Native quantile regression for xgb 2.0.0 and above (#2051)

* use native quantile regression for xgb 2.0.0 and above * update changelog * revert change to objective --------- Co-authored-by: madtoinou <[email protected]>
unit8co · Nov 3, 2023 · 2d0233f · 2d0233f
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -15,7 +15,9 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
   - 🚀🚀 Optimized `historical_forecasts()` for pre-trained `TorchForecastingModel` running up to 20 times faster than before!. [#2013](https://github.com/unit8co/darts/pull/2013) by [Dennis Bader](https://github.com/dennisbader).
   - Added callback `darts.utils.callbacks.TFMProgressBar` to customize at which model stages to display the progress bar. [#2020](https://github.com/unit8co/darts/pull/2020) by [Dennis Bader](https://github.com/dennisbader).
 - Improvements to documentation:
-  - Adapted the example notebooks to properly apply data transformers and avoid look-ahead bias. [#2020](https://github.com/unit8co/darts/pull/2020) by [Samriddhi Singh](https://github.com/SimTheGreat). 
+  - Adapted the example notebooks to properly apply data transformers and avoid look-ahead bias. [#2020](https://github.com/unit8co/darts/pull/2020) by [Samriddhi Singh](https://github.com/SimTheGreat).
+- Improvements to Regression Models:
+  - `XGBModel` now leverages XGBoost's native Quantile Regression support that was released in version 2.0.0 for improved probabilistic forecasts. [#2051](https://github.com/unit8co/darts/pull/2051) by [Dennis Bader](https://github.com/dennisbader).
 
 **Fixed**
 - Fixed a bug when calling optimized `historical_forecasts()` for a `RegressionModel` trained with unequal component-specific lags. [#2040](https://github.com/unit8co/darts/pull/2040) by [Antoine Madrona](https://github.com/madtoinou).

diff --git a/darts/models/forecasting/xgboost.py b/darts/models/forecasting/xgboost.py
@@ -25,6 +25,10 @@
 
 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.
@@ -183,8 +187,12 @@ 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.quantiles, self._median_idx = self._prepare_quantiles(quantiles)
                 self._model_container = self._get_model_container()
+
             self._rng = np.random.default_rng(seed=random_state)  # seed for sampling
 
         super().__init__(
@@ -249,12 +257,17 @@ def fit(
                 )
             ]
 
+        # TODO: XGBRegressor supports multi quantile reqression which we could leverage in the future
+        #  see https://xgboost.readthedocs.io/en/latest/python/examples/quantile_regression.html
         if self.likelihood == "quantile":
             # empty model container in case of multiple calls to fit, e.g. when backtesting
             self._model_container.clear()
             for quantile in self.quantiles:
-                obj_func = partial(xgb_quantile_loss, quantile=quantile)
-                self.kwargs["objective"] = obj_func
+                if xgb_200_or_above:
+                    self.kwargs["quantile_alpha"] = quantile
+                else:
+                    objective = partial(xgb_quantile_loss, quantile=quantile)
+                    self.kwargs["objective"] = objective
                 self.model = xgb.XGBRegressor(**self.kwargs)
 
                 super().fit(