How to use the pdpipe.sklearn_stages.Encode function in pdpipe

def test_encode_fit():
    df = _some_df()
    encode_stage = Encode()

    with pytest.raises(UnfittedPipelineStageError):
        encode_stage.transform(df)

    res_df = encode_stage.fit(df)
    assert 'lbl' in res_df.columns
    assert res_df['lbl'][1] == 'acd'
    assert res_df['lbl'][2] == 'alk'
    assert res_df['lbl'][3] == 'alk'

    # see only transform (no fit) when already fitted
    df2 = _some_df2()
    res_df2 = encode_stage.transform(df2)
    assert 'lbl' in res_df2.columns
    assert res_df2['lbl'][1] == 1
    assert res_df2['lbl'][2] == 0

def test_encode_in_pipelin_fit_n_transform():
    drop_name = pdp.ColDrop('name')
    encode_stage = Encode()
    pline = drop_name + encode_stage

    df = _some_df()

    with pytest.raises(UnfittedPipelineStageError):
        res_df = pline.transform(df)

    res_df = pline.fit(df)
    assert 'lbl' in res_df.columns
    assert 'name' in res_df.columns
    assert res_df['lbl'][1] == 'acd'
    assert res_df['lbl'][2] == 'alk'
    assert res_df['lbl'][3] == 'alk'

    res_df = pline.transform(df)
    assert 'lbl' in res_df.columns

def test_encode_with_args():
    df = _some_df()
    encode_stage = Encode("lbl", drop=False)
    res_df = encode_stage(df, verbose=True)
    assert 'lbl' in res_df.columns
    assert res_df['lbl_enc'][1] == 0
    assert res_df['lbl_enc'][2] == 1
    assert res_df['lbl_enc'][3] == 1

    # see only transform (no fit) when already fitted
    df2 = _some_df2()
    res_df2 = encode_stage(df2)
    assert 'lbl' in res_df.columns
    assert res_df2['lbl_enc'][1] == 1
    assert res_df2['lbl_enc'][2] == 0
    assert res_df2['lbl_enc'][3] == 1

    # check fit_transform when already fitted
    df2 = _some_df2()

def test_encode_in_pipeline():
    drop_name = pdp.ColDrop('name')
    encode_stage = Encode()
    pline = drop_name + encode_stage

    df = _some_df()
    res_df = pline(df)
    assert 'lbl' in res_df.columns
    assert 'name' not in res_df.columns
    assert res_df['lbl'][1] == 0
    assert res_df['lbl'][2] == 1
    assert res_df['lbl'][3] == 1

    # check fitted pipeline
    df2 = _some_df2()
    res_df2 = pline(df2)
    assert 'lbl' in res_df2.columns
    assert res_df2['lbl'][1] == 1
    assert res_df2['lbl'][2] == 0

def test_encode_with_exclude():
    df = _some_df()
    encode_stage = Encode("lbl", exclude_columns="name")
    res_df = encode_stage(df)
    assert 'lbl' in res_df.columns
    assert res_df['lbl'][1] == 0
    assert res_df['lbl'][2] == 1
    assert res_df['lbl'][3] == 1

def test_encode():
    df = _some_df()
    encode_stage = Encode()
    res_df = encode_stage(df)
    assert 'lbl' in res_df.columns
    assert res_df['lbl'][1] == 0
    assert res_df['lbl'][2] == 1
    assert res_df['lbl'][3] == 1
    assert res_df['name'][1] == 0
    assert res_df['name'][2] == 1
    assert res_df['name'][3] == 2

    # see only transform (no fit) when already fitted
    df2 = _some_df2()
    res_df2 = encode_stage(df2)
    assert 'lbl' in res_df2.columns
    assert res_df2['lbl'][1] == 1
    assert res_df2['lbl'][2] == 0
    assert res_df2['lbl'][3] == 1

self, columns=None, exclude_columns=None, drop=True, **kwargs
    ):
        if columns is None:
            self._columns = None
        else:
            self._columns = _interpret_columns_param(columns)
        if exclude_columns is None:
            self._exclude_columns = []
        else:
            self._exclude_columns = _interpret_columns_param(exclude_columns)
        self._drop = drop
        self.encoders = {}
        col_str = _list_str(self._columns)
        super_kwargs = {
            "exmsg": Encode._DEF_ENCODE_EXC_MSG.format(col_str),
            "appmsg": Encode._DEF_ENCODE_APP_MSG.format(col_str),
            "desc": "Encode {}".format(col_str or "all categorical columns"),
        }
        super_kwargs.update(**kwargs)
        super().__init__(**super_kwargs)

def __init__(
        self, columns=None, exclude_columns=None, drop=True, **kwargs
    ):
        if columns is None:
            self._columns = None
        else:
            self._columns = _interpret_columns_param(columns)
        if exclude_columns is None:
            self._exclude_columns = []
        else:
            self._exclude_columns = _interpret_columns_param(exclude_columns)
        self._drop = drop
        self.encoders = {}
        col_str = _list_str(self._columns)
        super_kwargs = {
            "exmsg": Encode._DEF_ENCODE_EXC_MSG.format(col_str),
            "appmsg": Encode._DEF_ENCODE_APP_MSG.format(col_str),
            "desc": "Encode {}".format(col_str or "all categorical columns"),
        }
        super_kwargs.update(**kwargs)
        super().__init__(**super_kwargs)