Refactoring, and categorical / numerical / mixed NaN values are now

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

diff --git a/predictops/learn/preprocessing.py b/predictops/learn/preprocessing.py
index a878a8215d83e8cd504ff7f345cbd1c15165a7e7..939a7fa30e79d45314adec6f8d526362137e3e9b 100644 (file)
--- a/predictops/learn/preprocessing.py
+++ b/predictops/learn/preprocessing.py
@@ -48,16 +48,23 @@ class Preprocessing:
         else:
             self._features = set(chain.from_iterable([tuple(u.keys())
                                                       for u in [*dict_features.values()]]))
         else:
             self._features = set(chain.from_iterable([tuple(u.keys())
                                                       for u in [*dict_features.values()]]))

-        for csv_file in listdir():
-            with open(csv_file, "r") as f:
-                reader = DictReader(f, delimiter=',')
-                dico_features = {{row['name']: row['type']  # qualitative (2) or quantitative (1)
-                                    }
-                                for row in reader if row['name'] in self._features}
+        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'):
+                config = ConfigParser()
+                config.read(feature_files / feature_file)
+                for section in config:
+                    if config.has_option(section, 'numerical'):
+                        self._features[section]['numerical'] = config[section].getboolean('numerical')

 
 

-        self._features = {feat : None for feat in self._features}
-        print(self._features)
-        exit()

 
 
     @property
 
 
     @property


@@ -137,21 +144,37 @@ class Preprocessing:
             logger.info("Creating feature dataframe from feature dictionary")
             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')

-            logger.info("Filling NaN values in the feature dataframe")
-
+            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':
             if self._config['PREPROCESSING']['fill_method'] == 'propagate':

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

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

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

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

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

 
             # 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')
 
 
             # 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)

             self._dataframe = self._dataframe.drop([k.to_pydatetime()
                                                    for k in self._dataframe.T
                                                    if k not in self._datetimes])
             self._dataframe = self._dataframe.drop([k.to_pydatetime()
                                                    for k in self._dataframe.T
                                                    if k not in self._datetimes])