Fix/hist fc predict kwargs

CHANGELOG.md

@@ -12,6 +12,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 **Improved**
 
 **Fixed**
+- Fixed a bug in historical forecasts, where some `fit/predict_kwargs` were not passed to the underlying model's fit/predict methods. [#2103](https://github.com/unit8co/darts/pull/2103) by [Dennis Bader](https://github.com/dennisbader).
 - Fixed an import error when trying to create a `TorchForecastingModel` with PyTorch Lightning v<2.0.0. [#2087](https://github.com/unit8co/darts/pull/2087) by [Eschibli](https://github.com/eschibli).
 
 ### For developers of the library:

darts/dataprocessing/transformers/base_data_transformer.py

@@ -77,7 +77,7 @@ def __init__(
             each parallel job. If `parallel_params` is a `Sequence` of fixed attribute names, only those
             attribute names specified will take on different values between different parallel jobs.
         mask_components
-            Optionally, whether or not to automatically apply any provided `component_mask`s to the
+            Optionally, whether to automatically apply any provided `component_mask`s to the
             `TimeSeries` inputs passed to `transform`, `fit`, `inverse_transform`, or `fit_transform`.
             If `True`, any specified `component_mask` will be applied to each input timeseries
             before passing them to the called method; the masked components will also be automatically

darts/dataprocessing/transformers/fittable_data_transformer.py

@@ -56,7 +56,7 @@ def __init__(
             each parallel job. If `parallel_params` is a `Sequence` of fixed attribute names, only those
             attribute names specified will take on different values between different parallel jobs.
         mask_components
-            Optionally, whether or not to automatically apply any provided `component_mask`s to the
+            Optionally, whether to automatically apply any provided `component_mask`s to the
             `TimeSeries` inputs passed to `transform`, `fit`, `inverse_transform`, or `fit_transform`.
             If `True`, any specified `component_mask` will be applied to each input timeseries
             before passing them to the called method; the masked components will also be automatically

darts/dataprocessing/transformers/invertible_data_transformer.py

@@ -65,7 +65,7 @@ def __init__(
             each parallel job. If `parallel_params` is a `Sequence` of fixed attribute names, only those
             attribute names specified will take on different values between different parallel jobs.
         mask_components
-            Optionally, whether or not to automatically apply any provided `component_mask`s to the
+            Optionally, whether to automatically apply any provided `component_mask`s to the
             `TimeSeries` inputs passed to `transform`, `fit`, `inverse_transform`, or `fit_transform`.
             If `True`, any specified `component_mask` will be applied to each input timeseries
             before passing them to the called method; the masked components will also be automatically

darts/metrics/metrics.py

@@ -184,7 +184,7 @@ def _get_values_or_raise(
     series_b
         A univariate (deterministic or stochastic) ``TimeSeries`` instance (the predicted series).
     intersect
-        A boolean for whether or not to only consider the time intersection between `series_a` and `series_b`
+        A boolean for whether to only consider the time intersection between `series_a` and `series_b`
     stochastic_quantile
         Optionally, for stochastic predicted series, return either all sample values with (`stochastic_quantile=None`)
         or any deterministic quantile sample values by setting `stochastic_quantile=quantile` {>=0,<=1}.

darts/models/forecasting/baselines.py

@@ -61,6 +61,7 @@ def predict(
         n: int,
         num_samples: int = 1,
         verbose: bool = False,
+        show_warnings: bool = True,
     ):
         super().predict(n, num_samples)
         forecast = np.tile(self.mean_val, (n, 1))
@@ -125,6 +126,7 @@ def predict(
         n: int,
         num_samples: int = 1,
         verbose: bool = False,
+        show_warnings: bool = True,
     ):
         super().predict(n, num_samples)
         forecast = np.array([self.last_k_vals[i % self.K, :] for i in range(n)])
@@ -174,6 +176,7 @@ def predict(
         n: int,
         num_samples: int = 1,
         verbose: bool = False,
+        show_warnings: bool = True,
     ):
         super().predict(n, num_samples)
         first, last = (
@@ -244,6 +247,7 @@ def predict(
         n: int,
         num_samples: int = 1,
         verbose: bool = False,
+        show_warnings: bool = True,
     ):
         super().predict(n, num_samples)
 

