+Jj4GBbib/iKhoRZIRnJ6XNtd21+ZvtZ9sR/UOpepPy4sSokLR4ZTafa7nVPKp+VMQIaLZ0QxXiStToS3FDVKY9CZls+Ws5+wVXy+mGV+9C71K0m9L5ZacVJTV2mqV5N90aBovDr2YkaqfQvu9hRNhvDX2FgDgBtcNaHY0qxzR8nSCDvta9sGkN2E2PItjs8fUDolW4I64paR7OoGm0ibc23JvTjfrXCUuZe7nZHASr468ur6EceUGAJc/bwlA5cb1hEkEQKpmiaVisBgsqLXVqh1OUemskNZqU8EpBOIBlaPJDLnar83ZhhJDicrRZF6LowUlhhKEEqHl9yCKeNxAoZIPRuTzwfm1yId2n1p+TcTEX67V7wS6HgAEAZg4CvS/xuQfERWlT+Y/Uar92hz5f5qzxFCCra6tAICPZ1n1R5Rtbc42CBAwH56/rnZ34UQY5zxSW5ysVfsB3CC9QH5+HPINLduqpRhmfJ3znMNbY29BhIhtrm24q/GunB16qbXV4uH2h5XkX6Za/hXK700URbw1/hbCyTAqLBW4rf42tUO6plJTKe5qvAsAcGz2GKaD0ypHRFfjjXrxwuALiKfiqLXV4v7W+2HQGXIeR42tBp9r/xz0gh6j/lG8MfbG9VeH3PUNKNXrAJSq9j3fyEywVNTkmUgtjpaCr2bRGofJgXp7PUSIBVH1l0wn0e+VDtptrtiscjTZodfplUrN3oXepV8s8nEDhWgxughfzAedoENTaZPa4eSEnOAcD4yvv2NBFmn1NRH/iqqhfsfF5N/kceD8q0z+EVFRiSajSiutfJ7td7kdVTtg0BkwH55XBi4TUXZYjVbU2+sB4LoGZ3/i/gQpMYUaaw3qbfWZDu8ibpACkCpOam21EEVRSbgWkx5PD94av5j0u7Phzpz/7aux1WB/x/4llX+xVCynMWjVWc9ZjPpHoRf02NeyT5XEzFptLN+IrvIuiBDRPdbN36UG+eN+HBo8hEgyAleJCw+2Pwij3qhaPA32Btzfdj90gg4DiwM4Mn7k+g6qbdkP/NqPgJqtgMEsvf/iM8DmhzMfNBUVURSV+X6FcDA0H22q2AQA6PP25f1B1iHfEGKpGOxGOxpLG9UOJ2s2VUq/sxH/CMKJ8MUvcNxAwZGr/RrsDTDpTeoGkyOuEhdsRhuS6eSyIyPo2pj4U0vddqDrc1Lyb+oE0PcKk39EVDQ+mf8E8VQclZbKgprdYDVasa2Ss/6IcqWjTGr3ObA4sKbvi6fiOOM+AwDYWbMzuwkYbpAqtlZKVX89np6imkNy1nMWh8cPA5BaSKqR9JNVW6vxSMcjMOvNmAnN4MVBJv8Wogt4b/I9AMCt9bdmr+1vFtzZeCccJgcC8QDenXhX7XDoEqFECC8MvoBQIoQycxkeapfm7KmtxdGCfc37IEDAuYVzeH/q/etP/j32HvDHc9L7IvybRpm3EF2AP+6HXtAXTTWL1rQ722HQGeCL+TAbnlU7nHWRK+A2VWwq6OrRCkuFcriuz3tJpSbHDaxOHrVDlRN/cvvLYiAIglL1p+V2n1pVuM98+aDuRmDTQ1Lyb/oU0Pcyk39EVPCiySg+cX8CoDBm+11uR7VU9TcXnmPVH1GWtTvbIUDAXHgO/rh/1d/X4+lBPBVHmbksNyfKuUEKQErUWgwWBBPBonnhdsZ9BkfGjwAAbqy6EZ9u+LTqf/eqrFXY37EfZr0Zs+FZvDD4QtEm/5LpJLpHu5ESU2gqbcKNrhvVDmlNTHoT9jbvhQABfd4+nPeeX/mb8miDK19FkhG8MPgCfDEfHCYH9nfsh9VoVTssxYbyDdjTtAeANOv26OxRdQMiukDu4NBY2qhqdWwxM+lNyhztfG736Yv5MBGYgABBqYgrZHKlZo+n5+JhDq2OG9DSOiSP2qFGk1FMh6TW7sWU+AMu/rwjvhEerl8jJv7UVrtN2vwRBGD6E6D3JSBdPCegiaj4yNV+FZYKtDvb1Q4n46xGq1LVcnTmKBcmRFlkNVpRa6sFAAwtDq3qe5LpJE7NnwIA3FRzk+pJmGJi0BmwqVzamDjrOatyNNl3ev403p54G4DUCvqO+js083irslbhkQ2PwGKwYC48h0ODhxBNRtUOK+c+mvkI7ogbFoMF9zTfo5nfz1rU2euwq2YXAODtibevfQgijza48lU8FcdLQy9hIboAm9GGhzseht1kVzusK2yu3IxPN3wagNSl4uTcSXUDIsLFapZC6giTjzorOgEA/Yv9mp6pdS1y0rKhtAEOk0PlaLJvQ9kGGHVG+GI+zIRmpE9qcdyA1tYhedQOdTwwDlEUUW4ph9PsVDucnGoobYBBZ0AwEYQn6lE7nLzCxJ8W1GwFtjwCCDpg5jTQ+yKTf0RUkGKp2MVqvwKa7Xe5ndU7oRf0mA3PYjwwrnY4RAVNbvc55LuY+Ose7caBQwew60e7cODQAXSPditfO+89j1AiBJvRho1lKp94LUJbKrcAAMb94/DFfCpHkz2fzH+CdybfASBVgt9Wf5vm/ua5SlzY37EfFoMF8+H5okv+jQfGlWTHnqY9sBlt6ga0Drtrd6PGWoN4Ko43Rt+4eivdPNrgykeJdAIvDb2EufAcLAYLHu54WNObczdW3Yhbam8BALw/9X5RHMgg7QolQpgLz0HAxbZupI4GewNsRhviqbgyczGfpMU0zi1I86Q3V2xWOZrcMOlNymuinoUe6ZNaHDegtXVIHrVDlQ9GFOPzo1FnRKNdmtOZj89JamLiTyuqN19M/s2eBXpfYPKPiArOpdV+8sK0EFmNVmxzSbP+js6y6o8om+TK4ZnQDILxILpHu3Hw8EH0e/sRT8fR7+3HwcMH0T3ajbSYxom5EwCA7VXbodfp1Qy9KJVZytBU2gQRIno8PWqHkxUn507i3Ulp3trO6p24rU57ST+Zq8SFRzoeQYmhBO6IG4cGDyGSjKgdVtZFk1G8OfYmAGn2ZL53INAJOuxr2Qejzojp0LTyPHeFPNrgyjepdAqvjryK6dA0THoTHm5/GBWWCrXDWtGuml3YUb0DAPD2+Nvo9/KxQOqQN3NrbDWaao1bjHSCDl0VXQCwdGZcnpgITCCUCMGsNxdV9ah8uG5wcRDxVPzCJzU2bkBr6xCttkO9TFpMY8wvjZEpxsQfALQ4pXafxTIuIlOY+NOS6k3A1s9fSP71AOcOMflHRAUjloop7fUKudpPJlf9zYRmMBGYUDscooJlN9kvtvv0DeGpU09BgADxwotKESIECHj61NMY9g3DF/PBrDcrLXkp9+T7vnehN29bSF3NybmTeH/qfQDShvqtdbdq/u9dZUklHtnwCKwGK9wRN14YfKGgk3+iKOLwxGGEEiE4zU7cXn+72iFlhNPsxJ2NdwKQWpjOhmavvFCebHDlm7SYxutjr2PMPwaDzoAH2x5ElbVK7bBWRRAE3FZ3G7ZWboUIEd1j3cqcNaJckjs3FOumttZ0lUuJv7HAGMKJsMrRrI1c8dZZ3gmDzqByNLlTY61BmbkMyXQS/YsaPcShtXWIFtuhLmMmNINYKgaz3owaW43a4aiipVRK/M2F5/LuOUlNTPxpTVUXsPULgE4PzJ0Dep4H0im1oyIiWje52q/cUl7Q1X4yq9GKra4Ls/5Y9UeUVfJzyuDioDT0+7KTpCJEDPuGlSqYG1w3wKg35jxOkrQ6W2Ez2hBJRgpqg/nE3Akl6be7Zjduqb1F80k/WYWlAvs37FeSf4cGDhXsi+rehV4MLQ5BEATc23JvQT0XdJV3oaOsA6IoJXASqcTSC+TJBlc+EUURb42/haHFIegEHR5ofQB19jq1w1oTQRDwmcbPoLO8E6Io4rWR13hojXIqnopjMjgJgPP9tKLcUo4aaw1EUcR573m1w1m1cCKsVI9uriyONp8yQRCUqr9eT6/K0VyF1tYhWmyHugy5zWeLowU6oThTOXaTHa4SF0SIGAuMqR1O3ijOR4vWVXUCW39FSv7N9zH5R0R5L56K45P5wp/tdzm56m86NI2JIDdQiLLl0nafzY5mCJedJBUgoLG0EXPhORh0BqUVL6lDJ+iUjYkz7jMqR5MZx2eP44OpDwAAN9fejFvq8ifpJ6uwVOCRDY/AZrTBE/Xg0GDhJf98MZ/ShvVTtZ9CtbVa5YgySxAE3NV4F2xG25KfVZEnG1z5QhRFvDv5LvoW+iAIAu5ruQ9Njia1w7ougiDgnuZ70OZsQ0pM4ZXhVzATmlE7LCoSY/4xpMU0nGYnyi3laodDF8jtPvMp8Xfeex5pMY0qaxVcJS61w8m5zvJOCIKA2fAsPBGP2uFcSYvrEK21Q12GnMwu9opo+WAI5/ytHhN/WuXaAGw7AOgMwPx54OzPmfwrNOkU4J8CFseBVGG1uSK63CfznyCWiqHMXFYU1X4ym9GmbG4fnWHVH1G2lJpKpVPJEPFw+8NKe08AStvP2+puAwBsqtjE2TEasLliMwRBwHRoWpsbEwDQcwh46nbgL6ql9z2Hlr3Y0Zmj+HD6QwBS0u/m2ptzGWVGlVvK8UiHlPxbiC7gXwf/tWCSf6l0Cq+Pvo5EOoF6e70y16zQWAwW7G3eCwECzi2cw9Di0NIL5MEGV774aOYjnHafBgDc03QP2svyf1bkvS33oqm0CYl0Ai8OvQh3xK12WFQE5GoWVvtpy4ayDdAJOrgj7rx4LhBFEb0LUqXb5oriqvaTWY1WtDmk/0fyfaE5XIesiS/mw2JsEYIg5O3hokxpcUjtPscD4wU3LiJbmPjTssoOYNuvSMk/dz9w5mdMEOWzdEpK8o2+D5z6F+DdJ4Bj/wc48Qzw7l9L74eOAAvDQDKudrREGRNPxS/O9qvdXXStCXZW74RO0GE6NK20sCGizJMPFVSUVOCJPU+gs7wTJp0JneWd+NPb/xQOswOCIBTsZn++sZvsyqnVHk+PusEsp+cQ8JMvS3O3kzHp/U++fEXy7+OZj/HRzEcAgE/VfSqvk36yMksZPr/h87AZbfBGvXh+4HmEEiG1w1q3Y7PHMBeeg0lvwt7mvQW9HmksbcT26u0AgLfG3yqI35/WHJ89jmOzxwAAn2n8jFIZk+8MOgPub70ftbZaxFNxvDD4ArxRr9phUQFLpVMXE38OJv60xGKwoNXZCgDoW+hTN5hVmA3PYiG6AL2gx8by4p1du6liEwCgz9uHlAYLSLpHu3Hg0AHs+tEuHDh0AN2j3WqHpGny82O9rR5mvVndYFRWVVIFm9GGRDqB6eC02uHkhcJ9tVMoKjuAGy5U/nkGgLNM/uWNVBLwjgLD7wAn/wl457LkXioBGC2AyXb1pODgW4BnUNpwIspTp92nlWq/DWUb1A4n5+wm+8Wqv9mjKkdDVLjkU+LTwWncXn87nt3/LI59+Rie3f8sbEYbAGBj2UY4TA41w6RLbKuUWq72efuunEWmtiN/CWXuCABlHsmRv5I+EkV8PPMxPp75GABwa92t2FWzS41Is8JpduLzGz4Pu9GOxdhi3if/poPTSpJmT+MelJpKVY4o+z5V+ym4SlyIpWJ4Y+wNdh3IoDPuM0qV7231txVc+2ij3ogH2x+Eq8SFSDKCQ4OH4I/71Q6LCtR0aBrxVBwlhhLU2GrUDocu01V+sd2nFpNIl5Ir3DrKOoo6QdLsaIbNaEM0GVWSRlrRPdqNg4cPot/bj3g6jn5vPw4ePsjk3zWM+kcBXKx2K2aCIKC5tBkANPfY1iom/vJBRTtww78B9AYpCXTmOSb/tCgZlxJ6Q0cuVvGd/Cdg5F0pAZhOAiYrUNUFbLwX2P0fgDu+Ctz+fwOf+o9A1wNSf2uLAxDTUhvQsQ+BT34iJQKPfh8YeEOq/kxE1P5piVYlkUoo1X67anYV9On6a7mp+iboBB2mglOs+stTvzgzjfuffBtdf/wK7n/ybfziDE+YaY3T7ESVtQoiRAz7hpXP+2I+pdUdq/20pbG0EU6zE/FUHP2L/WqHs5RnABeTfjIR8PRfkfS7rf423FRzU85DzDan2YlHNjwCu9EOX8yH5weeRzAeVDusa1umPWssFUP3WDdEiOiq6MKG8uI4hKTX6XFvy70w6AyYCEwo6zFan76FPrw98TYAaW27s3qnyhFlh1lvxkPtD6HMXIZQIoQXBl/I6+Q/aZe8ZmtxtBTta0Utay5tRomhBJFkBOOBcbXDuapEKoGBxQEAwObK4mzzKdMJOqUKXWtdNZ469ZQyhgGAMp7h6VNPqxyZNsVSMWX/iK2QJXIV8oh/hIfaVoF/VfNFRRtww69Jyb+FIeDMs1LFGKknGZMSsYNvSdV57z4hVeuNvi9V76VTUjVf9Wag8z7glt8Fbv8DqX1r426gtAYQBOnNWgHU75B6Xd/6e8CtjwKbHgTqbgRKygBRBAIzwPhHwOlngff+Bvj4u0D/68B8HxAvjNkrVHhOu08jmozCaXYWdbuNJVV/M6z6yze/ODONR585jr6ZAGLJNPpmAnj0meNM/mlQh1Nq9znoG1Q+d3LuJESIaHY0w1XiUis0WoYgCNhauRUAcNZzVuVoLlO5AbgwJ/IiAWLlBvxy5pdKBfft9bcX7MY/cKHyb+PnUWoqhS/mw78O/qt2k39Xac/6zgd/jUA8AIfJgTsb7lQ7ypwqt5Tjjvo7AAAfTn+YFzOatGxocQhvjr8JALjBdQNuqb1F5Yiyy2q0Yn/HfjhMDvhiPrww+AIiSR5ApcwRRZHz/TROr7vYNrPPq912n4O+QcRTcThMDtTb6tUOR3XyjMOJwAQC8YDK0Vw04htRkn6yyw9t0kXj/nGIoogycxmcZqfa4WhCo70RekGPQDyAheiC2uFoHhN/+aS8Bbjxi4DeKFWWnf4pk3+5lIhI1XYDb0jVd+8+IVXjjX0oVeeJaalar2arVL33qf8oVfNt/TzQsAuwuaQk30oEASgpl5J+mx4Ebn0MuO33pIG39TukJKEoAsE5YOKoNPvxvb8BPvrfwPlXpU2OmEY3ZKioJFIJnJw/CQDYXVN8s/0uJ8/6mwxOYio4pXY4tAZPdvdf2fBPAP7mDY1VKJEy528yOIlIMoJwIqy0/bmpuvAqsgpBV0UX9IIe8+F5zIZm1Q7noru+AaW9JwBcOJ384c5fxfHZ4wCAOxruKIoqUofJgUc2PKJs/j8/8LymNpEUy7RnPW804XzvsxAEAfta9sGkN6kYoDq2VG5Bq6MVaTGN10dfRyLN14/XY9w/jtdGX4MoSpWjn274NITVvLbLc3aTHQ93PAyb0YaF6AJeHHoR8RTn0VNmeKIeBOIBGHQGNJY2qh0OXYXc7nPYN4xoMqpyNMs75zkHQJpvVwzPzStxmp2ot9dDhKi8FtKCVmcrhMsO1gkQmPi/CrnNpzwXnaR25A2lDQAu3j90dVnfhf37v/97tLW1wWKxYNeuXXjnnXeuefkjR45g165dsFgsaG9vx9NPs9x3ibLmi8k/76iUeEpy4Z0V8bBUTdf/ulRd997fSNV24x9J1XeiKFXjKQm6R6VqvS37LyboMrXgsDiB2m3LJBRvkhKKABByA5PHgZ5/Bd7/H8Av/xfQ9wowcwaIciYD5d4ZzxlW+12i1FSqnLzjrL/8MuwOXdnwTwSG5tnySmucZidcJS6IonRy9NT8KaTEFGpttaiz1akdHi2jxFCiJGw1VfW3ZT/waz+SDnQZzBBrtuCD+/8EJyzSzJhPN3wa26u2qxxk7lya/PPH/fjXgX/V3syvy9qz+nUC3rZagPACdtfsRq2tVr3YVCQIAvY07YHVYIU36sUHUx+oHVLemQ5O45WRV5AW02gva8fdTXcX1cay0+zEwx0Pw2KwYD48j5eGXmICmTJCrvJptDfCqDOqHA1djavEhUpLJdJiGoOLgyt/Q455o15Mh6YhQMCmik1qh6MZ8t5D70KvZloiPrb9MaW9JwCl7edj2x9TLSatjvRIi2mMBi7M93Nyvt+l5EQoE38ry2ri78c//jG++tWv4o/+6I9w4sQJ3HnnnXjggQcwNja27OWHh4fxuc99DnfeeSdOnDiBP/zDP8Qf/MEf4LnnnstmmPmnrAnY/uuAwQQsjgGnmfzLiFhQqpY7/6pUPffe30jVdBNHpeo6UQSslVJSb/PDwG3/P6kaT2nJWZ65RN9KzPYLLUQ/K7UQvUNuIXozYK+W4ggvAFMngXMvAB/8T+DDp4BzLwLTnwARr/TzEGVJIpXAybmTAIp7tt/ldtZIVX8TgQlMB7WxoKSVtblsVzb8E4D2Kpsq8dC1yUmk3oVeJZG0s3pnUW3S5pttrm0AgIHFAW2dJN+yH3jsPYh/NIv39/83nLRYAAB3NtyJG6tuVDm43Cs1leLzGz4Pp9mpzeTfJe1Z0wDesFoRFwTUWlzYVbNL1dDUZjVacU/zPQCAM+4z3ChZg/nwPF4afgnJdBJNpU24t/neolzXVlgq8FD7QzDpTZgOTeMXw79AKp1SOyzKc3Lij9U+2iYIgjIzTkvVY7JzC1K1X5OjCXaTXeVotKO9rB0mvQmBeAATwQm1wwEA7GvZhyf2PIHO8k6YdCZ0lnfiyT1PYm/LXlXi+cWZafz+v/4QYyV/DmPHH2Ks5M/x+//6Q00k/2ZDs4gmozDpTTzAepkWh5QInQnNsAX5CrK6Yv3rv/5rfOUrX8Hv/M7vYPPmzXjyySfR1NSEp556atnLP/3002hubsaTTz6JzZs343d+53fwH/7Df8C3v/3tbIaZn5yNwI1y8m8c+OTH0iwLWr2oX6qG63tFqo57/39I1XKTx6XqOUCqpmu4Saquu/3/Bj71f0lVd7XbpLaeWmGyAVVdwMZ9wM1fAe74f4Ab/g3QdAvgqAMEHRBZBGZOA70vAR8+DXz499IslKmTUpKQiUDKoLOes4gkI3CYHOgs71Q7HM1wmBzKKcSPZz9WORpara/u26i098SF96II/D97+djWInnO30xoBvFUHOWWcrZH0bgaaw0qLZVIppOamx8jiiLem3oPp+ZPAQDuarwLN1TdoHJU6rGb7Hik4xE4zU4E4gE83/88fDGf2mFJLmnPetxixrTBAJMoYu9t/29RJmou1+xoxg0u6bH75tibCCc4I3wlC9EFvDD0AuKpOOpsdbi/7X7odXq1w1JNtbUaD7Y9CIPOgPHAOF4fex1pMa12WJSnAvEA3BE3BAjKJi5pV2d5JwQImA3PYjG6qHY4ilQ6hfML5wEAWyq2qByNthh1RmwskzovaSlhu69lH57d/yyOffkYnt3/rGpJPwD41pGfoqTxGejMMxB0SejMMyhpfAZ/+fZPVYtJJs8/bXG0cB17mVJTqdTlByLG/MsXl5HEkK0rjsfjOHbsGL7xjW8s+fx9992H999/f9nv+eCDD3Dfffct+dxnP/tZfPe730UikYDRqI3S/3Q6jdcGTqodBgBA79gG++gbEBbnkJwfR5wb7NcmJmEIz8MQnoMufuUcvJSlHElrDZK2GiStNRANUjsnLEaAxfM5DjYTyoHScgjWOPSReRhCszCE52CIuIHFOWBmQLmkaChB0laDtK0aLmcpDHpWRlyNJ5SAs7wKBntl7qo880nUj7f7X4MgxLGrtgm62R514xk6DBz9HuAbB8pbgTv//8ANvwqotHFzU81NOOc5h575YdRZj8NhVrlqLBEBEmGpvXFKWwdI5gIxJJLqbyiZAHz9dj/e6ffAE4zC5NiB37/3Zty/rTjbxi1n7NzHSIg6JK1VSJucKj836pCI2+CLL6CsxKiNar94GAjNaWoGrwgR074oogn1/48BQFlIxIBvAh/4XoGlXhunkgFgKjKPc8EJwGDGXS17sbVS3U2lUCyJcW9Y9fNam+334Ij/FcwHPPjfJ36MfW13oMSYtZeWq1PbBTz03xD/+H/j4/g8YKvEnTv/I5w3/Jq6cQGY9kXgLDHCalL3Prqt/jZMBiex4J9A9+n/g67SZlXjuZQnmEDQ4ETa7FQ7FACAKKZxeuEYIskwKiwudFjvxMBsBIB6J8sFAWgst8JuVu9xVGevwwOtD+ClgecxNHMCb3nHcc+mX4NQoo3fm1b0zA/hzNQ8bmws087LxbAHMFoBY4nakQAARn1TcAdjqLTUYNSdBKB+BbkhOAW9Vg6zXJA2WJBwtGpi30GfdmE6NIEj4x9iU6U2qjR94XmEfaMo0ZnQEgkC0TNqh4T5YAyBaFLtMAAA1ngUXvcEjs1PQzfru6KLTbHzm34CiEsP+EIEfMafoK+/Q7W4PvScwf8ZeQkLcR8aS6ohLI5jX80tqsUDAPFUGpOBNKKOVqm4RGVCogrzgUl8OPGJ2qFcFJoHwguorN0Ol0bas2Ztxeh2u5FKpVBTU7Pk8zU1NZiZmVn2e2ZmZpa9fDKZhNvtRl3dlaWtsVgMsdjFjUq/P/uLhbQo4h9Pv5T121ktU0qHqvAYhMVhYOojtcPJK3GdFTFDKWIGO+J6O9JhAQjPAe45AKfVDi9rBNEBcyoEUzIIcyoAUyoEASLglhI0drMB5SXaSLRrTSiewkI4DrvJgHK7BTCXLn3Tm9QOMbfSKSAWuOTNj3AkCk84jlpzCTqTJkBQcfE93wf0/Pzix+7zwM//o9TSt37nlb8/k/3Cvx1SS105+Z9BDpMDTn0L3p88hjl/NzZUZ6kdiZiW2kCnYhfeX/rvSz6n0ZPa4UQKnpC22li3Vkhv//bT9+LGjdpN+qmxNpo8/ipw4fRvSjAibKpA2Ci9hUyViBgrkNLl7vlxOubAdGwMDc5ybNyZwxmj6TQQWQCCs1Kb8NC89D4WyF0Mq+QNxzE6o524SpBGVBjBqJDGK/MnYNKr/4JSJggC7ippwJbQq0DfG4BJ/rtxyd8M5e+HXfq6Ljvxv/TJNCYXtdHSJpHehYnIW4imZjGy8AK21KvYDSOVBMIX/r/VbgCwARssLnSW1AELQ0BZa9Z+JyuZ8Ibx06MTqC+z4Is3q5hoiwVgmDuHfYtuPDdzAhNiGlpJsUeTacwHpb9bcb0dIWMlwsZyiIL61XUleiesJTvwxuKC2qEAAKpKzfh3t+ZwMykekrrhhN1AyAOE3WgKuXFfaAqvhsfQJ4pwmMtw85ZfzV1Ma5TrdVE6LeJ/f9QNd3QWvQE7XPbMv55Ys3gImDoh/Z2q08Z82rNTPvgjSYTNHfiFd/k9wlwqiS9g+8yzaoexrD7XffBaW9UOA95EOYYjvRj3HsN40yB0GkhGYv48EJxFl7kK+qj6+7TBWBKDk74r5sOrRYQInTAOnxBH90Th7nFer5TRhytnekiff+PMM6rENJTw4dXIuPLxWHgGB08+gSdcd2KftUmVmESIGJoJwBtOYLj8DsyWblUljkuFUhYMhIIYmOvFuyPn4bKb4bKbUWJSae043wcMvgnE/LhF74Drnv8ijY1QWdaPil1+wloUxWueul7u8st9Xvatb30Lf/qnf7rOKNeuvUw7pyMBwOBoRGlwGIKojVMl2iUgYSxF3FiGuKkMJp0B7AAOQEzBlPBDF1lAKuyFRQc0lWmolamGjC2EkdBHYU+F0aQ3A8kUkFwEQovSBYw2oKQMsJZLcx/NDk2chskIUQQSIWlGZGRR2tyOBoBLl7U6C2ZSApL6CjTpm6GvbFcrWsmJH0FayV229B55F3B1Si+CA9d4oWkwXbmhe/kmr8m25hOYTmxBuXEO6XgSDfbqtb1oEkUgnZQq9ZLRi28J+d8R6d+pFZJmghEwXEjw602A0QLoTJo4TQoAY94w7IY4TAYdTAZt/R+ymrX9lyPnayNRhMnVCl1oDuaYB4KYRiUWgNQCkAJwYWRb3ORAzFyJqLkSMbMLUXMlEsbsVAdWJ29AfCaEMrENQrY628tVfMELb6E5aVM0fZW1WEkZYCnTzP+xmagPPosTVpNevRdIlzAA6AqXYjTphsNkQVWpRe2QAEh/QTYbHeiAWXreTSUv/B30XuObBOlvw+WHSZQDJg7p34a1JcPjyTSmfdJ/qKYKK3SqP5SsaEw+iI9mfolEMoxaaw2MuUzYimkpue6bkP6WywdZjA6UGK2401gJYfYMMHvm4nzs6q1AaW1O/x8OzEmVvlOLUcwHYqgqzWESIBmTNiJmzwKLo4AowgVgr7UZ5/Siat0PLje2EEapPgqHGIBRJwCpBaRFPyKWGoStDUgYSlV57jTpStBu3wGz3prz217OhDeC+UAMnmAMlZlMJomitCa+JLmnJPviy7eEbTc6cU/ZZhxL+bHpwuwvrcr1ukinE7CttgnHJ4BwyIDGWpf6nQc8g4DBDqREwFoL6NU95LsQjgMJA1wmC3bWboVRp35ytNw7gLISI5IGK6LmSrXDAQCYEj6Y4n5sMHsxp3LHAQBoEjciNutGMOGHPuVEY7kGnhtn+2EylWGH6wZpbaWy/jEvFi2lmllfA8COdDX6k9MQNZKOXAjGMe4NI5pIw2LUoancigq7OgfoS0PT8Ivhpck/EajQ29FUps5e1s+nDy/5WGpiDzwd7MO+xs+oERJmfBFMJgC7sIBO/RQslTerEselRLEEppIdmAzMIplKwx8A/AHAUWJCfZkFdQ4LzMYc/R+cPA6c+onyYWlwBvjJl4Ff+5Hqyb+sJf5cLhf0ev0V1X1zc3NXVPXJamtrl728wWBAZeXyf3i/+c1v4vHHH1c+9vv9aGrKbgbcoNfjT/f++6zeBpEaIvEUnj4yCAC499MdsOTqSTKPfPfdYcQjCaTENO7Z7YQtOgP4p6Q3eTZkCkAgJL3pDUBpPeCol2ZzOuqlzcB8kIwDgQs/m28S8E9Km54AAAHQVwK2SmmB7WwAHA2Aox7fPxWBOSZiFEBwU7uq7Yjwwh/giqQfAER9wO7fVqoUEQtK/44HL34uGb/w5r74u12OoFt+Q1dJEpZK1R/6i/fDQkhAW8ntAIBbq5tR47iwwZ1OS8lVpYIyeCG+S2MLAKnEMoHoAFgBvRWQ/+vq9MtUMy5T4aiRjT+ZKIr47rvDsJYm8Ss3NaClMk/+z2hEztdGgoBdn/130r/TKWlurJwQC81L1W9Ki0uP9JY+L3VLixsBWxVgrwHsVYCtGrBXr7vaNp0WsXhESpS4gzFUO9aRRFryM80Cwflrt+3UG6WfwVYt/Uz2GulnzEIF8XocDY/AY4jj4e112FCt/kbJL85M4zsv92JyMYIahxn/6aEtuH+bxgbZpxKXPR9f8jfj0s+J6QtfC67ucMmS52j70s9dcrhk1h9FWhRRajHg3+xqzNEPvbLkB0Z4gnFsc9ahsyYHj6WQB5j5BJg7c+H/oRmwtkizuWtvBGq2Svebb0JKeM2fky43/rH0Zq0EarZIlyspz3q4o56LiZOeaT/uKq3K7g2mU1KV4+wZwD2w9DCCswGo3ooN1ZuwQSPr0WgihX94ZwglDhG/uqMCNeHzwPQp6ZAZACAI2K1A3Q7p96aRNoVqeP7EJIbdIQzOh64v8SeK0nNVyC21fVQq+dyXrPGXUVIGWF3Sut/qkv6mWSvRZTChI52EQadym98VqLFn9Bs33oeofxjxZBpbnfXoqFL50FjkZ4Dtwu+4ahdQrm4LsudPTGKDNYQbGpzYt2X5/cGcO3cC0DmA1k8DbXeqHY1k6iTQ9wpQngZ2aOPvftuoA2+fn4clZsSDba3QqXkKKRkDxs5Icxl2/AZgUjcR6QnGcHhuFEIJ8OVbWzJ7QGOdHlA7gAt+cWYajz5zXDmaLQAQ54Cn/91Nqqz7t4524+Dhg4AoAIKovP+jO/8c97buy3k8APCff7Tris+JAIbji8D2X895PNFECi98MIKUy4OHoi+gqSyE3TdWr/kAY3Y0IZ5MY8gdRO90AKOeMNKiiKQPmPADzRVWdNWWYkO1HWZDFve8XvsWEIxg6d6jABz5q8JN/JlMJuzatQuvv/46vvCFLyiff/311/HII48s+z233XYbXnjhhSWfe+2117B79+6rzvczm80wm7XzZEqUz0pMepRbjfCGE5j2RdHm0saGgFYEogn4IxcSLoIOUwk7NtbvlFpGAtILZjkJ6J+U3idjwOKY9CYrKbuQJJMSZbBXq594EUWpesE/eTHJF5rHFUOEdHrppLyj/uLPYLlYHRqMJbEYG1I+nvFF1N1QrtwAzPbgij/Aro3Sz1F6jXaNyfjyG7qXJuLiIWmDN+qX3q7FWAKY7UgY7HCMBmHRWWEQYwge+xA1jgubxPL1rYbRsnyi0XRJYs9YopnqorVYDCcQiCah1wmoLyveDb7rperaSKe/kOyqkjbVZUp13PzFBFrIIyVS5OfNS1mc0nOjkkCrljboV/l4fq1nBv/44ShmAzE8f3IS33xg0+peTMZDS1t0BmelzdF0avnLl5QvTVjaqtYUp1rC8SQ8QakquKFM/dPal28CTC1G8egzx1XbBLgqvRGwVkhvVyNXzigJQv/SJKH8udUeLtHpLzzX2xHzC2jxplBdXQMES6VElwYeay2VNniCcYy4Q9lL/CUiwNw5YOb00ucLowWo2QbU3iAl2S+9P8qapLeN9y5NhIU9wPA70pujXvr+6k1ZOZjlCyewcEnb6t5pPz69wQV9pjdKRVGaZTzbIyU6E9GLX5MTndVbrv3YVcnAXBCJlIhKuwnVrkpAuB1ovk2qUJw+daGN2xzQ/5rUQqmqS2pVWNasicd/Lm2otmPYHcLAXBC3tK3wPBQLLF/Bd+lj41KCIFWl21zSY8Z2IcFXUnHNzT2tJ/0AddZFFqMe2xvL8PHIAj4eXkC7y6Ze1Z8oSq/tZIFpVRN/c4Eoht0hCAKwuzX7hy9WzT8tvXfUqxvHpewXkqLBWen3qIHnvBsanPhoeAGL4QT654LoqlXxtX5wTnpvLlU96QcAH49I3SA6quyaSvppyZPd/Uv6MYmQHtZ/80a/Kmv+fS378MSeJ/D0qacx7BtGqb4e2+2/hir97pzHImt1tqLf27+kQlOAgDanOnM13+13IxRLocJZhfqyOiDmk9ZorhyO07gGk0GHTbUObKp1IBxP4vxsEL3Tfkz7ohj1hDHqCeOt3jm0V9nRVVuK1kpb5tfhngFcWXAgAp7+zN7OdcjqKu3xxx/Hl7/8ZezevRu33XYbvvOd72BsbAyPPvooAOnk1eTkJH74wx8CAB599FH83d/9HR5//HH87u/+Lj744AN897vfxT//8z9nM0wiukRdWYmU+FuMMPF3mRnf0hfKk4sRbLx0g8tYAlR2SG+AtDgPe6QT53IyMOS+0CZzUTqFDlyoCqxbmgzMdivBZEx60eebvBjbcid9LY4LSb4L1Yr2miWVa5ebvmzu0LQvqm7i765vSCX2S8+UAXu+sfL3GkyA4UJV49WkUxc2cYPX2OQNSK3hEhEgEUE4MoXq4MUkYSxpBHBJa11BJ20+Llf5sWSeZOHO4RxbkKoj6pyW3LaNo+wxWQFTK1DeevFzyjy8uaUtM2MBqSo36gPclyyW9YZLEmzVFxNuxqXVfJcnkUY94SuTSOmU9Pys3PaFysR4aPn4DaalyT35vcaq+FZr0is9V7vsJk20Ibp8EwCQnq3V2gRYF0G48Ny9wt/xZOzqVd3yWyIsPVYv/H9IzvhRF06gxTQEfHxSuo3y1otvKrW4aq204vioF6Oe8IpjHdYknQa8w1Kyz91/sXJN0ElrrdobgIqOa65LAEjJU9dG6U1ufTnXA3hHLh48GOgGKtqkAwuVGzN2innEIz2n1Dkt8EUSCMdTGPWE0J6p6p/gvJTQnOtZegBJxdama9UzJcW9pc5x8bEjCBcf14mItGaePnnh5z0rvZWUSwnA2m2aaO+WC+1VNgiCVP3riyTgtBik55DlKviSseWvRBCk+05O7ikVfBUFvbZUw87mMpwY82LaF8WEN4KmCpUSEzE/MHEMGH1X6mBw8hngs99SrQrh6IXkSGdNKcqsWqgYgZQQD3ukf5dqaN1hc0l/8xIRaV1wyYFbtZgMOuxsLsMHgx58NLKAzhq7ekltOfFnV79q1BdOoO/C7OxrHswocsPu0JXpEREYmr/Ka7Ac2NeyD/tapOq+k+OLeKt3Du8NuNFRZUOpJfd/Fx/b/hgOHj4IAQJEiMr7x7Y/lvNYxhfCOD3pAwDs21oL/VyH1NbSM6iZxN+lrCYDdjSVYUdTGRbDcfTOBNA77Yf3wv/PvpkALEY9Omvs2FTnQL3Tkpnnr6sVHFSqfx9lNfH3xS9+ER6PB3/2Z3+G6elpbNu2DS+//DJaWqTTRdPT0xgbu1gF09bWhpdffhkHDx7E//yf/xP19fX427/9Wxw4cCCbYRLRJeqdJeiZ8mPKd5XToEVMvk9sZj1CsZQyZ+eqBOHCaVkXUL9D+lwiKrXPvDThlowBi+PSm6ykbGlV3XqqAkVRepEnVyH6J6QNgSuq+QxAac3SBOQaX1xMXkj8lZj0iMRXcR9l25b9Ul/tI38lnbap3Cgl/TY/nJnr1+mlyiSL8+qXEUVp9t6FjdyxoQlMhKfgMiWwEBMwbrSjc8tmGEouJPiMNkBX3MmusYUwBuaC+PmJSfzx82fQ5rLhq/s2qpsA6DkEHPlL6TRX5QYpqayBYc15Tae7+ByJS+aWxMMXK+6UlqFuKYF+rerACwm57742CB1EpC/M9rMiiirBh9dePYT79fXS9V2tik+ueFBadF5IMGpoNl8mTFx4rtbEbBZcZRMA6m4CZJ3BLL2t6nBJAGIsgHOBXqQNQWyqEYH4hRa6M2ekN0D6v1TeJiVLyppylphuKCuBUS8gGEvCHYyvf4ZdyC218pw9u7Slrr1KauVZveX6D0gZzEDdjdJbLADM9UqJs8CMtInhGZSSH65OKQlY3rauv8ly4q+9yo5IIoXjo170TPvXl/iL+qTqx9kzUiJM+dlMgKtLirusJS/WEovhOCYXIxAEYFPdVdacxhKgcTfQsEs6tDZ9SnpsRLzA0GFg+G0pEVy3A6hoz4uf+7okorAGJnFjuheBhVksvvsGnJawVDm8HEEnJfiU9pyXVPCtlCynjLCZDdjW4MTJ8UV8PLKgXuLvxD8CPT+/+LFvQrXZQ95QHOdnpeTIza0aSo4ELlT7lZRponJMIXcaCLmltbEGEn8AsKOpDMdGvXAHYhh2Z/Awy1oFZ6X39mp1bv8Sx8YWkBZFtFRaL47x0ID/9s5P8U/nv4vE/8fenwdJkt33neD3edx3Rt6RR2VlVXVdXUCj+sIhoRtEg1BhSIBGzpJYUcASaxpoCBrNAK5RWtCGRoJjlAhqjRK4pjVCK8kMGoIaM86YYIYV2U0KDaJBEkd3o1BAN6q6us48I++4b3d/+8fz5xGZlUdEZkT4Eb+PWVrUkRnh6RHh7vG+v+/3q6zDp0/gl8//U/zz9/+iZdszPxrB7bXCbnmEicEWO/DETAK31/JYzVbxzbc28LEnpvoubO91Ic4n5vGZJz6DF+Ze6Ot2qJqOl2+J99g7ZxKYHgoBqiH87dy3jQv5IIbCfrznzAjePT+MjUINt9J5vL1eQKmm4cfLOfx4OYd4yIeLkzFcnIydzKX7/t8E/s//e8s/dGA46DE9v+L7tV/7Nfzar/3avv/3la985ZF/e/7553H9+vUebxVBEAeRGhIXKev5KnSdW5vZbjOkm+1ds0n8/d0tbORrqKs6/N4OFhh8QbEoMWwUBUtXYGvEZnm7xRV4U3yf6QpsEQMPWvRSa3siR1f2j/UJJpr3l5gWi9wnjByVQt87ZxL4/v0dbOSr0HTefSt9J1z+mLUCDWNi0coXAqLjuLcYxHIihXMXxnD3YQbFmoq0bwazcRt9yLQQXed4+dYG/uKNtOkAur1WsDb27+bXdztH12/apqzZlfjDgH9udwyVrotF3uL67sjQav4Rd+Bz2Tt41uNBFlGEUUWEieOSJ8eAtZapu1YXn3QRRsZs0lfQW5YNx9900h5RuvsuAsA+iwCW0TJcki838DDkgSfCEHv2LABdDPLsPBDOteK6sTC4BSy/Jhb9E9NN11RsqmeCiNejYHY4jPubJTzcLh1P+GtUhGtt7Y1m3Bogzp0Tj+8f5XlSAjFg9hnxVdoGNn4iju+VTNNV5g8DY5fENsSnOnp8VdPN99rpkTAYY7i+kMH9zRIqda0zt22jCmy+JbYpt9Qc3lI84ppy4nExlOIwx5Z0+50eiRzdCc2Ycd06BZx9QUSapn8krp+37oivQFSIw6l39qW/sWeYQ3vLLRH8IhL4UrmCh6UyipoXmEqI93p4+FEHXyhJAp8NeHIuiR8v57CwXcZ6vmqNIPD9L+/zj9Z0D72+kAHn4vx+4iGRbiKFPzvFfEqiE+L9X1xvJgtZTNDnwTtnEnj9YQavPdzBvEVRtt9Y/Tv8ydrf4uHy/4HTbwpxRDq3+kmxpuInK+J8ZidB+//1t/8H/rf7/yu4R5xCG2wF/9v9/xUALBP/Pvehx0QyC2vqRpwDn33hvCXbsxfGGF64NIH/8v1F3N8U0dqP9aO/eg+tLkSrePXhDjLlBqIBL/7BuVHxj0OnxLVnNSfWLSOjlm5jOzDGMBEPYiIexHOPjWEpU8atdAH3NovIVxp49cEOXn2wg/F4ABcnY7gwGT/6mnQvk+8ALv88sPRd8Tmi24aDE0BXggRB7GIk4kfAp6DW0LFVrGHcRtNKVqJqOjYKIjLnwkQMP17OolBVsZ6vnmx6s9UVmHpC/Jt0BbaKd43qo65AKdwlZsRCT2uc6L5uvsnm98enuh6LVFd1bOTFPnp8KoEfLeVQbWjYKtZsNfVmJZxzUxxNJUKYHa7iVrqAxZ2ydVPANmOzWMPf3RUOBrtk/+OVLwKPthHYoqx5YFAU4VqIjGCXO7BR2d3JV9pALLwMrVjGGLIAAM4ZcojCH5sA5t/fFPuCCVtPKfaKakPDdlEcq6dt0qG5dxEAEO8yuywC2IHVnBCQxmIBeD0KAKUp6gHCKZtdECJg5qEYHpLXDQ/+VgjaQ3NNR2B4uKuv/7mRiBD+tkrtL3qZUZ4/NqI8DRdua5TnyLn+9CBHRoD554DT7xfXU+s/EcJSvQys/EB8hZKiK2/iSltdeelcFXVVRyTgwVgsAMYYxuMBbORruL1ewLtmhw6/A00VLvONnwgnYqtLeWhWiH1jF4U46kA457iZNmI+pzp0sXj94ro59YS47k3fEM7XWhFY+I74Ss4JF+DoefsLYGq9JRHkkKG9UBKJ5GNYVhuo+IZw+l1XEUmMWd8VThxIIuTDhckYbqXzePXBDj76hAXCUmu/n0n/u4cK1QZupe0njgBopknEbCr8rf+k6W6zCU+eSuLGYharWWuibL/x8K/xG0v/P/MT2p3MHfzGt34D//YD/7bvgsn1hQxUnWN6KIQZmwzVAcB/efs/maIfIEU2hv/97f9kmfB37UoKX/7Ek/jjl+/g/mYJZ8Yi+OwL53HtyqQl27Mfo9EAnp5L4vsPdvA3tzcwOxxG0DdY59mtYs2MZf7AhbHm7+/1C/Fv54Fw/TlA+GtFURjmRiKYG4mgoem4v1nCW2t5PNwqYyNfw0a+hr+9s4WZZBgXJ2M4Nx5t77nfuS86qN/1j4Hz/6j3v0gH2PwK2J689GYaX/rGHTzYKtkjfsyG0D5yLowxpBJBPNwqYzVXJeHPYL1Qg6ZzRAIexENeTA2FcHutgNVsDy5y93UFGlO/reKedLls3Hr0PoIJMekvIzu74OY7ivV8FTrniAW9iAe9SCWCeLBVQjpn0XSrDcmWG6g2NHgVhrFYALPDYdxKF7BkdNoRIuYzU2488u+WZv/buKx54PGFxOJuiztw0v/T+OM/+zaGlQJKehDbSKABL778kaeA0/b5UGkVy5kKOAeGI35EOp1m7BGtiwD3NkqIh7x479kRfOiS9dFNdmHNHBo54HzqDxudbpfE3yuZphswuyBEBOmKAkRcWGs/oP9k7srTI+JaaDVbRU3VEPAecs1R3ATW3xBCTWuvZjeiPE8KY+L6KTENnHtB7L/1nwBbb4t9+vDvxVdsUgiA45cO3NYHW+J3mxtpOiEup+LYyG/i5mp+f+FP14HcoiE83t7d0xYZbT5maJ+fdRhLOxUUqioCPgVnTtIrHhkFzn0ImP+AOC+nf2QI4AviyxcU+y31hC3i4MA5UM3uFvmKG/sP7cVbOsAT04A/gjAAtb6I7VwV90pBvDM5WIuRTuSZ00m8tZbH3Y0itou1k8WJdYqminjX0sae/+h/99D1xSw0nWMmGcKUTQaPAIj3nhT+4jZcs5LHreLe59BaIgEvHp+O40dLOUuibP/kxv9nz1im6EL78o++3Ffhr9rQzA60Z+aHres73IeGsv7IjBdjHHXFWhH52pWU7deHn50fxtvrBWTKDfz93S28cMn6Hsl+wTnHy7fWoekcZ8YiODe+5zp3+Iz4jLF9D5h91pqN7AI+j4ILkzFcmIyhUtfw9rroAFzJVrC0U8bSThl/89YG5sciuDgZx+mRsDF8uQfOxb4Ammu4NsIen/YdxEtvpsVEMmwSP2ZDaB85n1QihIdbZaSzlaOnkQcEGfOZSoRMcfT2WsGcwO8pjDWdLtIVuDfOU2vsXhywYMFs1dhHU0NiH01K4Y9eRyby9TIRD8KjMJwyPiCt5auoNrSBmyTbj8XtMpJhH54u/x0+6/2vmGdpPOAp/LH2C1gY62+uvYmNy5qJR7n2jmngEx/AH798B0sbJSRCXjz32Bj+0eOD84HtMJYzYtDAThPJQHMRgHOO/++376Nc17BZrCGVsNd2WkWrW7wtQklgOglMPynEpOJa0w2YWxYxuekfiy9ALCwmTwPD80BituPIyKGwH8mwD5lyA0s7lUcXCRoVcRxd3y/K84pw98Vs9h5VPMJ5OHJWOLK27whBbueB6AQsrAH3Xhb7bfyymPRt6VVcMPr9To80Ra2Lk3H87Z0trOer2CzURNQd52JBd/1NMcxVKzS3IRBrugztIFp1kZtpsVB6cTK2/0JKp3i8TfG7khVO0vSPxf5cfl18xVPiWnr8ct86MKGpIkZQiny5ld2CtyQY3y3yHTK0d248irVcFXc3injnzFBvt584MSPRAM6ORXF3o4jXFzL4R4/3cQipuAbM/QOj429PekUfu4cqdQ1vLGcB2NDtV8uL9yRTxPvObshjfyUj1gD6dexqg6fmhvHGch4L22Ws5aqYPGg4qQc8LCztM5bJ8SD3oG/bAAA/XMyiruoYiwXMISi74NMn0GAru8Q/zhn8ug1f5zbD61HwwqUJ/J8/WMaPl3O4MBmzTTd6r/nxcg6r2Sr8XgUfvDj+qJg9fBbAyyJ6Xq27oiYj5PfgidkhPDE7hFy5gbfW8ri9XsB2sY4760XcWS8i6PPgsfEoLqZimDbWPAEIk0Y1J67ZhuYOfyALIOGvQ770jTuPhn1ZGT9mQ2gfOZ8pY1FpNbdPxMyAIvfFlNGBKOPRVrMWdSF6A2Jxbni+v497CFLUkm4EeZum15GJdGzID0WxoM9cKF3O7LNQOmComo7VbAX/bOwn+GfpL0HnDArjuIAlfNn3Jfzw4lkAz/V/w57//O6OPxuVNRP7I0UkVdPx5VfuoaFx7JTq/Z2ytykrxpCGXT+8ysGR+5slrGarJPwBaGg6No248WMtqilKsxtt7n3iQ3puyRACHwgHXnFDfC29KpxGielmLGh0oq1+wLnRCDKLWTzcKonzma6L6Ju1Hwvn9CNRnu8Ut06IKfT6RbTmxONigXjjLRHBmVsx4o4eAG//FTB6Dpi4gnx4FlvFOhiDOeQDiIWF+dEI7m4U8fbCMsZi60JMNPrbxGMFDAHrsohTspF7oFvUVA13N4oAgMupRPcfIDQkolvn/qF4jad/JNyu+bT4uvuy2MepJ4TQ1s19XM3vFvmK67tjWgHxmo9ONEW++LQQ/trk3FgUf3dnC0s7FRoccwjPnB7G3Y0i3koX8J4zI0iE+tTHmVsRQwnv+TXgwbeFezk4BJz5AHDxZ/uzDQB+uJRBQ+MYjwcwZzNxxBxGiY7ZsyfVHxFDILWCiLZPzFi9RSatUbavPexvlO3p4BjulFf3jGUyzCf6tz5SUzXcWMoCEA4xO7n9AOCXz/9T0fHHGRjj5u0/Pv8/Wb1pjmB2OIwr0wm8uZLDy7c28E/efao7g0o2plBt4O/uimvSf3BuFLHgPsfE8LBIGKvmRKrIqLuGoRNhH959ZgTPzg9js1DDrbUC3l4roFhT8cZKDm+s5BALenFxMo6LqRhGM4bbLzFrSxGUhL8OebBVenSqxMr4MRtC+8j5TCQCYAzIVxoo1tTOi01dBud8l+MPELnffq+Cuqpju1S3Vzm5Beg6x2pWiFpSFJ2IB8EYkKs0UKqptomUs5L0PlFtp0bCyJRzWMqUB174S+eqUHWOX85/FRxC9AMAhYnwlqv3/z2A/1v/N+zyx4Bf+lPR6bd9x1ZlzcTheD0KJhMhLO2UsZypDLzwV21opoA0bTPHXyupRAj3N0vmsMSgI6O0owERpX1ivP6mkw0QvWjZhWY0aK3QjEkERFRiayxoKLnv3Z4eieDGYhZr6SVw/5tg6z/ZE+U5LsS+icsnjha1FH8EmHlKfJV3gI2bws1Y3jYEwbdQLgPzxXEok1cQ8rUsEtXLuOq5j8D69xBcWYd+KgmFMSG2jpwVzr7hM/bvpDshd9aLaGgcI1E/JuI9PC4rSvO1XisKkTX9I/FcScer7NmeuCIicztB14Swl18VTtr8ihD+9uKPtETwT4uo2BMIDMmIH6OxALYKNdzfLHXekUj0nclEEKeGw1jcKeP6QgY/dbFPDl7Z73fpY8C1PxADGX/3R8KJWt7uSz/TLnHktP3EERRs3O8niU6Ic3Nx3VbCH2BdlO1nxt6D31j4r2BgZswnB8dnnvhMXx4fAN5cyaHa0JAM+3BuzH6f42WP3//+9n9CXVmHX5/APz7/P+Gfv///YvGWOYf3PzaKB1tF7JTqeO1hBu89O2L1JvWUv7m9ibqqI5UI4p3TBwxmMSauq1auiwE/lwl/EtHNHcR4PIj3nxvFcqaCt9byuLNRRKGq4rWHO3jt4Q6eyb+KM0oFiVNzsOOnG3d/ougB86MR3F4r7J4qYcCZMTs+vdZA+8j5BLwejEYD2CzUsJar4Nx4zOpNspR8RUW5rsGjMIwbAp+iMEzGg1jcKWM1Wxl44W+7VEdd1eH3Khg1LvaDPg9GIn5sFetI56oDL2rVVA1bRbHgnmrptTg1HMaPlnLU8wfR7wcAQ5UFsD0jJMzqTr3LHxNfhOOYSQrhbyVbwRMDHju8mhX9fsmwz9ZDPU3HeB/itB1Aq1u8JwumgWjTzSZ7haUbUPYDGoIWAOGmkm7A5JyI66yXMVu+iSfWv4lQbRMVNYGw32vvKM9uEB4GTv9DEaVXWBMuwI1byK2nMVHaxuz2Q+B7PxCOm/IOsHMfU7qGNTWDugZs+VIYP/ckMHpBCKwDws1VIY5dTsX7JwIEosCpd4s+mtyyEAA3bwm35d2XgfvfEotXqSfE63u/7aqXdot8hbQQUFphTIjcrbGdwaGuOzfPjUWxVajh7maRhD+H8Oz8MBZ3ynhzJYdn54d7PxTJeVP4S0yLW0UBopPGa3i1L8LfG8s51Bo6hiN+e34elI6/uJ2Fv3HhnLdZzx+wO8r2tYcZXLvShyhbzvEhbxL/dvT9+HJ9BQ+Ky5hPzOMzT3wGL8z1pxpC1XRcX8gCAJ4+Pdz/BKg2+efv/0VTACQ6J+jz4AMXxvEXP07jtYc7OD8Rde0g6d2NAu5tFKEwhhcuTRz+mh4+0xT+OHdlOkUrisJwaiSMUyNh/NRFHQ+2SnhrrYDFjRxYbgkLXMOP3/ZgOLOES6k4zo1HbZPGYN9P/Dblcx96TPTXseZrm3Pgsy+ct3rTbAPtI3cwNRTEZqGG1Wx14IW/Zi9bYJe1f2oohMWdMtI5WkyW/X6T8eCuC4TJRAhbxTrWSPjDRr4GzoFY0LtrwX0mGQZjwHaxPvAOWyl+NobOwrPzFqhTj+gG0oW8nCmDc26/SfM+spwRx+ppm8Z8SqRjvFBVUag29o+ZGSBW93GL94zWXuGZp4SjqZBuugHzq6I/rfJDYPWHxvePAuUdeHUNKU8eGTCs+U/hzJX3OifK86QwZnQtp6DN/xRezf4tEngbV2IZEYW09Kr5rUp8Ep7HnsL382PYTkziZ1M2XmzuAdlyHSvZChgDLqYsEKwYA4Zmxde5DwnHZvpHhnBrCNzBBJB6pxC3ixvN2M5K5tH78wV3i3yxqb5EPZ0bj+J797exsFUyh+8IezOTDCGVCCKdq+KHi1n8w8d6LLrV8sLpyhQg1lK5Ep8Swl8hLV7nPUTVdFxfFO+bp08n7XcNputiPwA2F/6MwZniurXbcQDPzoso29trBbz3zAgS4R5ft9UKQKOCD0Xm8KFr/29LXPI303kUaypiQS8uWXEuI/rGY+NRnBmL4P5mCS/f2sAvPj1jv2PZCak2NPzNW5sAhIv3SGPD0Jy4vq9kxWBbxN1OyFZ8HgXnJ2I4PxFDba2AbDmI1VoAZc8QypkKljMVrGYr+HA/+3wPYXBXF4/JtSspfPkTT+KPX76D+5slnBmL4LMvnO/PVItDoH3kDibjIfwIOZq2R9NxMLmnZ0j2/a1kKYpMCn9TQ7v3USoRxJsrOVM8HWSaMZ8hvPRmGl/6xh082CphfjSCJ0+Ji6vF7fLATm1XGxrW8mIf6c//P4GvfQrUqUd0g1QiCK/CUKppyJYbSEbsl73fL5r9fjaM+bz5deCVLwLbd+EfOYerqX+K65H3Yy1XHWjhj3OONdmhO2TB86Z4RKxYYgaYfz+g1oDsouEIfCjcUkWxUIDYBFjs3fjB1gim4iM4M2avOLJ+kc7XsOWbQik1i+g/mAV27gmnRiAuXJWRUcwUaqh/bwH3N0uo1DWE/AMgjhpIt9/pkYj1w06+IDD9pPgqrInoz/U3hVj74G/F114ioy3dfDPC9WnBAuBo1I+hsA/ZcgML2yU8NjHYg5pOgDGGZ+aH8fUbq/jRchZPn0721hGQM9x+0fHd0bJS4Mqv9u6xDW6m8yjVNLMPyXaUtwCtIcT60LDVW3MwUSMatrgpxMo2enf7yUQ8iLmRMBa2y/jB4g4+eLHHDn/pfAwPWyL66TrH6w+FoP3UXBIem7r9iO7AGMNPXRzHcmYBK9kK3ljJ4Z0zQ1ZvVlf5+7tbKNZUJMM+PDvfxrHQ6xeddpmHwvU3QMJfK4H8AibiQUxMXcWZU2fw9noBb6XztjrfkfB3DK5dSeHaldTR3zjA0D5yPlLUWs/XoGq660tsD0N2103tmbQXkVuiC3HQHQnSjTC9j/AHABv5KnSd2zYCox9IAXlhu4R//Ve3TUnr9loBb60V8DPvSOHy1OAKf8uZZgRh+ImfB3we6tQjuoLXo2AiEcSKMYE3qMJfTdWwkbdpv9/NrwN//kmYIv/6TTy//v9A/uIfIp37Hwd6QTtfVVGqaVBYM27cUrwBEYco+zyqebF4HEoCsQmMl+pQv/MQK9nKwLqQFraFe31uOAzm9QPjl8RXC2OxAMbjAWzka7i9XsC7BiQ5gnOOm2kj5rP1eqdF+MfIOeD5z/c/Xjs2Kb7O/hSweVu4AEubwmlj9vNNifhaG8AYw7nxKF5/mMHdjeJAHyedxJnRCEajogrhx8u59hZYj4sU9uLTu/9duv+KGyKqtkfCiSPEEbmPYinbiWm7CCWFeKs1gMpOXyJaO+XZ+WEsbJfxk5U8np0f6e1gh3Q+Rq2JEL+9XkCu0kDY78GVg3rQCFcRD/rw3rMjeOX2Jv72zhbOjEWtH17qEivZCn68nAMAvHBpov213+EzhvB3D5h9pncbaFc4F787AIycRSLkwzOnh/H03P5d6FZh4zMbQRBWkgj5EPZ7oOkcG4Wa1ZtjGQf1sgGiC1Fa4KWbaxApVBvIVxpgDJhI7F6UHI74EfApaGjc3I+DCOfcfI381x8um6If0PSzff/BNpZ2RBThILKUEQulp0aMCMLLHwM+8/fAb2+IWxL9iBMgHW4r2cHt0kxnq9A5RyLkQ9xugyqvfBHYc2TkYHj34n8c+OQB+fuPxQLw2XEIKxgHxi+a/X3JsA/xkA+azrGcGcz328PtEgDg9Ojh/eYyGkw64AaBpZ0KClUVAZ+CM3L/SOF//aZwlK7fFH+/+XVrNtLjAyavAFf/CfAPPwe86x8D88+J2FqbiH4SGaN/f6sEVdMt3hqiHRhjePq0EPuuL2bQ6OXzll8Wt4k9wl8wAfjDANd7Gh3pCHFExnzGUnjpzTSufenbuPDbL+Lal76Nl95MW7ttrcjuUMC2cZ/TQyFMD4Wg6hzXF/aJRe4mFgp/nHO89nAHAHD1VNKe12ZET3jXzBAmE0HUVR1/85b9+jaPg6rpePmWeD9dmU5gdriDOoiRs+I2uySGEgaNSkZEnSoeYOiU+c+MMVtFwdIRiiCIfWGMmULXIC+6redEL1s85Nt3omcqIReTB3cfSUFrLBZAwLs7roYxZrr+VgdYHM1VGqjUNXgUhsWdCvZKexxAptxAoaoiUx7AiyY0+/1mbd49RjiTmSHxuhLO0sEU15v9fvZauAYgXD57jowMHMOVBWzka9D0wXzOgJaY6KE+9Pt1AcYY5kfF+0063waJUk01nbVzI4efzy5OxqAwhvV8FZsDMmR3My0myi9OxpoT5fsI/wATrn/iUCbjQcSCXtRVHYs7g/d+cyoXJmJIhHyo1DW8uZLrzYNoajMOcW93HWOiixLoWdwn5xyvO0EcMX7/v1334Ve/eh231wqoqTpurxXwq1+9bi/xz+Y9fzLKFgDeWMmh2tB692AlI2JciqF95N5mCdvFOvxeBe+csamgTfQERWF44dI4FMZwd6OIuxtFqzfpxLy+kMF2sY6w34P3d9o7Gx4RA4C6CmQWerOBdmbbcPslZkQiik2x6dmX6BRbTycR++KE50xGW65a1WF38+vAn7wP+P1xcWvB5K/sptsb8ymRnXarAyz8SdFzKrH/YvJkXPz72gALyPI9NBEP4MxoBHvnfxgDJgz36NIALtwUayq2i3Uwhs6mzAiiTVJDQXgUhkJVRb6iWr05liDdjrbs9xs5B+w5MnIwZMOnoep8YESR/Vgz+2GdIfwBwNyIcHJJ59sgIX/niXgQYf/hEVBhvxfzY2Jf3Uq73/VXUzVzkUy6HQHsK/wDXER9E4fCGMPZMeH6c8MC5KCgKAxPnxZRYD9YyPRmuKW4BuiacPYFhx79/7gR91nojfB3f6uELbuLI2pd9NQC+LffLz46fsCAP37ZRsch0/FnX6fR6ZEwxmIB1FUdP1zM9uZB1Jpw2gB9F/5a3X7vmh3qbUcnYUvGY0E8ZUQ5fuv2BmpqDwXuHrNdrOHVB+L1/IEL452/nhkDhg3X3879Lm+dA5C/s9wHNoWEPxfw0ptp+08nEbtwynPW6vjru0PCJrE/0u24N+ZTIifwtwp11NXBjNiRoufUQfvIWKwc5DjUtbzYR5OJED73ocfMD5MwbjkHfvndIh5gECe2Fw1XyHgsSB+giJ7g8yiYiBvi+gDGD9ZVHWs5IZ5J96OteP7zaAYfAwADA8f9x38dQHMIZ9BoaLrpHkvFbSjYHsBMMgSPwpAtN5Ap1a3enL4iXY6nj3D7SS4bAthba3noLne23lkvoqFxDEf8mIy3CNn7CP8AE/2+xJG0xn26/TXkJi6n4ogEPChUVby11gPhP7cibuPTzQ8drUgXYL776w+cc7xmLCY/MWNjcaS4LuJOAzH8ZJs/On7AgfubNhpgaXX82TS9gjFm9lbeWMr2RhQpbYrfPxAD/IdHanebpZ0K1nJV+DwMV08N7f9NNhheJ3rLu88MYyjsQ6Gq4jt3t63enGPBOcfLb21A0znmRyM4PxE93h0NnxG3O/dse1zqCVoDyC6KP8t9YFNI+HMBX/rGHftPJxG7cMpzNhELwKMwlGpa/x0SNoj9ae1lO8jxFw/6EAt6oXNuTuUPEjVVM50YUwfEkE0a+y5bbqBcH0ynTbrFsXHtSgpf/sSTuDgZQ8Cr4OJkDF/+xFP4H5+aASBEiUFbuDH7/cjtR/SQGSNGdhCjmdO5CnTOEQt6EQ/ZsIj+8seAX/pTYOJxEZUy8Tjw8a+CX/xZABjI8ysAbBRq0DlHJOCx5/N2AAGvxxwGGiTXn65zU/ibO6LfTzI/GkHY70Gpprl+X900XI2Xp+K7u0/2Ef4BDnzg833eQmcyPRRCyO9Bpa4N5PnNqXg9iukaef1hpvvX/vkW4W8/Yobjr5IB6t0diFrOVJDOVeFVDhFH7ICMOY2nMH9AIsuZsf4KS4cSGRMbVS8Ddfs6fM+NRZEM+1Bt9CjK1sJ+v1cNt9/j04n9Xf02GV4neovPo+CFi+L196PlrCPTv95cyWMlU4Hfq+CnLo4fv5MuOSc67irZphN3EMguiojTYByIdBiR2mdI+HMBD7ZK9p9OInbhlOfM61EwbsQP9n3a3gaxP9ulOmoNHX6vgtHowZnN00OD2/MnOxBjQS9iQd++3xP0eTAS9QMYTNdfXdWxVRCOB+l+vHYlhRc/+xxu//5H8OJnn8O1K5OYiAUR8CmoNXRsFgcn1o5z3uz3G3aOo4VwHvJYLbvuBokV43eeSYZsVTa+i8sfAz7z98Bvb4jbSx81xaNBPHcAzYjsyYSNn7cDkI63Qer5W8tXUW1oCPgUpOLtRbN6FIYLkzEATWHMjWTLdaxkKmBM9Pvt4gDhH5c+as3GOgxFYThjCM0U9+ksrkwnEPR5sFOq495mF587zpvCX+IA4c8XAsLCmYVCd11/MjruynQCkYCNh1ZkzGls6sBEls++cN6yzXsEj090agG2jvsUUbbitXV9IQtV63Iqkvzd+xzzuZqtYGmnDI/CTNH+EWwwvE70h1MjYVyeioNz4OVb647qIy/WVPztXdGT+d6zI0iE9l/HawtvQHTcAc3Ou0FA/q7DZ/Z31dsIEv5cgCOmk2yAnTr1nPSctcZ99hUbxP6kzV62IBTl4IO5ZfvIBkixc/qAmE+JjHQaRNfGer5qOm0OEkcB8SFJOpIGKe4zU26gUFXhUdiBcbEE0Q2mhkJQGEO+0kCu0rB6c/rKsin8OctVOx4PgDEgX2mgWBs8x7jshz0odcDOnDaEiOVMGY1uL/rZFOnYmxuOHHrduJfLUyLu8/5mCZW6c7tiDkOKmnMj4f2vhfYR/on2eWxCiKn3Nov9r2cgjk3A68ETs6L/7tWHO9177mp5oFYEmNJ09u2H/L8uCn9ruSoWd8pQGMOTB4kjdkHGnMZTByayXLsyae027sXs+Vu3djuO4FIqjljQi2JN7f5Qi0WOP9ntdykVR/ygz/Q2GF4n+sdzj40h7Pdgq1jH68brwwl86/YGag0dk4kg3jUzdPI7HMSeP4f0+wEk/LkCW04n2SzX2m6derZ8zg5ALjbJxae+YYPYH+lyPGrBTUZcpnPVgYtolGLnUYKN/H8nxiCclLW8eO9MtrFwK6MuFwfIISHdflNDIfg8dFlC9A6/t9nztzJArr+GppvHoZmks8T1gNeDEcNxvzZgwzW8JUK8nfOH3RiJ+BELetHQ+MC838yYzzb7/STjsSDGYgFoOsft9UIvNs1SOOe4uWrEfKYSFm+NO5lNhuD3KihUVfN4T1hHJwPHV2eT8HkYNvK17jmkZb9fdFy4xA7C7Plb7c7joimOXEzFTuYi6TX1ElDNiYUYQwDdL5HFdrT2/NmYVlfca92MstV1oCicSv10/G0Uqri/WQJjwNOHCdo2GF4n+kfI78HzF8YAAN9/sIMdB/Ra39ss4s56EQpjeOHSeEeDagciO+6yi6L7zu2Ud0SsqeIRUac2h1bYXIDtppNsmGttt0492z1nhyDdbFvFWm/KmQ/CBrE/aUOkSh0hao1GAvB7FRHpWBqciEZdb3Ygpg7o95PIRcuNQm3gxFEpdqYSRy+4zyabAmnXY1FsinQ3zjpMkCCcyXRSxn0Ojri+lqtC0zmiAa+9F+EOQA7fDFrcZ6GmolhToTCGiTZjI+0EYwxzI8L15/buOgAo11WsG4LL6Tb7/VqRrj8pkLmJ5UwFhaqKgE/BWRumm7gBr0ehuE+b0OnAccjvwTsMx8Vr3XKMmN11B8R8SqTjL78qppBPyHaxhrsbxaPFETsg3X7hEbHW4BRMx599oz4lV6YTCPs9yFca3RtqqeyIXi2PDwj17zX2+kPRXXZ+IoZkxH/wN9pgeJ3oLxcmYjg9Goamc3zj1rqtXfc1VcPfvCWOHU/NJTEe69Lni8io6LrTVSH+uR3p9kvMOOL8QcKfS7imvIYX/Z/H7eCv4EX/53FNedW6jbFhrrUdO/UcMVEGIBrwIh7ygXNgI99nUcvC2J9KXUOmLKZVUkdM2isKM7+n785IC9kq1lBXjQ7EyOEnvJGIfyDF0VbHxlGvIwAYjvgRDXihtoiqbkbXOZYMAeZUhw4JgjgOMupykDpZl53Q73cIcnAkPUDnV6D5+47FAo51Qw9Sz9/Cdhmci+creoxOq4uTMSiMYT1fxZbLen5/YoiZFyZi8Dr0tewEzo1HAQjhz84Lj27nOAPHT54agkdhWM5UunN9kl8Wtwf1+0miE8Kx0KgA1eyJH/Y1Qxw5OxY13fq2Rfb7SdejU5COv0oGUO3tLvJ5FFw9JV1/XYqyNWM+x/vWq5Up1fG2IVw+ffoIsdEGw+tEf2GM4YMXJ+DzMKxkKuY1jx35zt1tFKoqhsI+vPvMcPfumLGm628Q4j7NmM8z1m5Hm9CVtxuwm8POhrnWTurUsyPNuM/BWSiVEZYjUT+CPs+R3z+IUZarhjA1NXR4ByIgLohSA7h4m6s0UK5r8CgM47GjPwAzxjA7PDg9f5vFGmoNIR5PdGvijCAOYWooCMaAbLmBQnUAokjQdDdOO9RVK93S63nhXBwU5HVIO0MjdmV2OAyFMeyU6siV3f1+WzBcjadHjvfZIuz3Yt74XOIm119N1XB3QyyYSlcj0RvmRiLwKgzZcgNbRXsLAm7mOAPHsaAPl1Li/XHinihNbbrBjhK1PN6mgyx/sgqUXKWB22vivf7sfBcXlHuFdEUe1oFoR/wRIBAVL6qS/V1/75xJwO9VsF2s4143hu4t6Pd7fSEDzsXaYVsOKeqsHTgSIR/ee3YUAPDtO5so2bCXPJ2r4EfLWQDACxcnuj9UKLvutu91937thtYAMgvizw7o9wNI+HMHdnPY2TDX2kmdenZERl0OggNJYkZYthHPCADTgyj8dRBhCbS4NgbwdTQeC7Q95T47LPbnIAh/8necSYa6ky9PEEcQ8HrMD+2D4PpTNd10HUu3o9NIhn0I+jxQde46J9RhOLnfTxL0ecwocDfHfXLOj93v18plY+H/rbW8a2LR76wX0dA4hiN+TDowstZJ+L0K5iju03KOO3D89FwSjAmBcKNwgs9KxTVA1wB/GAgOHf39MUMcLJys5+/6QgY655gbCds/nprzljhUhzn+AMf0/AHiOuBds0MAuuT6k6J2n/r9CtUGbqXFMM4zpx0gaBOWcXV2CBPxIGoNHd+6vWn15uxC0zm+cXMdnIshrJ4kLSXnAKYIN3K5S7HVdiS7KCJNAzERceoASPhzA3Zz2Nkw19pJnXp2xIyxzFUGJjqmKWq198FlIh6EwhgKVRX5AXGRyH00vacD8aBCeykQruXcv9guOc7C7SnD8beer6La6GOvpgUsGgul8ncmiH5g9vztuP9YtJavQtU5IgEPkuHd/X4HHavtRqtjfFCGa1RNx0ZBiJxTbQ7X2JXTA9Dzt1GooVzX4PcqZgLEcZgfjSDs96BU01yzv24aC6aXp+KOjBp2GufGjLjPTRL+rOK4A8fJiB/nJ2IAmn1ixyK3Im7j0+1FIcZlz9/xrwFKNRVvruQAtC+OWHoNUsmIpCzFC0TG+ve43cLs+bOXuHAQV08NwedhWMtVsXSSa2/O++74+8FCBprOMZMMnej8TrgfRWH40KVxKIzh7fUC7tvoPPz6wx1sFesI+T147rEeHfO8AdF5B7g77lP+biNn+xY3fFJI+HMDdnPY2TTX2imdenZkNBqAz8NQa+jYKbk/OkbTOdbz7feyAWLKdsyIchyEhcl8tYFCVYXC2K6pzsMK7eW+zJQbqNTdLWhJVnOduSIBEfczHPGD82Y3lxtRNd18r5DwR/STGUP4GwTH30pGDmiEdy26H3astiNyeGJtQBzjG4UaNJ0j7PcgHuq8L85OyJ6/5UwFqqZbvDW94cGWEOlODYfhOYF73aMwXJgUC/9SMHMy2XIdK5kKGBMdhkTvOTMWgcIYtgo1ZAbgM5sdOcnAsewPe3u9gGz5mM9fvkX4awfT8Wc4BY/BDxezUHWOVCJoXmMdhuXXIGbMp9Fx6DQc5PgDRJT149MJAMD3H2wf/47qRaBeFovtfRBsy/WmoO2I+FrCcsbjQVw9NQQA+OZbG6ip1q95ZUp1vPpAOPCePz+GkL+Hx7wRI/pyEIQ/h/T7AST8uQMbOuwo19pdeJSmuDMIMY1bxRoaGkfQ58FwxN/2z8k4q0HosJOCzVgsAL+3eSo5rNC+dX+mB8D119B0bBXEh3b52mgXGfe55OK4z3ROOJGiAW9H7zOCOCnTQyEwBuyU6rbsYOgmcnhgb7/fYcdqOyJdb4NwDQI0f8/JRNDxLqmxWACRgAd1VXft83fSfr9WZA/e/c2S44ekpHg5NxJGLOg74ruJbhD0ecxrSHL9WcdxB47HY0HMj0bA+TFdf5w3hb9Em8JfeFgMa+sqUOrcQVZtaGZv1DPzw22dsyy/BikYAmPM3jGfB7oipfBX2gB0ZwzUPDWXhEdhWM5Ujj8kLWM+wyOAp/fnlBuLWTQ0jol4kIZUibZ5z5kRJEI+FKoqvnPvBEJ3F+Cc4xu31qHqHKdHw70fwpJiWGZBdOG5jfKO+GIKkDxt9da