pre-commit: standardize pre-commit across repos

*ref: cern-sis/issues-inspire/issues/498

.github/workflows/build.yml

@@ -54,4 +54,4 @@ jobs:
             type=ref,event=pr
             type=ref,event=tag
           username: ${{ secrets.HARBOR_USERNAME }}
-          password: ${{ secrets.HARBOR_SECRET }}
+          password: ${{ secrets.HARBOR_SECRET }}

.github/workflows/pre-commit.yml

@@ -23,4 +23,4 @@ jobs:
           python-version: "3.11"
 
       - name: Run pre-commit
-        uses: pre-commit/[email protected]
+        uses: pre-commit/[email protected]

.github/workflows/pull-request-master.yml

@@ -9,4 +9,4 @@ jobs:
     uses: ./.github/workflows/pre-commit.yml
     with:
       ref: ${{ github.event.pull_request.head.sha }}
-    secrets: inherit
+    secrets: inherit

.gitignore

@@ -165,4 +165,4 @@ cython_debug/
 *.csv
 *.h5
 
-!/tests/integration/fixtures/inspire_test_data.df
+!/tests/integration/fixtures/inspire_test_data.df

.pre-commit-config.yaml

@@ -1,14 +1,17 @@
 repos:
-  - repo: https://github.com/psf/black
-    rev: '24.4.2'
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
     hooks:
-      - id: black
-  - repo: https://github.com/pycqa/isort
-    rev: '5.13.2'
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: fix-byte-order-marker
+      - id: mixed-line-ending
+      - id: name-tests-test
+        args: [ --pytest-test-first ]
+        exclude: '^(?!factories/)'
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.6.9
     hooks:
-      - id: isort
-  - repo: https://github.com/pycqa/flake8
-    rev: '7.1.0'
-    hooks:
-      - id: flake8
-        args: ['--config=setup.cfg']
+      - id: ruff
+        args: [ --fix]

README.md

@@ -1,9 +1,9 @@
 # Inspire Classifier
 
-## About 
+## About
 INSPIRE module aimed at automatically classifying the new papers that are added to INSPIRE, such as if they are core or not, or the arXiv category corresponding to each of them.
 
-The current implemntation uses the ULMfit approach. Universal Language Model Fine-tuning, is a method for training text classifiers by first pretraining a language model on a large corpus to learn general language features (in this case a pre-loaded model, which was trained using the WikiText-103 dataset is used). The pretrained model is then fine-tuned on the title and abstract of the inpsire dataset before training the classifier on top. 
+The current implemntation uses the ULMfit approach. Universal Language Model Fine-tuning, is a method for training text classifiers by first pretraining a language model on a large corpus to learn general language features (in this case a pre-loaded model, which was trained using the WikiText-103 dataset is used). The pretrained model is then fine-tuned on the title and abstract of the inpsire dataset before training the classifier on top.
 
 
 
@@ -28,7 +28,7 @@ poetry run python scripts/create_dataset.py --year-from $YEAR_FROM --month-from
 
