Add gaussian process model function traces

newrelic/config.py

@@ -2902,6 +2902,30 @@ def _process_module_builtin_defaults():
         "instrument_sklearn_ensemble_hist_models",
     )
 
+    _process_module_definition(
+        "sklearn.gaussian_process._gpc",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_gaussian_process_models",
+    )
+
+    _process_module_definition(
+        "sklearn.gaussian_process.gpc",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_gaussian_process_models",
+    )
+
+    _process_module_definition(
+        "sklearn.gaussian_process._gpr",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_gaussian_process_models",
+    )
+
+    _process_module_definition(
+        "sklearn.gaussian_process.gpr",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_gaussian_process_models",
+    )
+
     _process_module_definition(
         "sklearn.dummy",
         "newrelic.hooks.mlmodel_sklearn",

newrelic/hooks/mlmodel_sklearn.py

@@ -265,6 +265,14 @@ def instrument_sklearn_ensemble_hist_models(module):
     _instrument_sklearn_models(module, model_classes)
 
 
+def instrument_sklearn_gaussian_process_models(module):
+    model_classes = (
+        "GaussianProcessClassifier",
+        "GaussianProcessRegressor",
+    )
+    _instrument_sklearn_models(module, model_classes)
+
+
 def instrument_sklearn_dummy_models(module):
     model_classes = (
         "DummyClassifier",

tests/mlmodel_sklearn/test_gaussian_process_models.py

@@ -0,0 +1,83 @@
+# 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 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(
+    "gaussian_process_model_name",
+    [
+        "GaussianProcessClassifier",
+        "GaussianProcessRegressor",
+    ],
+)
+def test_model_methods_wrapped_in_function_trace(gaussian_process_model_name, run_gaussian_process_model):
+    expected_scoped_metrics = {
+        "GaussianProcessClassifier": [
+            ("Function/MLModel/Sklearn/Named/GaussianProcessClassifier.fit", 1),
+            ("Function/MLModel/Sklearn/Named/GaussianProcessClassifier.predict", 1),
+            ("Function/MLModel/Sklearn/Named/GaussianProcessClassifier.predict_proba", 1),
+        ],
+        "GaussianProcessRegressor": [
+            ("Function/MLModel/Sklearn/Named/GaussianProcessRegressor.fit", 1),
+            ("Function/MLModel/Sklearn/Named/GaussianProcessRegressor.predict", 1),
+        ],
+    }
+
+    expected_transaction_name = (
+        "test_gaussian_process_models:test_model_methods_wrapped_in_function_trace.<locals>._test"
+        if six.PY3
+        else "test_gaussian_process_models:_test"
+    )
+
+    @validate_transaction_metrics(
+        expected_transaction_name,
+        scoped_metrics=expected_scoped_metrics[gaussian_process_model_name],
+        rollup_metrics=expected_scoped_metrics[gaussian_process_model_name],
+        background_task=True,
+    )
+    @background_task()
+    def _test():
+        run_gaussian_process_model(gaussian_process_model_name)
+
+    _test()
+
+
+@pytest.fixture
+def run_gaussian_process_model():
+    def _run(gaussian_process_model_name):
+        import sklearn.gaussian_process
+        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)
+
+        clf = getattr(sklearn.gaussian_process, gaussian_process_model_name)(random_state=0)
+
+        model = clf.fit(x_train, y_train)
+        if hasattr(model, "predict"):
+            model.predict(x_test)
+        if hasattr(model, "predict_proba"):
+            model.predict_proba(x_test)
+
+        return model
+
+    return _run