0DQl/bsCmDjvCXUwNUIdda8xnJwtuMvJyEFwkUtyc2iNoHVVoP0iujfV8FToXwlYs0JljQ364WMq4V/iT/X6zwyHHL2wTziLo82A0Khzabj5eazo3hyz2TuAfday2GxOJABgDcpWG68VaoDkc44ZYKcYY5gxBTDrj3ES1oZmC5tzoyRcGx2NBjMUC0HSO2+uFE9+fVXDOcXPViPlMJSzemsHi3LgR90k9f47kGcNZdDOdR6HT+ohaHqgVxaJkLNXezzDW7LnLd97z96OlLOqqjtGoH2dG2xt+sPwaxAH9foe6IoNDQvjSVBFb6gDiQR8uGT22rz08Zv+XGfPZ+36/akPDDUPQfnY+SZ9VibbxexW8cEm8Rn+0lLV03esnq3ksZyrweRg+eGGi96/jyJjovtNV0YXnNnYeiNvEjNBeHAIJf26BHHZEj5ExjW6d1m5F/o7txnxK5ALdVrFmC1t/L5GL5XsXJedHI7imvIoX/Z/HW4FfwYv+z+Oa51Wz0F66NlYH6HV0HMfGTDIMxoDtYr3zD/0OoSn80QQl0X/Mnj8Xi+vr+SoaGkfI78HIHlft/Gjk0ZB4BvNYbTcC3ubvMAjXIWY/bLyz6xC7Ip1wCy7prWtlcacMzoGRqB/xLrnapOtPCmdOZDlTQaGqIuBTbHtccStnx6JgTBxHBqV33E1MD4UwnQxB0zmuL2Y7+2HZ7xcd78wRJUXCQmdRmw1Nxw+XsgDad/sBFl+DaGpTQIq3KY5awKGuSEVp6flzRtwnADw9lwRjQuDdKBzjWq6P/X5vrORQa+gYifpx1uhOJYh2mRuJ4FIqBs6B/35rHZq+d9Sh95RqKv72zhYA4L1nR5AI9yF5gbGm668PcZ9974rduSduHRTzCZDwRxBEm8iOsp1S3fHRQ0exeoCodRTRgBfxkA+cu9vRVlM1bBVrAB7dR39w6QG+7P8SLrAlBFkDF9gSvuz7Ev7VxYcAmo6/9XwVugUXQP1ELk7vdUW2Q9DnwXhM/NyJStBtSrWhmT2aJPwRVjAre/5c3KNpxnwOPeqq/dyHHjMXkWDccg589oXzfd7K9pHXIW4+vwJA4YAOXSczNyKGWbaKddcJEdLF2I2YT8nFyRgUxrCer5rXW07jJ4ZoeWEiBp+Hlhz6SSTgNa/P75Hrz5E8e1q4/t5cyXX2udt0srUZ8yk5puNPbl8i5MP58fYj5Cy9BiltiC5DX0g452zKka5Ih/X8AUAy4sf5CfE6OVaUrYz67LHjr6HpuL4gtu/pufYFbYJo5TmjT2+rUMP1xf47c195exPVhobxeABXZ5P9e+A+9fz1vStWU4Hsgviz/B0dAl2FEwTRFiH/YPSznXTBbdoQedwcH7eWq4JzIBHyIbonwvLq/X8PDgaFiY8qCuPgYLh6/98DAEYifvi9Cuqqju3SMUvrHQDnHGvG+2QycbyoNhn3uejCnr/lTAWcA8mwr2sOCYLohOkh8f7aKtZRrrszOnIlK44de2M+AdE/9OVPPImLkzEEvAouTsbw5U881XYPkRXIwZFVF1+DAM2hkdGYf1eHrpMJ+jxmisLitnvOaZzzrvb7ScJ+L+YN58uttPNcfzVVw90NEVMq3YtEf6G4T2czNxLGeDyAuqrjhuGoa4v8srhtt99PIh1/5W1AbW/YQNM5fiDFkdNJKEr74oil1yB5Y1E4PtVUHm3Ika5I0/G30dftOilPnxYCxNvrBWQ6WQtQ681Y0x47/n6ymke5riEe8uFCrzvRCNcS9nvx3GNjAIDv3dtGtty/ta8HWyXcXiuAMeCnL010dHw+McnTIm5a9uH1iL53xWYXhPgXiIlIUwfhjk+TBEH0hUGI+zzpgpucsJUdeG6kGfO5jzC6fRdsz3wiAwe2xQlYUZgZXeZmATlfUVGqafAoDOOx4+V/S+FvOVMG5+5yRy4ZYuapEXL7EdYQ8nswGhXDLG50/ek6x6pxHpreR/gDxMLbi599Drd//yN48bPP2Vr0A5rXIBv5qiWROf3iuHHjdseNPX+bxRpKNQ0+DzuWu/8wLhtdSLfSecclJNxZL6KhcQxH/K6Jq3UaMppuJVtx7XCLm2GM4RnD9XdjKdtehYSmNkWgTrvrAlEgGBeWsjbjPm+l8yhUVUQDXvN41QmWXYMUDFdjux2IFnGkK9KBjj9A9NieGYuAc+D1hQ5cUKUNsQMCUcDfu0hYTed43eggfHouCU8/BRPCdVxKxXBqOAxV5/jGrY2+rOnUVR0v3xLHhSdPJTHepeuwtmM1vQHRgQc0O/F6QN+7YuXvMnzG1kMj+0HCHzE43Pw68CfvA35/XNze/LrVW2Q7jjqYS1Fr1cVuNjPm85guLTOKzMVRlnIxed8o1JFzwKPzicDIY+bfBkJAzovX0VgscOyIq9RQEF6FoVBVkSm7KxptyehVO0Uxn4SFzCQNcd2F57SNQg11VUfQ58FY1Dnl44cxHPEj4FPQ0Di2HRp/2A6mWzx+vOsQuyIdcYs7ZdcItwvbza5ab5fjLOdHIwj5PSjVNCw4zPl/03ApXp6KU0SaRSRCPkzEg71dBCN6yrmxKJJhH6oNDW+u5I7+geKaiLD0R44XYSmFsPzRwp/eIo48OTfU9eNfT2l1/NmYI12RkTGx+FwvATVnOXulqH0rnW8//rtP/X5S0I4EPHicHOvECWGM4YVL4/B5GJZ2yub1US/5zr0tFKoq4iEf3nNmpCv32XGsZh96/vreFevQfj+gx8JfJpPBJz/5SSQSCSQSCXzyk59ENps99Gc+9alPgTG26+s973lPLzeTGARufh34808C6zdFfMX6TfF3Ev9M2jmYp1r62dyyaLMXc9L+mJPbo1GxMFlXdWy6cGFS17nZzZbaTxx9/vMwjPbGPxgG/A983vwWGdfm5p4m+TqaPIFjw+dRkDLEVTfFfRaqDWwX62CsKbwQzqbvxdpdQjrhll3o+Fs2xPWpoaBrFt4ZY+Z1yKpLzx+qpmM9Lzt03eWUmogHEPJ7UFd11zj+e9HvJ/EoDBeNiLGbq86J+8yW61jJVMAYzO1vB6eeR+wMxX06G0VheNoQSK4vZKFq+uE/kFsRt8eNsJRCWOHonr97m0Vkyg0EfR5cmU50/lhW0aiKOFPA9o4/4AhXpMcHhMTrw2muvyoxbm4AAI3BSURBVKmhEGaSIWg6N7v0jqQP/X6tgvZTc0lnCdqEbRkK+00B7ttvb/XUhb+Wq5rx0C9cHO9aZUDHsZqyAy/7ULjRe0Bfu2IrGRFbyhQRZeowenok++Vf/mXcuHEDL730El566SXcuHEDn/zkJ4/8uWvXriGdTptff/mXf9nLzSQGgVe+CDx6qAJe+UPrtslmtHMwb52233KhqNXQdGwYC277ilptwBgz3YJudEZuFoWLJOBTzJi8XVz+GPBLfwpMPC5s/hOPAx//KnDpo+a3yH27U6qj2uigsN5ByKjX4zpHJdIRt+Qi4W9pR7wvxmNBBH0ei7eGOCl9L9buItOGsL5drLnuWCQjmd0mrksX3JpLhKO9bBZr0HSOkN+DRMhd/aeMMZw24p0XXNDzV21o5rm+F8If0OzHu7dZdMwxSk6zz42EEWuzw9fJ5xE7I4W/xZ2yY14/xG4upeKIBb0o1tSjnSJ5Kfx12O8nkcLfEY4/zjleNcSRJ2YTCHgddC0vY0xDScDvgusjh/b8AcCz80K0fHMl154Q0gfH312nCtqE7bl6KomxWADVhoZXbm/25DE0neMbt9bBuYgYPT3avWvTjmM1I2OiC09Tgdxi17ajlb52xUrnYmIa8DlvMLNnwt+tW7fw0ksv4T/+x/+I9773vXjve9+L//Af/gP+23/7b7h9+/ahPxsIBDA5OWl+DQ8P92oziUFh+y7w6KHK7B0j2juYt4paboxpXM9XoXOOaMCLeNB77PsxHQku7PlrjUI90EVy+WPAZ/4e+O0Ncdsi+gGiWysZFotBbnwdNTQdmwUhIJ/E8Qe0CH+ZsmuiY6V7kWI+3UHfi7W7SCTgxXDED87d5frTdW7+PjMH9Ps5FemCc+O5A9jd7+cWp2Yrbur5W86UoXOOZNiHRLg3Iu14LIixWACaznF7rdCTx+gmnHPcSovtvJxqf9HUyecROzMc8WMk6oemczzcdv57bhDxKAxPzSUBAK8/zBz8WYDzpvCXOKbwF50Ub7xaQXwdwMJ2GRv5GvxeBVdnk8d7LKvIG27GuP3dfm3h0J4/QHwOnIgH0dA4bixmD/9mXQdKhljSI+GPc45XHwhB+12zQ84StAnb41EYfvryBBgD3lor9OQ6+PpiBpuFGoI+D547P9bV++44VpOxZiTmdu/iPvvWFSt/h+Gzvbn/HtMz4e+73/0uEokE3v3ud5v/9p73vAeJRALf+c53Dv3Zb33rWxgfH8f58+fx6U9/GhsbzptgIWxGG71jg067B3Ozn82FbrbWmM+TLLi1diH2o8C3n0gxM3VCQWvSFJDd9zraKNSgc45IwHMiARkAxmMBBHwKag0dGwXnu2w552YE4eywuwSJQaXvxdpdRgpjKy46p20Zzmy/V3FNv59kwiioz5YbPY3KsYp09pAobRcwNxIGY8BmoYZizdnP38MtcS6b6+JE9X5I118/emFOynKmgnylgYBP6ahjxY7nETtGjx5nm86NCdffnXWK+3QqV6YTCPk9yFUaeHvjAEGulhc9b0w5foSl1w9ERsWfD3H9Sbef3C5HIR1/MXv3+7WNgx1/jDE8Oy+E4xvL2cNdyZWMcA55fMfrr2yDh9tlbBaEoP2u2d48BjHYTMSDuHpKvOa/+dYG6uoR8c0dkC3X8b17Isb4ufOjCPtPtga1l2PFavah568vaKqILAUc2e8H9FD4W1tbw/j4o/nL4+PjWFtbO/DnPvKRj+DP/uzP8M1vfhN/9Ed/hNdeew0f/OAHUavtv+BZq9WQz+d3fRHEI7TROzbotHswN0UtF07bNyftT7bgNpkIQmEMxZqKfNXZC1utcM6bjr+hk+0j07XhQlekFMVTh7ki20RRmBnVt5RxfjRaptxAoarCq7ATv4aIg+nntVHfi7W7TLPnz/nvL8mS4fabHgpBUdzlGgv6PBgxYqbd6PqTwzAnHa6xK2G/F+Mx8bstONiBxHnTQdWrmE/JxckYFMawlqvaPmb/J0YX4YWJGHwddCPZ7Txix+jR426TjPtc2C6hcVRHnItx8pqRz6PgqiFEvPZgZ/+hUtnvFx0X4shxiR3e87eSrWAlU4FHYXjy1NDxH8cKOHev46+yA6h1a7flGJwdi2Ik6ketoeONldzB32jGfI4DSm+WsF8z3H7vcKKgTTiG954ZQTzkQ77SwHfvb3flPjnnePnWBlSd49RwGJdT8a7cbyvHitVMnhbDKOVtId47ldyiEP8C0Z52jPaSjo+aX/jCF8AYO/Tr9ddfB4B9Fzw554cuhH784x/Hz/zMz+DKlSv46Ec/ihdffBFvv/02/uIv/mLf7/+DP/gDJBIJ82t2drbTX4kYBNroHRt02j2YT8SDYAzIVxooVBsWbW334Zy3CDYnW3DzeRSMx4XLwk09f/mqimJNhcLYiSMs5c+v5auuibCUtEa1dQMZibnogk4kGfOZGgp1tChIdEY/r436WqzdA6SwvllwT89fs9/PneK6HM5x2+BIoSoGIxhrOhvdyOlR5/f8bZfq5hBLO++zk7jHwn4v5g0B7JaNXX81VcNdw40kXYrtYrfziB2jR4+7TWOxAOIhHxoad7TYflKcvmb0xOwQ/F4FW8U67u8XEWcKWseM+ZRIQSy/v/D3uuH2u5yKt93haRtqeaBeEgvRPeyJ6yuBKOCPiANmqTe9Yb2EMYan50St0/WFzMHDCT3u91vOlLGSNQTtOYfF1xKOwu9V8MGLQjz64WIG6/mTf5a5lS5gcacMr8LwwqXxnlUFdByr6Qs2o6ed7PqT2z58pnmh6jA6XnX79V//ddy6devQrytXrmBychLr649mTW9ubmJiov0DdiqVwtzcHO7c2f+i9rd+67eQy+XMr6WlpU5/JWJQOKJ3jGjvYO73Khg1osPWXDRtn6s0UK5r8CgM47GTR6NJN5OboiyliDkeD5xYtBmNBOD3KqirOnbKzptQPAjOufm+OKk4KplNNqNjnT6tvUT9fn2hn9dGfS3W7gHRgBdDYR84d8egBuccK9Lx51rhT/b8Of/5akWeO0aj4vzoVqRDbmHbud21UkCZGT56iKUb7jE5vX0rnbftPruzXkRD4xiO+DHZoXBtt/OIHaNHj7tNjDHT9Xd3Y3DjPp2+ZhT0efDEzBCAA1x/+WVxe9x+P4np+EuLF1gLG4Uq7m+WwBjw9GkHiiMyvjQ6djJXpN1wcM8fAFyYjCEe8qFc10zX+CPIKNMeuW1eMwTtx6fiiAa6G5FIEHuZH43gwmQMnAPfuLV+ouu6cl3Ft+8I0f89Z0cwFPZ3azO7Qx96/nrOdovw51A6PqqNjo5idHT0yO9773vfi1wuh1dffRXPPvssAOD73/8+crkc3ve+97X9eNvb21haWkIqtb8dPxAIIBBwV38JQdidqaEgNgs1rOaqeGwiZvXmdAXZXTcRD8B72CLOza8Dr3wR2L4ruiOf/7wQlfcwlQjiOoAVFzkSuhXzCYgIy4l4EEs7ZaSzVVNMdjqtrshuOTaGI35EA14UayrS2SpOjThTNNN1bsaVUr9fb+n3tdG1Kylcu+Lc2KSZZBjZcg4r2QrOGH1ITmWzKJyLfq9iRiq6DTlUsW44xt0SZ9ptt7hdmYwHEfR5UG1oWMtXHRn7bPb7tRHzeZhTq93j5vxoBCG/B6WahoWdMuZ73Ct4HGQH4aVU/FjT5nY6j8yPRnB7rbBLaLM6wvok2/TYeBTXFzK4v1WCpnN4XHLM7AQ3rBldPTWEHy5mkM5VsZypYFYO0WlqUxiJn7C7LjIGeLwiNrK83ez8A/D6QxHVdn4iZr/F5XYodMkVaTei48KR4sCePwDwKAxPzyXxzbc28PrDHbxjOvHoMaqHjr/1fBUPt8pQWtyHBNFrnj8/hoXtMjbyNVxfzODp08d77X377U1U6hrGYgE8ecqGAxnDZ4H7r4iOPE0V5xcnUcmKcyFTgOS81VtzbHo2Tnrp0iVcu3YNn/70p/G9730P3/ve9/DpT38aP/uzP4sLFy6Y33fx4kV87WtfAwAUi0X85m/+Jr773e/i4cOH+Na3voWPfvSjGB0dxc///M/3alMJguiQZsyWe6btm706hyxA3fw68OefBNZvAmpN3P75J8W/70EuZG0X3RMfJ3sdp4e6syjpRteGdGyMxU7uipQwxswP907u+dso1FBr6PB7FUy4VJAgnMmM2fPn/GORdPtNDQVdu7g7EvHD71XQ0LjtO886odtucbuiKMx0fT90YPRgXdXNON12+v264R7zKAwXJ8Wg3c2DHBEWki3XsZKpgDHgUsr5A4F2ix496TalEkFEAh7UGrqZvEA4j0jAi8enhftXOpQACHeeronIx+DQyR5EUYDYo3GfmVIdb6+LKF9Huv2A5u8Ts8eAQddwuOMPEE67SMCDQlV9NNK6VjQiWpkQpruMfC9dmIwiEXaRE5SwNZGAF+9/TAxWfO/+NnLlziuUHm6VcCtdAGPAhy5N2PNzX3RcRBJrqujKcxo798RtfEpElzqUnubI/Nmf/Rne8Y534MMf/jA+/OEP453vfCf+9E//dNf33L59G7mcKHL1eDx444038HM/93M4f/48fuVXfgXnz5/Hd7/7XcRizv8QQRBuYcoQxzYKNagOjx6USFFr6jBR65UvAo/ObQOv/OEj3xppiY9zQyRqtaFh21hgPVQc7YDWnj+30BSQu3thYPb8OXjBpun2C7vGoUO4AxmJuZGvoaY6e1BDipfTQ850BrcDY6xlcMQd5w9N52bPx1SXzrF2Zm7EuT1/S5kyNJ0jEfIh2cYi4fxoBHvPeMdxj8nevHubRdsNlEm336nhsPN6v/bBbtGjJ90mivt0D0/NDUNhDAvb5WY3lNnvN9Wd/iEpjBWaccSvL2TAuThuOTJNQNebv89JXZF2Qwp/pQ3xezoQr0fBU0a33usPd3ZHH0pBMzzS9YjW7WLNPCYe13FFEMfl8ak4ZofDaGgcL7+1/miE8yHUVR0vvyVcvu+aHbLv0CBjzYhMJ/b87TwQtyNnrd2OE9JTn+Xw8DC++tWvHvo9rS/uUCiEv/qrv+rlJhEE0QXiIS8iARE5tF6oYdqBMU2ttC1qbd8FHp3bBrb37yBNJULIlhtYzVZw2oaxTJ2wlquCc2Ao7EOkS9n3cuF2u1hHtaEh6PN05X6txIxq65IrUiKjMdfzVcfuq0VjgXfWpb1jhHOJB31IhHzIVRpIZ6uOPV5zzk0n0ozL32eTiSAWtstI56p4YtbqrTk5m4UaVJ0j6PNg6BAx6aU30/jSN+7gwVYJ86MRfO5Dj9kmHrET5HtsPV9Fua4i7HdO9I/s9zs9Gm4r0vJzH3oMv/rV66ZD67jusfFYEGOxADYLNdxeK+CJ2aHjbH7X4ZzjVlo4gaQ46QbsFD0qOck2nRuL4UdLOdzbLOKD+jgNYDmURMiHC5Mx3Ern8eqDHXz0iSkgvyL+s1sRllIYMwTFQrVhurCecao4Ut4SjhOvXwhIbiKUFPF5mgpUMkDEmb/flekEXn2QQabcwN3NIs7LOpke9vtJQfvseNQ1tSOEc2CM4YWL4/jq9xawsF3GW2sFXEq1dx31vfvbyFcaiAW9eN/Zo6vYLGX4DJD+cVNEcwqaCmQeij87uN8P6LHjjyAIdyKm7d0T97meF6JWInS4qJWPnIbOd39Q1jlDPrJ/3rMURFdcsI9kv1+33H4AEPZ7zQVON7giVU3HZsEQkOPdXXSPBX0YjvjBuTPjCBuabr6GpHuRIOzEtAviPrdLdVTqGnye7nWM2hXpinNLVHSrW/wgMemlN9P41a9ex+21AmqqjttrBfzqV6/jpTfT+36/nYkGvBiLBcC5s1x/nPOO+v2A7rrH5ILQzb1RaBaynKkgX2nA71Vw1uEdqW5mOhlC0OdBua5h1SXHzUHlGSNq8+5GEduFalP4S3RJ+JOOv+IGoDVwfTELTeeYToYc2ckKYHfMZzdckXZCUYCIIYo5OO4z4PXgXcZAy6sPdpoGEfk7Rbor/OUqDbxlDK0861RBm3A8yYgf7z4jxPpXjL6+o9jIV3F9UXSufvDiOPxem8s6yXnRkVfaEp15TiG3BGgNEaPdg37RfmLzVwhBEH3l5teBP3kf8Pvj4naf7jqJjMRcdYFgs5ptI+YTwB83fgEK46b4p3MGhXH8sfoL+36/dH2t56vQ9Pat+3ZEipfddne6Ka5to1CDpnOE/R7EQ913L0jBzIn9LOlsFarOEQ14MRzxW705BPEI0iG3knXe+0si+/1SiZA9ex66iIy0yZYbbX1ItjumW/yQqJ4vfePOo2HjDPjjl/dPHbA7sh9vwUE9f9lyA7lKAx6FYTbZ/hDLtSspvPjZ53D79z+CFz/73LEjIy+lYlAYw1quaiZVWI0UIS9MxLrWbUx0H4/CzHhZivt0NiPRgBndeuPekuhAY0r3uuuCCcAfBriOamYVbyxnAThcHJExn27r95O4oOcPEJGFfq+CzUIND+VQUI8cf9cXMtA5x6nhsH1jEomB4Km5JEajflTqGl55e/PQ79V1jv9+ax2cAxcmYzjjhIErX7DpJHdS3Kfs9xs+4/iBEbo6JwhCcPPrwJ9/Eli/Cag1cfvnnzxQ/Gt1/HWSR21HmpP2h4taX80/gf+5/jm8xWdR5T68xWfxP9d/A1/NvXPf7x+J+BH0edDQuOkEcyK7uoe6HGGZcpFrw3wdDYXaiv/qFBn36cSev2a/X2/2DUGclBmjE28tV0NddWZHitnv5/KYTwAI+jzmEIE7zh9S+Dv4uXuwVXo0bJwD9zedI5y10trz55TryAeGSDk9FLJkwjrs9+L0qNhvdnD91VXdFJHcFPPpVlp7/pzyniP2R0ZuphfuoqpqQhTpVv8ZY2Zs6Nt330ZD4xiPB8xjtiMxexC75Iq0G1IUkyKZQwn5PXjHdAIA8NqDHUCtA5Ud8Z9ddNyUaireXMkBAJ6dd7CgTbgCj8LwocsTYAy4lc4fOhD3w6UMNvI1BHwKnj8/1setPCGyI89Rwp87+v0AEv4IgpC88kXg0Vly4JU/3Pfbx2MBeBSGcl1DrtLo00Z2H13nbfeyzY9G8Nf6s/gf6l/Exdp/xv9Q/yL+mj9jTtDuhTFmCmVOjvvcLNTQ0PiuhdZuId0Na/mq4xch2nFsnISZZBiMATulOgpVZ73npFg5SzGfhE2Jh7yIBb3QOXdk9LDo9xPvM7f3+0nc4hgv1lTkKw0wBkwkDu6YmR+NYO/YBGM48BrE7kwZ4lm5rmHDIcNRrf1+VvG4IbC9lS5AtzhN4s5GAXVVRzLs69m1D9E95obD8HsVFKoq1vPOeM8R+zOZCOLUcBjh2jrS2Wr3Ba1YCqquY3VJLNI+e3rYuYN7ah0oGS6auMsdfyVnC38A8ORcEh6FYSVbQXp1UUw4+SNAoHvOpuuLGag6RyoRHJhrZsLepBIhs7v55VsbaGiPDqHmyg189942AOC5x8YOrSiyHbIjL/NQdOfZnUpWRJMyBiRPW701J4aEP4IgBNt3gUdnyYHt/SOkvB4FE3GxQCWjMp3IdqmOuqrD71UwGjm81PlzH3rMjNaCccs58NkXzh/4M25wtEnRcmro4O6h4zIaDcDnYag1dOyU6l29734jxYLJHnVrBX0es7dracc5r6dqQzMdo9TvR9gVxpj54X854zxXbabcQKmmwauwnh2D7Ebz/OrcaxAAWDOuD0aiAQS8ngO/7zjXIHbGozDznPBgy/6uxYamY9k497bb79cL5kejCPk9KNZULFicAHBzVbgOL08lnCsKDBBej2JG7FLcp/N5dn4Ysdo6NgpVlEPHiw8+kHgKG4UaAuV1JMM+Z/d3FtfEyTIQE19uJDImLgpqRfHlYKIBb3PA5e5d4x+75/arNjT8eLnp9qNzF2EX3nd2BLGgF7lKA9+7v73r/zjn+ObtdTQ0jplkyHyPOIbohBDwtYbozrM70pkYnwZ8zh8OIOGPIAjByDng0VlyYOSxA39ELrqt5Z0jQuxFCnKT8SCUIzqRrl1J4cufeBIXJ2MIeBVcnIzhy5946tCuFrML0cGRqHIf9aLQXVGYKWY5efE2X22gUFWhsObv0wtkp5CT4j6XMxVwDgxH/IgFuxRDRBA9YMZ4fy070KEt+/0mE0F4B6RnS3ayrOerljufToLpFj/i3HGcaxC746Sev5VMBarOEQt6MWJhV61HYbgwKRavpfBmBblyA8uZChgT3YOEM2jGfRYc+7mEEMwkfJhUctA58KNcd4cR1PAE0tkKgmoez8yEjvyMbGvyRr+fW91+AOD1AyEjstIFrr+n54ahMIbc5gqKNbWr/X43lrKoqzpGYwHMjzozMYFwJwGvBz91UbzWry9ksZFvro29tVbAw60yvArDhy5NOE+wZqzp+nNC3KfcRrnNDsdB3lCCIHrK858XnX5m3Kdx+4HPH/gjU0NB/GDB2Y4/ue1HxXxKrl1J4dqV9j84TMSD8CgMpZqGfEVFIuws4YNzjtWs7EDsjaCVSoSwnKkgnaviipHr7zSk22805u9p78+p4TBee7iDpR3RieSEi76lnWa/H0HYmWljuGEtV0VD0+FzkIAmXYqD0O8nGYmI421d1bFVqmE85kynoxT+Jts4x3Z6DWJ35ozIzHSuimpDQ9B3sOPRamS/3+mRiOXn3sdTcdxYzOLeZtGy/SY7Bk8Nh2mox0GcHg3DqzBkyg1sl+oYjR6edkLYF1ZYw/RQAG9uevDGho6rXTwW3NxqIM/iSHgLuBh2toMMBaPfLzZl7Xb0mug4UN4WPX8OX6xOhH24MBmFJ72N1WwF57vk+KurOn64mAXg8PhawrWcHYvi/EQMb68X8I1bG/i/PjOLmqrjlbdFXPGz88NIWjh8diKGzwBrbxii2gtWb83B6JqIJAUcfyyVOGdFgyCI3nL5Y8Av/Skw8TjgDYjbj38VuPTRA39k0nD8bRVrqKlav7a0q5hutkRvFkt9HgXjMfGh2ok9f7mKiI/zKL1zssnFzjUHx6H2ut9PkhoKwqswFGsqMmVn9PxJdyLFfBJ2ZyjsQzTghaY7q+ePc45lw/EnXcGDgNISa+qk56sVTefmRG8vXPV2Jx70YTTqB+f2d7IvbFnf7ycZiwUwGgtA0zlurxX6/vicc1P4u+y0uKkBJ+D14NSIeA1T3KfDya8iGfaBxadQ17gZX3hSdJ3j9YcZFANjSCWC8BTTXblfy3Cy4+/m14E/eR/w++Pi9ubXD/5eKY4V1/uzbT3m6bkhhBs72CnVscOGunKfb6zkUG1oGAr78Ni4g+NrCVfz/IUxBHwK1vNV/HApi2/f2USlrmE06sfTp4et3rzjMzwvnH+lLaDanfNVT8gtiUhSfxiIOTdVpRUS/giCaHL5Y8Bn/h747Q1xe4joB4gM9njIB86B9ZzzSuLLdRVZQzxpZ9L+uMjFvFUHCn/SETkRD/TM/SLjULdLdVQbDhWQTVdkbxdufR4FKeP1ZPdFUgAoVBvYKdXBWDNGkSDsCmPMdMxJIc0J5CoNFGsqPArr6bnMjshhC6dGRW8Va2hoHAGfgqTDEgEkL72ZxrUvfRsXfvtFXPvSt/HSm50tEsu+PDv3/OXKDWTKDSiM2eJcxhjD5ZQQ3KQA10+WMxXkKw34vYqzu78GlGbcJwl/jia/AgaG2XnR8/rDxQwamn7iu729XkCu0kAjksJ4PAgUHCz81YpigZkxIOYw4e/m10Ua0/pNQK2J2z//5MHin4zDLDo/6hMARpUyRsMKNObFa134lVRNx/WFDADgmdPDzo6vJVxNNODF+8+NAQC+c3cLN1fzYAx44dIEPE5+3fpCQNxwXm/fs3ZbDkNu2/CZZrG6wyHhjyCIEzFlLLqtOtCtJRcKR6L+nsYkSeEv7cB9tNoHQSvs9yIhBeS88xZvVU3HRkEI3712/AFN55wThL+lHfH6GY8FbR3hRhCSGUP4c5JDW4qUk/Ggo+JJu4EchEg76PlqpdUt7sTIqZfeTONXv3odt9cKqKk6bq8V8Ktfvd6R+Nfa82fXzrGHRsxnasg+57JLqRgUxrCWq2K72N/hOyk2XpiIDdwxxw2cGY1CYQybhRpyDkmPIPbAOZBfAQCcmjuHRMiHcl3Dmysnc1FwzvH6wx0AwOn5c/AwBuRXxeM5ESlahkdEopGTeOWLaFawAGYVyyt/uP/3S8dfeVu4VXrESYd92qa4jumhEMq+Yby1VkKucrLf6Va6gGJNRSzoxcVJ6qUl7M2V6TimkyGoRof5EzND7kgGGT4rbu3c82f2+521dju6CF2pEwRxIlIOFrXSst+vxy4t6WjbKjrP0WZGofb4QsPJro3NYg2azhHye5AI9d6xIYW/5UwZum7vD+IU80k4DenmSWcrULswOd8PpPA3M0D9fhIZ9ZkpN1CpO+v8CvTPLd4rvvSNO48uSzLgj1++0/Z9TA0F4fcqKNU0bPZZwGoXKfzNj0Ys3pImYb/XjB3tp+uvruqmU4xiPp1JyO8xzxd3N/sfFUt0gVpeuNmYAiUxhadPJwEAP1jIQDvBZ4P7WyVsFevwexVcfuwcoHiARgWoZru04X0mb/T7xR3Y77d9F82zq4QD2wecXwNRwB8RIm1psyeb1I1hn7YpriMa8CI8MgWdc/xgYefYd6XrHK8ZgvaTc0l4aWCFsDmMMXzo0gT8XgXxkA/vOzdi9SZ1B9mZl3kouvTsRjUnokgZE9GkLoGOeARBnIipFsHGrpPaByFdir12aYX9XjPCy0lxn9WGhq1iHUBTvOwVThaQV7P9dWyMxwII+BTUGk2noR3hnGOJhD/CYSTDPkQCHqg6x5pDHMjLGfE+mx5A4S/k95jnV6c8X630qx+2VzzYKj26LMmB+5vtx3Z6PYopQixs28/Jrmq6eS6bG7HXuexxQ3h7K13o2yDQnY0C6qqOZNjn2NctQXGfjicn3H6IjgMeHy6n4ogEPChUVby1drxBAM45XnsgxJEnZoYQDASa8ZF5h8Z9Ssef02I+AWDkHITjrxUGjDx28M+YcZ+96fnrxrBP2xiRpWfnhVDwk5U8SjX1WHf19oaIrw35PbgylejaJhJELxmO+PGp953GP3n3KQS89kibODGxSdGdpzVEl57dkG6/+JSIJnUJJPwRBHEiRqMB+L1ChNgu1a3enLbRdI51Y8GtH7b5prDlnIVJKVImwz6E/d6ePlbKwQLyWq4/zlGJojQ7huwc95kpi94xr8KQ6rFwTBDdgjGG6SHx/lpxQM9frtJAoapCYazzY9DNrwN/8j7g98fF7UG9MTbHqXGfpZqKXKUBxoCJuDOPkfOjkUeXJRlwZqwzZ9xpG/f8rWaraGgckYAHY1F7RcXNj0YR8ntQrKlY6NP1wM1VISpcnko4Mp6WEJw1hL/VbBXFYy6mExZiOtmmAYgBiidPCdff6w8zxxoEWM5UkM5V4VUYrp4aEv8Ym9r9eE6Cc2c7/p7/PMx4TwBm7OcHPn/wz/S4568bwz5tY4iXE1OnMDUUhKpzXF/MdHw3rYL21dkh+L20BE44h0jAa5uI+a7AWNP1Z8e4z9Z+PxdBRz2CIE6EojBzwUpGZzqBzUINqs4R9DXdAr1kesh5vVHpPgqjo9EAfB6GWkNHxmF9I+k+OUdbkQ66JRsLf1KUTA2FqAOIcBTSObfsAOFPuv0m4oHOFjNufh34808C6zcBtSZu//yTjhT/nBoVbfYMR3rbM9xLPvehx8yJfxi3nAOffeF8R/cjhb90tmq7SHQZ8zk3ErGd0OVRGC4YXUVSkOsluXIDy5kKGAMupqgjyclEA14zzeMeuf6cR35Z3CamzX96x0wCAZ+CnVId9zY7f05fNcSRK9MJRALGwGfccMoVHCj8VTLi+kbxApExq7emcy5/DPilPwUmHhf9hBOPAx//KnDpowf/jOz565Hjr1vDPkdSKwL1EsAYWGQcz5weBgD8eDnX8TVCa3ztE7ND3d1OgiA6R3bnSZHNLuiaiCAFXNXvB5DwRxBEF5Bxn6sOimlcNbvr+hPPKMWz9Vz1RN0L/USKlP0Q/jwKw7ghIDspDrVQFW6bfjs2pPC3mq2gYdMeMur3I5yKjB1M5yq2P16vmP1+Hb7PXvki8GhgE/DKH3Zx6/rDpHENspav2r73tBXpFp90aL8fAFy7ksKXP/EkLk7GEPAquDgZw5c/8RSuXZns6H4SYR+SYR90zk0x2y5I4U+Kk3bj8ZSI+7y3Wey5aCq7BE8NhxEP9n5ojugtMu7zDgl/zkJTm46uFidbwOvBuwxh49WHOx0lqKzlqljcKUNhDE/OJZv/IR1/hXV79jEdhnT7xSZFV6ETufwx4DN/D/z2hrg9TPQDWoS/DTGF02W6NexzJFK4DA0DXj/mRyMYjQVQV3XcWMq2fTecc1PQfmJmyLFDVgThKobnxcGjtCU69exCbklEkPrD4rzhIkj4IwjixDgxZiud7W88YzLsQ8gveqM2CvZ3JfQ7ChVoujbWHOTakNsqI2/7RTLsQzTghapzWzptdb25eEvCH+E0RiJ+hPweNDSOdZv3xklXYsf9ftt3gUcDm4DtHvS09JjRiDj+1lVnRY5b4RbvBdeupPDiZ5/D7d//CF787HMdi36S06NCWHu4ZR/hL19tYLtYB2P26/eTjMUCGI0FoOkcb68XevY4nHPcSsuYz3jPHofoH+fGhGtzJVNBpe4wUWeQKaSFCOePAMGhXf91dTYJn4dhI1/rqDP1tYdCHLkwGUMi1CLqh4eF20xXgdJmN7a+f8h+v7gD+/2OS2gY8HjF4nWl81jMo+jWsM+RyNeaEV3KGMOzhuvvh4tZ1NX2hl6XMxWs7Y2v7QMvvZnGtS99Gxd++0Vc+9K38dKbDu3IJIhWulUR4Qs1h1bsFPcpt2X4THO6wSWQ8EcQxImRi1aZcsMxHxz7veDGGDMfywmOto1CFarOEfL3JwoVaIlrs/lCeytps9+vvwu3jDHMDtu352+jUEOtoSPgUzAes1cnEkEchej5s388c6HaMDvipjrt0Rw5Bzwa2ASMPNatzesbrZHjThkc0fWmqOx04a9bSEfdw+2Sbbp+FwwRMpUI2tYpwBjDZcP118u4z+VMBblKA36vgrNj0Z49DtE/EmEfxmIB6JwfKxqSsIjW3ro9i5MhvwdXphMAmmLeUWwXa7hruD6fOZ3c/Z+MNRdondbzl18RtzEH9vsdF0Vpxpr2KO6zW8M+hyK3XToYATw2HsVQ2IdqQ8MbK+25hPaNr+0xL72Zxq9+9TpurxVQU3XcXivgV796ncQ/wtl0uyLCjj1/rcKfyyDhjyCIExP0eTAS9QNwRtxn3ohnVBjrazyjXEhetaFDay9SnEwl+hOFKh5L7J/tYg011WkCcv+j2syeP5vFogFNMXImGYaiuGtiihgMmj1/9nt/SaTbbzwWRMDboSjx/OdhxnsCMGM/P/D5Lm5h/2j2/Nn/GgQAtoo1NDSOgE/BcMRv9ebYgulkCF6FoVBVbePcbO33szMXJ2NQGEM6V8V2sdaTx5AxnxcmYtTb6yJk3CcJfw5CClqJmX3/+6m5JDwKw3Km0taw6WsPhTPs3HgUI9F9hvVisufPQcLFrjjUAXL8AT3v+esL8rkzHH+AGPKSXX/XFzJQj6i6SOcq+8fX9pgvfePOo0H6DPjjl52XqEEQJt2uiJDiWuahPWKkq3mguCnerMl5q7em69BVO0EQXUEKH3aMHdyL3MaxWH/jGVOm8FexzTT7QawY+2i6TzGfABAJeBEP+cA5sJ7rzcJVN9F0jo282E4rHBuzw0ZvZL7a816fTlmifj/C4ciev9WsfXvjls1+v2Mcpy9/DPilPwUmHhcxXhOPAx//6tH9MTalKfzZ/xoEaG7nZLx/wzV2x+dRMGOc1xYMwc1KNJ2bQyx27feTRAJenB4V59tb6e7HfdZV3XQEUcynu3jMEP4WtsuOGbobaDhvCn/x/Z1ssaAPlwwX8FGuv1ylgdtr4pghRZVHcKLjr7QhFpN9oUfiUF2PFMukeOY0tAZQ3hZ/bnH8AWLIJRb0olhTjzzXSbffxdSe+Noe82Cr9GiQPgfub1p/XUMQx6bbFRGxlOjSU+tAbvmkW3dypNtPbpfLIOGPIIiu4KRpe+lKTHUajXZCJmIBeBSGcl1Dttzo62N3Aufc7GtM9VH4A5z1Otos1Mw41KE+xaG2Egv6MBzxg3N7uZIamm5OGM8eR5AgCBswGgkg4BO9cRsFew4irBjv+477/SSXPwZ85u+B394Qtw4V/YDm8NFOqW67QYj9sNItbmfMuE8b9PytZiuoqzpCfg8m4vaPrH7cEORupfNdH1a4s1FAXdWRDPsomtZlDEf8GI74oencFu874ghqeaBWBJjSdOLtw9NzSTAmxIbDuuWvL2Sgc45Tw2FMHvTelo9T3hYRb04gL/v9Ho1DdT1Od/yVNoVS5o8Agd2x0l6PYrr3Xnu4c+C5brNQw/3NEhg7RNDuEfOjkUeD9BlwZszeA0QEcSjdrohoddbt3DvJlnUHuQ0ujPkESPgjCKJLTA013UeaTd0REun4m+rzgpvXo2DSiBa1cyRqttxAua7BqzBM9LmfzUmujdVc/+NQ92LGfe7Y5/WUzop+yGjASxF2hGNRlNaeP/sthhZrKjJl0e/XT2e2XWkdwHBCz59V/bB2Rwp/K4boZiUL29LtF3aEK3N+NIqQ34NiTe1696/sDrw8lXDEviDahzFmxn1KVydhY3KG2y86DngOHjpMRvw4PxEDALxuRHnupVRT8abRlfbs/CHiSCAKBONCjHFK3GfBcCceIo66lsi4WFSvFYG6A11m+/T7tXJlKoGQ34NcpYG3N/Z3/b1uOF0fG4/1/bPo5z70mBnvCeOWc+CzL5zv63YQRFfpRUXEyFlxa3XPn66JyFGguU0ug4Q/giC6QjLsQ9DnQUPj2OpRv0g3aGg6Ng33xoGTjT1Eugzt3PMnBa2JeBDePve4mJGxuart41DXWqLarGLWEP66vch3EuS2zA47Y7GUIA5iJineXzJS006sGNs0Fgsg6Ouw38+lyPOHnQdrAKBcV03XvxXXIXZmKOxDIuSDpnPL+2ud0u8n8SgMFybFQr/s4+sGuXIDy5kKGBORaYT7kMLfw+0SGkf0ZhEWI+M249NHfuvTp4Uz6u31ArLlR3tTf7iYhapzpBLBoyPDzbhPhwh/rY6/QcPrB0JGp50TXX/79Pu14vcquDo7BAB47cHOI+sF2XIdt9dlfG3/uv0k166k8OVPPImLkzEEvAouTsbw5U88hWtXJvu+LQTRNXpREZGcF8p4cVN07FlFbllEjvpCrh0WIeGPIIiuwBgzJ9fbKRK3ivV8FToXbqR40Nv3x59q6fmzK1KU7HcUKiAWsb0KQ7Vh7zhUoNWxYZ3bZiYZAmMi3q5Qtcf+kou1soOQIJyKXAhbyVZs1/Mn433J7ddEXoPY3fEnzx0jUT+JtntgjJlddVb2/BVrKjYLNTAGzI04p+vjcaPX695GsWuRt1JEnE2GEQ/2P9ac6D3jsQBiQS/qqm6rQTJiH/JGF1LiaOFvPBbE/GgEnD/q+qs2NPxoOQsAeGZ++OhBvZghoBUc0PPXqDQ74ly6iHskTu75Mx1/+wt/APDE7BD8XgVbxTrub+2+Vnj9YQaci8jNcYuGc69dSeHFzz6H27//Ebz42edI9CPcQbcrIvzh5jHaSteffOzhM66NhibhjyCIruGEmEZTrBmyJp5xqqWHqFK3Zw+RFCWnLFhQ9igMEw6IQy3WVOQrImZvImFd90/Q5zH3lx3iPqsNDet58R6TMaQE4VTGogH4vQpqDd12TvYV4zgtXYlEi/CXt7dj3A5ucTvT2vNn1fP40FhInIgHEfb3f0jsuIzFAhiNBaDqHG+v7x+B1gmcc9xKy5jP+Invj7AnFPfpEDS1KeS06WR7xojwvJnO7xoQ/PFyDnVVx2jUjzOjbbia48birBMcfzKONJQUC8uDiFN7/nS9xfG3f9QnID7/PjEzBAB4tcX1V6g2zGGVZw6LryUIwh7ITj1LhT939/sBJPwRBNFFnOFmk71s1rgkQn6PmTVvR2GrUtewUxJxMP3uQJRMOsC1sWY8dyPRAAJeax0bp2wU97mcKYNzYDjiR4ycAYTDae35W7JR3Ge5rmK7KI7T5PhrMtoi1G6XHo01swt2cIvbmZlkGB6FIVdpWOb8l/1+TnL7AULAuWy4/mQv30lYzlSQqzTg9yqmMES4E/n83t8s2b6rfWAppEUXkT8CBIfa+pHpoRCmkyFoOsf1xSwAUXtxfVE4AJ8+3YbbDwCik0ZvXMHaSLZ2MGM+B9TtB7QIfw5z/FWzgNYAPF4gdLhwd/XUELwKw1quag6/Xl/MQtM5ppMhuj4mCCcgO/UyD8T5rd9U8yJqlDES/giCINphIh6EwhgKVdU2sYOtcM7NBbcpC2IsJVIgTduw50+KkcMRP0J+awQtMzLWxsKfuXBrA8fGrOH4Wdqxzh0hkR+8yO1HuIXWuE+7IPv9RmMBy47TdkRRGMZjwoFt18ERXeemK9qKOG0n4Pcq5oLdAwviPnWdY2FHPO5ph/T7tXJxMgaFMaRzVWyf0KksnRPnJ2Lw9bnzmegvU4kQwn4Pqg3NPMcQNsPs95vqKI7s2dNCQHlzJYdKXTNvEyEfLky02dvp9QORUfHngs1df3L7YgPY7yeRMZnlbSGkOQXpUIyMAcrh55xIwIsr0wkAwKsPd1Cpa3jDiK+Vr3mCIGxOLCW69dQ6kF/p/+NLp2Fs0tUOcbqCJwiia/i9CkZjws1mx7jPbLmBSl2DV2EYj1m34GbnLkQrYz4lKeOxt4s11FR7xqGmLexB3MvUUBBehaFYU023plVI1yH1+xFuYVoKf5mK5cK6ZNlYlJ2haeZHkC46O16DAMBWqYa6qsPvVTBiuP+JR7Gy528tX0WtoSPo8zgyjjUS8Jr771b6+HGfdVU3Yx8p5tP9KArD2THh+ruzcfKYWKIHyEXRxExHPzY3EsZ4PIC6Kpx+P1iQbr8kFKWD2gsppOVt3PPHeYtAOsCOP39ULGJzDpQ2rd6a9jH7/Q6O+WzlqdNJKIxhaaeMv765hobGMR4POM6tTxADS6vTbvte/x/f7Pc72//H7iMk/BEE0VVkPKQtRS3DzTYRD8LTyQedLiMn2dfzVaiabtl27IcUtKx0REYDXsSCXnAObOTt1asFAFqrY8MGUW1ej2IKtVbGERaqDeyU6mCMescI9zAeC8LvVVBtaNgq2iM+ctns97P++GM35DBG2oZR2sDufj8reoadwpzhtFveqaDR5+sk2e83NxLubFHcRjxuCHW30nnox4xtvLNRQF3VMRT2YSrhPAGU6BwZ93lvs3js1w3RIzhvCn9t9vtJGGN4xnBAvfpgB4WqikjAY8YCt418XDs7/mp5oF4CmCLiSQcVxpwZ92n2+4239e3xoA+XUsK1en9TnLufbTe+liAIe2BVz5+uiYjR1m1wKST8EQTRVZqLbvabtreLS2so7EPY74Gqc2wU7CNsqZqONUPQsqrfT5KysYC8VaxB1TmCPg+SYXv02M3aoOdPxnxOxIMI+ih+kHAHHoWZLu3ljPU9mpW6hi3jvDFNwt8jyOdqu1hHtWE/x/iqTa5D7M5IxI9Y0AtV532PHXzo0H6/VuZHowj5PSjW1GNfF8iOwMupOC2iDgizw2EEfApKNQ3pvP0+xw00tTxQKwpBK9a5k+3cWHTXZ5YnTyXh7TS+t1X40+01uGoi3X7RcdETN8hI8cxRwl9njj9A9lSKPw9H/NRHSxBOY/iMGFYoboge2X6RXxERo77Qsc6rToKEP4IguooUbDbytb5PaR+FdABY7dJijJlxlnYSttYLNWg6R9jvwZDFgpZcFF2z4cKDfM5SCfs4NmSn3nKmbNmUthnzSW4/wmVIB6sdev7kNoxE/Qj7B3xRax/Cfi8SIXH+Wrfh+WPNJtchdocxZvbr9bPnr1xXzdfNnAP7/SQeheHCpHBByJ6+TshVGljOVMAYcIliPgcGj8JwZlQsmsuYV8Im5Ay3X3Qc8HT+GU1RGJ42XH8Bn4J3zCQ634bwqBDT1DpQ2en85/tBaw/ioGM6/tat3Y52qZcMcZsBkfYcf4AQ+y5OivPUe86M2OazOUEQbeIPi449oL+uPzPmc/7ITlGn09Pf7l/+y3+J973vfQiHwxgaGmrrZzjn+MIXvoCpqSmEQiF84AMfwE9+8pNebiZBEF0kHvQiGvBC59xWi27VhoZto/8sZYPIomlD2LLDQrIkLQWtoZDlF83yOUrnqrbp1ZKYUW02eB1JxmMBBHwKag3dEhcp5xxLhvAnRUhigLj5deBP3gf8/ri4vfl1q7eoq9ip50+6Dqep3+9AZFS1dNfZhUpdQ6bcAGCP6xC7Y/b8bfVP+Fsw3H5jsQCiAWcL648bMX73Noodu19vGWLhbDKMeNAeyQZEf5BumbsbRcvPd0QLpqA1fey7uJyK47nzY/joO6cQ8B4jmUNpcRvatedPxpC63L3RFq3CnxPey1KgDCUBb2cdyD99eQK/8r7T5sALQRAOw4qeP/lYLo/5BHos/NXrdfziL/4iPvOZz7T9M//6X/9r/Jt/82/w7/7dv8Nrr72GyclJ/PRP/zQKBSqZJggnINxs9ov7XMtVwbmI2YzYYDFHdrLZSdiSIuS0DSLIxqIBeBSGSl1D1lgotQvydW2nhVtFYaYryYq4z51SHcWaCq/CKMJu0Lj5deDPPwms3wTUmrj980+6SvybjAfh8zCU6xp2Stb2/C1nZL8fCewHMWm46dby9hmsAZqpA8MRP8Uht8HscBgKY8iUG8j16TpA9vvNjzrX7ScZiwUwGgtA1TneXm//czTnvBnzSW6/gWNuJAyfhyFfaWDTRnUEA09+Wdwmji/8KQrDU3NJsx7gWNhZ+NP1pvBHjj8gNAwoXkBrAJWM1VtzNB32+7XiURiGI52JhQRB2Ijhs+I287A/UdK1QvOYQ8Lfyfi93/s9/MZv/Abe8Y53tPX9nHN86Utfwv/yv/wv+IVf+AVcuXIF//k//2eUy2X8l//yX3q5qQRBdBE79rOt2ixeazwWhNcQtjI2ELY456agNWUDJ4nXo2AiHgBgLwG5VFORqzTAmL0cf0DTaWeF8LeUabpFfZ12hhDO5pUvAmAA5AADF39/5Q+t26YuI3r+xHFxuc99Y61UGxq2itTvdxR2dYzb0S1uZwJej+nefNiHuE9d51jYcX6/n4QxhsspI+5ztf24z5VsBblKA36vQl1JA4jPo+C0IXxT3KdN0NTmAqXVgpbZ82dD4a+8JfaV1w+ER6zeGutRFCA6Jv7shJ6/Y/T7EQThEmIp0bWn1pqDLr1ExnzGJgG/84f9jsJWq3MPHjzA2toaPvzhD5v/FggE8Pzzz+M73/mOhVtGEEQnyIWaNRstuqWzUtSyx4KbR2GYSMg4MusF0ky5gUpdg1dhGI/ZYx/Z0bUhRciRiP94MTk9RAp/6Wyl7/2aixTzObhs30VT9JNwYPuOFVvTM8y4TwuP1yvZCjgHkmGf42MIe8loNACfh6HW0C13aLZiR7e43ZECRD+Ev41CDZW6Br9Xsc2Q2Em5OBmHwhjSuWrb7wUpEp6fiNEgz4Bixn1ukvBnCwppQNfE4mRwyNptkcJfcVM4yeyEdCHGpkRP3HFwW3S97MpzQs+f6fgj4Y8gBg5FEV17QH96/uRjjJzt/WPZAFtdza+trQEAJiZ2H+wnJibM/9tLrVZDPp/f9UUQhLWMRQPwKiIWLVex/kOBrnOs5eWCm30Wc6Zs5IyU2zCRCMKj2KMUO5WwX09T07Fhn9eRJBn2IRb0QtW5KXT3A13nZu8YCX/2oK/XRiPnIBx/rTBg5LHePaYFzCSl469s2UDLCsV8toVHYRiP2yty3K7XIXZHOu+WdspQezzQIsXFU8Nh21wHnZRIwGt2Jbbj+qurOu4YLi+K+RxcTo9E4FEYtot1bBfdEffp6DUjs9/vBIJWtwjEhQDJdfuJSeZ+Oma/nxuj682eP5s7/rQGUN4Wfz5G1CdBEC5ARm72WvjTdWDnwe7HdDkdC39f+MIXwBg79Ov1118/0UaxPRc0nPNH/k3yB3/wB0gkEubX7OzsiR6bIIiT4/UoGDdiGu0g2myVaqirOvxeBSM2yn+X7kM7CX/TNoj5lEjhb6sonj870IyMtZ9jgzFrev42CjXUGjoCPgXjsUDfHpc4mL5eGz3/eZjxngDM2M8PfL53j2kBk3ERz1yqWdc7KmNGKebzaOQxes0mwt92qW7L6xC7MxYNIBrwoqHxnl9Pyn6/0yPuivy5nBIC3q10Hrp++NDC3Y0i6qqOobAPUza8ziH6Q9DnMQe57m323m3bDxy9ZmT2+81Yux2AEB6l6y+ftnZb9lJocfwdBzdG10cd4vgrbQKcA/4w4KeIaYIYSKQIV1gHaj1MHMiviOEOX/D45wuH0bHw9+u//uu4devWoV9Xrlw51sZMTk4CwCPuvo2NjUdcgJLf+q3fQi6XM7+WlpaO9dgEQXQXOdGezlkvakn302Q8CMVGU9yySy9TbqBcVy3dFin82UnQigWFg41zYD1v/eKtrnNs5O0d1WZFz598rJlk2Fbvr0Gmr9dGlz8G/NKfAhOPA96AuP34V4FLH+3dY1qA16OY3WxW9PzVVA0bBXH8mSHh70jsdA0CNAXICZtdh9gdxpjp+utl3GelrpmOzLlRdzlq50cjCPo8KNbUI68NbqaFC+pyKn7g0C0xGJhxny7p+XPsmhHnux1/diBmOOrs1POn1oHSlvjzcR1/boyul8JfrQDUbSzit/b70bmHIAYTf0R07gG9df3t3BO3yXkRMToAdFwQMjo6itHR0V5sC+bn5zE5OYn//t//O65evQoAqNfreOWVV/CHf7j/pE0gEEAgQA4DgrAbU0NB/GABWLXBtL3Zq2OTfj9J0OfBSNSP7WIdq9mq+SG735TrKjKGg2XKRo4/AJhMBFGoFpHOVTFrcYzkVrGGhsYR8CkYtqljY3ZYPH8bhSqqDQ1BX+97CKnfz370/dro8sfEl8uZToawnKlgOVPGO2YSfX3s1WwVnAOJkA+xoK+vj+1E5HDGdqnet2PhYUi3OLmoOuf0aAQ/Wc3j4XYJz2GsJ4+xuFMG58Bo1I+4y95fXo+Ci5Mx3FjK4mY6b/Ym7iVXaWBppwzGgEsU8znwnBmLgDExeJerNJAIOft94dg1o1peOB+Y0hTcrEYKa3kbCX/FNSGSBmLi6ziMnBPxnrvEP4dH13sDQCgJVDIi7lN2aNkNs9+PYj4JYqAZOQsU1oQ4l3pnbx5jwPr9gB53/C0uLuLGjRtYXFyEpmm4ceMGbty4gWKxOTl28eJFfO1rXwMgpjo/97nP4V/9q3+Fr33ta3jzzTfxqU99CuFwGL/8y7/cy00lCKLLyGn77WIN1YZm6bakzQU3e4lagD16/mR81mjUb/ni6F7s5NqQInYqEbTtJHws6MNwxA/OYfbu9ZKGpiNtvHZJ+CPczqwRpbuSrfS956/Z72e/85gdiQS8iId84BzYyFvfUdXshyXhr1NODYfBGLBdrCNf7U3MrnQTzrks5lMi+/rubRQPvCa/Zbj9ZpNh14mfROeE/V4z/t8trj9HklsRt9FxwGOT96UUICtZoN6/hJFDkbGjx3X7Ae6NrjfjPm3c82cKf/unvBEEMSCYPX8PRBdft6kVRZQoIBx/A0JPhb/f+Z3fwdWrV/G7v/u7KBaLuHr1Kq5evbqrA/D27dvI5XLm3//Fv/gX+NznPodf+7Vfw9NPP42VlRX89V//NWKxY07uEARhCZGAFwlj0c3KmMZSTUW23ABj9lxwkw47K4WtZsyn/RaUW3ua+r3Qvpc14zmajNtvP7XSz7jPdLYKVeeIBrxIhm2yIEEQPWIyEYRHYShUVeQq/e35k0I+9fu1j3TXrVo8OFJtaNgp1QHY8zxrd4I+j3ktsLDV/fMa5xwL2+7s95OMxwIYjfqh6hxvrxce+X/OOW6uGjGf5PYjDGQSyT0S/qzDjPmctnY7WvGFgPCw+HPBJj1/hS7sJ7dG10sxza49f5zvjvokCGJwiU2J7j21Jrr4uo10+8UmgcDg9In2VPj7yle+As75I18f+MAHzO/hnONTn/qU+XfGGL7whS8gnU6jWq3ilVdeOXZnIEEQ1jJlRGtKR5kVyJjPkYj93GxAcx+t52toaD2YamkD0xFps5hPQCxWeRSGcl3r+0L7XtI5e/f7SWQk6tJO7xe7pbg4Oxy2rQuSILqFz6NgMt7/nr+6qmPdcK3NJMlZ2y6TLYMjViLPHcmwDyG//a5DnIAU5HrR87dZqKFU0+D3KuY1mdtgjJmCnhT4WlnJVpCrNOD3Kjg7NjgLIcThSOFvNVdBqWZtF/nAkl8WtwkbCX9As2/QLnGf0vF30jjUyx8DPvP3wG9viFuni36A/YW/SgbQGoDiBULDVm8NQRBWoihNJ14vev5kv590Fg4Ig9FkSBCEJdghplE+tl2n7BMhHyIBDzSdW+KMbGjNBWU7Lnh5PQrGY6KTI23h4m25LpyjgD2do63MJENgDNgp1VHoUSyaZClD/X7EYCEdd/0U/tK5CnTOEQt6Hd+z1E+a1yDWOsbldcikTa9DnIDspVvcKUPTu/tcPtwW57GZZAhej3s/Gl+cjENhDOlc1XSgSqQYeH4iBr/XvfuA6IxY0IdUIgjOgXub5PrrO5rajECUQptdiBnbYwfHX60IVHMAY8LFQexGRn2Wd8Rrym6YMZ9jYtGfIIjBRnbvSZGuW+i6iBBtfYwBgY6sBEH0jJQhJKVzVehdXqhpl7ThNkzZUNQCxBR26+Jkv1nPV6HpHJGAx7YLynZwbZjOURv2IO4l6PNgwnAl9TLus9rQTLF6dpgWtInBQHbsrfSxl3XZ7Pcjgb0TxmIBeBWGakNDpmydY3zNIW5xOzMeCyDs96Cu6l3vRH7o8phPSSTgxelRcQxpdf3VVR13jChHivkk9iJdf9TzZwGFNKBrgD8CBIes3prdyC69/KqIarQSKT6GR0REJ7GbQEzEs3IdKG1avTWPQjGfBEG0Ih1/hXUx2NEt8isiQtQXbA6vDAgk/BEE0TNGIwH4vQrqqo7tPdPF/aDVRTdl40l7GbHZ7cWsdpCC1tRQyLZRjVIYtbKnSS7cypg/u3OqD3Gfy5kyOAeGI37EgvYUjQmi26QSISiMIV9p9C1+eMUU/ux7HrMjHoWZQxBWJQ9wzpsx0TYdQHICjDHMjYjz2sJ29wZaqg3NHBBzu/AHAJdTQti7lc6bA3l3N4qoqzqGwj6zF5MgJDL6dWmngmpDs3hrBgyz329KuNnsRGQcUDxAowJUs9ZuS+t+Ih6FMXvHfZqOv3Frt4MgCHsQiAIx45jVzbhPeV/J0wPnLh6s35YgiL6iKMwUSqxYdNsoVKHqHCG/B0Nh+woT06bw1/84Mik22jUKFWgulm4V6pb1IMr9ZMcexP1oCn/lnr2mpJuQYj6JQcLvVTARFxPlK32I+2xoOtaMARYS/jrHasf4dqmOuqrD71UwGiEnwkmQcZ/d7Plb2ilD5xzDET8SNr5O7BbzoxEEfR4Ua6oZ1X0zLdx/l1Nx2w6AEdaRjPgxGgtA5xz3N7vfsUkcgtnvN2PtduyHx9sUaqzu+St0qd/PzcjnSopsdoIcfwRB7GVYxn32QPgbHqyYT4CEP4IgeowUbVaz/V90k4+ZSgRtvZgxFgvA5xFxZHt7V3oJ59zcR9M2FrRiAS+iAS90bk0Poq5zbBRED6Ld+/0kqUQQXoWhWFN79pqSbsJZEv6IAUNGbi5nehelK1nLiThm6vc7HrK7dtUi4U8KjuOxABTFvtchTmBuOALGgM1CDcVad3qKZL+fdBO6Ha9HwcXJGAAR95mrNLC0UwZjwCWK+SQO4Jzh+rtLPX/9g3P7O9lkVFrewp4/J+wnO2BXx1+9BNQK4s+RMWu3hSAI+zB8RtxmHohuvpNSKwKFtd33PUCQ8EcQRE+ZMvvrrIixdIZLqzWOrJ8C6U6pjmpDg8/DMBazrxOBMWYKblb0IG6VaqZjYyTi7/vjHwevRzFf90s9cCUVqg3slOpgjFxIxOAx3ceeP+nKmbZxHLOdmTSuQbaLNdTU/sfUOc0tbmdC/mZ/7cOtkzuPOOdYGJB+v1Zkj9/djSJuLGUBALPJMOIU2U0cgOz5W9gqoa5ak7wxcNTyYqGSKfZ1skmhrWCh46+SEZ1NHi8JR4chhb/ShvWdjK1IB2IoSf2MBEE0iU+LY0Kj2p1zTOaBuI1NiCjRAYOEP4IgeooUbLLlBsr17kxotwPn3OxtSTnApWXGffZRIJUi40Q8CI/NnQjStWGF8Jd2iHN0L6cMB4OM5Owm8j4n4kEEfZ6u3z9B2JmpoSAYE+e1QrW3PX/Nfr/BcCR1m2jAi1jQC86BjXyt748vY1qd4ha3O93s+dsu1VGoqvAqzBTzB4HxWACjUT9UneOHixkATTGQIPZjNOrHUNgHVW+K5USPya2I2+g44LGpKG8Kf+uAblH/Y17up0nROUjsT3gYULyAWhdiqV2gfj+CIPZDUYDhefHnbsR9bt8TtwPo9gNI+CMIoscEfR6MRoVLqp+iTb6qolhTobCmm87OpMyev/4Jf9KtYueYT4l0bazlKn3vQZSvW6ct3M62xBHqenf3mYz5pH4/YhAJeD0Yj4njQS9df6qmm1GRgyRMdJspC86vAFBtaNguiqhlJwwgOYF5o+dvYad04vOadA3ODofh8wzOR2LGmCn0cS56S8+ODd70M9E+jDHT9Xdng+I++4IZXzlt7XYchnRp6SpQ2rRmG2TMaNymrki7oHiAyKj4s516/qjfjyCIg5BdfFK0Oy663nT8DWC/H0DCH0EQfUCKNuk+xljKmE/Rn2f/Q51wkwkHSalL3TVHIfdRygHC33gsAIUxlGoa8pX+OUcBITYCQCph//3UyngsgIBPQa2hY73Qvfce5xxLhuNvllxIxIAiI26Xd3onJq3lq1B1jkjAg2TYphP/DkAObaz1uSNWirZDYR/Cfm9fH9utTMSEy7zW0E/8fA5av18rFybjUIwEg8fGo/B77X+dTFiLFP4ebJWgahT32XPyy+I2YWPhj7Gm6y9vUdynjICLUb/fkdix54+EP4IgDkK68wprog/0uBRWRWSoN2DvYZoeQlf5BEH0HDnp3s8YSzOeccgZU/ZBnwcjUZFt348+xFJNRbbcAGPOcCL4PArG48b+yffvdVSpa8iURZSfE/ZTK4rCTGFuqYvixE6pjmJNxKNNOeT9RRDdRjrwljPdj9KVLGekKzvsqJhhu5Fq6Yjtp2NcusWddu6wM4rCTKHuJD1/NVUzHaCD1O8niQa8uJiKwaswvGt2yOrNIRzAZDyIWNCLuqr3JEKeaEFTm66suM0FLdk/aIXwt2s/kePvSMyeP4vcmXvRVKC8I/5MUZ8EQewlEBWdfMDJ4j7lzw7PiwjRAWQwf2uCIPqKjNlaz1WhdTly8CCkyDjlIJfWVEJGx/XelSDFxZFowDEdbZOJ/vf8yf00HPE7Zj+1Mjvc/Z6/JUOMmBoKwesANy1B9ILpoRAYAzLlBoo9cmk3+/2ccx6zI+OxILwKQ6WuIVvubSdjK2mHusXtjin8naDnbzlTgaZzJEI+DA2om/anL03gnz1/BuMOiMMnrIcxhrOG6+8uxX32lkJadOb5I0BwyOqtORyz5y/d/8cubYj95AvZfz/ZASmu2cXxV9oEuC6ev0DM6q0hCMKOSNffSYS/Ae/3A0j4IwiiDyTDPgR9Hqg6x2ah1vPHq6s6tgpGr46DHEn97CGS4uKUg5wIpmujj5Gxaw7t95PIDr50toJGl6KZpIg4S/1+xAAj+muFC1kKdN1E07kpHFG/38nwKKzpGO/T4Ajn3IyiJMdfd5EOvfV8FeX68UR36RacH40MrJtWURgCXucNNBHWcc7ogry/dfKOTeIQzH6/KRGnaWek46+8Dai9/4y/C7Pfb9r++8kOSOGvmgfqNnDttsZ80vNHEMR+yE6+nfuiq69T6iURFQqQ8EcQBNFLGGtGAvYj7nM9X4XOOWJBL+JB50xyS3fiRr7WNZHmIKS4OOWAfj+JdE1sFnq/fySrOSmQOmc/tZIM+xALeqHqvCuCqa5zM9rwFAl/xIAjnXgr2e4voKzlq2hoHCG/ByMRf9fvf9CQXcNrfYqK3inVUWvo8HmYKRAT3SES8JpC7sIxXH+c84Hu9yOI4zI9FELI70GlrmGlD0OKA4vZ7zdj7Xa0QyAKBBMA5/13/eVXxC3FfLaHNwCEkuLPpQ1rtwVoxrRSzCdBEAcRnxbHrkb1eOcY6RSMjg+0s5iEP4Ig+oIUbfrh1mr26jhLrImHvIgGvNA5N51mvaCh6djIi6lMJwl/8aAXkYAHOufY6INzVNc51vPOdvwxxjCT7F7c53qhilpDR8CnYDxGi9nEYDNj9vx1fwF0xez3Cw2sI6mbSHf7ap8c4/I6ZCIehKLQ89dtpOvvOD1/mXID+UoDHqV5fiQI4mgUheHsGMV99hTOdzv+nIAU3vJ9Fv7kInCMhL+2MeM+7SD8tTj+CIIg9kNRRDcfAOzc6/znpfA3crZ72+RASPgjCKIvmDGNfXD8mb06Dor5BKQz0hBIeyj8reWEIzIa8CIe9PbscboNY8x0baT7MGm8XaqjrurwexVHO25OdbHnb2lH7PfZZJgWs4mBZ3pIvLe2i/VjRw4ehHTWUr9fd5DDG1vFGupq7x3jTh1AcgrSqbewU+44cvDhthALp4dC8HvpozBBdMK5lp4/zinus+vU8kCtCDDFOYJWTPb8rfbvMRsVoLwj/uwUgdQOSJHN6p4/zpuuQxL+CII4jOP2/Ol682cGOOYTIOGPIIg+MREPQmEMhaqKfLXRs8fhnJsT/U6MZ5RiZS97/lpjPp3mJJkyBeR+OEfFfpp0uGPjlLFAulGootrQTnRf1O9HEE1Cfg9GY93v+RP9fuIYR46k7hALithjzmE6uXvJmjx/ONQtbndSCSHaVepaxwkA0iV4epTeWwTRKbNJ8d4r1lSzx5ToIjkjvjI6DngcUldhOv76KPxJt18oCfic93nfMkzHn8XCXyUDqHVA8QLhEWu3hSAIeyNFu8Ka6Oxrl0JaRIR6/SIydIAh4Y8giL7g9yoYMxZIexn3mSk3UG1o8CrMfDwnMW04/lZzlZ5N0soF5SmHOSKB5iJquof7R9J0bDhvP7USDXgxEvWD86aL6Dg0NN10WlK/H0EIZoxj9nIXhzU2ClXUVR1BnwejUee6je2GGTne48GRakPDdqluPKazzx92xaMw0/UnHXzt0NB0U6SXcaEEQbSP16PgzKh471DcZw+Q4pkT+v0k0UnhUKwVgWq+P48pY0Wp368zpPBX2ga07iZVdISMGo2Miig/giCIgwjExLGLc2DnQfs/J91+yXlA8fRm2xwCHWUJgugbpputh3Gf0s02kQjC40CX1lg0AL9XQa2hmwuH3YRzbu5/J/X7SaRztFTTkK/29gOL7Fl0g2Njtgs9f+lsFarOEQt6kQw7ZAqZIHpML3r+zH6/pPNc2XZmsk+R4+v5KjgHEiEfIgHnxGk7jeP0/C1nKuZ5bNjBEd4EYSUU99lD8svi1knxlV6/EHCAphOv18jHGXAXR8cE4oAvCHAdKG9Ztx3U70cQRCccJ+5TdgIOeMwnQMIfQRB9ZMrsZ+vdtL3pZnNgzCcAKArDRLx3cZ9bxTpqDdFbNxZ1niPS52k6R9d66NqoNjTsmI4NZ76WWpHRnIvbxxf+pGg4kwyTGEEQBtOG8LddrJ04SlciRcRpBw5n2JlUS1R0Lxer3eIWtzvS8beWr6JSb++9J92Bp0cidB4jiGMyNxKBV2HIlhvYLHYWtUscgqY2nVBOEv6A5vb2I+6TcyBvRKI6pQfRLjBmj56/IvX7EQTRASNnxe3OfXEOOIp6SUSDAiT8gYQ/giD6iHT8bRZqaGh6Tx5DTvKnHBhjKZnqYc+f3D8TDu6tS/XBtSEXbpNhH0J+50cDzCRDYExE4RaO2bEphT+K+SSIJmF/a5TuyY9Jus6xYhz7Z5Mk/HWT8VgAHoWhUteQq/Sua7h5HULPXy+JBX0YNd577brZqd+PIE6O36tgjuI+u08hDega4I8AwSGrt6YzpADXD8dfNQfUyyK6jYSjzjF7/jas2wbT8Tdu3TYQBOEc4tPCXd6otHee2XkgBMLoGBCM9377bA4JfwRB9I1YwItY0Audc6z3oBC+2tCwXXR+r47Z89cDZ6QUE53Y7yeRom4ve5rkwu2kC9x+ABD0eUwn6XHiPqsNDRsFsb9PjdCCKUG0Io/ZK10Y1tgs1lBXdQR8CkYd6Mq2M16PgnHZNdyj8wfnHGs54YBx8nWIUzhtiA/t9Pxly3Vkyw0ojJkueIIgjse5MRH3eY+Ev+4h3XLxKeHMchLS8VdIA3pvhntN5KJvZAzwUJx2x1jt+KuXgVrB2BYS/giCaAPFI7r6AGD73tHfLyNBh8/2bpscBAl/BEH0DcZYS8dO9xfd5H0OhX0I+537QWAyEQRjQK7SQLHW3R47KSY6OUIuFRfbvlmoQe2Vc9TYT04WSPcinXpLxxD+ljNlcA4MR/yIUmcVQexixujQXM4cP0pX0hrz6VRXtp3pdc9fptxAtaHB52Ek3PYB2fO3sF06Mr71oRF1PTUURMDrfCc/QVjJmbEIFMawVawj04NO8oFE9vslZqzdjuMQHgU8PkCtA+Xt3j5Wq0BKdE6r8GdFR2fJcBqGkoCXrpMIgmiTdnv+OG8R/ijmEyDhjyCIPiP70noSY2ncp9M72QJej7lg2M39VKypyFUaYKy5+OlE4iEvwn4PNJ1jo9D9bhFd51gzHKlO3k97aQp/lY77rSjmkyAORvb8bRZO3vMnxUMnD2fYmSljv/bK8SfP2ePxIDwk3PacqaEQ/F4FpZqGzSOuBxZkv5/hEiQI4vgEfR6cGhHH07ub5Po7MZw7W9BSFCA2Kf5c6HHPn3T8Ub/f8QiPCPeMWgeq2f4/vtnvR24/giA6QIp4hbRwDh9EIS0iQb1+Zw7S9AAS/giC6CtTLTGNnYoPR7Gac49Lqxc9f/K+RqMBR0+7M8bM7qReuDZ2ynXUVR1+r4LRiHsmEVOJILwKQ7GmYqfD6exFwylB8WgE8SjRgBfJsA+cn+yYzTk3XdnSRUh0FznMsVUQx/lus2Zch1DMZ3/wKAwzhvAuHX37oWq66Xafo7hqgugK58ZiAKjnrytUc0CtCDDFuYKW3O58D3v+dL0p/DlRILUDigeIjIo/W9HzZ/b7UT8jQRAdEIyLzj7OgcyDg79PRoEmT4vjHUHCH0EQ/WU8JsSHSl1Dttzo2v3qerM30OmOP6DpSuhmz59ckHaDkyTVy8hYY59PxIOuitrzehTzddVJz1+h2kCmLJyicoGVIIjdTJtxn8cX/jaLwjHo9za76IjuEgt4EQ30rms4nSfhr9/Mt9Hzt5KtoKFxRANejFEEK0F0hTNjETAmBh7y1e59phtIpNsvOi4iM51IfFrc9tLxV94CNFU4OcIjvXsct2Nlzx8JfwRBHBfZ2XdYzx/1+z0CCX8EQfQVj8IwETfcbF10a22VaqZLayTi79r9WoUUaDYLta65EqSImHKBI3LSeA2t9aQrUkbGOn8/7eWU4XRY