Refactoring, fin du lever/coucher de soleil, et début de sentinelles

[predictops.git] / predictops / learn / preprocessing.py

diff --git a/predictops/learn/preprocessing.py b/predictops/learn/preprocessing.py
index 4197b8fed13cb4137e33655753976532e42987a2..55cffbd2a0e094a610580e1e4dbcdf8adc80de5d 100644 (file)
--- a/predictops/learn/preprocessing.py
+++ b/predictops/learn/preprocessing.py
@@ -1,10 +1,7 @@
 from configparser import ConfigParser
 from configparser import ConfigParser

-from csv import DictReader
-from datetime import datetime, timedelta

 from itertools import chain
 from logging import getLogger
 from logging.config import fileConfig
 from itertools import chain
 from logging import getLogger
 from logging.config import fileConfig

-from os import listdir

 from pathlib import Path
 from sklearn import preprocessing
 
 from pathlib import Path
 from sklearn import preprocessing
 


@@ -14,6 +11,7 @@ import pandas as pd
 fileConfig((Path.cwd() / 'config') / 'logging.cfg')
 logger = getLogger()
 
 fileConfig((Path.cwd() / 'config') / 'logging.cfg')
 logger = getLogger()
 

+

 class Preprocessing:
     '''
     Generate a pandas dataframe from a dictionary of features per datetime, which
 class Preprocessing:
     '''
     Generate a pandas dataframe from a dictionary of features per datetime, which


@@ -25,48 +23,47 @@ class Preprocessing:
      - NaN values are then filled with last known values.
     '''
 
      - NaN values are then filled with last known values.
     '''
 

-    def __init__(self, config_file = None, dict_features = None, features = None):
+    def __init__(self, config_file=None,
+                 start=None, end=None, timestep=None,
+                 dict_features=None, dict_target=None):

         '''
         Constructor that defines all needed attributes and collects features.
         '''
         '''
         Constructor that defines all needed attributes and collects features.
         '''

-        self._config = ConfigParser()
-        self._config.read(config_file)
+        self._config = config_file

 
 

-        self._start = datetime.strptime(self._config['DATETIME']['start'],
-                                        '%m/%d/%Y %H:%M:%S')
-        self._end = datetime.strptime(self._config['DATETIME']['end'],
-                                        '%m/%d/%Y %H:%M:%S')
-        self._timestep = timedelta(hours =
-                                   self._config['DATETIME'].getfloat('hourStep'))
+        self._start = start
+        self._end = end
+        self._timestep = timestep

         self._dict_features = dict_features
         self._dict_features = dict_features

+        self._dict_target = dict_target
+

         self._full_dict = None
         self._dataframe = None
         self._datetimes = []
         self._full_dict = None
         self._dataframe = None
         self._datetimes = []

-        # If features are not provided to the constructor, then we collect
-        # any existing feature in the dictionary
-        if features != None:
-            self._features = features
-        else:
-            self._features = set(chain.from_iterable([tuple(u.keys())
-                                                      for u in [*dict_features.values()]]))
-        feature_files = Path.cwd() / 'config' / 'features'
-        self._features = {feat : {'numerical': False} for feat in self._features}
-        for feature_file in listdir(feature_files):
-            if feature_file.endswith('csv'):
-                with open(feature_files / feature_file , "r") as f:
-                    reader = DictReader(f, delimiter=',')
-                    typed_names = {row['name']: row['type'] for row in reader}
-                for feature in self._features:
-                    if feature.split('_')[0] in typed_names:
-                        self._features[feature]['type'] = int(typed_names[feature.split('_')[0]])
-            elif feature_file.endswith('cfg'):
+
+        self._features = set(chain.from_iterable([tuple(u.keys())
+                                                  for u in [*dict_features.values()]]))
+
+        #feature_files = Path.cwd() / 'config' / 'features'
+        self._features = {feat: {'numerical': False, 'categorical': False}
+                          for feat in self._features}
+
+        for feature in self._config['FEATURES']:
+            if self._config['FEATURES'][feature]:
+                feature_file = self._config['FEATURE_CONFIG'][feature]

                 config = ConfigParser()
                 config = ConfigParser()

-                config.read(feature_files / feature_file)
+                config.read(eval(feature_file))

                 for section in config:
                     if config.has_option(section, 'numerical'):
                 for section in config:
                     if config.has_option(section, 'numerical'):

-                        self._features[section]['numerical'] = config[section].getboolean('numerical')
-
+                        for feature in self._features:
+                            if feature.split('_')[0] == section:
+                                self._features[feature]['binary'] = config[section].getboolean('binary')
+                                self._features[feature]['categorical'] = config[section].getboolean('categorical')
+                                self._features[feature]['numerical'] = config[section].getboolean('numerical')

 
 

+        self._binary_columns = [k for k in self._features if self._features[k]['binary']]
+        self._categorical_columns = [k for k in self._features if self._features[k]['categorical']]
+        self._numerical_columns = [k for k in self._features if self._features[k]['numerical']]

 
     @property
     def start(self):
 
     @property
     def start(self):


