wip

python/cuml/linear_model/base.pyx

@@ -68,9 +68,14 @@ class LinearPredictMixin:
         Predicts `y` values for `X`.
 
         """
-        coef_cp, n_feat, n_targets, _ = input_to_cupy_array(self.coef_)
-        if 1 < n_targets:
+        if self.coef_ is None:
+            raise ValueError(
+                "LinearModel.predict() cannot be called before fit(). "
+                "Please fit the model first."
+            )
+        if len(self.coef_.shape) == 2 and self.coef_.shape[1] > 1:
             # Handle multi-target prediction in Python.
+            coef_cp = input_to_cupy_array(self.coef_).array
             X_cp = input_to_cupy_array(
                 X,
                 check_dtype=self.dtype,
@@ -79,8 +84,8 @@ class LinearPredictMixin:
             ).array
             intercept_cp = input_to_cupy_array(self.intercept_).array
             preds_cp = X_cp @ coef_cp + intercept_cp
-            preds = input_to_cuml_array(preds_cp).array
-            return preds
+            # preds = input_to_cuml_array(preds_cp).array # TODO:remove
+            return preds_cp
 
         # Handle single-target prediction in C++
         X_m, n_rows, n_cols, dtype = \

python/cuml/linear_model/linear_regression.pyx

@@ -322,12 +322,10 @@ class LinearRegression(Base,
             self.algo = 0
 
         if 1 < y_cols:
-            del X_m
-            del y_m
-            if sample_weight is not None:
-                del sample_weight_m
+            if sample_weight is None:
+               sample_weight_m = None
 
-            return self._fit_multi_target(X, y, convert_dtype, sample_weight)
+            return self._fit_multi_target(X_m, y_m, convert_dtype, sample_weight_m)
 
         self.coef_ = CumlArray.zeros(self.n_cols, dtype=self.dtype)
         cdef uintptr_t coef_ptr = self.coef_.ptr