darts/models/forecasting/block_rnn_model.py

@@ -231,7 +231,7 @@ def __init__(
             If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will
             be discarded). Default: ``False``.
         save_checkpoints
-            Whether or not to automatically save the untrained model and checkpoints from training.
+            Whether to automatically save the untrained model and checkpoints from training.
             To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where
             :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,
             :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using

darts/models/forecasting/dlinear.py

@@ -328,7 +328,7 @@ def __init__(
             If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will
             be discarded). Default: ``False``.
         save_checkpoints
-            Whether or not to automatically save the untrained model and checkpoints from training.
+            Whether to automatically save the untrained model and checkpoints from training.
             To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where
             :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,
             :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using

darts/models/forecasting/exponential_smoothing.py

@@ -137,7 +137,13 @@ def fit(self, series: TimeSeries):
 
         return self
 
-    def predict(self, n, num_samples=1, verbose: bool = False):
+    def predict(
+        self,
+        n: int,
+        num_samples: int = 1,
+        verbose: bool = False,
+        show_warnings: bool = True,
+    ):
         super().predict(n, num_samples)
 
         if num_samples == 1:

darts/models/forecasting/fft.py

@@ -366,7 +366,13 @@ def fit(self, series: TimeSeries):
 
         return self
 
-    def predict(self, n: int, num_samples: int = 1, verbose: bool = False):
+    def predict(
+        self,
+        n: int,
+        num_samples: int = 1,
+        verbose: bool = False,
+        show_warnings: bool = True,
+    ):
         super().predict(n, num_samples)
         trend_forecast = np.array(
             [self.trend_function(i + len(self.training_series)) for i in range(n)]

darts/models/forecasting/forecasting_model.py

@@ -235,6 +235,14 @@ def supports_likelihood_parameter_prediction(self) -> bool:
         """
         return getattr(self, "likelihood", None) is not None
 
+    @property
+    @abstractmethod
+    def supports_transferrable_series_prediction(self) -> bool:
+        """
+        Whether the model supports prediction for any input `series`.
+        """
+        pass
+
     @property
     def uses_past_covariates(self) -> bool:
         """
@@ -278,7 +286,13 @@ def output_chunk_length(self) -> Optional[int]:
         return None
 
     @abstractmethod
-    def predict(self, n: int, num_samples: int = 1) -> TimeSeries:
+    def predict(
+        self,
+        n: int,
+        num_samples: int = 1,
+        verbose: bool = False,
+        show_warnings: bool = True,
+    ) -> TimeSeries:
         """Forecasts values for `n` time steps after the end of the training series.
 
         Parameters
@@ -288,6 +302,10 @@ def predict(self, n: int, num_samples: int = 1) -> TimeSeries:
         num_samples
             Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
             for deterministic models.
+        verbose
+            Optionally, set the prediction verbosity. Not effective for all models.
+        show_warnings
+            Optionally, control whether warnings are shown. Not effective for all models.
 
         Returns
         -------
@@ -315,49 +333,59 @@ def predict(self, n: int, num_samples: int = 1) -> TimeSeries:
     def _fit_wrapper(
         self,
         series: TimeSeries,
-        past_covariates: Optional[TimeSeries],
-        future_covariates: Optional[TimeSeries],
+        past_covariates: Optional[TimeSeries] = None,
+        future_covariates: Optional[TimeSeries] = None,
         **kwargs,
     ):
-        supported_params = inspect.signature(self.fit).parameters
-        kwargs_ = {k: v for k, v in kwargs.items() if k in supported_params}
-
+        add_kwargs = {}
         # handle past and future covariates based on model support
-        for covs, name in zip([past_covariates, future_covariates], ["past", "future"]):
-            covs_name = f"{name}_covariates"
+        for covs, covs_name in zip(
+            [past_covariates, future_covariates],
+            ["past_covariates", "future_covariates"],
+        ):
             if getattr(self, f"supports_{covs_name}"):
-                kwargs_[covs_name] = covs
+                add_kwargs[covs_name] = covs
             elif covs is not None:
                 raise_log(
                     ValueError(f"Model cannot be fit/trained with `{covs_name}`."),
                     logger,
                 )
-        self.fit(series, **kwargs_)
+        self.fit(series=series, **add_kwargs, **kwargs)
 
