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fix: imports from modAL.models fixed
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‎tests/core_tests.py

+41-40
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,8 @@
33
import numpy as np
44

55
import mock
6-
import modAL.models
6+
import modAL.models.base
7+
import modAL.models.learners
78
import modAL.uncertainty
89
import modAL.disagreement
910
import modAL.density
@@ -124,7 +125,7 @@ def test_make_query_strategy(self):
124125
proba = proba/np.sum(proba, axis=1).reshape(n_samples, 1)
125126
X = np.random.rand(n_samples, 3)
126127

127-
learner = modAL.models.ActiveLearner(
128+
learner = modAL.models.learners.ActiveLearner(
128129
estimator=mock.MockEstimator(predict_proba_return=proba)
129130
)
130131

@@ -174,7 +175,7 @@ def test_optimizer_PI(self):
174175

175176
# 1. fitted estimator
176177
mock_estimator = mock.MockEstimator(predict_return=(mean, std))
177-
optimizer = modAL.models.BayesianOptimizer(estimator=mock_estimator)
178+
optimizer = modAL.models.learners.BayesianOptimizer(estimator=mock_estimator)
178179
optimizer._set_max([0], [max_val])
179180
true_PI = ndtr((mean - max_val - tradeoff)/std)
180181

@@ -185,7 +186,7 @@ def test_optimizer_PI(self):
185186

186187
# 2. unfitted estimator
187188
mock_estimator = mock.MockEstimator(fitted=False)
188-
optimizer = modAL.models.BayesianOptimizer(estimator=mock_estimator)
189+
optimizer = modAL.models.learners.BayesianOptimizer(estimator=mock_estimator)
189190
optimizer._set_max([0], [max_val])
190191
true_PI = ndtr((np.zeros(shape=(len(mean), 1)) - max_val - tradeoff) / np.ones(shape=(len(mean), 1)))
191192

@@ -205,7 +206,7 @@ def test_optimizer_EI(self):
205206
mock_estimator = mock.MockEstimator(
206207
predict_return=(mean, std)
207208
)
208-
optimizer = modAL.models.BayesianOptimizer(estimator=mock_estimator)
209+
optimizer = modAL.models.learners.BayesianOptimizer(estimator=mock_estimator)
209210
optimizer._set_max([0], [max_val])
210211
true_EI = (mean - optimizer.y_max - tradeoff) * ndtr((mean - optimizer.y_max - tradeoff) / std) \
211212
+ std * norm.pdf((mean - optimizer.y_max - tradeoff) / std)
@@ -217,7 +218,7 @@ def test_optimizer_EI(self):
217218

218219
# 2. unfitted estimator
219220
mock_estimator = mock.MockEstimator(fitted=False)
220-
optimizer = modAL.models.BayesianOptimizer(estimator=mock_estimator)
221+
optimizer = modAL.models.learners.BayesianOptimizer(estimator=mock_estimator)
221222
optimizer._set_max([0], [max_val])
222223
true_EI = (np.zeros(shape=(len(mean), 1)) - optimizer.y_max - tradeoff) * ndtr((np.zeros(shape=(len(mean), 1)) - optimizer.y_max - tradeoff) / np.ones(shape=(len(mean), 1))) \
223224
+ np.ones(shape=(len(mean), 1)) * norm.pdf((np.zeros(shape=(len(mean), 1)) - optimizer.y_max - tradeoff) / np.ones(shape=(len(mean), 1)))
@@ -237,7 +238,7 @@ def test_optimizer_UCB(self):
237238
mock_estimator = mock.MockEstimator(
238239
predict_return=(mean, std)
239240
)
240-
optimizer = modAL.models.BayesianOptimizer(estimator=mock_estimator)
241+
optimizer = modAL.models.learners.BayesianOptimizer(estimator=mock_estimator)
241242
true_UCB = mean + beta*std
242243

243244
np.testing.assert_almost_equal(
@@ -247,7 +248,7 @@ def test_optimizer_UCB(self):
247248

248249
# 2. unfitted estimator
249250
mock_estimator = mock.MockEstimator(fitted=False)
250-
optimizer = modAL.models.BayesianOptimizer(estimator=mock_estimator)
251+
optimizer = modAL.models.learners.BayesianOptimizer(estimator=mock_estimator)
251252
true_UCB = np.zeros(shape=(len(mean), 1)) + beta * np.ones(shape=(len(mean), 1))
252253

253254
np.testing.assert_almost_equal(
@@ -267,7 +268,7 @@ def test_selection(self):
267268
predict_return=(mean, std)
268269
)
269270

270-
optimizer = modAL.models.BayesianOptimizer(estimator=mock_estimator)
271+
optimizer = modAL.models.learners.BayesianOptimizer(estimator=mock_estimator)
271272
optimizer._set_max([0], [max_val])
272273

