sklearn-instrumentation Documentation¶
Generalized instrumentation tooling for scikit-learn models. sklearn_instrumentation
allows instrumenting the sklearn
package and any scikit-learn compatible packages with estimators and transformers inheriting from sklearn.base.BaseEstimator
.
Instrumentation works by applying decorators to methods of BaseEstimator
-derived classes or instances. By default the instrumentor applies instrumentation to the following methods (except when they are properties of instances):
fit
fit_transform
predict
predict_log_proba
predict_proba
transform
_fit
_fit_transform
_predict
_predict_log_proba
_predict_proba
_transform
sklearn-instrumentation supports instrumentation of full sklearn-compatible packages, as well as recursive instrumentation of models (metaestimators like Pipeline
, or even single estimators like RandomForestClassifier
)
- Estimator Instrumentation
- Package Instrumentation
- Class Instrumentation
- Instrumentor
- Configuration
- Base Instruments
- Logging Instruments
- cProfile Instruments
- Memory Profiler Instruments
- PyInstrument Instruments
- Prometheus Instruments
- Datadog Instruments
- OpenTelemetry Instruments
- Statsd Instruments
- Custom Instruments
- Utils