X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/predictops.git/blobdiff_plain/20a117b07643f7b3ef305d1e7a6f62f05e33698e..ef7617a10d088cccaa6acd8b45a0db76bd8fb61e:/predictops/learn/preprocessing.py?ds=inline diff --git a/predictops/learn/preprocessing.py b/predictops/learn/preprocessing.py index 939a7fa..106a626 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 @@ -24,12 +25,12 @@ class Preprocessing: - NaN values are then filled with last known values. ''' - def __init__(self, config_file = None, dict_features = None, features = None): + def __init__(self, config_file = None, + dict_features = None, dict_target = None): ''' Constructor that defines all needed attributes and collects features. ''' - self._config = ConfigParser() - self._config.read(config_file) + self._config = config_file self._start = datetime.strptime(self._config['DATETIME']['start'], '%m/%d/%Y %H:%M:%S') @@ -38,16 +39,15 @@ class Preprocessing: self._timestep = timedelta(hours = self._config['DATETIME'].getfloat('hourStep')) self._dict_features = dict_features + self._dict_target = dict_target + self._full_dict = None self._dataframe = None self._datetimes = [] - # If features are not provided to the constructor, then we collect - # any existing feature in the dictionary - if features != None: - self._features = features - else: - self._features = set(chain.from_iterable([tuple(u.keys()) + + self._features = set(chain.from_iterable([tuple(u.keys()) for u in [*dict_features.values()]])) + feature_files = Path.cwd() / 'config' / 'features' self._features = {feat : {'numerical': False} for feat in self._features} for feature_file in listdir(feature_files): @@ -65,6 +65,12 @@ class Preprocessing: if config.has_option(section, 'numerical'): self._features[section]['numerical'] = config[section].getboolean('numerical') + self._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._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'])] + @property @@ -134,6 +140,89 @@ 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 + # The interpolation + if self._config['PREPROCESSING']['fill_method'] == 'propagate': + self._dataframe[self._numerical_columns] =\ + self._dataframe[self._numerical_columns].fillna(method='ffill') + elif self._config['PREPROCESSING']['fill_method'] == 'linear': + self._dataframe[self._numerical_columns] =\ + self._dataframe[self._numerical_columns].interpolate() + elif self._config['PREPROCESSING']['fill_method'] == 'spline': + self._dataframe[self._numerical_columns] =\ + self._dataframe[self._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") + self._dataframe[self._categorical_columns] =\ + self._dataframe[self._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) + logger.info("Dropping rows that are not related to our datetime window") + self._dataframe['datetime'] =\ + self._dataframe.apply(lambda x: datetime(int(x.year), int(x.month), int(x.dayInMonth), int(x.hour)), axis=1) + self._dataframe['row_ok'] =\ + self._dataframe.apply(lambda x:x.datetime in self._datetimes, axis=1) + self._dataframe = self._dataframe[self._dataframe['row_ok']] + self._dataframe = self._dataframe.drop(['datetime', 'row_ok'], axis=1) + logger.info("Rows dropped") + + + def _add_history(self): + ''' + Integrating previous nb of interventions as features + ''' + logger.info("Integrating previous nb of interventions as features") + nb_lines = self._config['HISTORY_KNOWLEDGE'].getint('nb_lines') + for k in range(1,nb_lines+1): + name = 'history_'+str(nb_lines-k+1) + self._dataframe[name] = [np.NaN]*k + list(self._dict_target.values())[:-k] + self._numerical_columns.append(name) + self._dataframe = self._dataframe[nb_lines:] + + + + def _standardize(self): + ''' + Normalizing numerical features + ''' + logger.info("Standardizing numerical values in the feature dataframe") + # We operate only on numerical columns + self._dataframe[self._numerical_columns] =\ + preprocessing.scale(self._dataframe[self._numerical_columns]) + + + + def _one_hot_encoding(self): + ''' + Apply a one hot encoding for category features + ''' + logger.info("One hot encoding for categorical feature") + + # We store numerical columns + df_out = pd.DataFrame() + for col in self._numerical_columns: + df_out[col] = self._dataframe[col] + # The one hot encoding + for col in self._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 + @property def dataframe(self): @@ -144,40 +233,14 @@ 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() + # Adding previous (historical) nb_interventions as features + self._add_history() + # Normalizing numerical values + self._standardize() + # Dealing with categorical features + self._one_hot_encoding() return self._dataframe