X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/predictops.git/blobdiff_plain/f5357178353432c5431fc0fd875b9928a326e4c7..a2faba3f0797b7be72d0c8fa9cb9db67456136d6:/predictops/learn/preprocessing.py?ds=sidebyside diff --git a/predictops/learn/preprocessing.py b/predictops/learn/preprocessing.py index 4197b8f..885aad3 100644 --- a/predictops/learn/preprocessing.py +++ b/predictops/learn/preprocessing.py @@ -25,12 +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') @@ -39,32 +39,31 @@ 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): - 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'): + + #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(feature_files / feature_file) + config.read(feature_file) for section in config: if config.has_option(section, 'numerical'): self._features[section]['numerical'] = config[section].getboolean('numerical') + self._features[section]['categorical'] = config[section].getboolean('categorical') + + self._numerical_columns = [k for k in self._features if self._features[k]['numerical']] + self._categorical_columns = [k for k in self._features if self._features[k]['categorical']] @@ -141,27 +140,23 @@ class Preprocessing: ''' 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') + self._dataframe[self._numerical_columns] =\ + self._dataframe[self._numerical_columns].fillna(method='ffill') elif self._config['PREPROCESSING']['fill_method'] == 'linear': - self._dataframe[numerical_columns] =\ - self._dataframe[numerical_columns].interpolate() + self._dataframe[self._numerical_columns] =\ + self._dataframe[self._numerical_columns].interpolate() elif self._config['PREPROCESSING']['fill_method'] == 'spline': - self._dataframe[numerical_columns] =\ - self._dataframe[numerical_columns].interpolate(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") - 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') + 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 @@ -170,9 +165,27 @@ class Preprocessing: # 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]) + 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 = 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): @@ -181,9 +194,9 @@ class Preprocessing: ''' 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]) + self._dataframe[self._numerical_columns] =\ + preprocessing.scale(self._dataframe[self._numerical_columns]) + def _one_hot_encoding(self): @@ -191,18 +204,17 @@ class Preprocessing: 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 + # We store numerical columns df_out = pd.DataFrame() - for col in categorical_columns: + 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 - print(self._dataframe.head()) - @property @@ -216,6 +228,8 @@ class Preprocessing: 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