6kCckCLhRDyIoI+mpghiP6QovnICx58UDaeG3DV0YCeEY9w4f3RZ+KupGraLIm5y0gUDSE5hzuj5S2erB0btSjfg3EgYjNF7iyC6QSTgNT+v3CPX38mQwp+TY8nihuOvuAloPRKC8yviNjYF0LH8+JjCX58df5oKlIwOSIr6JAiiU2TcZ+bB/h2l9XLTFU79fiYk/BEE0Xfkolu6i242eV+phDsWTONBH2LB7rkS6qpu9t9MucDxNxEPQmEMhara9SljN/b7SZo9f2XoentRu0vU70cQRyJ7/jbyNdTU4/X8rRjCH8V89pZeOcbXczVwDsRDPkQD3q7eN3EwiZAPwxE/dM7N89VeqN+PIHrDu2aH8Nz5UZwZi1q9Kc4mvyxunRxfGYgD/gjA9d45yWSMaNyhcah2QYpu/Xb8lbfE68MXBAKx/j42QRDOJzEjHN+tAl8rUhCMjoloUAIACX8EQViAjOLsZj9b06XlfFFLIgW6kzhIJOv5KnTOEQt6EQ86NEKmBb9XwWhMODu76fqrNjRsF0X/nRsdf2PRAAI+BXVVx3rh6P3GOcfSjnj9kfBHEAcTD/qQCPmgc36soRbOuXmsd0Mcs52Rbrx0ttLVrmE3u8Xtjuzt26/nL1dpYLtYB2N0HiOIbnN+Ioan5oaRCDn/s4VlaGrTeeVk4Y+x5vb3qudPxojGHLyf7EDEEP6qeaDRvfWYI2mN+STHJkEQnaJ4RHcf0Iz0bEVGgJLbbxck/BEE0XemDMffdql+YCxTp8gYyykXxFhKpPDXDYFULii7we0n6YVrQ4qIQ2Efwn73OTYUhWHWcBMt7rNAupedUh3FmgqvwmgxmyCOQMZ9Hqfnb7tUR6WuwedpxmETvWEiFoBHYSjXNeQratfuV56L6FjZf2TP38J26RExV7r9phIhiqsmCMJ+FNKArgm3XHDI6q05GTHDiScjObuJWgdKW+LP5Pg7Gb4gEBoSf+6n608K3BTzSRDEcZGi3t6eP86F46/1ewgAJPwRBGEBYb8XQ2EfOO+OW6tYU5GrNMAYXLVgOmUsHq5mq23HMh7EqiuFv6Zro1sMwsKtGffZhjgh+/2mhkLweuiSgSAOY9rs+TtaVN+LFAtTiRA8LoirtjNej4KxWAAAkM535/zBOW85f7jnPOsUpodC8HlE/Pd2qb7r/1r7/QiCIGyH7PeLu6C3Tgpy+0WwnZTimljYDcQoJrIbmHGffez5a3X8EQRBHIfhs+K2kBaRn5LCmvi7xwckZq3ZNpvS01W8f/kv/yXe9773IRwOY2hoqK2f+dSnPgXG2K6v97znPb3cTIIgLEAujK12wc22ZtzHSDTgqmnu0WgAfq+IZdwq1Y59P7reXJCccpGgJcW5jUINqqZ35T7dGBm7l1lD+FvNVtA4Yr9JcfAULZgSxJHIbr61XA11tbNjUrPfz73HHjshO1y71TWcLTdQbWjwKswUFYn+4fUo5vvv4VbJ/HdNb/b+Ub8fQRC2RPb7JWas3Y5uICM4K9ndC7LdwOz3o5jPriDFt345/jgn4Y8giJMTjAORUcPh97D57zuGAzB5WkSCEiY9Ff7q9Tp+8Rd/EZ/5zGc6+rlr164hnU6bX3/5l3/Zoy0kCMIqZCRnNxbdzJhPF4lagIhlTHVhcXKrJBah/V4Fo1H3LEgmQj6E/B5oOsdm8fjCqIRzjrW8+x1/ybAPsaAXms5NJ+h+6C0LpjIelCCIg0mExHtL57wjNzvnHMsZ8V6bJuGvL0yZXcPdEf7kENNEPEiOTYvYr+dvNVtBXdUR9nswToIsQRB2g/Pdjj+n4wsC4RHx5267/mR8qBv2kx3ot/BXzYq4VsXTfI0QBEEchxHD9bfTEvcpO//k/xEmPRX+fu/3fg+/8Ru/gXe84x0d/VwgEMDk5KT5NTw83KMtJAjCKqSjai1/8hhLN7u0TGfkCeIspWiYSgShuGhBkrGmMLraBQF5p1RHraHD52GuEkj3whgzXX9LOwe/rtYLVdRVHQGfQgumBNEm0nUkhbx2yJQbKNeFW2zSRXHVdkY6/jYLtSOdz+0ghd5JFw+N2B3Z87earaCmiv7oh0a/39xIBMzpEXoEQbiPag6oFQGmNPvxnI6M+5SCZreQQqJb9pPVyKjP8o7omOw1MlI0MkpuHIIgTobs8Nu5LwZoGpXmOYf6/R7BloU93/rWtzA+Po7z58/j05/+NDY2+pg7TRBEXxiJ+LsSY6lqOtbz4ueli9BNTA/JzqjjC39SNHSzMNqNrkjp/JiIu0sg3Q/p4JMdfvuxuN10+7l9fxBEt5BRncsdHLOlSDiZCFKXZp+IB72IBDzQOcd6vnvnDze7xe3OUNiPobAPms7Nzkzp/js9Sq51giBsiFyojI6LXiI3IOM+u+n4qxWBal50IJLw1x0CceHQ1DWgtNX7x6OYT4IgukViVpwz62XR7bfzQAiAkVEgmLB662yH7VYXPvKRj+DP/uzP8M1vfhN/9Ed/hNdeew0f/OAHUavtLwzUajXk8/ldXwRB2J/WGMuTiDYbhRo0nSPs9yARcskHphYmEgEwBhSqKgrVxrHuQ4qGUkR0E2YUahe6IpsLt+7bT3uRnX0bhSqqjf2nPM1+v2FaMHUadG1kHVL4W8tV23aSNfv96L3WL4RjvDuDIzVVw5YRN51y4XnWSZweEa6/h1slFKoNbBVqYAyYG6Z+P4IYZGx7XSSFPzf0+0laHX/8ZKk+JlJEDI8AXn937nPQYay/cZ/S8UfCH0EQJ0XxiC4/QLj+ZOQnuf32pWPh7wtf+AIYY4d+vf7668feoI9//OP4mZ/5GVy5cgUf/ehH8eKLL+Ltt9/GX/zFX+z7/X/wB3+ARCJhfs3Ozh77sQmC6C+TXYhplILPZCLoyhingNeDMSNm8Tj7qVBtoFBVoTCGiYT74hon4sETC6OStZbXktuJBrwYifrB+f6RhA1NN52isyT8OQ66NrKORMiHaEB0aLYjKIl+Pyn8kWjUT5qDIycT/jbyNXAOxIJeRAPebmwacUxae/4WDLffRDyIkJ9ixQhikLHtdVF+Wdy6qbcuMi4WZRsVoJLpzn26qQfRTsi4z2IfEtZMx9947x+LIAj309rzR/1+h9Kx8Pfrv/7ruHXr1qFfV65c6doGplIpzM3N4c6dO/v+/2/91m8hl8uZX0tLS117bIIgesuUMW1/EreWFMOmXDxlL3+31WPsJ7l/RmN+BLzuW/jyexWzj+8kro1qQ8N2qQ7AnZGx+yEFvf3iPlezFWg6RyzoRTLsPiet26FrI+tgjDXjPjNHH7Oz5QaKNRUehQ3E0IGdmGxxjPMTuBIGyS1ud2aSYXgUhnylgRtLWQBNMZAgiMHFltdFmtoUXNwkaHm8TVdXt+I+5f24aT/ZgX45/hoVEdUKCGGYIAjipEh3X25FRH56fCIClHiEjsdSR0dHMTo62ott2Zft7W0sLS0hldo/yzsQCCAQcJ+LhSAGAeHSEwufpZqKSIeT8pxzUzR0c6/OVCKEG8iaDqxOkD/jZmE0lQhis1BDOlfFYxOxY93Her4KzoVbJ+wfDMfGbDKMG4tZs8uvlaWdptvPjU5at0PXRtYynQzhrbWC4aYdOfR7ZRTzZDwIH/X79ZWJeBAKYyjVNOSr6rHjws3rkAEZGrEzfq+CmWQIC9tlbBZE/Or8KMV8EsSgY8vrokJa9Kv5I0BwyOqt6S7xKeHSy6eBicdPdl+cNx1/MRL+uooU4YrrYj/36jOfFBZDQ6JXkCAI4qQEE6LTT3aUJk8LtznxCD1dYVhcXMSNGzewuLgITdNw48YN3LhxA8Vi0fyeixcv4mtf+xoAoFgs4jd/8zfx3e9+Fw8fPsS3vvUtfPSjH8Xo6Ch+/ud/vpebShCEBQR9HoxERE7/caK28hUVpZomYizj7r2IlA60zUINNXX/PraDkC7BKRc7EVJdcI7K19+guP0AESvIGJApN5DfE5MqXYCz1DlGEB0ju/rWclWoR/T8yahdivnsPz6PYkZpH9cxLgaQpONvcM4fdmZupCn0BX0eTMToeSEIwoa0xle6bcguZgztF1ZPfl/lHUCtCSdhZOzk90c0iYyKhXK1BlRzvXscs9+P3H4EQXSR1k4/6vc7kJ4Kf7/zO7+Dq1ev4nd/93dRLBZx9epVXL16dVcH4O3bt5HLiZOMx+PBG2+8gZ/7uZ/D+fPn8Su/8is4f/48vvvd7yIWO56LgyAIe3MS0UaKWuPxgKudErGgD/GQD5wD67la2z9XUzVz4t3NgpZcbN3I16Dpx4tra3ZFDs7ie9DnwaQhmC+1xH1WGxo2CmIh+xRFpBFExyTDPkQCHqg6x1r+YEFpd78fvdesQJ4/jhOlDQC5SgOVugaPwjAWtZmbZEA53XLemhsJQ1FctqBOEIQ7kP1+iRlrt6MXyEjOwrpwNZ4EKR5GJwHFvZ/3LUHxAGEjmaKXPX9mv99E7x6DIIjBg4S/tuhpntlXvvIVfOUrXzn0e1o7NUKhEP7qr/6ql5tEEITNSA0F8cZKDuls59P2awM0ZT+VCCJfaWAlW2lbjFnP1cA5EA/5EAu6t6dtKOxDyO9BpS6Ezk57sgbZsTE7HEY6V8XSThmPTyUACAcS58BI1I9oh/G7BEGInr/poTDeXi9gJVM5UNTLV1QUqioURv1+VpEaCuLG0vEdf7JHdyIegNfFA0hOYjjiRzzkQ77SoH4/giDsSWt8pRt760JJwBsQTrLiBhDfv7anLfKy3+8E90EcTHRCPEfFdWDsfG8eg4Q/giB6wdApYOwC4I+KKGFiX+gTKkEQliIjKNfz1Y7dWmaMpYv76yTyd+yk5092R025fEGZMXYi10am3ECtocPnYRgdMMfGqWGxKLq4UzYHcSjmkyBOjozulI6+/VjOivfaZCIAv5cuya0gFRfP02ahdmQs636s5QfPLW53GGP48OUJPHN6GBcn41ZvDkEQxKNUc0CtCDClGYvpJhhrcf2dMO6zQP1+PUWKcVKc6zaaCpS2jceiqE+CILqI4gGu/AJw/sNWb4mtoVUGgiAsRbq1VJ2b8YLtUFd1M8ZyEFxaKSOqcy1fhd6mQCpFwkEQRmVk5XFcG3I/jceD8AxYJFgqEYRXYSjVNOyU6gCAxW1D+Bsm4Y8gjst0shljfdBQixQFp4fovWYV8ZAXYb8Hms6xXmg/SlsyqG5xuzM7HMY/fGx04M7pBEE4BOn2i44DHpemskjhTzr2joOmNiMo3eiMtANSjOtV1Gd5C+A64AsCARrGIQiC6Dck/BEEYSm73FodxH2u56vgHIgFva6OsZSMRoQjpK7q2CoevTipt3RLDYLw1+yKpMjYTvB6FPP1sbhTRr7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Run summary:


epoch32
eps_01e-05
eps_11e-05
lr_00.00142
lr_10.00142
mom_00.85157
mom_10.85157
raw_loss0.15201
sqr_mom_00.99
sqr_mom_10.99
train_loss0.38384
train_samples_per_sec16304.13929
valid_loss0.67672
wd_00.01
wd_10.01

" + "

Run summary:


epoch34
eps_01e-05
eps_11e-05
lr_00.00117
lr_10.00117
mom_00.85278
mom_10.85278
raw_loss0.22496
sqr_mom_00.99
sqr_mom_10.99
train_loss0.37858
train_samples_per_sec36677.16832
valid_loss0.63232
wd_00.01
wd_10.01

" ], "text/plain": [ "" @@ -1847,7 +1869,7 @@ { "data": { "text/html": [ - " View run 02c_encoder_MVP-sliding_window_view at: https://wandb.ai/mi-santamaria/deepvats/runs/lei5rxhx
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" + " View run 02c_encoder_MVP-sliding_window_view at: https://wandb.ai/mi-santamaria/deepvats/runs/kl9g41ca
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" @@ -1859,7 +1881,7 @@ { "data": { "text/html": [ - "Find logs at: /home/macu/work/wandb/run-20240301_152213-lei5rxhx/logs" + "Find logs at: /home/macu/work/wandb/run-20240723_124854-kl9g41ca/logs" ], "text/plain": [ "" @@ -1920,6 +1942,12 @@ "display_name": "Python 3.10 (XPython)", "language": "python", "name": "xpython" + }, + "language_info": { + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "version": "3.10.12" } }, "nbformat": 4, diff --git a/nbs_pipeline/_ploomber_engine_example_.ipynb b/nbs_pipeline/_ploomber_engine_example_.ipynb index 609c3e3a..9477efe1 100644 --- a/nbs_pipeline/_ploomber_engine_example_.ipynb +++ b/nbs_pipeline/_ploomber_engine_example_.ipynb @@ -79,2397 +79,31 @@ { "name": "stdout", "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - 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"output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# packages in environment at /usr/local/share/miniconda3/envs/env:\n", - "#\n", - "# Name Version Build Channel\n", - "brotli-python 1.0.9 py310hd8f1fbe_9 conda-forge\n", - "cuda-python 11.8.3 py310hf9913ef_0 nvidia\n", - "gitpython 3.1.42 pyhd8ed1ab_0 conda-forge\n", - "ipython 8.16.1 pyh0d859eb_0 conda-forge\n", - "ipython_genutils 0.2.0 py_1 conda-forge\n", - "msgpack-python 1.0.7 py310hd41b1e2_0 conda-forge\n", - "python 3.10.13 hd12c33a_0_cpython conda-forge\n", - "python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge\n", - "python-fastjsonschema 2.19.1 pyhd8ed1ab_0 conda-forge\n", - "python-json-logger 2.0.7 pyhd8ed1ab_0 conda-forge\n", - "python_abi 3.10 4_cp310 conda-forge\n", - "types-python-dateutil 2.8.19.20240106 pyhd8ed1ab_0 conda-forge\n", - "xeus-python 0.15.10 py310hd41b1e2_1 conda-forge\n", - "xeus-python-shell 0.5.0 pyhd8ed1ab_0 conda-forge\n", - "xeus-python-shell-raw 0.5.0 pyhd8ed1ab_0 conda-forge\n" - ] - } - ], - "source": [ - "! conda list python" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "29251880-4fd0-4ef3-996d-efff44ba93db", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - 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"output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [] + "text": [ + "# conda environments:\n", + "#\n", + "base /usr/local/share/miniconda3\n", + "env * /usr/local/share/miniconda3/envs/env\n", + "\n" + ] + } + ], + "source": [ + "! conda info --envs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a6dce6ab-c5e1-4e9e-a220-a50ae195c18f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, + "tags": [] + }, + "outputs": [ { "name": "stdout", "output_type": "stream", @@ -2983,8 +617,44 @@ { "name": "stdout", "output_type": "stream", - "text": [] + "text": [ + "# packages in environment at /usr/local/share/miniconda3/envs/env:\n", + "#\n", + "# Name Version Build Channel\n", + "brotli-python 1.0.9 py310hd8f1fbe_9 conda-forge\n", + "cuda-python 11.8.3 py310hf9913ef_0 nvidia\n", + "gitpython 3.1.42 pyhd8ed1ab_0 conda-forge\n", + "ipython 8.16.1 pyh0d859eb_0 conda-forge\n", + "ipython_genutils 0.2.0 py_1 conda-forge\n", + "msgpack-python 1.0.7 py310hd41b1e2_0 conda-forge\n", + "python 3.10.13 hd12c33a_0_cpython conda-forge\n", + "python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge\n", + "python-fastjsonschema 2.19.1 pyhd8ed1ab_0 conda-forge\n", + "python-json-logger 2.0.7 pyhd8ed1ab_0 conda-forge\n", + "python_abi 3.10 4_cp310 conda-forge\n", + "types-python-dateutil 2.8.19.20240106 pyhd8ed1ab_0 conda-forge\n", + "xeus-python 0.15.10 py310hd41b1e2_1 conda-forge\n", + "xeus-python-shell 0.5.0 pyhd8ed1ab_0 conda-forge\n", + "xeus-python-shell-raw 0.5.0 pyhd8ed1ab_0 conda-forge\n" + ] + } + ], + "source": [ + "! conda list python" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "29251880-4fd0-4ef3-996d-efff44ba93db", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, + "tags": [] + }, + "outputs": [ { "name": "stdout", "output_type": "stream", @@ -3539,7 +1209,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "id": "bd4ac0b5-b867-49ea-a983-5a4bb5cb3e3a", "metadata": { "editable": true, @@ -3579,6 +1249,21 @@ " 'frequency_factor_change_alias' : True,\n", " 'cuda_device' : torch.cuda.current_device()\n", " }\n", + " case 3: \n", + " filename = \"02c_encoder_MVP-sliding_window_view\"\n", + " parameters = parameters_02 = {\n", + " 'print_flag' : False,\n", + " 'check_memory_usage' : False,\n", + " 'time_flag' : False,\n", + " 'window_size_percentage' : None,\n", + " 'show_plots' : False,\n", + " 'reset_kernel' : False,\n", + " 'pre_configured_case' : False,\n", + " 'case_id' : None,\n", + " 'frequency_factor' : 1,\n", + " 'frequency_factor_change_alias' : True,\n", + " 'cuda_device' : torch.cuda.current_device()\n", + " }\n", " case _:\n", " print(\"Invalid configuration\")\n", " filename = \"\"\n", @@ -3588,7 +1273,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "12e3a2a8-d568-455a-bab5-103c4e600f9b", "metadata": { "editable": true, @@ -3659,7 +1344,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "e44f66e3-91c0-4820-abfe-56c4dedcd86b", "metadata": { "editable": true, @@ -3682,16 +1367,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Executing cell: 25: 76%|#######6 | 42/55 [00:03<00:00, 19.22it/s]wandb: Currently logged in as: mi-santamaria. Use `wandb login --relogin` to force relogin\n", - "Executing cell: 27: 100%|##########| 55/55 [00:23<00:00, 2.31it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There's a new ploomber-engine version available (0.0.32), you're running 0.0.31. To upgrade: pip install ploomber-engine --upgrade\n", - "Deploy Panel apps for free on Ploomber Cloud! Learn more: https://ploomber.io/s/signup\n" + "Executing cell: 27: 100%|##########| 55/55 [00:08<00:00, 6.16it/s]\n" ] } ], @@ -3723,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "id": "98a23721-98ff-4466-8c6c-2a18d1baf865", "metadata": { "editable": true, @@ -3746,8 +1422,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Executing cell: 16: 42%|####1 | 28/67 [00:25<00:34, 1.12it/s]wandb: 1 of 1 files downloaded. \n", - "Executing cell: 34: 100%|##########| 67/67 [00:55<00:00, 1.22it/s]\n" + "Executing cell: 16: 41%|####1 | 28/68 [00:09<00:13, 2.90it/s]wandb: 1 of 1 files downloaded. \n", + "Executing cell: 35: 100%|##########| 68/68 [00:24<00:00, 2.76it/s]\n" ] } ], @@ -3762,6 +1438,50 @@ " remove_tagged_cells = ['skip', 'hide']\n", ")" ] + }, + { + "cell_type": "markdown", + "id": "63ad0856-6aae-4e20-9ea4-ebf9a535cecd", + "metadata": {}, + "source": [ + "## Execute notebook 02c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db9f1155-eaa9-4099-bc43-d9a15f53da41", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "02c_encoder_MVP-sliding_window_view\n", + "{'print_flag': False, 'check_memory_usage': False, 'time_flag': False, 'window_size_percentage': None, 'show_plots': False, 'reset_kernel': False, 'pre_configured_case': False, 'case_id': None, 'frequency_factor': 1, 'frequency_factor_change_alias': True, 'cuda_device': 0}\n", + "Executing /home/macu/work/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb into /home/macu/work/nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Executing cell: 23: 32%|###2 | 33/103 [00:04<00:10, 6.89it/s]wandb: 1 of 1 files downloaded. \n", + "Executing cell: 68: 97%|#########7| 100/103 [00:15<00:00, 7.92it/s]" + ] + } + ], + "source": [ + "inputnb, outputnb, parameters = get_input_output(3)\n", + "_ = execute_notebook(\n", + " input_path = inputnb,\n", + " output_path = outputnb,\n", + " log_output = False,\n", + " progress_bar = True, \n", + " parameters = parameters,\n", + " remove_tagged_cells = ['skip', 'hide']\n", + ")" + ] } ], "metadata": { diff --git a/nbs_pipeline/config/02c-encoder_mvp-sliding_window_view.yaml b/nbs_pipeline/config/02c-encoder_mvp-sliding_window_view.yaml index 7baa8400..6b8753d0 100644 --- a/nbs_pipeline/config/02c-encoder_mvp-sliding_window_view.yaml +++ b/nbs_pipeline/config/02c-encoder_mvp-sliding_window_view.yaml @@ -37,7 +37,7 @@ configuration: #ws2: 75 #21600 #30 (15*60=900 = 1hora intervalos 4 secs) #ws1: 15 #TitlABP #ws2: 30 #TitlABP - ws1: 15 #Solar_4_seconds + ws1: 10 ws2: &wlen 30 #Solar_4_seconds #ws1: 7 #sunspot #ws2: &wlen 365 #sunspot @@ -58,4 +58,4 @@ configuration: # This will set the percentage of items that go to val # window size for the sliding window (taxi=48, steamflow=640) #w - size: *wlen \ No newline at end of file + size: *wlen diff --git a/nbs_pipeline/config/base.yaml b/nbs_pipeline/config/base.yaml index b1758bc0..95aa7412 100644 --- a/nbs_pipeline/config/base.yaml +++ b/nbs_pipeline/config/base.yaml @@ -13,17 +13,17 @@ user_preferences: # Whether to use or not wandb for experiment tracking use_wandb: &use_wandb true wdb: - user: &wdb_user angel-gutierrez - project_name: &wdb_project ream + user: &wdb_user mi-santamaria + project_name: &wdb_project deepvats version: &wdb_version 'latest' # 'online' if the model will be tested on future data, or 'offline' if not mode: &wdb_mode 'online' artifacts_path: &artifacts_path './data/wandb_artifacts' data: folder: &path '~/data/' - fname: &fname "Semantic_Segmentation_TiltABP" + fname: &fname "penguin_sample" ftype: &ftype '.csv' - cols: &cols [1] + cols: &cols [0] freq: &freq '10h' artifact: # Alias of the artifact resulting of this run. null will create one automatically diff --git a/nbs_pipeline/output/01_dataset_artifact-output.ipynb b/nbs_pipeline/output/01_dataset_artifact-output.ipynb index 53651ac7..0cf2a2f2 100644 --- a/nbs_pipeline/output/01_dataset_artifact-output.ipynb +++ b/nbs_pipeline/output/01_dataset_artifact-output.ipynb @@ -6,8 +6,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568791.004735, - "timestamp_start": 1714568791.003454 + "timestamp_end": 1721738696.173764, + "timestamp_start": 1721738696.173425 }, "slideshow": { "slide_type": "" @@ -33,11 +33,11 @@ { "cell_type": "code", "execution_count": 2, - "id": "84ff5f6e", + "id": "eebde9db", "metadata": { "ploomber": { - "timestamp_end": 1714568791.006043, - "timestamp_start": 1714568791.005209 + "timestamp_end": 1721738696.174074, + "timestamp_start": 1721738696.173824 }, "tags": [ "injected-parameters" @@ -46,14 +46,15 @@ "outputs": [], "source": [ "# Injected parameters\n", - "print_flag = False\n", + "print_flag = True\n", "show_plots = False\n", "reset_kernel = False\n", "pre_configured_case = False\n", "case_id = None\n", "frequency_factor = 1\n", "frequency_factor_change_alias = True\n", - "cuda_device = 0\n" + "cuda_device = 0\n", + "check_parameters = True\n" ] }, { @@ -75,8 +76,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568791.007214, - "timestamp_start": 1714568791.006315 + "timestamp_end": 1721738696.174375, + "timestamp_start": 1721738696.174123 }, "slideshow": { "slide_type": "" @@ -89,7 +90,7 @@ "output_type": "stream", "text": [ "--- Check parameters ---\n", - "print_flag: False show_plots: False reset_kernel: False pre_configured_case: False case_id: None frequency_factor: 1 frequency_factor_change_alias: True cuda_device: 0\n" + "print_flag: True show_plots: False reset_kernel: False pre_configured_case: False case_id: None frequency_factor: 1 frequency_factor_change_alias: True cuda_device: 0\n" ] } ], @@ -129,8 +130,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568791.009344, - "timestamp_start": 1714568791.00794 + "timestamp_end": 1721738696.174909, + "timestamp_start": 1721738696.174547 }, "slideshow": { "slide_type": "" @@ -180,8 +181,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568792.760766, - "timestamp_start": 1714568791.009639 + "timestamp_end": 1721738696.175173, + "timestamp_start": 1721738696.174958 }, "slideshow": { "slide_type": "" @@ -220,8 +221,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568792.762731, - "timestamp_start": 1714568792.762171 + "timestamp_end": 1721738696.175301, + "timestamp_start": 1721738696.175222 }, "slideshow": { "slide_type": "" @@ -260,8 +261,8 @@ "execution_count": 7, "metadata": { "ploomber": { - "timestamp_end": 1714568793.308667, - "timestamp_start": 1714568792.762999 + "timestamp_end": 1721738696.175443, + "timestamp_start": 1721738696.175342 } }, "outputs": [], @@ -276,8 +277,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568793.310527, - "timestamp_start": 1714568793.309817 + "timestamp_end": 1721738696.17559, + "timestamp_start": 1721738696.175483 }, "slideshow": { "slide_type": "" @@ -312,8 +313,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568793.311866, - "timestamp_start": 1714568793.31148 + "timestamp_end": 1721738696.175811, + "timestamp_start": 1721738696.175728 }, "slideshow": { "slide_type": "" @@ -351,8 +352,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568793.323595, - "timestamp_start": 1714568793.312146 + "timestamp_end": 1721738696.176197, + "timestamp_start": 1721738696.175849 }, "slideshow": { "slide_type": "" @@ -387,8 +388,8 @@ "execution_count": 11, "metadata": { "ploomber": { - "timestamp_end": 1714568793.324733, - "timestamp_start": 1714568793.324098 + "timestamp_end": 1721738696.17639, + "timestamp_start": 1721738696.176252 } }, "outputs": [], @@ -403,8 +404,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568793.351289, - "timestamp_start": 1714568793.325031 + "timestamp_end": 1721738696.182399, + "timestamp_start": 1721738696.176434 }, "slideshow": { "slide_type": "" @@ -461,8 +462,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568794.180187, - "timestamp_start": 1714568793.351836 + "timestamp_end": 1721738696.189032, + "timestamp_start": 1721738696.182461 }, "slideshow": { "slide_type": "" @@ -503,15 +504,77 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568794.183956, - "timestamp_start": 1714568794.181058 + "timestamp_end": 1721738696.19212, + "timestamp_start": 1721738696.189098 }, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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penguin_sample
00.253906
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20.269287
30.271240
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\n", + "
" + ], + "text/plain": [ + " penguin_sample\n", + "0 0.253906\n", + "1 0.259033\n", + "2 0.269287\n", + "3 0.271240\n", + "4 0.265137" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#| export\n", "if config.time_col is not None:\n", @@ -555,15 +618,23 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568794.201037, - "timestamp_start": 1714568794.184334 + "timestamp_end": 1721738696.194951, + "timestamp_start": 1721738696.192191 }, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<10 * Hours>\n" + ] + } + ], "source": [ "#| export\n", "df = infer_or_inject_freq(\n", @@ -587,11 +658,20 @@ "execution_count": 16, "metadata": { "ploomber": { - "timestamp_end": 1714568794.204033, - "timestamp_start": 1714568794.201598 + "timestamp_end": 1721738696.195765, + "timestamp_start": 1721738696.195102 } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data_cols: [0]\n", + "Num. variables: 1\n" + ] + } + ], "source": [ "#| export\n", "# Subset of variables\n", @@ -615,15 +695,24 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568794.220762, - "timestamp_start": 1714568794.204467 + "timestamp_end": 1721738696.19715, + "timestamp_start": 1721738696.195921 }, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "df shape before dropping duplicates (109842, 1)\n", + "df shape after dropping duplicates (109842, 1)\n" + ] + } + ], "source": [ "#| export\n", "#Duplicated rows\n", @@ -640,8 +729,8 @@ "execution_count": 18, "metadata": { "ploomber": { - "timestamp_end": 1714568794.222035, - "timestamp_start": 1714568794.221287 + "timestamp_end": 1721738696.197491, + "timestamp_start": 1721738696.197307 } }, "outputs": [], @@ -697,8 +786,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568794.223922, - "timestamp_start": 1714568794.222344 + "timestamp_end": 1721738696.197986, + "timestamp_start": 1721738696.197544 }, "slideshow": { "slide_type": "" @@ -738,8 +827,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568794.282495, - "timestamp_start": 1714568794.224232 + "timestamp_end": 1721738696.207365, + "timestamp_start": 1721738696.198041 }, "slideshow": { "slide_type": "" @@ -751,8 +840,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "About to write df to /home/macu/data/wandb_artifacts/-2617748540682500409\n" + "About to write df to /home/macu/data/wandb_artifacts/3058220854446460321\n" ] + }, + { + "data": { + "text/plain": [ + "{'TS': {'sd': '1970-01-01 00:00:00',\n", + " 'ed': '2095-04-22 02:00:00',\n", + " 'created': 'from-df',\n", + " 'n_vars': 1,\n", + " 'handle_missing_values_technique': 'None',\n", + " 'has_missing_values': 'False',\n", + " 'n_samples': 109842,\n", + " 'freq': '<10 * Hours>',\n", + " 'vars': ['penguin_sample'],\n", + " 'hash': '3058220854446460321'}}" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -774,8 +881,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568794.284654, - "timestamp_start": 1714568794.283685 + "timestamp_end": 1721738696.207716, + "timestamp_start": 1721738696.207507 }, "slideshow": { "slide_type": "" @@ -810,15 +917,23 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568794.287788, - "timestamp_start": 1714568794.284993 + "timestamp_end": 1721738696.208613, + "timestamp_start": 1721738696.207775 }, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rg None | test_split None\n" + ] + } + ], "source": [ "#| export\n", "# Testing data\n", @@ -875,8 +990,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568794.289775, - "timestamp_start": 1714568794.288146 + "timestamp_end": 1721738696.209243, + "timestamp_start": 1721738696.208772 }, "slideshow": { "slide_type": "" @@ -932,8 +1047,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568794.291033, - "timestamp_start": 1714568794.290084 + "timestamp_end": 1721738696.209568, + "timestamp_start": 1721738696.209297 }, "slideshow": { "slide_type": "" @@ -965,8 +1080,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568813.781523, - "timestamp_start": 1714568794.29177 + "timestamp_end": 1721738701.741686, + "timestamp_start": 1721738696.20971 }, "slideshow": { "slide_type": "" @@ -975,16 +1090,16 @@ }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "wandb: Currently logged in as: mi-santamaria. Use `wandb login --relogin` to force relogin\n" + "Logging training_artifact \n" ] }, { "data": { "text/html": [ - "wandb version 0.16.6 is available! To upgrade, please run:\n", + "wandb version 0.17.5 is available! To upgrade, please run:\n", " $ pip install wandb --upgrade" ], "text/plain": [ @@ -1009,7 +1124,7 @@ { "data": { "text/html": [ - "Run data is saved locally in /home/macu/work/wandb/run-20240501_130636-hidvy0xp" + "Run data is saved locally in /home/macu/work/app/wandb/run-20240723_124456-6z6ejjh6" ], "text/plain": [ "" @@ -1021,7 +1136,7 @@ { "data": { "text/html": [ - "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" + "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -1045,7 +1160,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/mi-santamaria/deepvats/runs/hidvy0xp" + " View run at https://wandb.ai/mi-santamaria/deepvats/runs/6z6ejjh6" ], "text/plain": [ "" @@ -1069,12 +1184,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "16d13078c98d49c5af44d6096afee146", + "model_id": "406efb5bf5a24361931be8e5e1456cac", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "VBox(children=(Label(value='0.006 MB of 0.012 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=0.509076…" + "VBox(children=(Label(value='0.003 MB of 0.003 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…" ] }, "metadata": {}, @@ -1083,7 +1198,7 @@ { "data": { "text/html": [ - " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/hidvy0xp
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" + " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/6z6ejjh6
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" @@ -1095,7 +1210,7 @@ { "data": { "text/html": [ - "Find logs at: /home/macu/work/wandb/run-20240501_130636-hidvy0xp/logs" + "Find logs at: ./wandb/run-20240723_124456-6z6ejjh6/logs" ], "text/plain": [ "" @@ -1119,6 +1234,7 @@ " run.log_artifact(train_test_artifact, aliases=['all'])\n", " \n", " else:\n", + " if print_flag: print(\"Logging training_artifact\", training_artifact)\n", " run.log_artifact(training_artifact, aliases=['all'])" ] }, @@ -1128,8 +1244,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568813.786532, - "timestamp_start": 1714568813.784343 + "timestamp_end": 1721738701.744144, + "timestamp_start": 1721738701.742865 }, "slideshow": { "slide_type": "" @@ -1148,8 +1264,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568814.798882, - "timestamp_start": 1714568813.787229 + "timestamp_end": 1721738702.748999, + "timestamp_start": 1721738701.744481 }, "slideshow": { "slide_type": "" diff --git a/nbs_pipeline/output/02b_encoder_MVP-output.ipynb b/nbs_pipeline/output/02b_encoder_MVP-output.ipynb index d161863d..73f91f2f 100644 --- a/nbs_pipeline/output/02b_encoder_MVP-output.ipynb +++ b/nbs_pipeline/output/02b_encoder_MVP-output.ipynb @@ -7,8 +7,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568815.121533, - "timestamp_start": 1714568815.119915 + "timestamp_end": 1721729941.044309, + "timestamp_start": 1721729941.043785 }, "slideshow": { "slide_type": "" @@ -37,11 +37,11 @@ { "cell_type": "code", "execution_count": 2, - "id": "ee7b255c", + "id": "ab0a488c", "metadata": { "ploomber": { - "timestamp_end": 1714568815.122833, - "timestamp_start": 1714568815.121863 + "timestamp_end": 1721729941.044803, + "timestamp_start": 1721729941.044435 }, "tags": [ "injected-parameters" @@ -84,8 +84,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568815.12411, - "timestamp_start": 1714568815.123099 + "timestamp_end": 1721729941.04526, + "timestamp_start": 1721729941.044904 }, "slideshow": { "slide_type": "" @@ -142,8 +142,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568815.126461, - "timestamp_start": 1714568815.124859 + "timestamp_end": 1721729941.046017, + "timestamp_start": 1721729941.045492 }, "slideshow": { "slide_type": "" @@ -195,8 +195,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568815.127529, - "timestamp_start": 1714568815.126746 + "timestamp_end": 1721729941.046358, + "timestamp_start": 1721729941.046109 }, "slideshow": { "slide_type": "" @@ -208,10 +208,10 @@ "#| export\n", "# This is only needed if the notebook is run in VSCode\n", "import sys\n", - "import dvats.utils as ut\n", "if '--vscode' in sys.argv:\n", " print(\"Executing inside vscode\")\n", - " ut.DisplayHandle.update = ut.update_patch" + " import nbs_pipeline.utils.vscode as vs\n", + " vs.DisplayHandle.update = vs.update_patch" ] }, { @@ -242,8 +242,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568815.128131, - "timestamp_start": 1714568815.127799 + "timestamp_end": 1721729941.04654, + "timestamp_start": 1721729941.046441 }, "slideshow": { "slide_type": "" @@ -283,8 +283,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568815.128725, - "timestamp_start": 1714568815.128364 + "timestamp_end": 1721729941.046721, + "timestamp_start": 1721729941.04661 }, "slideshow": { "slide_type": "" @@ -304,8 +304,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568815.12924, - "timestamp_start": 1714568815.128951 + "timestamp_end": 1721729941.046864, + "timestamp_start": 1721729941.046784 }, "slideshow": { "slide_type": "" @@ -340,8 +340,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568827.14534, - "timestamp_start": 1714568815.129449 + "timestamp_end": 1721729945.929103, + "timestamp_start": 1721729941.046929 }, "slideshow": { "slide_type": "" @@ -366,7 +366,9 @@ "from fastai.callback.progress import ShowGraphCallback\n", "from fastai.callback.schedule import *\n", "from fastai.callback.tracker import EarlyStoppingCallback\n", - "import wandb" + "import wandb\n", + "if check_memory_usage: \n", + " import nbs_pipeline.utils.memory as mem" ] }, { @@ -390,8 +392,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568827.147569, - "timestamp_start": 1714568827.146081 + "timestamp_end": 1721729945.93016, + "timestamp_start": 1721729945.929445 }, "slideshow": { "slide_type": "" @@ -425,8 +427,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568827.148499, - "timestamp_start": 1714568827.147768 + "timestamp_end": 1721729945.930607, + "timestamp_start": 1721729945.930263 }, "slideshow": { "slide_type": "" @@ -440,7 +442,7 @@ "torch.cuda.set_device(device)\n", "if check_memory_usage:\n", " gpu_device = torch.cuda.current_device()\n", - " gpu_memory_status(gpu_device)" + " mem.gpu_memory_status(gpu_device)" ] }, { @@ -459,8 +461,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568827.168334, - "timestamp_start": 1714568827.148674 + "timestamp_end": 1721729945.938055, + "timestamp_start": 1721729945.9307 }, "slideshow": { "slide_type": "" @@ -496,8 +498,8 @@ "id": "f30caa23", "metadata": { "ploomber": { - "timestamp_end": 1714568827.169249, - "timestamp_start": 1714568827.168555 + "timestamp_end": 1721729945.938479, + "timestamp_start": 1721729945.938153 } }, "outputs": [], @@ -517,8 +519,8 @@ "id": "e4411368-d772-4381-9cc0-5c9b7ea5361a", "metadata": { "ploomber": { - "timestamp_end": 1714568839.789061, - "timestamp_start": 1714568827.169422 + "timestamp_end": 1721729950.297892, + "timestamp_start": 1721729945.938569 } }, "outputs": [ @@ -537,7 +539,7 @@ { "data": { "text/html": [ - "wandb version 0.16.6 is available! To upgrade, please run:\n", + "wandb version 0.17.5 is available! To upgrade, please run:\n", " $ pip install wandb --upgrade" ], "text/plain": [ @@ -562,7 +564,7 @@ { "data": { "text/html": [ - "Run data is saved locally in /home/macu/work/wandb/run-20240501_130707-bc344htx" + "Run data is saved locally in /home/macu/work/wandb/run-20240723_101905-ektjz2ry" ], "text/plain": [ "" @@ -574,7 +576,7 @@ { "data": { "text/html": [ - "Syncing run 02a_encoder_MVP to Weights & Biases (docs)
