Fix checkpoint_model

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.3
+    rev: v0.5.5
     hooks:
       - id: ruff
         args: [--fix]

aviary/cgcnn/data.py

@@ -148,9 +148,7 @@ def __getitem__(self, idx: int):
         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
-                )
+                np.sum([self.elem_features[el] * amt for el, amt in site.items()], axis=0)
                 for site in site_atoms
             ]
         )

aviary/core.py

@@ -261,9 +261,7 @@ def evaluate(
         # *_ discards identifiers like material_id and formula which we don't need when
         # training tqdm(disable=None) means suppress output in non-tty (e.g. CI/log
         # files) but keep in terminal (i.e. tty mode) https://git.io/JnBOi
-        for inputs, targets_list, *_ in tqdm(
-            data_loader, disable=None if pbar else True
-        ):
+        for inputs, targets_list, *_ in tqdm(data_loader, disable=None if pbar else True):
             inputs = [  # noqa: PLW2901
                 tensor.to(self.device) if hasattr(tensor, "to") else tensor
                 for tensor in inputs

aviary/networks.py

@@ -34,15 +34,15 @@ def __init__(
         dims = [input_dim, *list(hidden_layer_dims)]
 
         self.fcs = nn.ModuleList(
-            nn.Linear(dims[i], dims[i + 1]) for i in range(len(dims) - 1)
+            nn.Linear(dims[idx], dims[idx + 1]) for idx in range(len(dims) - 1)
         )
 
         if batch_norm:
             self.bns = nn.ModuleList(
-                nn.BatchNorm1d(dims[i + 1]) for i in range(len(dims) - 1)
+                nn.BatchNorm1d(dims[idx + 1]) for idx in range(len(dims) - 1)
             )
         else:
-            self.bns = nn.ModuleList(nn.Identity() for i in range(len(dims) - 1))
+            self.bns = nn.ModuleList(nn.Identity() for _ in range(len(dims) - 1))
 
         self.acts = nn.ModuleList(activation() for _ in range(len(dims) - 1))
 
@@ -95,21 +95,21 @@ def __init__(
         dims = [input_dim, *list(hidden_layer_dims)]
 
         self.fcs = nn.ModuleList(
-            nn.Linear(dims[i], dims[i + 1]) for i in range(len(dims) - 1)
+            nn.Linear(dims[idx], dims[idx + 1]) for idx in range(len(dims) - 1)
         )
 
         if batch_norm:
             self.bns = nn.ModuleList(
-                nn.BatchNorm1d(dims[i + 1]) for i in range(len(dims) - 1)
+                nn.BatchNorm1d(dims[idx + 1]) for idx in range(len(dims) - 1)
             )
         else:
-            self.bns = nn.ModuleList(nn.Identity() for i in range(len(dims) - 1))
+            self.bns = nn.ModuleList(nn.Identity() for _ in range(len(dims) - 1))
 
         self.res_fcs = nn.ModuleList(
-            nn.Linear(dims[i], dims[i + 1], bias=False)
-            if (dims[i] != dims[i + 1])
+            nn.Linear(dims[idx], dims[idx + 1], bias=False)
+            if (dims[idx] != dims[idx + 1])
             else nn.Identity()
-            for i in range(len(dims) - 1)
+            for idx in range(len(dims) - 1)
         )
         self.acts = nn.ModuleList(activation() for _ in range(len(dims) - 1))
 

aviary/predict.py

@@ -69,7 +69,7 @@ def make_ensemble_predictions(
     # (i.e. tty mode) https://git.io/JnBOi
     print(f"Pytorch running on {device=}")
     for idx, checkpoint_path in tqdm(
-        enumerate(tqdm(checkpoint_paths), 1), disable=None if pbar else True
+        enumerate(tqdm(checkpoint_paths), start=1), disable=None if pbar else True
     ):
         try:
             checkpoint = torch.load(checkpoint_path, map_location=device)
@@ -189,7 +189,7 @@ def predict_from_wandb_checkpoints(
 
     checkpoint_paths: list[str] = []
 
