datset_classification = r'datasets\classification\autos.csv'
data = pd.read_csv(datset_classification)
X, y = data.drop(columns=['class']), data['class']
X_train, X_test, y_train, y_test = train_test_split(X,
y,
stratify=y,
random_state=42)
preprocessor = Preprocessor()
preprocessor.add_branch("categorical")
preprocessor.add_branch('numerical')
preprocessor.add_transformer_to_branch("numerical", DtypeSelector(np.number))
preprocessor.add_transformer_to_branch("numerical", GeneralImputer('Simple'))
preprocessor.add_transformer_to_branch("categorical", DtypeSelector(np.object))
preprocessor.add_transformer_to_branch(
"categorical", GeneralImputer('Simple', strategy='most_frequent'))
preprocessor.add_transformer_to_branch("categorical",
GeneralEncoder(kind='LE'))
final = preprocessor.merge()
model = GridSelector('classification')
clf_pipe = make_pipeline(final, model)
clf_pipe.fit(X_train, y_train)