" + "Syncing run 02a_encoder_MVP to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -598,7 +600,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/mi-santamaria/deepvats/runs/bc344htx" + " View run at https://wandb.ai/mi-santamaria/deepvats/runs/ektjz2ry" ], "text/plain": [ "" @@ -655,8 +657,8 @@ "id": "78dced3c-8280-460e-bd11-8188495bf470", "metadata": { "ploomber": { - "timestamp_end": 1714568840.386254, - "timestamp_start": 1714568839.7898 + "timestamp_end": 1721729950.747868, + "timestamp_start": 1721729950.298113 } }, "outputs": [], @@ -675,8 +677,8 @@ "id": "8acf714f-0fe1-4aed-8b57-23ccd31b22fe", "metadata": { "ploomber": { - "timestamp_end": 1714568840.903495, - "timestamp_start": 1714568840.387691 + "timestamp_end": 1721729951.176553, + "timestamp_start": 1721729950.748913 } }, "outputs": [ @@ -699,8 +701,8 @@ "id": "b10283f9", "metadata": { "ploomber": { - "timestamp_end": 1714568840.90863, - "timestamp_start": 1714568840.905904 + "timestamp_end": 1721729951.180512, + "timestamp_start": 1721729951.178273 } }, "outputs": [], @@ -730,8 +732,8 @@ "id": "8d25ab5a-b1e6-4eb7-8542-e4dc87f2a883", "metadata": { "ploomber": { - "timestamp_end": 1714568840.92364, - "timestamp_start": 1714568840.909149 + "timestamp_end": 1721729951.186238, + "timestamp_start": 1721729951.180939 } }, "outputs": [], @@ -758,8 +760,8 @@ "id": "bb1e270e-c6a2-4dc0-a54d-8f6fdc7565b1", "metadata": { "ploomber": { - "timestamp_end": 1714568840.927629, - "timestamp_start": 1714568840.924094 + "timestamp_end": 1721729951.188039, + "timestamp_start": 1721729951.186477 } }, "outputs": [], @@ -804,8 +806,8 @@ "id": "c7c3cd99", "metadata": { "ploomber": { - "timestamp_end": 1714568840.930001, - "timestamp_start": 1714568840.928022 + "timestamp_end": 1721729951.18906, + "timestamp_start": 1721729951.188218 } }, "outputs": [], @@ -829,8 +831,8 @@ "id": "b97038fe-116f-4d6c-8569-e9a9c015a434", "metadata": { "ploomber": { - "timestamp_end": 1714568840.93178, - "timestamp_start": 1714568840.930332 + "timestamp_end": 1721729951.189854, + "timestamp_start": 1721729951.189201 } }, "outputs": [], @@ -856,8 +858,8 @@ "id": "668e7b5a", "metadata": { "ploomber": { - "timestamp_end": 1714568841.072785, - "timestamp_start": 1714568840.932035 + "timestamp_end": 1721729951.224889, + "timestamp_start": 1721729951.189984 } }, "outputs": [], @@ -881,8 +883,8 @@ "id": "f17ebe25-b894-4674-aed5-32e9d8f284a9", "metadata": { "ploomber": { - "timestamp_end": 1714568841.220977, - "timestamp_start": 1714568841.073636 + "timestamp_end": 1721729951.243702, + "timestamp_start": 1721729951.225076 } }, "outputs": [ @@ -960,8 +962,8 @@ "id": "ab1d5cc8-fac5-4f9d-bbd9-4083c2c60d8a", "metadata": { "ploomber": { - "timestamp_end": 1714568841.224015, - "timestamp_start": 1714568841.222634 + "timestamp_end": 1721729951.244375, + "timestamp_start": 1721729951.244011 } }, "outputs": [], @@ -985,8 +987,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568841.225923, - "timestamp_start": 1714568841.224501 + "timestamp_end": 1721729951.244873, + "timestamp_start": 1721729951.244483 }, "slideshow": { "slide_type": "" @@ -1006,8 +1008,8 @@ "id": "ce3a3112-ba6b-4756-9ce1-e15e4c2af1fd", "metadata": { "ploomber": { - "timestamp_end": 1714568841.227552, - "timestamp_start": 1714568841.226364 + "timestamp_end": 1721729951.245306, + "timestamp_start": 1721729951.244969 } }, "outputs": [ @@ -1041,8 +1043,8 @@ "id": "9272a132-9026-419c-81b0-d16d03894f70", "metadata": { "ploomber": { - "timestamp_end": 1714568841.230374, - "timestamp_start": 1714568841.228439 + "timestamp_end": 1721729951.245947, + "timestamp_start": 1721729951.245529 } }, "outputs": [], @@ -1061,8 +1063,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1714568841.231968, - "timestamp_start": 1714568841.230814 + "timestamp_end": 1721729951.246326, + "timestamp_start": 1721729951.246042 }, "slideshow": { "slide_type": "" @@ -1090,8 +1092,8 @@ "id": "437a49d0-7ee1-4519-a871-b4f82f94c1a4", "metadata": { "ploomber": { - "timestamp_end": 1714568841.240744, - "timestamp_start": 1714568841.232377 + "timestamp_end": 1721729951.247926, + "timestamp_start": 1721729951.246416 } }, "outputs": [], @@ -1126,8 +1128,8 @@ "id": "610dd97f-1cb2-4364-9748-db882ac6162a", "metadata": { "ploomber": { - "timestamp_end": 1714568863.691527, - "timestamp_start": 1714568841.241209 + "timestamp_end": 1721729959.8771, + "timestamp_start": 1721729951.248032 } }, "outputs": [ @@ -1138,676 +1140,1814 @@ "█\r", "█\r", "\r", - "Epoch 1/5 : || 0.00% [0/24 00:00\n", - "

Run summary:


epoch10
eps_01e-05
eps_11e-05
lr_00.00064
lr_10.00064
mom_00.9157
mom_10.9157
raw_loss0.10031
sqr_mom_00.99
sqr_mom_10.99
train_loss0.11684
train_samples_per_sec22755.49884
valid_lossnan
wd_00.01
wd_10.01

" + "

Run summary:


epoch48
eps_01e-05
eps_11e-05
lr_00.00114
lr_10.00114
mom_00.87131
mom_10.87131
raw_loss0.09436
sqr_mom_00.99
sqr_mom_10.99
train_loss0.11699
train_samples_per_sec48571.24212
valid_loss0.06691
wd_00.01
wd_10.01

" ], "text/plain": [ "" @@ -1984,7 +3118,7 @@ { "data": { "text/html": [ - " View run 02a_encoder_MVP at: https://wandb.ai/mi-santamaria/deepvats/runs/bc344htx
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" + " View run 02a_encoder_MVP at: https://wandb.ai/mi-santamaria/deepvats/runs/ektjz2ry
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" @@ -1996,7 +3130,7 @@ { "data": { "text/html": [ - "Find logs at: /home/macu/work/wandb/run-20240501_130707-bc344htx/logs" + "Find logs at: /home/macu/work/wandb/run-20240723_101905-ektjz2ry/logs" ], "text/plain": [ "" @@ -2018,8 +3152,8 @@ "id": "9c0422ca-555a-42ab-8d37-6191ec9d6c82", "metadata": { "ploomber": { - "timestamp_end": 1714568870.195519, - "timestamp_start": 1714568869.188294 + "timestamp_end": 1721729965.670452, + "timestamp_start": 1721729964.665212 } }, "outputs": [ @@ -2047,6 +3181,19 @@ "from dvats.imports import beep\n", "beep(1)" ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "a7759ac9-ed1f-49dd-9a5b-65e4090b9aec", + "metadata": { + "ploomber": { + "timestamp_end": 1721729965.671926, + "timestamp_start": 1721729965.671496 + } + }, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index 97d3c3dd..84a6ee42 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -1151,7 +1151,7 @@ shinyServer(function(input, output, session) { textOutput("file_path_text"), tags$hr(), h4("Configuration"), - numericInput("cols_input", "Calls:", value = 5, min = 1), + numericInput("cols_input", "Calls:", value = 5, min = 0), numericInput("freq_input", "Frequency:", value = 10, min = 1), numericInput("n_epoch_input", "Epochs:", value = 100, min = 1), numericInput("ws1_input", "Window:", value = 10, min = 1), @@ -1197,32 +1197,120 @@ shinyServer(function(input, output, session) { } removeModal() }) + + #---------------------------------------------Execution notebook------------------------------------------------------- + get_parameters <- function(nb_id) { + print("Just before switch") + print("... a little check") + cuda_device = torch$cuda$current_device() + print("a little check...") + switch(nb_id, + "1" = { + filename <- "01_dataset_artifact" + parameters <- list( + print_flag = TRUE, + show_plots = FALSE, + reset_kernel = FALSE, + pre_configured_case = FALSE, + case_id = NULL, + frequency_factor = as.integer(1), + frequency_factor_change_alias = TRUE, + cuda_device = torch$cuda$current_device(), + check_parameters = TRUE + ) + }, + "2" = { + filename <- "02c_encoder_MVP-sliding_window_view" + parameters <- list( + print_flag = FALSE, + check_memory_usage = FALSE, + time_flag = FALSE, + window_size_percentage = NULL, + show_plots = FALSE, + reset_kernel = FALSE, + pre_configured_case = FALSE, + + + case_id = NULL, + frequency_factor = as.integer(1), + frequency_factor_change_alias = TRUE, + cuda_device = torch$cuda$current_device() + ) + }, + { + print("Invalid configuration") + filename <- "" + parameters <- list() + } + ) + print("Just after switch") + return(list(filename = filename, parameters = parameters)) + } + + get_input_output <- function(nb_id) { + print("About to get parameters") + params <- get_parameters(nb_id) + filename <- params$filename + parameters <- params$parameters + + print(filename) + print(parameters) + + inbpath <- path.expand("~/work/nbs_pipeline") + onbpath <- path.expand("~/work/nbs_pipeline/output") + extension <- ".ipynb" + reportname <- paste0(filename, "-output") + inputnb <- file.path(inbpath, paste0(filename, extension)) + outputnb <- file.path(onbpath, paste0(reportname, extension)) + + print(paste("Executing", inputnb, "into", outputnb)) + + return(list(inputnb = inputnb, outputnb = outputnb, parameters = parameters)) + } ejecucion_notebooks <- function() { - print("-------------Inicio------------------------") - ruta_a_primer_nb <- "~/work/nbs_pipeline/01_dataset_artifact.ipynb" - print("Se ha inicializado Primer notebook") + print("------------- START ------------------------") + nb_path_1 <- "~/work/nbs_pipeline/01_dataset_artifact.ipynb" + print("--> 1st Jupyter Notebook: start") + + + result <- get_input_output(1) + print(result) + input_nb <- result$inputnb + output_nb <- result$outputnb + parameters <- result$parameters + print("Execute notebook") + res <- ploomber$execute_notebook( + input_path = input_nb, + output_path = output_nb, + log_output = FALSE, + progress_bar = TRUE, + parameters = parameters, + remove_tagged_cells = c('skip', 'hide') + ) + system(paste("jupyter nbconvert --execute --to notebook --inplace", "--TagRemovePreprocessor.enabled=True --TagRemovePreprocessor.remove_cell_tags=hide", - ruta_a_primer_nb)) + nb_path_1)) - print("El primer notebook ha finalizado") + print("First notebook: end -->") updateProgressBar(session, "progress_bar", value = 66, total = 100, status = "info") - ruta_a_segundo_nb <- "~/work/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb" - print("Se ha inicializado Segundo notebook") + nb_path_2 <- "~/work/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb" + print("--> 2st Jupyter Notebook: start") system(paste("jupyter nbconvert --execute --to notebook --inplace", "--TagRemovePreprocessor.enabled=True --TagRemovePreprocessor.remove_cell_tags=hide", - ruta_a_segundo_nb)) + nb_path_2)) - print("El segundo notebook ha finalizado.\n") + print("Second notebook: end -->") updateProgressBar(session, "progress_bar", value = 100, total = 100, status = "info") - print("Ambos notebooks han finalizado completamente.\n") + print("All notebooks: end -->") } + mod_file_base <- function(filepath){ yaml_content <- readLines("~/work/nbs_pipeline/config/base.yaml") @@ -1345,9 +1433,10 @@ shinyServer(function(input, output, session) { output$data_preview_plot <- renderDygraph({ data <- dataset() print("-----data load------") - print(data) + #print(data) data_info <- dataset() + print(data_info) data <- data_info$data date_col <- data_info$date_col #print(data) From 9310e631f548ced6a3429a16691d85b9e7993382 Mon Sep 17 00:00:00 2001 From: "mi.santamaria" Date: Tue, 23 Jul 2024 14:55:47 +0200 Subject: [PATCH 17/37] =?UTF-8?q?Comentados=20los=20system=20de=20ejecucio?= =?UTF-8?q?n=20de=20los=20notebooks.=20A=C3=B1adido=20ploomber=20para=20el?= =?UTF-8?q?=20segundo=20nb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../output/01_dataset_artifact-output.ipynb | 124 +++++++++--------- r_shiny_app/server.R | 25 ++-- 2 files changed, 79 insertions(+), 70 deletions(-) diff --git a/nbs_pipeline/output/01_dataset_artifact-output.ipynb b/nbs_pipeline/output/01_dataset_artifact-output.ipynb index 0cf2a2f2..f83fddc9 100644 --- a/nbs_pipeline/output/01_dataset_artifact-output.ipynb +++ b/nbs_pipeline/output/01_dataset_artifact-output.ipynb @@ -6,8 +6,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.173764, - "timestamp_start": 1721738696.173425 + "timestamp_end": 1721739264.018716, + "timestamp_start": 1721739264.018366 }, "slideshow": { "slide_type": "" @@ -33,11 +33,11 @@ { "cell_type": "code", "execution_count": 2, - "id": "eebde9db", + "id": "710e91ea", "metadata": { "ploomber": { - "timestamp_end": 1721738696.174074, - "timestamp_start": 1721738696.173824 + "timestamp_end": 1721739264.019031, + "timestamp_start": 1721739264.01878 }, "tags": [ "injected-parameters" @@ -76,8 +76,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.174375, - "timestamp_start": 1721738696.174123 + "timestamp_end": 1721739264.019335, + "timestamp_start": 1721739264.019083 }, "slideshow": { "slide_type": "" @@ -130,8 +130,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.174909, - "timestamp_start": 1721738696.174547 + "timestamp_end": 1721739264.019877, + "timestamp_start": 1721739264.019511 }, "slideshow": { "slide_type": "" @@ -181,8 +181,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.175173, - "timestamp_start": 1721738696.174958 + "timestamp_end": 1721739264.020152, + "timestamp_start": 1721739264.019928 }, "slideshow": { "slide_type": "" @@ -221,8 +221,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.175301, - "timestamp_start": 1721738696.175222 + "timestamp_end": 1721739264.020289, + "timestamp_start": 1721739264.020209 }, "slideshow": { "slide_type": "" @@ -261,8 +261,8 @@ "execution_count": 7, "metadata": { "ploomber": { - "timestamp_end": 1721738696.175443, - "timestamp_start": 1721738696.175342 + "timestamp_end": 1721739264.020431, + "timestamp_start": 1721739264.020332 } }, "outputs": [], @@ -277,8 +277,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.17559, - "timestamp_start": 1721738696.175483 + "timestamp_end": 1721739264.020578, + "timestamp_start": 1721739264.020471 }, "slideshow": { "slide_type": "" @@ -313,8 +313,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.175811, - "timestamp_start": 1721738696.175728 + "timestamp_end": 1721739264.020804, + "timestamp_start": 1721739264.020722 }, "slideshow": { "slide_type": "" @@ -352,8 +352,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.176197, - "timestamp_start": 1721738696.175849 + "timestamp_end": 1721739264.021193, + "timestamp_start": 1721739264.020843 }, "slideshow": { "slide_type": "" @@ -388,8 +388,8 @@ "execution_count": 11, "metadata": { "ploomber": { - "timestamp_end": 1721738696.17639, - "timestamp_start": 1721738696.176252 + "timestamp_end": 1721739264.021386, + "timestamp_start": 1721739264.021246 } }, "outputs": [], @@ -404,8 +404,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.182399, - "timestamp_start": 1721738696.176434 + "timestamp_end": 1721739264.027467, + "timestamp_start": 1721739264.021432 }, "slideshow": { "slide_type": "" @@ -462,8 +462,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.189032, - "timestamp_start": 1721738696.182461 + "timestamp_end": 1721739264.032988, + "timestamp_start": 1721739264.027529 }, "slideshow": { "slide_type": "" @@ -504,8 +504,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.19212, - "timestamp_start": 1721738696.189098 + "timestamp_end": 1721739264.036115, + "timestamp_start": 1721739264.033057 }, "slideshow": { "slide_type": "" @@ -618,8 +618,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.194951, - "timestamp_start": 1721738696.192191 + "timestamp_end": 1721739264.038612, + "timestamp_start": 1721739264.036183 }, "slideshow": { "slide_type": "" @@ -658,8 +658,8 @@ "execution_count": 16, "metadata": { "ploomber": { - "timestamp_end": 1721738696.195765, - "timestamp_start": 1721738696.195102 + "timestamp_end": 1721739264.039305, + "timestamp_start": 1721739264.038772 } }, "outputs": [ @@ -695,8 +695,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.19715, - "timestamp_start": 1721738696.195921 + "timestamp_end": 1721739264.040537, + "timestamp_start": 1721739264.039459 }, "slideshow": { "slide_type": "" @@ -729,8 +729,8 @@ "execution_count": 18, "metadata": { "ploomber": { - "timestamp_end": 1721738696.197491, - "timestamp_start": 1721738696.197307 + "timestamp_end": 1721739264.040866, + "timestamp_start": 1721739264.040685 } }, "outputs": [], @@ -786,8 +786,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.197986, - "timestamp_start": 1721738696.197544 + "timestamp_end": 1721739264.041372, + "timestamp_start": 1721739264.040921 }, "slideshow": { "slide_type": "" @@ -827,8 +827,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.207365, - "timestamp_start": 1721738696.198041 + "timestamp_end": 1721739264.05057, + "timestamp_start": 1721739264.041425 }, "slideshow": { "slide_type": "" @@ -881,8 +881,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.207716, - "timestamp_start": 1721738696.207507 + "timestamp_end": 1721739264.050927, + "timestamp_start": 1721739264.050715 }, "slideshow": { "slide_type": "" @@ -917,8 +917,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.208613, - "timestamp_start": 1721738696.207775 + "timestamp_end": 1721739264.051753, + "timestamp_start": 1721739264.050987 }, "slideshow": { "slide_type": "" @@ -990,8 +990,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.209243, - "timestamp_start": 1721738696.208772 + "timestamp_end": 1721739264.052373, + "timestamp_start": 1721739264.051909 }, "slideshow": { "slide_type": "" @@ -1047,8 +1047,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738696.209568, - "timestamp_start": 1721738696.209297 + "timestamp_end": 1721739264.052705, + "timestamp_start": 1721739264.052431 }, "slideshow": { "slide_type": "" @@ -1080,8 +1080,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738701.741686, - "timestamp_start": 1721738696.20971 + "timestamp_end": 1721739269.819416, + "timestamp_start": 1721739264.052858 }, "slideshow": { "slide_type": "" @@ -1093,7 +1093,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Logging training_artifact \n" + "Logging training_artifact \n" ] }, { @@ -1124,7 +1124,7 @@ { "data": { "text/html": [ - "Run data is saved locally in /home/macu/work/app/wandb/run-20240723_124456-6z6ejjh6" + "Run data is saved locally in /home/macu/work/app/wandb/run-20240723_125424-3ei04uuv" ], "text/plain": [ "" @@ -1136,7 +1136,7 @@ { "data": { "text/html": [ - "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" + "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -1160,7 +1160,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/mi-santamaria/deepvats/runs/6z6ejjh6" + " View run at https://wandb.ai/mi-santamaria/deepvats/runs/3ei04uuv" ], "text/plain": [ "" @@ -1184,7 +1184,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "406efb5bf5a24361931be8e5e1456cac", + "model_id": "32c3282e32e34aaa88903e9dc50cbbd3", "version_major": 2, "version_minor": 0 }, @@ -1198,7 +1198,7 @@ { "data": { "text/html": [ - " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/6z6ejjh6
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" + " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/3ei04uuv
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" @@ -1210,7 +1210,7 @@ { "data": { "text/html": [ - "Find logs at: ./wandb/run-20240723_124456-6z6ejjh6/logs" + "Find logs at: ./wandb/run-20240723_125424-3ei04uuv/logs" ], "text/plain": [ "" @@ -1244,8 +1244,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738701.744144, - "timestamp_start": 1721738701.742865 + "timestamp_end": 1721739269.821886, + "timestamp_start": 1721739269.820618 }, "slideshow": { "slide_type": "" @@ -1264,8 +1264,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721738702.748999, - "timestamp_start": 1721738701.744481 + "timestamp_end": 1721739270.826678, + "timestamp_start": 1721739269.822185 }, "slideshow": { "slide_type": "" diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index 84a6ee42..9e8291da 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -1277,7 +1277,7 @@ shinyServer(function(input, output, session) { result <- get_input_output(1) - print(result) + #print(result) input_nb <- result$inputnb output_nb <- result$outputnb parameters <- result$parameters @@ -1291,19 +1291,28 @@ shinyServer(function(input, output, session) { remove_tagged_cells = c('skip', 'hide') ) - system(paste("jupyter nbconvert --execute --to notebook --inplace", - "--TagRemovePreprocessor.enabled=True --TagRemovePreprocessor.remove_cell_tags=hide", - nb_path_1)) + #system(paste("jupyter nbconvert --execute --to notebook --inplace","--TagRemovePreprocessor.enabled=True --TagRemovePreprocessor.remove_cell_tags=hide",nb_path_1)) print("First notebook: end -->") updateProgressBar(session, "progress_bar", value = 66, total = 100, status = "info") nb_path_2 <- "~/work/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb" print("--> 2st Jupyter Notebook: start") - system(paste("jupyter nbconvert --execute --to notebook --inplace", - "--TagRemovePreprocessor.enabled=True --TagRemovePreprocessor.remove_cell_tags=hide", - nb_path_2)) - + #system(paste("jupyter nbconvert --execute --to notebook --inplace","--TagRemovePreprocessor.enabled=True --TagRemovePreprocessor.remove_cell_tags=hide",nb_path_2)) + result <- get_input_output(3) + #print(result) + input_nb <- result$inputnb + output_nb <- result$outputnb + parameters <- result$parameters + print("Execute notebook") + res <- ploomber$execute_notebook( + input_path = input_nb, + output_path = output_nb, + log_output = FALSE, + progress_bar = TRUE, + parameters = parameters, + remove_tagged_cells = c('skip', 'hide') + ) print("Second notebook: end -->") updateProgressBar(session, "progress_bar", value = 100, total = 100, status = "info") From 5a4d8f197af73fbb5a6a8d8bbb22af10683b18cd Mon Sep 17 00:00:00 2001 From: "mi.santamaria" Date: Tue, 23 Jul 2024 14:58:00 +0200 Subject: [PATCH 18/37] Revisado get_input_output id para el nb 02c. --- .../output/01_dataset_artifact-output.ipynb | 136 +- ...coder_MVP-sliding_window_view-output.ipynb | 1467 +++++++++++++++++ r_shiny_app/server.R | 2 +- 3 files changed, 1529 insertions(+), 76 deletions(-) create mode 100644 nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb diff --git a/nbs_pipeline/output/01_dataset_artifact-output.ipynb b/nbs_pipeline/output/01_dataset_artifact-output.ipynb index f83fddc9..9645bf45 100644 --- a/nbs_pipeline/output/01_dataset_artifact-output.ipynb +++ b/nbs_pipeline/output/01_dataset_artifact-output.ipynb @@ -6,8 +6,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.018716, - "timestamp_start": 1721739264.018366 + "timestamp_end": 1721739446.092216, + "timestamp_start": 1721739446.091866 }, "slideshow": { "slide_type": "" @@ -33,11 +33,11 @@ { "cell_type": "code", "execution_count": 2, - "id": "710e91ea", + "id": "08390527", "metadata": { "ploomber": { - "timestamp_end": 1721739264.019031, - "timestamp_start": 1721739264.01878 + "timestamp_end": 1721739446.092528, + "timestamp_start": 1721739446.092276 }, "tags": [ "injected-parameters" @@ -76,8 +76,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.019335, - "timestamp_start": 1721739264.019083 + "timestamp_end": 1721739446.092831, + "timestamp_start": 1721739446.092575 }, "slideshow": { "slide_type": "" @@ -130,8 +130,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.019877, - "timestamp_start": 1721739264.019511 + "timestamp_end": 1721739446.093385, + "timestamp_start": 1721739446.093005 }, "slideshow": { "slide_type": "" @@ -181,8 +181,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.020152, - "timestamp_start": 1721739264.019928 + "timestamp_end": 1721739446.093657, + "timestamp_start": 1721739446.093437 }, "slideshow": { "slide_type": "" @@ -221,8 +221,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.020289, - "timestamp_start": 1721739264.020209 + "timestamp_end": 1721739446.093783, + "timestamp_start": 1721739446.093705 }, "slideshow": { "slide_type": "" @@ -261,8 +261,8 @@ "execution_count": 7, "metadata": { "ploomber": { - "timestamp_end": 1721739264.020431, - "timestamp_start": 1721739264.020332 + "timestamp_end": 1721739446.093925, + "timestamp_start": 1721739446.093825 } }, "outputs": [], @@ -277,8 +277,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.020578, - "timestamp_start": 1721739264.020471 + "timestamp_end": 1721739446.094082, + "timestamp_start": 1721739446.093969 }, "slideshow": { "slide_type": "" @@ -313,8 +313,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.020804, - "timestamp_start": 1721739264.020722 + "timestamp_end": 1721739446.094312, + "timestamp_start": 1721739446.094226 }, "slideshow": { "slide_type": "" @@ -352,8 +352,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.021193, - "timestamp_start": 1721739264.020843 + "timestamp_end": 1721739446.094705, + "timestamp_start": 1721739446.094353 }, "slideshow": { "slide_type": "" @@ -388,8 +388,8 @@ "execution_count": 11, "metadata": { "ploomber": { - "timestamp_end": 1721739264.021386, - "timestamp_start": 1721739264.021246 + "timestamp_end": 1721739446.094896, + "timestamp_start": 1721739446.094757 } }, "outputs": [], @@ -404,8 +404,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.027467, - "timestamp_start": 1721739264.021432 + "timestamp_end": 1721739446.100998, + "timestamp_start": 1721739446.094942 }, "slideshow": { "slide_type": "" @@ -462,8 +462,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.032988, - "timestamp_start": 1721739264.027529 + "timestamp_end": 1721739446.106589, + "timestamp_start": 1721739446.101059 }, "slideshow": { "slide_type": "" @@ -504,8 +504,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.036115, - "timestamp_start": 1721739264.033057 + "timestamp_end": 1721739446.109691, + "timestamp_start": 1721739446.106655 }, "slideshow": { "slide_type": "" @@ -618,8 +618,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.038612, - "timestamp_start": 1721739264.036183 + "timestamp_end": 1721739446.112288, + "timestamp_start": 1721739446.109753 }, "slideshow": { "slide_type": "" @@ -658,8 +658,8 @@ "execution_count": 16, "metadata": { "ploomber": { - "timestamp_end": 1721739264.039305, - "timestamp_start": 1721739264.038772 + "timestamp_end": 1721739446.112971, + "timestamp_start": 1721739446.112441 } }, "outputs": [ @@ -695,8 +695,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.040537, - "timestamp_start": 1721739264.039459 + "timestamp_end": 1721739446.114194, + "timestamp_start": 1721739446.11312 }, "slideshow": { "slide_type": "" @@ -729,8 +729,8 @@ "execution_count": 18, "metadata": { "ploomber": { - "timestamp_end": 1721739264.040866, - "timestamp_start": 1721739264.040685 + "timestamp_end": 1721739446.114536, + "timestamp_start": 1721739446.114351 } }, "outputs": [], @@ -786,8 +786,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.041372, - "timestamp_start": 1721739264.040921 + "timestamp_end": 1721739446.115028, + "timestamp_start": 1721739446.114588 }, "slideshow": { "slide_type": "" @@ -827,8 +827,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.05057, - "timestamp_start": 1721739264.041425 + "timestamp_end": 1721739446.124292, + "timestamp_start": 1721739446.115083 }, "slideshow": { "slide_type": "" @@ -881,8 +881,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.050927, - "timestamp_start": 1721739264.050715 + "timestamp_end": 1721739446.124648, + "timestamp_start": 1721739446.124437 }, "slideshow": { "slide_type": "" @@ -917,8 +917,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.051753, - "timestamp_start": 1721739264.050987 + "timestamp_end": 1721739446.125471, + "timestamp_start": 1721739446.124707 }, "slideshow": { "slide_type": "" @@ -990,8 +990,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.052373, - "timestamp_start": 1721739264.051909 + "timestamp_end": 1721739446.126106, + "timestamp_start": 1721739446.125629 }, "slideshow": { "slide_type": "" @@ -1047,8 +1047,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739264.052705, - "timestamp_start": 1721739264.052431 + "timestamp_end": 1721739446.126434, + "timestamp_start": 1721739446.126162 }, "slideshow": { "slide_type": "" @@ -1080,8 +1080,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739269.819416, - "timestamp_start": 1721739264.052858 + "timestamp_end": 1721739451.655433, + "timestamp_start": 1721739446.126575 }, "slideshow": { "slide_type": "" @@ -1093,7 +1093,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Logging training_artifact \n" + "Logging training_artifact \n" ] }, { @@ -1124,7 +1124,7 @@ { "data": { "text/html": [ - "Run data is saved locally in /home/macu/work/app/wandb/run-20240723_125424-3ei04uuv" + "Run data is saved locally in /home/macu/work/app/wandb/run-20240723_125726-0juve5p4" ], "text/plain": [ "" @@ -1136,7 +1136,7 @@ { "data": { "text/html": [ - "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" + "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -1160,7 +1160,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/mi-santamaria/deepvats/runs/3ei04uuv" + " View run at https://wandb.ai/mi-santamaria/deepvats/runs/0juve5p4" ], "text/plain": [ "" @@ -1181,24 +1181,10 @@ "metadata": {}, "output_type": "display_data" }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "32c3282e32e34aaa88903e9dc50cbbd3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Label(value='0.003 MB of 0.003 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ - " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/3ei04uuv
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" + " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/0juve5p4
