add pylint to pre-commit

.github/workflows/pre-commit.yaml

@@ -10,5 +10,19 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
-      - uses: actions/setup-python@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+          cache: "pip"
+      - name: Install requirements
+        run: |
+          pip install -U pip
+          pip install pylint
+          pip install -U black
+          pip install .[dev]
+          pip install wandb
+          pip install tqdm
+      - name: Run black
+        run: |
+          python -m black .
       - uses: pre-commit/[email protected]

.pre-commit-config.yaml

@@ -1,50 +1,58 @@
+exclude: &exclude_files >
+    (?x)^(
+        docs/.*|
+        tests/.*|
+        .github/.*|
+        LICENSE.md|
+        README.md|
+    )$
+
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v2.5.0
     hooks:
-      - id: end-of-file-fixer
-      - id: trailing-whitespace
+    -   id: mixed-line-ending
+    -   id: trailing-whitespace
+        exclude: *exclude_files
 
   - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.12.1
+    rev: 24.4.0
     hooks:
       - id: black
         name: Black Formating
+        exclude: *exclude_files
 
   - repo: https://github.com/pycqa/isort
     rev: 5.13.2
     hooks:
       - id: isort
         name: Sort imports
+        exclude: *exclude_files
 
-  # Failing - to be investigated separately
-  # - repo: local
-  #   hooks:
-  #     - id: pylint
-  #       name: Pylint Checks
-  #       entry: pylint
-  #       language: system
-  #       types: [python]
-  #       args:
-  #         [
-  #           "--rcfile=pyproject.toml", 
-  #           "mace", 
-  #           "tests", 
-  #           "scripts"
-  #         ]  
-
-  # - repo: local
-  #   hooks:
-  #     - id: mypy
-  #       name: mypy type checks
-  #       entry: mypy
-  #       language: system
-  #       types: [python]
-  #       args:
-  #         [
-  #           --config-file=.mypy.ini,
-  #           mace,
-  #           tests,
-  #           scripts
-  #         ]
-
+  - repo: https://github.com/PyCQA/pylint
+    rev: pylint-2.5.2
+    hooks:
+    -   id: pylint
+        language: system
+        args: [
+          '--disable=line-too-long',
+          '--disable=no-member',
+          '--disable=missing-module-docstring',
+          '--disable=missing-class-docstring',
+          '--disable=missing-function-docstring',
+          '--disable=too-many-arguments',
+          '--disable=too-many-locals',
+          '--disable=not-callable',
+          '--disable=logging-fstring-interpolation',
+          '--disable=logging-not-lazy',
+          '--disable=invalid-name',
+          '--disable=too-few-public-methods',
+          '--disable=too-many-instance-attributes',
+          '--disable=too-many-statements',
+          '--disable=too-many-branches',
+          '--disable=import-outside-toplevel',
+          '--disable=cell-var-from-loop',
+          '--disable=duplicate-code',
+          '--disable=use-dict-literal',
+        ]
+        exclude: *exclude_files

mace/cli/active_learning_md.py

@@ -1,4 +1,5 @@
 """Demonstrates active learning molecular dynamics with constant temperature."""
+
 import argparse
 import os
 import time

mace/cli/preprocess_data.py

@@ -78,8 +78,7 @@ def split_array(a: np.ndarray, max_size: int):
         for j in range(0, len(factors) - i + 1):
             if np.prod(factors[j : j + i]) <= max_size:
                 test = np.prod(factors[j : j + i])
-                if test > max_factor:
-                    max_factor = test
+                max_factor = max(test, max_factor)
     return np.array_split(a, max_factor), drop_last
 
 

mace/cli/run_train.py

@@ -823,7 +823,7 @@ def main() -> None:
                         path_complied,
                         _extra_files=extra_files,
                     )
-                except Exception as e:  # pylint: disable=W070344
+                except Exception as e:  # pylint: disable=W0703
                     pass
 
         if args.distributed:

mace/data/utils.py

@@ -246,9 +246,9 @@ def load_from_xyz(
                 isolated_atom_config = atoms.info.get("config_type") == "IsolatedAtom"
                 if isolated_atom_config:
                     if energy_key in atoms.info.keys():
-                        atomic_energies_dict[
-                            atoms.get_atomic_numbers()[0]
-                        ] = atoms.info[energy_key]
+                        atomic_energies_dict[atoms.get_atomic_numbers()[0]] = (
+                            atoms.info[energy_key]
+                        )
                     else:
                         logging.warning(
                             f"Configuration '{idx}' is marked as 'IsolatedAtom' "

