fix historical forecasts retraining of TFMs

darts/models/forecasting/forecasting_model.py

@@ -691,17 +691,16 @@ def historical_forecasts(
             retrain_func = _retrain_wrapper(
                 lambda counter: counter % int(retrain) == 0 if retrain else False
             )
-
         elif isinstance(retrain, Callable):
             retrain_func = _retrain_wrapper(retrain)
-
         else:
             raise_log(
                 ValueError(
                     "`retrain` argument must be either `bool`, positive `int` or `Callable` (returning `bool`)"
                 ),
                 logger,
             )
+
         retrain_func_signature = tuple(
             inspect.signature(retrain_func).parameters.keys()
         )
@@ -728,7 +727,6 @@ def historical_forecasts(
 
         forecasts_list = []
         for idx, series_ in enumerate(outer_iterator):
-
             past_covariates_ = past_covariates[idx] if past_covariates else None
             future_covariates_ = future_covariates[idx] if future_covariates else None
 
@@ -765,15 +763,12 @@ def historical_forecasts(
 
             # prepare the start parameter -> pd.Timestamp
             if start is not None:
-
                 historical_forecasts_time_index = drop_before_index(
                     historical_forecasts_time_index,
                     series_.get_timestamp_at_point(start),
                 )
-
             else:
                 if (retrain is not False) or (not self._fit_called):
-
                     if train_length:
                         historical_forecasts_time_index = drop_before_index(
                             historical_forecasts_time_index,
@@ -804,9 +799,9 @@ def historical_forecasts(
                         (not self._fit_called)
                         and (retrain is False)
                         and (not train_length),
-                        " The model has not been fitted yet, and `start` and train_length are not specified. "
-                        " The model is not retraining during the historical forecasts. Hence the "
-                        "the first and only training would be done on 2 samples.",
+                        "The model has not been fitted yet, and `start` and train_length are not specified. "
+                        "The model is not retraining during the historical forecasts. Hence the "
+                        "first and only training would be done on 2 samples.",
                         logger,
                     )
 
@@ -837,7 +832,6 @@ def historical_forecasts(
 
             # iterate and forecast
             for _counter, pred_time in enumerate(iterator):
-
                 # build the training series
                 if min_timestamp > series_.time_index[0]:
                     train_series = series_.drop_before(
@@ -866,13 +860,17 @@ def historical_forecasts(
                     if future_covariates_
                     else None,
                 ):
-                    self._fit_wrapper(
+                    # avoid fitting the same model multiple times
+                    model = self.untrained_model()
+                    model._fit_wrapper(
                         series=train_series,
                         past_covariates=past_covariates_,
                         future_covariates=future_covariates_,
                     )
+                else:
+                    model = self
 
-                forecast = self._predict_wrapper(
+                forecast = model._predict_wrapper(
                     n=forecast_horizon,
                     series=train_series,
                     past_covariates=past_covariates_,
@@ -901,7 +899,6 @@ def historical_forecasts(
                         hierarchy=series_.hierarchy,
                     )
                 )
-
             else:
                 forecasts_list.append(forecasts)
 
@@ -1526,7 +1523,7 @@ def _extract_model_creation_params(self):
         return model_params
 
     def untrained_model(self):
-        return self.__class__(**self.model_params)
+        return self.__class__(**copy.deepcopy(self.model_params))
 
     @property
     def model_params(self) -> dict:

darts/tests/models/forecasting/test_backtesting.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 import pandas as pd
+import pytest
 
 from darts import TimeSeries
 from darts.logging import get_logger
@@ -219,12 +220,28 @@ def test_backtest_forecasting(self):
             self.assertEqual(pred.end_time(), linear_series.end_time())
 
             # multivariate model + multivariate series
-            with self.assertRaises(ValueError):
+            # historical forecasts doesn't overwrite model object -> we can use different input dimensions
+            tcn_model.backtest(
+                linear_series_multi,
+                start=pd.Timestamp("20000125"),
+                forecast_horizon=3,
+                verbose=False,
+                retrain=False,
+            )
+
+            # univariate model
+            tcn_model = TCNModel(
+                input_chunk_length=12, output_chunk_length=1, batch_size=1, n_epochs=1
+            )
+            tcn_model.fit(linear_series, verbose=False)
+            # univariate fitted model + multivariate series
+            with pytest.raises(ValueError):
                 tcn_model.backtest(
                     linear_series_multi,
                     start=pd.Timestamp("20000125"),
                     forecast_horizon=3,
                     verbose=False,
+                    retrain=False,
                 )
 
             tcn_model = TCNModel(

darts/tests/models/forecasting/test_ensemble_models.py

@@ -50,6 +50,13 @@ def test_untrained_models(self):
         with self.assertRaises(ValueError):
             NaiveEnsembleModel([model])
 
+        # an untrained ensemble model should also give untrained underlying models
+        model_ens = NaiveEnsembleModel([NaiveDrift()])
+        model_ens.fit(self.series1)
+        assert model_ens.models[0]._fit_called
+        new_model = model_ens.untrained_model()
+        assert not new_model.models[0]._fit_called
+
     def test_input_models_local_models(self):
         with self.assertRaises(ValueError):
             NaiveEnsembleModel([])