Replace Black, isort and flake8 with Ruff check and formatter

.devcontainer/devcontainer.json

@@ -5,25 +5,22 @@
 		"vscode": {
 			"settings": {
 				"python.linting.enabled": true,
-				"python.linting.flake8Enabled": true,
 				"python.linting.pylintEnabled": false,
 				"python.testing.pytestEnabled": true,
 				"editor.formatOnSave": true,
 				"editor.codeActionsOnSave": {
 					"source.organizeImports": true
 				},
 				"[python]": {
-					"editor.defaultFormatter": "ms-python.black-formatter"
+					"editor.defaultFormatter": "charliermarsh.ruff"
 				},
 				"editor.rulers": [
 					80
 				]
 			},
 			"extensions": [
-				"ms-python.python",
-				"ms-python.isort",
-				"ms-python.flake8",
-				"ms-python.black-formatter"
+				"charliermarsh.ruff",
+				"ms-python.python"
 			]
 		}
 	},

autokeras/auto_model.py

@@ -130,7 +130,7 @@ def __init__(
         overwrite: bool = False,
         seed: Optional[int] = None,
         max_model_size: Optional[int] = None,
-        **kwargs
+        **kwargs,
     ):
         self.inputs = tree.flatten(inputs)
         self.outputs = tree.flatten(outputs)
@@ -152,7 +152,7 @@ def __init__(
             seed=self.seed,
             project_name=project_name,
             max_model_size=max_model_size,
-            **kwargs
+            **kwargs,
         )
         self.overwrite = overwrite
         self._heads = [output_node.in_blocks[0] for output_node in self.outputs]
@@ -225,7 +225,7 @@ def fit(
         validation_split=0.2,
         validation_data=None,
         verbose=1,
-        **kwargs
+        **kwargs,
     ):
         """Search for the best model and hyperparameters for the AutoModel.
 
@@ -307,7 +307,7 @@ def fit(
             validation_data=validation_data,
             validation_split=validation_split,
             verbose=verbose,
-            **kwargs
+            **kwargs,
         )
 
         return history
@@ -475,7 +475,7 @@ def predict(self, x, batch_size=32, verbose=1, **kwargs):
             batch_size=batch_size,
             x=dataset,
             verbose=verbose,
-            **kwargs
+            **kwargs,
         )
         return pipeline.postprocess(y)
 
@@ -515,7 +515,7 @@ def evaluate(self, x, y=None, batch_size=32, verbose=1, **kwargs):
             batch_size=batch_size,
             x=dataset,
             verbose=verbose,
-            **kwargs
+            **kwargs,
         )
 
     def export_model(self):

autokeras/blocks/heads.py

@@ -64,7 +64,7 @@ def __init__(
         loss: Optional[types.LossType] = None,
         metrics: Optional[types.MetricsType] = None,
         dropout: Optional[float] = None,
-        **kwargs
+        **kwargs,
     ):
         self.num_classes = num_classes
         self.multi_label = multi_label
@@ -217,7 +217,7 @@ def __init__(
         loss: types.LossType = "mean_squared_error",
         metrics: Optional[types.MetricsType] = None,
         dropout: Optional[float] = None,
-        **kwargs
+        **kwargs,
     ):
         if metrics is None:
             metrics = ["mean_squared_error"]

autokeras/blocks/preprocessing.py

@@ -100,7 +100,7 @@ def __init__(
         contrast_factor: Optional[
             Union[float, Tuple[float, float], hyperparameters.Choice]
         ] = None,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(**kwargs)
         self.translation_factor = utils.get_hyperparameter(

autokeras/blocks/wrapper.py

@@ -51,7 +51,7 @@ def __init__(
         block_type: Optional[str] = None,
         normalize: Optional[bool] = None,
         augment: Optional[bool] = None,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(**kwargs)
         self.block_type = block_type

autokeras/engine/head.py

@@ -65,7 +65,7 @@ def __init__(
         self,
         loss: Optional[types.LossType] = None,
         metrics: Optional[types.MetricsType] = None,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(**kwargs)
         self.loss = loss

autokeras/engine/tuner.py

@@ -150,7 +150,7 @@ def search(
         callbacks=None,
         validation_split=0,
         verbose=1,
-        **fit_kwargs
+        **fit_kwargs,
     ):
         """Search for the best HyperParameters.
 
@@ -203,7 +203,7 @@ def search(
             epochs=epochs,
             callbacks=new_callbacks,
             verbose=verbose,
-            **fit_kwargs
+            **fit_kwargs,
         )
 
         # Train the best model use validation data.

autokeras/tasks/image.py

@@ -86,7 +86,7 @@ def __init__(
         overwrite: bool = False,
         seed: Optional[int] = None,
         max_model_size: Optional[int] = None,
-        **kwargs
+        **kwargs,
     ):
         if tuner is None:
             tuner = task_specific.ImageClassifierTuner
@@ -105,7 +105,7 @@ def __init__(
             overwrite=overwrite,
             seed=seed,
             max_model_size=max_model_size,
-            **kwargs
+            **kwargs,
         )
 
     def fit(
@@ -118,7 +118,7 @@ def fit(
         validation_data: Union[
             tf.data.Dataset, Tuple[types.DatasetType, types.DatasetType], None
         ] = None,
-        **kwargs
+        **kwargs,
     ):
         """Search for the best model and hyperparameters for the AutoModel.
 
