Chronos: Add support for id_sensitive=True to Forecaster.from_tsdat…

…aset (#5551)

* supports id_sensitive=True

* new params is_predict

* rollback is_predict

* fix known issues

* fix code style

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

@@ -1096,7 +1096,7 @@ 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'.
@@ -1151,6 +1151,11 @@ def check_time_steps(tsdataset, past_seq_len, future_seq_len):
                           fixMsg="Do not specify past_seq_len and future seq_len "
                           "or call tsdataset.roll method again and specify time step")
 
+        if tsdataset.id_sensitive:
+            _id_list_len = len(tsdataset.id_col)
+            input_feature_num *= _id_list_len
+            output_feature_num *= _id_list_len
+
         return cls(past_seq_len=past_seq_len,
                    future_seq_len=future_seq_len,
                    input_feature_num=input_feature_num,

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'.
@@ -198,6 +198,11 @@ def check_time_steps(tsdataset, past_seq_len):
                           fixMsg="Do not specify past_seq_len "
                           "or call tsdataset.roll method again and specify time step.")
 
+        if tsdataset.id_sensitive:
+            _id_list_len = len(tsdataset._id_list)
+            input_feature_num *= _id_list_len
+            output_feature_num *= _id_list_len
+
         return cls(past_seq_len=past_seq_len,
                    input_feature_num=input_feature_num,
                    output_feature_num=output_feature_num,

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'.
@@ -225,6 +225,9 @@ def check_time_steps(tsdataset, past_seq_len, future_seq_len):
                           fixMsg="Do not specify past_seq_len and future seq_len "
                           "or call tsdataset.roll method again and specify time step")
 
+        invalidInputError(not all([tsdataset.id_sensitive, len(tsdataset._id_list) > 1]),
+                          "NBeats only supports univariate forecasting.")
+
         return cls(past_seq_len=past_seq_len,
                    future_seq_len=future_seq_len,
                    **kwargs)