Merge branch 'microsoft:main' into main

.pre-commit-config.yaml

@@ -1,14 +1,17 @@
 
 repos:
   - repo: https://github.com/psf/black
-    rev: stable
+    rev: 22.3.0
     hooks:
     - id: black
       language_version: python3
+  -   repo: https://github.com/pycqa/flake8
+      rev: 4.0.1
+      hooks:
+      -   id: flake8
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v1.2.3
+    rev: v4.3.0
     hooks:
-    - id: flake8
     - id: check-added-large-files
     - id: check-ast
     - id: check-byte-order-marker

test/automl/test_constraints.py

@@ -1,11 +1,11 @@
-import unittest
-
+from urllib.error import URLError
 from sklearn.datasets import fetch_openml
 from sklearn.model_selection import train_test_split
-from flaml.automl import AutoML
+from sklearn.externals._arff import ArffException
+from functools import partial
+from flaml.automl import AutoML, size
 from flaml import tune
 
-
 dataset = "credit-g"
 
 
@@ -24,11 +24,10 @@ def test_metric_constraints():
         "time_budget": 2,
         "pred_time_limit": 5.1e-05,
     }
-    from sklearn.externals._arff import ArffException
 
     try:
         X, y = fetch_openml(name=dataset, return_X_y=True)
-    except (ArffException, ValueError):
+    except (ArffException, ValueError, URLError):
         from sklearn.datasets import load_wine
 
         X, y = load_wine(return_X_y=True)
@@ -42,10 +41,6 @@ def test_metric_constraints():
     config = automl.best_config.copy()
     config["learner"] = automl.best_estimator
     automl.trainable(config)
-
-    from flaml.automl import size
-    from functools import partial
-
     print("metric constraints used in automl", automl.metric_constraints)
 
     analysis = tune.run(
@@ -117,7 +112,6 @@ def test_metric_constraints_custom():
             ("val_train_loss_gap", "<=", 0.05),
         ],
     }
-    from sklearn.externals._arff import ArffException
 
     try:
         X, y = fetch_openml(name=dataset, return_X_y=True)
@@ -151,11 +145,8 @@ def test_metric_constraints_custom():
     config = automl.best_config.copy()
     config["learner"] = automl.best_estimator
     automl.trainable(config)
-
-    from flaml.automl import size
-    from functools import partial
-
     print("metric constraints in automl", automl.metric_constraints)
+
     analysis = tune.run(
         automl.trainable,
         automl.search_space,
@@ -176,4 +167,5 @@ def test_metric_constraints_custom():
 
 
 if __name__ == "__main__":
-    unittest.main()
+    test_metric_constraints()
+    test_metric_constraints_custom()