class FeatureSelection:
def __init__(self, train, test, id_column, y_column_name):
self.train = train
self.number_of_train = train.shape[0]
self.y_column_name = y_column_name
self.id_column = id_column
self.test = test
self.data = pd.concat([train, test], ignore_index=True)
self.feature_engineering = FeatureEngineering(train, test, id_column,
y_column_name)
self.y = self.data[[self.id_column, self.y_column_name]]
def preprocess_my_data(self):
self.data = self.feature_engineering.fill_na_categorical()
self.data = self.feature_engineering.fill_na_numerical()
self.data = self.feature_engineering.input_rare_categorical()
self.data = self.feature_engineering.label_encoder()
self.data = self.feature_engineering.get_scale_features()
return self.data
def perform_feature_selection(self, num_of_features_to_select):
data = self.preprocess_my_data()
train_data = data[:self.number_of_train]
ytrain = train_data[[self.y_column_name]]
xtrain = train_data.drop([self.id_column, self.y_column_name], axis=1)
feature_sel_model = ExtraTreesRegressor().fit(xtrain, ytrain)
feat_importances = pd.Series(feature_sel_model.feature_importances_,
index=xtrain.columns)
selected_features = feat_importances.nlargest(
num_of_features_to_select)
selected_features_df = selected_features.to_frame()
selected_features_list = selected_features_df.index.tolist()
data = self.data[selected_features_list]
self.data = pd.concat([self.y, data], axis=1)
return self.data
class PreprocessedData:
def __init__(self, train, test, id_column, y_column_name):
self.train = train
self.test = test
self.y_column_name = y_column_name
self.id_column = id_column
self.data = pd.concat([train, test], ignore_index=True)
self.feature_engineering = FeatureEngineering(train, test, id_column,
y_column_name)
self.feature_selection = FeatureSelection(train, test, id_column,
y_column_name)
def preprocess_my_data(self, num_of_features_to_select):
self.data = self.feature_engineering.fill_na_categorical()
self.data = self.feature_engineering.fill_na_numerical()
self.data = self.feature_engineering.input_rare_categorical()
self.data = self.feature_engineering.label_encoder()
self.data = self.feature_engineering.get_scale_features()
self.data = self.feature_selection.perform_feature_selection(
num_of_features_to_select)
return self.data