I would like to implement Onehot encoding and label encoding to my dataset using Pipeline into my random forest model. I have created a function that utilize pipeline from scikit learn together with OneHotEncoder and LabelEncoder.

def create_pipeline(self, train_feature, train_label, encoding_method, model):
        if encoding_method == EncodingMethod.ONE_HOT:
            categorical_cols = [col for col in train_feature.columns if train_feature[col].dtype == 'object'] 
            categorical_transformer = Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore',
                                                                              sparse=False))])

            label_encoder = Pipeline(steps=[('label', LabelEncoder())])

            preprocessor = ColumnTransformer(transformers=[('category', categorical_transformer, categorical_cols),
                                                           ('label', label_encoder, [train_label.name])],
                                                           remainder='passthrough')

        elif encoding_method == EncodingMethod.LABEL:
            categorical_cols = [col for col in train_feature.columns if train_feature[col].dtype == 'object']
            categorical_transformer = Pipeline(steps=[('label', LabelEncoder())])

            preprocessor = ColumnTransformer(transformers=[('category', categorical_transformer, categorical_cols),
                                                           ('label', categorical_transformer, [train_label.name])],
                                             remainder='passthrough')
            
        pipeline = Pipeline(steps=[('preprocessor', preprocessor),
                                   ('classifier', model)])
        
        return pipeline

Using the function above, I would pass into my model script (using iris dataset, code as below) and expect the y_train (species column) will be encoded as 0,1,2 etc but when I print the output, it is still categorical values.

partial script:

df = self._dataset.as_dataframe()

train_feature = df[self._train_configs.feature_cols]
train_label = df[self._train_configs.target_col]

self._model = **self.create_pipeline(train_feature, train_label, self._train_configs.encoding_method, self._model)**

print("n")
print("This is model")
print(self._model)

X_train, X_test, y_train, y_test = train_test_split(train_feature, train_label, random_state=0, train_size=0.8)

print("n")
print("This is y_train")
print(y_train)

output of print y_train:

137 Iris-virginica
84 Iris-versicolor
27 Iris-setosa
127 Iris-virginica
132 Iris-virginica
…
9 Iris-setosa
103 Iris-virginica
67 Iris-versicolor
117 Iris-virginica
47 Iris-setosa