Custom Instruments

The package comes with a handful of instruments which log information about X or timing of execution. You can create your own instrument just by creating a decorator, following this pattern:

from functools import wraps


def my_instrument(obj func, **dkwargs):
    """Wrap an estimator method with instrumentation.

    :param obj: The class or instance on which to apply instrumentation
    :param func: The method to be instrumented.
    :param dkwargs: Decorator kwargs, which can be passed to the
        decorator at decoration time. For estimator instrumentation
        this allows different parametrizations for each ml model.
    """
    @wraps(func)
    def wrapper(*args, **kwargs):
        """Wrapping function.

        :param args: The args passed to methods, typically
            just ``X`` and/or ``y``
        :param kwargs: The kwargs passed to methods, usually
            weights or other params
        """
        # Code goes here before execution of the estimator method
        retval = func(*args, **kwargs)
        # Code goes here after execution of the estimator method
        return retval

    return wrapper

To create a stateful instrument, inherit from the BaseInstrument class and use the __call__ method for implementing the decorator:

from functools import wraps

from sklearn_instrumentation.instruments.base import BaseInstrument


class MyInstrument(BaseInstrument)

    def __init__(self, *args, **kwargs):
        # handle any statefulness here
        pass

    def __call__(obj, self, func, **dkwargs):
        """Wrap an estimator method with instrumentation.

       :param obj: The class or instance on which to apply instrumentation
       :param func: The method to be instrumented.
        :param dkwargs: Decorator kwargs, which can be passed to the
            decorator at decoration time. For estimator instrumentation
            this allows different parametrizations for each ml model.
        """
        @wraps(func)
        def wrapper(*args, **kwargs):
            """Wrapping function.

            :param args: The args passed to methods, typically
                just ``X`` and/or ``y``
            :param kwargs: The kwargs passed to methods, usually
                weights or other params
            """
            # Code goes here before execution of the estimator method
            retval = func(*args, **kwargs)
            # Code goes here after execution of the estimator method
            return retval

        return wrapper

To pass decorator kwargs for different ml models using dkwargs:

instrumentor = SklearnInstrumentor(instrument=my_instrument)

instrumentor.instrument_instance(estimator=ml_model_1, decorator_kwargs={"name": "awesome_model"})
instrumentor.instrument_instance(estimator=ml_model_2, decorator_kwargs={"name": "better_model"})