273274
modAL.acquisition.max_PI(optimizer, X, tradeoff=np.random.rand(), n_instances=n_instances)
@@ -515,7 +516,7 @@ def test_add_training_data(self):
515516
y_initial = np.random.randint(0, 2, size=(n_samples,))
516517
X_new = np.random.rand(n_new_samples, n_features)
517518
y_new = np.random.randint(0, 2, size=(n_new_samples,))
518-
learner = modAL.models.ActiveLearner(
519+
learner = modAL.models.learners.ActiveLearner(
519520
estimator=mock.MockEstimator(),
520521
X_training=X_initial, y_training=y_initial
521522
)
@@ -531,7 +532,7 @@ def test_add_training_data(self):
531532
# 2. vector class labels
532533
y_initial = np.random.randint(0, 2, size=(n_samples, n_features+1))
533534
y_new = np.random.randint(0, 2, size=(n_new_samples, n_features+1))
534-
learner = modAL.models.ActiveLearner(
535+
learner = modAL.models.learners.ActiveLearner(
535536
estimator=mock.MockEstimator(),
536537
X_training=X_initial, y_training=y_initial
537538
)
@@ -543,7 +544,7 @@ def test_add_training_data(self):
543544
# 3. data with shape (n, )
544545
X_initial = np.random.rand(n_samples, )
545546
y_initial = np.random.randint(0, 2, size=(n_samples,))
546-
learner = modAL.models.ActiveLearner(
547+
learner = modAL.models.learners.ActiveLearner(
547548
estimator=mock.MockEstimator(),
548549
X_training=X_initial, y_training=y_initial
549550
)
@@ -569,7 +570,7 @@ def test_predict(self):
569570
X = np.random.rand(n_samples, n_features)
570571
predict_return = np.random.randint(0, 2, size=(n_samples, ))
571572
mock_classifier = mock.MockEstimator(predict_return=predict_return)
572-
learner = modAL.models.ActiveLearner(
573+
learner = modAL.models.learners.ActiveLearner(
573574
estimator=mock_classifier
574575
)
575576
np.testing.assert_equal(
@@ -583,7 +584,7 @@ def test_predict_proba(self):
583584
X = np.random.rand(n_samples, n_features)
584585
predict_proba_return = np.random.randint(0, 2, size=(n_samples,))
585586
mock_classifier = mock.MockEstimator(predict_proba_return=predict_proba_return)
586-
learner = modAL.models.ActiveLearner(
587+
learner = modAL.models.learners.ActiveLearner(
587588
estimator=mock_classifier
588589
)
589590
np.testing.assert_equal(
@@ -597,7 +598,7 @@ def test_query(self):
597598
X = np.random.rand(n_samples, n_features)
598599
query_idx = np.random.randint(0, n_samples)
599600
mock_query = mock.MockFunction(return_val=(query_idx, X[query_idx]))
600-
learner = modAL.models.ActiveLearner(
601+
learner = modAL.models.learners.ActiveLearner(
601602
estimator=None,
602603
query_strategy=mock_query
603604
)
@@ -610,7 +611,7 @@ def test_score(self):
610611
test_cases = (np.random.rand() for _ in range(10))
611612
for score_return in test_cases:
612613
mock_classifier = mock.MockEstimator(score_return=score_return)
613-
learner = modAL.models.ActiveLearner(mock_classifier, mock.MockFunction(None))
614+
learner = modAL.models.learners.ActiveLearner(mock_classifier, mock.MockFunction(None))
614615
np.testing.assert_almost_equal(
615616
learner.score(np.random.rand(5, 2), np.random.rand(5, )),
616617
score_return
@@ -625,7 +626,7 @@ def test_teach(self):
625626
X = np.random.rand(n_samples, 2)
626627
y = np.random.randint(0, 2, size=n_samples)
627628

628-
learner = modAL.models.ActiveLearner(
629+
learner = modAL.models.learners.ActiveLearner(
629630
X_training=X_training, y_training=y_training,
630631
estimator=mock.MockEstimator()
631632
)
@@ -636,7 +637,7 @@ def test_keras(self):
636637
pass
637638

638639
def test_sklearn(self):
639-
learner = modAL.models.ActiveLearner(
640+
learner = modAL.models.learners.ActiveLearner(
640641
estimator=RandomForestClassifier(),
641642
X_training=np.random.rand(10, 10),
642643
y_training=np.random.randint(0, 2, size=(10,))
@@ -661,7 +662,7 @@ def test_sparse_matrices(self):
661662
y_pool = np.random.randint(0, 2, size=(n_samples, ))
662663
initial_idx = np.random.choice(range(n_samples), size=5, replace=False)
663664

