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)