rename_train_result

paddlets/utils/utils.py

@@ -188,8 +188,7 @@ def check_train_valid_continuity(train_data: TSDataset,
                                pd.to_timedelta(train_index.freq))
     elif isinstance(train_index, pd.RangeIndex):
         if isinstance(valid_index, pd.RangeIndex):
-            continuious = (
-                valid_index[0] - train_index[-1] == train_index.step)
+            continuious = (valid_index[0] - train_index[-1] == train_index.step)
     else:
         raise_log("Unsupport data index format")
 
@@ -324,8 +323,7 @@ def get_tsdataset_max_len(dataset: TSDataset) -> int:
     return len(all_index)
 
 
-def repr_results_to_tsdataset(reprs: np.array,
-                              dataset: TSDataset) -> TSDataset:
+def repr_results_to_tsdataset(reprs: np.array, dataset: TSDataset) -> TSDataset:
     """
     Convert representation model output to a TSDataset 
 
@@ -455,9 +453,8 @@ def build_ts_infer_input(tsdataset: TSDataset,
     #build sample base on DataAdapter
     data_adapter = DataAdapter()
     if json_data['model_type'] == 'forecasting':
-        raise_if_not(
-            tsdataset.get_target() is not None,
-            "The target of tsdataset can not be None for forecasting!")
+        raise_if_not(tsdataset.get_target() is not None,
+                     "The target of tsdataset can not be None for forecasting!")
         size_keys = ['in_chunk_len', 'out_chunk_len', 'skip_chunk_len']
         for key in size_keys:
             raise_if_not(
@@ -480,8 +477,7 @@ def build_ts_infer_input(tsdataset: TSDataset,
             raise_if_not(
                 key in json_data['size'],
                 f"The {key} in json_data['size'] can not be None for anomaly!")
-        dataset = data_adapter.to_sample_dataset(tsdataset,
-                                                 **json_data['size'])
+        dataset = data_adapter.to_sample_dataset(tsdataset, **json_data['size'])
     else:
         raise_log(ValueError(f"Invalid model_type: {json_data['model_type']}"))
 
@@ -522,7 +518,7 @@ def convert_and_remove_types(data):
 
 def update_train_results(save_path, score, model_name="", done_flag=True):
 
-    train_results_path = os.path.join(save_path, "train_results.json")
+    train_results_path = os.path.join(save_path, "train_result.json")
     save_model_tag = ["pdparams", "pdopt", "pdstates", "pdema"]
     save_inference_tag = [
         "inference_config", "pdmodel", "pdiparams", "pdiparams.info"