X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/predictops.git/blobdiff_plain/d6469a787c80df2c938f21d4ae107b84213e238f..eb6d0d929146ad8df7760eb586653ee67a2f6fed:/predictops/learn/preprocessing.py

diff --git a/predictops/learn/preprocessing.py b/predictops/learn/preprocessing.py
index 833e483..5400d1d 100644
--- a/predictops/learn/preprocessing.py
+++ b/predictops/learn/preprocessing.py
@@ -1,3 +1,5 @@
+from configparser import ConfigParser
+from datetime import datetime, timedelta
 from itertools import chain
 from logging import getLogger
 from logging.config import fileConfig
@@ -18,19 +20,22 @@ class Preprocessing:
      - 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, dict_features,
-                 start, end, timestep,
-                 features = None):
+
+    def __init__(self, config_file = None, dict_features = None, features = None):
         '''
         Constructor that defines all needed attributes and collects features.
         '''
-        logger.info("Entering  NaN values in the feature dataframe")
+        self._config = ConfigParser()
+        self._config.read(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._start = start
-        self._end = end
-        self._timestep = timestep
         self._full_dict = None
         self._dataframe = None
         self._datetimes = []
@@ -43,6 +48,33 @@ class Preprocessing:
                                                       for u in [*dict_features.values()]]))
 
 
+    @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):
         '''
         Add datetime keys in the dated feature dictionary that are missing. The
@@ -94,14 +126,22 @@ class Preprocessing:
             self._dataframe = pd.DataFrame.from_dict(self.full_dict,
                                                      orient='index')
             logger.info("Filling NaN values in the feature dataframe")
-            #TODO: add other filling methods like linear interpolation
-            self._dataframe = self._dataframe.fillna(method='ffill')
+
+            if self._config['PREPROCESSING']['fill_method'] == 'propagate':
+                self._dataframe = self._dataframe.fillna(method='ffill')
+            elif self._config['PREPROCESSING']['fill_method'] == 'linear':
+                self._dataframe = self._dataframe.interpolate()
+            elif self._config['PREPROCESSING']['fill_method'] == 'spline':
+                self._dataframe = self._dataframe.interpolate(method='spline',
+                                                              order=self._config['PREPROCESSING'].getint('order'))
             self._dataframe = self._dataframe.fillna(method='bfill')
+
             self._dataframe = self._dataframe.drop([k.to_pydatetime()
                                                    for k in self._dataframe.T
                                                    if k not in self._datetimes])
         return self._dataframe
 
+
     @dataframe.setter
     def dataframe(self, df):
         self._dataframe = df