Add multiclass model function traces

newrelic/config.py

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

newrelic/hooks/mlmodel_sklearn.py

@@ -243,6 +243,15 @@ def instrument_sklearn_cluster_kmeans_models(module):
  _instrument_sklearn_models(module, model_classes)
 
 
+def instrument_sklearn_multiclass_models(module):
+ model_classes = (
+ "OneVsRestClassifier",
+ "OneVsOneClassifier",
+ "OutputCodeClassifier",
+ )
+ _instrument_sklearn_models(module, model_classes)
+
+
 def instrument_sklearn_multioutput_models(module):
  model_classes = (
  "MultiOutputEstimator",

tests/mlmodel_sklearn/test_multiclass_models.py

@@ -0,0 +1,91 @@
+# 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.ensemble import AdaBoostClassifier
+from testing_support.validators.validate_transaction_metrics import (
+ validate_transaction_metrics,
+)
+
+from newrelic.api.background_task import background_task
+from newrelic.packages import six
+
+
+@pytest.mark.parametrize(
+ "multiclass_model_name",
+ [
+ "OneVsRestClassifier",
+ "OneVsOneClassifier",
+ "OutputCodeClassifier",
+ ],
+)
+def test_model_methods_wrapped_in_function_trace(multiclass_model_name, run_multiclass_model):
+ expected_scoped_metrics = {
+ "OneVsRestClassifier": [
+ ("Function/MLModel/Sklearn/Named/OneVsRestClassifier.fit", 1),
+ ("Function/MLModel/Sklearn/Named/OneVsRestClassifier.predict", 1),
+ ("Function/MLModel/Sklearn/Named/OneVsRestClassifier.predict_proba", 1),
+ ],
+ "OneVsOneClassifier": [
+ ("Function/MLModel/Sklearn/Named/OneVsOneClassifier.fit", 1),
+ ("Function/MLModel/Sklearn/Named/OneVsOneClassifier.predict", 1),
+ ],
+ "OutputCodeClassifier": [
+ ("Function/MLModel/Sklearn/Named/OutputCodeClassifier.fit", 1),
+ ("Function/MLModel/Sklearn/Named/OutputCodeClassifier.predict", 1),
+ ],
+ }
+
+ expected_transaction_name = (
+ "test_multiclass_models:test_model_methods_wrapped_in_function_trace.<locals>._test"
+ if six.PY3
+ else "test_multiclass_models:_test"
+ )
+
+ @validate_transaction_metrics(
+ expected_transaction_name,
+ scoped_metrics=expected_scoped_metrics[multiclass_model_name],
+ rollup_metrics=expected_scoped_metrics[multiclass_model_name],
+ background_task=True,
+ )
+ @background_task()
+ def _test():
+ run_multiclass_model(multiclass_model_name)
+
+ _test()
+
+
+@pytest.fixture
+def run_multiclass_model():
+ def _run(multiclass_model_name):
+ import sklearn.multiclass
+ from sklearn.datasets import load_iris
+ from sklearn.model_selection import train_test_split
+
+ X, y = load_iris(return_X_y=True)
+ x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)
+
+ # This is an example of a model that has all the available attributes
+ # We could have choosen any estimator that has predict, score,
+ # predict_log_proba, and predict_proba
+ clf = getattr(sklearn.multiclass, multiclass_model_name)(estimator=AdaBoostClassifier())
+
+ model = clf.fit(x_train, y_train)
+ model.predict(x_test)
+ if hasattr(model, "predict_proba"):
+ model.predict_proba(x_test)
+
+ return model
+
+ return _run