def load(cls, path):
"""Load the evaluation results from the specified local filesystem path"""
with open(os.path.join(path, "metrics.json"), "r") as fp:
metrics = json.load(fp)
with open(os.path.join(path, "artifacts_metadata.json"), "r") as fp:
artifacts_metadata = json.load(fp)
artifacts = {}
artifacts_dir = os.path.join(path, "artifacts")
for artifact_name, meta in artifacts_metadata.items():
uri = meta["uri"]
ArtifactCls = _get_class_from_string(meta["class_name"])
artifact = ArtifactCls(uri=uri)
artifact._load(os.path.join(artifacts_dir, artifact_name))
artifacts[artifact_name] = artifact
return EvaluationResult(metrics=metrics, artifacts=artifacts)
def _load_model(path):
"""
Load Model Implementation.
:param path: Local filesystem path to
the MLflow Model with the ``xgboost`` flavor (MLflow < 1.22.0) or
the top-level MLflow Model directory (MLflow >= 1.22.0).
"""
model_dir = os.path.dirname(path) if os.path.isfile(path) else path
flavor_conf = _get_flavor_configuration(model_path=model_dir, flavor_name=FLAVOR_NAME)
# XGBoost models saved in MLflow >=1.22.0 have `model_class`
# in the XGBoost flavor configuration to specify its XGBoost model class.
# When loading models, we first get the XGBoost model from
# its flavor configuration and then create an instance based on its class.
model_class = flavor_conf.get("model_class", "xgboost.core.Booster")
xgb_model_path = os.path.join(model_dir, flavor_conf.get("data"))
model = _get_class_from_string(model_class)()
model.load_model(xgb_model_path)
return model
def test_get_class_from_string():
assert _get_class_from_string(
"mlflow.tracking.MlflowClient") == mlflow.tracking.MlflowClient