migrate style check to precommit

.devtools/license

@@ -46,10 +46,11 @@ external_license = "# LICENSE: EXTERNAL"
 
 
 def check_file(path: Path) -> bool:
-    """Check whether `path` is complicit.
+    """
+    Check whether `path` is complicit.
 
-    The license header needs to start on the 3rd line latest. This allows
-    to have a shebang on top of the file.
+    The license header needs to start on the 3rd line latest. This allows to
+    have a shebang on top of the file.
     """
     num_lines = len(license.splitlines()) + 2
 

.github/workflows/just_checks.yml

@@ -0,0 +1,23 @@
+name: Type & license checks
+
+on: [push, pull_request]
+
+jobs:
+  just-checks:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        just_cmd: ["license", "mypy"]
+    steps:
+    - uses: actions/checkout@v3
+    - uses: extractions/setup-just@v1
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.8'
+    - name: Install dependencies
+      run: pip install -r requirements/requirements-ci-just.txt
+    - name: Check ${{ matrix.just_cmd }}
+      run: just ${{ matrix.just_cmd }}

.github/workflows/lint_precommit.yml

@@ -0,0 +1,11 @@
+name: Lint checks by pre-commit
+
+on: [push, pull_request]
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v3
+      - uses: pre-commit/action@v3.0.1

.pre-commit-config.yaml

@@ -0,0 +1,52 @@
+default_language_version:
+  python: python3
+
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: "[pre-commit.ci] pre-commit suggestions"
+  autoupdate_schedule: monthly
+  # submodules: true
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      #  - id: end-of-file-fixer
+      #  - id: trailing-whitespace
+      - id: check-json
+      - id: check-yaml
+      - id: check-toml
+      #  - id: check-docstring-first
+      #  - id: check-executables-have-shebangs
+      #  - id: check-case-conflict
+      #  - id: check-added-large-files
+      - id: detect-private-key
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.3.3
+    hooks:
+      - id: ruff
+        name: Run Ruff & fixing (Flake8)
+        args: ["--fix"]
+
+  - repo: https://github.com/PyCQA/docformatter
+    rev: v1.5.0
+    hooks:
+      - id: docformatter
+        name: Format docstrings with Docformatter
+        additional_dependencies: [tomli]
+        args: ["--in-place"]
+
+  - repo: https://github.com/psf/black
+    rev: 24.2.0
+    hooks:
+      - id: black
+        name: Format code with Black
+
+  #- repo: https://github.com/executablebooks/mdformat
+  #  rev: 0.7.17
+  #  hooks:
+  #    - id: mdformat
+  #      additional_dependencies:
+  #        - mdformat-gfm
+  #        - mdformat_frontmatter

Justfile

@@ -33,9 +33,6 @@ compile_notebooks:
 release:
   python setup.py sdist
 
-black:
-  black --check --diff --color src test examples
-
 mypy:
   python setup.py type_check
 

docs/conf.py

@@ -95,9 +95,9 @@
 }
 
 if os.environ.get("GITHUB_REF_NAME") == "dev":
-    html_theme_options[
-        "announcement"
-    ] = "<strong>Note:</strong> You are looking at the development docs."
+    html_theme_options["announcement"] = (
+        "<strong>Note:</strong> You are looking at the development docs."
+    )
 
 
 # Add any paths that contain custom static files (such as style sheets) here,

examples/anomaly_detection.py

@@ -13,11 +13,11 @@
 
 """
 This example shows how to do anomaly detection with DeepAR.
-The model is first trained and then time-points with the largest negative log-likelihood are plotted.
 
+The model is first trained and then time-points with the largest negative log-
+likelihood are plotted.
 """
 import numpy as np
-from itertools import islice
 from functools import partial
 import mxnet as mx
 import matplotlib.pyplot as plt

examples/warm_start.py

@@ -12,8 +12,8 @@
 # permissions and limitations under the License.
 
 """
-This example show how to intialize the network with parameters from a model that was previously trained.
-
+This example show how to intialize the network with parameters from a model
+that was previously trained.
 """
 
 from gluonts.dataset.repository import get_dataset, dataset_recipes

pyproject.toml

@@ -20,13 +20,13 @@ filterwarnings = "ignore"
 
 [tool.ruff]
 line-length = 79
-
 lint.ignore = [
     "E501",  # line-length is handled by black
     "E741"  # TODO: remove usage of `l`
 ]
-
-exclude = ["src/gluonts/nursery"]
+exclude = ["src/gluonts/nursery"]  # todo
+[tool.ruff.lint.per-file-ignores]
+"examples/*" = ["E402"]
 
 
 [tool.docformatter]

requirements/requirements-ci-just.txt

@@ -0,0 +1,5 @@
+click
+mypy==1.8.0
+types-python-dateutil
+types-waitress
+types-PyYAML

setup.py

@@ -60,7 +60,8 @@ def run(self):
         import mypy.api
 
         excluded_folders = [
-            str(p.parent.relative_to(ROOT)) for p in ROOT.glob("src/**/.typeunsafe")
+            str(p.parent.relative_to(ROOT))
+            for p in ROOT.glob("src/**/.typeunsafe")
         ]
 
         if len(excluded_folders) > 0:

src/gluonts/itertools.py

@@ -298,7 +298,7 @@ def split_into(xs: Sequence, n: int) -> Sequence:
     # e.g. 10 by 3 -> 4, 3, 3
     relative_splits[:remainder] += 1
 
-    return split(xs, np.cumsum(relative_splits))
+    return split(xs, np.cumsum(relative_splits))  # type: ignore[arg-type]
 