664-
learner = modAL.models.ActiveLearner(
665+
learner = modAL.models.learners.ActiveLearner(
665666
estimator=RandomForestClassifier(), query_strategy=query_strategy,
666667
X_training=X_pool[initial_idx], y_training=y_pool[initial_idx]
667668
)
@@ -673,7 +674,7 @@ class TestBayesianOptimizer(unittest.TestCase):
673674
def test_set_max(self):
674675
# case 1: the estimator is not fitted yet
675676
regressor = mock.MockEstimator()
676-
learner = modAL.models.BayesianOptimizer(estimator=regressor)
677+
learner = modAL.models.learners.BayesianOptimizer(estimator=regressor)
677678
self.assertEqual(-np.inf, learner.y_max)
678679

679680
# case 2: the estimator is fitted already
@@ -683,7 +684,7 @@ def test_set_max(self):
683684
max_val = np.max(y)
684685

685686
regressor = mock.MockEstimator()
686-
learner = modAL.models.BayesianOptimizer(
687+
learner = modAL.models.learners.BayesianOptimizer(
687688
estimator=regressor,
688689
X_training=X, y_training=y
689690
)
@@ -697,7 +698,7 @@ def test_set_new_max(self):
697698
y = np.random.rand(n_samples)
698699
max_idx = np.argmax(y)
699700
regressor = mock.MockEstimator()
700-
learner = modAL.models.BayesianOptimizer(estimator=regressor)
701+
learner = modAL.models.learners.BayesianOptimizer(estimator=regressor)
701702
learner._set_max(X, y)
702703
np.testing.assert_equal(learner.X_max, X[max_idx])
703704
np.testing.assert_equal(learner.y_max, y[max_idx])
@@ -708,7 +709,7 @@ def test_set_new_max(self):
708709
y = np.random.rand(n_samples)
709710

710711
regressor = mock.MockEstimator()
711-
learner = modAL.models.BayesianOptimizer(
712+
learner = modAL.models.learners.BayesianOptimizer(
712713
estimator=regressor,
713714
X_training=X, y_training=y
714715
)
@@ -727,7 +728,7 @@ def test_set_new_max(self):
727728
y = np.random.rand(n_samples)
728729

729730
regressor = mock.MockEstimator()
730-
learner = modAL.models.BayesianOptimizer(
731+
learner = modAL.models.learners.BayesianOptimizer(
731732
estimator=regressor,
732733
X_training=X, y_training=y
733734
)
@@ -747,7 +748,7 @@ def test_get_max(self):
747748
y[max_idx] = 10
748749

749750
regressor = mock.MockEstimator()
750-
optimizer = modAL.models.BayesianOptimizer(regressor, X_training=X, y_training=y)
751+
optimizer = modAL.models.learners.BayesianOptimizer(regressor, X_training=X, y_training=y)
751752
X_max, y_max = optimizer.get_max()
752753
np.testing.assert_equal(X_max, X[max_idx])
753754
np.testing.assert_equal(y_max, y[max_idx])
@@ -758,7 +759,7 @@ def test_teach(self):
758759
for n_samples in range(1, 100):
759760
for n_features in range(1, 100):
760761
regressor = mock.MockEstimator()
761-
learner = modAL.models.BayesianOptimizer(estimator=regressor)
762+
learner = modAL.models.learners.BayesianOptimizer(estimator=regressor)
762763

763764
X = np.random.rand(n_samples, 2)
764765
y = np.random.rand(n_samples)
@@ -771,7 +772,7 @@ def test_teach(self):
771772
y = np.random.rand(n_samples)
772773

773774
regressor = mock.MockEstimator()
774-
learner = modAL.models.BayesianOptimizer(
775+
learner = modAL.models.learners.BayesianOptimizer(
775776
estimator=regressor,
776777
X_training=X, y_training=y
777778
)
@@ -783,17 +784,17 @@ class TestCommittee(unittest.TestCase):
783784
def test_set_classes(self):
784785
# 1. test unfitted learners
785786
for n_learners in range(1, 10):
786-
learner_list = [modAL.models.ActiveLearner(estimator=mock.MockEstimator(fitted=False))
787+
learner_list = [modAL.models.learners.ActiveLearner(estimator=mock.MockEstimator(fitted=False))
787788
for idx in range(n_learners)]
788-
committee = modAL.models.Committee(learner_list=learner_list)
789+
committee = modAL.models.learners.Committee(learner_list=learner_list)
789790
self.assertEqual(committee.classes_, None)
790791
self.assertEqual(committee.n_classes_, 0)
791792

