Swap to using nn.Embedding

.github/workflows/test.yml

@@ -8,12 +8,17 @@ on:
     paths: ["**/*.py", .github/workflows/test.yml]
     branches: [main]
 
+concurrency:
+  # Cancel only on same PR number
+  group: ${{ github.workflow }}-pr-${{ github.event.pull_request.number }}
+  cancel-in-progress: true
+
 jobs:
   tests:
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest, macos-14]
+        os: [ubuntu-latest, macos-latest, windows-latest]
         version:
           - { python: "3.10", resolution: highest }
           - { python: "3.12", resolution: lowest-direct }
@@ -33,7 +38,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip install torch --index-url https://download.pytorch.org/whl/cpu
+          uv pip install torch --index-url https://download.pytorch.org/whl/cpu --system
           uv pip install .[test] --system
 
       - name: Run Tests

.pre-commit-config.yaml

@@ -45,4 +45,4 @@ repos:
     rev: 0.8.1
     hooks:
       - id: nbstripout
-        args: [--drop-empty-cells, --keep-output]
+        args: [--drop-empty-cells]

aviary/cgcnn/data.py

@@ -1,5 +1,4 @@
 import itertools
-import json
 from collections.abc import Sequence
 from functools import cache
 from typing import Any
@@ -12,8 +11,6 @@
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
-from aviary import PKG_DIR
-
 
 class CrystalGraphData(Dataset):
     """Dataset class for the CGCNN structure model."""
@@ -22,58 +19,32 @@ def __init__(
         self,
         df: pd.DataFrame,
         task_dict: dict[str, str],
-        elem_embedding: str = "cgcnn92",
         structure_col: str = "structure",
         identifiers: Sequence[str] = (),
-        radius: float = 5,
+        radius_cutoff: float = 5,
         max_num_nbr: int = 12,
-        dmin: float = 0,
-        step: float = 0.2,
     ):
         """Featurize crystal structures into neighborhood graphs with this data class
         for CGCNN.
 
         Args:
             df (pd.Dataframe): Pandas dataframe holding input and target values.
             task_dict ({target: task}): task dict for multi-task learning
-            elem_embedding (str, optional): One of matscholar200, cgcnn92, megnet16,
-                onehot112 or path to a file with custom element embeddings.
-                Defaults to matscholar200.
             structure_col (str, optional): df column holding pymatgen Structure objects
                 as input.
             identifiers (list[str], optional): df columns for distinguishing data
                 points. Will be copied over into the model's output CSV. Defaults to ().
-            radius (float, optional): Cut-off radius for neighborhood. Defaults to 5.
+            radius_cutoff (float, optional): Cut-off radius for neighborhood.
+                Defaults to 5.
             max_num_nbr (int, optional): maximum number of neighbors to consider.
                 Defaults to 12.
-            dmin (float, optional): minimum distance in Gaussian basis. Defaults to 0.
-            step (float, optional): increment size of Gaussian basis. Defaults to 0.2.
         """
         self.task_dict = task_dict
         self.identifiers = list(identifiers)
 
-        self.radius = radius
+        self.radius_cutoff = radius_cutoff
         self.max_num_nbr = max_num_nbr
 
-        if elem_embedding in ("matscholar200", "cgcnn92", "megnet16", "onehot112"):
-            elem_embedding = f"{PKG_DIR}/embeddings/element/{elem_embedding}.json"
-
-        with open(elem_embedding) as file:
-            self.elem_features = json.load(file)
-
-        for key, value in self.elem_features.items():
-            self.elem_features[key] = np.array(value, dtype=float)
-            if not hasattr(self, "elem_emb_len"):
-                self.elem_emb_len = len(value)
-            elif self.elem_emb_len != len(value):
-                raise ValueError(
-                    f"Element embedding length mismatch: len({key})="
-                    f"{len(value)}, expected {self.elem_emb_len}"
-                )
-
-        self.gaussian_dist_func = GaussianDistance(dmin=dmin, dmax=radius, step=step)
-        self.nbr_fea_dim = self.gaussian_dist_func.embedding_size
-
         self.df = df
         self.structure_col = structure_col
 
