def test_XGBoost(self):
model=XGBClassifier()
sk_model=iris_dataset(model)
derived_col_names=['sepal_length','petal_length']
col_names=['sepal_length','petal_length']
target_name='species'
mining_imp_value=()
categoric_values=()
self.assertEqual(
len(xgb.get_segments_for_xgbc(sk_model,derived_col_names,col_names,target_name,mining_imp_value,categoric_values)),
4)
self.assertEqual(
xgb.get_segments_for_xgbc(sk_model,derived_col_names,col_names,target_name,mining_imp_value,categoric_values)[
3].get_id(),4)
self.assertEqual(
xgb.get_segments_for_xgbc(sk_model, derived_col_names, col_names, target_name, mining_imp_value,categoric_values)[
3].get_RegressionModel().get_RegressionTable()[0].get_intercept(), '0.0')
self.assertEqual(
xgb.mining_Field_For_First_Segment(col_names).__class__.__name__,
pml.MiningSchema().__class__.__name__)
self.assertEqual(
xgb.mining_Field_For_First_Segment(col_names).get_MiningField()[0].get_name(),
'sepal_length')
self.assertEqual(len(xgb.mining_Field_For_First_Segment(['a','b','d','e']).get_MiningField())
,4)
self.assertEqual(
type(xgb.generate_Segments_Equal_To_Estimators([],derived_col_names,col_names)),
type([]))
self.assertEqual(
xgb.add_segmentation(sk_model,[],[],pml.Output,1).__class__.__name__,
pml.Segment().__class__.__name__)
self.assertEqual(
xgb.add_segmentation(sk_model,[],[],pml.Output,1).get_MiningModel().__class__.__name__,
pml.MiningModel().__class__.__name__)
self.assertEqual(xgb.add_segmentation(sk_model, [], [], pml.Output, 1).get_id(),
2)
self.assertEqual(type(xgb.get_regrs_tabl(sk_model,col_names,'species',categoric_values)),type([]))
def test_LightGradientBoostingRegressor(self):
model = LGBMRegressor()
sk_model, feature_names, target_name = auto_dataset_for_regression(
model)
derived_col_names = feature_names
mining_imp_value = ()
categoric_values = ()
self.assertEqual(
lgbToPmml.get_segments_for_lgbr(
sk_model, derived_col_names, feature_names, target_name,
mining_imp_value, categoric_values).__class__.__name__,
pml.Segmentation().__class__.__name__)
self.assertEqual(
len(
lgbToPmml.get_segments_for_lgbr(
sk_model, derived_col_names, feature_names, target_name,
mining_imp_value, categoric_values).get_Segment()),
model.n_estimators)
self.assertEqual(
lgbToPmml.get_ensemble_models(
sk_model, derived_col_names, feature_names, target_name,
mining_imp_value, categoric_values)[0].__class__.__name__,
pml.MiningModel().__class__.__name__)
self.assertEqual(
lgbToPmml.get_ensemble_models(
sk_model, derived_col_names, feature_names, target_name,
mining_imp_value, categoric_values)[0].get_functionName(),
'regression')
self.assertEqual(
len(
lgbToPmml.get_ensemble_models(
sk_model, derived_col_names, feature_names, target_name,
mining_imp_value,
categoric_values)[0].get_MiningSchema().get_MiningField()),
model.n_features_ + 1)