def test_pmdarima_signature_and_examples_saved_correctly(
auto_arima_model, test_data):
# NB: Signature inference will only work on the first element of the tuple return
prediction = auto_arima_model.predict(n_periods=20,
return_conf_int=True,
alpha=0.05)
signature_ = infer_signature(test_data, prediction[0])
example_ = test_data[0:5].copy(deep=False)
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.pmdarima.save_model(auto_arima_model,
path=path,
signature=signature,
input_example=example)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
r_example = _read_example(mlflow_model,
path).copy(deep=False)
np.testing.assert_array_equal(r_example, example)
def export_model(run, pipe, used_columns, X_val, val_pred, export_artifact):
# Infer the signature of the model
# Get the columns that we are really using from the pipeline
signature = infer_signature(X_val[used_columns], val_pred)
with tempfile.TemporaryDirectory() as temp_dir:
export_path = os.path.join(temp_dir, "model_export")
mlflow.sklearn.save_model(
pipe,
export_path,
serialization_format=mlflow.sklearn.SERIALIZATION_FORMAT_CLOUDPICKLE,
signature=signature,
input_example=X_val.iloc[:2],
)
artifact = wandb.Artifact(
export_artifact,
type="model_export",
description="Random Forest pipeline export",
)
artifact.add_dir(export_path)
run.log_artifact(artifact)
# Make sure the artifact is uploaded before the temp dir
# gets deleted
artifact.wait()
def test_signature_and_examples_are_saved_correctly(sklearn_knn_model,
iris_data):
data = iris_data
signature_ = infer_signature(*data)
example_ = data[0][:3, ]
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
with open(tmp.path("skmodel"), "wb") as f:
pickle.dump(sklearn_knn_model, f)
path = tmp.path("model")
mlflow.pyfunc.save_model(
path=path,
data_path=tmp.path("skmodel"),
loader_module=os.path.basename(__file__)[:-3],
code_path=[__file__],
signature=signature,
input_example=example)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model,
path) == example).all())
def test_pmdarima_signature_and_example_for_confidence_interval_mode(
auto_arima_model, model_path, test_data, use_signature, use_example):
model_path_primary = model_path.joinpath("primary")
model_path_secondary = model_path.joinpath("secondary")
mlflow.pmdarima.save_model(pmdarima_model=auto_arima_model,
path=model_path_primary)
loaded_pyfunc = mlflow.pyfunc.load_model(model_uri=model_path_primary)
predict_conf = pd.DataFrame([{
"n_periods": 10,
"return_conf_int": True,
"alpha": 0.2
}])
forecast = loaded_pyfunc.predict(predict_conf)
signature = infer_signature(test_data["orders"],
forecast) if use_signature else None
example = test_data[0:10].copy(deep=False) if use_example else None
mlflow.pmdarima.save_model(auto_arima_model,
path=model_path_secondary,
signature=signature,
input_example=example)
mlflow_model = Model.load(model_path_secondary)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
r_example = _read_example(mlflow_model,
model_path_secondary).copy(deep=False)
np.testing.assert_array_equal(r_example, example)
def test_signature_and_examples_saved_correctly(prophet_model):
data = prophet_model.data
model = prophet_model.model
horizon_df = future_horizon_df(model, FORECAST_HORIZON)
signature_ = infer_signature(data, model.predict(horizon_df))
example_ = data[0:5].copy(deep=False)
example_["y"] = pd.to_numeric(
example_["y"]) # cast to appropriate precision
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.prophet.save_model(model,
path=path,
signature=signature,
input_example=example)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
r_example = _read_example(mlflow_model,
path).copy(deep=False)
r_example["ds"] = pd.to_datetime(r_example["ds"],
