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
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 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])
+ # Dealing with NaN values
+ self._fill_nan()
+ # Normalizing numerical values
+ self._standardize()
+ # Dealing with categorical features
+ self._one_hot_encoding()
return self._dataframe