@@ -84,7 +55,7 @@ def __init__(
             self.df[structure_col].items(), total=len(df), desc=desc, disable=None
         ):
             self_idx, nbr_idx, _ = get_structure_neighbor_info(
-                struct, radius, max_num_nbr
+                struct, self.radius_cutoff, self.max_num_nbr
             )
             material_ids = [idx, *self.df.loc[idx][self.identifiers]]
             if 0 in (len(self_idx), len(nbr_idx)):
@@ -140,16 +111,10 @@ def __getitem__(self, idx: int):
         material_ids = [self.df.index[idx], *row[self.identifiers]]
 
         # atom features for disordered sites
-        site_atoms = [atom.species.as_dict() for atom in struct]
-        atom_features = np.vstack(
-            [
-                np.sum([self.elem_features[el] * amt for el, amt in site.items()], axis=0)
-                for site in site_atoms
-            ]
-        )
+        atom_features = [atom.specie.Z for atom in struct]
 
         self_idx, nbr_idx, nbr_dist = get_structure_neighbor_info(
-            struct, self.radius, self.max_num_nbr
+            struct, self.radius_cutoff, self.max_num_nbr
         )
 
         if len(self_idx) == 0:
@@ -161,9 +126,7 @@ def __getitem__(self, idx: int):
         if set(self_idx) != set(range(len(struct))):
             raise ValueError(f"At least one atom in {material_ids} is isolated")
 
-        nbr_dist = self.gaussian_dist_func.expand(nbr_dist)
-
-        atom_fea_t = Tensor(atom_features)
+        atom_fea_t = LongTensor(atom_features)
         nbr_dist_t = Tensor(nbr_dist)
         self_idx_t = LongTensor(self_idx)
         nbr_idx_t = LongTensor(nbr_idx)
@@ -278,27 +241,25 @@ def __init__(
                 "Max radii below minimum radii + step size - please increase dmax."
             )
 
-        self.filter = np.arange(dmin, dmax + step, step)
+        self.filter = torch.arange(dmin, dmax + step, step)
         self.embedding_size = len(self.filter)
 
         if var is None:
             var = step
 
         self.var = var
 
-    def expand(self, distances: np.ndarray) -> np.ndarray:
+    def expand(self, distances: Tensor) -> Tensor:
         """Apply Gaussian distance filter to a numpy distance array.
 
         Args:
             distances (ArrayLike): A distance matrix of any shape.
 
         Returns:
-            np.ndarray: Expanded distance matrix with the last dimension of length
+            Tensor: Expanded distance matrix with the last dimension of length
                 len(self.filter)
         """
-        distances = np.array(distances)
-
-        return np.exp(-((distances[..., None] - self.filter) ** 2) / self.var**2)
+        return torch.exp(-((distances[..., None] - self.filter) ** 2) / self.var**2)
 
 
 def get_structure_neighbor_info(

aviary/cgcnn/model.py

@@ -5,9 +5,11 @@
 from pymatgen.util.due import Doi, due
 from torch import LongTensor, Tensor, nn
 
+from aviary.cgcnn.data import GaussianDistance
 from aviary.core import BaseModelClass
 from aviary.networks import SimpleNetwork
 from aviary.scatter import scatter_reduce
+from aviary.utils import get_element_embedding
 
