X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/predictops.git/blobdiff_plain/66b4627c14e9f89a2e5ab73bbf48819f8f3a1455..4b6d71d96bb92791cc31640e5f30378ae6fe63e4:/predictops/learn/preprocessing.py

diff --git a/predictops/learn/preprocessing.py b/predictops/learn/preprocessing.py
index 5400d1d..49d7ef8 100644
--- a/predictops/learn/preprocessing.py
+++ b/predictops/learn/preprocessing.py
@@ -1,8 +1,10 @@
 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 os import listdir
 from pathlib import Path
 
 import numpy as np
@@ -46,6 +48,16 @@ class Preprocessing:
         else:
             self._features = set(chain.from_iterable([tuple(u.keys())
                                                       for u in [*dict_features.values()]]))
+        csv_files = Path.cwd() / 'config' / 'features'
+        self._features = {feat : None for feat in self._features}
+        for csv_file in listdir(csv_files):
+            with open(csv_files / csv_file, "r") as f:
+                reader = DictReader(f, delimiter=',')
+                for row in reader:
+                    if row['name'] in self._features:
+                        self._features[row['name']] = row['type']
+        print(self._features)
+        exit()
 
 
     @property
@@ -134,7 +146,11 @@ class Preprocessing:
             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')
+
+            # 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')
 
             self._dataframe = self._dataframe.drop([k.to_pydatetime()
                                                    for k in self._dataframe.T