792793
# 2. test fitted learners
793794
for n_classes in range(1, 10):
794-
learner_list = [modAL.models.ActiveLearner(estimator=mock.MockEstimator(classes_=np.asarray([idx])))
795+
learner_list = [modAL.models.learners.ActiveLearner(estimator=mock.MockEstimator(classes_=np.asarray([idx])))
795796
for idx in range(n_classes)]
796-
committee = modAL.models.Committee(learner_list=learner_list)
797+
committee = modAL.models.learners.Committee(learner_list=learner_list)
797798
np.testing.assert_equal(
798799
committee.classes_,
799800
np.unique(range(n_classes))
@@ -803,7 +804,7 @@ def test_predict(self):
803804
for n_learners in range(1, 10):
804805
for n_instances in range(1, 10):
805806
prediction = np.random.randint(10, size=(n_instances, n_learners))
806-
committee = modAL.models.Committee(
807+
committee = modAL.models.learners.Committee(
807808
learner_list=[mock.MockActiveLearner(
808809
mock.MockEstimator(classes_=np.asarray([0])),
809810
predict_return=prediction[:, learner_idx]
@@ -825,7 +826,7 @@ def test_predict_proba(self):
825826
predict_proba_return=vote_proba_output[:, learner_idx, :],
826827
predictor=mock.MockEstimator(classes_=list(range(n_classes)))
827828
) for learner_idx in range(n_learners)]
828-
committee = modAL.models.Committee(learner_list=learner_list)
829+
committee = modAL.models.learners.Committee(learner_list=learner_list)
829830
np.testing.assert_almost_equal(
830831
committee.predict_proba(np.random.rand(n_samples, 1)),
831832
np.mean(vote_proba_output, axis=1)
@@ -841,7 +842,7 @@ def test_vote(self):
841842
predictor=mock.MockEstimator(classes_=[0])
842843
)
843844
for member_idx in range(n_members)]
844-
committee = modAL.models.Committee(learner_list=learner_list)
845+
committee = modAL.models.learners.Committee(learner_list=learner_list)
845846
np.testing.assert_array_almost_equal(
846847
committee.vote(np.random.rand(n_instances).reshape(-1, 1)),
847848
vote_output
@@ -857,7 +858,7 @@ def test_vote_proba(self):
857858
predict_proba_return=vote_proba_output[:, learner_idx, :],
858859
predictor=mock.MockEstimator(classes_=list(range(n_classes)))
859860
) for learner_idx in range(n_learners)]
860-
committee = modAL.models.Committee(learner_list=learner_list)
861+
committee = modAL.models.learners.Committee(learner_list=learner_list)
861862
np.testing.assert_almost_equal(
862863
committee.vote_proba(np.random.rand(n_samples, 1)),
863864
vote_proba_output
@@ -872,16 +873,16 @@ def test_teach(self):
872873
X = np.random.rand(n_samples, 2)
873874
y = np.random.randint(0, 2, size=n_samples)
874875

875-
learner_1 = modAL.models.ActiveLearner(
876+
learner_1 = modAL.models.learners.ActiveLearner(
876877
X_training=X_training, y_training=y_training,
877878
estimator=mock.MockEstimator(classes_=[0, 1])
878879
)
879-
learner_2 = modAL.models.ActiveLearner(
880+
learner_2 = modAL.models.learners.ActiveLearner(
880881
X_training=X_training, y_training=y_training,
881882
estimator=mock.MockEstimator(classes_=[0, 1])
882883
)
883884

884-
committee = modAL.models.Committee(
885+
committee = modAL.models.learners.Committee(
885886
learner_list=[learner_1, learner_2]
886887
)
887888

@@ -897,7 +898,7 @@ def test_predict(self):
897898
# assembling the Committee
898899
learner_list = [mock.MockActiveLearner(predict_return=vote[:, member_idx])
899900
for member_idx in range(n_members)]
900-
committee = modAL.models.CommitteeRegressor(learner_list=learner_list)
901+
committee = modAL.models.learners.CommitteeRegressor(learner_list=learner_list)
901902
np.testing.assert_array_almost_equal(
902903
committee.predict(np.random.rand(n_instances).reshape(-1, 1), return_std=False),
903904
np.mean(vote, axis=1)
@@ -914,7 +915,7 @@ def test_vote(self):
914915
# assembling the Committee
915916
learner_list = [mock.MockActiveLearner(predict_return=vote_output[:, member_idx])
916917
for member_idx in range(n_members)]
917-
committee = modAL.models.CommitteeRegressor(learner_list=learner_list)
918+
committee = modAL.models.learners.CommitteeRegressor(learner_list=learner_list)
918919
np.testing.assert_array_almost_equal(
919920
committee.vote(np.random.rand(n_instances).reshape(-1, 1)),
920921
vote_output

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