Catboost native multi-output with RMSE (#2659)

* Replace MultioutputRegressor by native multioutput from CastBoost, wip

* Add _native_support_multioutput() to regression models, wip

* Fix logic, replace RMSE with MultiRMSE when multioutput is required

* Add entry to change log

* Adapt shapexplainer test to support all native multioutput

* Let user decide on native multioutput usage

* Update CHANGELOG.md

* Update CHANGELOG.md

* Add recursive call for native multioutput

* Add test for native multioutput support

* Fix logic, only sklearn model should be passed to RegressionModel

* Set loss_function in kwargs following model default

* Fix SNM logic to be independant of MOR wrapper

* Use __sklearn_tags__ instead of _get_tags as required in sklearn 1.7

* Update CHANGELOG.md

* Minor rewriting

* Remove logic for xboost<2.0.0

* minor updates

---------

Co-authored-by: dennisbader <[email protected]>

Author: jonasblanc

CHANGELOG.md

@@ -11,6 +11,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Improved**
 
+- Improved `CatBoostModel` documentation by describing how to use native multi-output regression. [#2659](https://github.com/unit8co/darts/pull/2659) by [Jonas Blanc](https://github.com/jonasblanc)
 - `TimeSeries.from_dataframe()` and `from_series()` now support creating `TimeSeries` from additional backends (Polars, PyArrow, ...). We leverage `narwhals` as the compatibility layer between dataframe libraries. See the `narwhals` [documentation](https://narwhals-dev.github.io/narwhals/) for all supported backends. [#2661](https://github.com/unit8co/darts/pull/2661) by [Jules Authier](https://github.com/authierj)
 - Added ONNX support for torch-based models with method `TorchForecastingModel.to_onnx()`. Check out [this example](https://unit8co.github.io/darts/userguide/gpu_and_tpu_usage.html#exporting-model-to-onnx-format-for-inference) from the user guide on how to export and load a model for inference. [#2620](https://github.com/unit8co/darts/pull/2620) by [Antoine Madrona](https://github.com/madtoinou)
 - Made method `ForecastingModel.untrained_model()` public. Use this method to get a new (untrained) model instance created with the same parameters. [#2684](https://github.com/unit8co/darts/pull/2684) by [Timon Erhart](https://github.com/turbotimon)
@@ -24,8 +25,15 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Dependencies**
 
+- Bumped minimum scikit-learn version from `1.0.1` to `1.6.0`. This was required due to sklearn deprecating `_get_tags` in favor of `BaseEstimator.__sklearn_tags__` in version 1.7. This leads to increasing both sklearn and XGBoost minimum supported version to 1.6 and 2.1.4 respectively. [#2659](https://github.com/unit8co/darts/pull/2659) by [Jonas Blanc](https://github.com/jonasblanc)
+- Bumped minimum xgboost version from `1.6.0` to `2.1.4` for the same reason as bumping the minimum sklearn version. [#2659](https://github.com/unit8co/darts/pull/2659) by [Jonas Blanc](https://github.com/jonasblanc)
+
 ### For developers of the library:
 
+**Improved**
+
+- Refactored and improved the multi-output support handling for `RegressionModel`. [#2659](https://github.com/unit8co/darts/pull/2659) by [Jonas Blanc](https://github.com/jonasblanc)
+
 ## [0.33.0](https://github.com/unit8co/darts/tree/0.33.0) (2025-02-14)
 
 ### For users of the library:

darts/models/forecasting/catboost_model.py

@@ -132,6 +132,9 @@ def encode_year(idx):
             that all target `series` have the same static covariate dimensionality in ``fit()`` and ``predict()``.
         **kwargs
             Additional keyword arguments passed to `catboost.CatBoostRegressor`.
+            Native multi-output support can be achieved by using an appropriate `loss_function` ('MultiRMSE',
+            'MultiRMSEWithMissingValues'). Otherwise, Darts uses its `MultiOutputRegressor` wrapper to add multi-output
+            support.
 
         Examples
         --------
@@ -208,6 +211,9 @@ def encode_year(idx):
             use_static_covariates=use_static_covariates,
         )
 
+        # if no loss provided, get the default loss from the model
+        self.kwargs["loss_function"] = self.model.get_params().get("loss_function")
+
     def fit(
         self,
         series: Union[TimeSeries, Sequence[TimeSeries]],
@@ -404,3 +410,11 @@ def min_train_series_length(self) -> int:
                 else self.output_chunk_length
             ),
         )
+
+    @property
+    def _supports_native_multioutput(self):
+        # CatBoostRegressor supports multi-output natively, but only with selected loss functions
+        # ("MultiRMSE", "MultiRMSEWithMissingValues", ...)
+        return CatBoostRegressor._is_multiregression_objective(
+            self.kwargs.get("loss_function")
+        )

darts/models/forecasting/regression_model.py

@@ -846,36 +846,21 @@ def fit(
             "future": future_covariates[0].width if future_covariates else None,
         }
 
