class FeatureSelection:
def __init__(self, train, test, id_column, y_column_name):
self.train = train
self.number_of_train = train.shape[0]
self.id_column = id_column
self.y_column_name = y_column_name
self.test = test
self.data = pd.concat([self.train, self.test], ignore_index=True)
self.feature_engineering = FeatureEngineering(self.train, self.test,
self.id_column,
self.y_column_name)
self.id_and_output = self.data[[self.id_column, self.y_column_name]]
def preprocess_my_data(self):
self.data = self.feature_engineering.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 = ExtraTreesClassifier().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()
features_df = self.data[selected_features_list]
self.data = pd.concat([self.id_and_output, features_df], axis=1)
return self.data
class PreprocessedData:
"""
This class combines the feature engineering and feature selection
class to one to preprocess the data.
It takes in the cleaned data and the y_column_name(ratings)
"""
def __init__(self, data, y_column_name):
self.data = data
self.y_column_name = y_column_name
self.feature_engineering = FeatureEngineering(data, y_column_name)
self.feature_selection = FeatureSelection(data, y_column_name)
def preprocess_my_data(self, num_of_features_to_select):
"""
This method preprocesses the cleaned data and performs
feature selection to select best n-features
:param num_of_features_to_select: n-best features of the model
:return: Full preprocesse data with n-selected features
"""
self.data = self.feature_engineering.input_rare_categorical()
self.data = self.feature_engineering.encode_categorical_features()
self.data = self.feature_engineering.scale_features()
self.data = self.feature_selection.perform_feature_selection(
num_of_features_to_select)
return self.data
class ProcessedData:
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([self.train, self.test], ignore_index=True)
self.feature_engineering = FeatureEngineering(self.train, self.test,
self.id_column,
self.y_column_name)
self.feature_selection = FeatureSelection(self.train, self.test,
self.id_column,
self.y_column_name)
def preprocess_my_data(self, num_of_features_to_select):
self.data = self.feature_engineering.scale_features()
self.data = self.feature_selection.perform_feature_selection(
num_of_features_to_select)
return self.data
class FeatureSelection:
"""
This class peroforms feature selection.
It takes the cleaned data and the the y_column_name(ratings)
"""
def __init__(self, data, y_column_name):
self.data = data
self.y_column_name = y_column_name
self.y = self.data[[self.y_column_name]]
self.feature_engineering = FeatureEngineering(data, y_column_name)
def preprocess_my_data(self):
"""
This method preprocessed the data by inputing rare categorical,
perform feature engineering and feature scaling
:return: preprocessed full data
"""
self.data = self.feature_engineering.input_rare_categorical()
self.data = self.feature_engineering.encode_categorical_features()
self.data = self.feature_engineering.scale_features()
return self.data
def perform_feature_selection(self, num_of_features_to_select):
"""
This method performs the feature selection technique
:param num_of_features_to_select: number of best features to select
:return: full data with n-selected features
"""
data = self.preprocess_my_data()
self.train = data[0: 300000]
label_encoder = LabelEncoder()
ytrain = self.train[self.y_column_name]
ytrain= label_encoder.fit_transform(ytrain)
xtrain = self.train.drop([self.y_column_name], axis=1)
feature_sel_model = ExtraTreesClassifier().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
