feat: Add v2 interface support for N-BEATS model

Author: harshsomankar123-tech

Dockerfile

@@ -0,0 +1,9 @@
+FROM public.ecr.aws/d3j8x8q7/olympus-base-python:latest
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install --no-cache-dir .[dev,all_extras]
+
+CMD ["/bin/bash"]

pytorch_forecasting/models/nbeats/__init__.py

@@ -14,13 +14,15 @@
 from pytorch_forecasting.models.nbeats._nbeats import NBeats
 from pytorch_forecasting.models.nbeats._nbeats_adapter import NBeatsAdapter
 from pytorch_forecasting.models.nbeats._nbeats_pkg import NBeats_pkg
+from pytorch_forecasting.models.nbeats._nbeats_pkg_v2 import NBeats_pkg_v2
 from pytorch_forecasting.models.nbeats._nbeatskan import NBeatsKAN
 from pytorch_forecasting.models.nbeats._nbeatskan_pkg import NBeatsKAN_pkg
 
 __all__ = [
     "NBeats",
     "NBeatsKAN",
     "NBeats_pkg",
+    "NBeats_pkg_v2",
     "NBeatsKAN_pkg",
     "NBEATSGenericBlock",
     "NBEATSSeasonalBlock",

pytorch_forecasting/models/nbeats/_nbeats_pkg_v2.py

@@ -0,0 +1,79 @@
+"""NBeats package container."""
+
+from pytorch_forecasting.base._base_pkg import Base_pkg
+
+
+class NBeats_pkg_v2(Base_pkg):
+    """NBeats package container."""
+
+    _tags = {
+        "info:name": "NBeats",
+        "authors": ["jdb78"],
+        "capability:exogenous": False,
+        "capability:multivariate": False,
+        "capability:pred_int": True,
+        "capability:flexible_history_length": False,
+    }
+
+    @classmethod
+    def get_cls(cls):
+        """Get model class."""
+        from pytorch_forecasting.models.nbeats._nbeats_v2 import NBeats
+
+        return NBeats
+
+    @classmethod
+    def get_datamodule_cls(cls):
+        """Get the underlying DataModule class."""
+        from pytorch_forecasting.data.data_module import (
+            EncoderDecoderTimeSeriesDataModule,
+        )
+
+        return EncoderDecoderTimeSeriesDataModule
+
+    @classmethod
+    def get_test_train_params(cls):
+        """Return testing parameter settings for the trainer.
+
+        Returns
+        -------
+        params : dict or list of dict, default = {}
+            Parameters to create testing instances of the class
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+            `create_test_instance` uses the first (or only) dictionary in `params`
+        """
+        from pytorch_forecasting.metrics import QuantileLoss
+
+        params = [
+            {},
+            dict(
+                stack_types=["generic"],
+                num_blocks=[1],
+                num_block_layers=[4],
+                widths=[16],
+                backcast_loss_ratio=1.0,
+            ),
+            dict(
+                stack_types=["trend", "seasonality"],
+                num_blocks=[2, 2],
+                widths=[16, 32],
+                backcast_loss_ratio=0.5,
+            ),
+            dict(
+                loss=QuantileLoss(quantiles=[0.1, 0.5, 0.9]),
+                stack_types=["generic"],
+                num_blocks=[1],
+                widths=[16],
+            ),
+        ]
+
+        default_dm_cfg = {"max_encoder_length": 8, "max_prediction_length": 2}
+
+        for param in params:
+            current_dm_cfg = param.get("datamodule_cfg", {})
+            default_dm_cfg.update(current_dm_cfg)
+
+            param["datamodule_cfg"] = default_dm_cfg
+
+        return params