 
 @dataclass

src/gluonts/model/forecast.py

@@ -178,14 +178,14 @@ class Quantile:
     @classmethod
     def from_float(cls, quantile: float) -> "Quantile":
         assert isinstance(quantile, float)
-        return cls(value=quantile, name=str(quantile))
+        return cls(value=quantile, name=str(quantile))  # type: ignore[call-arg]
 
     @classmethod
     def from_str(cls, quantile: str) -> "Quantile":
         assert isinstance(quantile, str)
 
         try:
-            return cls(value=float(quantile), name=quantile)
+            return cls(value=float(quantile), name=quantile)  # type: ignore[call-arg]
         except ValueError:
             m = re.match(r"^p(\d+)$", quantile)
 

src/gluonts/mx/trainer/learning_rate_scheduler.py

@@ -247,8 +247,8 @@ def __init__(
             0 <= min_lr <= base_lr
         ), "The value of `min_lr` should be >= 0 and <= base_lr"
 
-        self.lr_scheduler = MetricAttentiveScheduler(
-            patience=Patience(
+        self.lr_scheduler = MetricAttentiveScheduler(  # type: ignore[call-arg]
+            patience=Patience(  # type: ignore[call-arg]
                 patience=patience, objective=Objective.from_str(objective)
             ),
             learning_rate=base_lr,

src/gluonts/torch/model/patch_tst/module.py

@@ -229,7 +229,7 @@ def forward(
             # shift time features by `prediction_length` so that they are
             # aligned with the target input.
             time_feat = take_last(
-                torch.cat((past_time_feat, future_time_feat), dim=1),
+                torch.cat((past_time_feat, future_time_feat), dim=1),  # type: ignore[arg-type]
                 dim=1,
                 num=self.context_length,
             )

src/gluonts/torch/model/tft/layers.py

@@ -27,7 +27,7 @@ def forward(self, features: torch.Tensor) -> List[torch.Tensor]:  # type: ignore
         concat_features = super().forward(features=features)
 
         if self._num_features > 1:
-            return torch.chunk(concat_features, self._num_features, dim=-1)
+            return torch.chunk(concat_features, self._num_features, dim=-1)  # type: ignore[return-value]
         else:
             return [concat_features]
 

src/gluonts/torch/modules/feature.py

@@ -38,7 +38,7 @@ def forward(self, features: torch.Tensor) -> torch.Tensor:
                 features, self._num_features, dim=-1
             )
         else:
-            cat_feature_slices = [features]
+            cat_feature_slices = [features]  # type: ignore[assignment]
 
         return torch.cat(
             [

src/gluonts/zebras/_time_frame.py

@@ -453,11 +453,11 @@ def split(
 
         # If past_length is not provided, it will equal to `index`, since
         # `len(tf.split(5).past) == 5`
-        past_length: int = maybe.unwrap_or(past_length, index)
+        past_length: int = maybe.unwrap_or(past_length, index)  # type: ignore[annotation-unchecked]
 
         # Same logic applies to future_length, except that we deduct from the
         # right. (We can't use past_length, since it can be unequal to index).
-        future_length: int = maybe.unwrap_or(future_length, len(self) - index)
+        future_length: int = maybe.unwrap_or(future_length, len(self) - index)  # type: ignore[annotation-unchecked]
 
         if self.index is None:
             new_index = None

test/core/test_component.py

@@ -24,8 +24,8 @@ def __init__(self, x: float, y: float) -> None:
         self.x = x
         self.y = y
 
-        assert type(self.x) == float
-        assert type(self.y) == float
+        assert isinstance(self.x, float)
+        assert isinstance(self.y, float)
 
     def __eq__(self, that):
         return self.x == that.x and self.y == that.y
@@ -41,9 +41,9 @@ def __init__(self, a: int, c: Complex, **kwargs) -> None:
         self.b = kwargs["b"]
         self.c = c
 
-        assert type(self.a) == int
-        assert type(self.b) == float
-        assert type(self.c) == Complex
+        assert isinstance(self.a, int)
+        assert isinstance(self.b, float)
+        assert isinstance(self.c, Complex)
 
     def __eq__(self, that):
         return self.a == that.a and self.b == that.b and self.c == that.c

test/ev/test_metrics_compared_to_previous_approach.py

@@ -121,7 +121,7 @@ def get_data_batches(predictor, test_data):
         )
 
         seasonality = get_seasonality(freq=forecast.start_date.freqstr)
-        freq = forecast.start_date.freqstr
+        # freq = forecast.start_date.freqstr
         other_data = {
             "label": np.array([label["target"]]),
             "seasonal_error": np.array(

test/ext/hierarchicalforecast/test_predictor.py

@@ -110,7 +110,7 @@ def test_predictor_serialization(
 ):
     train_datasets = sine7(nonnegative=True)
 
-    (train_dataset, test_dataset, metadata) = (
+    _, _, metadata = (
         train_datasets.train,
         train_datasets.test,
         train_datasets.metadata,
@@ -152,7 +152,7 @@ def test_predictor_working(
         bias=1,
     )
 
-    (train_dataset, test_dataset, metadata) = (
+    _, test_dataset, metadata = (
         train_datasets.train,
         train_datasets.test,
         train_datasets.metadata,

test/mx/model/deepvar_hierarchical/test_projection.py

@@ -146,4 +146,4 @@ def test_projection_mat(D):
 )
 def test_projection_mat_expected_fail(D):
     with pytest.raises(AssertionError):
-        p = projection_mat(S=S, D=D)
+        projection_mat(S=S, D=D)

test/test_itertools.py

@@ -289,7 +289,7 @@ def test_join_items():
     ]
 
     with pytest.raises(Exception):
-        oin_items(left, right, "strict")
+        oin_items(left, right, "strict")  # noqa: F821
 
 
 @pytest.mark.parametrize(

test/zebras/test_batch.py

@@ -11,7 +11,6 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
-import pytest
 
 import numpy as np