format=DS_FORMAT)
np.testing.assert_array_equal(r_example, example)
def test_signature_and_examples_are_saved_correctly(onnx_model, data,
onnx_custom_env):
import mlflow.onnx
model = onnx_model
signature_ = infer_signature(*data)
example_ = data[0].head(3)
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.onnx.save_model(
model,
path=path,
conda_env=onnx_custom_env,
signature=signature,
input_example=example,
)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model,
path) == example).all())
def test_signature_and_examples_are_saved_correctly(iris_data, main_scoped_model_class):
def test_predict(sk_model, model_input):
return sk_model.predict(model_input) * 2
data = iris_data
signature_ = infer_signature(*data)
example_ = data[0][
:3,
]
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.pyfunc.save_model(
path=path,
artifacts={},
python_model=main_scoped_model_class(test_predict),
signature=signature,
input_example=example,
)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert np.array_equal(_read_example(mlflow_model, path), example)
def test_pmdarima_signature_and_example_for_confidence_interval_mode(
auto_arima_model, model_path, test_data):
mlflow.pmdarima.save_model(pmdarima_model=auto_arima_model,
path=model_path)
loaded_pyfunc = mlflow.pyfunc.load_model(model_uri=model_path)
predict_conf = pd.DataFrame([{
"n_periods": 10,
"return_conf_int": True,
"alpha": 0.2
}])
forecast = loaded_pyfunc.predict(predict_conf)
signature_ = infer_signature(test_data["orders"], forecast)
example_ = test_data[0:10].copy(deep=False)
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.pmdarima.save_model(auto_arima_model,
path=path,
signature=signature,
input_example=example)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
r_example = _read_example(mlflow_model,
path).copy(deep=False)
np.testing.assert_array_equal(r_example, example)
def after_pipeline_run(
self,
run_params: Dict[str, Any],
pipeline: Pipeline,
catalog: DataCatalog,
) -> None:
"""Hook to be invoked after a pipeline runs.
Args:
run_params: The params needed for the given run.
Should be identical to the data logged by Journal.
# @fixme: this needs to be modelled explicitly as code, instead of comment
Schema: {
"run_id": str,
"project_path": str,
"env": str,
"kedro_version": str,
"tags": Optional[List[str]],
"from_nodes": Optional[List[str]],
"to_nodes": Optional[List[str]],
"node_names": Optional[List[str]],
"from_inputs": Optional[List[str]],
"load_versions": Optional[List[str]],
"pipeline_name": str,
"extra_params": Optional[Dict[str, Any]],
}
pipeline: The ``Pipeline`` that was run.
catalog: The ``DataCatalog`` used during the run.
"""
if self._is_mlflow_enabled:
if isinstance(pipeline, PipelineML):
with TemporaryDirectory() as tmp_dir:
# This will be removed at the end of the context manager,
# but we need to log in mlflow beforeremoving the folder
pipeline_catalog = pipeline._extract_pipeline_catalog(catalog)
artifacts = pipeline.extract_pipeline_artifacts(
pipeline_catalog, temp_folder=Path(tmp_dir)
)
if pipeline.model_signature == "auto":
input_data = pipeline_catalog.load(pipeline.input_name)
model_signature = infer_signature(model_input=input_data)
else:
model_signature = pipeline.model_signature
mlflow.pyfunc.log_model(
artifact_path=pipeline.model_name,
python_model=KedroPipelineModel(
pipeline_ml=pipeline,
catalog=pipeline_catalog,
**pipeline.kwargs,
),
artifacts=artifacts,
conda_env=_format_conda_env(pipeline.conda_env),
signature=model_signature,
)
# Close the mlflow active run at the end of the pipeline to avoid interactions with further runs
mlflow.end_run()
else:
switch_catalog_logging(catalog, True)
def infer_model_signature(input_example):
