[ENH] Refactor _loss_mapping

pytorch_forecasting/metrics/_distributions_pkg/_beta/_beta_distribution_loss_pkg.py

@@ -3,6 +3,7 @@
 """
 
 from pytorch_forecasting.data import TorchNormalizer
+from pytorch_forecasting.data.encoders import GroupNormalizer
 from pytorch_forecasting.metrics.base_metrics._base_object import _BasePtMetric
 
 
@@ -16,6 +17,15 @@ class BetaDistributionLoss_pkg(_BasePtMetric):
         "distribution_type": "beta",
         "info:metric_name": "BetaDistributionLoss",
         "requires:data_type": "beta_distribution_forecast",
+        "clip_target": True,
+        "data_loader_kwargs": {
+            "target_normalizer": GroupNormalizer(
+                groups=["agency", "sku"], transformation="logit"
+            )
+        },
+        "info:pred_type": ["distr"],
+        "info:y_type": ["numeric"],
+        "expected_loss_ndim": 2,
     }
 
     @classmethod
@@ -30,3 +40,10 @@ def get_encoder(cls):
         Returns a TorchNormalizer instance for rescaling parameters.
         """
         return TorchNormalizer(transformation="logit")
+
+    @classmethod
+    def _get_test_dataloaders(cls, params=None):
+        """
+        Returns test dataloaders configured for BetaDistributionLoss.
+        """
+        return super()._get_test_dataloaders_from(params, target="agency")

pytorch_forecasting/metrics/_distributions_pkg/_implicit_quantile_network/_implicit_quantile_network_distribution_loss_pkg.py

@@ -18,6 +18,8 @@ class ImplicitQuantileNetworkDistributionLoss_pkg(_BasePtMetric):
         "requires:data_type": "implicit_quantile_network_distribution_forecast",
         "capability:quantile_generation": True,
         "shape:adds_quantile_dimension": True,
+        "info:pred_type": ["distr"],
+        "info:y_type": ["numeric"],
     }
 
     @classmethod
@@ -44,3 +46,10 @@ def get_metric_test_params(cls):
         fixture for testing the ImplicitQuantileNetworkDistributionLoss metric.
         """
         return [{"input_size": 5}]
+
+    @classmethod
+    def _get_test_dataloaders(cls, params=None):
+        """
+        Returns test dataloaders configured for ImplicitQuantileNetworkDistributionLoss.
+        """
+        return super()._get_test_dataloaders_from(params)

pytorch_forecasting/metrics/_distributions_pkg/_log_normal/_log_normal_distribution_loss_pkg.py

@@ -5,6 +5,7 @@
 import torch
 
 from pytorch_forecasting.data import TorchNormalizer
+from pytorch_forecasting.data.encoders import GroupNormalizer
 from pytorch_forecasting.metrics.base_metrics._base_object import _BasePtMetric
 
 
@@ -18,6 +19,15 @@ class LogNormalDistributionLoss_pkg(_BasePtMetric):
         "distribution_type": "log_normal",
         "info:metric_name": "LogNormalDistributionLoss",
         "requires:data_type": "log_normal_distribution_forecast",
+        "clip_target": True,
+        "data_loader_kwargs": {
+            "target_normalizer": GroupNormalizer(
+                groups=["agency", "sku"], transformation="log1p"
+            )
+        },
+        "info:pred_type": ["distr"],
+        "info:y_type": ["numeric"],
+        "expected_loss_ndim": 2,
     }
 
     @classmethod
@@ -48,3 +58,10 @@ def prepare_test_inputs(cls, test_case):
             )
 
         return y_pred, y
+
+    @classmethod
+    def _get_test_dataloaders(cls, params=None):
+        """
+        Returns test dataloaders configured for LogNormalDistributionLoss.
+        """
+        super()._get_test_dataloaders_from(params=params, target="agency")

pytorch_forecasting/metrics/_distributions_pkg/_mqf2/_mqf2_distribution_loss_pkg.py

@@ -3,6 +3,7 @@
 """
 
 from pytorch_forecasting.data import TorchNormalizer
+from pytorch_forecasting.data.encoders import GroupNormalizer
 from pytorch_forecasting.metrics.base_metrics._base_object import _BasePtMetric
 
 
@@ -18,6 +19,13 @@ class MQF2DistributionLoss_pkg(_BasePtMetric):
         "python_dependencies": ["cpflows"],
         "capability:quantile_generation": True,
         "requires:data_type": "mqf2_distribution_forecast",
+        "clip_target": True,
+        "data_loader_kwargs": {
+            "target_normalizer": GroupNormalizer(
+                groups=["agency", "sku"], center=False, transformation="log1p"
+            )
+        },
+        "trainer_kwargs": dict(accelerator="cpu"),
     }
 
