resolving problems with lint tests (by ashishpatel16)

scikit-learn-contrib · Nov 23, 2023 · e7d1b3e · e7d1b3e
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diff --git a/tests/test_LocalClassifierPerLevel.py b/tests/test_LocalClassifierPerLevel.py
@@ -215,3 +215,20 @@ def test_knn():
     check_is_fitted(lcpl)
     # predictions = lcpl.predict(X)
     # assert_array_equal(y, predictions)
+
+
+def test_fit_multiple_dim_input():
+    lcpl = LocalClassifierPerLevel()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcpl.fit(X, y)
+    check_is_fitted(lcpl)
+
+
+def test_predict_multiple_dim_input():
+    lcpl = LocalClassifierPerLevel()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcpl.fit(X, y)
+    predictions = lcpl.predict(X)
+    assert predictions is not None
diff --git a/tests/test_LocalClassifierPerNode.py b/tests/test_LocalClassifierPerNode.py
@@ -276,3 +276,20 @@ def test_knn():
     check_is_fitted(lcpn)
     # predictions = lcpn.predict(X)
     # assert_array_equal(y, predictions)
+
+
+def test_fit_multiple_dim_input():
+    lcpn = LocalClassifierPerNode()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcpn.fit(X, y)
+    check_is_fitted(lcpn)
+
+
+def test_predict_multiple_dim_input():
+    lcpn = LocalClassifierPerNode()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcpn.fit(X, y)
+    predictions = lcpn.predict(X)
+    assert predictions is not None
diff --git a/tests/test_LocalClassifierPerParentNode.py b/tests/test_LocalClassifierPerParentNode.py
@@ -267,3 +267,20 @@ def test_knn():
     check_is_fitted(lcppn)
     # predictions = lcppn.predict(X)
     # assert_array_equal(y, predictions)
+
+
+def test_fit_multiple_dim_input():
+    lcppn = LocalClassifierPerParentNode()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcppn.fit(X, y)
+    check_is_fitted(lcppn)
+
+
+def test_predict_multiple_dim_input():
+    lcppn = LocalClassifierPerParentNode()
+    X = np.random.rand(1, 275, 3)
+    y = np.array([["a", "b", "c"]])
+    lcppn.fit(X, y)
+    predictions = lcppn.predict(X)
+    assert predictions is not None