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" @@ -1210,7 +1196,7 @@ { "data": { "text/html": [ - "Find logs at: ./wandb/run-20240723_125424-3ei04uuv/logs" + "Find logs at: ./wandb/run-20240723_125726-0juve5p4/logs" ], "text/plain": [ "" @@ -1244,8 +1230,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739269.821886, - "timestamp_start": 1721739269.820618 + "timestamp_end": 1721739451.658173, + "timestamp_start": 1721739451.656773 }, "slideshow": { "slide_type": "" @@ -1264,8 +1250,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739270.826678, - "timestamp_start": 1721739269.822185 + "timestamp_end": 1721739452.663431, + "timestamp_start": 1721739451.658506 }, "slideshow": { "slide_type": "" diff --git a/nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb b/nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb new file mode 100644 index 00000000..e2d87836 --- /dev/null +++ b/nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb @@ -0,0 +1,1467 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "404075c4", + "metadata": { + "tags": [ + "ploomber-engine-error-cell" + ] + }, + "source": [ + "## An Exception has occured at cell 14" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bb4a66b6", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.694645, + "timestamp_start": 1721739452.69428 + }, + "tags": [ + "injected-parameters" + ] + }, + "outputs": [], + "source": [ + "# Injected parameters\n", + "print_flag = False\n", + "check_memory_usage = False\n", + "time_flag = False\n", + "window_size_percentage = None\n", + "show_plots = False\n", + "reset_kernel = False\n", + "pre_configured_case = False\n", + "case_id = None\n", + "frequency_factor = 1\n", + "frequency_factor_change_alias = True\n", + "cuda_device = 0\n" + ] + }, + { + "cell_type": "markdown", + "id": "c7b6c9b7-dd2a-4d74-bf5a-cedadcc9347f", + "metadata": {}, + "source": [ + "# Encoder - MVP\n", + "\n", + "Self-supervised learning Masked Value Prediction (MVP) as a way to create the embeddings.\n", + "Based on tsai's MVP" + ] + }, + { + "cell_type": "markdown", + "id": "e5c4844a-ef9f-48f8-a575-254bd0aa04fe", + "metadata": {}, + "source": [ + "## Set-up\n", + "Initial notebook setup and specific debugging and pre-configured cases selection.\n", + "### VsCode update patch\n", + "Initial notebook setup when using VSCode." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "185023c6", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.694938, + "timestamp_start": 1721739452.694702 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "# This is only needed if the notebook is run in VSCode\n", + "import sys\n", + "import dvats.utils as ut\n", + "if '--vscode' in sys.argv:\n", + " print(\"Executing inside vscode\")\n", + " ut.DisplayHandle.update = ut.update_patch" + ] + }, + { + "cell_type": "markdown", + "id": "7b767ea2-3eaf-4233-b144-926264ec1c25", + "metadata": {}, + "source": [ + "### Debugging variables\n", + "\n", + "- `print_flag`. If `True` it adds debbuging messages in those functions that allows so.\n", + "- `reset_kernel`. If `True` it resets the kernel by the end of the execution. Use only in case that memory management is needed.\n", + "- `check_memory_usage`. If `True`, it adds some lines for checking the GPU memmory ussage along the execution.\n", + "- `time_flag`. If `True` it get the execution time along the notebook as well as inside those functions that allows so.\n", + "- `window_size_percentage`. If `True`, MVP will be used directly with the proposed windows sizes. Otherwise, it will be asumed that they have been taken as absolute values and execution will be take that into account." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f3bed3a6-eaea-4f74-9411-ea4bea5bfa26", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.695138, + "timestamp_start": 1721739452.694988 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "print_flag = False\n", + "check_memory_usage = True\n", + "time_flag = True\n", + "window_size_percentage = False" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "da23843a", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.695336, + "timestamp_start": 1721739452.695182 + } + }, + "outputs": [], + "source": [ + "#| hide\n", + "print_flag = True\n", + "reset_kernel = True\n", + "check_memory_usage = True\n", + "time_flag = True\n", + "window_size_percentage = False" + ] + }, + { + "cell_type": "markdown", + "id": "a1162273-f25b-4040-934e-943389baaca6", + "metadata": {}, + "source": [ + "## Preconfigurated cases selection\n", + "- `pre_configured_case`. If `True`, a preconfigured case will be selected, forcing the artifact to get the expected configuration based on the information in `config\\*.yml` and `utils\\config.py`.\n", + "- `case_id`. If `preconfigured_case` is `True`, it forces to select the configuration of the `case_id` preconfigured samples. The available preconfigured samples are shown in the next cell.\n", + "- `frequency_factor`. If `pre_configured_case` is `True`, frequency will be resampled by `config.freq*frequency_factor`\n", + " `frequency_factor_change_alias`. If `pre_configured_case` is `True` and `frequency_factor != 1` then the dataset alias will be modified for adding the new frequency as suffix." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fa0e189a-1cc3-4e40-93d9-86d56cee23a0", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.695473, + "timestamp_start": 1721739452.695378 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "import dvats.config as cfg_" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e2ba1f8c", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.695617, + "timestamp_start": 1721739452.695512 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available datasets: \n", + "0 - monash_australian_electricity_demand_0\n", + "1 - monash_solar_4_seconds_0\n", + "2 - wikipedia_0\n", + "3 - traffic_san_francisco_0\n", + "4 - monash_solar_10_minutes_0\n", + "5 - etth1_0\n", + "6 - stumpy_abp_0\n", + "7 - stumpy_toy_0\n" + ] + } + ], + "source": [ + "#| hide\n", + "cfg_.show_available_configs()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6f53b9f6", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.695947, + "timestamp_start": 1721739452.695771 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "pre_configured_case = False\n", + "case_id = -1\n", + "frequency_factor = 1\n", + "frequency_factor_change_alias = True" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "59a05e15-80e7-44fd-91b3-637a0d2e7a7f", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.696142, + "timestamp_start": 1721739452.695995 + } + }, + "outputs": [], + "source": [ + "#| hide\n", + "pre_configured_case = True\n", + "case_id = 7\n", + "frequency_factor = 1\n", + "frequency_factor_change_alias = True" + ] + }, + { + "cell_type": "markdown", + "id": "84bafe60-84e9-440a-ae92-29562567df86", + "metadata": {}, + "source": [ + "## Main code\n", + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4a511d12-df7f-420e-b570-f37bc13d1781", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.697002, + "timestamp_start": 1721739452.696193 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", module=\"umap\")\n", + "import os\n", + "import sys\n", + "sys.path.append(os.path.abspath('..'))\n", + "from dvats.all import *\n", + "from fastcore.all import *\n", + "from tsai.basics import *\n", + "from tsai.models.InceptionTimePlus import *\n", + "from tsai.callback.MVP import *\n", + "import matplotlib.colors as colors\n", + "from fastai.callback.wandb import WandbCallback\n", + "from fastai.callback.progress import ShowGraphCallback\n", + "from fastai.callback.schedule import *\n", + "from fastai.callback.tracker import EarlyStoppingCallback\n", + "from tsai.data.preparation import prepare_forecasting_data\n", + "from tsai.data.validation import get_long_term_forecasting_splits, get_forecasting_splits #TODO: Quitar 1 cuando esté decidida la opción\n", + "import wandb" + ] + }, + { + "cell_type": "markdown", + "id": "fb2deef3-db00-430a-b426-49d7e58e22c0", + "metadata": {}, + "source": [ + "### Initialize and Configurate Artifact" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "be090327-2eda-44be-9a71-89e20242dc9a", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.697622, + "timestamp_start": 1721739452.697062 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "wandb_api = wandb.Api()" + ] + }, + { + "cell_type": "markdown", + "id": "961dbf47-71de-4471-80d5-2fdabbd6e360", + "metadata": {}, + "source": [ + "#### Setup CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e773d2a8", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.697825, + "timestamp_start": 1721739452.697682 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "cuda_device = torch.cuda.current_device()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "210b085c", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.697974, + "timestamp_start": 1721739452.697874 + } + }, + "outputs": [], + "source": [ + "#| hide\n", + "cuda_device = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cb164924-13e2-4099-ba35-06e675035d34", + "metadata": { + "ploomber": { + "timestamp_end": 1721739452.725631, + "timestamp_start": 1721739452.698018 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPU | Used mem: 0\n", + "GPU | Used mem: 12\n", + "GPU | Memory Usage: [\u001b[90m--------------------\u001b[0m] \u001b[90m0%\u001b[0m\n" + ] + } + ], + "source": [ + "#| export\n", + "device = torch.device(f'cuda:{cuda_device}' if torch.cuda.is_available() else 'cpu')\n", + "torch.cuda.set_device(device)\n", + "if check_memory_usage:\n", + " gpu_device = torch.cuda.current_device()\n", + " gpu_memory_status(gpu_device)" + ] + }, + { + "cell_type": "markdown", + "id": "28cb7848-0ac0-4f55-a6e6-49478d7cac25", + "metadata": {}, + "source": [ + "#### Get configutation from yml\n", + "> This file used the configuration files './config/base.yml' and './config/02b_encoder_MVP.ipynb'" + ] + }, + { + "cell_type": "markdown", + "id": "c5002065", + "metadata": { + "tags": [ + "ploomber-engine-error-cell" + ] + }, + "source": [ + "## Ploomber Engine raised an exception due to the cell below " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5b845205-b133-4ee1-baaf-acc2ddd6533b", + "metadata": { + "ploomber": { + "timestamp_end": 1721739453.009878, + "timestamp_start": 1721739452.726309 + } + }, + "outputs": [ + { + "ename": "UnicodeDecodeError", + "evalue": "'ascii' codec can't decode byte 0xc2 in position 2683: ordinal not in range(128)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 2\u001b[0m", + "\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#| export\u001b[39;00m", + "\u001b[0;32m----> 2\u001b[0m user, project, version, data, config, job_type \u001b[38;5;241m=\u001b[39m \u001b[43mcfg_\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_artifact_config_MVP_SWV\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m", + "", + "File \u001b[0;32m~/dvats/config.py:425\u001b[0m, in \u001b[0;36mget_artifact_config_MVP_SWV\u001b[0;34m(print_flag, base_filename, path)\u001b[0m", + "\u001b[1;32m 416\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_artifact_config_MVP_SWV\u001b[39m(", + "\u001b[1;32m 417\u001b[0m print_flag : \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,", + "\u001b[1;32m 418\u001b[0m base_filename : \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m config_base_filename,", + "\u001b[1;32m 419\u001b[0m path : \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m config_path", + "\u001b[1;32m 420\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mstr\u001b[39m, AttrDict, \u001b[38;5;28mstr\u001b[39m]:", + "\u001b[1;32m 421\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m", + "\u001b[1;32m 422\u001b[0m \u001b[38;5;124;03m Gathers and structures the MVP_SWV artifact configuration, including user, project, version, data details, and various configuration settings.\u001b[39;00m", + "\u001b[1;32m 423\u001b[0m \u001b[38;5;124;03m Returns a tuple comprising user, project, version, data, the structured artifact configuration as an AttrDict, and the job type.\u001b[39;00m", + "\u001b[1;32m 424\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m", + "\u001b[0;32m--> 425\u001b[0m user, project, version, data, config, train_artifact_, mvp_ws, user_preferences \u001b[38;5;241m=\u001b[39m \u001b[43mget_artifact_config_MVP_auxiliar_variables_SWV\u001b[49m\u001b[43m(\u001b[49m", + "\u001b[1;32m 426\u001b[0m \u001b[43m \u001b[49m\u001b[43mprint_flag\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mprint_flag\u001b[49m\u001b[43m,\u001b[49m", + "\u001b[1;32m 427\u001b[0m \u001b[43m \u001b[49m\u001b[43mbase_filename\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbase_filename\u001b[49m\u001b[43m,\u001b[49m", + "\u001b[1;32m 428\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m", + "\u001b[1;32m 429\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m", + "\u001b[1;32m 431\u001b[0m artifact_config \u001b[38;5;241m=\u001b[39m AttrDict(", + "\u001b[1;32m 432\u001b[0m alias \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39malias,", + "\u001b[1;32m 433\u001b[0m analysis_mode \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39mwandb\u001b[38;5;241m.\u001b[39mmode, ", + "\u001b[0;32m (...)\u001b[0m", + "\u001b[1;32m 449\u001b[0m wandb_group \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39mwandb\u001b[38;5;241m.\u001b[39mgroup", + "\u001b[1;32m 450\u001b[0m )", + "\u001b[1;32m 451\u001b[0m get_artifact_config_MVP_check_errors(artifact_config, user, project)", + "", + "File \u001b[0;32m~/dvats/config.py:356\u001b[0m, in \u001b[0;36mget_artifact_config_MVP_auxiliar_variables_SWV\u001b[0;34m(print_flag, base_filename, path)\u001b[0m", + "\u001b[1;32m 354\u001b[0m \u001b[38;5;66;03m#Get neccesary variables\u001b[39;00m", + "\u001b[1;32m 355\u001b[0m user, project, version, data \u001b[38;5;241m=\u001b[39m get_project_data(print_flag, base_filename, path)", + "\u001b[0;32m--> 356\u001b[0m config \u001b[38;5;241m=\u001b[39m \u001b[43mget_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprint_flag\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m02c-encoder_mvp-sliding_window_view\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m", + "\u001b[1;32m 357\u001b[0m user_preferences \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39muser_preferences", + "\u001b[1;32m 358\u001b[0m config \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39mconfiguration", + "", + "File \u001b[0;32m~/dvats/config.py:147\u001b[0m, in \u001b[0;36mget_config\u001b[0;34m(print_flag, filename, path)\u001b[0m", + "\u001b[1;32m 144\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGetting content\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m path \u001b[38;5;241m+\u001b[39m filename)", + "\u001b[1;32m 146\u001b[0m \u001b[38;5;66;03m# Get file content\u001b[39;00m", + "\u001b[0;32m--> 147\u001b[0m full_content \u001b[38;5;241m=\u001b[39m \u001b[43mreplace_includes_with_content\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_flag\u001b[49m\u001b[43m)\u001b[49m", + "\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (print_flag):", + "\u001b[1;32m 150\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLoad content\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m path \u001b[38;5;241m+\u001b[39m filename)", + "", + "File \u001b[0;32m~/dvats/config.py:88\u001b[0m, in \u001b[0;36mreplace_includes_with_content\u001b[0;34m(filename, path, print_flag)\u001b[0m", + "\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# Lee el archivo incluido\u001b[39;00m", + "\u001b[1;32m 87\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(path\u001b[38;5;241m+\u001b[39minclude_filename, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m include_file:", + "\u001b[0;32m---> 88\u001b[0m included_content \u001b[38;5;241m=\u001b[39m \u001b[43minclude_file\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m", + "\u001b[1;32m 90\u001b[0m \u001b[38;5;66;03m# Reemplaza la directiva por el contenido del archivo incluido\u001b[39;00m", + "\u001b[1;32m 91\u001b[0m content \u001b[38;5;241m=\u001b[39m content[:start_idx] \u001b[38;5;241m+\u001b[39m included_content \u001b[38;5;241m+\u001b[39m content[end_quote_idx\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m:]", + "", + "File \u001b[0;32m/usr/local/share/miniconda3/envs/env/lib/python3.10/encodings/ascii.py:26\u001b[0m, in \u001b[0;36mIncrementalDecoder.decode\u001b[0;34m(self, input, final)\u001b[0m", + "\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecode\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m, final\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):", + "\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcodecs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mascii_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]", + "", + "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'ascii' codec can't decode byte 0xc2 in position 2683: ordinal not in range(128)" + ] + } + ], + "source": [ + "#| export\n", + "user, project, version, data, config, job_type = cfg_.get_artifact_config_MVP_SWV(False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "494fae16", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if pre_configured_case: \n", + " cfg_.show_config(case_id)\n", + " cfg_.force_artifact_config_mvp(\n", + " config = config,\n", + " id = case_id,\n", + " frequency_factor = frequency_factor,\n", + " frequency_factor_change_alias = frequency_factor_change_alias\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "71052bbf-f65b-45ea-aa8f-e3ee665f27ba", + "metadata": {}, + "source": [ + "### Setup W&B artiffact" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f30caa23", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "path = os.path.expanduser(\"~/work/nbs_pipeline/\")\n", + "name=\"02c_encoder_MVP-sliding_window_view\"\n", + "os.environ[\"WANDB_NOTEBOOK_NAME\"] = path+name+\".ipynb\"\n", + "runname=name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbc1edf2", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(\"runname: \"+runname)\n", + " cfg_.show_attrdict(config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4411368-d772-4381-9cc0-5c9b7ea5361a", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "run = wandb.init(\n", + " entity = user,\n", + " # work-nbs is a place to log draft runs\n", + " project=project,\n", + " group=config.wandb_group,\n", + " job_type=job_type,\n", + " allow_val_change=True,\n", + " mode=config.analysis_mode,\n", + " config=config,\n", + " # When use_wandb is false the run is not linked to a personal account\n", + " #NOTE: This is not working right now\n", + " anonymous = 'never' if config.use_wandb else 'must', resume=False,\n", + " name = runname\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9ad0515d-4f2a-4ba6-8c41-a6f480ff6f4b", + "metadata": {}, + "source": [ + "### Split data using Sliding Window & Get training artiffact" + ] + }, + { + "cell_type": "markdown", + "id": "b4a82ad4-45ca-4c9e-8d87-aa56f4d2fdd2", + "metadata": {}, + "source": [ + "#### Get W&B train artifact\n", + "##### Build artifact selector\n", + "Botch to use artifacts offline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc36de96", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "config = run.config # Object for storing hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ebeba46-eb67-405c-9de7-c1acb8e4085b", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag: cfg_.show_attrdict(config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2cd3daa2-d550-424a-9113-df1d9c7bef14", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "artifacts_gettr = run.use_artifact if config.use_wandb else wandb_api.artifact" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78dced3c-8280-460e-bd11-8188495bf470", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "train_artifact = artifacts_gettr(config.train_artifact)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02f7a835", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "df_train = train_artifact.to_df()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8acf714f-0fe1-4aed-8b57-23ccd31b22fe", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(df_train.shape)\n", + " display(df_train.head)" + ] + }, + { + "cell_type": "markdown", + "id": "a7e58744-f25a-4e25-baad-07013879e89f", + "metadata": {}, + "source": [ + "### Get training set\n", + "Use `prepare_forecasting_data` from tsai. Must take into account it uses the following variables:\n", + "> | Variable | Definition | Default Value | Value Utilised |\n", + "> |------------------|------------------------------------------------------|---------------|------------------|\n", + "> | `df` | Time series DataFrame. | - | `df_train` |\n", + "> | `fcst_history` | Input historical steps. Window size. | - | `config.w` |\n", + "> | `fcst_horizon` | Future predicted steps. | `1` | - (no forecasts) |\n", + "> | `x_vars` | Input variables. | `None` | - (all columns) |\n", + "> | `y_vars` | Output variables. | `None` | - |\n", + "> | `dtype` | Output datatype (for example, `'float32'`). | `None` | - |\n", + "> | `unique_id_cols` | None or unique identifier column id. | - | - |\n", + "> \n", + "> For more information, visit [tsai - data - preparation - prepare_forecasting_data](https://timeseriesai.github.io/tsai/data.preparation.html#prepare_forecasting_data)recasting_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ca6f7fd-ca88-449a-81db-9ebcce6beb80", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag: \n", + " print(\"df_train ~ \", df_train.shape)\n", + " print(\"window_sizes = \", config.mvp_ws)\n", + " print(\"wlen = \", config.w)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "973a9e71-2e00-4cbd-a93b-2824e2f45c4e", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "X_train, _ = prepare_forecasting_data(df_train, fcst_history = config.w)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3deacf2c-348f-414c-8cdd-2de61c49733a", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(\"X ~\", X_train.shape)\n", + " print(\"stride ~\", config.stride)" + ] + }, + { + "cell_type": "markdown", + "id": "4c1d9653-82e7-42a1-a659-b61b13708dcf", + "metadata": {}, + "source": [ + "#### Apply strides\n", + "Once we have build the windows, we can apply strides in order to check have the same structure as when used via sliding window\n", + "> TODO: Check if it is the same to set fcst_horizon = stride " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ff39cfd-bab8-4b9e-96c4-7a8600652afe", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "X_strided = X_train[::config.stride]\n", + "X = X_train" + ] + }, + { + "cell_type": "markdown", + "id": "1565617e-74e5-4727-91ba-fd5470e864fc", + "metadata": {}, + "source": [ + "- df_train ~ (num_samples, num_vars)\n", + "- X_train ~ (num_samples - window_size, num_vars, window_size)\n", + "- X_train_strided ~ ((num_samples - window_size)/stride, num_vars, window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d25ab5a-b1e6-4eb7-8542-e4dc87f2a883", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(\"X ~ \", X.shape)\n", + " print(\"X_strided ~ \", X_strided.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "447e726d-a175-4629-8024-0156cdfe599a", + "metadata": {}, + "source": [ + "### Split Training Set into Training and Test Dataset\n", + "\n", + "> Use the `get_forecasting_splits` function from tsai to split your time series data. Understand and adapt the parameters to suit your needs:\n", + ">\n", + "> | Variable | Definition | Default Value | Value Utilised |\n", + "> |------------------------|--------------------------------------------------------|---------------|--------------------|\n", + "> | `df` | DataFrame containing a sorted time series. | - | `df_train` |\n", + "> | `fcst_history` | Number of historical steps used as input. | - | `config.w` |\n", + "> | `fcst_horizon` | Number of steps forecasted into the future. | `1` | 1 (no forecasts) |\n", + "> | `stride` | Strides of the sliding windows (input and target). | `1` | `config.stride` |\n", + "> | `valid_size` | Size of the training set (based on datetimes). | `0.0` | `config.valid_size`|\n", + "> | `test_size` | Size of the test set (based on datetimes). | `0.2` | `0.2` |\n", + "> | `valid_cutoff_datetime`| First prediction datetime of validation dataset. | `None` | - |\n", + "> | `test_cutoff_datetime` | First prediction datetime of test dataset. | `None` | - |\n", + "> | `datetime_col` | Column with the datetime values. | `None` | `config.time_col |\n", + "> | `use_index` | Flag to indicate if datetime is in the index. | `False` | `True` |\n", + "> | `unique_id_cols` | Column/s with the unique identifier/s for each entity. | `None` | - |\n", + "> | `show_plot` | Flag to indicate if splits should be plotted. | `True` | `True` |\n", + ">\n", + "> For more information, visit [tsai - Splitting data - get_forecasting_splits](https://timeseriesai.github.io/tsai/data.validation.html#get_forecasting_splits)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "854c7680-e590-449a-a454-6b6a7595b0ea", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "assert config.analysis_mode in ['offline','online'], 'Invalid analysis mode'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98bb418e", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "if config.analysis_mode == 'online': \n", + " splits = get_forecasting_splits(\n", + " df = df_train, \n", + " fcst_history = config.w,\n", + " fcst_horizon = 1,\n", + " stride = config.stride, \n", + " test_size = 0.2,\n", + " show_plot = True\n", + " )\n", + " \n", + "elif config.analysis_mode == 'offline':\n", + " splits = get_splits(np.arange(len(X_strided)), valid_size=config.valid_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "539d98ef-0953-446c-8a47-7c3278a929fc", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag:\n", + " display(splits)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2fb702b4-8b10-4164-8755-456ffd2759f9", + "metadata": {}, + "source": [ + "## MVP - Encoder training\n", + "Train MVP with optional adaptable window sizes, to allow for inference with different\n", + "window sizes, to provide an easier exploration of the embedding space through different\n", + "ways of sliding the data." + ] + }, + { + "cell_type": "markdown", + "id": "156687d8-27b4-451c-b4ae-d5e6859fce25", + "metadata": {}, + "source": [ + "### Set callback list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7c3cd99", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "cbs = L(WandbCallback(log_preds=False)) if config.use_wandb else L()" + ] + }, + { + "cell_type": "markdown", + "id": "31668027-6769-405d-8223-09699a4f1b7f", + "metadata": {}, + "source": [ + "### Set transformations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b97038fe-116f-4d6c-8569-e9a9c015a434", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "tfms = [ToFloat(), None]\n", + "batch_tfms = [TSStandardize(by_sample=config.norm_by_sample, \n", + " use_single_batch=config.norm_use_single_batch)]" + ] + }, + { + "cell_type": "markdown", + "id": "496ae849-6b83-4298-9132-b68746b982f8", + "metadata": {}, + "source": [ + "### Get data loaders" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5565e966", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag: print(X.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02951c77", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "dls = get_ts_dls(X, splits=splits, tfms=tfms, bs=config.batch_size, batch_tfms=batch_tfms)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "668e7b5a", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag: display(dls.show_at(0))" + ] + }, + { + "cell_type": "markdown", + "id": "7824ad8e-868d-4840-a985-00627f538439", + "metadata": {}, + "source": [ + "#### Check dls" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9e0746c-7d67-474b-85db-f65e5fe147b8", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(\"X ~\", X.shape) \n", + " print(\"dls batch size\", dls.bs)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3b997218-945b-49df-b48e-e83eff7f3463", + "metadata": {}, + "source": [ + "#### Build MVP TS Learner" + ] + }, + { + "cell_type": "markdown", + "id": "210e4f21-d911-446d-8df0-d8c6fc4e112c", + "metadata": {}, + "source": [ + "##### Auxiliar functions for ensuring absolute/percentage window size management and checking the result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68800c87-ba6e-4097-bfb0-6862a0fb1a2c", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "if (not window_size_percentage):\n", + " from copy import deepcopy\n", + " def ensure_expected_window_size(expected_window_size, print_flag: bool = False):\n", + " window_size = deepcopy(expected_window_size)\n", + " if print_flag: print(window_size)\n", + " window_size[0] = window_size[0] / window_size[1]\n", + " if print_flag: \n", + " print(window_size)\n", + " print(int(round(window_size[0]*window_size[1])))\n", + " return window_size\n", + " def check_expected_window_size(learn, expected_window_size, print_flag: bool = False):\n", + " # Find MVP calback\n", + " obtained_window_size = deepcopy(learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size)\n", + " if print_flag: print(\"obtained percentage\", obtained_window_size)\n", + " obtained_window_size[0] = int(round(obtained_window_size[0]*obtained_window_size[1]))\n", + " if print_flag: print(\"obtained absolute\", obtained_window_size)\n", + " return obtained_window_size == expected_window_size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a291d3f9-ab4a-4233-9fe7-2febc19e80f9", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "if not window_size_percentage:\n", + " window_size = ensure_expected_window_size(config.mvp_ws)\n", + "else:\n", + " window_size = config.mvp_ws\n", + "window_size" + ] + }, + { + "cell_type": "markdown", + "id": "fe45a7c5-6670-4098-8112-fe57d6e51e21", + "metadata": {}, + "source": [ + "##### Initialize learner" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5f2b562-c1d8-4b01-aa69-6aa974ee959b", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "sgc = ShowGraphCallback2()\n", + "learn = ts_learner(dls, \n", + " InceptionTimePlus, \n", + " cbs= cbs + sgc + MVP(\n", + " r = config.r, \n", + " window_size=window_size, \n", + " future_mask = config.mask_future, \n", + " target_dir='./models', \n", + " sync = config.mask_sync, \n", + " stateful = config.mask_stateful,\n", + " fname=f'encoder_MVP',\n", + " verbose=False\n", + " ), y_range=[X.min(), X.max()])" + ] + }, + { + "cell_type": "markdown", + "id": "5dbf260d-9df4-48c6-9f9a-191d5b956631", + "metadata": {}, + "source": [ + "#### Check learner" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3deaf33e-2d3d-4fac-a10f-21704a2cca60", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "if not window_size_percentage:\n", + " check_expected_window_size(learn, config.mvp_ws, print_flag = print_flag)\n", + " if print_flag:\n", + " print(\"learn dls.bs\", learn.dls.bs)" + ] + }, + { + "cell_type": "markdown", + "id": "675240ed-a3b9-4a8d-b5cb-41c48b68909c", + "metadata": {}, + "source": [ + "#### Example mask" + ] + }, + { + "cell_type": "markdown", + "id": "a74c923f-31e8-49f2-b3d9-8b1fe370bcc6", + "metadata": {}, + "source": [ + "##### Create mask" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb9dd304-6691-49b7-9ffc-f004757f3cd8", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "if config.mask_future:\n", + " example_mask = create_future_mask(torch.from_numpy(X[0]), config.r, sync=config.mask_sync)\n", + "else:\n", + " example_mask = create_subsequence_mask(torch.from_numpy(X[0]), config.r, stateful=config.mask_stateful, sync=config.mask_sync)" + ] + }, + { + "cell_type": "markdown", + "id": "e58d81f4-2d4a-484c-bf9b-88ccab1cd4d6", + "metadata": {}, + "source": [ + "##### Show mask" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "563efbd4-bc93-4969-b7e8-bed4b191291e", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide \n", + "fig, ax = plt.subplots(figsize=(20, 2))\n", + "plt.pcolormesh(example_mask[0], cmap=colors.ListedColormap(['whitesmoke', 'orchid']))\n", + "plt.title(f'r={config.r}, future={config.mask_future}, stateful={config.mask_stateful}, sync={config.mask_sync}')\n", + "ax.set_ylabel('variables')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2ffb64be-3389-4f2b-997d-a8799e1a5469", + "metadata": {}, + "source": [ + " #### Check window size configuration" + ] + }, + { + "cell_type": "markdown", + "id": "9d63c043-6d89-47f6-a579-2f028980237e", + "metadata": {}, + "source": [ + "##### Check config attributes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab1d5cc8-fac5-4f9d-bbd9-4083c2c60d8a", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "expected_window_size = config.mvp_ws\n", + "np.random.randint(*config.mvp_ws)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce0cfbe7", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(\"w\", config.w, \"mvp_ws\", config.mvp_ws)\n", + " print(\"expected \", expected_window_size)\n", + " print(*config.mvp_ws)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99425d9b", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "obtained_window_size = deepcopy(learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bf85abb", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag: print(\"obtained \", obtained_window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4a428d5", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "obtained_window_size[0] = int(round(obtained_window_size[0]*obtained_window_size[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "385e5d4c-84de-440c-9572-8688951c2873", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag: print(\"obtained \", obtained_window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce3a3112-ba6b-4756-9ce1-e15e4c2af1fd", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "if (expected_window_size != obtained_window_size):\n", + " raise ValueError(\"Obtained window_size for MVP training different from expected window size. Check size, ws1 & ws2 parameters in '02b-encoder_MVP.yaml'\")\n", + "else: \n", + " print(\"Obtained window size tuple is the expected one. Continue!