Reducing the computation time and adding holidays features

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

diff --git a/predictops/learn/preprocessing.py b/predictops/learn/preprocessing.py
index b58ffac00588fc22d7f7f3d37edcf63b791d16f4..885aad3393979b897e3e0d8c40f3378dbba08e5a 100644 (file)
--- a/predictops/learn/preprocessing.py
+++ b/predictops/learn/preprocessing.py
@@ -1,7 +1,12 @@
+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 pathlib import Path

+from sklearn import preprocessing

 
 import numpy as np
 import pandas as pd
 
 import numpy as np
 import pandas as pd


@@ -10,50 +15,230 @@ fileConfig((Path.cwd() / 'config') / 'logging.cfg')
 logger = getLogger()
 
 class Preprocessing:
 logger = getLogger()
 
 class Preprocessing:

-    def __init__(self, dict_features,
-                 start, end, timestep,
-                 features = None):
+    '''
+    Generate a pandas dataframe from a dictionary of features per datetime, which
+    respects the starting and ending dates of the study, and its precision (the
+    time step) as passed to the constructor. Missing feature values are completed.
+
+     - Missing datetimes are added first with np.NaN feature values,
+     - The dataframe is then constructed based on the filled feature dictionary,
+     - NaN values are then filled with last known values.
+    '''
+
+    def __init__(self, config_file = None,
+                 dict_features = None, dict_target = None):
+        '''
+        Constructor that defines all needed attributes and collects features.
+        '''
+        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._dict_features = dict_features
         self._dict_features = dict_features

-        self._start = start
-        self._end = end
-        self._timestep = timestep
+        self._dict_target = dict_target
+
+        self._full_dict = None

         self._dataframe = None
         self._dataframe = None

+        self._datetimes = []

 
 

-        if features != None:
-            self._features = features
-        else:
-            self._features = set(chain.from_iterable([tuple(u.keys())
+        self._features = set(chain.from_iterable([tuple(u.keys())

                                                       for u in [*dict_features.values()]]))
 
                                                       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.read(feature_file)
+                for section in config:
+                    if config.has_option(section, 'numerical'):
+                        self._features[section]['numerical'] = config[section].getboolean('numerical')
+                        self._features[section]['categorical'] = config[section].getboolean('categorical')
+
+        self._numerical_columns = [k for k in self._features if self._features[k]['numerical']]
+        self._categorical_columns = [k for k in self._features if self._features[k]['categorical']]
+
+
+
+    @property
+    def start(self):
+        return self._start
+
+    @start.setter
+    def start(self, x):
+        self._start = x
+
+
+    @property
+    def end(self):
+        return self._end
+
+    @end.setter
+    def end(self, x):
+        self._end = x
+
+
+    @property
+    def timestep(self):
+        return self._timestep
+
+    @timestep.setter
+    def timestep(self, x):
+        self._timestep = x
+

 
     def _fill_dict(self):
 
     def _fill_dict(self):

+        '''
+        Add datetime keys in the dated feature dictionary that are missing. The
+        features are then set to np.NaN. Add missing features in existing datetimes
+        too.
+        '''
+        logger.info("Adding missing dates and filling missing features with NaN values")

         current = self._start
         while current <= self._end:
         current = self._start
         while current <= self._end:

+            self._datetimes.append(current)

             if current not in self._dict_features:
             if current not in self._dict_features:

-                self._dict_features[current] = {feature:np.NaN for feature in self._features}
+                self._dict_features[current] = {feature:np.NaN
+                                                for feature in self._features}

             else:
             else:

-                null_dict = {feature:np.NaN for feature in self._features}
+                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
                 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
+                         for feature in self._features}
+            null_dict.update(self._dict_features[k])
+            self._dict_features[k] = null_dict
+
+        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):

-        self._fill_dict()
-        return {k: self._dict_features[k] for k in sorted(self._dict_features.keys())}
+        '''
+        Returns the fully filled dated feature dictionary, ordered by datetimes
+        '''
+        if self._full_dict is None:
+            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
+        # The interpolation
+        if self._config['PREPROCESSING']['fill_method'] == 'propagate':
+            self._dataframe[self._numerical_columns] =\
+                self._dataframe[self._numerical_columns].fillna(method='ffill')
+        elif self._config['PREPROCESSING']['fill_method'] == 'linear':
+            self._dataframe[self._numerical_columns] =\
+                self._dataframe[self._numerical_columns].interpolate()
+        elif self._config['PREPROCESSING']['fill_method'] == 'spline':
+            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")
+        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
+        # that are available only for recent years, e.g., air quality
+        #self._dataframe = self._dataframe.fillna(method='bfill')
+
+        # Dropping rows that are not related to our datetime window (start/
+        # step / end)
+        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 = self._config['HISTORY_KNOWLEDGE'].getint('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):
+        '''
+        Normalizing numerical features
+        '''
+        logger.info("Standardizing numerical values in the feature dataframe")
+        # We operate only on 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")
+
+        # We store numerical columns
+        df_out = pd.DataFrame()
+        for col in  self._numerical_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

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

+        '''
+        Returns the feature dataframe, after creating it if needed.
+        '''

         if self._dataframe is None:
         if self._dataframe is None:

-            self._dataframe = pd.DataFrame.from_dict(self.full_dict, orient='index')
+            logger.info("Creating feature dataframe from feature dictionary")
+            self._dataframe = pd.DataFrame.from_dict(self.full_dict,
+                                                     orient='index')
+            # Dealing with NaN values
+            self._fill_nan()
+            # Adding previous (historical) nb_interventions as features
+            self._add_history()
+            # Normalizing numerical values
+            self._standardize()
+            # Dealing with categorical features
+            self._one_hot_encoding()

         return self._dataframe
 
         return self._dataframe
 

+

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

-    def fill_na(self):
-        self.dataframe = self.dataframe.fillna(method='ffill')
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