fix tests + add tests for best params and score

SimonBlanke · Aug 27, 2024 · 160e668 · 160e668
1 parent 1b772ff
commit 160e668
Showing 1 changed file with 30 additions and 19 deletions.
diff --git a/tests/integrations/sklearn/test_sklearn_api.py b/tests/integrations/sklearn/test_sklearn_api.py
@@ -3,7 +3,6 @@
 
 from sklearn import svm, datasets
 from sklearn.naive_bayes import GaussianNB
-from sklearn.isotonic import IsotonicRegression
 from sklearn.decomposition import PCA
 
 
@@ -16,114 +15,126 @@
 iris = datasets.load_iris()
 X, y = iris.data, iris.target
 
-
-ir = IsotonicRegression()
 nb = GaussianNB()
 svc = svm.SVC()
-pca = PCA(n_components=2)
+pca = PCA()
+
 
+parameters_svc = {"kernel": ["linear", "rbf"], "C": [1, 10]}
+parameters_nb = {"var_smoothing": [1e-7, 1e-8, 1e-9]}
+parameters_pca = {"n_components": [2, 3, 4]}
 
-parameters = {"kernel": ["linear", "rbf"], "C": [1, 10]}
 opt = RandomSearchOptimizer()
 
 
 def test_fit():
-    search = HyperactiveSearchCV(svc, opt, parameters)
+    search = HyperactiveSearchCV(svc, opt, parameters_svc)
     search.fit(X, y)
 
     check_is_fitted(search)
 
 
 def test_score():
-    search = HyperactiveSearchCV(svc, opt, parameters)
+    search = HyperactiveSearchCV(svc, opt, parameters_svc)
     search.fit(X, y)
     score = search.score(X, y)
 
     assert isinstance(score, float)
 
 
 def test_classes_():
-    search = HyperactiveSearchCV(svc, opt, parameters)
+    search = HyperactiveSearchCV(svc, opt, parameters_svc)
     search.fit(X, y)
 
     assert [0, 1, 2] == list(search.classes_)
 
 
 def test_score_samples():
-    search = HyperactiveSearchCV(svc, opt, parameters)
+    search = HyperactiveSearchCV(svc, opt, parameters_svc)
     search.fit(X, y)
 
     with pytest.raises(AttributeError):
         search.score_samples(X)
 
 
 def test_predict():
-    search = HyperactiveSearchCV(svc, opt, parameters)
+    search = HyperactiveSearchCV(svc, opt, parameters_svc)
     search.fit(X, y)
     result = search.predict(X)
 
     assert isinstance(result, np.ndarray)
 
 
 def test_predict_proba():
-    search = HyperactiveSearchCV(svc, opt, parameters)
+    search = HyperactiveSearchCV(svc, opt, parameters_svc)
     search.fit(X, y)
 
     with pytest.raises(AttributeError):
         search.predict_proba(X)
 
-    search = HyperactiveSearchCV(nb, opt, parameters)
+    search = HyperactiveSearchCV(nb, opt, parameters_nb)
     search.fit(X, y)
     result = search.predict(X)
 
     assert isinstance(result, np.ndarray)
 
 
 def test_predict_log_proba():
-    search = HyperactiveSearchCV(svc, opt, parameters)
+    search = HyperactiveSearchCV(svc, opt, parameters_svc)
     search.fit(X, y)
 
     with pytest.raises(AttributeError):
         search.predict_log_proba(X)
 
-    search = HyperactiveSearchCV(nb, opt, parameters)
+    search = HyperactiveSearchCV(nb, opt, parameters_nb)
     search.fit(X, y)
     result = search.predict_log_proba(X)
 
     assert isinstance(result, np.ndarray)
 
 
 def test_decision_function():
-    search = HyperactiveSearchCV(svc, opt, parameters)
+    search = HyperactiveSearchCV(svc, opt, parameters_svc)
     search.fit(X, y)
     result = search.decision_function(X)
 
     assert isinstance(result, np.ndarray)
 
 
 def test_transform():
-    search = HyperactiveSearchCV(svc, opt, parameters)
+    search = HyperactiveSearchCV(svc, opt, parameters_svc)
     search.fit(X, y)
 
     with pytest.raises(AttributeError):
         search.transform(X)
 
-    search = HyperactiveSearchCV(pca, opt, parameters)
+    search = HyperactiveSearchCV(pca, opt, parameters_pca)
     search.fit(X, y)
     result = search.transform(X)
 
     assert isinstance(result, np.ndarray)
 
 
 def test_inverse_transform():
-    search = HyperactiveSearchCV(svc, opt, parameters)
+    search = HyperactiveSearchCV(svc, opt, parameters_svc)
     search.fit(X, y)
 
     with pytest.raises(AttributeError):
         search.inverse_transform(X)
 
-    search = HyperactiveSearchCV(pca, opt, parameters)
+    search = HyperactiveSearchCV(pca, opt, parameters_pca)
     search.fit(X, y)
     result = search.inverse_transform(search.transform(X))
 
     assert isinstance(result, np.ndarray)
+
+
+def test_best_params_and_score():
+    search = HyperactiveSearchCV(svc, opt, parameters_svc)
+    search.fit(X, y)
+
+    best_params = search.best_params_
+    best_score = search.best_score_
+
+    assert "kernel" in best_params and "C" in best_params
+    assert isinstance(best_score, float)