Add multioutput model function traces

newrelic/config.py

@@ -3046,6 +3046,12 @@ def _process_module_builtin_defaults():
         "instrument_sklearn_cluster_clustering_models",
     )
 
+    _process_module_definition(
+        "sklearn.multioutput",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_multioutput_models",
+    )
+
     _process_module_definition(
         "sklearn.naive_bayes",
         "newrelic.hooks.mlmodel_sklearn",

newrelic/hooks/mlmodel_sklearn.py

@@ -243,6 +243,16 @@ def instrument_sklearn_cluster_kmeans_models(module):
     _instrument_sklearn_models(module, model_classes)
 
 
+def instrument_sklearn_multioutput_models(module):
+    model_classes = (
+        "MultiOutputEstimator",
+        "MultiOutputClassifier",
+        "ClassifierChain",
+        "RegressorChain",
+    )
+    _instrument_sklearn_models(module, model_classes)
+
+
 def instrument_sklearn_naive_bayes_models(module):
     model_classes = (
         "GaussianNB",

tests/mlmodel_sklearn/test_multioutput_models.py

@@ -0,0 +1,129 @@
+# Copyright 2010 New Relic, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+from sklearn import __init__  # noqa: Needed for get_package_version
+from sklearn.ensemble import AdaBoostClassifier
+from testing_support.validators.validate_transaction_metrics import (
+    validate_transaction_metrics,
+)
+
+from newrelic.api.background_task import background_task
+from newrelic.common.package_version_utils import get_package_version
+from newrelic.packages import six
+
+SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split(".")))
+
+
+# Python 2 will not allow instantiation of abstract class
+# (abstract method is __init__ here)
+@pytest.mark.skipif(SKLEARN_VERSION >= (1, 0, 0) or six.PY2, reason="Requires sklearn < 1.0 and Python3")
+@pytest.mark.parametrize(
+    "multioutput_model_name",
+    [
+        "MultiOutputEstimator",
+    ],
+)
+def test_below_v1_0_model_methods_wrapped_in_function_trace(multioutput_model_name, run_multioutput_model):
+    expected_scoped_metrics = {
+        "MultiOutputEstimator": [
+            ("Function/MLModel/Sklearn/Named/MultiOutputEstimator.fit", 1),
+            ("Function/MLModel/Sklearn/Named/MultiOutputEstimator.predict", 2),
+        ],
+    }
+    expected_transaction_name = (
+        "test_multioutput_models:test_below_v1_0_model_methods_wrapped_in_function_trace.<locals>._test"
+        if six.PY3
+        else "test_multioutput_models:_test"
+    )
+
+    @validate_transaction_metrics(
+        expected_transaction_name,
+        scoped_metrics=expected_scoped_metrics[multioutput_model_name],
+        rollup_metrics=expected_scoped_metrics[multioutput_model_name],
+        background_task=True,
+    )
+    @background_task()
+    def _test():
+        run_multioutput_model(multioutput_model_name)
+
+    _test()
+
+
+@pytest.mark.parametrize(
+    "multioutput_model_name",
+    [
+        "MultiOutputClassifier",
+        "ClassifierChain",
+        "RegressorChain",
+    ],
+)
+def test_above_v1_0_model_methods_wrapped_in_function_trace(multioutput_model_name, run_multioutput_model):
+    expected_scoped_metrics = {
+        "MultiOutputClassifier": [
+            ("Function/MLModel/Sklearn/Named/MultiOutputClassifier.fit", 1),
+            ("Function/MLModel/Sklearn/Named/MultiOutputClassifier.predict_proba", 1),
+            ("Function/MLModel/Sklearn/Named/MultiOutputClassifier.score", 1),
+        ],
+        "ClassifierChain": [
+            ("Function/MLModel/Sklearn/Named/ClassifierChain.fit", 1),
+            ("Function/MLModel/Sklearn/Named/ClassifierChain.predict_proba", 1),
+        ],
+        "RegressorChain": [
+            ("Function/MLModel/Sklearn/Named/RegressorChain.fit", 1),
+        ],
+    }
+    expected_transaction_name = (
+        "test_multioutput_models:test_above_v1_0_model_methods_wrapped_in_function_trace.<locals>._test"
+        if six.PY3
+        else "test_multioutput_models:_test"
+    )
+
+    @validate_transaction_metrics(
+        expected_transaction_name,
+        scoped_metrics=expected_scoped_metrics[multioutput_model_name],
+        rollup_metrics=expected_scoped_metrics[multioutput_model_name],
+        background_task=True,
+    )
+    @background_task()
+    def _test():
+        run_multioutput_model(multioutput_model_name)
+
+    _test()
+
+
+@pytest.fixture
+def run_multioutput_model():
+    def _run(multioutput_model_name):
+        import sklearn.multioutput
+        from sklearn.datasets import make_multilabel_classification
+
+        X, y = make_multilabel_classification(n_classes=3, random_state=0)
+
+        kwargs = {"estimator": AdaBoostClassifier()}
+        if multioutput_model_name in ["RegressorChain", "ClassifierChain"]:
+            kwargs = {"base_estimator": AdaBoostClassifier()}
+        clf = getattr(sklearn.multioutput, multioutput_model_name)(**kwargs)
+
+        model = clf.fit(X, y)
+        if hasattr(model, "predict"):
+            model.predict(X)
+        if hasattr(model, "score"):
+            model.score(X, y)
+        if hasattr(model, "predict_proba"):
+            model.predict_proba(X)
+
+        return model
+
+    return _run