     def _predict_wrapper(
         self,
         n: int,
+        series: Optional[TimeSeries] = None,
+        past_covariates: Optional[TimeSeries] = None,
+        future_covariates: Optional[TimeSeries] = None,
+        predict_likelihood_parameters: bool = False,
         **kwargs,
     ) -> Union[TimeSeries, Sequence[TimeSeries]]:
-        supported_params = set(inspect.signature(self.predict).parameters)
-
-        # if predict() accepts covariates, the model might not support them at inference
-        for covs_name in ["past_covariates", "future_covariates"]:
-            if covs_name in kwargs and not getattr(self, f"supports_{covs_name}"):
-                if kwargs[covs_name] is None:
-                    supported_params = supported_params - {covs_name}
-                else:
-                    raise_log(
-                        ValueError(
-                            f"Model prediction does not support `{covs_name}`, either because it "
-                            f"does not support `{covs_name}` in general, or because it was fit/trained "
-                            f"without using `{covs_name}`."
-                        ),
-                        logger,
-                    )
+        add_kwargs = {}
+        # not all models supports input `series` at inference
+        if self.supports_transferrable_series_prediction:
+            add_kwargs["series"] = series
+
+        # even if predict() accepts covariates, the model might not support them at inference
+        for covs, name in zip(
+            [past_covariates, future_covariates],
+            ["past_covariates", "future_covariates"],
+        ):
+            if getattr(self, f"supports_{name}"):
+                add_kwargs[name] = covs
+            elif covs is not None:
+                raise_log(
+                    ValueError(
+                        f"Model prediction does not support `{name}`, either because it "
+                        f"does not support `{name}` in general, or because it was fit/trained "
+                        f"without using `{name}`."
+                    ),
+                    logger,
+                )
 
-        kwargs_ = {k: v for k, v in kwargs.items() if k in supported_params}
-        return self.predict(n, **kwargs_)
+        if self.supports_likelihood_parameter_prediction:
+            add_kwargs["predict_likelihood_parameters"] = predict_likelihood_parameters
+        return self.predict(n=n, **add_kwargs, **kwargs)
 
     @property
     def min_train_series_length(self) -> int:
@@ -2099,6 +2127,13 @@ def extreme_lags(
         #  that use an input to predict an output.
         return -self.min_train_series_length, -1, None, None, None, None
 
+    @property
+    def supports_transferrable_series_prediction(self) -> bool:
+        """
+        Whether the model supports prediction for any input `series`.
+        """
+        return False
+
 
 class GlobalForecastingModel(ForecastingModel, ABC):
     """The base class for "global" forecasting models, handling several time series and optional covariates.
@@ -2314,6 +2349,13 @@ def supports_optimized_historical_forecasts(self) -> bool:
         """
         return True
 
+    @property
+    def supports_transferrable_series_prediction(self) -> bool:
+        """
+        Whether the model supports prediction for any input `series`.
+        """
+        return True
+
     def _sanity_check_predict_likelihood_parameters(
         self, n: int, output_chunk_length: Union[int, None], num_samples: int
     ):
@@ -2420,6 +2462,8 @@ def predict(
         n: int,
         future_covariates: Optional[TimeSeries] = None,
         num_samples: int = 1,
+        verbose: bool = False,
+        show_warnings: bool = True,
         **kwargs,
     ) -> TimeSeries:
         """Forecasts values for `n` time steps after the end of the training series.
@@ -2437,6 +2481,10 @@ def predict(
         num_samples
             Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
             for deterministic models.
+        verbose
+            Optionally, set the prediction verbosity. Not effective for all models.
+        show_warnings
+            Optionally, control whether warnings are shown. Not effective for all models.
 
