[python] handle list for prediction (#625), fix #621

* handle list for prediction

* fix error msg

python-package/lightgbm/basic.py

@@ -414,16 +414,21 @@ class _InnerPredictor(object):
         elif isinstance(data, np.ndarray):
             preds, nrow = self.__pred_for_np2d(data, num_iteration,
                                                predict_type)
-        elif isinstance(data, DataFrame):
-            preds, nrow = self.__pred_for_np2d(data.values, num_iteration,
-                                               predict_type, early_stop_instance_handle)
+        elif isinstance(data, list):
+            try:
+                data = np.array(data)
+            except:
+                raise ValueError('Cannot convert data list to numpy array.')
+            preds, nrow = self.__pred_for_np2d(data, num_iteration,
+                                               predict_type)
         else:
             try:
+                warnings.warn('Converting data to scipy sparse matrix.')
                 csr = scipy.sparse.csr_matrix(data)
-                preds, nrow = self.__pred_for_csr(csr, num_iteration,
-                                                  predict_type)
             except:
                 raise TypeError('Cannot predict data for type {}'.format(type(data).__name__))
+            preds, nrow = self.__pred_for_csr(csr, num_iteration,
+                                              predict_type)
         if pred_leaf:
             preds = preds.astype(np.int32)
         if is_reshape and preds.size != nrow:
@@ -452,7 +457,7 @@ class _InnerPredictor(object):
         Predict for a 2-D numpy matrix.
         """
         if len(mat.shape) != 2:
-            raise ValueError('Input numpy.ndarray must be 2 dimensional')
+            raise ValueError('Input numpy.ndarray or list must be 2 dimensional')
 
         if mat.dtype == np.float32 or mat.dtype == np.float64:
             data = np.array(mat.reshape(mat.size), dtype=mat.dtype, copy=False)