-    for idx, run in enumerate(runs, 1):
+    for idx, run in enumerate(runs, start=1):
         run_path = "/".join(run.path)
         out_dir = f"{cache_dir}/{run_path}"
         os.makedirs(out_dir, exist_ok=True)

aviary/roost/data.py

@@ -176,7 +176,7 @@ def collate_batch(
     batch_cry_ids = []
 
     cry_base_idx = 0
-    for i, (inputs, target, *cry_ids) in enumerate(samples):
+    for idx, (inputs, target, *cry_ids) in enumerate(samples):
         elem_weights, elem_fea, self_idx, nbr_idx = inputs
 
         n_sites = elem_fea.shape[0]  # number of atoms for this crystal
@@ -190,7 +190,7 @@ def collate_batch(
         batch_nbr_idx.append(nbr_idx + cry_base_idx)
 
         # mapping from atoms to crystals
-        crystal_elem_idx.append(torch.tensor([i] * n_sites))
+        crystal_elem_idx.append(torch.tensor([idx] * n_sites))
 
         # batch the targets and ids
         batch_targets.append(target)

aviary/train.py

@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import os
+from datetime import datetime
 from typing import TYPE_CHECKING, Any, Literal
 
 import numpy as np
@@ -20,7 +21,7 @@
 try:
     import wandb
 except ImportError:
-    wandb = None
+    wandb = None  # type: ignore[assignment]
 
 if TYPE_CHECKING:
     from torch import nn
@@ -319,13 +320,14 @@ def train_model(
     if checkpoint is not None:
         checkpoint_model(
             checkpoint_endpoint=checkpoint,
-            model=inference_model,
+            model_params=model_params,
+            inference_model=inference_model,
             optimizer_instance=optimizer_instance,
             lr_scheduler=lr_scheduler,
             loss_dict=loss_dict,
-            epoch=epochs,
+            epochs=epochs,
             test_metrics=test_metrics,
-            timestamp=timestamp,
+            timestamp=timestamp or datetime.now().astimezone().strftime("%Y%m%d-%H%M%S"),
             run_name=run_name,
             normalizer_dict=normalizer_dict,
             run_params=run_params,
@@ -364,7 +366,7 @@ def train_model(
 
 def checkpoint_model(
     checkpoint_endpoint: str,
-    model_params: dict,
+    model_params: dict | None,
     inference_model: nn.Module,
     optimizer_instance: torch.optim.Optimizer,
     lr_scheduler: torch.optim.lr_scheduler._LRScheduler,

aviary/utils.py

@@ -174,13 +174,9 @@ def initialize_optim(
             momentum=momentum,
         )
     elif optim == "Adam":
-        optimizer = Adam(
-            model.parameters(), lr=learning_rate, weight_decay=weight_decay
-        )
+        optimizer = Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
     elif optim == "AdamW":
-        optimizer = AdamW(
-            model.parameters(), lr=learning_rate, weight_decay=weight_decay
-        )
+        optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
     else:
         raise NameError("Only SGD, Adam or AdamW are allowed as --optim")
 
@@ -361,9 +357,7 @@ def train_ensemble(
                 sample_target = Tensor(train_set.df[target].values)
                 if not restart_params["resume"]:
                     normalizer.fit(sample_target)
-                print(
-                    f"Dummy MAE: {(sample_target - normalizer.mean).abs().mean():.4f}"
-                )
+                print(f"Dummy MAE: {(sample_target - normalizer.mean).abs().mean():.4f}")
 
         if log:
             writer = SummaryWriter(
@@ -528,9 +522,7 @@ def results_multitask(
             elif task_type == "classification":
                 if model.robust:
                     mean, log_std = output.chunk(2, dim=1)
-                    logits = (
-                        sampled_softmax(mean, log_std, samples=10).data.cpu().numpy()
-                    )
+                    logits = sampled_softmax(mean, log_std, samples=10).data.cpu().numpy()
                     pre_logits = mean.data.cpu().numpy()
                     pre_logits_std = torch.exp(log_std).data.cpu().numpy()
                     res_dict["pre-logits_ale"].append(pre_logits_std)  # type: ignore[union-attr]

aviary/wren/data.py

@@ -139,16 +139,16 @@ def __getitem__(self, idx: int):
         n_wyks = len(elements)
         self_idx = []
         nbr_idx = []
-        for i in range(n_wyks):
-            self_idx += [i] * n_wyks
+        for wyk_idx in range(n_wyks):
+            self_idx += [wyk_idx] * n_wyks
             nbr_idx += list(range(n_wyks))
 
         self_aug_fea_idx = []
         nbr_aug_fea_idx = []
         n_aug = len(augmented_wyks)
-        for i in range(n_aug):
-            self_aug_fea_idx += [x + i * n_wyks for x in self_idx]
-            nbr_aug_fea_idx += [x + i * n_wyks for x in nbr_idx]
+        for aug_idx in range(n_aug):
+            self_aug_fea_idx += [x + aug_idx * n_wyks for x in self_idx]
+            nbr_aug_fea_idx += [x + aug_idx * n_wyks for x in nbr_idx]
 
