X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/predictops.git/blobdiff_plain/4b6d71d96bb92791cc31640e5f30378ae6fe63e4..90e69cb2125d4bae76a27b9c38defb4f70bf2ca6:/predictops/learn/preprocessing.py diff --git a/predictops/learn/preprocessing.py b/predictops/learn/preprocessing.py index 49d7ef8..9bc09ad 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 @@ -13,6 +14,7 @@ import pandas as pd fileConfig((Path.cwd() / 'config') / 'logging.cfg') logger = getLogger() + class Preprocessing: ''' Generate a pandas dataframe from a dictionary of features per datetime, which @@ -24,41 +26,48 @@ 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') self._end = datetime.strptime(self._config['DATETIME']['end'], - '%m/%d/%Y %H:%M:%S') - self._timestep = timedelta(hours = - self._config['DATETIME'].getfloat('hourStep')) + '%m/%d/%Y %H:%M:%S') + 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()) - for u in [*dict_features.values()]])) - csv_files = Path.cwd() / 'config' / 'features' - self._features = {feat : None for feat in self._features} - for csv_file in listdir(csv_files): - with open(csv_files / csv_file, "r") as f: - reader = DictReader(f, delimiter=',') - for row in reader: - if row['name'] in self._features: - self._features[row['name']] = row['type'] - print(self._features) - exit() + 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, 'categorical': False} + for feat in self._features} + + for feature in self._config['FEATURES']: + if self._config['FEATURES'][feature]: + feature_file = self._config['FEATURE_CONFIG'][feature] + config = ConfigParser() + config.read(eval(feature_file)) + for section in config: + if config.has_option(section, 'numerical'): + for feature in self._features: + if feature.split('_')[0] == section: + self._features[feature]['binary'] = config[section].getboolean('binary') + self._features[feature]['categorical'] = config[section].getboolean('categorical') + self._features[feature]['numerical'] = config[section].getboolean('numerical') + + self._binary_columns = [k for k in self._features if self._features[k]['binary']] + self._categorical_columns = [k for k in self._features if self._features[k]['categorical']] + self._numerical_columns = [k for k in self._features if self._features[k]['numerical']] @property def start(self): @@ -68,7 +77,6 @@ class Preprocessing: def start(self, x): self._start = x - @property def end(self): return self._end @@ -77,7 +85,6 @@ class Preprocessing: def end(self, x): self._end = x - @property def timestep(self): return self._timestep @@ -86,7 +93,6 @@ class Preprocessing: def timestep(self, x): self._timestep = x - def _fill_dict(self): ''' Add datetime keys in the dated feature dictionary that are missing. The @@ -98,16 +104,16 @@ class Preprocessing: while current <= self._end: self._datetimes.append(current) if current not in self._dict_features: - self._dict_features[current] = {feature:np.NaN + self._dict_features[current] = {feature: np.NaN for feature in self._features} else: - null_dict = {feature:np.NaN + 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 + null_dict = {feature: np.NaN for feature in self._features} null_dict.update(self._dict_features[k]) self._dict_features[k] = null_dict @@ -115,8 +121,6 @@ class Preprocessing: self._full_dict = {k: self._dict_features[k] for k in sorted(self._dict_features.keys())} - - @property def full_dict(self): ''' @@ -126,7 +130,84 @@ class Preprocessing: 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") + dates = tuple((x.year, x.month, x.day, x.hour) for x in self._datetimes) + self._dataframe['row_ok'] =\ + self._dataframe.apply(lambda x: (int(x.year), int(x.month), int(x.dayInMonth), int(x.hour)) in dates, axis=1) + self._dataframe = self._dataframe[self._dataframe['row_ok']] + self._dataframe = self._dataframe.drop(['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 = eval(self._config['HISTORY_KNOWLEDGE']['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] + # Idem for binary features + for col in self._binary_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): @@ -137,27 +218,16 @@ 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 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() + # 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