if not hasattr(estimator, "predict"):
raise Exception(
"the trained model does not specify a `predict` function, "
+ "which is required in order to infer the signature")
return infer_signature(input_example,
estimator.predict(input_example))
def _infer_model_signature(input_example_slice):
input_slice_df = _find_and_set_features_col_as_vector_if_needed(
spark.createDataFrame(input_example_slice),
spark_model)
model_output = spark_model.transform(input_slice_df).drop(
*input_slice_df.columns)
return infer_signature(input_example_slice,
model_output.toPandas())
def test_parse_with_schema(pandas_df_with_all_types):
schema = Schema([ColSpec(c, c) for c in pandas_df_with_all_types.columns])
df = _shuffle_pdf(pandas_df_with_all_types)
json_str = json.dumps(df.to_dict(orient="split"), cls=NumpyEncoder)
df = pyfunc_scoring_server.parse_json_input(json_str,
orient="split",
schema=schema)
json_str = json.dumps(df.to_dict(orient="records"), cls=NumpyEncoder)
df = pyfunc_scoring_server.parse_json_input(json_str,
orient="records",
schema=schema)
assert schema == infer_signature(df[schema.input_names()]).inputs
# The current behavior with pandas json parse with type hints is weird. In some cases, the
# types are forced ignoting overflow and loss of precision:
bad_df = """{
"columns":["bad_integer", "bad_float", "bad_string", "bad_boolean"],
"data":[
[9007199254740991.0, 1.1, 1, 1.5],
[9007199254740992.0, 9007199254740992.0, 2, 0],
[9007199254740994.0, 3.3, 3, "some arbitrary string"]
]
}"""
schema = Schema([
ColSpec("integer", "bad_integer"),
ColSpec("float", "bad_float"),
ColSpec("float", "good_float"),
ColSpec("string", "bad_string"),
ColSpec("boolean", "bad_boolean"),
])
df = pyfunc_scoring_server.parse_json_input(bad_df,
orient="split",
schema=schema)
# Unfortunately, the current behavior of pandas parse is to force numbers to int32 even if
# they don't fit:
assert df["bad_integer"].dtype == np.int32
assert all(df["bad_integer"] == [-2147483648, -2147483648, -2147483648])
# The same goes for floats:
assert df["bad_float"].dtype == np.float32
assert all(df["bad_float"] == np.array([1.1, 9007199254740992, 3.3],
dtype=np.float32))
# However bad string is recognized as int64:
assert all(df["bad_string"] == np.array([1, 2, 3], dtype=np.object))
# Boolean is forced - zero and empty string is false, everything else is true:
assert df["bad_boolean"].dtype == np.bool
assert all(df["bad_boolean"] == [True, False, True])
def test_signature_and_examples_are_saved_correctly(xgb_model):
model = xgb_model.model
for signature in (None, infer_signature(xgb_model.inference_dataframe)):
for example in (None, xgb_model.inference_dataframe.head(3)):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.xgboost.save_model(
xgb_model=model, path=path, signature=signature, input_example=example
)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model, path) == example).all())
def test_signature_and_examples_are_saved_correctly(h2o_iris_model):
model = h2o_iris_model.model
signature_ = infer_signature(h2o_iris_model.inference_data.as_data_frame())
example_ = h2o_iris_model.inference_data.as_data_frame().head(3)
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.h2o.save_model(model, path=path, signature=signature, input_example=example)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model, path) == example).all())
def test_signature_and_examples_are_saved_correctly(model, data):
signature_ = infer_signature(*data)
example_ = data[0].head(3)
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.keras.save_model(
model, path=path, signature=signature, input_example=example
)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model, path) == example).all())
