from logging.config import fileConfig
from os import listdir
from pathlib import Path
+from sklearn import preprocessing
import numpy as np
import pandas as pd
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
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
+ 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':
+ self._dataframe[numerical_columns] =\
+ self._dataframe[numerical_columns].fillna(method='ffill')
+ elif self._config['PREPROCESSING']['fill_method'] == 'linear':
+ self._dataframe[numerical_columns] =\
+ self._dataframe[numerical_columns].interpolate()
+ elif self._config['PREPROCESSING']['fill_method'] == 'spline':
+ 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')
+
+ # 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])
+
+
+ def _standardize(self):
+ '''
+ Normalizing numerical features
+ '''
+ 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])
+
+
+ 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'])]
+
+ # On fait un codage disjonctif complet des variables qualitatives
+ df_out = pd.DataFrame()
+ for col in 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
+ print(self._dataframe.head())
+
+
@property
def dataframe(self):
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")
-
- 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'))
-
- # 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
- if k not in self._datetimes])
+ # Dealing with NaN values
+ self._fill_nan()
+ # Normalizing numerical values
+ self._standardize()
+ # Dealing with categorical features
+ self._one_hot_encoding()
return self._dataframe
