Add cluster model function traces

newrelic/config.py

@@ -2902,6 +2902,120 @@ def _process_module_builtin_defaults():
         "instrument_sklearn_ensemble_hist_models",
     )
 
+    _process_module_definition(
+        "sklearn.cluster._affinity_propagation",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster.affinity_propagation_",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster._agglomerative",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_agglomerative_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster.hierarchical",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_agglomerative_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster._birch",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster.birch",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster._bisect_k_means",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_kmeans_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster._dbscan",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster.dbscan_",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster._feature_agglomeration",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster._kmeans",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_kmeans_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster.k_means_",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_kmeans_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster._mean_shift",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster.mean_shift_",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster._optics",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster._spectral",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_clustering_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster.spectral",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_clustering_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster._bicluster",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_clustering_models",
+    )
+
+    _process_module_definition(
+        "sklearn.cluster.bicluster",
+        "newrelic.hooks.mlmodel_sklearn",
+        "instrument_sklearn_cluster_clustering_models",
+    )
+
     _process_module_definition(
         "rest_framework.views",
         "newrelic.hooks.component_djangorestframework",

newrelic/hooks/mlmodel_sklearn.py

@@ -189,6 +189,43 @@ def instrument_sklearn_ensemble_hist_models(module):
     _instrument_sklearn_models(module, model_classes)
 
 
+def instrument_sklearn_cluster_models(module):
+    model_classes = (
+        "AffinityPropagation",
+        "Birch",
+        "DBSCAN",
+        "MeanShift",
+        "OPTICS",
+    )
+    _instrument_sklearn_models(module, model_classes)
+
+
+def instrument_sklearn_cluster_agglomerative_models(module):
+    model_classes = (
+        "AgglomerativeClustering",
+        "FeatureAgglomeration",
+    )
+    _instrument_sklearn_models(module, model_classes)
+
+
+def instrument_sklearn_cluster_clustering_models(module):
+    model_classes = (
+        "SpectralBiclustering",
+        "SpectralCoclustering",
+        "SpectralClustering",
+    )
+    _instrument_sklearn_models(module, model_classes)
+
+
+def instrument_sklearn_cluster_kmeans_models(module):
+    model_classes = (
+        "BisectingKMeans",
+        "KMeans",
+        "MiniBatchKMeans",
+    )
+    _instrument_sklearn_models(module, model_classes)
+
+
 def instrument_sklearn_metrics(module):
     for scorer in METRIC_SCORERS:
         if hasattr(module, scorer):