         Returns
         -------
@@ -2580,6 +2628,8 @@ def predict(
         series: Optional[TimeSeries] = None,
         future_covariates: Optional[TimeSeries] = None,
         num_samples: int = 1,
+        verbose: bool = False,
+        show_warnings: bool = True,
         **kwargs,
     ) -> TimeSeries:
         """If the `series` parameter is not set, forecasts values for `n` time steps after the end of the training
@@ -2605,6 +2655,10 @@ def predict(
         num_samples
             Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
             for deterministic models.
+        verbose
+            Optionally, set the prediction verbosity. Not effective for all models.
+        show_warnings
+            Optionally, control whether warnings are shown. Not effective for all models.
 
         Returns
         -------
@@ -2699,6 +2753,13 @@ def _predict(
         """
         pass
 
+    @property
+    def supports_transferrable_series_prediction(self) -> bool:
+        """
+        Whether the model supports prediction for any input `series`.
+        """
+        return True
+
     @property
     def _supports_non_retrainable_historical_forecasts(self) -> bool:
         return True

darts/models/forecasting/kalman_forecaster.py

@@ -124,6 +124,8 @@ def predict(
         series: Optional[TimeSeries] = None,
         future_covariates: Optional[TimeSeries] = None,
         num_samples: int = 1,
+        verbose: bool = False,
+        show_warnings: bool = True,
         **kwargs,
     ) -> TimeSeries:
         # we override `predict()` to pass a non-None `series`, so that historic_future_covariates

darts/models/forecasting/nbeats.py

@@ -653,7 +653,7 @@ def __init__(
             If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will
             be discarded). Default: ``False``.
         save_checkpoints
-            Whether or not to automatically save the untrained model and checkpoints from training.
+            Whether to automatically save the untrained model and checkpoints from training.
             To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where
             :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,
             :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using

darts/models/forecasting/nhits.py

@@ -589,7 +589,7 @@ def __init__(
             If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will
             be discarded). Default: ``False``.
         save_checkpoints
-            Whether or not to automatically save the untrained model and checkpoints from training.
+            Whether to automatically save the untrained model and checkpoints from training.
             To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where
             :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,
             :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using

darts/models/forecasting/nlinear.py

@@ -282,7 +282,7 @@ def __init__(
             If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will
             be discarded). Default: ``False``.
         save_checkpoints
-            Whether or not to automatically save the untrained model and checkpoints from training.
+            Whether to automatically save the untrained model and checkpoints from training.
             To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where
             :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,
             :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using

darts/models/forecasting/regression_model.py

@@ -772,6 +772,8 @@ def predict(
         **kwargs : dict, optional
             Additional keyword arguments passed to the `predict` method of the model. Only works with
             univariate target series.
+        show_warnings
+            Optionally, control whether warnings are shown. Not effective for all models.
         """
         if series is None:
             # then there must be a single TS, and that was saved in super().fit as self.training_series

darts/models/forecasting/rnn_model.py

@@ -308,7 +308,7 @@ def __init__(
             If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will
             be discarded). Default: ``False``.
         save_checkpoints
-            Whether or not to automatically save the untrained model and checkpoints from training.
+            Whether to automatically save the untrained model and checkpoints from training.
             To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where
             :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,
             :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using

darts/models/forecasting/sf_auto_ces.py

@@ -62,6 +62,7 @@ def predict(
         n: int,
         num_samples: int = 1,
         verbose: bool = False,
+        show_warnings: bool = True,
     ):
         super().predict(n, num_samples)
         forecast_dict = self.model.predict(