X = X[self.raw_features].copy()
X_pp = self.preprocessing_pipeline.transform(X)
X_pp = pd.DataFrame(X_pp, columns=self.get_feature_names())
return X_pp
def get_feature_names(self):
"""
Feature names after preprocessing.
Replicates the get_feature_names function in the sklearn Transformer classes.
"""
return self.raw_features
if __name__ == "__main__":
from preprocessing import PreProcessor # noqa
# Load data
db_config = db.get_config()
train = db.load(*db_config, 'raw_train')
X_train = train.drop('SalePrice', axis=1)
# Fit and transform training data
pp = PreProcessor()
X_train_pp = pp.fit_transform(X_train)
train_pp = X_train_pp.assign(SalePrice=train['SalePrice'])
# Save preprocessed data and fitted preprocessor
db.save(train_pp, *db_config, 'processed_train')
joblib.dump(pp, os.path.join(DIR, '../pickle/PreProcessor.pkl'))