\")" + ] + }, + { + "cell_type": "markdown", + "id": "bcc784e8-59a1-45e9-8af1-b61ba01a9fc6", + "metadata": {}, + "source": [ + "##### Check w1 < w2 for MVP random window size selection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9272a132-9026-419c-81b0-d16d03894f70", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "if (obtained_window_size[1] < obtained_window_size[0]):\n", + " raise ValueError(\"Ws2 must be greater than Ws1 as they are the maximun and minimum window size respectively. Please ensure w2 > w1\")\n", + "else: \n", + " if print_flag: print(obtained_window_size)\n", + " ws = np.random.randint(*obtained_window_size)\n", + " if print_flag: print(ws)" + ] + }, + { + "cell_type": "markdown", + "id": "05917f56-5e62-4e45-b546-5996a72106bd", + "metadata": {}, + "source": [ + "### Train the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca10238a-f7f2-4fa2-9497-1b59f83cc6b2", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "obtained_window_size = deepcopy(learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size)\n", + "if print_flag: print(\"obtained \", obtained_window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4402b8e-a0e3-40ac-b208-8172d9ba0457", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if check_memory_usage: gpu_memory_status(gpu_device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4870d10-dcd3-4cfc-937e-3db61ed57f3a", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "lr_valley, lr_steep = learn.lr_find(suggest_funcs=(valley, steep))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc5f53b3-5c75-4d9b-94c9-0385f65dd22f", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "obtained_window_size = deepcopy(learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de465ad5", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if print_flag: print(\"obtained \", obtained_window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd3a6e2e-6b3b-44f4-9f8a-7585931b2c5b", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "if not window_size_percentage:\n", + " if not check_expected_window_size(learn=learn, expected_window_size=config.mvp_ws, print_flag=True):\n", + " learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size = ensure_expected_window_size(config.mvp_ws, True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c26833f1-ba03-470e-b209-b20ddebc7294", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "if check_memory_usage: gpu_memory_status(gpu_device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "610dd97f-1cb2-4364-9748-db882ac6162a", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "learn.fit_one_cycle(n_epoch=config.epochs, lr_max=lr_valley, cbs=[EarlyStoppingCallback(monitor='valid_loss', min_delta=0.000001, patience=10)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e92b920f-09d3-4bdc-9dbe-0d0ecf673138", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "if check_memory_usage: gpu_memory_status(gpu_device)" + ] + }, + { + "cell_type": "markdown", + "id": "2ae82aae-70dd-40de-baf8-22a2bf3d99c0", + "metadata": {}, + "source": [ + "#### Validate the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2ab4815-050b-475e-b0e7-7278fbae4215", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "if not window_size_percentage:\n", + " if not check_expected_window_size(learn=learn, expected_window_size=config.mvp_ws, print_flag=True):\n", + " learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size = ensure_expected_window_size(config.mvp_ws, True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b546f8d3", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "learn.validate()" + ] + }, + { + "cell_type": "markdown", + "id": "98db4834-1e28-4251-8f58-662cdf5f24bf", + "metadata": {}, + "source": [ + "## Visualize predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfdf3667-a698-451a-9900-0ac1d6fa0cf5", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "learn.MVP.show_preds(sharey=True, nrows=2) # error with nwors=1 or ncols=1" + ] + }, + { + "cell_type": "markdown", + "id": "5ad4ff29-1d59-4c1d-8499-a8e2b0b6c012", + "metadata": {}, + "source": [ + "## Save artifact to W&B\n", + "> Remove extra information and saving the learner object as an weight and biases artifact" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf3bf350-4f52-4a18-ad19-9d260bd060d1", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "# Remove the ShowGraphCallback2 callback to avoid errors in the frontend (TODO)\n", + "learn.remove_cb(sgc)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9cc0de64-aced-433e-9c10-28fbcf4f9a91", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "# Log the learner without the datasets\n", + "aux_learn = learn.export_and_get()\n", + "if config.use_wandb: \n", + " run.log_artifact(\n", + " ReferenceArtifact(\n", + " aux_learn, \n", + " f'mvp-SWV', \n", + " type='learner', \n", + " metadata=dict(run.config)\n", + " ), \n", + " aliases=config.alias\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "c97de068-dc91-4cfc-b0a0-2a52b52a9f80", + "metadata": {}, + "source": [ + "## Close W&B" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddfe12cd-6e5f-40eb-8f84-12d2db7a355b", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "run.finish()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d253c2fa-520a-4c55-a972-7fa9f4b0e250", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "print(\"Execution ended\")\n", + "beep(1)\n", + "if reset_kernel:\n", + " import os\n", + " os._exit(00)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10 (XPython)", + "language": "python", + "name": "xpython" + }, + "language_info": { + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/r_shiny_app/server.R b/r_shiny_app/server.R index 9e8291da..c5a4103a 100644 --- a/r_shiny_app/server.R +++ b/r_shiny_app/server.R @@ -1299,7 +1299,7 @@ shinyServer(function(input, output, session) { nb_path_2 <- "~/work/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb" print("--> 2st Jupyter Notebook: start") #system(paste("jupyter nbconvert --execute --to notebook --inplace","--TagRemovePreprocessor.enabled=True --TagRemovePreprocessor.remove_cell_tags=hide",nb_path_2)) - result <- get_input_output(3) + result <- get_input_output(2) #print(result) input_nb <- result$inputnb output_nb <- result$outputnb From 5a1f6e5f959a0d38a67d5447dc8b712fecfe3815 Mon Sep 17 00:00:00 2001 From: "mi.santamaria" Date: Tue, 23 Jul 2024 15:24:01 +0200 Subject: [PATCH 19/37] tratando de arreglar problemas con ficheros grandes --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 28d2372d..9d521f33 100644 --- a/.gitignore +++ b/.gitignore @@ -166,3 +166,5 @@ checklink/cookies.txt /docker_test/ /docker/tmp_configComits.sh .Trash-1004 +.installing +quarto-linux-amd64.deb \ No newline at end of file From 63eeed7e6bbfcc650501b2505e445a73d1ddf0b3 Mon Sep 17 00:00:00 2001 From: "mi.santamaria" Date: Tue, 23 Jul 2024 15:25:26 +0200 Subject: [PATCH 20/37] modificados utf-8 en el segundo while de replace_includes_with_content --- dvats/config.py | 2 +- nbs/config.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/dvats/config.py b/dvats/config.py index 258fc7f6..d4d9c4f7 100644 --- a/dvats/config.py +++ b/dvats/config.py @@ -84,7 +84,7 @@ def replace_includes_with_content( include_filename = content[start_quote_idx:end_quote_idx] # Lee el archivo incluido - with open(path+include_filename, 'r') as include_file: + with open(path+include_filename, 'r', encoding = 'utf-8') as include_file: included_content = include_file.read() # Reemplaza la directiva por el contenido del archivo incluido diff --git a/nbs/config.ipynb b/nbs/config.ipynb index 85264a94..ab9cff1f 100644 --- a/nbs/config.ipynb +++ b/nbs/config.ipynb @@ -212,7 +212,7 @@ " include_filename = content[start_quote_idx:end_quote_idx]\n", " \n", " # Lee el archivo incluido\n", - " with open(path+include_filename, 'r') as include_file:\n", + " with open(path+include_filename, 'r', encoding = 'utf-8') as include_file:\n", " included_content = include_file.read()\n", " \n", " # Reemplaza la directiva por el contenido del archivo incluido\n", From 42abbdbcd871df41e48e2b36457d92300ebabc1c Mon Sep 17 00:00:00 2001 From: "mi.santamaria" Date: Tue, 23 Jul 2024 15:34:49 +0200 Subject: [PATCH 21/37] =?UTF-8?q?A=C3=B1adida=20la=20celda=20de=20parametr?= =?UTF-8?q?os=20de=20entrada=20en=2002c?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../files/01_dataset_artifact-output.ipynb | 1331 +++++++++++++++++ .../files}/02b_encoder_MVP-output.ipynb | 0 ...coder_MVP-sliding_window_view-output.ipynb | 62 +- ...01_dataset_artifact-output.ipynb.trashinfo | 3 + .../02b_encoder_MVP-output.ipynb.trashinfo | 3 + ...sliding_window_view-output.ipynb.trashinfo | 3 + .../02c_encoder_MVP-sliding_window_view.ipynb | 22 + nbs_pipeline/config/base.yaml | 2 +- .../output/01_dataset_artifact-output.ipynb | 139 +- r_shiny_app/models/encoder_MVP.pth | Bin 0 -> 1860470 bytes 10 files changed, 1467 insertions(+), 98 deletions(-) create mode 100644 .Trash-1005/files/01_dataset_artifact-output.ipynb rename {nbs_pipeline/output => .Trash-1005/files}/02b_encoder_MVP-output.ipynb (100%) rename {nbs_pipeline/output => .Trash-1005/files}/02c_encoder_MVP-sliding_window_view-output.ipynb (97%) create mode 100644 .Trash-1005/info/01_dataset_artifact-output.ipynb.trashinfo create mode 100644 .Trash-1005/info/02b_encoder_MVP-output.ipynb.trashinfo create mode 100644 .Trash-1005/info/02c_encoder_MVP-sliding_window_view-output.ipynb.trashinfo create mode 100644 r_shiny_app/models/encoder_MVP.pth diff --git a/.Trash-1005/files/01_dataset_artifact-output.ipynb b/.Trash-1005/files/01_dataset_artifact-output.ipynb new file mode 100644 index 00000000..3cc6e762 --- /dev/null +++ b/.Trash-1005/files/01_dataset_artifact-output.ipynb @@ -0,0 +1,1331 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.762873, + "timestamp_start": 1721741469.76253 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "#| export\n", + "print_flag = None \n", + "show_plots = None\n", + "reset_kernel = None \n", + "pre_configured_case = None\n", + "case_id = None\n", + "frequency_factor = None\n", + "frequency_factor_change_alias = None\n", + "cuda_device = None\n", + "check_parameters = True" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f0024f3e", + "metadata": { + "ploomber": { + "timestamp_end": 1721741469.763363, + "timestamp_start": 1721741469.763101 + }, + "tags": [ + "injected-parameters" + ] + }, + "outputs": [], + "source": [ + "# Injected parameters\n", + "print_flag = True\n", + "show_plots = False\n", + "reset_kernel = False\n", + "pre_configured_case = False\n", + "case_id = None\n", + "frequency_factor = 1\n", + "frequency_factor_change_alias = True\n", + "cuda_device = 0\n", + "check_parameters = True\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Checking input parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.763658, + "timestamp_start": 1721741469.763411 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Check parameters ---\n", + "print_flag: True show_plots: False reset_kernel: False pre_configured_case: False case_id: None frequency_factor: 1 frequency_factor_change_alias: True cuda_device: 0\n" + ] + } + ], + "source": [ + "#| export\n", + "if check_parameters:\n", + " print(\"--- Check parameters ---\")\n", + " print(\n", + " \"print_flag:\", print_flag,\n", + " \"show_plots:\",show_plots,\n", + " \"reset_kernel:\",reset_kernel,\n", + " \"pre_configured_case:\",pre_configured_case,\n", + " \"case_id:\",case_id,\n", + " \"frequency_factor:\", frequency_factor, \n", + " \"frequency_factor_change_alias:\", frequency_factor_change_alias,\n", + " \"cuda_device:\", cuda_device\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Set default input parameter values ensuring no errors\n", + "### Values explained below in their natural execution place" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.764196, + "timestamp_start": 1721741469.763832 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#| export\n", + "print_flag = True if print_flag is None else print_flag\n", + "show_plots = False if show_plots is None else show_plots\n", + "reset_kernel = True if reset_kernel is None else reset_kernel\n", + "pre_configured_case = False if pre_configured_case is None else pre_configured_case\n", + "case_id = 1 if case_id is None else case_id\n", + "frequency_factor = 5 if frequency_factor is None else frequency_factor\n", + "frequency_factor_change_alias = True if frequency_factor_change_alias is None else frequency_factor_change_alias\n", + "cuda_device = 0 if cuda_device is None else cuda_device" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Create artifact from time series dataframe\n", + "Gets a .tsf or .csv with a time serie, convert int to np.dataframe and loads it to weights and biases (W&B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set-up\n", + "Initial notebook setup and specific debugging and pre-configured cases selection\n", + "### VsCode update patch\n", + "Initial notebook setup when using VSCode" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.76446, + "timestamp_start": 1721741469.764249 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#| export\n", + "import sys\n", + "import dvats.utils as ut\n", + "if '--vscode' in sys.argv:\n", + " print(\"Executing inside vscode\")\n", + " ut.DisplayHandle.update = ut.update_patch" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Debugging variables\n", + "- `print_flag`. If `True` it adds debbuging messages in those functions that allows so (eg. `get_enc_embeddings`)\n", + "- `reset_kernel`. If `True` it resets the kernel by the end of the execution. Use only in case that memory management is needed.\n", + "- `show_plots`. If `True` all plots are shown within the execution of the notebook. Otherwise, none of them will be plotted." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.764587, + "timestamp_start": 1721741469.764508 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#| export\n", + "##### ----- This cell should be substituted by input parameters ------ #####\n", + "##### See _ploomber_engine_example_.ipynb\n", + "##### Uncomment for direct Notebook execution\n", + "# print_flag = True\n", + "# reset_kernel = True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Preconfigurated cases selection\n", + "- `pre_configured_case`. If `True`, a preconfigured case will be selected, forcing the artifact to get the expected configuration based on the information in `config\\*.yml` and `utils\\config.py`.\n", + "- `case_id`. If `preconfigured_case` is `True`, it forces to select the configuration of the `case_id` preconfigured samples. The available preconfigured samples are shown in the next cell.\n", + "- `frequency_factor`. If `pre_configured_case` is `True`, frequency will be resampled by `config.freq*frequency_factor`\n", + " `frequency_factor_change_alias`. If `pre_configured_case` is `True` and `frequency_factor != 1` then the dataset alias will be modified for adding the new frequency as suffix." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ploomber": { + "timestamp_end": 1721741469.764731, + "timestamp_start": 1721741469.764631 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "import dvats.config as cfg_" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.764878, + "timestamp_start": 1721741469.764771 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available datasets: \n", + "0 - monash_australian_electricity_demand_0\n", + "1 - monash_solar_4_seconds_0\n", + "2 - wikipedia_0\n", + "3 - traffic_san_francisco_0\n", + "4 - monash_solar_10_minutes_0\n", + "5 - etth1_0\n", + "6 - stumpy_abp_0\n", + "7 - stumpy_toy_0\n" + ] + } + ], + "source": [ + "#| hide\n", + "cfg_.show_available_configs()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.765102, + "timestamp_start": 1721741469.765022 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#| export \n", + "##### ----- This cell should be substituted by input parameters ------ #####\n", + "##### See _ploomber_engine_example_.ipynb\n", + "##### Uncomment for direct Notebook execution\n", + "#pre_configured_case = False\n", + "#case_id = None\n", + "#frequency_factor = 1\n", + "#frequency_factor_change_alias = True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Main code\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.765475, + "timestamp_start": 1721741469.76514 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#| export\n", + "import pandas as pd\n", + "import numpy as np\n", + "from fastcore.all import *\n", + "import wandb\n", + "from dvats.load import TSArtifact, infer_or_inject_freq\n", + "import pickle\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from tsai.data.external import convert_tsf_to_dataframe\n", + "from tsai.utils import stack_pad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Path and Artiffact configurattions\n", + "This notebook gets configuration from `config\\base.yaml` and `config\\01-dataset_artifact.yaml`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ploomber": { + "timestamp_end": 1721741469.765661, + "timestamp_start": 1721741469.765527 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "base_path = Path.home()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.772738, + "timestamp_start": 1721741469.765705 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#| export\n", + "config = cfg_.get_artifact_config_sd2a(print_flag = False)\n", + "if pre_configured_case: \n", + " cfg_.force_artifact_config_sd2a(\n", + " config = config, \n", + " id = case_id, \n", + " print_flag = print_flag, \n", + " both = print_flag, \n", + " frequency_factor = frequency_factor, \n", + " frequency_factor_change_alias = frequency_factor_change_alias\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Extraction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data is assumed to come as a dataframe, either as a binarized picke file or\n", + "as a csv file. It can also come as a `.tsf` file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Check file content (if wanted)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extract data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.778742, + "timestamp_start": 1721741469.772802 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#| export\n", + "ext = str(config.data_fpath).split('.')[-1]\n", + "\n", + "if ext == 'pickle':\n", + " df = pd.read_pickle(config.data_fpath)\n", + " \n", + "elif ext in ['csv','txt']:\n", + " df = pd.read_csv(config.data_fpath, **config.csv_config)\n", + " \n", + "elif ext == 'tsf':\n", + " data, _, _, _, _ = convert_tsf_to_dataframe(os.path.expanduser(config.data_fpath))\n", + " config.update({'start_date': data.start_timestamp[0]}, allow_val_change=True)\n", + " date_format = config.date_format\n", + " df = pd.DataFrame(stack_pad(data.series_value).T)\n", + " \n", + "else:\n", + " raise Exception('The data file path has an unsupported extension')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set the time column (if any) as index" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.78251, + "timestamp_start": 1721741469.778809 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " penguin_sample\n", + "0 0.253906\n", + "1 0.259033\n", + "2 0.269287\n", + "3 0.271240\n", + "4 0.265137" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| export\n", + "if config.time_col is not None:\n", + " if print_flag: print(\"time_col: \"+str(config.time_col))\n", + " \n", + " if isinstance(config.time_col, int): \n", + " if print_flag: print(\"Op 1: time_col int\")\n", + " datetime = df.iloc[:, config.time_col]\n", + " \n", + " elif isinstance(config.time_col, list): \n", + " if print_flag: print(\"Op 2: time_col list\")\n", + " datetime = df.iloc[:, config.time_col].apply(lambda x: x.astype(str).str.cat(sep='-'), axis=1)\n", + " \n", + " index = pd.DatetimeIndex(datetime)\n", + " \n", + " if config.date_offset:\n", + " index += config.date_offset\n", + " \n", + " df = df.set_index(index, drop=False) \n", + " \n", + " #Delete Timestamp col\n", + " col_name = df.columns[config.time_col]\n", + " \n", + " if print_flag: print(\"... drop Timestamp col \" + str(col_name))\n", + " \n", + " df = df.drop(col_name, axis=1)\n", + " \n", + "if print_flag: display(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set dataframe frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.785019, + "timestamp_start": 1721741469.782575 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<10 * Hours>\n" + ] + } + ], + "source": [ + "#| export\n", + "df = infer_or_inject_freq(\n", + " df, \n", + " injected_freq=config.freq, \n", + " start_date=config.start_date, \n", + " format=config.date_format\n", + ")\n", + "if print_flag: print(df.index.freq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select only the needed variables" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ploomber": { + "timestamp_end": 1721741469.78571, + "timestamp_start": 1721741469.785175 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data_cols: [0]\n", + "Num. variables: 1\n" + ] + } + ], + "source": [ + "#| export\n", + "# Subset of variables\n", + "if config.data_cols:\n", + " if print_flag: print(\"data_cols: \", config.data_cols)\n", + " df = df.iloc[:, config.data_cols]\n", + "\n", + "if print_flag: print(f'Num. variables: {len(df.columns)}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ensure data integrity" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.786932, + "timestamp_start": 1721741469.785864 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "df shape before dropping duplicates (109842, 1)\n", + "df shape after dropping duplicates (109842, 1)\n" + ] + } + ], + "source": [ + "#| export\n", + "#Duplicated rows\n", + "if print_flag: print(\"df shape before dropping duplicates\", df.shape)\n", + "df.drop_duplicates()\n", + "if print_flag: print(\"df shape after dropping duplicates\", df.shape)\n", + "# Verificar si hay duplicados en el índice del dataframe\n", + "if df.index.duplicated().any():\n", + " raise ValueError(\"Duplicated index names\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "ploomber": { + "timestamp_end": 1721741469.787265, + "timestamp_start": 1721741469.787082 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "# Replace the default missing values by np.NaN\n", + "if config.missing_values_constant:\n", + " df.replace(config.missing_values_constant, np.nan, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Show time series plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Transformation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Handle Missing Values, Resample and Normalize__\n", + "\n", + "> In this second part, Time Series Artifact (TSArtifact) object can be created and missing values handling techniques, resampling and normalization can be applied.\n", + "> \n", + "> This techniques should be applied on the three subsets that must be previously created: training, validation and testing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Build dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.787749, + "timestamp_start": 1721741469.787317 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#| export\n", + "rg = config.range_training\n", + "\n", + "if isinstance(rg, list):\n", + " rg_training = rg\n", + " \n", + "elif isinstance(rg, dict):\n", + " rg_training = pd.date_range(rg['start'], rg['end'], freq=rg['freq'])\n", + " \n", + "elif config.test_split:\n", + " rg_training = df.index[:math.ceil(len(df) * (1-config.test_split))]\n", + "\n", + "else:\n", + " rg_training = None\n", + " \n", + "df_training = df[df.index.isin(rg_training)] if rg_training is not None else df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Build training artifact" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.797428, + "timestamp_start": 1721741469.7878 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "About to write df to /home/macu/data/wandb_artifacts/3058220854446460321\n" + ] + }, + { + "data": { + "text/plain": [ + "{'TS': {'sd': '1970-01-01 00:00:00',\n", + " 'ed': '2095-04-22 02:00:00',\n", + " 'created': 'from-df',\n", + " 'n_vars': 1,\n", + " 'handle_missing_values_technique': 'None',\n", + " 'has_missing_values': 'False',\n", + " 'n_samples': 109842,\n", + " 'freq': '<10 * Hours>',\n", + " 'vars': ['penguin_sample'],\n", + " 'hash': '3058220854446460321'}}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| export\n", + "training_artifact = TSArtifact.from_df(\n", + " df_training, \n", + " name=config.artifact_name, \n", + " missing_values_technique=config.missing_values_technique,\n", + " resampling_freq=config.resampling_freq, \n", + " normalize=config.normalize_training, \n", + " path=str(Path.home()/config.wandb_artifacts_path)\n", + ")\n", + "if print_flag: display(training_artifact.metadata)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.797793, + "timestamp_start": 1721741469.797576 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#| export\n", + "#Debugging \n", + "if df_training.index.duplicated().any():\n", + " raise ValueError(\"Duplicated index names\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Testing data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Build dataframe & artifact" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.7986, + "timestamp_start": 1721741469.797853 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rg None | test_split None\n" + ] + } + ], + "source": [ + "#| export\n", + "# Testing data\n", + "rg = config.range_testing\n", + "\n", + "if rg or config.test_split:\n", + " \n", + " if isinstance(rg, list):\n", + " rg_testing = rg\n", + "\n", + " elif isinstance(rg, dict):\n", + " rg_testing = pd.date_range(rg['start'], rg['end'], freq=rg['freq'])\n", + "\n", + " elif config.test_split:\n", + " rg_testing = df.index[math.ceil(len(df) * (1 - config.test_split)):]\n", + "\n", + " else:\n", + " rg_testing = None\n", + " \n", + " df_testing = df[df.index.isin(rg_testing)]\n", + " testing_artifact = TSArtifact.from_df(df_testing,\n", + " name=config.artifact_name, \n", + " missing_values_technique=config.missing_values_technique,\n", + " resampling_freq=config.resampling_freq, \n", + " normalize=False,\n", + " path=str(Path.home()/config.wandb_artifacts_path))\n", + " display(testing_artifact.metadata)\n", + " if df_testing.index.duplicated().any():\n", + " print(\"There exist duplicated value(s) in the index dataframe.\")\n", + " else:\n", + " if print_flag: print(\"There is no duplicated value in the index dataframe.\")\n", + "else:\n", + " if print_flag: print(\"rg \"+ str(rg) + \" | test_split \"+ str(config.test_split))\n", + " testing_artifact = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training + Testing data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Build dataframe & artifact" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.799205, + "timestamp_start": 1721741469.798758 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#| export\n", + "# Training + Testing data\n", + "if(config.joining_train_test):\n", + " print(\"joining_train_test: \"+ str(config.joining_train_test))\n", + " df_train_test = pd.concat([df_training, df_testing])\n", + " train_test_artifact = TSArtifact.from_df(\n", + " df_train_test,\n", + " name=config.artifact_name, \n", + " missing_values_technique=config.missing_values_technique,\n", + " resampling_freq=config.resampling_freq, \n", + " normalize=False,\n", + " path=str(Path.home()/config.wandb_artifacts_path)\n", + " )\n", + " if df_train_test.index.duplicated().any():\n", + " print(\"There exist duplicated value(s) within the dataframe index.\")\n", + " else:\n", + " if print_flag: print(\"There is no duplicated value in the dtaframe index\")\n", + " if print_flag: display(train_test_artifact.metadata)\n", + "else:\n", + " train_test_artifact = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Storing artifacts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the experiment tracking and hyperparameter we will use the tool **Weights & Biases**. \n", + "\n", + "> \n", + "Before running this notebook part, make sure you have the `$WANDB_API_KEY`, `$WANDB_ENTITY` and `$WANDB_PROJECT` environment varibales defined with your API_KEY and your ENTITY and PROJECT names (run in a terminal `echo $WANDB_API_KEY` to see it, same with the other variables). If not, run in a terminal `wandb login [API_KEY]` to set the first one. You can see your API_KEY [here](https://wandb.ai/authorize) or in the settings of your W&B account. Run in a terminal `export WANDB_ENTITY=entity_name` and/or `export WANDB_PROJECT=project_name` to set the other two\n", + "> \n", + "> TODO: Modify config.ipynb so it gets wandb config from base.yml ." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741469.799529, + "timestamp_start": 1721741469.799261 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "runname: 01_dataset_artifact\n" + ] + } + ], + "source": [ + "#| export\n", + "import os\n", + "path = os.path.expanduser(\"~/work/nbs_pipeline/\")\n", + "name=\"01_dataset_artifact\"\n", + "os.environ[\"WANDB_NOTEBOOK_NAME\"] = path+name+\".ipynb\"\n", + "runname=name\n", + "print(\"runname: \"+runname)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741476.423326, + "timestamp_start": 1721741469.799674 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Logging training_artifact \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "wandb: Currently logged in as: mi-santamaria. Use `wandb login --relogin` to force relogin\n" + ] + }, + { + "data": { + "text/html": [ + "wandb version 0.17.5 is available! To upgrade, please run:\n", + " $ pip install wandb --upgrade" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.14.2" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /home/macu/work/app/wandb/run-20240723_133110-1o04j3va" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/mi-santamaria/deepvats" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/mi-santamaria/deepvats/runs/1o04j3va" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Waiting for W&B process to finish... (success)." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8c8039657cdc4edb92a2b606841de9b5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='0.003 MB of 0.003 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/1o04j3va
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Find logs at: ./wandb/run-20240723_133110-1o04j3va/logs" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| export\n", + "mode = 'online' if config.use_wandb else 'disabled'\n", + "\n", + "# Make the run that will produce the artifact\n", + "with wandb.init(job_type='create_dataset', resume=True, mode=mode, config=config, name=runname) as run:\n", + " if testing_artifact: \n", + " run.log_artifact(training_artifact, aliases=['train'])\n", + " run.log_artifact(testing_artifact, aliases=['test'])\n", + " \n", + " if train_test_artifact:\n", + " run.log_artifact(train_test_artifact, aliases=['all'])\n", + " \n", + " else:\n", + " if print_flag: print(\"Logging training_artifact\", training_artifact)\n", + " run.log_artifact(training_artifact, aliases=['all'])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741476.426171, + "timestamp_start": 1721741476.425034 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#| export\n", + "run.finish()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "editable": true, + "ploomber": { + "timestamp_end": 1721741477.431858, + "timestamp_start": 1721741476.426423 + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution ended\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| export\n", + "from dvats.imports import beep\n", + "print(\"Execution ended\")\n", + "beep(1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10 (XPython)", + "language": "python", + "name": "xpython" + }, + "language_info": { + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nbs_pipeline/output/02b_encoder_MVP-output.ipynb b/.Trash-1005/files/02b_encoder_MVP-output.ipynb similarity index 100% rename from nbs_pipeline/output/02b_encoder_MVP-output.ipynb rename to .Trash-1005/files/02b_encoder_MVP-output.ipynb diff --git a/nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb b/.Trash-1005/files/02c_encoder_MVP-sliding_window_view-output.ipynb similarity index 97% rename from nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb rename to .Trash-1005/files/02c_encoder_MVP-sliding_window_view-output.ipynb index e2d87836..091addd3 100644 --- a/nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb +++ b/.Trash-1005/files/02c_encoder_MVP-sliding_window_view-output.