 
 ### 2. Run training and validate model
-The [`train_classifier.py`](scripts/train_classifier.py) script will run the commands to train and validate a new model. Configurations changes like the amount of training epochs as well as the train-test split can be adjusted here. In short, the script first splits the pkl file from the first step into a training and a test dataset inside the `classifier/data` folder. The training set is then used to train the model, while the test set is used to evaluate the model after the training is finished. The model will be saved into `classifier/models/language_model/finetuned_language_model_encoder.h5` 
+The [`train_classifier.py`](scripts/train_classifier.py) script will run the commands to train and validate a new model. Configurations changes like the amount of training epochs as well as the train-test split can be adjusted here. In short, the script first splits the pkl file from the first step into a training and a test dataset inside the `classifier/data` folder. The training set is then used to train the model, while the test set is used to evaluate the model after the training is finished. The model will be saved into `classifier/models/language_model/finetuned_language_model_encoder.h5`
 
 ```
 poetry run python scripts/train_classifier.py
@@ -49,15 +49,13 @@ poetry run python scripts/upload_to_s3.py
 
 1. Build docker image: `docker build -t inspirehep/classifier:<NEW TAG> .`
 2. Login with inspirehep user on dockerhub: `docker login`
-3. Push image to dockerhub: `docker push inspirehep/classifier:<NEW TAG>` 
+3. Push image to dockerhub: `docker push inspirehep/classifier:<NEW TAG>`
 4. Change `newTag` in the `kustomization.yml` file in the [k8s repo](https://github.com/cern-sis/kubernetes/tree/master/classifier).
 
 
 
 
 ## How to run
-For testing, the cli of the classifier can be used via `poetry run inspire-classifier 'example title' 'exmaple abstract'`, with the `-b` flag, the basepath to check for the training data, can be passed (which currently should be `-b classifier`). 
+For testing, the cli of the classifier can be used via `poetry run inspire-classifier 'example title' 'exmaple abstract'`, with the `-b` flag, the basepath to check for the training data, can be passed (which currently should be `-b classifier`).
 
 In the production, the api is used to predict the 'coreness' of records using the `/api/predict/coreness` endpoint and passing `title` and `abstract` as json fields in a POST request (see [this file](inspire_classifier/app.py) for details).
-
-

inspire_classifier/__init__.py

@@ -20,4 +20,6 @@
 # granted to it by virtue of its status as an Intergovernmental Organization
 # or submit itself to any jurisdiction.
 
-"""INSPIRE module aimed at automatically classifying the new papers that are added to INSPIRE, such as if they are core or not, or the arXiv category corresponding to each of them."""
+"""INSPIRE module aimed at automatically classifying the new papers that are added to
+INSPIRE, such as if they are core or not, or the arXiv category corresponding to each
+of them."""

inspire_classifier/api.py

@@ -56,9 +56,9 @@ def split_data():
         )
     except IOError as error:
         raise IOError(
-            "Training dataframe not found. Make sure the file is present in the right directory. "
-            "Please use the path specified in config.py for CLASSIFIER_DATAFRAME_PATH relative to the "
-            "CLASSIFIER_BASE_PATH."
+            "Training dataframe not found. Make sure the file is present in the right "
+            "directory. Please use the path specified in config.py for "
+            "CLASSIFIER_DATAFRAME_PATH relative to the CLASSIFIER_BASE_PATH."
         ) from error
 
 
@@ -93,8 +93,8 @@ def finetune_and_save_language_model():
         )
     except IOError as error:
         raise IOError(
-            "Unable to save the finetuned language model. Please check that the language model data directory "
-            "exists."
+            "Unable to save the finetuned language model. Please check that the "
+            "language model data directory exists."
         ) from error
 
 
@@ -182,7 +182,7 @@ def predict_coreness(classifier, title, abstract):
 
     predicted_class = categories[np.argmax(class_probabilities)]
     output_dict = {"prediction": predicted_class}
-    output_dict["scores"] = dict(zip(categories, class_probabilities))
+    output_dict["scores"] = dict(zip(categories, class_probabilities, strict=False))
 
     return output_dict
 

inspire_classifier/app.py

@@ -28,13 +28,13 @@
 from prometheus_flask_exporter.multiprocess import GunicornInternalPrometheusMetrics
 from webargs.flaskparser import use_args
 
+from inspire_classifier import serializers
 from inspire_classifier.api import initialize_classifier, predict_coreness
 
-from . import serializers
-
 
 class JsonResponse(Response):
-    """ "By creaitng this Response class, we force the response to always be in json, getting rid of the jsonify function."""
+    """ By creating this Response class, we force the response to always be in json,
+    getting rid of the jsonify function."""
 
     @classmethod
     def force_type(cls, rv, environ=None):
@@ -60,7 +60,8 @@ def create_app():
 
     @app.route("/api/health")
     def date():
-        """Basic endpoint that returns the date, used to check if everything is up and working."""
+        """Basic endpoint that returns the date, used to check if everything is up
+        and working."""
         now = datetime.datetime.now()
         return jsonify(now)
 

inspire_classifier/cli.py

@@ -43,11 +43,10 @@ def inspire_classifier():
     "-b", "--base-path", type=click.Path(exists=True), required=False, nargs=1
 )
 def predict(title, abstract, base_path):
-    with click_spinner.spinner():
-        with current_app.app_context():
-            if base_path:
-                current_app.config["CLASSIFIER_BASE_PATH"] = base_path