mace/tools/finetuning_utils.py

@@ -50,24 +50,24 @@ def load_foundations(
         for j in range(4):  # Assuming 4 layers in conv_tp_weights,
             layer_name = f"layer{j}"
             if j == 0:
-                getattr(
-                    model.interactions[i].conv_tp_weights, layer_name
-                ).weight = torch.nn.Parameter(
-                    getattr(
-                        model_foundations.interactions[i].conv_tp_weights,
-                        layer_name,
+                getattr(model.interactions[i].conv_tp_weights, layer_name).weight = (
+                    torch.nn.Parameter(
+                        getattr(
+                            model_foundations.interactions[i].conv_tp_weights,
+                            layer_name,
+                        )
+                        .weight[:num_radial, :]
+                        .clone()
                     )
-                    .weight[:num_radial, :]
-                    .clone()
                 )
             else:
-                getattr(
-                    model.interactions[i].conv_tp_weights, layer_name
-                ).weight = torch.nn.Parameter(
-                    getattr(
-                        model_foundations.interactions[i].conv_tp_weights,
-                        layer_name,
-                    ).weight.clone()
+                getattr(model.interactions[i].conv_tp_weights, layer_name).weight = (
+                    torch.nn.Parameter(
+                        getattr(
+                            model_foundations.interactions[i].conv_tp_weights,
+                            layer_name,
+                        ).weight.clone()
+                    )
                 )
 
         model.interactions[i].linear.weight = torch.nn.Parameter(
@@ -105,24 +105,24 @@ def load_foundations(
     for i in range(2):  # Assuming 2 products modules
         max_range = max_L + 1 if i == 0 else 1
         for j in range(max_range):  # Assuming 3 contractions in symmetric_contractions
-            model.products[i].symmetric_contractions.contractions[
-                j
-            ].weights_max = torch.nn.Parameter(
-                model_foundations.products[i]
-                .symmetric_contractions.contractions[j]
-                .weights_max[indices_weights, :, :]
-                .clone()
-            )
-
-            for k in range(2):  # Assuming 2 weights in each contraction
-                model.products[i].symmetric_contractions.contractions[j].weights[
-                    k
-                ] = torch.nn.Parameter(
+            model.products[i].symmetric_contractions.contractions[j].weights_max = (
+                torch.nn.Parameter(
                     model_foundations.products[i]
                     .symmetric_contractions.contractions[j]
-                    .weights[k][indices_weights, :, :]
+                    .weights_max[indices_weights, :, :]
                     .clone()
                 )
+            )
+
+            for k in range(2):  # Assuming 2 weights in each contraction
+                model.products[i].symmetric_contractions.contractions[j].weights[k] = (
+                    torch.nn.Parameter(
+                        model_foundations.products[i]
+                        .symmetric_contractions.contractions[j]
+                        .weights[k][indices_weights, :, :]
+                        .clone()
+                    )
+                )
 
         model.products[i].linear.weight = torch.nn.Parameter(
             model_foundations.products[i].linear.weight.clone()

mace/tools/scripts_utils.py

@@ -388,9 +388,9 @@ def step(self, metrics=None, epoch=None):  # pylint: disable=E1123
         if self.scheduler == "ExponentialLR":
             self.lr_scheduler.step(epoch=epoch)
         elif self.scheduler == "ReduceLROnPlateau":
-            self.lr_scheduler.step(
+            self.lr_scheduler.step(  # pylint: disable=E1123
                 metrics=metrics, epoch=epoch
-            )  # pylint: disable=E1123
+            )
 
     def __getattr__(self, name):
         if name == "step":

pyproject.toml

@@ -30,6 +30,7 @@ disable = [
     "import-outside-toplevel",
     "cell-var-from-loop",
     "duplicate-code",
+    "use-dict-literal",
 ]
 
 [tool.pylint.MASTER]

setup.cfg

@@ -27,6 +27,7 @@ install_requires =
     python-hostlist
     configargparse
     GitPython
+    tqdm
     # for plotting:
     matplotlib
     pandas