new params is_predict

python/chronos/src/bigdl/chronos/data/tsdataset.py

@@ -59,6 +59,7 @@ def __init__(self, data, **schema):
         self.scaler_index = [i for i in range(len(self.target_col))]
         self.id_sensitive = None
         self._has_generate_agg_feature = False
+        self.is_predict = False
         self._check_basic_invariants()
 
         self._id_list = list(np.unique(self.df[self.id_col]))
@@ -609,7 +610,8 @@ def roll(self,
         # horizon_time is only for time_enc, the time_enc numpy ndarray won't have any
         # shape change when the dataset is for prediction.
         horizon_time = self.horizon
-        if is_predict:
+        self.is_predict = is_predict
+        if self.is_predict:
             self.horizon = 0
 
         if self.lookback == 'auto':
@@ -785,6 +787,7 @@ def to_torch_data_loader(self,
                                   "of lookback and horizon, while get lookback+horizon="
                                   f"{need_dflen} and the length of dataset is {len(self.df)}.")
 
+            self.is_predict = is_predict
             torch_dataset = RollDataset(self.df,
                                         dt_col=self.dt_col,
                                         freq=self._freq,
@@ -795,7 +798,7 @@ def to_torch_data_loader(self,
                                         id_col=self.id_col,
                                         time_enc=time_enc,
                                         label_len=label_len,
-                                        is_predict=is_predict)
+                                        is_predict=self.is_predict)
             # TODO gen_rolling_feature and gen_global_feature will be support later
             self.roll_target = target_col
             self.roll_feature = feature_col

python/chronos/src/bigdl/chronos/forecaster/base_forecaster.py

@@ -450,7 +450,7 @@ def predict(self, data, batch_size=32, quantize=False):
                                              feature_col=data.roll_feature,
                                              target_col=data.roll_target,
                                              shuffle=False,
-                                             is_predict=True)
+                                             is_predict=data.is_predict)
         # data transform
         is_local_data = isinstance(data, (np.ndarray, DataLoader))
         if is_local_data and self.distributed:
@@ -544,7 +544,7 @@ def predict_with_onnx(self, data, batch_size=32, quantize=False):
                                              feature_col=data.roll_feature,
                                              target_col=data.roll_target,
                                              shuffle=False,
-                                             is_predict=True)
+                                             is_predict=data.is_predict)
         if quantize:
             return _pytorch_fashion_inference(model=self.onnxruntime_int8,
                                               input_data=data,
@@ -654,7 +654,8 @@ def evaluate(self, data, batch_size=32, multioutput="raw_values", quantize=False
                                              horizon=self.data_config['future_seq_len'],
                                              feature_col=data.roll_feature,
                                              target_col=data.roll_target,
-                                             shuffle=False)
+                                             shuffle=False,
+                                             is_predict=False)
         is_local_data = isinstance(data, (tuple, DataLoader))
         if not is_local_data and not self.distributed:
             data = xshard_to_np(data, mode="fit")
@@ -754,7 +755,8 @@ def evaluate_with_onnx(self, data,
                                              horizon=self.data_config['future_seq_len'],
                                              feature_col=data.roll_feature,
                                              target_col=data.roll_target,
-                                             shuffle=False)
+                                             shuffle=False,
+                                             is_predict=False)
         if isinstance(data, DataLoader):
             input_data = data
             target = np.concatenate(tuple(val[1] for val in data), axis=0)
@@ -1098,7 +1100,7 @@ def from_tsdataset(cls, tsdataset, past_seq_len=None, future_seq_len=None, **kwa
         """
         Build a Forecaster Model.
 
-        :param tsdataset: A bigdl.chronos.data.tsdataset.TSDataset instance.
+        :param tsdataset: Train tsdataset, a bigdl.chronos.data.tsdataset.TSDataset instance.
         :param past_seq_len: int or "auto", Specify the history time steps (i.e. lookback).
                Do not specify the 'past_seq_len' if your tsdataset has called
                the 'TSDataset.roll' method or 'TSDataset.to_torch_data_loader'.

python/chronos/src/bigdl/chronos/forecaster/lstm_forecaster.py

@@ -152,7 +152,7 @@ def from_tsdataset(cls, tsdataset, past_seq_len=None, **kwargs):
         '''
         Build a LSTM Forecaster Model.
 
-        :param tsdataset: A bigdl.chronos.data.tsdataset.TSDataset instance.
+        :param tsdataset: Train tsdataset, a bigdl.chronos.data.tsdataset.TSDataset instance.
         :param past_seq_len: Specify the history time steps (i.e. lookback).
                Do not specify the 'past_seq_len' if your tsdataset has called
                the 'TSDataset.roll' method or 'TSDataset.to_torch_data_loader'.

python/chronos/src/bigdl/chronos/forecaster/nbeats_forecaster.py

@@ -174,7 +174,7 @@ def from_tsdataset(cls, tsdataset, past_seq_len=None, future_seq_len=None, **kwa
         """
         Build a NBeats Forecaster Model.
 
-        :param tsdataset: A bigdl.chronos.data.tsdataset.TSDataset instance.
+        :param tsdataset: Train tsdataset, a bigdl.chronos.data.tsdataset.TSDataset instance.
         :param past_seq_len: Specify the history time steps (i.e. lookback).
                Do not specify the 'past_seq_len' if your tsdataset has called
                the 'TSDataset.roll' method or 'TSDataset.to_torch_data_loader'.