def test_model_export_with_signature_and_examples(iris_df, spark_model_iris):
_, _, iris_spark_df = iris_df
signature_ = infer_signature(iris_spark_df)
example_ = iris_spark_df.toPandas().head(3)
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
sparkm.save_model(
spark_model_iris.model, path=path, signature=signature, input_example=example
)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model, path) == example).all())
def test_signature_and_examples_are_saved_correctly():
model, _, X = ols_model()
signature_ = infer_signature(X)
example_ = X[0:3, :]
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.statsmodels.save_model(
model, path=path, signature=signature, input_example=example
)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert np.array_equal(_read_example(mlflow_model, path), example)
def test_model_export_with_schema_and_examples(spacy_model_with_data):
spacy_model = spacy_model_with_data.model
signature_ = infer_signature(spacy_model_with_data.inference_data)
example_ = spacy_model_with_data.inference_data.head(3)
for signature in (None, signature_):
for example in (None, example_):
print(signature is None, example is None)
with TempDir() as tmp:
path = tmp.path("model")
mlflow.spacy.save_model(
spacy_model, path=path, signature=signature, input_example=example
)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model, path) == example).all())
def generate_report(
self,
model: OpenstfRegressor,
) -> Report:
"""Generate a report on a given model
Args:
model (OpenstfRegressor): the model to create a report on
Returns:
Report: reporter object containing info about the model
"""
# Get training (input_data_list[0]) and validation (input_data_list[1]) set
train_x, train_y = (
self.input_data_list[0].iloc[:, 1:-1],
self.input_data_list[0].iloc[:, 0],
)
valid_x, valid_y = (
self.input_data_list[1].iloc[:, 1:-1],
self.input_data_list[1].iloc[:, 0],
)
data_series_figures = self._make_data_series_figures(model)
# feature_importance_dataframe should be a dataframe, to create a figure
# can be None if we have no feature importance
if isinstance(model.feature_importance_dataframe, pd.DataFrame):
feature_importance_figure = figure.plot_feature_importance(
model.feature_importance_dataframe)
# If it isn't a dataframe we will set feature_importance_figure, so it will not create the figure
else:
feature_importance_figure = None
with warnings.catch_warnings():
warnings.simplefilter("ignore")
report = Report(
data_series_figures=data_series_figures,
feature_importance_figure=feature_importance_figure,
metrics=self.get_metrics(model.predict(valid_x), valid_y),
signature=infer_signature(train_x, train_y),
)
return report
def test_signature_and_examples_are_saved_correctly(gluon_model, model_data):
model = gluon_model
signature_ = infer_signature(model_data[0].asnumpy())
example_ = model_data[0].asnumpy()[:3, ]
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.gluon.save_model(model,
path=path,
signature=signature,
input_example=example)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model,
path) == example).all())
def test_diviner_signature_and_examples_saved_correctly(
grouped_prophet, diviner_data, model_path, use_signature, use_example
):
prediction = grouped_prophet.forecast(horizon=20, frequency="D")
signature = infer_signature(diviner_data.df, prediction) if use_signature else None
example = diviner_data.df[0:5].copy(deep=False) if use_example else None
mlflow.diviner.save_model(
grouped_prophet, path=model_path, signature=signature, input_example=example
)
mlflow_model = Model.load(model_path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
r_example = _read_example(mlflow_model, model_path).copy(deep=False)
