def test_predict_batched(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
default = cs.get_default_configuration()
cls = ParamSklearnClassifier(default)
# Multiclass
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647,), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
# Multilabel
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
Y_train = np.array([(y, 26 - y) for y in Y_train])
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647, 2), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
def test_predict_batched(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space()
default = cs.get_default_configuration()
cls = ParamSklearnClassifier(default)
# Multiclass
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647, ), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
# Multilabel
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
Y_train = np.array([(y, 26 - y) for y in Y_train])
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647, 2), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
def test_predict_batched_sparse(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'sparse': True})
config = Configuration(
cs,
values={
"balancing:strategy": "none",
"classifier:__choice__": "random_forest",
"imputation:strategy": "mean",
"one_hot_encoding:minimum_fraction": 0.01,
"one_hot_encoding:use_minimum_fraction": "True",
"preprocessor:__choice__": "no_preprocessing",
'classifier:random_forest:bootstrap': 'True',
'classifier:random_forest:criterion': 'gini',
'classifier:random_forest:max_depth': 'None',
'classifier:random_forest:min_samples_split': 2,
'classifier:random_forest:min_samples_leaf': 2,
'classifier:random_forest:max_features': 0.5,
'classifier:random_forest:max_leaf_nodes': 'None',
'classifier:random_forest:n_estimators': 100,
'classifier:random_forest:min_weight_fraction_leaf': 0.0,
"rescaling:__choice__": "min/max"
})
cls = ParamSklearnClassifier(config)
# Multiclass
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=True)
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647, ), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
# Multilabel
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=True)
Y_train = np.array([(y, 26 - y) for y in Y_train])
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647, 2), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
def test_predict_batched_sparse(self):
cs = ParamSklearnClassifier.get_hyperparameter_search_space(
dataset_properties={'sparse': True})
config = Configuration(cs,
values={"balancing:strategy": "none",
"classifier:__choice__": "random_forest",
"imputation:strategy": "mean",
"one_hot_encoding:minimum_fraction": 0.01,
"one_hot_encoding:use_minimum_fraction": "True",
"preprocessor:__choice__": "no_preprocessing",
'classifier:random_forest:bootstrap': 'True',
'classifier:random_forest:criterion': 'gini',
'classifier:random_forest:max_depth': 'None',
'classifier:random_forest:min_samples_split': 2,
'classifier:random_forest:min_samples_leaf': 2,
'classifier:random_forest:max_features': 0.5,
'classifier:random_forest:max_leaf_nodes': 'None',
'classifier:random_forest:n_estimators': 100,
'classifier:random_forest:min_weight_fraction_leaf': 0.0,
"rescaling:__choice__": "min/max"})
cls = ParamSklearnClassifier(config)
# Multiclass
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=True)
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647,), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
# Multilabel
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
make_sparse=True)
Y_train = np.array([(y, 26 - y) for y in Y_train])
cls.fit(X_train, Y_train)
X_test_ = X_test.copy()
prediction_ = cls.predict(X_test_)
cls_predict = mock.Mock(wraps=cls.pipeline_)
cls.pipeline_ = cls_predict
prediction = cls.predict(X_test, batch_size=20)
self.assertEqual((1647, 2), prediction.shape)
self.assertEqual(83, cls_predict.predict.call_count)
assert_array_almost_equal(prediction_, prediction)