         # convert all data to tensors
         wyckoff_weights = Tensor(wyk_site_multiplcities)
@@ -291,9 +291,7 @@ def parse_protostructure_label(
         )
 
         # Separate out pairs of Wyckoff letters and their number of occurrences
-        sep_n_wyks = [
-            "".join(g) for _, g in groupby(wyk_letters_normalized, str.isalpha)
-        ]
+        sep_n_wyks = ["".join(g) for _, g in groupby(wyk_letters_normalized, str.isalpha)]
 
         # Process Wyckoff letters and multiplicities
         mults = map(int, sep_n_wyks[0::2])

aviary/wren/model.py

@@ -205,7 +205,7 @@ def __init__(
                 msg_gate_layers=elem_gate,
                 msg_net_layers=elem_msg,
             )
-            for i in range(n_graph)
+            for _ in range(n_graph)
         )
 
         # define a global pooling function for materials
@@ -259,9 +259,7 @@ def forward(
             for attnhead in self.cry_pool
         ]
 
-        return scatter_mean(
-            torch.mean(torch.stack(head_fea), dim=0), aug_cry_idx, dim=0
-        )
+        return scatter_mean(torch.mean(torch.stack(head_fea), dim=0), aug_cry_idx, dim=0)
 
     def __repr__(self) -> str:
         return (

aviary/wren/utils.py

@@ -361,13 +361,11 @@ def sort_and_score_element_wyckoffs(element_wyckoffs: str) -> tuple[str, int]:
         wp_counts = split_alpha_numeric(el_wyks)
         sorted_element_wyckoffs.append(
             "".join(
-                [
-                    f"{count}{wyckoff_letter}" if count != "1" else wyckoff_letter
-                    for count, wyckoff_letter in sorted(
-                        zip(wp_counts["numeric"], wp_counts["alpha"]),
-                        key=lambda x: x[1],
-                    )
-                ]
+                f"{count}{wyckoff_letter}" if count != "1" else wyckoff_letter
+                for count, wyckoff_letter in sorted(
+                    zip(wp_counts["numeric"], wp_counts["alpha"]),
+                    key=lambda x: x[1],
+                )
             )
         )
         score += sum(
@@ -391,19 +389,19 @@ def get_prototype_formula_from_composition(composition: Composition) -> str:
     """
     reduced = composition.element_composition
     if all(x == int(x) for x in composition.values()):
-        reduced /= gcd(*(int(i) for i in composition.values()))
+        reduced /= gcd(*(int(amt) for amt in composition.values()))
 
     amounts = [reduced[key] for key in sorted(reduced, key=str)]
 
     anon = ""
-    for e, amt in zip(ascii_uppercase, amounts):
+    for elem, amt in zip(ascii_uppercase, amounts):
         if amt == 1:
             amt_str = ""
         elif abs(amt % 1) < 1e-8:
             amt_str = str(int(amt))
         else:
             amt_str = str(amt)
-        anon += f"{e}{amt_str}"
+        anon += f"{elem}{amt_str}"
     return anon
 
 
@@ -415,13 +413,8 @@ def get_anonymous_formula_from_prototype_formula(prototype_formula: str) -> str:
     anom_list = split_alpha_numeric(prototype_formula)
 
     return "".join(
-        [
-            f"{el}{num}" if num != 1 else el
-            for el, num in zip(
-                anom_list["alpha"],
-                sorted(map(int, anom_list["numeric"])),
-            )
-        ]
+        f"{el}{num}" if num != 1 else el
+        for el, num in zip(anom_list["alpha"], sorted(map(int, anom_list["numeric"])))
     )
 
