X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/predictops.git/blobdiff_plain/f04d85cc8028a2721d7d5e8ec866ff8022797bb5..f5357178353432c5431fc0fd875b9928a326e4c7:/predictops/learn/preprocessing.py?ds=sidebyside diff --git a/predictops/learn/preprocessing.py b/predictops/learn/preprocessing.py index 939a7fa..4197b8f 100644 --- a/predictops/learn/preprocessing.py +++ b/predictops/learn/preprocessing.py @@ -6,6 +6,7 @@ from logging import getLogger 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 @@ -134,6 +135,75 @@ class Preprocessing: 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): @@ -144,40 +214,12 @@ class Preprocessing: 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