     @classmethod

pytorch_forecasting/metrics/_distributions_pkg/_multivariate_normal/_multivariate_normal_distribution_loss_pkg.py

@@ -2,6 +2,7 @@
 Package container for multivariate normal distribution loss metric.
 """
 
+from pytorch_forecasting.data.encoders import GroupNormalizer
 from pytorch_forecasting.metrics.base_metrics._base_object import _BasePtMetric
 
 
@@ -17,6 +18,14 @@ class MultivariateNormalDistributionLoss_pkg(_BasePtMetric):
         "distribution_type": "multivariate_normal",
         "info:metric_name": "MultivariateNormalDistributionLoss",
         "requires:data_type": "multivariate_normal_distribution_forecast",
+        "data_loader_kwargs": {
+            "target_normalizer": GroupNormalizer(
+                groups=["agency", "sku"], transformation="log1p"
+            )
+        },
+        "info:pred_type": ["distr"],
+        "info:y_type": ["numeric"],
+        "expected_loss_ndim": 2,
     }
 
     @classmethod
@@ -26,3 +35,10 @@ def get_cls(cls):
         )
 
         return MultivariateNormalDistributionLoss
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for MultivariateNormalDistributionLoss.
+        """
+        super()._get_test_dataloaders_from(params=params, target="agency")

pytorch_forecasting/metrics/_distributions_pkg/_negative_binomial/_negative_binomial_distribution_loss_pkg.py

@@ -3,6 +3,7 @@
 """
 
 from pytorch_forecasting.data import TorchNormalizer
+from pytorch_forecasting.data.encoders import GroupNormalizer
 from pytorch_forecasting.metrics.base_metrics._base_object import _BasePtMetric
 
 
@@ -16,6 +17,13 @@ class NegativeBinomialDistributionLoss_pkg(_BasePtMetric):
         "distribution_type": "negative_binomial",
         "info:metric_name": "NegativeBinomialDistributionLoss",
         "requires:data_type": "negative_binomial_distribution_forecast",
+        "clip_target": False,
+        "data_loader_kwargs": {
+            "target_normalizer": GroupNormalizer(groups=["agency", "sku"], center=False)
+        },
+        "info:pred_type": ["distr"],
+        "info:y_type": ["numeric"],
+        "expected_loss_ndim": 2,
     }
 
     @classmethod
@@ -32,3 +40,10 @@ def get_encoder(cls):
         Returns a TorchNormalizer instance for rescaling parameters.
         """
         return TorchNormalizer(center=False)
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for NegativeBinomialDistributionLoss.
+        """
+        super()._get_test_dataloaders_from(params, target="agency")

pytorch_forecasting/metrics/_distributions_pkg/_normal/_normal_distribution_loss_pkg.py

@@ -17,10 +17,20 @@ class NormalDistributionLoss_pkg(_BasePtMetric):
         "distribution_type": "normal",
         "info:metric_name": "NormalDistributionLoss",
         "requires:data_type": "normal_distribution_forecast",
+        "info:pred_type": ["distr"],
+        "info:y_type": ["numeric"],
+        "expected_loss_ndim": 2,
     }
 
     @classmethod
     def get_cls(cls):
         from pytorch_forecasting.metrics.distributions import NormalDistributionLoss
 
         return NormalDistributionLoss
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for NormalDistributionLoss.
+        """
+        super()._get_test_dataloaders_from(params=params, target="agency")

pytorch_forecasting/metrics/_point_pkg/_cross_entropy/_cross_entropy_pkg.py

@@ -18,10 +18,19 @@ class CrossEntropy_pkg(_BasePtMetric):
         "requires:data_type": "classification_forecast",
         "info:metric_name": "CrossEntropy",
         "no_rescaling": True,
+        "info:pred_type": ["point"],
+        "info:y_type": ["category"],
     }
 
     @classmethod
     def get_cls(cls):
         from pytorch_forecasting.metrics import CrossEntropy
 
         return CrossEntropy
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for CrossEntropy.
+        """
+        super()._get_test_dataloaders_from(params=params, target="category")

pytorch_forecasting/metrics/_point_pkg/_mae/_mae_pkg.py

@@ -16,10 +16,19 @@ class MAE_pkg(_BasePtMetric):
         "metric_type": "point",
         "requires:data_type": "point_forecast",
         "info:metric_name": "MAE",
+        "info:pred_type": ["point"],
+        "info:y_type": ["numeric"],
     }
 