@@ -76,7 +73,6 @@ class Preprocessing:
     def start(self, x):
         self._start = x
 
     def start(self, x):
         self._start = x
 

-

     @property
     def end(self):
         return self._end
     @property
     def end(self):
         return self._end


@@ -85,7 +81,6 @@ class Preprocessing:
     def end(self, x):
         self._end = x
 
     def end(self, x):
         self._end = x
 

-

     @property
     def timestep(self):
         return self._timestep
     @property
     def timestep(self):
         return self._timestep


@@ -94,7 +89,6 @@ class Preprocessing:
     def timestep(self, x):
         self._timestep = x
 
     def timestep(self, x):
         self._timestep = x
 

-

     def _fill_dict(self):
         '''
         Add datetime keys in the dated feature dictionary that are missing. The
     def _fill_dict(self):
         '''
         Add datetime keys in the dated feature dictionary that are missing. The


@@ -106,16 +100,16 @@ class Preprocessing:
         while current <= self._end:
             self._datetimes.append(current)
             if current not in self._dict_features:
         while current <= self._end:
             self._datetimes.append(current)
             if current not in self._dict_features:

-                self._dict_features[current] = {feature:np.NaN
+                self._dict_features[current] = {feature: np.NaN

                                                 for feature in self._features}
             else:
                                                 for feature in self._features}
             else:

-                null_dict = {feature:np.NaN
+                null_dict = {feature: np.NaN

                              for feature in self._features}
                 null_dict.update(self._dict_features[current])
                 self._dict_features[current] = null_dict
             current += self._timestep
         for k in self._dict_features:
                              for feature in self._features}
                 null_dict.update(self._dict_features[current])
                 self._dict_features[current] = null_dict
             current += self._timestep
         for k in self._dict_features:

-            null_dict = {feature:np.NaN
+            null_dict = {feature: np.NaN

                          for feature in self._features}
             null_dict.update(self._dict_features[k])
             self._dict_features[k] = null_dict
                          for feature in self._features}
             null_dict.update(self._dict_features[k])
             self._dict_features[k] = null_dict


@@ -123,8 +117,6 @@ class Preprocessing:
         self._full_dict = {k: self._dict_features[k]
                            for k in sorted(self._dict_features.keys())}
 
         self._full_dict = {k: self._dict_features[k]
                            for k in sorted(self._dict_features.keys())}
 

-
-

     @property
     def full_dict(self):
         '''
     @property
     def full_dict(self):
         '''


@@ -134,34 +126,29 @@ class Preprocessing:
             self._fill_dict()
         return self._full_dict
 
             self._fill_dict()
         return self._full_dict
 

-

     def _fill_nan(self):
         '''
         Fill NaN values, either by propagation or by interpolation (linear or splines)
         '''
         logger.info("Filling NaN numerical values in the feature dataframe")
         # We interpolate (linearly or with splines) only numerical columns
     def _fill_nan(self):
         '''
         Fill NaN values, either by propagation or by interpolation (linear or splines)
         '''
         logger.info("Filling NaN numerical values in the feature dataframe")
         # We interpolate (linearly or with splines) only numerical columns

-        numerical_columns = [k for k in self._features if self._features[k]['type'] == 1
-                   or (self._features[k]['type'] == 3 and self._features[k]['numerical'])]

         # The interpolation
         if self._config['PREPROCESSING']['fill_method'] == 'propagate':
         # The interpolation
         if self._config['PREPROCESSING']['fill_method'] == 'propagate':

-            self._dataframe[numerical_columns] =\
-                self._dataframe[numerical_columns].fillna(method='ffill')
+            self._dataframe[self._numerical_columns] =\
+                self._dataframe[self._numerical_columns].fillna(method='ffill')

         elif self._config['PREPROCESSING']['fill_method'] == 'linear':
         elif self._config['PREPROCESSING']['fill_method'] == 'linear':

-            self._dataframe[numerical_columns] =\
-                self._dataframe[numerical_columns].interpolate()
+            self._dataframe[self._numerical_columns] =\
+                self._dataframe[self._numerical_columns].interpolate()

         elif self._config['PREPROCESSING']['fill_method'] == 'spline':
         elif self._config['PREPROCESSING']['fill_method'] == 'spline':

-            self._dataframe[numerical_columns] =\
-                self._dataframe[numerical_columns].interpolate(method='spline',
-                     order=self._config['PREPROCESSING'].getint('order'))
+            self._dataframe[self._numerical_columns] =\
+                self._dataframe[self._numerical_columns].interpolate(method='spline',
+                                                                     order=self._config['PREPROCESSING'].getint('order'))

 
         # For the categorical columns, NaN values are filled by duplicating
         # the last known value (forward fill method)
         logger.info("Filling NaN categorical values in the feature dataframe")
 
         # For the categorical columns, NaN values are filled by duplicating
         # the last known value (forward fill method)
         logger.info("Filling NaN categorical values in the feature dataframe")

