test: add simple outline to test the predict method in sklearn frontend.

the integration with already exisiting strategies requires some work as predict method assume fit called etc.

ivy_tests/test_ivy/test_frontends/test_sklearn/test_tree/test_tree.py

@@ -1,8 +1,41 @@
 import ivy.functional.frontends.sklearn as sklearn_frontend
+import numpy as np
+import ivy
+from hypothesis import given
+import ivy_tests.test_ivy.helpers as helpers
+
+
+# --- Helpers --- #
+# --------------- #
 
 
 # helper functions
-def _get_sklearn_predict(X, y, max_depth):
-    ivy_clf = sklearn_frontend.tree.DecisionTreeClassifier(max_depth=max_depth)
-    ivy_clf.fit(X, y)
-    return ivy_clf.predict
+def _get_sklearn_predict(X, y, max_depth, module=None):
+    clf = module.tree.DecisionTreeClassifier(max_depth=max_depth)
+    clf.fit(X, y)
+    return clf.predict
+
+
+# --- Main --- #
+# ------------ #
+
+
+# todo: integrate with already existing strats and generalize
+@given(
+    X=helpers.array_values(shape=(5, 2), dtype=helpers.get_dtypes("float")),
+    y=helpers.array_values(shape=(5,), dtype=helpers.get_dtypes("integer")),
+)
+def test_sklearn_tree_predict(X, y):
+    try:
+        import sklearn
+    except ImportError:
+        print("sklearn not installed, skipping test_sklearn_tree_predict")
+        return
+    sklearn_pred = _get_sklearn_predict(X, y, max_depth=3, module=sklearn)(X)
+    for fw in helpers.available_frameworks:
+        ivy.set_backend(fw)
+        ivy_pred = _get_sklearn_predict(
+            ivy.array(X), ivy.array(y), max_depth=3, module=sklearn_frontend
+        )(X)
+        assert np.allclose(ivy_pred.to_numpy(), sklearn_pred)
+        ivy.unset_backend()