add ML system

Author: weilixu

.idea/workspace.xml

@@ -1,3 +1,3 @@
 user_api_key= buildsimhub
-base_url=https://develop.buildsimhub.net/
+base_url=https://my.buildsim.io/
 vendor_id=buildsimhub

BuildSimHubAPI/__pycache__/version.cpython-36.pyc

@@ -0,0 +1,79 @@
+"""
+BuildingLoad class - post-process bldg level load data
+and form it into a pandas dataframe
+"""
+
+try:
+    import pandas as pd
+except ImportError:
+    pd = None
+    print('pandas is not installed')
+
+
+class BuildingLoad(object):
+
+    def __init__(self, bldg_load_profile):
+        """
+        
+        :param bldg_load_profile: bldg_load_profile: jsonarray returned
+            from BSH server by calling the building load api
+        """
+
+        # reform the dict
+        index_list = list()
+        self._cooling_unit = ''
+        self._heating_unit = ''
+        self._cooling_density_unit = ''
+        self._heating_density_unit = ''
+        data = list()
+        for d_dict in bldg_load_profile:
+            data_dict = dict()
+            if len(d_dict.keys()) == 1:
+                continue
+            index_list.append(d_dict['model'].upper())
+            if self._cooling_unit == '':
+                self._cooling_unit = d_dict['cooling_unit']
+            if self._heating_unit == '':
+                self._heating_unit = d_dict['heating_unit']
+            if self._heating_density_unit == '':
+                self._heating_density_unit = d_dict['heating_load_density_unit']
+            if self._cooling_density_unit == '':
+                self._cooling_density_unit = d_dict['cooling_load_density_unit']
+            # remove name and units from the dict
+            data_dict['heating_load'] = d_dict['heating_load']
+            data_dict['cooling_load'] = d_dict['cooling_load']
+            data_dict['heating_load_density'] = d_dict['heating_load_density']
+            data_dict['cooling_load_density'] = d_dict['cooling_load_density']
+            data.append(data_dict)
+            self._df = pd.DataFrame(data, index=index_list)
+
+    @property
+    def cooling_load_unit(self):
+        return self._cooling_unit
+
+    @property
+    def heating_load_unit(self):
+        return self._heating_unit
+
+    @property
+    def cooling_load_density_unit(self):
+        return self._cooling_density_unit
+
+    @property
+    def heating_load_density_unit(self):
+        return self._heating_density_unit
+
+    def get_df(self):
+        """get the dataframe"""
+        return self._df
+
+    def plot(self):
+        """Plotly scatter plot"""
+        try:
+            from plotly.offline import plot
+            from plotly import tools
+            import plotly.graph_objs as go
+        except ImportError:
+            print('plotly is not installed')
+
+        data = list()

BuildSimHubAPI/helpers/__pycache__/parametric_job.cpython-36.pyc

@@ -0,0 +1,82 @@
+
+try:
+    import numpy as np
+except ImportError:
+    np = None
+    print('numpy is not installed')
+
+try:
+    import pandas as pd
+except ImportError:
+    pd = None
+    print('pandas is not installed')
+
+
+class Regression(object):
+    def __init__(self, data, unit=""):
+        """
+        Construct parametric plot
+
+        :param data: returned from the parametric result call
+        :param unit:
+        """
+        self._value = data['value']
+        self._unit = unit
+
+        self._model_plot = data['model_plot']
+        self._model_des = data['model']
+
+        data_list = list()
+
+        # create data dictionary
+        for j in range(len(self._model_des)):
+            col_list = self._model_des[j]
+            parameters = col_list.split(",")
+            data_dict = dict()
+            for k in range(len(parameters)):
+                title, val = parameters[k].split(":")
+                # for cases like on off options
+                if val.strip() == 'Off':
+                    val = '0'
+                if val.strip() == 'On':
+                    val = '1'
+
+                data_dict[title.strip()] = float(val.strip())
+            # data_dict['Value'] = self._value[j]
+
+            data_list.append(data_dict)
+
+        self._df = pd.DataFrame(data_list, index=self._model_plot)
+        self._df['Value'] = self._value
+
+    def pandas_df(self):
+        """get the data in pandas dataframe"""
+        return self._df
+
+    def add_column(self, head, col):
+        """
+
+        :param head:
+        :param col:
+        :type head: str
+        :type col: list
+        :return:
+        """
+        if head == 'Value':
+            return False
+        self._df.assign(head=col)
+
+    def x_validate(self):
+        y = self._df[['Value']]
+        try:
+            from sklearn.model_selection import cross_val_score
+        except ImportError:
+            print("Sci-kit learn package is required for model training. Please install the package at: "
+                  "http://scikit-learn.org/stable/index.html")
+            return
+
+        
+
+
+
+