 
 @due.dcite(Doi("10.1103/PhysRevLett.120.145301"), description="CGCNN model")
@@ -25,8 +27,10 @@ def __init__(
         self,
         robust: bool,
         n_targets: Sequence[int],
-        elem_emb_len: int,
-        nbr_fea_len: int,
+        elem_embedding: str = "cgcnn92",
+        radius_cutoff: float = 5.0,
+        radius_min: float = 0.0,
+        radius_step: float = 0.2,
         elem_fea_len: int = 64,
         n_graph: int = 4,
         h_fea_len: int = 128,
@@ -42,8 +46,15 @@ def __init__(
                 (uncertainty inherent to the sample) which can be used with a robust
                 loss function to attenuate the weighting of uncertain samples.
             n_targets (list[int]): Number of targets to train on
-            elem_emb_len (int): Number of atom features in the input.
-            nbr_fea_len (int): Number of bond features.
+            elem_embedding (str, optional): One of matscholar200, cgcnn92, megnet16,
+                onehot112 or path to a file with custom element embeddings.
+                Defaults to matscholar200.
+            radius_cutoff (float, optional): Cut-off radius for neighborhood.
+                Defaults to 5.
+            radius_min (float, optional): minimum distance in Gaussian basis.
+                Defaults to 0.
+            radius_step (float, optional): increment size of Gaussian basis.
+                Defaults to 0.2.
             elem_fea_len (int, optional): Number of hidden atom features in the
                 convolutional layers. Defaults to 64.
             n_graph (int, optional): Number of convolutional layers. Defaults to 4.
@@ -57,6 +68,14 @@ def __init__(
         """
         super().__init__(robust=robust, **kwargs)
 
+        self.elem_embedding = get_element_embedding(elem_embedding)
+        elem_emb_len = self.elem_embedding.weight.shape[1]
+
+        self.gaussian_dist_func = GaussianDistance(
+            dmin=radius_min, dmax=radius_cutoff, step=radius_step
+        )
+        nbr_fea_len = self.gaussian_dist_func.embedding_size
+
         desc_dict = {
             "elem_emb_len": elem_emb_len,
             "nbr_fea_len": nbr_fea_len,
@@ -107,6 +126,9 @@ def forward(
         Returns:
             tuple[Tensor, ...]: tuple of predictions for all targets
         """
+        nbr_fea = self.gaussian_dist_func.expand(nbr_fea)
+        atom_fea = self.elem_embedding(atom_fea)
+
         atom_fea = self.node_nn(atom_fea, nbr_fea, self_idx, nbr_idx)
 
         crys_fea = scatter_reduce(atom_fea, crystal_atom_idx, dim=0, reduce="mean")

aviary/core.py

@@ -32,6 +32,7 @@ def __init__(
         epoch: int = 0,
         device: str | None = None,
         best_val_scores: dict[str, float] | None = None,
+        **kwargs,
     ) -> None:
         """Store core model parameters.
 
@@ -47,6 +48,7 @@ def __init__(
             device (str, optional): Device to store the model parameters on.
             best_val_scores (dict[str, float], optional): Validation score to use for
                 early stopping. Defaults to None.
+            **kwargs: Additional keyword arguments.
         """
         super().__init__()
         self.task_dict = task_dict
@@ -299,8 +301,9 @@ def evaluate(
                         preds = output.squeeze(1)
                         loss = loss_func(preds, targets)
 
-                    z_scored_error = preds - targets
-                    error = normalizer.std * z_scored_error.data.cpu()
+                    denormed_preds = normalizer.denorm(preds)
+                    denormed_targets = normalizer.denorm(targets)
+                    error = denormed_preds - denormed_targets
                     target_metrics["MAE"].append(float(error.abs().mean()))
                     target_metrics["MSE"].append(float(error.pow(2).mean()))
 

aviary/roost/data.py

@@ -1,4 +1,3 @@
-import json
 from collections.abc import Sequence
 from functools import cache
 from typing import Any
@@ -10,8 +9,6 @@
 from torch import LongTensor, Tensor
 from torch.utils.data import Dataset
 