tests/mlmodel_sklearn/test_cluster_models.py

@@ -0,0 +1,186 @@
+# 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 __version__  # noqa: this is needed for get_package_version
+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(
+    "cluster_model_name",
+    [
+        "AffinityPropagation",
+        "AgglomerativeClustering",
+        "Birch",
+        "DBSCAN",
+        "FeatureAgglomeration",
+        "KMeans",
+        "MeanShift",
+        "MiniBatchKMeans",
+        "SpectralBiclustering",
+        "SpectralCoclustering",
+        "SpectralClustering",
+    ],
+)
+def test_below_v1_1_model_methods_wrapped_in_function_trace(cluster_model_name, run_cluster_model):
+    expected_scoped_metrics = {
+        "AffinityPropagation": [
+            ("Function/MLModel/Sklearn/Named/AffinityPropagation.fit", 2),
+            ("Function/MLModel/Sklearn/Named/AffinityPropagation.predict", 1),
+            ("Function/MLModel/Sklearn/Named/AffinityPropagation.fit_predict", 1),
+        ],
+        "AgglomerativeClustering": [
+            ("Function/MLModel/Sklearn/Named/AgglomerativeClustering.fit", 2),
+            ("Function/MLModel/Sklearn/Named/AgglomerativeClustering.fit_predict", 1),
+        ],
+        "Birch": [
+            ("Function/MLModel/Sklearn/Named/Birch.fit", 2),
+            (
+                "Function/MLModel/Sklearn/Named/Birch.predict",
+                1 if SKLEARN_VERSION >= (1, 0, 0) else 3,
+            ),
+            ("Function/MLModel/Sklearn/Named/Birch.fit_predict", 1),
+            ("Function/MLModel/Sklearn/Named/Birch.transform", 1),
+        ],
+        "DBSCAN": [
+            ("Function/MLModel/Sklearn/Named/DBSCAN.fit", 2),
+            ("Function/MLModel/Sklearn/Named/DBSCAN.fit_predict", 1),
+        ],
+        "FeatureAgglomeration": [
+            ("Function/MLModel/Sklearn/Named/FeatureAgglomeration.fit", 1),
+            ("Function/MLModel/Sklearn/Named/FeatureAgglomeration.transform", 1),
+        ],
+        "KMeans": [
+            ("Function/MLModel/Sklearn/Named/KMeans.fit", 2),
+            ("Function/MLModel/Sklearn/Named/KMeans.predict", 1),
+            ("Function/MLModel/Sklearn/Named/KMeans.fit_predict", 1),
+            ("Function/MLModel/Sklearn/Named/KMeans.transform", 1),
+        ],
+        "MeanShift": [
+            ("Function/MLModel/Sklearn/Named/MeanShift.fit", 2),
+            ("Function/MLModel/Sklearn/Named/MeanShift.predict", 1),
+            ("Function/MLModel/Sklearn/Named/MeanShift.fit_predict", 1),
+        ],
+        "MiniBatchKMeans": [
+            ("Function/MLModel/Sklearn/Named/MiniBatchKMeans.fit", 2),
+            ("Function/MLModel/Sklearn/Named/MiniBatchKMeans.predict", 1),
+            ("Function/MLModel/Sklearn/Named/MiniBatchKMeans.fit_predict", 1),
+        ],
+        "SpectralBiclustering": [
+            ("Function/MLModel/Sklearn/Named/SpectralBiclustering.fit", 1),
+        ],
+        "SpectralCoclustering": [
+            ("Function/MLModel/Sklearn/Named/SpectralCoclustering.fit", 1),
+        ],
+        "SpectralClustering": [
+            ("Function/MLModel/Sklearn/Named/SpectralClustering.fit", 2),
+            ("Function/MLModel/Sklearn/Named/SpectralClustering.fit_predict", 1),
+        ],
+    }
+    expected_transaction_name = "test_cluster_models:_test"
+    if six.PY3:
+        expected_transaction_name = (
+            "test_cluster_models:test_below_v1_1_model_methods_wrapped_in_function_trace.<locals>._test"
+        )
+
+    @validate_transaction_metrics(
+        expected_transaction_name,
+        scoped_metrics=expected_scoped_metrics[cluster_model_name],
+        rollup_metrics=expected_scoped_metrics[cluster_model_name],
+        background_task=True,
+    )
+    @background_task()
+    def _test():
+        run_cluster_model(cluster_model_name)
+
+    _test()
+
+
+@pytest.mark.skipif(SKLEARN_VERSION < (1, 1, 0), reason="Requires sklearn > 1.1")
+@pytest.mark.parametrize(
+    "cluster_model_name",
+    [
+        "BisectingKMeans",
+        "OPTICS",
+    ],
+)
+def test_above_v1_1_model_methods_wrapped_in_function_trace(cluster_model_name, run_cluster_model):
+    expected_scoped_metrics = {
+        "BisectingKMeans": [
+            ("Function/MLModel/Sklearn/Named/BisectingKMeans.fit", 2),
+            ("Function/MLModel/Sklearn/Named/BisectingKMeans.predict", 1),
+            ("Function/MLModel/Sklearn/Named/BisectingKMeans.fit_predict", 1),
+        ],
+        "OPTICS": [
+            ("Function/MLModel/Sklearn/Named/OPTICS.fit", 2),
+            ("Function/MLModel/Sklearn/Named/OPTICS.fit_predict", 1),
+        ],
+    }
+    expected_transaction_name = "test_cluster_models:_test"
+    if six.PY3:
+        expected_transaction_name = (
+            "test_cluster_models:test_above_v1_1_model_methods_wrapped_in_function_trace.<locals>._test"
+        )
+
+    @validate_transaction_metrics(
+        expected_transaction_name,
+        scoped_metrics=expected_scoped_metrics[cluster_model_name],
+        rollup_metrics=expected_scoped_metrics[cluster_model_name],
+        background_task=True,
+    )
+    @background_task()
+    def _test():
+        run_cluster_model(cluster_model_name)
+
+    _test()
+
+
+@pytest.fixture
+def run_cluster_model():
+    def _run(cluster_model_name):
+        import sklearn.cluster
+        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.cluster, cluster_model_name)()
+
+        model = clf.fit(x_train, y_train)
+
+        if hasattr(model, "predict"):
+            model.predict(x_test)
+        if hasattr(model, "score"):
+            model.score(x_test, y_test)
+        if hasattr(model, "fit_predict"):
+            model.fit_predict(x_test)
+        if hasattr(model, "predict_log_proba"):
+            model.predict_log_proba(x_test)
+        if hasattr(model, "predict_proba"):
+            model.predict_proba(x_test)
+        if hasattr(model, "transform"):
+            model.transform(x_test)
+
+        return model
+
+    return _run