diff --git a/epftoolbox/data/_wrangling.py b/epftoolbox/data/_wrangling.py index 36e587f1..47f0ac04 100644 --- a/epftoolbox/data/_wrangling.py +++ b/epftoolbox/data/_wrangling.py @@ -17,56 +17,34 @@ def __init__(self): self.fitted = False def fit(self, data): - if len(data.shape)!=2: - raise IndexError('Error: Provide 2-D array. First dimension is datapoints and' + - ' second features') - return -1 - + raise IndexError('Error: Provide 2-D array. First dimension is datapoints and second features') self.median = np.median(data, axis=0) self.mad = mad(data, axis=0) self.fitted = True def fit_transform(self, data): - self.fit(data) return self.transform(data) def transform(self, data): - if not self.fitted: - print('Error: The scaler has not been yet fitted. Called fit or fit_transform') - return -1 + raise RuntimeError('Error: The scaler has not been yet fitted. Called fit or fit_transform') + if len(data.shape) != 2: + raise IndexError('Error: Provide 2-D array. First dimension is datapoints and second features') - if len(data.shape)!=2: - raise IndexError('Error: Provide 2-D array. First dimension is datapoints and' + - ' second features') - - transformed_data = np.zeros(shape=data.shape) + numerator = data - self.median + denominator = self.mad + return np.divide(numerator, denominator, out=np.zeros_like(numerator), where=denominator != 0) - for i in range(data.shape[1]): - - transformed_data[:, i] = (data[:, i] - self.median[i]) / self.mad[i] - - return transformed_data def inverse_transform(self, data): - if not self.fitted: - print('Error: The scaler has not been yet fitted. Called fit or fit_transform') - return -1 - + raise RuntimeError('Error: The scaler has not been yet fitted. Call fit or fit_transform') if len(data.shape)!=2: - raise IndexError('Error: Provide 2-D array. First dimension is datapoints and' + - ' second features') - - transformed_data = np.zeros(shape=data.shape) + raise IndexError('Error: Provide 2-D array. First dimension is datapoints and second features') - for i in range(data.shape[1]): - - transformed_data[:, i] = data[:, i] * self.mad[i] + self.median[i] - - return transformed_data + return data * self.mad + self.median class InvariantScaler(MedianScaler): @@ -343,4 +321,4 @@ def scaling(datasets, normalize): datasets[i] = dataset - return datasets, scaler \ No newline at end of file + return datasets, scaler diff --git a/epftoolbox/models/_dnn.py b/epftoolbox/models/_dnn.py index 7aa43da5..faffed50 100755 --- a/epftoolbox/models/_dnn.py +++ b/epftoolbox/models/_dnn.py @@ -112,13 +112,13 @@ def __init__(self, neurons, n_features, outputShape=24, dropout=0, batch_normali opt = 'adam' else: if optimizer == 'adam': - opt = kr.optimizers.Adam(lr=lr, clipvalue=10000) + opt = kr.optimizers.Adam(learning_rate=lr, clipvalue=10000) if optimizer == 'RMSprop': - opt = kr.optimizers.RMSprop(lr=lr, clipvalue=10000) + opt = kr.optimizers.RMSprop(learning_rate=lr, clipvalue=10000) if optimizer == 'adagrad': - opt = kr.optimizers.Adagrad(lr=lr, clipvalue=10000) + opt = kr.optimizers.Adagrad(learning_rate=lr, clipvalue=10000) if optimizer == 'adadelta': - opt = kr.optimizers.Adadelta(lr=lr, clipvalue=10000) + opt = kr.optimizers.Adadelta(learning_rate=lr, clipvalue=10000) self.model.compile(loss=loss, optimizer=opt) @@ -161,20 +161,19 @@ def _build_model(self): for k, neurons in enumerate(self.neurons): if self.activation == 'LeakyReLU': - past_Dense = Dense(neurons, activation='linear', batch_input_shape=inputShape, + past_Dense = Dense(neurons, activation='linear', kernel_initializer=self.initializer, kernel_regularizer=self._reg(self.lambda_reg))(past_Dense) past_Dense = LeakyReLU(alpha=.001)(past_Dense) elif self.activation == 'PReLU': - past_Dense = Dense(neurons, activation='linear', batch_input_shape=inputShape, + past_Dense = Dense(neurons, activation='linear', kernel_initializer=self.initializer, kernel_regularizer=self._reg(self.lambda_reg))(past_Dense) past_Dense = PReLU()(past_Dense) else: past_Dense = Dense(neurons, activation=self.activation, - batch_input_shape=inputShape, kernel_initializer=self.initializer, kernel_regularizer=self._reg(self.lambda_reg))(past_Dense) diff --git a/epftoolbox/models/_lear.py b/epftoolbox/models/_lear.py index c10b71ba..0197b897 100644 --- a/epftoolbox/models/_lear.py +++ b/epftoolbox/models/_lear.py @@ -412,7 +412,7 @@ def evaluate_lear_in_test_dataset(path_datasets_folder=os.path.join('.', 'datase data_available = pd.concat([df_train, df_test.loc[:date + pd.Timedelta(hours=23), :]], axis=0) # We set the real prices for current date to NaN in the dataframe of available data - data_available.loc[date:date + pd.Timedelta(hours=23), 'Price'] = np.NaN + data_available.loc[date:date + pd.Timedelta(hours=23), 'Price'] = np.nan # Recalibrating the model with the most up-to-date available data and making a prediction # for the next day diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 00000000..db4b2002 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,2093 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. + +[[package]] +name = "absl-py" +version = "2.3.1" +description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." +optional = false +python-versions = ">=3.8" +files = [ + {file = "absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d"}, + {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"}, +] + +[[package]] +name = "astunparse" +version = "1.6.3" +description = "An AST unparser for Python" +optional = false +python-versions = "*" +files = [ + {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"}, + {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"}, +] + +[package.dependencies] +six = ">=1.6.1,<2.0" +wheel = ">=0.23.0,<1.0" + +[[package]] +name = "certifi" +version = "2025.10.5" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.7" +files = [ + {file = "certifi-2025.10.5-py3-none-any.whl", hash = "sha256:0f212c2744a9bb6de0c56639a6f68afe01ecd92d91f14ae897c4fe7bbeeef0de"}, + {file = "certifi-2025.10.5.tar.gz", hash = "sha256:47c09d31ccf2acf0be3f701ea53595ee7e0b8fa08801c6624be771df09ae7b43"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.4.3" +description = "The Real First Universal Charset Detector. 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[ + { include = "epftoolbox" }, +] + +[tool.poetry.dependencies] +python = "~3.11" +hyperopt = ">=0.2" +keras = ">=3" +matplotlib = ">=3" +pandas = ">=1" +scikit-learn = ">=0.22" +scipy = ">=1.4" +statsmodels = ">=0.11" +numpy = "^2.3.3" +tensorflow = "^2.20.0" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" \ No newline at end of file diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 9d5f7979..00000000 --- a/setup.cfg +++ /dev/null @@ -1,3 +0,0 @@ -# Inside of setup.cfg -[metadata] -description-file = README.md \ No newline at end of file diff --git a/setup.py b/setup.py index 5a7421f5..e69de29b 100644 --- a/setup.py +++ b/setup.py @@ -1,36 +0,0 @@ -from setuptools import setup, find_packages -from os import path - -here = path.abspath(path.dirname(__file__)) - -# Get the long description from the README file -with open(path.join(here, 'README.md'), encoding='utf-8') as f: - long_description = f.read() - -setup( - 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