script for postprocessing custom data

FitNewModel/postprocessing.py

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+data_folder = 'Folder/'  # * fill in with location *
+year = '2019'
+subfolder = 'NewData/'
+import os
+import shutil
+# if not os.path.isdir('../Data/NewData'):
+#     os.mkdir('../Data/NewData')
+num_clusters = 9
+
+'''Process the data post clustering to be used by speech.'''
+
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import pickle
+
+# Load data
+driver_subset = pd.read_csv(data_folder+'sessions'+year+'_driverdata_unscaled_withlabels.csv', index_col=0)
+
+# P(G)
+pg = pd.DataFrame(dict(driver_subset['Agglom Cluster Number'].value_counts(normalize=True)), index=['pg']).T.sort_index()
+pg.to_csv(subfolder+'pg.csv', index=None)
+
+# P(z|G)
+relabel = {'Home': 'home_l2', 'Work': 'work_l2', 'MUD': 'mud_l2', 'Other Slow': 'public_l2', 'Other Fast': 'public_l3'}
+col_mapping = {}
+for key, val in relabel.items():
+    for w in ['weekdays', 'weekenddays']:
+        col_mapping[key+' - Fraction of '+w+' with session'] = key+' - Fraction of '+w+' with session'
+        # col_mapping[key+' - Fraction of '+w+' with session'] = val+' - Fraction of '+w+' with session'
+for i in range(num_clusters):
+    inds = driver_subset[driver_subset['Agglom Cluster Number'] == i].index
+    pz_subset = driver_subset.loc[inds, col_mapping.keys()].reset_index(drop=True)
+    pz_subset = pz_subset.rename(columns=col_mapping)
+    pz_subset['home_l1 - Fraction of weekdays with session'] = 0
+    pz_subset['home_l1 - Fraction of weekenddays with session'] = 0
+    pz_subset.to_csv(subfolder+'pz_weekday_g_'+str(i)+'.csv')
+    pz_subset.to_csv(subfolder+'pz_weekend_g_'+str(i)+'.csv')
+
+# P(s | z, G) are the GMMs already done.
+
+shutil.copytree('NewData', '../Data/NewData')
+# Final to-do:
+# Copy the following as a method into the class `DataSetConfigurations` in `speech.py`
+
+
+def new_data(self):
+    """New Clustering. Also note new ng = """
+
+    self.categories = ['Home', 'MUD', 'Work', 'Other Slow', 'Other Fast']
+    self.labels = ['Residential L2', 'MUD L2', 'Workplace L2', 'Public L2', 'Public DCFC']
+    self.colours = {'Residential L2': '#dfc27d', 'MUD L2': '#f6e8c3', 'Workplace L2': '#80cdc1', 'Public L2': '#01665e', 'Public DCFC': '#003c30'}
+    self.num_categories = 5
+    self.rates = [6.6, 6.6, 6.6, 6.6, 50]
+    self.gmm_names = {'Home': 'home', 'Work': 'work', 'Other Slow': 'other_slow', 'MUD': 'mud', 'Other Fast': 'other_fast'}
+    self.start_time_scaler = 1/60
+    self.zkey_weekday = ' - Fraction of weekdays with session'
+    self.zkey_weekend = ' - Fraction of weekenddays with session'
+    self.start_mod = 24*3600  # since start time is in seconds
+    self.timers_dict = {}
+    # Optional: record shifts for removing timers from fitted model
+    # self.timers_dict = {group number with timers in it:
+    # {gmm segment number with timers in it: 0,
+    # other gmm segment number to switch to: current weight + weight from timer segment}}
+    self.shift_timers_dict = {}
+    # Similarly: self.shift_timers_dict = {'Components': {group number with timers: [list of gmm segments with timers],
+    # another group number with timers: [list of gmm segments with timers],},
+    # 'Targets': {'PGE': 23, 'SMUD': 0, 'SCE': 21, 'SDGE': 0}}
+    self.timer_cat = 'Home'
+