-        # if multi-output regression
-        use_mor = False
-        if not series[0].is_univariate or (
-            self.output_chunk_length > 1
-            and self.multi_models
+        # Check if multi-output regression is required
+        requires_multioutput = not series[0].is_univariate or (
+            self.output_chunk_length > 1 and self.multi_models
+        )
+
+        # If multi-output required and model doesn't support it natively, wrap it in a MultiOutputRegressor
+        if (
+            requires_multioutput
             and not isinstance(self.model, MultiOutputRegressor)
+            and (
+                not self._supports_native_multioutput
+                or sample_weight
+                is not None  # we have 2D sample (and time) weights, only supported in Darts
+            )
         ):
-            if sample_weight is not None:
-                # we have 2D sample (and time) weights, only supported in Darts
-                use_mor = True
-            elif not (
-                callable(getattr(self.model, "_get_tags", None))
-                and isinstance(self.model._get_tags(), dict)
-                and self.model._get_tags().get("multioutput")
-            ):
-                # model does not support multi-output regression natively
-                use_mor = True
-            elif (
-                self.model.__class__.__name__ == "CatBoostRegressor"
-                and self.model.get_params()["loss_function"] == "RMSEWithUncertainty"
-            ):
-                use_mor = True
-            elif (
-                self.model.__class__.__name__ == "XGBRegressor"
-                and self.likelihood is not None
-            ):
-                # since xgboost==2.1.0, likelihoods do not support native multi output regression
-                use_mor = True
-
-        if use_mor:
             val_set_name, val_weight_name = self.val_set_params
             mor_kwargs = {
                 "eval_set_name": val_set_name,
@@ -884,7 +869,6 @@ def fit(
             }
             self.model = MultiOutputRegressor(self.model, **mor_kwargs)
 
-        # warn if n_jobs_multioutput_wrapper was provided but not used
         if (
             not isinstance(self.model, MultiOutputRegressor)
             and n_jobs_multioutput_wrapper is not None
@@ -1391,6 +1375,18 @@ def _optimized_historical_forecasts(
             )
         return series2seq(hfc, seq_type_out=series_seq_type)
 
+    @property
+    def _supports_native_multioutput(self) -> bool:
+        """
+        Returns True if the model supports multi-output regression natively.
+        """
+        model = (
+            self.model.estimator
+            if isinstance(self.model, MultiOutputRegressor)
+            else self.model
+        )
+        return model.__sklearn_tags__().target_tags.multi_output
+
 
 class _LikelihoodMixin:
     """

darts/models/forecasting/xgboost.py

@@ -8,7 +8,6 @@
 """
 
 from collections.abc import Sequence
-from functools import partial
 from typing import Optional, Union
 
 import numpy as np
@@ -25,10 +24,6 @@
 
 logger = get_logger(__name__)
 
-# Check whether we are running xgboost >= 2.0.0 for quantile regression
-tokens = xgb.__version__.split(".")
-xgb_200_or_above = int(tokens[0]) >= 2
-
 
 def xgb_quantile_loss(labels: np.ndarray, preds: np.ndarray, quantile: float):
     """Custom loss function for XGBoost to compute quantile loss gradient.
@@ -205,9 +200,7 @@ def encode_year(idx):
             if likelihood in {"poisson"}:
                 self.kwargs["objective"] = f"count:{likelihood}"
             elif likelihood == "quantile":
-                if xgb_200_or_above:
-                    # leverage built-in Quantile Regression
-                    self.kwargs["objective"] = "reg:quantileerror"
+                self.kwargs["objective"] = "reg:quantileerror"
                 self.quantiles, self._median_idx = self._prepare_quantiles(quantiles)
                 self._model_container = self._get_model_container()
 
@@ -289,11 +282,7 @@ def fit(
             # empty model container in case of multiple calls to fit, e.g. when backtesting
             self._model_container.clear()
             for quantile in self.quantiles:
-                if xgb_200_or_above:
-                    self.kwargs["quantile_alpha"] = quantile
-                else:
-                    objective = partial(xgb_quantile_loss, quantile=quantile)
-                    self.kwargs["objective"] = objective
+                self.kwargs["quantile_alpha"] = quantile
                 self.model = xgb.XGBRegressor(**self.kwargs)
                 super().fit(
                     series=series,
@@ -368,3 +357,8 @@ def min_train_series_length(self) -> int:
                 else self.output_chunk_length
             ),
         )
+
+    @property
+    def _supports_native_multioutput(self):
+        # since xgboost==2.1.0, likelihoods do not support native multi output regression
+        return super()._supports_native_multioutput and self.likelihood is None

darts/tests/ad/test_scorers.py

@@ -3,7 +3,6 @@
 
 import numpy as np
 import pytest
-import sklearn
 from pyod.models.knn import KNN
 from scipy.stats import cauchy, expon, gamma, laplace, norm, poisson
 