     @classmethod
     def get_cls(cls):
         from pytorch_forecasting.metrics import MAE
 
         return MAE
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for MAE.
+        """
+        return super()._get_test_dataloaders_from(params=params, target="agency")

pytorch_forecasting/metrics/_point_pkg/_mape/_mape_pkg.py

@@ -18,10 +18,19 @@ class MAPE_pkg(_BasePtMetric):
         "metric_type": "point",
         "info:metric_name": "MAPE",
         "requires:data_type": "point_forecast",
+        "info:pred_type": ["point"],
+        "info:y_type": ["numeric"],
     }
 
     @classmethod
     def get_cls(cls):
         from pytorch_forecasting.metrics.point import MAPE
 
         return MAPE
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for MAPE.
+        """
+        return super()._get_test_dataloaders_from(params=params, target="agency")

pytorch_forecasting/metrics/_point_pkg/_mase/_mase_pkg.py

@@ -14,10 +14,19 @@ class MASE_pkg(_BasePtMetric):
         "metric_type": "point",
         "info:metric_name": "MASE",
         "requires:data_type": "point_forecast",
+        "info:pred_type": ["point"],
+        "info:y_type": ["numeric"],
     }
 
     @classmethod
     def get_cls(cls):
         from pytorch_forecasting.metrics import MASE
 
         return MASE
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for MASE.
+        """
+        return super()._get_test_dataloaders_from(params=params, target="agency")

pytorch_forecasting/metrics/_point_pkg/_poisson/_poisson_loss_pkg.py

@@ -18,10 +18,19 @@ class PoissonLoss_pkg(_BasePtMetric):
         "requires:data_type": "point_forecast",
         "capability:quantile_generation": True,
         "shape:adds_quantile_dimension": True,
+        "info:pred_type": ["point"],
+        "info:y_type": ["numeric"],
     }
 
     @classmethod
     def get_cls(cls):
         from pytorch_forecasting.metrics.point import PoissonLoss
 
         return PoissonLoss
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for PoissonLoss.
+        """
+        return super()._get_test_dataloaders_from(params=params, target="agency")

pytorch_forecasting/metrics/_point_pkg/_rmse/_rmse_pkg.py

@@ -16,10 +16,19 @@ class RMSE_pkg(_BasePtMetric):
         "metric_type": "point",
         "info:metric_name": "RMSE",
         "requires:data_type": "point_forecast",
+        "info:pred_type": ["point"],
+        "info:y_type": ["numeric"],
     }  # noqa: E501
 
     @classmethod
     def get_cls(cls):
         from pytorch_forecasting.metrics.point import RMSE
 
         return RMSE
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for RMSE.
+        """
+        return super()._get_test_dataloaders_from(params=params, target="agency")

pytorch_forecasting/metrics/_point_pkg/_smape/_smape_pkg.py

@@ -18,10 +18,19 @@ class SMAPE_pkg(_BasePtMetric):
         "metric_type": "point",
         "info:metric_name": "SMAPE",
         "requires:data_type": "point_forecast",
+        "info:pred_type": ["point"],
+        "info:y_type": ["numeric"],
     }  # noqa: E501
 
     @classmethod
     def get_cls(cls):
         from pytorch_forecasting.metrics.point import SMAPE
 
         return SMAPE
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for SMAPE.
+        """
+        return super()._get_test_dataloaders_from(params=params, target="agency")

pytorch_forecasting/metrics/_point_pkg/_tweedie/_tweedie_loss_pkg.py

@@ -16,10 +16,19 @@ class TweedieLoss_pkg(_BasePtMetric):
         "metric_type": "point",
         "info:metric_name": "TweedieLoss",
         "requires:data_type": "point_forecast",
+        "info:pred_type": ["point"],
+        "info:y_types": ["numeric"],
     }  # noqa: E501
 
     @classmethod
     def get_cls(cls):
         from pytorch_forecasting.metrics.point import TweedieLoss
 
         return TweedieLoss
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for TweedieLoss.
+        """
+        return super()._get_test_dataloaders_from(params, target="agency")

pytorch_forecasting/metrics/_quantile_pkg/_quantile_loss_pkg.py

@@ -16,6 +16,8 @@ class QuantileLoss_pkg(_BasePtMetric):
         "metric_type": "quantile",
         "info:metric_name": "QuantileLoss",
         "requires:data_type": "quantile_forecast",
+        "info:pred_type": ["quantile"],
+        "info:y_type": ["numeric"],
     }  # noqa: E501
 
     @classmethod
@@ -34,3 +36,10 @@ def get_metric_test_params(cls):
                 "quantiles": [0.2, 0.5],
             },
         ]
+
+    @classmethod
+    def _get_test_dataloaders_from(cls, params=None):
+        """
+        Returns test dataloaders configured for QuantileLoss.
+        """
+        return super()._get_test_dataloaders_from(params, target="agency")