-        categorical_columns = [k for k in self._features if self._features[k]['type'] == 2
-                   or (self._features[k]['type'] == 3 and not self._features[k]['numerical'])]
-        self._dataframe[categorical_columns] =\
-            self._dataframe[categorical_columns].fillna(method='ffill')
+        self._dataframe[self._categorical_columns] =\
+            self._dataframe[self._categorical_columns].fillna(method='ffill')

 
         # Uncomment this line to fill NaN values at the beginning of the
         # dataframe. This may not be a good idea, especially for features
 
         # Uncomment this line to fill NaN values at the beginning of the
         # dataframe. This may not be a good idea, especially for features


@@ -170,10 +157,25 @@ class Preprocessing:
 
         # Dropping rows that are not related to our datetime window (start/
         # step / end)
 
         # Dropping rows that are not related to our datetime window (start/
         # step / end)

-        self._dataframe = self._dataframe.drop([k.to_pydatetime()
-                                               for k in self._dataframe.T
-                                               if k not in self._datetimes])
-
+        logger.info("Dropping rows that are not related to our datetime window")
+        dates = tuple((x.year, x.month, x.day, x.hour) for x in self._datetimes)
+        self._dataframe['row_ok'] =\
+            self._dataframe.apply(lambda x: (int(x.year), int(x.month), int(x.dayInMonth), int(x.hour)) in dates, axis=1)
+        self._dataframe = self._dataframe[self._dataframe['row_ok']]
+        self._dataframe = self._dataframe.drop(['row_ok'], axis=1)
+        logger.info("Rows dropped")
+
+    def _add_history(self):
+        '''
+        Integrating previous nb of interventions as features
+        '''
+        logger.info("Integrating previous nb of interventions as features")
+        nb_lines = eval(self._config['HISTORY_KNOWLEDGE']['nb_lines'])
+        for k in range(1, nb_lines + 1):
+            name = 'history_' + str(nb_lines - k + 1)
+            self._dataframe[name] = [np.NaN] * k + list(self._dict_target.values())[:-k]
+            self._numerical_columns.append(name)
+        self._dataframe = self._dataframe[nb_lines:]

 
     def _standardize(self):
         '''
 
     def _standardize(self):
         '''


@@ -181,29 +183,28 @@ class Preprocessing:
         '''
         logger.info("Standardizing numerical values in the feature dataframe")
         # We operate only on numerical columns
         '''
         logger.info("Standardizing numerical values in the feature dataframe")
         # We operate only on numerical columns

-        numerical_columns = [k for k in self._features if self._features[k]['type'] == 1
-                   or (self._features[k]['type'] == 3 and self._features[k]['numerical'])]
-        self._dataframe[numerical_columns] = preprocessing.scale(self._dataframe[numerical_columns])
-
+        self._dataframe[self._numerical_columns] =\
+            preprocessing.scale(self._dataframe[self._numerical_columns])

 
     def _one_hot_encoding(self):
         '''
         Apply a one hot encoding for category features
         '''
         logger.info("One hot encoding for categorical feature")
 
     def _one_hot_encoding(self):
         '''
         Apply a one hot encoding for category features
         '''
         logger.info("One hot encoding for categorical feature")

-        categorical_columns = [k for k in self._features if self._features[k]['type'] == 2
-                   or (self._features[k]['type'] == 3 and not self._features[k]['numerical'])]
+        # We store numerical columns

 
 

-        # On fait un codage disjonctif complet des variables qualitatives

         df_out = pd.DataFrame()
         df_out = pd.DataFrame()

-        for col in categorical_columns:
-            pd1 = pd.get_dummies(self._dataframe[col],prefix=col)
+        for col in self._numerical_columns:
+            df_out[col] = self._dataframe[col]
+        # Idem for binary features
+        for col in self._binary_columns:
+            df_out[col] = self._dataframe[col]
+        # The one hot encoding
+        for col in self._categorical_columns:
+            pd1 = pd.get_dummies(self._dataframe[col], prefix=col)

             for col1 in pd1.columns:
                 df_out[col1] = pd1[col1]
         self._dataframe = df_out
             for col1 in pd1.columns:
                 df_out[col1] = pd1[col1]
         self._dataframe = df_out

-        print(self._dataframe.head())
-
-

 
     @property
     def dataframe(self):
 
     @property
     def dataframe(self):


@@ -216,13 +217,16 @@ class Preprocessing:
                                                      orient='index')
             # Dealing with NaN values
             self._fill_nan()
                                                      orient='index')
             # Dealing with NaN values
             self._fill_nan()

+            # Adding previous (historical) nb_interventions as features
+            self._add_history()
+            # self._dataframe.to_csv('toto.csv')
+            # exit()

             # Normalizing numerical values
             self._standardize()
             # Dealing with categorical features
             self._one_hot_encoding()
         return self._dataframe
 
             # Normalizing numerical values
             self._standardize()
             # Dealing with categorical features
             self._one_hot_encoding()
         return self._dataframe
 

-

     @dataframe.setter
     def dataframe(self, df):
         self._dataframe = df
     @dataframe.setter
     def dataframe(self, df):
         self._dataframe = df