X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/predictops.git/blobdiff_plain/910a056eaa0181df00d21fa836f3c68504051717..ef7617a10d088cccaa6acd8b45a0db76bd8fb61e:/predictops/learn/preprocessing.py diff --git a/predictops/learn/preprocessing.py b/predictops/learn/preprocessing.py index b58ffac..106a626 100644 --- a/predictops/learn/preprocessing.py +++ b/predictops/learn/preprocessing.py @@ -1,7 +1,12 @@ +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 +from sklearn import preprocessing import numpy as np import pandas as pd @@ -10,50 +15,237 @@ fileConfig((Path.cwd() / 'config') / 'logging.cfg') logger = getLogger() class Preprocessing: - def __init__(self, dict_features, - start, end, timestep, - features = None): + ''' + Generate a pandas dataframe from a dictionary of features per datetime, which + respects the starting and ending dates of the study, and its precision (the + time step) as passed to the constructor. Missing feature values are completed. + + - Missing datetimes are added first with np.NaN feature values, + - The dataframe is then constructed based on the filled feature dictionary, + - NaN values are then filled with last known values. + ''' + + def __init__(self, config_file = None, + dict_features = None, dict_target = None): + ''' + Constructor that defines all needed attributes and collects features. + ''' + self._config = config_file + + self._start = datetime.strptime(self._config['DATETIME']['start'], + '%m/%d/%Y %H:%M:%S') + self._end = datetime.strptime(self._config['DATETIME']['end'], + '%m/%d/%Y %H:%M:%S') + self._timestep = timedelta(hours = + self._config['DATETIME'].getfloat('hourStep')) self._dict_features = dict_features - self._start = start - self._end = end - self._timestep = timestep + self._dict_target = dict_target + + self._full_dict = None self._dataframe = None + self._datetimes = [] - 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): + 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._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 + def start(self): + return self._start + + @start.setter + def start(self, x): + self._start = x + + + @property + def end(self): + return self._end + + @end.setter + def end(self, x): + self._end = x + + + @property + def timestep(self): + return self._timestep + + @timestep.setter + def timestep(self, x): + self._timestep = x + def _fill_dict(self): + ''' + Add datetime keys in the dated feature dictionary that are missing. The + features are then set to np.NaN. Add missing features in existing datetimes + too. + ''' + logger.info("Adding missing dates and filling missing features with NaN values") current = self._start while current <= self._end: + self._datetimes.append(current) if current not in self._dict_features: - self._dict_features[current] = {feature:np.NaN for feature in self._features} + self._dict_features[current] = {feature:np.NaN + for feature in self._features} else: - null_dict = {feature:np.NaN for feature in self._features} + null_dict = {feature:np.NaN + for feature in self._features} null_dict.update(self._dict_features[current]) self._dict_features[current] = null_dict current += self._timestep + for k in self._dict_features: + null_dict = {feature:np.NaN + for feature in self._features} + null_dict.update(self._dict_features[k]) + self._dict_features[k] = null_dict + + self._full_dict = {k: self._dict_features[k] + for k in sorted(self._dict_features.keys())} + @property def full_dict(self): - self._fill_dict() - return {k: self._dict_features[k] for k in sorted(self._dict_features.keys())} + ''' + Returns the fully filled dated feature dictionary, ordered by datetimes + ''' + if self._full_dict is None: + self._fill_dict() + 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): + ''' + Returns the feature dataframe, after creating it if needed. + ''' if self._dataframe is None: - self._dataframe = pd.DataFrame.from_dict(self.full_dict, orient='index') + logger.info("Creating feature dataframe from feature dictionary") + self._dataframe = pd.DataFrame.from_dict(self.full_dict, + orient='index') + # 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 + @dataframe.setter def dataframe(self, df): self._dataframe = df - def fill_na(self): - self.dataframe = self.dataframe.fillna(method='ffill') \ No newline at end of file