def test_groupby1(self): df = get_iris_randomgroup() enc_out = scale(df, input_cols=[ 'sepal_length', 'sepal_width', 'petal_length', 'petal_width' ], scaler='RobustScaler', group_by=['random_group1', 'random_group2']) print(enc_out['out_table']) print(enc_out['model'].keys()) model_out = scale_model(df, enc_out['model']) print(model_out['out_table'])
def test(self): nm = scale(self.testdata, input_cols=['sepal_length'], scaler='Min_Max', suffix=None) DF1 = nm['out_table'].values # print(DF1) np.testing.assert_almost_equal(DF1[0][5], 0.2222222222222221, 10) np.testing.assert_almost_equal(DF1[1][5], 0.16666666666666674, 10) np.testing.assert_almost_equal(DF1[2][5], 0.11111111111111116, 10) np.testing.assert_almost_equal(DF1[3][5], 0.08333333333333326, 10) np.testing.assert_almost_equal(DF1[4][5], 0.19444444444444442, 10) nm_model = scale_model(self.testdata, model=nm['model']) DF2 = nm_model['out_table'].values # print(DF2) np.testing.assert_almost_equal(DF2[0][5], 0.2222222222222221, 10) np.testing.assert_almost_equal(DF2[1][5], 0.16666666666666674, 10) np.testing.assert_almost_equal(DF2[2][5], 0.11111111111111116, 10) np.testing.assert_almost_equal(DF2[3][5], 0.08333333333333326, 10) np.testing.assert_almost_equal(DF2[4][5], 0.19444444444444442, 10)