BuildSimHubAPI/info.config

@@ -7,8 +7,8 @@
 import BuildSimHubAPI.postprocess as pp
 
 # 1. set your folder key
-project_key = '87e86a33-964e-4809-9e72-802791a8e968'
-model_key = '82f244cf-adb6-4f69-9d5d-183ca5e89106'
+project_key = '7e140eec-b37f-4213-8640-88b5f96c0065'
+model_key = '48003d7a-143f-45c1-b175-4ab4d1968bbd'
 
 file_dir = "/Users/weilixu/Desktop/data/jsontest/5ZoneAirCooled_UniformLoading.epJSON"
 
@@ -17,11 +17,11 @@
 
 # Define EEMs
 wwr = bsh_api.measures.WindowWallRatio()
-wwr_ratio = [0.6, 0.4, 0.3]
+wwr_ratio = [0.6, 0.4]
 wwr.set_datalist(wwr_ratio)
 
 lpd = bsh_api.measures.LightLPD('ip')
-lpdValue = [1.2, 0.9, 0.7]
+lpdValue = [1.2, 0.9]
 lpd.set_datalist(lpdValue)
 
 heatEff = bsh_api.measures.HeatingEfficiency()
@@ -44,6 +44,7 @@
 
     # Plot
     plot = pp.ParametricPlot(result_dict, result_unit)
+    print(plot.pandas_df())
 
     plot.scatter_chart_plotly("Scatter plot demo")
     plot.parallel_coordinate_plotly('LPD')

BuildSimHubAPI/postprocess/bldg_load.py

@@ -2,17 +2,18 @@
 8.9 test
 """
 import BuildSimHubAPI as bshapi
+import BuildSimHubAPI.postprocess as pp
 import time
 
 bsh = bshapi.BuildSimHubAPIClient()
-project_key = "d43f2d17-7da6-4fd1-800c-62651001d453"
+project_key = "7e140eec-b37f-4213-8640-88b5f96c0065"
 # model_key = "39ed84d0-062b-470d-96e6-b03abff9c31c"
 
 # 1. define the absolute directory of your energy model
 file_dir = ["/Users/weilixu/Desktop/data/jsontest/5ZoneAirCooled_UniformLoading.epJSON",
            "/Users/weilixu/Desktop/data/jsontest/5ZoneAirCooled.idf"]
 
-file_dir = "/Users/weilixu/Desktop/data/jsontest/5ZoneAirCooled.idf"
+# file_dir = "/Users/weilixu/Desktop/data/jsontest/5ZoneAirCooled.idf"
 
 wea_dir = "/Users/weilixu/Desktop/data/jsontest/in.epw"
 
@@ -21,8 +22,10 @@
 
 if results:
     load_data = results.zone_load()
-    # print(load_data)
-    results.bldg_geo()
+    print(load_data)
+    zl = pp.ZoneLoad(load_data)
+    print(zl.get_df())
+    # results.bldg_geo()
 # if results:
 #    print(str(results.not_met_hour_cooling()) + " " + results.last_parameter_unit)
 #    load_data = results.zone_load()