[python-package] remove is_reshape argument in Booster.predict (fixes #5115) (#5117)

python-package/lightgbm/basic.py

@@ -752,8 +752,7 @@ class _InnerPredictor:
         return this
 
     def predict(self, data, start_iteration=0, num_iteration=-1,
-                raw_score=False, pred_leaf=False, pred_contrib=False, data_has_header=False,
-                is_reshape=True):
+                raw_score=False, pred_leaf=False, pred_contrib=False, data_has_header=False):
         """Predict logic.
 
         Parameters
@@ -774,8 +773,6 @@ class _InnerPredictor:
         data_has_header : bool, optional (default=False)
             Whether data has header.
             Used only for txt data.
-        is_reshape : bool, optional (default=True)
-            Whether to reshape to (nrow, ncol).
 
         Returns
         -------
@@ -832,7 +829,7 @@ class _InnerPredictor:
         if pred_leaf:
             preds = preds.astype(np.int32)
         is_sparse = scipy.sparse.issparse(preds) or isinstance(preds, list)
-        if is_reshape and not is_sparse and preds.size != nrow:
+        if not is_sparse and preds.size != nrow:
             if preds.size % nrow == 0:
                 preds = preds.reshape(nrow, -1)
             else:
@@ -1403,8 +1400,8 @@ class Dataset:
         if predictor is not None:
             init_score = predictor.predict(data,
                                            raw_score=True,
-                                           data_has_header=data_has_header,
-                                           is_reshape=False)
+                                           data_has_header=data_has_header)
+            init_score = init_score.ravel()
             if used_indices is not None:
                 assert not self.need_slice
                 if isinstance(data, (str, Path)):
@@ -3489,7 +3486,7 @@ class Booster:
 
     def predict(self, data, start_iteration=0, num_iteration=None,
                 raw_score=False, pred_leaf=False, pred_contrib=False,
-                data_has_header=False, is_reshape=True, **kwargs):
+                data_has_header=False, **kwargs):
         """Make a prediction.
 
         Parameters
@@ -3523,8 +3520,6 @@ class Booster:
         data_has_header : bool, optional (default=False)
             Whether the data has header.
             Used only if data is str.
-        is_reshape : bool, optional (default=True)
-            If True, result is reshaped to [nrow, ncol].
         **kwargs
             Other parameters for the prediction.
 
@@ -3542,7 +3537,7 @@ class Booster:
                 num_iteration = -1
         return predictor.predict(data, start_iteration, num_iteration,
                                  raw_score, pred_leaf, pred_contrib,
-                                 data_has_header, is_reshape)
+                                 data_has_header)
 
     def refit(
         self,