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "404075c4", + "id": "f55f2731", "metadata": { "tags": [ "ploomber-engine-error-cell" @@ -15,11 +15,11 @@ { "cell_type": "code", "execution_count": 1, - "id": "bb4a66b6", + "id": "3a140bc2", "metadata": { "ploomber": { - "timestamp_end": 1721739452.694645, - "timestamp_start": 1721739452.69428 + "timestamp_end": 1721741236.833208, + "timestamp_start": 1721741236.832847 }, "tags": [ "injected-parameters" @@ -69,8 +69,8 @@ "id": "185023c6", "metadata": { "ploomber": { - "timestamp_end": 1721739452.694938, - "timestamp_start": 1721739452.694702 + "timestamp_end": 1721741236.833504, + "timestamp_start": 1721741236.833265 } }, "outputs": [], @@ -104,8 +104,8 @@ "id": "f3bed3a6-eaea-4f74-9411-ea4bea5bfa26", "metadata": { "ploomber": { - "timestamp_end": 1721739452.695138, - "timestamp_start": 1721739452.694988 + "timestamp_end": 1721741236.833719, + "timestamp_start": 1721741236.833555 } }, "outputs": [], @@ -123,8 +123,8 @@ "id": "da23843a", "metadata": { "ploomber": { - "timestamp_end": 1721739452.695336, - "timestamp_start": 1721739452.695182 + "timestamp_end": 1721741236.833929, + "timestamp_start": 1721741236.833767 } }, "outputs": [], @@ -155,8 +155,8 @@ "id": "fa0e189a-1cc3-4e40-93d9-86d56cee23a0", "metadata": { "ploomber": { - "timestamp_end": 1721739452.695473, - "timestamp_start": 1721739452.695378 + "timestamp_end": 1721741236.834075, + "timestamp_start": 1721741236.833973 } }, "outputs": [], @@ -171,8 +171,8 @@ "id": "e2ba1f8c", "metadata": { "ploomber": { - "timestamp_end": 1721739452.695617, - "timestamp_start": 1721739452.695512 + "timestamp_end": 1721741236.834227, + "timestamp_start": 1721741236.834116 } }, "outputs": [ @@ -203,8 +203,8 @@ "id": "6f53b9f6", "metadata": { "ploomber": { - "timestamp_end": 1721739452.695947, - "timestamp_start": 1721739452.695771 + "timestamp_end": 1721741236.834555, + "timestamp_start": 1721741236.83438 } }, "outputs": [], @@ -222,8 +222,8 @@ "id": "59a05e15-80e7-44fd-91b3-637a0d2e7a7f", "metadata": { "ploomber": { - "timestamp_end": 1721739452.696142, - "timestamp_start": 1721739452.695995 + "timestamp_end": 1721741236.834749, + "timestamp_start": 1721741236.8346 } }, "outputs": [], @@ -250,8 +250,8 @@ "id": "4a511d12-df7f-420e-b570-f37bc13d1781", "metadata": { "ploomber": { - "timestamp_end": 1721739452.697002, - "timestamp_start": 1721739452.696193 + "timestamp_end": 1721741236.835693, + "timestamp_start": 1721741236.834791 } }, "outputs": [], @@ -291,8 +291,8 @@ "id": "be090327-2eda-44be-9a71-89e20242dc9a", "metadata": { "ploomber": { - "timestamp_end": 1721739452.697622, - "timestamp_start": 1721739452.697062 + "timestamp_end": 1721741236.836396, + "timestamp_start": 1721741236.835752 } }, "outputs": [], @@ -315,8 +315,8 @@ "id": "e773d2a8", "metadata": { "ploomber": { - "timestamp_end": 1721739452.697825, - "timestamp_start": 1721739452.697682 + "timestamp_end": 1721741236.836603, + "timestamp_start": 1721741236.836455 } }, "outputs": [], @@ -331,8 +331,8 @@ "id": "210b085c", "metadata": { "ploomber": { - "timestamp_end": 1721739452.697974, - "timestamp_start": 1721739452.697874 + "timestamp_end": 1721741236.836759, + "timestamp_start": 1721741236.836654 } }, "outputs": [], @@ -347,8 +347,8 @@ "id": "cb164924-13e2-4099-ba35-06e675035d34", "metadata": { "ploomber": { - "timestamp_end": 1721739452.725631, - "timestamp_start": 1721739452.698018 + "timestamp_end": 1721741236.864335, + "timestamp_start": 1721741236.836805 } }, "outputs": [ @@ -382,7 +382,7 @@ }, { "cell_type": "markdown", - "id": "c5002065", + "id": "57b33752", "metadata": { "tags": [ "ploomber-engine-error-cell" @@ -398,8 +398,8 @@ "id": "5b845205-b133-4ee1-baaf-acc2ddd6533b", "metadata": { "ploomber": { - "timestamp_end": 1721739453.009878, - "timestamp_start": 1721739452.726309 + "timestamp_end": 1721741237.005302, + "timestamp_start": 1721741236.864947 } }, "outputs": [ diff --git a/.Trash-1005/info/01_dataset_artifact-output.ipynb.trashinfo b/.Trash-1005/info/01_dataset_artifact-output.ipynb.trashinfo new file mode 100644 index 00000000..a6429345 --- /dev/null +++ b/.Trash-1005/info/01_dataset_artifact-output.ipynb.trashinfo @@ -0,0 +1,3 @@ +[Trash Info] +Path=nbs_pipeline/output/01_dataset_artifact-output.ipynb +DeletionDate=2024-07-23T13:32:34 diff --git a/.Trash-1005/info/02b_encoder_MVP-output.ipynb.trashinfo b/.Trash-1005/info/02b_encoder_MVP-output.ipynb.trashinfo new file mode 100644 index 00000000..42fa65f0 --- /dev/null +++ b/.Trash-1005/info/02b_encoder_MVP-output.ipynb.trashinfo @@ -0,0 +1,3 @@ +[Trash Info] +Path=nbs_pipeline/output/02b_encoder_MVP-output.ipynb +DeletionDate=2024-07-23T13:32:34 diff --git a/.Trash-1005/info/02c_encoder_MVP-sliding_window_view-output.ipynb.trashinfo b/.Trash-1005/info/02c_encoder_MVP-sliding_window_view-output.ipynb.trashinfo new file mode 100644 index 00000000..3e7809e1 --- /dev/null +++ b/.Trash-1005/info/02c_encoder_MVP-sliding_window_view-output.ipynb.trashinfo @@ -0,0 +1,3 @@ +[Trash Info] +Path=nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb +DeletionDate=2024-07-23T13:32:34 diff --git a/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb b/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb index 89f3f9b2..f45da4dc 100644 --- a/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb +++ b/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb @@ -1,5 +1,27 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "856a14ef-9c60-4bbe-b51f-153374342556", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "print_flag = None\n", + "check_memory_usage = None\n", + "time_flag = None\n", + "window_size_percentage = None\n", + "show_plots = None\n", + "reset_kernel = None\n", + "pre_configured_case = None\n", + "case_id = None\n", + "frequency_factor = None\n", + "frequency_factor_change_alias = None\n", + "check_parameters = True\n", + "cuda_device = None" + ] + }, { "cell_type": "markdown", "id": "c7b6c9b7-dd2a-4d74-bf5a-cedadcc9347f", diff --git a/nbs_pipeline/config/base.yaml b/nbs_pipeline/config/base.yaml index 95aa7412..02d2a199 100644 --- a/nbs_pipeline/config/base.yaml +++ b/nbs_pipeline/config/base.yaml @@ -24,7 +24,7 @@ user_preferences: fname: &fname "penguin_sample" ftype: &ftype '.csv' cols: &cols [0] - freq: &freq '10h' + freq: &freq '1h' artifact: # Alias of the artifact resulting of this run. null will create one automatically alias: &alias 'Monash-Australian_electricity_demand' diff --git a/nbs_pipeline/output/01_dataset_artifact-output.ipynb b/nbs_pipeline/output/01_dataset_artifact-output.ipynb index 9645bf45..07e064d2 100644 --- a/nbs_pipeline/output/01_dataset_artifact-output.ipynb +++ b/nbs_pipeline/output/01_dataset_artifact-output.ipynb @@ -6,8 +6,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.092216, - "timestamp_start": 1721739446.091866 + "timestamp_end": 1721741571.778358, + "timestamp_start": 1721741571.778018 }, "slideshow": { "slide_type": "" @@ -33,11 +33,11 @@ { "cell_type": "code", "execution_count": 2, - "id": "08390527", + "id": "ae2e6a96", "metadata": { "ploomber": { - "timestamp_end": 1721739446.092528, - "timestamp_start": 1721739446.092276 + "timestamp_end": 1721741571.778835, + "timestamp_start": 1721741571.778583 }, "tags": [ "injected-parameters" @@ -76,8 +76,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.092831, - "timestamp_start": 1721739446.092575 + "timestamp_end": 1721741571.779129, + "timestamp_start": 1721741571.778883 }, "slideshow": { "slide_type": "" @@ -130,8 +130,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.093385, - "timestamp_start": 1721739446.093005 + "timestamp_end": 1721741571.779654, + "timestamp_start": 1721741571.7793 }, "slideshow": { "slide_type": "" @@ -181,8 +181,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.093657, - "timestamp_start": 1721739446.093437 + "timestamp_end": 1721741571.779913, + "timestamp_start": 1721741571.779703 }, "slideshow": { "slide_type": "" @@ -221,8 +221,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.093783, - "timestamp_start": 1721739446.093705 + "timestamp_end": 1721741571.780032, + "timestamp_start": 1721741571.779957 }, "slideshow": { "slide_type": "" @@ -261,8 +261,8 @@ "execution_count": 7, "metadata": { "ploomber": { - "timestamp_end": 1721739446.093925, - "timestamp_start": 1721739446.093825 + "timestamp_end": 1721741571.780182, + "timestamp_start": 1721741571.780074 } }, "outputs": [], @@ -277,8 +277,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.094082, - "timestamp_start": 1721739446.093969 + "timestamp_end": 1721741571.780336, + "timestamp_start": 1721741571.780224 }, "slideshow": { "slide_type": "" @@ -313,8 +313,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.094312, - "timestamp_start": 1721739446.094226 + "timestamp_end": 1721741571.780565, + "timestamp_start": 1721741571.780483 }, "slideshow": { "slide_type": "" @@ -352,8 +352,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.094705, - "timestamp_start": 1721739446.094353 + "timestamp_end": 1721741571.780934, + "timestamp_start": 1721741571.780602 }, "slideshow": { "slide_type": "" @@ -388,8 +388,8 @@ "execution_count": 11, "metadata": { "ploomber": { - "timestamp_end": 1721739446.094896, - "timestamp_start": 1721739446.094757 + "timestamp_end": 1721741571.781118, + "timestamp_start": 1721741571.780984 } }, "outputs": [], @@ -404,8 +404,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.100998, - "timestamp_start": 1721739446.094942 + "timestamp_end": 1721741571.78827, + "timestamp_start": 1721741571.781162 }, "slideshow": { "slide_type": "" @@ -462,8 +462,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.106589, - "timestamp_start": 1721739446.101059 + "timestamp_end": 1721741571.794356, + "timestamp_start": 1721741571.788332 }, "slideshow": { "slide_type": "" @@ -504,8 +504,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.109691, - "timestamp_start": 1721739446.106655 + "timestamp_end": 1721741571.798136, + "timestamp_start": 1721741571.794421 }, "slideshow": { "slide_type": "" @@ -618,8 +618,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.112288, - "timestamp_start": 1721739446.109753 + "timestamp_end": 1721741571.800656, + "timestamp_start": 1721741571.798198 }, "slideshow": { "slide_type": "" @@ -631,7 +631,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "<10 * Hours>\n" + "\n" ] } ], @@ -658,8 +658,8 @@ "execution_count": 16, "metadata": { "ploomber": { - "timestamp_end": 1721739446.112971, - "timestamp_start": 1721739446.112441 + "timestamp_end": 1721741571.801363, + "timestamp_start": 1721741571.800818 } }, "outputs": [ @@ -695,8 +695,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.114194, - "timestamp_start": 1721739446.11312 + "timestamp_end": 1721741571.802589, + "timestamp_start": 1721741571.801513 }, "slideshow": { "slide_type": "" @@ -729,8 +729,8 @@ "execution_count": 18, "metadata": { "ploomber": { - "timestamp_end": 1721739446.114536, - "timestamp_start": 1721739446.114351 + "timestamp_end": 1721741571.802923, + "timestamp_start": 1721741571.802744 } }, "outputs": [], @@ -786,8 +786,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.115028, - "timestamp_start": 1721739446.114588 + "timestamp_end": 1721741571.803413, + "timestamp_start": 1721741571.802977 }, "slideshow": { "slide_type": "" @@ -827,8 +827,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.124292, - "timestamp_start": 1721739446.115083 + "timestamp_end": 1721741571.812929, + "timestamp_start": 1721741571.803468 }, "slideshow": { "slide_type": "" @@ -840,22 +840,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "About to write df to /home/macu/data/wandb_artifacts/3058220854446460321\n" + "About to write df to /home/macu/data/wandb_artifacts/1852077221503164997\n" ] }, { "data": { "text/plain": [ "{'TS': {'sd': '1970-01-01 00:00:00',\n", - " 'ed': '2095-04-22 02:00:00',\n", + " 'ed': '1982-07-13 17:00:00',\n", " 'created': 'from-df',\n", " 'n_vars': 1,\n", " 'handle_missing_values_technique': 'None',\n", " 'has_missing_values': 'False',\n", " 'n_samples': 109842,\n", - " 'freq': '<10 * Hours>',\n", + " 'freq': '',\n", " 'vars': ['penguin_sample'],\n", - " 'hash': '3058220854446460321'}}" + " 'hash': '1852077221503164997'}}" ] }, "metadata": {}, @@ -881,8 +881,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.124648, - "timestamp_start": 1721739446.124437 + "timestamp_end": 1721741571.813281, + "timestamp_start": 1721741571.81307 }, "slideshow": { "slide_type": "" @@ -917,8 +917,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.125471, - "timestamp_start": 1721739446.124707 + "timestamp_end": 1721741571.814085, + "timestamp_start": 1721741571.813339 }, "slideshow": { "slide_type": "" @@ -990,8 +990,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.126106, - "timestamp_start": 1721739446.125629 + "timestamp_end": 1721741571.814704, + "timestamp_start": 1721741571.81424 }, "slideshow": { "slide_type": "" @@ -1047,8 +1047,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739446.126434, - "timestamp_start": 1721739446.126162 + "timestamp_end": 1721741571.815031, + "timestamp_start": 1721741571.814757 }, "slideshow": { "slide_type": "" @@ -1080,8 +1080,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721739451.655433, - "timestamp_start": 1721739446.126575 + "timestamp_end": 1721741580.278162, + "timestamp_start": 1721741571.815177 }, "slideshow": { "slide_type": "" @@ -1093,7 +1093,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Logging training_artifact \n" + "Logging training_artifact \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "wandb: Currently logged in as: mi-santamaria. Use `wandb login --relogin` to force relogin\n" ] }, { @@ -1124,7 +1131,7 @@ { "data": { "text/html": [ - "Run data is saved locally in /home/macu/work/app/wandb/run-20240723_125726-0juve5p4" + "Run data is saved locally in /home/macu/work/app/wandb/run-20240723_133252-f2bs0185" ], "text/plain": [ "" @@ -1136,7 +1143,7 @@ { "data": { "text/html": [ - "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" + "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -1160,7 +1167,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/mi-santamaria/deepvats/runs/0juve5p4" + " View run at https://wandb.ai/mi-santamaria/deepvats/runs/f2bs0185" ], "text/plain": [ "" @@ -1184,7 +1191,7 @@ { "data": { "text/html": [ - " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/0juve5p4
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" + " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/f2bs0185
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literal 0 HcmV?d00001 From 851c5ab02078005b2bece4ae28eba10a5789fee9 Mon Sep 17 00:00:00 2001 From: "mi.santamaria" Date: Tue, 23 Jul 2024 15:39:18 +0200 Subject: [PATCH 22/37] Segunda ejecucion completada correctamente --- .../02c_encoder_MVP-sliding_window_view.ipynb | 2 +- nbs_pipeline/config/base.yaml | 2 +- .../output/01_dataset_artifact-output.ipynb | 132 +- ...coder_MVP-sliding_window_view-output.ipynb | 4045 +++++++++++++++++ r_shiny_app/models/encoder_MVP.pth | Bin 1860470 -> 1860470 bytes 5 files changed, 4113 insertions(+), 68 deletions(-) create mode 100644 nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb diff --git a/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb b/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb index f45da4dc..1c41e126 100644 --- a/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb +++ b/nbs_pipeline/02c_encoder_MVP-sliding_window_view.ipynb @@ -97,7 +97,7 @@ "source": [ "#| hide\n", "print_flag = True\n", - "reset_kernel = True\n", + "reset_kernel = False\n", "check_memory_usage = True\n", "time_flag = True\n", "window_size_percentage = False" diff --git a/nbs_pipeline/config/base.yaml b/nbs_pipeline/config/base.yaml index 02d2a199..95aa7412 100644 --- a/nbs_pipeline/config/base.yaml +++ b/nbs_pipeline/config/base.yaml @@ -24,7 +24,7 @@ user_preferences: fname: &fname "penguin_sample" ftype: &ftype '.csv' cols: &cols [0] - freq: &freq '1h' + freq: &freq '10h' artifact: # Alias of the artifact resulting of this run. null will create one automatically alias: &alias 'Monash-Australian_electricity_demand' diff --git a/nbs_pipeline/output/01_dataset_artifact-output.ipynb b/nbs_pipeline/output/01_dataset_artifact-output.ipynb index 07e064d2..9d796783 100644 --- a/nbs_pipeline/output/01_dataset_artifact-output.ipynb +++ b/nbs_pipeline/output/01_dataset_artifact-output.ipynb @@ -6,8 +6,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.778358, - "timestamp_start": 1721741571.778018 + "timestamp_end": 1721741921.66956, + "timestamp_start": 1721741921.669217 }, "slideshow": { "slide_type": "" @@ -33,11 +33,11 @@ { "cell_type": "code", "execution_count": 2, - "id": "ae2e6a96", + "id": "cadc50f0", "metadata": { "ploomber": { - "timestamp_end": 1721741571.778835, - "timestamp_start": 1721741571.778583 + "timestamp_end": 1721741921.670036, + "timestamp_start": 1721741921.669784 }, "tags": [ "injected-parameters" @@ -76,8 +76,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.779129, - "timestamp_start": 1721741571.778883 + "timestamp_end": 1721741921.670328, + "timestamp_start": 1721741921.670083 }, "slideshow": { "slide_type": "" @@ -130,8 +130,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.779654, - "timestamp_start": 1721741571.7793 + "timestamp_end": 1721741921.670857, + "timestamp_start": 1721741921.670497 }, "slideshow": { "slide_type": "" @@ -181,8 +181,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.779913, - "timestamp_start": 1721741571.779703 + "timestamp_end": 1721741921.671121, + "timestamp_start": 1721741921.670907 }, "slideshow": { "slide_type": "" @@ -221,8 +221,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.780032, - "timestamp_start": 1721741571.779957 + "timestamp_end": 1721741921.671244, + "timestamp_start": 1721741921.671167 }, "slideshow": { "slide_type": "" @@ -261,8 +261,8 @@ "execution_count": 7, "metadata": { "ploomber": { - "timestamp_end": 1721741571.780182, - "timestamp_start": 1721741571.780074 + "timestamp_end": 1721741921.671383, + "timestamp_start": 1721741921.671283 } }, "outputs": [], @@ -277,8 +277,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.780336, - "timestamp_start": 1721741571.780224 + "timestamp_end": 1721741921.671535, + "timestamp_start": 1721741921.671425 }, "slideshow": { "slide_type": "" @@ -313,8 +313,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.780565, - "timestamp_start": 1721741571.780483 + "timestamp_end": 1721741921.671762, + "timestamp_start": 1721741921.671681 }, "slideshow": { "slide_type": "" @@ -352,8 +352,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.780934, - "timestamp_start": 1721741571.780602 + "timestamp_end": 1721741921.672143, + "timestamp_start": 1721741921.671802 }, "slideshow": { "slide_type": "" @@ -388,8 +388,8 @@ "execution_count": 11, "metadata": { "ploomber": { - "timestamp_end": 1721741571.781118, - "timestamp_start": 1721741571.780984 + "timestamp_end": 1721741921.672337, + "timestamp_start": 1721741921.6722 } }, "outputs": [], @@ -404,8 +404,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.78827, - "timestamp_start": 1721741571.781162 + "timestamp_end": 1721741921.679319, + "timestamp_start": 1721741921.672381 }, "slideshow": { "slide_type": "" @@ -462,8 +462,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.794356, - "timestamp_start": 1721741571.788332 + "timestamp_end": 1721741921.685353, + "timestamp_start": 1721741921.679381 }, "slideshow": { "slide_type": "" @@ -504,8 +504,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.798136, - "timestamp_start": 1721741571.794421 + "timestamp_end": 1721741921.689177, + "timestamp_start": 1721741921.685423 }, "slideshow": { "slide_type": "" @@ -618,8 +618,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.800656, - "timestamp_start": 1721741571.798198 + "timestamp_end": 1721741921.691731, + "timestamp_start": 1721741921.689241 }, "slideshow": { "slide_type": "" @@ -631,7 +631,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "<10 * Hours>\n" ] } ], @@ -658,8 +658,8 @@ "execution_count": 16, "metadata": { "ploomber": { - "timestamp_end": 1721741571.801363, - "timestamp_start": 1721741571.800818 + "timestamp_end": 1721741921.692461, + "timestamp_start": 1721741921.691894 } }, "outputs": [ @@ -695,8 +695,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.802589, - "timestamp_start": 1721741571.801513 + "timestamp_end": 1721741921.693702, + "timestamp_start": 1721741921.692614 }, "slideshow": { "slide_type": "" @@ -729,8 +729,8 @@ "execution_count": 18, "metadata": { "ploomber": { - "timestamp_end": 1721741571.802923, - "timestamp_start": 1721741571.802744 + "timestamp_end": 1721741921.694035, + "timestamp_start": 1721741921.693852 } }, "outputs": [], @@ -786,8 +786,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.803413, - "timestamp_start": 1721741571.802977 + "timestamp_end": 1721741921.694511, + "timestamp_start": 1721741921.694086 }, "slideshow": { "slide_type": "" @@ -827,8 +827,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.812929, - "timestamp_start": 1721741571.803468 + "timestamp_end": 1721741921.704262, + "timestamp_start": 1721741921.69456 }, "slideshow": { "slide_type": "" @@ -840,22 +840,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "About to write df to /home/macu/data/wandb_artifacts/1852077221503164997\n" + "About to write df to /home/macu/data/wandb_artifacts/3058220854446460321\n" ] }, { "data": { "text/plain": [ "{'TS': {'sd': '1970-01-01 00:00:00',\n", - " 'ed': '1982-07-13 17:00:00',\n", + " 'ed': '2095-04-22 02:00:00',\n", " 'created': 'from-df',\n", " 'n_vars': 1,\n", " 'handle_missing_values_technique': 'None',\n", " 'has_missing_values': 'False',\n", " 'n_samples': 109842,\n", - " 'freq': '',\n", + " 'freq': '<10 * Hours>',\n", " 'vars': ['penguin_sample'],\n", - " 'hash': '1852077221503164997'}}" + " 'hash': '3058220854446460321'}}" ] }, "metadata": {}, @@ -881,8 +881,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.813281, - "timestamp_start": 1721741571.81307 + "timestamp_end": 1721741921.704622, + "timestamp_start": 1721741921.704407 }, "slideshow": { "slide_type": "" @@ -917,8 +917,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.814085, - "timestamp_start": 1721741571.813339 + "timestamp_end": 1721741921.705439, + "timestamp_start": 1721741921.704681 }, "slideshow": { "slide_type": "" @@ -990,8 +990,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.814704, - "timestamp_start": 1721741571.81424 + "timestamp_end": 1721741921.706042, + "timestamp_start": 1721741921.705593 }, "slideshow": { "slide_type": "" @@ -1047,8 +1047,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741571.815031, - "timestamp_start": 1721741571.814757 + "timestamp_end": 1721741921.706366, + "timestamp_start": 1721741921.706098 }, "slideshow": { "slide_type": "" @@ -1080,8 +1080,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741580.278162, - "timestamp_start": 1721741571.815177 + "timestamp_end": 1721741930.047431, + "timestamp_start": 1721741921.706509 }, "slideshow": { "slide_type": "" @@ -1093,7 +1093,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Logging training_artifact \n" + "Logging training_artifact \n" ] }, { @@ -1131,7 +1131,7 @@ { "data": { "text/html": [ - "Run data is saved locally in /home/macu/work/app/wandb/run-20240723_133252-f2bs0185" + "Run data is saved locally in /home/macu/work/app/wandb/run-20240723_133842-j8icgfh1" ], "text/plain": [ "" @@ -1143,7 +1143,7 @@ { "data": { "text/html": [ - "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" + "Syncing run 01_dataset_artifact to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -1167,7 +1167,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/mi-santamaria/deepvats/runs/f2bs0185" + " View run at https://wandb.ai/mi-santamaria/deepvats/runs/j8icgfh1" ], "text/plain": [ "" @@ -1191,7 +1191,7 @@ { "data": { "text/html": [ - " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/f2bs0185
Synced 5 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)" + " View run 01_dataset_artifact at: https://wandb.ai/mi-santamaria/deepvats/runs/j8icgfh1
Synced 5 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" @@ -1203,7 +1203,7 @@ { "data": { "text/html": [ - "Find logs at: ./wandb/run-20240723_133252-f2bs0185/logs" + "Find logs at: ./wandb/run-20240723_133842-j8icgfh1/logs" ], "text/plain": [ "" @@ -1237,8 +1237,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741580.280861, - "timestamp_start": 1721741580.279746 + "timestamp_end": 1721741930.050684, + "timestamp_start": 1721741930.049519 }, "slideshow": { "slide_type": "" @@ -1257,8 +1257,8 @@ "metadata": { "editable": true, "ploomber": { - "timestamp_end": 1721741581.286677, - "timestamp_start": 1721741580.281114 + "timestamp_end": 1721741931.056395, + "timestamp_start": 1721741930.050976 }, "slideshow": { "slide_type": "" diff --git a/nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb b/nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb new file mode 100644 index 00000000..0a49ad57 --- /dev/null +++ b/nbs_pipeline/output/02c_encoder_MVP-sliding_window_view-output.ipynb @@ -0,0 +1,4045 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d1b334e6", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.091976, + "timestamp_start": 1721741931.091616 + }, + "tags": [ + "injected-parameters" + ] + }, + "outputs": [], + "source": [ + "# Injected parameters\n", + "print_flag = False\n", + "check_memory_usage = False\n", + "time_flag = False\n", + "window_size_percentage = None\n", + "show_plots = False\n", + "reset_kernel = False\n", + "pre_configured_case = False\n", + "case_id = None\n", + "frequency_factor = 1\n", + "frequency_factor_change_alias = True\n", + "cuda_device = 0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "856a14ef-9c60-4bbe-b51f-153374342556", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.092321, + "timestamp_start": 1721741931.092034 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "print_flag = None\n", + "check_memory_usage = None\n", + "time_flag = None\n", + "window_size_percentage = None\n", + "show_plots = None\n", + "reset_kernel = None\n", + "pre_configured_case = None\n", + "case_id = None\n", + "frequency_factor = None\n", + "frequency_factor_change_alias = None\n", + "check_parameters = True\n", + "cuda_device = None" + ] + }, + { + "cell_type": "markdown", + "id": "c7b6c9b7-dd2a-4d74-bf5a-cedadcc9347f", + "metadata": {}, + "source": [ + "# Encoder - MVP\n", + "\n", + "Self-supervised learning Masked Value Prediction (MVP) as a way to create the embeddings.\n", + "Based on tsai's MVP" + ] + }, + { + "cell_type": "markdown", + "id": "e5c4844a-ef9f-48f8-a575-254bd0aa04fe", + "metadata": {}, + "source": [ + "## Set-up\n", + "Initial notebook setup and specific debugging and pre-configured cases selection.\n", + "### VsCode update patch\n", + "Initial notebook setup when using VSCode." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "185023c6", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.092589, + "timestamp_start": 1721741931.092371 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "# This is only needed if the notebook is run in VSCode\n", + "import sys\n", + "import dvats.utils as ut\n", + "if '--vscode' in sys.argv:\n", + " print(\"Executing inside vscode\")\n", + " ut.DisplayHandle.update = ut.update_patch" + ] + }, + { + "cell_type": "markdown", + "id": "7b767ea2-3eaf-4233-b144-926264ec1c25", + "metadata": {}, + "source": [ + "### Debugging variables\n", + "\n", + "- `print_flag`. If `True` it adds debbuging messages in those functions that allows so.\n", + "- `reset_kernel`. If `True` it resets the kernel by the end of the execution. Use only in case that memory management is needed.\n", + "- `check_memory_usage`. If `True`, it adds some lines for checking the GPU memmory ussage along the execution.\n", + "- `time_flag`. If `True` it get the execution time along the notebook as well as inside those functions that allows so.\n", + "- `window_size_percentage`. If `True`, MVP will be used directly with the proposed windows sizes. Otherwise, it will be asumed that they have been taken as absolute values and execution will be take that into account." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f3bed3a6-eaea-4f74-9411-ea4bea5bfa26", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.092777, + "timestamp_start": 1721741931.092637 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "print_flag = False\n", + "check_memory_usage = True\n", + "time_flag = True\n", + "window_size_percentage = False" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "da23843a", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.092969, + "timestamp_start": 1721741931.09282 + } + }, + "outputs": [], + "source": [ + "#| hide\n", + "print_flag = True\n", + "reset_kernel = False\n", + "check_memory_usage = True\n", + "time_flag = True\n", + "window_size_percentage = False" + ] + }, + { + "cell_type": "markdown", + "id": "a1162273-f25b-4040-934e-943389baaca6", + "metadata": {}, + "source": [ + "## Preconfigurated cases selection\n", + "- `pre_configured_case`. If `True`, a preconfigured case will be selected, forcing the artifact to get the expected configuration based on the information in `config\\*.yml` and `utils\\config.py`.\n", + "- `case_id`. If `preconfigured_case` is `True`, it forces to select the configuration of the `case_id` preconfigured samples. The available preconfigured samples are shown in the next cell.\n", + "- `frequency_factor`. If `pre_configured_case` is `True`, frequency will be resampled by `config.freq*frequency_factor`\n", + " `frequency_factor_change_alias`. If `pre_configured_case` is `True` and `frequency_factor != 1` then the dataset alias will be modified for adding the new frequency as suffix." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fa0e189a-1cc3-4e40-93d9-86d56cee23a0", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.093116, + "timestamp_start": 1721741931.09301 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "import dvats.config as cfg_" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e2ba1f8c", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.093266, + "timestamp_start": 1721741931.093158 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available datasets: \n", + "0 - monash_australian_electricity_demand_0\n", + "1 - monash_solar_4_seconds_0\n", + "2 - wikipedia_0\n", + "3 - traffic_san_francisco_0\n", + "4 - monash_solar_10_minutes_0\n", + "5 - etth1_0\n", + "6 - stumpy_abp_0\n", + "7 - stumpy_toy_0\n" + ] + } + ], + "source": [ + "#| hide\n", + "cfg_.show_available_configs()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6f53b9f6", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.093572, + "timestamp_start": 1721741931.093413 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "pre_configured_case = False\n", + "case_id = -1\n", + "frequency_factor = 1\n", + "frequency_factor_change_alias = True" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "59a05e15-80e7-44fd-91b3-637a0d2e7a7f", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.093753, + "timestamp_start": 1721741931.093615 + } + }, + "outputs": [], + "source": [ + "#| hide\n", + "pre_configured_case = True\n", + "case_id = 7\n", + "frequency_factor = 1\n", + "frequency_factor_change_alias = True" + ] + }, + { + "cell_type": "markdown", + "id": "84bafe60-84e9-440a-ae92-29562567df86", + "metadata": {}, + "source": [ + "## Main code\n", + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4a511d12-df7f-420e-b570-f37bc13d1781", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.094603, + "timestamp_start": 1721741931.093797 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", module=\"umap\")\n", + "import os\n", + "import sys\n", + "sys.path.append(os.path.abspath('..'))\n", + "from dvats.all import *\n", + "from fastcore.all import *\n", + "from tsai.basics import *\n", + "from tsai.models.InceptionTimePlus import *\n", + "from tsai.callback.MVP import *\n", + "import matplotlib.colors as colors\n", + "from fastai.callback.wandb import WandbCallback\n", + "from fastai.callback.progress import ShowGraphCallback\n", + "from fastai.callback.schedule import *\n", + "from fastai.callback.tracker import EarlyStoppingCallback\n", + "from tsai.data.preparation import prepare_forecasting_data\n", + "from tsai.data.validation import get_long_term_forecasting_splits, get_forecasting_splits #TODO: Quitar 1 cuando esté decidida la opción\n", + "import wandb" + ] + }, + { + "cell_type": "markdown", + "id": "fb2deef3-db00-430a-b426-49d7e58e22c0", + "metadata": {}, + "source": [ + "### Initialize and Configurate Artifact" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "be090327-2eda-44be-9a71-89e20242dc9a", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.095286, + "timestamp_start": 1721741931.094661 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "wandb_api = wandb.Api()" + ] + }, + { + "cell_type": "markdown", + "id": "961dbf47-71de-4471-80d5-2fdabbd6e360", + "metadata": {}, + "source": [ + "#### Setup CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e773d2a8", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.095485, + "timestamp_start": 1721741931.095343 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "cuda_device = torch.cuda.current_device()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "210b085c", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.095632, + "timestamp_start": 1721741931.095533 + } + }, + "outputs": [], + "source": [ + "#| hide\n", + "cuda_device = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cb164924-13e2-4099-ba35-06e675035d34", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.119432, + "timestamp_start": 1721741931.095675 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPU | Used mem: 0\n", + "GPU | Used mem: 12\n", + "GPU | Memory Usage: [\u001b[90m--------------------\u001b[0m] \u001b[90m0%\u001b[0m\n" + ] + } + ], + "source": [ + "#| export\n", + "device = torch.device(f'cuda:{cuda_device}' if torch.cuda.is_available() else 'cpu')\n", + "torch.cuda.set_device(device)\n", + "if check_memory_usage:\n", + " gpu_device = torch.cuda.current_device()\n", + " gpu_memory_status(gpu_device)" + ] + }, + { + "cell_type": "markdown", + "id": "28cb7848-0ac0-4f55-a6e6-49478d7cac25", + "metadata": {}, + "source": [ + "#### Get configutation from yml\n", + "> This file used the configuration files './config/base.yml' and './config/02b_encoder_MVP.ipynb'" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5b845205-b133-4ee1-baaf-acc2ddd6533b", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.131029, + "timestamp_start": 1721741931.119929 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "user, project, version, data, config, job_type = cfg_.get_artifact_config_MVP_SWV(False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "494fae16", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.131363, + "timestamp_start": 1721741931.131097 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alias: toy\n", + "fname: toy\n", + "ftype: .csv\n", + "cols: []\n", + "freq: 1s\n", + "time_col: None\n", + "mvp: {'batch_size': 512, 'n_epoch': 100, 'ws': [10, 30], 'stride': 1}\n", + "dcae: {'batch_size': 512, 'n_epoch': 100, 'stride': 48, 'w': 224, 'delta': 60, 'nfs': [64, 32, 16], 'kss': [10, 5, 5], 'output_filter_size': 10, 'top_k': [2, 2, 4], 'pool_szs': [2, 2, 4]}\n" + ] + } + ], + "source": [ + "#| hide\n", + "if pre_configured_case: \n", + " cfg_.show_config(case_id)\n", + " cfg_.force_artifact_config_mvp(\n", + " config = config,\n", + " id = case_id,\n", + " frequency_factor = frequency_factor,\n", + " frequency_factor_change_alias = frequency_factor_change_alias\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "71052bbf-f65b-45ea-aa8f-e3ee665f27ba", + "metadata": {}, + "source": [ + "### Setup W&B artiffact" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f30caa23", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.131768, + "timestamp_start": 1721741931.131532 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "path = os.path.expanduser(\"~/work/nbs_pipeline/\")\n", + "name=\"02c_encoder_MVP-sliding_window_view\"\n", + "os.environ[\"WANDB_NOTEBOOK_NAME\"] = path+name+\".ipynb\"\n", + "runname=name" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "fbc1edf2", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.13199, + "timestamp_start": 1721741931.131817 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "runname: 02c_encoder_MVP-sliding_window_view\n", + "alias: toy\n", + "analysis_mode: online\n", + "batch_size: 512\n", + "epochs: 100\n", + "mask_future: False\n", + "mask_stateful: True\n", + "mask_sync: False\n", + "mvp_ws: [10, 30]\n", + "norm_by_sample: False\n", + "norm_use_single_batch: False\n", + "r: 0.71\n", + "stride: 1\n", + "train_artifact: mi-santamaria/deepvats/toy:latest\n", + "valid_artifact: None\n", + "use_wandb: True\n", + "valid_size: 0.2\n", + "w: 30\n", + "wandb_group: None\n", + "artifact_name: toy\n", + "data_cols: []\n", + "data_fpath: ~/data/toy.csv\n", + "freq: 1s\n", + "time_col: None\n", + "csv_config: {}\n", + "norm_use_by_single_batch: (False,)\n" + ] + } + ], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(\"runname: \"+runname)\n", + " cfg_.show_attrdict(config)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e4411368-d772-4381-9cc0-5c9b7ea5361a", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.602569, + "timestamp_start": 1721741931.132133 + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "Changes to your `wandb` environment variables will be ignored because your `wandb` session has already started. For more information on how to modify your settings with `wandb.init()` arguments, please refer to the W&B docs." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "wandb version 0.17.5 is available! To upgrade, please run:\n", + " $ pip install wandb --upgrade" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.14.2" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /home/macu/work/app/wandb/run-20240723_133851-uvfns4mt" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run 02c_encoder_MVP-sliding_window_view to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/mi-santamaria/deepvats" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/mi-santamaria/deepvats/runs/uvfns4mt" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| export\n", + "run = wandb.init(\n", + " entity = user,\n", + " # work-nbs is a place to log draft runs\n", + " project=project,\n", + " group=config.wandb_group,\n", + " job_type=job_type,\n", + " allow_val_change=True,\n", + " mode=config.analysis_mode,\n", + " config=config,\n", + " # When use_wandb is false the run is not linked to a personal account\n", + " #NOTE: This is not working right now\n", + " anonymous = 'never' if config.use_wandb else 'must', resume=False,\n", + " name = runname\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9ad0515d-4f2a-4ba6-8c41-a6f480ff6f4b", + "metadata": {}, + "source": [ + "### Split data using Sliding Window & Get training artiffact" + ] + }, + { + "cell_type": "markdown", + "id": "b4a82ad4-45ca-4c9e-8d87-aa56f4d2fdd2", + "metadata": {}, + "source": [ + "#### Get W&B train artifact\n", + "##### Build artifact selector\n", + "Botch to use artifacts offline" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fc36de96", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.603283, + "timestamp_start": 1721741931.602778 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "config = run.config # Object for storing hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "3ebeba46-eb67-405c-9de7-c1acb8e4085b", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.603896, + "timestamp_start": 1721741931.603386 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alias: toy\n", + "analysis_mode: online\n", + "batch_size: 512\n", + "epochs: 100\n", + "mask_future: False\n", + "mask_stateful: True\n", + "mask_sync: False\n", + "mvp_ws: [10, 30]\n", + "norm_by_sample: False\n", + "norm_use_single_batch: False\n", + "r: 0.71\n", + "stride: 1\n", + "train_artifact: mi-santamaria/deepvats/toy:latest\n", + "valid_artifact: None\n", + "use_wandb: True\n", + "valid_size: 0.2\n", + "w: 30\n", + "wandb_group: None\n", + "artifact_name: toy\n", + "data_cols: []\n", + "data_fpath: ~/data/toy.csv\n", + "freq: 1s\n", + "time_col: None\n", + "csv_config: {}\n", + "norm_use_by_single_batch: [False]\n" + ] + } + ], + "source": [ + "#| hide\n", + "if print_flag: cfg_.show_attrdict(config)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "2cd3daa2-d550-424a-9113-df1d9c7bef14", + "metadata": { + "ploomber": { + "timestamp_end": 1721741931.604682, + "timestamp_start": 1721741931.604194 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "artifacts_gettr = run.use_artifact if config.use_wandb else wandb_api.artifact" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "78dced3c-8280-460e-bd11-8188495bf470", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.107458, + "timestamp_start": 1721741931.604788 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "train_artifact = artifacts_gettr(config.train_artifact)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "02f7a835", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.512275, + "timestamp_start": 1721741932.108009 + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "wandb: 1 of 1 files downloaded. \n" + ] + } + ], + "source": [ + "#| export\n", + "df_train = train_artifact.to_df()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8acf714f-0fe1-4aed-8b57-23ccd31b22fe", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.524573, + "timestamp_start": 1721741932.513199 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(550, 3)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(df_train.shape)\n", + " display(df_train.head)" + ] + }, + { + "cell_type": "markdown", + "id": "a7e58744-f25a-4e25-baad-07013879e89f", + "metadata": {}, + "source": [ + "### Get training set\n", + "Use `prepare_forecasting_data` from tsai. Must take into account it uses the following variables:\n", + "> | Variable | Definition | Default Value | Value Utilised |\n", + "> |------------------|------------------------------------------------------|---------------|------------------|\n", + "> | `df` | Time series DataFrame. | - | `df_train` |\n", + "> | `fcst_history` | Input historical steps. Window size. | - | `config.w` |\n", + "> | `fcst_horizon` | Future predicted steps. | `1` | - (no forecasts) |\n", + "> | `x_vars` | Input variables. | `None` | - (all columns) |\n", + "> | `y_vars` | Output variables. | `None` | - |\n", + "> | `dtype` | Output datatype (for example, `'float32'`). | `None` | - |\n", + "> | `unique_id_cols` | None or unique identifier column id. | - | - |\n", + "> \n", + "> For more information, visit [tsai - data - preparation - prepare_forecasting_data](https://timeseriesai.github.io/tsai/data.preparation.html#prepare_forecasting_data)recasting_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2ca6f7fd-ca88-449a-81db-9ebcce6beb80", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.525688, + "timestamp_start": 1721741932.524963 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "df_train ~ (550, 3)\n", + "window_sizes = [10, 30]\n", + "wlen = 30\n" + ] + } + ], + "source": [ + "#| hide\n", + "if print_flag: \n", + " print(\"df_train ~ \", df_train.shape)\n", + " print(\"window_sizes = \", config.mvp_ws)\n", + " print(\"wlen = \", config.w)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "973a9e71-2e00-4cbd-a93b-2824e2f45c4e", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.526586, + "timestamp_start": 1721741932.525967 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "X_train, _ = prepare_forecasting_data(df_train, fcst_history = config.w)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3deacf2c-348f-414c-8cdd-2de61c49733a", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.527178, + "timestamp_start": 1721741932.52669 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X ~ (520, 3, 30)\n", + "stride ~ 1\n" + ] + } + ], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(\"X ~\", X_train.shape)\n", + " print(\"stride ~\", config.stride)" + ] + }, + { + "cell_type": "markdown", + "id": "4c1d9653-82e7-42a1-a659-b61b13708dcf", + "metadata": {}, + "source": [ + "#### Apply strides\n", + "Once we have build the windows, we can apply strides in order to check have the same structure as when used via sliding window\n", + "> TODO: Check if it is the same to set fcst_horizon = stride " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "9ff39cfd-bab8-4b9e-96c4-7a8600652afe", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.527765, + "timestamp_start": 1721741932.527397 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "X_strided = X_train[::config.stride]\n", + "X = X_train" + ] + }, + { + "cell_type": "markdown", + "id": "1565617e-74e5-4727-91ba-fd5470e864fc", + "metadata": {}, + "source": [ + "- df_train ~ (num_samples, num_vars)\n", + "- X_train ~ (num_samples - window_size, num_vars, window_size)\n", + "- X_train_strided ~ ((num_samples - window_size)/stride, num_vars, window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "8d25ab5a-b1e6-4eb7-8542-e4dc87f2a883", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.528238, + "timestamp_start": 1721741932.527844 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X ~ (520, 3, 30)\n", + "X_strided ~ (520, 3, 30)\n" + ] + } + ], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(\"X ~ \", X.shape)\n", + " print(\"X_strided ~ \", X_strided.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "447e726d-a175-4629-8024-0156cdfe599a", + "metadata": {}, + "source": [ + "### Split Training Set into Training and Test Dataset\n", + "\n", + "> Use the `get_forecasting_splits` function from tsai to split your time series data. Understand and adapt the parameters to suit your needs:\n", + ">\n", + "> | Variable | Definition | Default Value | Value Utilised |\n", + "> |------------------------|--------------------------------------------------------|---------------|--------------------|\n", + "> | `df` | DataFrame containing a sorted time series. | - | `df_train` |\n", + "> | `fcst_history` | Number of historical steps used as input. | - | `config.w` |\n", + "> | `fcst_horizon` | Number of steps forecasted into the future. | `1` | 1 (no forecasts) |\n", + "> | `stride` | Strides of the sliding windows (input and target). | `1` | `config.stride` |\n", + "> | `valid_size` | Size of the training set (based on datetimes). | `0.0` | `config.valid_size`|\n", + "> | `test_size` | Size of the test set (based on datetimes). | `0.2` | `0.2` |\n", + "> | `valid_cutoff_datetime`| First prediction datetime of validation dataset. | `None` | - |\n", + "> | `test_cutoff_datetime` | First prediction datetime of test dataset. | `None` | - |\n", + "> | `datetime_col` | Column with the datetime values. | `None` | `config.time_col |\n", + "> | `use_index` | Flag to indicate if datetime is in the index. | `False` | `True` |\n", + "> | `unique_id_cols` | Column/s with the unique identifier/s for each entity. | `None` | - |\n", + "> | `show_plot` | Flag to indicate if splits should be plotted. | `True` | `True` |\n", + ">\n", + "> For more information, visit [tsai - Splitting data - get_forecasting_splits](https://timeseriesai.github.io/tsai/data.validation.html#get_forecasting_splits)." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "854c7680-e590-449a-a454-6b6a7595b0ea", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.52873, + "timestamp_start": 1721741932.52842 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "assert config.analysis_mode in ['offline','online'], 'Invalid analysis mode'" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "98bb418e", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.628447, + "timestamp_start": 1721741932.528793 + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| export\n", + "if config.analysis_mode == 'online': \n", + " splits = get_forecasting_splits(\n", + " df = df_train, \n", + " fcst_history = config.w,\n", + " fcst_horizon = 1,\n", + " stride = config.stride, \n", + " test_size = 0.2,\n", + " show_plot = True\n", + " )\n", + " \n", + "elif config.analysis_mode == 'offline':\n", + " splits = get_splits(np.arange(len(X_strided)), valid_size=config.valid_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "539d98ef-0953-446c-8a47-7c3278a929fc", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.629328, + "timestamp_start": 1721741932.628576 + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((#416) [0,1,2,3,4,5,6,7,8,9...],\n", + " (#104) [416,417,418,419,420,421,422,423,424,425...])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| hide\n", + "if print_flag:\n", + " display(splits)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2fb702b4-8b10-4164-8755-456ffd2759f9", + "metadata": {}, + "source": [ + "## MVP - Encoder training\n", + "Train MVP with optional adaptable window sizes, to allow for inference with different\n", + "window sizes, to provide an easier exploration of the embedding space through different\n", + "ways of sliding the data." + ] + }, + { + "cell_type": "markdown", + "id": "156687d8-27b4-451c-b4ae-d5e6859fce25", + "metadata": {}, + "source": [ + "### Set callback list" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "c7c3cd99", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.629941, + "timestamp_start": 1721741932.629399 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "cbs = L(WandbCallback(log_preds=False)) if config.use_wandb else L()" + ] + }, + { + "cell_type": "markdown", + "id": "31668027-6769-405d-8223-09699a4f1b7f", + "metadata": {}, + "source": [ + "### Set transformations" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "b97038fe-116f-4d6c-8569-e9a9c015a434", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.630424, + "timestamp_start": 1721741932.630006 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "tfms = [ToFloat(), None]\n", + "batch_tfms = [TSStandardize(by_sample=config.norm_by_sample, \n", + " use_single_batch=config.norm_use_single_batch)]" + ] + }, + { + "cell_type": "markdown", + "id": "496ae849-6b83-4298-9132-b68746b982f8", + "metadata": {}, + "source": [ + "### Get data loaders" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "5565e966", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.630747, + "timestamp_start": 1721741932.630486 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(520, 3, 30)\n" + ] + } + ], + "source": [ + "#| hide\n", + "if print_flag: print(X.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "02951c77", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.651412, + "timestamp_start": 1721741932.630923 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "dls = get_ts_dls(X, splits=splits, tfms=tfms, bs=config.batch_size, batch_tfms=batch_tfms)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "668e7b5a", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.776713, + "timestamp_start": 1721741932.651485 + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "None" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| hide\n", + "if print_flag: display(dls.show_at(0))" + ] + }, + { + "cell_type": "markdown", + "id": "7824ad8e-868d-4840-a985-00627f538439", + "metadata": {}, + "source": [ + "#### Check dls" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "f9e0746c-7d67-474b-85db-f65e5fe147b8", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.777369, + "timestamp_start": 1721741932.776843 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X ~ (520, 3, 30)\n", + "dls batch size 416\n" + ] + } + ], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(\"X ~\", X.shape) \n", + " print(\"dls batch size\", dls.bs)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3b997218-945b-49df-b48e-e83eff7f3463", + "metadata": {}, + "source": [ + "#### Build MVP TS Learner" + ] + }, + { + "cell_type": "markdown", + "id": "210e4f21-d911-446d-8df0-d8c6fc4e112c", + "metadata": {}, + "source": [ + "##### Auxiliar functions for ensuring absolute/percentage window size management and checking the result." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "68800c87-ba6e-4097-bfb0-6862a0fb1a2c", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.778414, + "timestamp_start": 1721741932.77756 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "if (not window_size_percentage):\n", + " from copy import deepcopy\n", + " def ensure_expected_window_size(expected_window_size, print_flag: bool = False):\n", + " window_size = deepcopy(expected_window_size)\n", + " if print_flag: print(window_size)\n", + " window_size[0] = window_size[0] / window_size[1]\n", + " if print_flag: \n", + " print(window_size)\n", + " print(int(round(window_size[0]*window_size[1])))\n", + " return window_size\n", + " def check_expected_window_size(learn, expected_window_size, print_flag: bool = False):\n", + " # Find MVP calback\n", + " obtained_window_size = deepcopy(learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size)\n", + " if print_flag: print(\"obtained percentage\", obtained_window_size)\n", + " obtained_window_size[0] = int(round(obtained_window_size[0]*obtained_window_size[1]))\n", + " if print_flag: print(\"obtained absolute\", obtained_window_size)\n", + " return obtained_window_size == expected_window_size" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a291d3f9-ab4a-4233-9fe7-2febc19e80f9", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.779183, + "timestamp_start": 1721741932.778483 + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.3333333333333333, 30]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| export\n", + "if not window_size_percentage:\n", + " window_size = ensure_expected_window_size(config.mvp_ws)\n", + "else:\n", + " window_size = config.mvp_ws\n", + "window_size" + ] + }, + { + "cell_type": "markdown", + "id": "fe45a7c5-6670-4098-8112-fe57d6e51e21", + "metadata": {}, + "source": [ + "##### Initialize learner" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "f5f2b562-c1d8-4b01-aa69-6aa974ee959b", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.789716, + "timestamp_start": 1721741932.779252 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "sgc = ShowGraphCallback2()\n", + "learn = ts_learner(dls, \n", + " InceptionTimePlus, \n", + " cbs= cbs + sgc + MVP(\n", + " r = config.r, \n", + " window_size=window_size, \n", + " future_mask = config.mask_future, \n", + " target_dir='./models', \n", + " sync = config.mask_sync, \n", + " stateful = config.mask_stateful,\n", + " fname=f'encoder_MVP',\n", + " verbose=False\n", + " ), y_range=[X.min(), X.max()])" + ] + }, + { + "cell_type": "markdown", + "id": "5dbf260d-9df4-48c6-9f9a-191d5b956631", + "metadata": {}, + "source": [ + "#### Check learner" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "3deaf33e-2d3d-4fac-a10f-21704a2cca60", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.790594, + "timestamp_start": 1721741932.789842 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "obtained percentage [0.3333333333333333, 30]\n", + "obtained absolute [10, 30]\n", + "learn dls.bs 416\n" + ] + } + ], + "source": [ + "#| export\n", + "if not window_size_percentage:\n", + " check_expected_window_size(learn, config.mvp_ws, print_flag = print_flag)\n", + " if print_flag:\n", + " print(\"learn dls.bs\", learn.dls.bs)" + ] + }, + { + "cell_type": "markdown", + "id": "675240ed-a3b9-4a8d-b5cb-41c48b68909c", + "metadata": {}, + "source": [ + "#### Example mask" + ] + }, + { + "cell_type": "markdown", + "id": "a74c923f-31e8-49f2-b3d9-8b1fe370bcc6", + "metadata": {}, + "source": [ + "##### Create mask" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "cb9dd304-6691-49b7-9ffc-f004757f3cd8", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.801579, + "timestamp_start": 1721741932.790906 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "if config.mask_future:\n", + " example_mask = create_future_mask(torch.from_numpy(X[0]), config.r, sync=config.mask_sync)\n", + "else:\n", + " example_mask = create_subsequence_mask(torch.from_numpy(X[0]), config.r, stateful=config.mask_stateful, sync=config.mask_sync)" + ] + }, + { + "cell_type": "markdown", + "id": "e58d81f4-2d4a-484c-bf9b-88ccab1cd4d6", + "metadata": {}, + "source": [ + "##### Show mask" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "563efbd4-bc93-4969-b7e8-bed4b191291e", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.91356, + "timestamp_start": 1721741932.801675 + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| hide \n", + "fig, ax = plt.subplots(figsize=(20, 2))\n", + "plt.pcolormesh(example_mask[0], cmap=colors.ListedColormap(['whitesmoke', 'orchid']))\n", + "plt.title(f'r={config.r}, future={config.mask_future}, stateful={config.mask_stateful}, sync={config.mask_sync}')\n", + "ax.set_ylabel('variables')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2ffb64be-3389-4f2b-997d-a8799e1a5469", + "metadata": {}, + "source": [ + " #### Check window size configuration" + ] + }, + { + "cell_type": "markdown", + "id": "9d63c043-6d89-47f6-a579-2f028980237e", + "metadata": {}, + "source": [ + "##### Check config attributes" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "ab1d5cc8-fac5-4f9d-bbd9-4083c2c60d8a", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.914548, + "timestamp_start": 1721741932.913694 + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| export\n", + "expected_window_size = config.mvp_ws\n", + "np.random.randint(*config.mvp_ws)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "ce0cfbe7", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.915021, + "timestamp_start": 1721741932.914627 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w 30 mvp_ws [10, 30]\n", + "expected [10, 30]\n", + "10 30\n" + ] + } + ], + "source": [ + "#| hide\n", + "if print_flag:\n", + " print(\"w\", config.w, \"mvp_ws\", config.mvp_ws)\n", + " print(\"expected \", expected_window_size)\n", + " print(*config.mvp_ws)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "99425d9b", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.915561, + "timestamp_start": 1721741932.915194 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "obtained_window_size = deepcopy(learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "8bf85abb", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.915879, + "timestamp_start": 1721741932.915621 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "obtained [0.3333333333333333, 30]\n" + ] + } + ], + "source": [ + "#| hide\n", + "if print_flag: print(\"obtained \", obtained_window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "c4a428d5", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.916351, + "timestamp_start": 1721741932.916031 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "obtained_window_size[0] = int(round(obtained_window_size[0]*obtained_window_size[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "385e5d4c-84de-440c-9572-8688951c2873", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.916674, + "timestamp_start": 1721741932.916412 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "obtained [10, 30]\n" + ] + } + ], + "source": [ + "#| hide\n", + "if print_flag: print(\"obtained \", obtained_window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "ce3a3112-ba6b-4756-9ce1-e15e4c2af1fd", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.917133, + "timestamp_start": 1721741932.916826 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Obtained window size tuple is the expected one. Continue!\n" + ] + } + ], + "source": [ + "#| export\n", + "if (expected_window_size != obtained_window_size):\n", + " raise ValueError(\"Obtained window_size for MVP training different from expected window size. Check size, ws1 & ws2 parameters in '02b-encoder_MVP.yaml'\")\n", + "else: \n", + " print(\"Obtained window size tuple is the expected one. Continue!\")" + ] + }, + { + "cell_type": "markdown", + "id": "bcc784e8-59a1-45e9-8af1-b61ba01a9fc6", + "metadata": {}, + "source": [ + "##### Check w1 < w2 for MVP random window size selection" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "9272a132-9026-419c-81b0-d16d03894f70", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.917713, + "timestamp_start": 1721741932.917282 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10, 30]\n", + "20\n" + ] + } + ], + "source": [ + "#| export\n", + "if (obtained_window_size[1] < obtained_window_size[0]):\n", + " raise ValueError(\"Ws2 must be greater than Ws1 as they are the maximun and minimum window size respectively. Please ensure w2 > w1\")\n", + "else: \n", + " if print_flag: print(obtained_window_size)\n", + " ws = np.random.randint(*obtained_window_size)\n", + " if print_flag: print(ws)" + ] + }, + { + "cell_type": "markdown", + "id": "05917f56-5e62-4e45-b546-5996a72106bd", + "metadata": {}, + "source": [ + "### Train the model" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "ca10238a-f7f2-4fa2-9497-1b59f83cc6b2", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.918259, + "timestamp_start": 1721741932.917864 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "obtained [0.3333333333333333, 30]\n" + ] + } + ], + "source": [ + "#| export\n", + "obtained_window_size = deepcopy(learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size)\n", + "if print_flag: print(\"obtained \", obtained_window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "f4402b8e-a0e3-40ac-b208-8172d9ba0457", + "metadata": { + "ploomber": { + "timestamp_end": 1721741932.957482, + "timestamp_start": 1721741932.918408 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPU | Used mem: 0\n", + "GPU | Used mem: 12\n", + "GPU | Memory Usage: [\u001b[90m--------------------\u001b[0m] \u001b[90m0%\u001b[0m\n" + ] + } + ], + "source": [ + "#| hide\n", + "if check_memory_usage: gpu_memory_status(gpu_device)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "d4870d10-dcd3-4cfc-937e-3db61ed57f3a", + "metadata": { + "ploomber": { + "timestamp_end": 1721741934.800921, + "timestamp_start": 1721741932.957764 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "█\r", + "█\r", + "\r", + "Epoch 1/101 : |----------------------------------------| 0.00% [0/1 00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| export\n", + "lr_valley, lr_steep = learn.lr_find(suggest_funcs=(valley, steep))" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "fc5f53b3-5c75-4d9b-94c9-0385f65dd22f", + "metadata": { + "ploomber": { + "timestamp_end": 1721741934.8018, + "timestamp_start": 1721741934.801313 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "obtained_window_size = deepcopy(learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "de465ad5", + "metadata": { + "ploomber": { + "timestamp_end": 1721741934.802179, + "timestamp_start": 1721741934.801868 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "obtained [10, 30]\n" + ] + } + ], + "source": [ + "#| hide\n", + "if print_flag: print(\"obtained \", obtained_window_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "bd3a6e2e-6b3b-44f4-9f8a-7585931b2c5b", + "metadata": { + "ploomber": { + "timestamp_end": 1721741934.802846, + "timestamp_start": 1721741934.802354 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "obtained percentage [10, 30]\n", + "obtained absolute [300, 30]\n", + "[10, 30]\n", + "[0.3333333333333333, 30]\n", + "10\n" + ] + } + ], + "source": [ + "#| export\n", + "if not window_size_percentage:\n", + " if not check_expected_window_size(learn=learn, expected_window_size=config.mvp_ws, print_flag=True):\n", + " learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size = ensure_expected_window_size(config.mvp_ws, True)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "c26833f1-ba03-470e-b209-b20ddebc7294", + "metadata": { + "ploomber": { + "timestamp_end": 1721741934.825018, + "timestamp_start": 1721741934.803012 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPU | Used mem: 0\n", + "GPU | Used mem: 12\n", + "GPU | Memory Usage: [\u001b[90m--------------------\u001b[0m] \u001b[90m0%\u001b[0m\n" + ] + } + ], + "source": [ + "#| export\n", + "if check_memory_usage: gpu_memory_status(gpu_device)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "610dd97f-1cb2-4364-9748-db882ac6162a", + "metadata": { + "ploomber": { + "timestamp_end": 1721741935.849101, + "timestamp_start": 1721741934.825348 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "█\r", + "epoch train_loss valid_loss time \n", + "█\r", + "\r", + "Epoch 1/100 : |----------------------------------------| 0.00% [0/1 00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| export\n", + "learn.fit_one_cycle(n_epoch=config.epochs, lr_max=lr_valley, cbs=[EarlyStoppingCallback(monitor='valid_loss', min_delta=0.000001, patience=10)])" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "e92b920f-09d3-4bdc-9dbe-0d0ecf673138", + "metadata": { + "ploomber": { + "timestamp_end": 1721741935.8721, + "timestamp_start": 1721741935.849343 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPU | Used mem: 0\n", + "GPU | Used mem: 12\n", + "GPU | Memory Usage: [\u001b[90m--------------------\u001b[0m] \u001b[90m0%\u001b[0m\n" + ] + } + ], + "source": [ + "#| hide\n", + "if check_memory_usage: gpu_memory_status(gpu_device)" + ] + }, + { + "cell_type": "markdown", + "id": "2ae82aae-70dd-40de-baf8-22a2bf3d99c0", + "metadata": {}, + "source": [ + "#### Validate the model" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "f2ab4815-050b-475e-b0e7-7278fbae4215", + "metadata": { + "ploomber": { + "timestamp_end": 1721741935.873429, + "timestamp_start": 1721741935.872501 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "obtained percentage [10, 30]\n", + "obtained absolute [300, 30]\n", + "[10, 30]\n", + "[0.3333333333333333, 30]\n", + "10\n" + ] + } + ], + "source": [ + "#| export\n", + "if not window_size_percentage:\n", + " if not check_expected_window_size(learn=learn, expected_window_size=config.mvp_ws, print_flag=True):\n", + " learn.cbs.filter(lambda cb: isinstance(cb, MVP))[0].window_size = ensure_expected_window_size(config.mvp_ws, True)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "b546f8d3", + "metadata": { + "ploomber": { + "timestamp_end": 1721741935.897726, + "timestamp_start": 1721741935.873704 + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "█\r", + "\r", + " |----------------------------------------| 0.00% [0/1 00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| hide\n", + "learn.MVP.show_preds(sharey=True, nrows=2) # error with nwors=1 or ncols=1" + ] + }, + { + "cell_type": "markdown", + "id": "5ad4ff29-1d59-4c1d-8499-a8e2b0b6c012", + "metadata": {}, + "source": [ + "## Save artifact to W&B\n", + "> Remove extra information and saving the learner object as an weight and biases artifact" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "bf3bf350-4f52-4a18-ad19-9d260bd060d1", + "metadata": { + "ploomber": { + "timestamp_end": 1721741936.426313, + "timestamp_start": 1721741936.425432 + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| export\n", + "# Remove the ShowGraphCallback2 callback to avoid errors in the frontend (TODO)\n", + "learn.remove_cb(sgc)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "9cc0de64-aced-433e-9c10-28fbcf4f9a91", + "metadata": { + "ploomber": { + "timestamp_end": 1721741936.619767, + "timestamp_start": 1721741936.426388 + } + }, + "outputs": [], + "source": [ + "#| export\n", + "# Log the learner without the datasets\n", + "aux_learn = learn.export_and_get()\n", + "if config.use_wandb: \n", + " run.log_artifact(\n", + " ReferenceArtifact(\n", + " aux_learn, \n", + " f'mvp-SWV', \n", + " type='learner', \n", + " metadata=dict(run.config)\n", + " ), \n", + " aliases=config.alias\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "c97de068-dc91-4cfc-b0a0-2a52b52a9f80", + "metadata": {}, + "source": [ + "## Close W&B" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "ddfe12cd-6e5f-40eb-8f84-12d2db7a355b", + "metadata": { + "ploomber": { + "timestamp_end": 1721741940.850039, + "timestamp_start": 1721741936.619898 + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "Waiting for W&B process to finish... (success)." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "403e35801ac24f4b92152f3852c19c51", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='0.003 MB of 0.003 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "

Run summary:


epoch12
eps_01e-05
eps_11e-05
lr_00.00062
lr_10.00062
mom_00.90937
mom_10.90937
raw_loss0.5904
sqr_mom_00.99
sqr_mom_10.99
train_loss0.81198
train_samples_per_sec34625.30782
valid_loss1.29739
wd_00.01
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