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

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zsk 2022-08-22 17:05:18 -04:00 committed by GitHub
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3 changed files with 19 additions and 3 deletions
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7
flaml/automl.py
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@ -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"
]:
@ -3079,7 +3081,8 @@ class AutoML(BaseEstimator):
"iter_counter", self._iter_per_learner[estimator]
)
del search_state.metric_for_logging["intermediate_results"]
mlflow.log_metrics(search_state.metric_for_logging)
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)
mlflow.log_metric("validation_loss", search_state.val_loss)

2
flaml/version.py
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@ -1 +1 @@
__version__ = "1.0.11"
__version__ = "1.0.12"

13
test/automl/test_notebook_example.py
View File

@ -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)