Merge branch 'main' into dependabot/github_actions/actions/cache-3

flaml/automl.py

@@ -3069,7 +3069,9 @@ class AutoML(BaseEstimator):
         if mlflow is not None and mlflow.active_run():
             with mlflow.start_run(nested=True):
                 mlflow.log_metric("iter_counter", self._track_iter)
-                if "intermediate_results" in search_state.metric_for_logging:
+                if (search_state.metric_for_logging is not None) and (
+                    "intermediate_results" in search_state.metric_for_logging
+                ):
                     for each_entry in search_state.metric_for_logging[
                         "intermediate_results"
                     ]:
@@ -3079,6 +3081,7 @@ class AutoML(BaseEstimator):
                                 "iter_counter", self._iter_per_learner[estimator]
                             )
                     del search_state.metric_for_logging["intermediate_results"]
+                if search_state.metric_for_logging:
                     mlflow.log_metrics(search_state.metric_for_logging)
                 mlflow.log_metric("trial_time", search_state.trial_time)
                 mlflow.log_metric("wall_clock_time", self._state.time_from_start)

flaml/version.py

@@ -1 +1 @@
-__version__ = "1.0.11"
+__version__ = "1.0.12"

test/automl/test_notebook_example.py

@@ -154,6 +154,19 @@ def test_mlflow():
         pass
     # subprocess.check_call([sys.executable, "-m", "pip", "uninstall", "mlflow"])
 
+    from sklearn.datasets import load_iris
+
+    with mlflow.start_run():
+        automl = AutoML()
+        automl_settings = {
+            "time_budget": 2,  # in seconds
+            "metric": "accuracy",
+            "task": "classification",
+            "log_file_name": "iris.log",
+        }
+        X_train, y_train = load_iris(return_X_y=True)
+        automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
+
 
 if __name__ == "__main__":
     test_automl(600)