# NB: datetime values are implicitly cast, so this needs to be reverted.
r_example["ds"] = pd.to_datetime(r_example["ds"], format=DS_FORMAT)
np.testing.assert_array_equal(r_example, example)
def test_signature_and_examples_are_saved_correctly(sklearn_knn_model):
data = sklearn_knn_model.inference_data
model = sklearn_knn_model.model
signature_ = infer_signature(data)
example_ = data[:3, ]
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.sklearn.save_model(model,
path=path,
signature=signature,
input_example=example)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model,
path) == example).all())
def test_pmdarima_signature_and_examples_saved_correctly(
auto_arima_model, test_data, model_path, use_signature, use_example):
# NB: Signature inference will only work on the first element of the tuple return
prediction = auto_arima_model.predict(n_periods=20,
return_conf_int=True,
alpha=0.05)
signature = infer_signature(test_data,
prediction[0]) if use_signature else None
example = test_data[0:5].copy(deep=False) if use_example else None
mlflow.pmdarima.save_model(auto_arima_model,
path=model_path,
signature=signature,
input_example=example)
mlflow_model = Model.load(model_path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
r_example = _read_example(mlflow_model, model_path).copy(deep=False)
np.testing.assert_array_equal(r_example, example)
def test_log_model_with_signature_and_examples(iris_df, spark_model_iris):
_, _, iris_spark_df = iris_df
signature_ = infer_signature(iris_spark_df)
example_ = iris_spark_df.toPandas().head(3)
artifact_path = "model"
for signature in (None, signature_):
for example in (None, example_):
with mlflow.start_run():
sparkm.log_model(spark_model_iris.model,
artifact_path=artifact_path,
signature=signature,
input_example=example)
artifact_uri = mlflow.get_artifact_uri()
model_path = os.path.join(artifact_uri, artifact_path)
mlflow_model = Model.load(model_path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model,
model_path) == example).all())
def test_signature_and_examples_are_saved_correctly(ols_model):
model = ols_model.model
X = ols_model.inference_dataframe
signature_ = infer_signature(X)
example_ = X[0:3, :]
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.statsmodels.save_model(model,
path=path,
signature=signature,
input_example=example)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model,
path) == example).all())
def test_schema_and_examples_are_save_correctly(saved_tf_iris_model):
train_x, train_y = iris_data_utils.load_data()[0]
X = pd.DataFrame(train_x)
y = pd.Series(train_y)
for signature in (None, infer_signature(X, y)):
for example in (None, X.head(3)):
with TempDir() as tmp:
path = tmp.path("model")
mlflow.tensorflow.save_model(
tf_saved_model_dir=saved_tf_iris_model.path,
tf_meta_graph_tags=saved_tf_iris_model.meta_graph_tags,
tf_signature_def_key=saved_tf_iris_model.signature_def_key,
path=path,
signature=signature,
input_example=example,
)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model, path) == example).all())
def infer_model_signature(input_example):
model_output = model.predict(input_example)
model_signature = infer_signature(input_example, model_output)
return model_signature
def train(*args, **kwargs):
def record_eval_results(eval_results):
"""
Create a callback function that records evaluation results.
"""
def callback(env):
eval_results.append(dict(env.evaluation_result_list))
return callback
if not mlflow.active_run():
try_mlflow_log(mlflow.start_run)
auto_end_run = True
else:
auto_end_run = False
def log_feature_importance_plot(features, importance, importance_type):
"""
Log feature importance plot.
"""
import matplotlib.pyplot as plt
features = np.array(features)
importance = np.array(importance)
indices = np.argsort(importance)
features = features[indices]
importance = importance[indices]
num_features = len(features)
# If num_features > 10, increase the figure height to prevent the plot
# from being too dense.
w, h = [6.4, 4.8] # matplotlib's default figure size
h = h + 0.1 * num_features if num_features > 10 else h
fig, ax = plt.subplots(figsize=(w, h))
yloc = np.arange(num_features)
ax.barh(yloc, importance, align="center", height=0.5)
ax.set_yticks(yloc)
ax.set_yticklabels(features)
ax.set_xlabel("Importance")
ax.set_title("Feature Importance ({})".format(importance_type))
fig.tight_layout()
tmpdir = tempfile.mkdtemp()
try:
# pylint: disable=undefined-loop-variable
filepath = os.path.join(
tmpdir, "feature_importance_{}.png".format(imp_type))
fig.savefig(filepath)
try_mlflow_log(mlflow.log_artifact, filepath)
finally:
plt.close(fig)
shutil.rmtree(tmpdir)
original = gorilla.get_original_attribute(xgboost, "train")
# logging booster params separately via mlflow.log_params to extract key/value pairs
# and make it easier to compare them across runs.
params = args[0] if len(args) > 0 else kwargs["params"]
try_mlflow_log(mlflow.log_params, params)
unlogged_params = [
"params",
"dtrain",
"evals",
"obj",
"feval",
"evals_result",
"xgb_model",
"callbacks",
"learning_rates",
]
log_fn_args_as_params(original, args, kwargs, unlogged_params)
all_arg_names = inspect.getargspec(original)[0] # pylint: disable=W1505
num_pos_args = len(args)