-            click.echo(predict_coreness(title, abstract))
+    with click_spinner.spinner(),current_app.app_context():
+        if base_path:
+            current_app.config["CLASSIFIER_BASE_PATH"] = base_path
+        click.echo(predict_coreness(title, abstract))
 
 
 @inspire_classifier.command("train")
@@ -58,19 +57,18 @@ def predict(title, abstract, base_path):
     "-b", "--base-path", type=click.Path(exists=True), required=False, nargs=1
 )
 def train_classifier(language_model_epochs, classifier_epochs, base_path):
-    with click_spinner.spinner():
-        with current_app.app_context():
-            if language_model_epochs:
-                current_app.config["CLASSIFIER_LANGUAGE_MODEL_CYCLE_LENGTH"] = (
-                    language_model_epochs
-                )
-            if classifier_epochs:
-                current_app.config["CLASSIFIER_CLASSIFIER_CYCLE_LENGTH"] = (
-                    classifier_epochs
-                )
-            if base_path:
-                current_app.config["CLASSIFIER_BASE_PATH"] = base_path
-            train()
+    with click_spinner.spinner(),current_app.app_context():
+        if language_model_epochs:
+            current_app.config["CLASSIFIER_LANGUAGE_MODEL_CYCLE_LENGTH"] = (
+                language_model_epochs
+            )
+        if classifier_epochs:
+            current_app.config["CLASSIFIER_CLASSIFIER_CYCLE_LENGTH"] = (
+                classifier_epochs
+            )
+        if base_path:
+            current_app.config["CLASSIFIER_BASE_PATH"] = base_path
+        train()
 
 
 @inspire_classifier.command("validate")

inspire_classifier/domain/models.py

@@ -124,8 +124,11 @@ def initialize_learner(
         self,
         dropout_multiplier=0.5,
         weight_decay=1e-6,
-        learning_rates=np.array([1e-4, 1e-4, 1e-4, 1e-3, 1e-2]),
+        learning_rates=None,
     ):
+        if learning_rates is None:
+            learning_rates = np.array([1e-4, 1e-4, 1e-4, 1e-3, 1e-2])
+
         self.learner = text_classifier_learner(
             self.dataloader,
             AWD_LSTM,

inspire_classifier/domain/preprocessor.py

@@ -31,9 +31,11 @@
 def split_and_save_data_for_training(dataframe_path, dest_dir, val_fraction=0.1):
     """
     Args:
-        dataframe_path: The path to the pandas dataframe containing the records. The dataframe should have one
-                        column containing the title and abstract text appended (title + abstract). The second
-                        column should contain the label as an integer (0: Rejected, 1: Non-Core, 2: Core).
+        dataframe_path: The path to the pandas dataframe containing the records.
+                        The dataframe should have one column containing the title and
+                        abstract text appended (title + abstract). The second column
+                         should contain the label as an integer
+                         (0: Rejected, 1: Non-Core, 2: Core).
         dest_dir: Directory to save the training/validation csv.
         val_fraction: the fraction of data to use as the validation set.
     """

pyproject.toml

@@ -35,14 +35,12 @@ mock = "^5.1.0"
 
 
 [tool.poetry.group.dev.dependencies]
-black = "^24.4.2"
 pre-commit = "*"
 elasticsearch-dsl = "^7.4.0"
 elasticsearch = "<7.14.0"
 inspire-utils = "3.0.22"
 
 
-isort = "^5.13.2"
 boto3 = "^1.34.130"
 [build-system]
 requires = ["poetry-core"]
@@ -56,8 +54,3 @@ inspire-classifier = 'inspire_classifier.cli:inspire_classifier'
 testpaths = [
     "tests",
 ]
-
-[tool.isort]
-profile = "black"
-multi_line_output = 3
-atomic = true

ruff.toml

@@ -0,0 +1,28 @@
+target-version = "py311"
+[lint.flake8-tidy-imports]
+ban-relative-imports = "all"
+
+[lint]
+select = [
+    # pycodestyle
+    "E",
+    # Pyflakes
+    "F",
+    # flake8-bugbear
+    "B",
+    # flake8-simplify
+    "SIM",
+    # isort
+    "I",
+    # flake8-tidy-imports
+    "TID",
+    # flake8-pytest-style
+    "PT",
+]
+ignore = ["B904"]
+
+[lint.pycodestyle]
+ignore-overlong-task-comments = true
+
+[lint.pydocstyle]
+convention = "google"

scripts/train_classifier.py

@@ -46,7 +46,9 @@ def train_classifier(
     print("-----------------")
 
     os.system(
-        f"inspire-classifier train -b classifier --classifier-epochs {number_of_classifier_epochs} --language-model-epochs {number_of_lanuage_model_epochs}"
+        f"inspire-classifier train -b classifier "
+        f"--classifier-epochs {number_of_classifier_epochs} "
+        f"--language-model-epochs {number_of_lanuage_model_epochs}"
     )
     print("training finished successfully!")
     os.system(

tests/integration/conftest.py

@@ -46,7 +46,7 @@ def app():
         yield app
 
 
-@pytest.fixture()
+@pytest.fixture
 def app_client(app):
     return app.test_client()
 
@@ -55,9 +55,10 @@ class Mock_Learner(Learner):
     """
     Mocks the fit method of the Learner.
 
-    This is done to reduce the model training time during testing by making the fit run once (as opposed to 2 times and
-    3 times for the LanguageModel and Classifier respectively). It stores the result of the first run and then returns
-    the same result for the other times fit is run.
+    This is done to reduce the model training time during testing by making the fit
+    run once (as opposed to 2 times and 3 times for the LanguageModel and Classifier
+    respectively). It stores the result of the first run and then returns the same
+    result for the other times fit is run.
     """
 
     def fit(self, *args, **kwargs):
@@ -70,7 +71,7 @@ def fit(self, *args, **kwargs):
 
 @pytest.fixture(scope="session")
 @patch("fastai.text.learner.text_classifier_learner", Mock_Learner)
-def trained_pipeline(app, tmp_path_factory):
+def _trained_pipeline(app, tmp_path_factory):
     app.config["CLASSIFIER_BASE_PATH"] = tmp_path_factory.getbasetemp()
     create_directories()
     shutil.copy(