-from aviary import PKG_DIR
-
 
 class CompositionData(Dataset):
     """Dataset class for the Roost composition model."""
@@ -20,7 +17,6 @@ def __init__(
         self,
         df: pd.DataFrame,
         task_dict: dict[str, str],
-        elem_embedding: str = "matscholar200",
         inputs: str = "composition",
         identifiers: Sequence[str] = ("material_id", "composition"),
     ):
@@ -47,14 +43,6 @@ def __init__(
         self.identifiers = list(identifiers)
         self.df = df
 
-        if elem_embedding in ["matscholar200", "cgcnn92", "megnet16", "onehot112"]:
-            elem_embedding = f"{PKG_DIR}/embeddings/element/{elem_embedding}.json"
-
-        with open(elem_embedding) as file:
-            self.elem_features = json.load(file)
-
-        self.elem_emb_len = len(next(iter(self.elem_features.values())))
-
         self.n_targets = []
         for target, task in self.task_dict.items():
             if task == "regression":
@@ -88,24 +76,12 @@ def __getitem__(self, idx: int):
         composition = row[self.inputs]
         material_ids = row[self.identifiers].to_list()
 
-        comp_dict = Composition(composition).get_el_amt_dict()
-        elements = list(comp_dict)
-
+        comp_dict = Composition(composition).fractional_composition
         weights = list(comp_dict.values())
         weights = np.atleast_2d(weights).T / np.sum(weights)
+        elem_fea = [elem.Z for elem in comp_dict]
 
-        try:
-            elem_fea = np.vstack([self.elem_features[element] for element in elements])
-        except AssertionError as exc:
-            raise AssertionError(
-                f"{material_ids} contains element types not in embedding"
-            ) from exc
-        except ValueError as exc:
-            raise ValueError(
-                f"{material_ids} composition cannot be parsed into elements"
-            ) from exc
-
-        n_elems = len(elements)
+        n_elems = len(comp_dict)
         self_idx = []
         nbr_idx = []
         for elem_idx in range(n_elems):
@@ -114,7 +90,7 @@ def __getitem__(self, idx: int):
 
         # convert all data to tensors
         elem_weights = Tensor(weights)
-        elem_fea = Tensor(elem_fea)
+        elem_fea = LongTensor(elem_fea)
         self_idx = LongTensor(self_idx)
         nbr_idx = LongTensor(nbr_idx)
 

aviary/roost/model.py

@@ -8,6 +8,7 @@
 from aviary.core import BaseModelClass
 from aviary.networks import ResidualNetwork, SimpleNetwork
 from aviary.segments import MessageLayer, WeightedAttentionPooling
+from aviary.utils import get_element_embedding
 
 
 @due.dcite(Doi("10.1038/s41467-020-19964-7"), description="Roost model")
@@ -25,7 +26,7 @@ def __init__(
         self,
         robust: bool,
         n_targets: Sequence[int],
-        elem_emb_len: int,
+        elem_embedding: str = "matscholar200",
         elem_fea_len: int = 64,
         n_graph: int = 3,
         elem_heads: int = 3,
@@ -41,6 +42,8 @@ def __init__(
         """Composition-only model."""
         super().__init__(robust=robust, **kwargs)
 
+        self.elem_embedding = get_element_embedding(elem_embedding)
+        elem_emb_len = self.elem_embedding.weight.shape[1]
         desc_dict = {
             "elem_emb_len": elem_emb_len,
             "elem_fea_len": elem_fea_len,
@@ -60,6 +63,7 @@ def __init__(
             "n_targets": n_targets,
             "out_hidden": out_hidden,
             "trunk_hidden": trunk_hidden,
+            "elem_embedding": elem_embedding,
             **desc_dict,
         }
         self.model_params.update(model_params)
@@ -83,6 +87,8 @@ def forward(
         cry_elem_idx: LongTensor,
     ) -> tuple[Tensor, ...]:
         """Forward pass through the material_nn and output_nn."""
+        elem_fea = self.elem_embedding(elem_fea)
+
         crys_fea = self.material_nn(
             elem_weights, elem_fea, self_idx, nbr_idx, cry_elem_idx
         )