 
@@ -435,12 +428,8 @@ def get_formula_from_protostructure_label(protostructure_label: str) -> str:
     anom_list = split_alpha_numeric(prototype_formula)
 
     return "".join(
-        [
-            f"{el}{num}" if num != 1 else el
-            for el, num in zip(
-                chemsys.split("-"), map(int, anom_list["numeric"]), strict=True
-            )
-        ]
+        f"{el}{num}" if num != 1 else el
+        for el, num in zip(chemsys.split("-"), map(int, anom_list["numeric"]))
     )
 
 

aviary/wrenformer/data.py

@@ -66,9 +66,7 @@ def collate_batch(
 
 
 @cache
-def get_wyckoff_features(
-    equivalent_wyckoff_set: list[tuple], spg_num: int
-) -> np.ndarray:
+def get_wyckoff_features(equivalent_wyckoff_set: list[tuple], spg_num: int) -> np.ndarray:
     """Get Wyckoff set features from the precomputed dictionary. The output of this
     function is cached for speed.
 
@@ -204,6 +202,4 @@ def df_to_in_mem_dataloader(
         inputs[idx] = tensor.to(device)
 
     ids = df.get(id_col, df.index).to_numpy()
-    return InMemoryDataLoader(
-        [inputs, targets, ids], collate_fn=collate_batch, **kwargs
-    )
+    return InMemoryDataLoader([inputs, targets, ids], collate_fn=collate_batch, **kwargs)

examples/wrenformer/mat_bench/make_plots.py

@@ -40,7 +40,7 @@
 
 # %% --- Load other's scores ---
 # load benchmark data for models with existing Matbench submission
-for idx, dirname in enumerate(glob(f"{bench_dir}/*"), 1):
+for idx, dirname in enumerate(glob(f"{bench_dir}/*"), start=1):
     model_name = dirname.split("/matbench_v0.1_")[-1]
     print(f"{idx}. {model_name}")
     mbbm = MatbenchBenchmark.from_file(f"{dirname}/results.json.gz")
@@ -62,7 +62,7 @@
 # %% --- Load our scores ---
 our_score_files = sorted(glob("model_scores/*.json"), key=lambda s: s.split("@")[0])
 
-for idx, filename in enumerate(our_score_files, 1):
+for idx, filename in enumerate(our_score_files, start=1):
     date, model_name = re.split(r"@\d\d-\d\d-", filename.split("/")[-1])
 
     print(f"{idx}.  {date}  {model_name}")

examples/wrenformer/mat_bench/save_matbench_aflow_labels.py

@@ -17,7 +17,7 @@
 
 benchmark = MatbenchBenchmark()
 
-for idx, task in enumerate(benchmark.tasks, 1):
+for idx, task in enumerate(benchmark.tasks, start=1):
     print(f"\n\n{idx}/{len(benchmark.tasks)}")
     task.load()
     df: pd.DataFrame = task.df

examples/wrenformer/mat_bench/utils.py

@@ -54,7 +54,5 @@ def non_serializable_handler(obj: object) -> str:
         return f"<not serializable: {type(obj).__qualname__}>"
 
     with open(file_path, "w") as file:
-        default = (
-            non_serializable_handler if on_non_serializable == "annotate" else None
-        )
+        default = non_serializable_handler if on_non_serializable == "annotate" else None
         json.dump(dct, file, default=default, indent=2)

tests/conftest.py

@@ -19,7 +19,7 @@ def df_matbench_phonons():
     """Returns the dataframe for the Matbench phonon DOS peak task."""
 
     df = load_dataset("matbench_phonons")
-    df["material_id"] = [f"mb_phdos_{i + 1}" for i in range(len(df))]
+    df["material_id"] = [f"mb_phdos_{idx + 1}" for idx in range(len(df))]
     df = df.set_index("material_id", drop=False)
     df["composition"] = [x.composition.formula.replace(" ", "") for x in df.structure]
 
@@ -33,7 +33,7 @@ def df_matbench_jdft2d():
     """Returns Matbench experimental band gap task dataframe. Currently unused."""
 
     df = load_dataset("matbench_jdft2d")
-    df["material_id"] = [f"mb_jdft2d_{i + 1}" for i in range(len(df))]
+    df["material_id"] = [f"mb_jdft2d_{idx + 1}" for idx in range(len(df))]
     df = df.set_index("material_id", drop=False)
     df["composition"] = [x.composition.formula.replace(" ", "") for x in df.structure]