fix plkl param prediction with multi_models=False

darts/models/forecasting/regression_model.py

@@ -1143,10 +1143,18 @@ def predict(
         # same for covariate matrices
         for cov_type, data in covariate_matrices.items():
             covariate_matrices[cov_type] = np.repeat(data, num_samples, axis=0)
+
+        # for concatenating target with predictions (or quantile parameters)
+        if predict_likelihood_parameters and self.likelihood is not None:
+            # with `multi_models=False`, the predictions are concatenated with the past target, even if `n<=ocl`
+            # to make things work, we just append the first predicted parameter (it will never be accessed)
+            sample_slice = slice(0, None, self.num_parameters)
+        else:
+            sample_slice = slice(None)
+
         # prediction
         predictions = []
         last_step_shift = 0
-
         # t_pred indicates the number of time steps after the first prediction
         for t_pred in range(0, n, step):
             # in case of autoregressive forecast `(t_pred > 0)` and if `n` is not a round multiple of `step`,
@@ -1157,7 +1165,9 @@ def predict(
 
             # concatenate previous iteration forecasts
             if "target" in self.lags and predictions:
-                series_matrix = np.concatenate([series_matrix, predictions[-1]], axis=1)
+                series_matrix = np.concatenate(
+                    [series_matrix, predictions[-1][:, :, sample_slice]], axis=1
+                )
 
             # extract and concatenate lags from target and covariates series
             X = _create_lagged_data_autoregression(

darts/tests/models/forecasting/test_probabilistic_models.py

@@ -94,6 +94,23 @@
     ),
 ]
 
+xgb_test_params = {
+    "n_estimators": 1,
+    "max_depth": 1,
+    "max_leaves": 1,
+}
+lgbm_test_params = {
+    "n_estimators": 1,
+    "max_depth": 1,
+    "num_leaves": 2,
+    "verbosity": -1,
+}
+cb_test_params = {
+    "iterations": 1,
+    "depth": 1,
+    "verbose": -1,
+}
+
 if TORCH_AVAILABLE:
     models_cls_kwargs_errs += [
         (
@@ -294,15 +311,17 @@ def helper_test_probabilistic_forecast_accuracy(self, model, err, ts, noisy_ts):
     @pytest.mark.parametrize(
         "config",
         itertools.product(
-            [(LinearRegressionModel, False), (XGBModel, False)]
-            + ([(LightGBMModel, False)] if lgbm_available else [])
-            + ([(CatBoostModel, True)] if cb_available else []),
-            [1, 3],
+            [(LinearRegressionModel, False, {}), (XGBModel, False, xgb_test_params)]
+            + ([(LightGBMModel, False, lgbm_test_params)] if lgbm_available else [])
+            + ([(CatBoostModel, True, cb_test_params)] if cb_available else []),
+            [1, 3],  # n components
             [
                 "quantile",
                 "poisson",
                 "gaussian",
-            ],
+            ],  # likelihood
+            [True, False],  # multi models
+            [1, 2],  # horizon
         ),
     )
     def test_predict_likelihood_parameters_regression_models(self, config):
@@ -312,7 +331,13 @@ def test_predict_likelihood_parameters_regression_models(self, config):
 
         Note: values are not tested as it would be too time consuming
         """
-        (model_cls, supports_gaussian), n_comp, likelihood = config
+        (
+            (model_cls, supports_gaussian, model_kwargs),
+            n_comp,
+            likelihood,
+            multi_models,
+            horizon,
+        ) = config
 
         seed = 142857
         n_times, n_samples = 100, 1
@@ -340,10 +365,17 @@ def test_predict_likelihood_parameters_regression_models(self, config):
         else:
             assert False, f"unknown likelihood {likelihood}"
 
-        model = model_cls(lags=3, random_state=seed, **lkl["kwargs"])
+        model = model_cls(
+            lags=3,
+            output_chunk_length=horizon,
+            random_state=seed,
+            **lkl["kwargs"],
+            multi_models=multi_models,
+            **model_kwargs,
+        )
         model.fit(lkl["ts"])
         pred_lkl_params = model.predict(
-            n=1, num_samples=1, predict_likelihood_parameters=True
+            n=horizon, num_samples=1, predict_likelihood_parameters=True
         )
         if n_comp == 1:
             assert lkl["expected"].shape == pred_lkl_params.values()[0].shape, (
@@ -352,7 +384,7 @@ def test_predict_likelihood_parameters_regression_models(self, config):
             )
         else:
             assert (
-                1,
+                horizon,
                 len(lkl["expected"]) * n_comp,
                 1,
             ) == pred_lkl_params.all_values().shape, (