def test_skope_rules(): """Check various parameter settings.""" X_train = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [6, 3], [-4, -7]] y_train = [0] * 6 + [1] * 2 X_test = np.array([[2, 1], [1, 1]]) grid = ParameterGrid({ "feature_names": [None, ['a', 'b']], "precision_min": [0.], "recall_min": [0.], "n_estimators": [1], "max_samples": [0.5, 4], "max_samples_features": [0.5, 2], "bootstrap": [True, False], "bootstrap_features": [True, False], "max_depth": [2], "max_features": ["auto", 1, 0.1], "min_samples_split": [2, 0.1], "n_jobs": [-1, 2] }) with suppress_warnings(): for params in grid: SkopeRulesClassifier(random_state=rng, **params).fit(X_train, y_train).predict(X_test) # additional parameters: SkopeRulesClassifier(n_estimators=50, max_samples=1., recall_min=0., precision_min=0.).fit(X_train, y_train).predict(X_test)
def test_max_samples_attribute(): X = iris.data y = iris.target y = (y != 0) clf = SkopeRulesClassifier(max_samples=1.).fit(X, y) assert clf.max_samples_ == X.shape[0] clf = SkopeRulesClassifier(max_samples=500) assert_warns(UserWarning, clf.fit, X, y) assert clf.max_samples_ == X.shape[0] clf = SkopeRulesClassifier(max_samples=0.4).fit(X, y) assert clf.max_samples_ == 0.4 * X.shape[0]
def test_performances(): X, y = make_blobs(n_samples=1000, random_state=0, centers=2) # make labels imbalanced by remove all but 100 instances from class 1 indexes = np.ones(X.shape[0]).astype(bool) ind = np.array([False] * 100 + list(((y == 1)[100:]))) indexes[ind] = 0 X = X[indexes] y = y[indexes] n_samples, n_features = X.shape clf = SkopeRulesClassifier() # fit clf.fit(X, y) # with lists clf.fit(X.tolist(), y.tolist()) y_pred = clf.predict(X) assert y_pred.shape == (n_samples, ) # training set performance assert accuracy_score(y, y_pred) > 0.83 # decision_function agrees with predict decision = -clf.decision_function(X) assert decision.shape == (n_samples, ) dec_pred = (decision.ravel() < 0).astype(np.int) assert_array_equal(dec_pred, y_pred)
def test_f1_score(): clf = SkopeRulesClassifier() rule0 = ('a > 0', (0, 0, 0)) rule1 = ('a > 0', (0.5, 0.5, 0)) rule2 = ('a > 0', (0.5, 0, 0)) assert clf.f1_score(rule0) == 0 assert clf.f1_score(rule1) == 0.5 assert clf.f1_score(rule2) == 0
def test_deduplication_works(): # toy sample (the last two samples are outliers) X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [6, 3], [4, -7]] y = [0] * 6 + [1] * 2 X_test = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [10, 5], [5, -7]] # Test LOF clf = SkopeRulesClassifier(random_state=rng, max_samples=1., max_depth_duplication=3) clf.fit(X, y) decision_func = clf.decision_function(X_test) rules_vote = clf.rules_vote(X_test) score_top_rules = clf.score_top_rules(X_test) pred = clf.predict(X_test) pred_score_top_rules = clf.predict_top_rules(X_test, 1) assert True, 'deduplication works'
def test_skope_rules_works(): # toy sample (the last two samples are outliers) X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [6, 3], [4, -7]] y = [0] * 6 + [1] * 2 X_test = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [10, 5], [5, -7]] # Test LOF clf = SkopeRulesClassifier(random_state=rng, max_samples=1.) clf.fit(X, y) decision_func = clf.decision_function(X_test) rules_vote = clf.rules_vote(X_test) score_top_rules = clf.score_top_rules(X_test) pred = clf.predict(X_test) pred_score_top_rules = clf.predict_top_rules(X_test, 1) # assert detect outliers: assert np.min(decision_func[-2:]) > np.max(decision_func[:-2]) assert np.min(rules_vote[-2:]) > np.max(rules_vote[:-2]) assert np.min(score_top_rules[-2:]) > np.max(score_top_rules[:-2]) assert_array_equal(pred, 6 * [0] + 2 * [1]) assert_array_equal(pred_score_top_rules, 6 * [0] + 2 * [1])
def test_skope_rules_error(): """Test that it gives proper exception on deficient input.""" X = iris.data y = iris.target y = (y != 0) # Test max_samples assert_raises(ValueError, SkopeRulesClassifier(max_samples=-1).fit, X, y) assert_raises(ValueError, SkopeRulesClassifier(max_samples=0.0).fit, X, y) assert_raises(ValueError, SkopeRulesClassifier(max_samples=2.0).fit, X, y) # explicitly setting max_samples > n_samples should result in a warning. assert_warns(UserWarning, SkopeRulesClassifier(max_samples=1000).fit, X, y) assert_no_warnings(SkopeRulesClassifier(max_samples=np.int64(2)).fit, X, y) assert_raises(ValueError, SkopeRulesClassifier(max_samples='foobar').fit, X, y) assert_raises(ValueError, SkopeRulesClassifier(max_samples=1.5).fit, X, y) assert_raises(ValueError, SkopeRulesClassifier(max_depth_duplication=1.5).fit, X, y) assert_raises(ValueError, SkopeRulesClassifier().fit(X, y).predict, X[:, 1:]) assert_raises(ValueError, SkopeRulesClassifier().fit(X, y).decision_function, X[:, 1:]) assert_raises(ValueError, SkopeRulesClassifier().fit(X, y).rules_vote, X[:, 1:]) assert_raises(ValueError, SkopeRulesClassifier().fit(X, y).score_top_rules, X[:, 1:])