@@ -1221,12 +1220,7 @@ def test_multivariate_componentwise_kmeans(self):
 
         assert np.abs(1.0 - auc_roc_cwtrue[0]) < delta
         assert np.abs(0.97666 - auc_roc_cwtrue[1]) < delta
-        # sklearn changed the centroid initialization in version 1.3.0
-        # so the results are slightly different for older versions
-        if sklearn.__version__ < "1.3.0":
-            assert np.abs(0.9851 - auc_roc_cwfalse) < delta
-        else:
-            assert np.abs(0.99007 - auc_roc_cwfalse) < delta
+        assert np.abs(0.99007 - auc_roc_cwfalse) < delta
 
     def test_PyODScorer(self):
         # Check parameters and inputs

darts/tests/explainability/test_shap_explainer.py

@@ -205,8 +205,9 @@ def test_creation(self):
 
         # Good type of explainers
         shap_explain = ShapExplainer(m)
-        if isinstance(m, XGBModel):
-            # since xgboost > 2.1.0, model supports native multi output regression
+        if m._supports_native_multioutput:
+            # since xgboost > 2.1.0, model supports native multi-output regression
+            # CatBoostModel supports multi-output for certain loss functions
             assert isinstance(shap_explain.explainers.explainers, shap.explainers.Tree)
         else:
             assert isinstance(
@@ -270,7 +271,7 @@ def test_creation(self):
             future_covariates=self.fut_cov_ts,
         )
         shap_explain = ShapExplainer(m)
-        if isinstance(m, XGBModel):
+        if m._supports_native_multioutput:
             assert isinstance(shap_explain.explainers.explainers, shap.explainers.Tree)
         else:
             assert isinstance(

darts/tests/models/forecasting/test_regression_models.py

@@ -182,11 +182,7 @@ class NewCls(cls):
 class TestRegressionModels:
     np.random.seed(42)
     # default regression models
-    models = [
-        RandomForest,
-        LinearRegressionModel,
-        RegressionModel,
-    ]
+    models = [RandomForest, LinearRegressionModel, RegressionModel]
 
     # register likelihood regression models
     QuantileLinearRegressionModel = partialclass(
@@ -1355,6 +1351,31 @@ def test_multioutput_wrapper(self, config):
                 horizon=0, target_dim=1
             )
 
+    model_configs_multioutput = [
+        (
+            RegressionModel,
+            {"lags": 4, "model": LinearRegression()},
+            True,
+        ),
+        (LinearRegressionModel, {"lags": 4}, True),
+        (XGBModel, {"lags": 4}, True),
+        (XGBModel, {"lags": 4, "likelihood": "poisson"}, False),
+    ]
+    if lgbm_available:
+        model_configs_multioutput += [(LightGBMModel, {"lags": 4}, False)]
+    if cb_available:
+        model_configs_multioutput += [
+            (CatBoostModel, {"lags": 4, "loss_function": "RMSE"}, False),
+            (CatBoostModel, {"lags": 4, "loss_function": "MultiRMSE"}, True),
+            (CatBoostModel, {"lags": 4, "loss_function": "RMSEWithUncertainty"}, False),
+        ]
+
+    @pytest.mark.parametrize("config", model_configs_multioutput)
+    def test_supports_native_multioutput(self, config):
+        model_cls, model_config, supports_native_multioutput = config
+        model = model_cls(**model_config)
+        assert model._supports_native_multioutput == supports_native_multioutput
+
     model_configs = [(XGBModel, dict({"likelihood": "poisson"}, **xgb_test_params))]
     if lgbm_available:
         model_configs += [(LightGBMModel, lgbm_test_params)]

requirements/core.txt

@@ -8,7 +8,7 @@ pandas>=1.0.5
 pmdarima>=1.8.0
 pyod>=0.9.5
 requests>=2.22.0
-scikit-learn>=1.0.1
+scikit-learn>=1.6.0
 scipy>=1.3.2
 shap>=0.40.0
 statsforecast>=1.4
@@ -17,4 +17,4 @@ tbats>=1.1.0
 tqdm>=4.60.0
 typing-extensions
 xarray>=0.17.0
-xgboost>=1.6.0
+xgboost>=2.1.4