dev -> release

.github/workflows/python-publish-ezmsg-learn.yml

@@ -17,7 +17,7 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v2
+        uses: astral-sh/setup-uv@v6
 
       - name: Build Package
         run: uv build

.github/workflows/python-tests.yml

@@ -2,10 +2,13 @@ name: Test package
 
 on:
   push:
-    branches: [main]
+    branches:
+      - main
+      - dev
   pull_request:
     branches:
       - main
+      - dev
   workflow_dispatch:
 
 jobs:
@@ -23,16 +26,12 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v2
+        uses: astral-sh/setup-uv@v6
         with:
-          enable-cache: true
-          cache-dependency-glob: "uv.lock"
-
-      - name: Set up Python ${{ matrix.python-version }}
-        run: uv python install ${{ matrix.python-version }}
+          python-version: ${{ matrix.python-version }}
 
       - name: Install the project
-        run: uv sync --all-extras
+        run: uv sync
 
       - name: Lint
         run:

.pre-commit-config.yaml

@@ -0,0 +1,7 @@
+repos:
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: v0.11.12
+  hooks:
+    - id: ruff
+      args: [ --fix ]
+    - id: ruff-format

pyproject.toml

@@ -25,6 +25,7 @@ lint = [
     "ruff>=0.12.9",
 ]
 test = [
+    "hmmlearn>=0.3.3",
     "pytest>=8.4.1",
 ]
 
@@ -40,3 +41,9 @@ version-file = "src/ezmsg/learn/__version__.py"
 
 [tool.hatch.build.targets.wheel]
 packages = ["src/ezmsg"]
+
+[tool.pytest.ini_options]
+pythonpath = [
+    "src",
+    ".",
+]

src/ezmsg/learn/model/mlp.py

@@ -0,0 +1,133 @@
+import torch
+import torch.nn
+
+
+class MLP(torch.nn.Module):
+    """
+    A simple Multi-Layer Perceptron (MLP) model. Adapted from Ezmsg MLP.
+
+    Attributes:
+        feature_extractor (torch.nn.Sequential): The sequential feature extractor part of the MLP.
+        heads (torch.nn.ModuleDict): A dictionary of output linear layers for each output head.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int | list[int],
+        num_layers: int | None = None,
+        output_heads: int | dict[str, int] = 2,
+        norm_layer: str | None = None,
+        activation_layer: str | None = "ReLU",
+        inplace: bool | None = None,
+        bias: bool = True,
+        dropout: float = 0.0,
+    ):
+        """
+        Initialize the MLP model.
+        Args:
+            input_size (int): The size of the input features.
+            hidden_size (int | list[int]): The sizes of the hidden layers. If a list, num_layers must be None or the length
+                of the list. If a single integer, num_layers must be specified and determines the number of hidden layers.
+            num_layers (int, optional): The number of hidden layers. Length of hidden_size if None. Default is None.
+            output_heads (int | dict[str, int], optional): Number of output features or classes if single head output or a
+                dictionary mapping head names to output sizes if multi-head output. Default is 2 (single head).
+            norm_layer (str, optional): A normalization layer to be applied after each linear layer. Default is None.
+                Common choices are "BatchNorm1d" or "LayerNorm".
+            activation_layer (str, optional): An activation function to be applied after each normalization
+                layer. Default is "ReLU".
+            inplace (bool, optional): Whether the activation function is performed in-place. Default is None.
+            bias (bool, optional): Whether to use bias in the linear layers. Default is True.
+            dropout (float, optional): The dropout rate to be applied after each linear layer. Default is 0.0.
+        """
+        super().__init__()
+        if isinstance(hidden_size, int):
+            if num_layers is None:
+                raise ValueError(
+                    "If hidden_size is an integer, num_layers must be specified."
+                )
+            hidden_size = [hidden_size] * num_layers
+        if len(hidden_size) == 0:
+            raise ValueError("hidden_size must have at least one element")
+        if any(not isinstance(x, int) for x in hidden_size):
+            raise ValueError("hidden_size must contain only integers")
+        if num_layers is not None and len(hidden_size) != num_layers:
+            raise ValueError(
+                "Length of hidden_size must match num_layers if num_layers is specified."
+            )
+
+        params = {} if inplace is None else {"inplace": inplace}
+
+        layers = []
+        in_dim = input_size
+
+        def _get_layer_class(layer_name: str):
+            if layer_name is not None and "torch.nn" in layer_name:
+                return getattr(torch.nn, layer_name.rsplit(".", 1)[1])
+            return None
+
+        norm_layer_class = _get_layer_class(norm_layer)
+        activation_layer_class = _get_layer_class(activation_layer)
+        for hidden_dim in hidden_size[:-1]:
+            layers.append(torch.nn.Linear(in_dim, hidden_dim, bias=bias))
+            if norm_layer_class is not None:
+                layers.append(norm_layer_class(hidden_dim))
+            if activation_layer_class is not None:
+                layers.append(activation_layer_class(**params))
+            layers.append(torch.nn.Dropout(dropout, **params))
+            in_dim = hidden_dim
+
+        layers.append(torch.nn.Linear(in_dim, hidden_size[-1], bias=bias))
+
+        self.feature_extractor = torch.nn.Sequential(*layers)
+
+        if isinstance(output_heads, int):
+            output_heads = {"output": output_heads}
+        self.heads = torch.nn.ModuleDict(
+            {
+                name: torch.nn.Linear(hidden_size[-1], output_size)
+                for name, output_size in output_heads.items()
+            }
+        )
+
+    @classmethod
+    def infer_config_from_state_dict(cls, state_dict: dict) -> dict[str, int | float]:
+        """
+        Infer the configuration from the state dict.
+
+        Args:
+            state_dict: The state dict of the model.
+
+        Returns:
+            dict[str, int | float]: A dictionary containing the inferred configuration.
+        """
+        input_size = state_dict["feature_extractor.0.weight"].shape[1]
+        hidden_size = [
+            param.shape[0]
+            for key, param in state_dict.items()
+            if key.startswith("feature_extractor.") and key.endswith(".weight")
+        ]
+        output_heads = {
+            key.split(".")[1]: param.shape[0]
+            for key, param in state_dict.items()
+            if key.startswith("heads.") and key.endswith(".bias")
+        }
+
+        return {
+            "input_size": input_size,
+            "hidden_size": hidden_size,
+            "output_heads": output_heads,
+        }
+
+    def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]:
+        """
+        Forward pass through the MLP.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape (batch, seq_len, input_size).
+
+        Returns:
+            dict[str, torch.Tensor]: A dictionary mapping head names to output tensors.
+        """
+        x = self.feature_extractor(x)
+        return {name: head(x) for name, head in self.heads.items()}