adding precommit check (#930)

* adding precommit check

* run precommit

* Apply suggestions from code review

Co-authored-by: Zvi Baratz <z.baratz@gmail.com>

* apply precommit

---------

Co-authored-by: Zvi Baratz <z.baratz@gmail.com>

.github/workflows/pre-commit.yml

@@ -0,0 +1,26 @@
+name: Code formatting
+
+# see: https://help.github.com/en/actions/reference/events-that-trigger-workflows
+on:  # Trigger the workflow on push or pull request, but only for the main branch
+  push:
+    branches: [main]
+  pull_request: {}
+
+defaults:
+  run:
+    shell: bash
+
+jobs:
+
+  pre-commit-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+      - name: Set $PY environment variable
+        run: echo "PY=$(python -VV | sha256sum | cut -d' ' -f1)" >> $GITHUB_ENV
+      - uses: actions/cache@v3
+        with:
+          path: ~/.cache/pre-commit
+          key: pre-commit|${{ env.PY }}|${{ hashFiles('.pre-commit-config.yaml') }}
+      - uses: pre-commit/action@v3.0.0

flaml/default/suggest.py

@@ -61,8 +61,15 @@ def load_config_predictor(estimator_name, task, location=None):
     return predictor
 
 
-def suggest_config(task, X, y, estimator_or_predictor, location=None, k=None, meta_feature_fn=meta_feature):
-
+def suggest_config(
+    task,
+    X,
+    y,
+    estimator_or_predictor,
+    location=None,
+    k=None,
+    meta_feature_fn=meta_feature,
+):
     """Suggest a list of configs for the given task and training data.
 
     The returned configs can be used as starting points for AutoML.fit().

flaml/tune/spark/utils.py

@@ -281,7 +281,8 @@ class PySparkOvertimeMonitor:
 
     def __enter__(self):
         """Enter the context manager.
-        This will start a monitor thread if spark is available and force_cancel is True."""
+        This will start a monitor thread if spark is available and force_cancel is True.
+        """
         if self._force_cancel and _have_spark:
             self._monitor_daemon = threading.Thread(target=self._monitor_overtime)
             # logger.setLevel("INFO")