Add svm model function traces

newrelic/config.py

@@ -3022,6 +3022,18 @@ def _process_module_builtin_defaults():
         "instrument_sklearn_cluster_clustering_models",
     )
 
+    _process_module_definition(
+        "sklearn.svm._classes",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_svm_models",
+    )
+
+    _process_module_definition(
+        "sklearn.svm.classes",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_svm_models",
+    )
+
     _process_module_definition(
         "rest_framework.views",
         "newrelic.hooks.component_djangorestframework",

newrelic/hooks/mlmodel_sklearn.py

@@ -243,6 +243,19 @@ def instrument_sklearn_cluster_kmeans_models(module):
     _instrument_sklearn_models(module, model_classes)
 
 
+def instrument_sklearn_svm_models(module):
+    model_classes = (
+        "LinearSVC",
+        "LinearSVR",
+        "SVC",
+        "NuSVC",
+        "SVR",
+        "NuSVR",
+        "OneClassSVM",
+    )
+    _instrument_sklearn_models(module, model_classes)
+
+
 def instrument_sklearn_metrics(module):
     for scorer in METRIC_SCORERS:
         if hasattr(module, scorer):

tests/mlmodel_sklearn/test_svm_models.py

@@ -0,0 +1,110 @@
+# 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.common.package_version_utils import get_package_version
+from newrelic.packages import six
+
+SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split(".")))
+
+
+@pytest.mark.parametrize(
+    "svm_model_name",
+    [
+        "LinearSVC",
+        "LinearSVR",
+        "SVC",
+        "NuSVC",
+        "SVR",
+        "NuSVR",
+        "OneClassSVM",
+    ],
+)
+def test_model_methods_wrapped_in_function_trace(svm_model_name, run_svm_model):
+    expected_scoped_metrics = {
+        "LinearSVC": [
+            ("Function/MLModel/Sklearn/Named/LinearSVC.fit", 1),
+            ("Function/MLModel/Sklearn/Named/LinearSVC.predict", 1),
+        ],
+        "LinearSVR": [
+            ("Function/MLModel/Sklearn/Named/LinearSVR.fit", 1),
+            ("Function/MLModel/Sklearn/Named/LinearSVR.predict", 1),
+        ],
+        "SVC": [
+            ("Function/MLModel/Sklearn/Named/SVC.fit", 1),
+            ("Function/MLModel/Sklearn/Named/SVC.predict", 1),
+        ],
+        "NuSVC": [
+            ("Function/MLModel/Sklearn/Named/NuSVC.fit", 1),
+            ("Function/MLModel/Sklearn/Named/NuSVC.predict", 1),
+        ],
+        "SVR": [
+            ("Function/MLModel/Sklearn/Named/SVR.fit", 1),
+            ("Function/MLModel/Sklearn/Named/SVR.predict", 1),
+        ],
+        "NuSVR": [
+            ("Function/MLModel/Sklearn/Named/NuSVR.fit", 1),
+            ("Function/MLModel/Sklearn/Named/NuSVR.predict", 1),
+        ],
+        "OneClassSVM": [
+            ("Function/MLModel/Sklearn/Named/OneClassSVM.fit", 1),
+            ("Function/MLModel/Sklearn/Named/OneClassSVM.predict", 1),
+        ],
+    }
+
+    expected_transaction_name = (
+        "test_svm_models:test_model_methods_wrapped_in_function_trace.<locals>._test"
+        if six.PY3
+        else "test_svm_models:_test"
+    )
+
+    @validate_transaction_metrics(
+        expected_transaction_name,
+        scoped_metrics=expected_scoped_metrics[svm_model_name],
+        rollup_metrics=expected_scoped_metrics[svm_model_name],
+        background_task=True,
+    )
+    @background_task()
+    def _test():
+        run_svm_model(svm_model_name)
+
+    _test()
+
+
+@pytest.fixture
+def run_svm_model():
+    def _run(svm_model_name):
+        import sklearn.svm
+        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)
+
+        kwargs = {"random_state": 0}
+        if svm_model_name in ["SVR", "NuSVR", "OneClassSVM"]:
+            kwargs = {}
+        clf = getattr(sklearn.svm, svm_model_name)(**kwargs)
+
+        model = clf.fit(x_train, y_train)
+        model.predict(x_test)
+
+        return model
+
+    return _run