Merge branch 'main' into add-nbeats-v2

Author: harshsomankar123-tech

.github/workflows/pypi_release.yml

@@ -12,7 +12,7 @@ jobs:
     steps:
       - uses: actions/checkout@v7
 
-      - uses: actions/setup-python@v6
+      - uses: actions/setup-python@v7
         with:
           python-version: '3.11'
 
@@ -34,11 +34,12 @@ jobs:
   build_wheels:
     name: Build wheels
     runs-on: ubuntu-latest
+    needs: [check_tag]
 
     steps:
       - uses: actions/checkout@v7
 
-      - uses: actions/setup-python@v6
+      - uses: actions/setup-python@v7
         with:
           python-version: '3.11'
 
@@ -67,7 +68,7 @@ jobs:
       - uses: actions/checkout@v7
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v7
         with:
           python-version: ${{ matrix.python-version }}
 

.github/workflows/test.yml

@@ -22,7 +22,7 @@ jobs:
         uses: actions/checkout@v7
 
       - name: python environment step
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v7
         with:
           python-version: "3.11"
 
@@ -59,7 +59,7 @@ jobs:
     steps:
       - uses: actions/checkout@v7
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v7
         with:
           python-version: "3.11"
       - name: Install dependencies
@@ -88,7 +88,7 @@ jobs:
       - uses: actions/checkout@v7
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v7
         with:
           python-version: ${{ matrix.python-version }}
 
@@ -132,7 +132,7 @@ jobs:
           enable-cache: true
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v7
         with:
           python-version: ${{ matrix.python-version }}
 
@@ -172,7 +172,7 @@ jobs:
       - uses: actions/checkout@v7
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v7
         with:
           python-version: ${{ matrix.python-version }}
 

docs/source/m_layer_v2.rst

@@ -47,3 +47,4 @@ See the detailed API documentation for the V2 base classes and specific model im
    models.samformer._samformer_v2.Samformer
    models.tide._tide_dsipts._tide_v2.TIDE
    models.timexer._timexer_v2.TimeXer
+   models.mlp._decodermlp_v2.DecoderMLP_v2

docs/source/pkg_v2.rst

@@ -99,3 +99,4 @@ See the detailed API documentation for the available V2 Package classes below:
    models.samformer._samformer_v2_pkg.Samformer_pkg_v2
    models.tide._tide_dsipts._tide_v2_pkg.TIDE_pkg_v2
    models.timexer._timexer_pkg_v2.TimeXer_pkg_v2
+   models.mlp._decodermlp_pkg_v2.DecoderMLP_pkg_v2

pyproject.toml

@@ -33,7 +33,7 @@ dependencies = [
   "scipy >=1.8,<2.0",
   "pandas >=1.3.0,<3.1.0",
   "scikit-learn >=1.2,<2.0",
-  "scikit-base <1.1.0",
+  "scikit-base <1.2.0",
 ]
 
 [project.optional-dependencies]

pytorch_forecasting/layers/__init__.py

@@ -19,6 +19,7 @@
     Encoder,
     EncoderLayer,
 )
+from pytorch_forecasting.layers._mlp import FullyConnectedModule
 from pytorch_forecasting.layers._normalization import RevIN
 from pytorch_forecasting.layers._output._flatten_head import (
     FlattenHead,
@@ -54,4 +55,5 @@
     "RevIN",
     "ResidualBlock",
     "embedding_cat_variables",
+    "FullyConnectedModule",
 ]

pytorch_forecasting/layers/_mlp/__init__.py

@@ -0,0 +1,7 @@
+"""
+Fully connected (MLP) layers.
+"""
+
+from pytorch_forecasting.layers._mlp._fully_connected import FullyConnectedModule
+
+__all__ = ["FullyConnectedModule"]

pytorch_forecasting/layers/_mlp/_fully_connected.py

@@ -0,0 +1,52 @@
+"""
+Fully connected (MLP) module.
+"""
+
+import torch
+from torch import nn
+
+
+class FullyConnectedModule(nn.Module):
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int,
+        hidden_size: int,
+        n_hidden_layers: int,
+        activation_class: nn.ReLU,
+        dropout: float = None,
+        norm: bool = True,
+    ):
+        super().__init__()
+        self.input_size = input_size
+        self.output_size = output_size
+        self.hidden_size = hidden_size
+        self.n_hidden_layers = n_hidden_layers
+        self.activation_class = activation_class
+        self.dropout = dropout
+        self.norm = norm
+
+        # input layer
+        module_list = [nn.Linear(input_size, hidden_size), activation_class()]
+        if dropout is not None:
+            module_list.append(nn.Dropout(dropout))
+        if norm:
+            module_list.append(nn.LayerNorm(hidden_size))
+        # hidden layers
+        for _ in range(n_hidden_layers):
+            module_list.extend(
+                [nn.Linear(hidden_size, hidden_size), activation_class()]
+            )
+            if dropout is not None:
+                module_list.append(nn.Dropout(dropout))
+            if norm:
+                module_list.append(nn.LayerNorm(hidden_size))
+        # output layer
+        module_list.append(nn.Linear(hidden_size, output_size))
+
+        self.sequential = nn.Sequential(*module_list)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x of shape: batch_size x n_timesteps_in
+        # output of shape batch_size x n_timesteps_out
+        return self.sequential(x)

pytorch_forecasting/metrics/base_metrics/_base_metrics.py

@@ -689,18 +689,22 @@ def to_quantiles(self, y_pred: torch.Tensor, **kwargs) -> torch.Tensor:
         return self._metrics[0].to_quantiles(y_pred, **kwargs)
 
     def __add__(self, metric: LightningMetric):
+        new_metrics = list(self._metrics)
+        new_weights = list(self._weights)
         if isinstance(metric, self.__class__):
-            self._metrics.extend(metric._metrics)
-            self._weights.extend(metric._weights)
+            new_metrics.extend(metric._metrics)
+            new_weights.extend(metric._weights)
         else:
-            self._metrics.append(metric)
-            self._weights.append(1.0)
+            new_metrics.append(metric)
+            new_weights.append(1.0)
 
-        return self
+        result = CompositeMetric(metrics=new_metrics, weights=new_weights)
+        return result
 
     def __mul__(self, multiplier: float):
-        self._weights = [w * multiplier for w in self._weights]
-        return self
+        new_weights = [w * multiplier for w in self._weights]
+        result = CompositeMetric(metrics=list(self._metrics), weights=new_weights)
+        return result
 
     __rmul__ = __mul__
 

pytorch_forecasting/models/mlp/__init__.py

@@ -2,6 +2,14 @@
 
 from pytorch_forecasting.models.mlp._decodermlp import DecoderMLP
 from pytorch_forecasting.models.mlp._decodermlp_pkg import DecoderMLP_pkg
+from pytorch_forecasting.models.mlp._decodermlp_pkg_v2 import DecoderMLP_pkg_v2
+from pytorch_forecasting.models.mlp._decodermlp_v2 import DecoderMLP_v2
 from pytorch_forecasting.models.mlp.submodules import FullyConnectedModule
 
-__all__ = ["DecoderMLP", "DecoderMLP_pkg", "FullyConnectedModule"]
+__all__ = [
+    "DecoderMLP",
+    "DecoderMLP_pkg",
+    "DecoderMLP_v2",
+    "DecoderMLP_pkg_v2",
+    "FullyConnectedModule",
+]