# adding a callback that records evaluation results.
eval_results = []
callbacks_index = all_arg_names.index("callbacks")
callback = record_eval_results(eval_results)
if num_pos_args >= callbacks_index + 1:
tmp_list = list(args)
tmp_list[callbacks_index] += [callback]
args = tuple(tmp_list)
elif "callbacks" in kwargs and kwargs["callbacks"] is not None:
kwargs["callbacks"] += [callback]
else:
kwargs["callbacks"] = [callback]
# training model
model = original(*args, **kwargs)
# logging metrics on each iteration.
for idx, metrics in enumerate(eval_results):
try_mlflow_log(mlflow.log_metrics, metrics, step=idx)
# If early_stopping_rounds is present, logging metrics at the best iteration
# as extra metrics with the max step + 1.
early_stopping_index = all_arg_names.index("early_stopping_rounds")
early_stopping = (num_pos_args >= early_stopping_index + 1
or "early_stopping_rounds" in kwargs)
if early_stopping:
extra_step = len(eval_results)
try_mlflow_log(mlflow.log_metric, "stopped_iteration",
len(eval_results) - 1)
try_mlflow_log(mlflow.log_metric, "best_iteration",
model.best_iteration)
try_mlflow_log(mlflow.log_metrics,
eval_results[model.best_iteration],
step=extra_step)
# logging feature importance as artifacts.
for imp_type in importance_types:
imp = None
try:
imp = model.get_score(importance_type=imp_type)
features, importance = zip(*imp.items())
log_feature_importance_plot(features, importance, imp_type)
except Exception: # pylint: disable=broad-except
_logger.exception(
"Failed to log feature importance plot. XGBoost autologging "
"will ignore the failure and continue. Exception: ")
if imp is not None:
tmpdir = tempfile.mkdtemp()
try:
filepath = os.path.join(
tmpdir, "feature_importance_{}.json".format(imp_type))
with open(filepath, "w") as f:
json.dump(imp, f)
try_mlflow_log(mlflow.log_artifact, filepath)
finally:
shutil.rmtree(tmpdir)
# dtrain must exist as the original train function already ran successfully
dtrain = args[1] if len(args) > 1 else kwargs.get("dtrain")
input_example = None
signature = None
try:
# it is possible that the dataset was constructed before the patched
# constructor was applied, so we cannot assume the input_example_info exists
input_example_info = getattr(dtrain, "input_example_info", None)
if input_example_info is None:
raise Exception("please ensure that autologging is " +
"enabled before constructing the dataset.")
input_example = input_example_info.input_example
if input_example is None:
# input example collection failed
raise Exception(input_example_info.error_msg)
model_output = model.predict(xgboost.DMatrix(input_example))
signature = infer_signature(input_example, model_output)
except Exception as e: # pylint: disable=broad-except
input_example = None
msg = "Failed to gather example input and model signature: " + str(
e)
_logger.warning(msg)
try_mlflow_log(
log_model,
model,
artifact_path="model",
signature=signature,
input_example=input_example,
)
if auto_end_run:
try_mlflow_log(mlflow.end_run)
return model
model_uri = mlflow.get_artifact_uri(artifact_path)
flavor_conf = Model.load(model_uri).flavors["catboost"]
assert "save_format" in flavor_conf
assert flavor_conf["save_format"] == save_format
if save_format in SUPPORTS_DESERIALIZATION:
mlflow.catboost.load_model(model_uri)
else:
with pytest.raises(cb.CatBoostError,
match="deserialization not supported or missing"):
mlflow.catboost.load_model(model_uri)
@pytest.mark.large
@pytest.mark.parametrize("signature", [None, infer_signature(get_iris()[0])])
@pytest.mark.parametrize("input_example", [None, get_iris()[0].head(3)])
def test_signature_and_examples_are_saved_correctly(reg_model, model_path,
signature, input_example):
mlflow.catboost.save_model(reg_model.model,
model_path,
signature=signature,
input_example=input_example)
mlflow_model = Model.load(model_path)
assert signature == mlflow_model.signature
if input_example is None:
assert mlflow_model.saved_input_example_info is None
else:
pd.testing.assert_frame_equal(_read_example(mlflow_model, model_path),
input_example)
def _log_posttraining_metadata(estimator, *args, **kwargs):
"""
Records metadata for a scikit-learn estimator after training has completed.
