def test_scale_with_exclude_cols(): df = _some_df1() scale_stage = Scale("StandardScaler", exclude_columns=["lbl"]) res_df = scale_stage(df) assert list(res_df.columns) == ["ph", "gt", "lbl"] assert "ph" in res_df.columns assert "gt" in res_df.columns assert res_df["ph"][1] < df["ph"][1] # see only transform (no fit) when already fitted df2 = _some_df1b() res_df2 = scale_stage(df2) assert "ph" in res_df2.columns assert "gt" in res_df2.columns assert df['ph'][1] < df2['ph'][1] assert res_df2["ph"][1] < df2["ph"][1] assert res_df["ph"][1] < res_df2["ph"][1] # check fit_transform when already fitted df3 = _some_df1b() res_df3 = scale_stage.fit_transform(df2) assert "ph" in res_df3.columns assert "gt" in res_df3.columns assert res_df3["ph"][1] < df3["ph"][1] assert res_df3["ph"][1] < res_df2["ph"][1]
def test_scale_with_exclude(): """Basic binning test.""" df = _some_df2() scale_stage = Scale("StandardScaler", with_std=False) res_df = scale_stage(df) assert 'ph' in res_df.columns assert 'gt' in res_df.columns
def test_scale_transform_exception(): df1 = _some_df1() scale_stage = Scale("StandardScaler", exmsg="ERR") scale_stage(df1) # test transform exception df2 = _bad_dtype_df1() with pytest.raises(PipelineApplicationError): scale_stage(df2)
def test_scale(): df = _some_df2() scale_stage = Scale("StandardScaler") res_df = scale_stage(df) assert 'ph' in res_df.columns assert 'gt' in res_df.columns assert res_df['ph'][1] < df['ph'][1] # see only transform (no fit) when already fitted df2 = _some_df2b() res_df2 = scale_stage(df2) assert 'ph' in res_df2.columns assert 'gt' in res_df2.columns assert res_df2['ph'][1] < df2['ph'][1] assert res_df['ph'][1] < res_df2['ph'][1] # check fit_transform when already fitted df3 = _some_df2b() res_df3 = scale_stage.fit_transform(df2) assert 'ph' in res_df3.columns assert 'gt' in res_df3.columns assert res_df3['ph'][1] < df3['ph'][1] assert res_df3['ph'][1] < res_df2['ph'][1]
def test_scale_with_exclude_cols(): df = _some_df1() scale_stage = Scale("StandardScaler", exclude_columns=['lbl'], exmsg='AA') res_df = scale_stage(df) assert list(res_df.columns) == ['ph', 'gt', 'lbl'] assert 'ph' in res_df.columns assert 'gt' in res_df.columns assert res_df['ph'][1] < df['ph'][1] # see only transform (no fit) when already fitted df2 = _some_df1b() res_df2 = scale_stage(df2) assert 'ph' in res_df2.columns assert 'gt' in res_df2.columns assert res_df2['ph'][1] < df2['ph'][1] assert res_df['ph'][1] < res_df2['ph'][1] # check fit_transform when already fitted df3 = _some_df1b() res_df3 = scale_stage.fit_transform(df2) assert 'ph' in res_df3.columns assert 'gt' in res_df3.columns assert res_df3['ph'][1] < df3['ph'][1] assert res_df3['ph'][1] < res_df2['ph'][1]
def test_scale_app_exception(): df1 = _some_df1() scale_stage = Scale("StandardScaler", exclude_columns=[], exclude_object_columns=False) with pytest.raises(PipelineApplicationError): scale_stage(df1) df2 = _some_df2() res_df = scale_stage(df2) assert "ph" in res_df.columns assert "gt" in res_df.columns # test transform exception with pytest.raises(PipelineApplicationError): scale_stage(df1)
def test_scale_app_exception(): df = _some_df1() scale_stage = Scale("StandardScaler", exclude_columns=[]) with pytest.raises(PipelineApplicationError): scale_stage(df)
def test_scale_fit_transform_exception(): df1 = _some_df1() scale_stage = Scale("StandardScaler", columns=['ph', 'lbl']) with pytest.raises(PipelineApplicationError): scale_stage(df1)