@@ -31,7 +31,7 @@ def build_pairs():
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X , pairs = build_data ()
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pairs , y = wrap_pairs (X , pairs )
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pairs , y = shuffle (pairs , y )
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- return ( pairs , y )
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+ return pairs , y
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def build_quadruplets ():
@@ -41,7 +41,7 @@ def build_quadruplets():
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c = np .column_stack (pairs )
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quadruplets = X [c ]
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quadruplets = shuffle (quadruplets )
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- return ( quadruplets , None )
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+ return quadruplets , None
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list_estimators = [(Covariance (), build_data ),
@@ -122,7 +122,7 @@ def test_score_pairs_finite(estimator, build_dataset):
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@pytest .mark .parametrize ('estimator, build_dataset' , list_estimators ,
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ids = ids_estimators )
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- def tests_score_pairs_dim (estimator , build_dataset ):
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+ def test_score_pairs_dim (estimator , build_dataset ):
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# scoring of 3D arrays should return 1D array (several tuples),
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# and scoring of 2D arrays (one tuple) should return a scalar (0D array).
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inputs , labels = build_dataset ()
@@ -162,7 +162,7 @@ def test_embed_toy_example(estimator, build_dataset):
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@pytest .mark .parametrize ('estimator, build_dataset' , list_estimators ,
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ids = ids_estimators )
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- def tests_embed_dim (estimator , build_dataset ):
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+ def test_embed_dim (estimator , build_dataset ):
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# Checks that the the dimension of the output space is as expected
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inputs , labels = build_dataset ()
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model = clone (estimator )
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