@@ -172,7 +172,7 @@ def fit(
             callbacks=callbacks,
             validation_split=validation_split,
             validation_data=validation_data,
-            **kwargs
+            **kwargs,
         )
         return history
 
@@ -222,7 +222,7 @@ def __init__(
         overwrite: bool = False,
         seed: Optional[int] = None,
         max_model_size: Optional[int] = None,
-        **kwargs
+        **kwargs,
     ):
         if tuner is None:
             tuner = greedy.Greedy
@@ -238,7 +238,7 @@ def __init__(
             overwrite=overwrite,
             seed=seed,
             max_model_size=max_model_size,
-            **kwargs
+            **kwargs,
         )
 
     def fit(
@@ -251,7 +251,7 @@ def fit(
         validation_data: Union[
             types.DatasetType, Tuple[types.DatasetType], None
         ] = None,
-        **kwargs
+        **kwargs,
     ):
         """Search for the best model and hyperparameters for the AutoModel.
 
@@ -306,6 +306,6 @@ def fit(
             callbacks=callbacks,
             validation_split=validation_split,
             validation_data=validation_data,
-            **kwargs
+            **kwargs,
         )
         return history

autokeras/tasks/text.py

@@ -81,7 +81,7 @@ def __init__(
         overwrite: bool = False,
         seed: Optional[int] = None,
         max_model_size: Optional[int] = None,
-        **kwargs
+        **kwargs,
     ):
         if tuner is None:
             tuner = task_specific.TextClassifierTuner
@@ -100,7 +100,7 @@ def __init__(
             overwrite=overwrite,
             seed=seed,
             max_model_size=max_model_size,
-            **kwargs
+            **kwargs,
         )
 
     def fit(
@@ -111,7 +111,7 @@ def fit(
         callbacks=None,
         validation_split=0.2,
         validation_data=None,
-        **kwargs
+        **kwargs,
     ):
         """Search for the best model and hyperparameters for the AutoModel.
 
@@ -167,7 +167,7 @@ def fit(
             callbacks=callbacks,
             validation_split=validation_split,
             validation_data=validation_data,
-            **kwargs
+            **kwargs,
         )
         return history
 
@@ -217,7 +217,7 @@ def __init__(
         overwrite: bool = False,
         seed: Optional[int] = None,
         max_model_size: Optional[int] = None,
-        **kwargs
+        **kwargs,
     ):
         if tuner is None:
             tuner = greedy.Greedy
@@ -233,7 +233,7 @@ def __init__(
             overwrite=overwrite,
             seed=seed,
             max_model_size=max_model_size,
-            **kwargs
+            **kwargs,
         )
 
     def fit(
@@ -244,7 +244,7 @@ def fit(
         callbacks=None,
         validation_split=0.2,
         validation_data=None,
-        **kwargs
+        **kwargs,
     ):
         """Search for the best model and hyperparameters for the AutoModel.
 
@@ -300,6 +300,6 @@ def fit(
             callbacks=callbacks,
             validation_split=validation_split,
             validation_data=validation_data,
-            **kwargs
+            **kwargs,
         )
         return history

autokeras/tuners/greedy.py

@@ -216,7 +216,7 @@ def __init__(
         hyperparameters: Optional[keras_tuner.HyperParameters] = None,
         tune_new_entries: bool = True,
         allow_new_entries: bool = True,
-        **kwargs
+        **kwargs,
     ):
         self.seed = seed
         oracle = GreedyOracle(

docker/pre-commit.Dockerfile

@@ -1,6 +1,6 @@
-FROM python:3.7
+FROM python:3.8
 
-RUN pip install flake8 black isort
+RUN pip install ruff
 
 WORKDIR /autokeras
 CMD ["python", "docker/pre_commit.py"]

docker/pre_commit.py

@@ -6,7 +6,7 @@ def check_bash_call(string):
     check_call(["bash", "-c", string])
 
 
-def _run_format_and_flake8():
+def _run_lint_and_format():
     files_changed = False
 
     try:
@@ -25,13 +25,13 @@ def _run_format_and_flake8():
         exit(1)
 
 
-def run_format_and_flake8():
+def run_lint_and_format():
     try:
-        _run_format_and_flake8()
+        _run_lint_and_format()
     except CalledProcessError as error:
         print("Pre-commit returned exit code", error.returncode)
         exit(error.returncode)
 
 
 if __name__ == "__main__":
-    run_format_and_flake8()
+    run_lint_and_format()

docs/ipynb/text_regression.ipynb

@@ -101,7 +101,8 @@
    "source": [
     "# Initialize the text regressor.\n",
     "reg = ak.TextRegressor(\n",
-    "    overwrite=True, max_trials=1  # It tries 10 different models.\n",
+    "    overwrite=True,\n",
+    "    max_trials=1,  # It tries 10 different models.\n",
     ")\n",
     "# Feed the text regressor with training data.\n",
     "reg.fit(x_train, y_train, epochs=1, batch_size=2)\n",

docs/py/text_regression.py

@@ -58,7 +58,8 @@
 
 # Initialize the text regressor.
 reg = ak.TextRegressor(
-    overwrite=True, max_trials=1  # It tries 10 different models.
+    overwrite=True,
+    max_trials=1,  # It tries 10 different models.
 )
 # Feed the text regressor with training data.
 reg.fit(x_train, y_train, epochs=1, batch_size=2)

docs/tutobooks.py

@@ -239,10 +239,7 @@ def nb_to_md(nb_path, md_path, img_dir, working_dir=None):
 
     os.system(
         # "jupyter nbconvert --to markdown --execute --debug "
-        "jupyter nbconvert --to markdown "
-        + nb_fname
-        + " --output "
-        + target_md
+        "jupyter nbconvert --to markdown " + nb_fname + " --output " + target_md
         # + " --ExecutePreprocessor.timeout="
         # + str(TIMEOUT)
     )