metric-learn/test/test_quadruplets_classifiers.py at master · scikit-learn-contrib/metric-learn · GitHub

Name: metric-learn/test/test_quadruplets_classifiers.py at master · scikit-learn-contrib/metric-learn · GitHub
Rating: 4.9 (7571 reviews)
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importpytest
fromsklearn.exceptionsimportNotFittedError
fromsklearn.model_selectionimporttrain_test_split

fromtest.test_utilsimportquadruplets_learners, ids_quadruplets_learners
frommetric_learn.sklearn_shimsimportset_random_state
fromsklearnimportclone
importnumpyasnp


@pytest.mark.parametrize('with_preprocessor', [True, False])
@pytest.mark.parametrize('estimator, build_dataset', quadruplets_learners,
ids=ids_quadruplets_learners)
deftest_predict_only_one_or_minus_one(estimator, build_dataset,
with_preprocessor):
"""Test that all predicted values are either +1 or -1"""
input_data, labels, preprocessor, _=build_dataset(with_preprocessor)
estimator=clone(estimator)
estimator.set_params(preprocessor=preprocessor)
set_random_state(estimator)
 (quadruplets_train,
quadruplets_test, y_train, y_test) =train_test_split(input_data, labels)
estimator.fit(quadruplets_train)
predictions=estimator.predict(quadruplets_test)
not_valid= [eforeinpredictionsifenotin [-1, 1]]
assertlen(not_valid) ==0


@pytest.mark.parametrize('with_preprocessor', [True, False])
@pytest.mark.parametrize('estimator, build_dataset', quadruplets_learners,
ids=ids_quadruplets_learners)
deftest_raise_not_fitted_error_if_not_fitted(estimator, build_dataset,
with_preprocessor):
"""Test that a NotFittedError is raised if someone tries to predict and
 the metric learner has not been fitted."""
input_data, labels, preprocessor, _=build_dataset(with_preprocessor)
estimator=clone(estimator)
estimator.set_params(preprocessor=preprocessor)
set_random_state(estimator)
withpytest.raises(NotFittedError):
estimator.predict(input_data)


@pytest.mark.parametrize('estimator, build_dataset', quadruplets_learners,
ids=ids_quadruplets_learners)
deftest_accuracy_toy_example(estimator, build_dataset):
"""Test that the default scoring for quadruplets (accuracy) works on some
 toy example"""
input_data, labels, preprocessor, X=build_dataset(with_preprocessor=False)
estimator=clone(estimator)
estimator.set_params(preprocessor=preprocessor)
set_random_state(estimator)
estimator.fit(input_data)
# We take the two first points and we build 4 regularly spaced points on the
# line they define, so that it's easy to build quadruplets of different
# similarities.
X_test=X[0] +np.arange(4)[:, np.newaxis] * (X[0] -X[1]) /4
quadruplets_test=np.array(
 [[X_test[0], X_test[2], X_test[0], X_test[1]],
 [X_test[1], X_test[3], X_test[1], X_test[0]],
 [X_test[1], X_test[2], X_test[0], X_test[3]],
 [X_test[3], X_test[0], X_test[2], X_test[1]]])
# we force the transformation to be identity so that we control what it does
estimator.components_=np.eye(X.shape[1])
assertestimator.score(quadruplets_test) ==0.25