undo more changes that broke tests

uxlfoundation · Dec 17, 2024 · 84afb62 · 84afb62
1 parent 9964c5a
commit 84afb62
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Showing 2 changed files with 38 additions and 61 deletions.
diff --git a/onedal/ensemble/forest.py b/onedal/ensemble/forest.py
@@ -330,20 +330,10 @@ def _fit(self, X, y, sample_weight, module, queue):
 
         if self.oob_score:
             if isinstance(self, ClassifierMixin):
-                # self.oob_score_ = from_table(train_result.oob_err_accuracy).item()
-                self.oob_score_ = from_table(
-                    train_result.oob_err_accuracy,
-                    sua_iface=sua_iface,
-                    sycl_queue=queue,
-                    xp=xp,
-                )[0]
-
+                self.oob_score_ = from_table(train_result.oob_err_accuracy).item()
                 self.oob_decision_function_ = from_table(
-                    train_result.oob_err_decision_function,
-                    sua_iface=sua_iface,
-                    sycl_queue=queue,
-                    xp=xp,
-                )[0]
+                    train_result.oob_err_decision_function
+                )
                 if xp.any(self.oob_decision_function_ == 0):
                     warnings.warn(
                         "Some inputs do not have OOB scores. This probably means "
@@ -352,21 +342,11 @@ def _fit(self, X, y, sample_weight, module, queue):
                         UserWarning,
                     )
             else:
-                # self.oob_score_ = from_table(train_result.oob_err_r2).item()
-                self.oob_score_ = from_table(
-                    train_result.oob_err_r2, sua_iface=sua_iface, sycl_queue=queue, xp=xp
-                )[0]
-                # self.oob_prediction_ = from_table(train_result.oob_err_prediction).reshape(-1)
-                self.oob_score_ = xp.reshape(
-                    from_table(
-                        train_result.oob_err_r2,
-                        sua_iface=sua_iface,
-                        sycl_queue=queue,
-                        xp=xp,
-                    ),
-                    -1,
-                )
-                if xp.any(self.oob_prediction_ == 0):
+                self.oob_score_ = from_table(train_result.oob_err_r2).item()
+                self.oob_prediction_ = from_table(
+                    train_result.oob_err_prediction
+                ).reshape(-1)
+                if np.any(self.oob_prediction_ == 0):
                     warnings.warn(
                         "Some inputs do not have OOB scores. This probably means "
                         "too few trees were used to compute any reliable OOB "

diff --git a/sklearnex/ensemble/_forest.py b/sklearnex/ensemble/_forest.py
@@ -812,42 +812,39 @@ def _onedal_gpu_supported(self, method_name, *data):
         return patching_status
 
     def _onedal_predict(self, X, queue=None):
-        xp, _ = get_namespace(X)
-        use_raw_input = get_config()["use_raw_input"]
-        if not use_raw_input:
-            if sklearn_check_version("1.0"):
-                X = validate_data(
-                    self,
+        if sklearn_check_version("1.0"):
+            X = validate_data(
+                self,
+                X,
+                dtype=[np.float64, np.float32],
+                force_all_finite=False,
+                reset=False,
+                ensure_2d=True,
+            )
+        else:
+            if not get_config()["use_raw_input"]:
+                X = check_array(
                     X,
                     dtype=[np.float64, np.float32],
                     force_all_finite=False,
-                    reset=False,
-                    ensure_2d=True,
-                )
-        # sklearn version < 1.0 is not supported
-        # else:
-        #     X = check_array(
-        #         X,
-        #         dtype=[np.float64, np.float32],
-        #         force_all_finite=False,
-        #     )  # Warning, order of dtype matters
-        #     if hasattr(self, "n_features_in_"):
-        #         try:
-        #             num_features = _num_features(X)
-        #         except TypeError:
-        #             num_features = _num_samples(X)
-        #         if num_features != self.n_features_in_:
-        #             raise ValueError(
-        #                 (
-        #                     f"X has {num_features} features, "
-        #                     f"but {self.__class__.__name__} is expecting "
-        #                     f"{self.n_features_in_} features as input"
-        #                 )
-        #             )
-        #     self._check_n_features(X, reset=False)
-        res = xp.reshape(self._onedal_estimator.predict(X, queue=queue), -1)
-        res = xp.astype(res, xp.int64)
-        return xp.take(self.classes_, res)
+                )  # Warning, order of dtype matters
+            if hasattr(self, "n_features_in_"):
+                try:
+                    num_features = _num_features(X)
+                except TypeError:
+                    num_features = _num_samples(X)
+                if num_features != self.n_features_in_:
+                    raise ValueError(
+                        (
+                            f"X has {num_features} features, "
+                            f"but {self.__class__.__name__} is expecting "
+                            f"{self.n_features_in_} features as input"
+                        )
+                    )
+            self._check_n_features(X, reset=False)
+
+        res = self._onedal_estimator.predict(X, queue=queue)
+        return np.take(self.classes_, res.ravel().astype(np.int64, casting="unsafe"))
 
     def _onedal_predict_proba(self, X, queue=None):
         xp, _ = get_namespace(X)