This is intended to be invoked within a patched scikit-learn training routine
(e.g., `fit()`, `fit_transform()`, ...) and assumes the existence of an active
MLflow run that can be referenced via the fluent Tracking API.
:param estimator: The scikit-learn estimator for which to log metadata.
:param args: The arguments passed to the scikit-learn training routine (e.g.,
`fit()`, `fit_transform()`, ...).
:param kwargs: The keyword arguments passed to the scikit-learn training routine.
"""
if hasattr(estimator, "score"):
try:
score_args = _get_args_for_score(estimator.score,
estimator.fit, args, kwargs)
training_score = estimator.score(*score_args)
except Exception as e: # pylint: disable=broad-except
msg = (
estimator.score.__qualname__ +
" failed. The 'training_score' metric will not be recorded. Scoring error: "
+ str(e))
_logger.warning(msg)
else:
try_mlflow_log(mlflow.log_metric, "training_score",
training_score)
# log common metrics and artifacts for estimators (classifier, regressor)
_log_specialized_estimator_content(estimator,
mlflow.active_run().info.run_id,
args, kwargs)
input_example = None
signature = None
if hasattr(estimator, "predict"):
try:
# Fetch an input example using the first several rows of the array-like
# training data supplied to the training routine (e.g., `fit()`)
SAMPLE_ROWS = 5
fit_arg_names = _get_arg_names(estimator.fit)
X_var_name, y_var_name = fit_arg_names[:2]
input_example = _get_Xy(args, kwargs, X_var_name,
y_var_name)[0][:SAMPLE_ROWS]
model_output = estimator.predict(input_example)
signature = infer_signature(input_example, model_output)
except Exception as e: # pylint: disable=broad-except
input_example = None
msg = "Failed to infer an input example and model signature: " + str(
e)
_logger.warning(msg)
try_mlflow_log(
log_model,
estimator,
artifact_path="model",
signature=signature,
input_example=input_example,
)
if _is_parameter_search_estimator(estimator):
if hasattr(estimator, "best_estimator_"):
try_mlflow_log(
log_model,
estimator.best_estimator_,
artifact_path="best_estimator",
signature=signature,
input_example=input_example,
)
if hasattr(estimator, "best_params_"):
best_params = {
"best_{param_name}".format(param_name=param_name):
param_value
for param_name, param_value in
estimator.best_params_.items()
}
try_mlflow_log(mlflow.log_params, best_params)
if hasattr(estimator, "cv_results_"):
try:
# Fetch environment-specific tags (e.g., user and source) to ensure that lineage
# information is consistent with the parent run
environment_tags = context_registry.resolve_tags()
_create_child_runs_for_parameter_search(
cv_estimator=estimator,
parent_run=mlflow.active_run(),
child_tags=environment_tags,
)
except Exception as e: # pylint: disable=broad-except
msg = (
"Encountered exception during creation of child runs for parameter search."
" Child runs may be missing. Exception: {}".format(
str(e)))
_logger.warning(msg)
try:
cv_results_df = pd.DataFrame.from_dict(
estimator.cv_results_)
_log_parameter_search_results_as_artifact(
cv_results_df,
mlflow.active_run().info.run_id)
except Exception as e: # pylint: disable=broad-except
msg = (
"Failed to log parameter search results as an artifact."
" Exception: {}".format(str(e)))
_logger.warning(msg)