sklearn-instrumentation 0.13.0
scikit-learn instrumentation tooling
License: MIT
Requires Python: >=3.7.0
Keywords: scikit-learn,instrumentation,machine,learning

sklearn-instrumentation
=======================

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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 applies 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``)

Installation
------------

The sklearn-instrumentation package is available on pypi and can be installed using pip

.. code-block:: bash

    pip install sklearn-instrumentation


Package instrumentation
-----------------------

Instrument any sklearn compatible package that has ``BaseEstimator``-derived classes.

.. code-block:: python

    from sklearn_instrumentation import SklearnInstrumentor

    instrumentor = SklearnInstrumentor(instrument=my_instrument)
    instrumentor.instrument_packages(["sklearn", "xgboost", "lightgbm"])


Full example:

.. code-block:: python

    import logging

    from sklearn.datasets import load_iris
    from sklearn.decomposition import PCA
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.pipeline import FeatureUnion
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler

    from sklearn_instrumentation import SklearnInstrumentor
    from sklearn_instrumentation.instruments.logging import TimeElapsedLogger

    logging.basicConfig(level=logging.INFO)

    # Create an instrumentor and instrument sklearn
    instrumentor = SklearnInstrumentor(instrument=TimeElapsedLogger())
    instrumentor.instrument_packages(["sklearn"])

    # Create a toy model for classification
    ss = StandardScaler()
    pca = PCA(n_components=3)
    rf = RandomForestClassifier()
    classification_model = Pipeline(
        steps=[
            (
                "fu",
                FeatureUnion(
                    transformer_list=[
                        ("ss", ss),
                        ("pca", pca),
                    ]
                ),
            ),
            ("rf", rf),
        ]
    )
    X, y = load_iris(return_X_y=True)

    # Observe logging
    classification_model.fit(X, y)
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline._fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.fit elapsed time: 0.0006406307220458984 seconds
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform elapsed time: 0.0001430511474609375 seconds
    # INFO:sklearn_instrumentation.instruments.logging:PCA._fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:PCA._fit elapsed time: 0.0006711483001708984 seconds
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline._fit elapsed time: 0.0026731491088867188 seconds
    # INFO:sklearn_instrumentation.instruments.logging:BaseForest.fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:BaseForest.fit elapsed time: 0.1768970489501953 seconds
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.fit elapsed time: 0.17983102798461914 seconds

    # Observe logging
    classification_model.predict(X)
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.predict starting.
    # INFO:sklearn_instrumentation.instruments.logging:FeatureUnion.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform elapsed time: 0.00024509429931640625 seconds
    # INFO:sklearn_instrumentation.instruments.logging:_BasePCA.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:_BasePCA.transform elapsed time: 0.0002181529998779297 seconds
    # INFO:sklearn_instrumentation.instruments.logging:FeatureUnion.transform elapsed time: 0.0012080669403076172 seconds
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict starting.
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict_proba starting.
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict_proba elapsed time: 0.013531208038330078 seconds
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict elapsed time: 0.013692140579223633 seconds
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.predict elapsed time: 0.015219926834106445 seconds

    # Remove instrumentation
    instrumentor.uninstrument_packages(["sklearn"])

    # Observe no logging
    classification_model.predict(X)


Instance instrumentation
------------------------

Instrument any sklearn compatible trained estimator or metaestimator.

.. code-block:: python

    from sklearn_instrumentation import SklearnInstrumentor

    instrumentor = SklearnInstrumentor(instrument=my_instrument)
    instrumentor.instrument_instance(estimator=my_ml_pipeline)


Example:

.. code-block:: python

    import logging

    from sklearn.datasets import load_iris
    from sklearn_instrumentation import SklearnInstrumentor
    from sklearn_instrumentation.instruments.logging import TimeElapsedLogger
    from sklearn.ensemble import RandomForestClassifier

    logging.basicConfig(level=logging.INFO)

    # Train a classifier
    X, y = load_iris(return_X_y=True)
    rf = RandomForestClassifier()

    rf.fit(X, y)

    # Create an instrumentor which decorates BaseEstimator methods with
    # logging output when entering and exiting methods, with time elapsed logged
    # on exit.
    instrumentor = SklearnInstrumentor(instrument=TimeElapsedLogger())

    # Apply the decorator to all BaseEstimators in each of these libraries
    instrumentor.instrument_instance(rf)

    # Observe the logging output
    rf.predict(X)
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict starting.
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict_proba starting.
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict_proba elapsed time: 0.014165163040161133 seconds
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict elapsed time: 0.014327764511108398 seconds

    # Remove the decorator from all BaseEstimators in each of these libraries
    instrumentor.uninstrument_instance(rf)

    # No more logging
    rf.predict(X)


Instance class instrumentation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

During fitting, some metaestimators will copy estimator instances using scikit-learn's ``clone`` function. This results in cloned fitted estimators not having instrumentation. To get around this we can instrument the classes rather than the instances.

.. code-block:: python

    from sklearn.datasets import load_iris
    from sklearn.decomposition import PCA
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.pipeline import FeatureUnion
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler

    from sklearn_instrumentation import SklearnInstrumentor
    from sklearn_instrumentation.instruments.logging import TimeElapsedLogger

    logging.basicConfig(level=logging.INFO)

    ss = StandardScaler()
    pca = PCA(n_components=3)
    rf = RandomForestClassifier()
    classification_model = Pipeline(
        steps=[
            (
                "fu",
                FeatureUnion(
                    transformer_list=[
                        ("ss", ss),
                        ("pca", pca),
                    ]
                ),
            ),
            ("rf", rf),
        ]
    )
    X, y = load_iris(return_X_y=True)

    instrumentor = SklearnInstrumentor(instrument=TimeElapsedLogger())
    instrumentor.instrument_instance_classes(classification_model)

    classification_model.fit(X, y)
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline._fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline._fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.fit elapsed time: 0.0006749629974365234 seconds
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.fit elapsed time: 0.0007731914520263672 seconds
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform elapsed time: 0.00016427040100097656 seconds
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform elapsed time: 0.0002810955047607422 seconds
    # INFO:sklearn_instrumentation.instruments.logging:PCA._fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:PCA._fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:PCA._fit elapsed time: 0.0004239082336425781 seconds
    # INFO:sklearn_instrumentation.instruments.logging:PCA._fit elapsed time: 0.0005612373352050781 seconds
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline._fit elapsed time: 0.002705097198486328 seconds
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline._fit elapsed time: 0.002802133560180664 seconds
    # INFO:sklearn_instrumentation.instruments.logging:BaseForest.fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:BaseForest.fit starting.
    # INFO:sklearn_instrumentation.instruments.logging:BaseForest.fit elapsed time: 0.16085195541381836 seconds
    # INFO:sklearn_instrumentation.instruments.logging:BaseForest.fit elapsed time: 0.16097569465637207 seconds
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.fit elapsed time: 0.1639721393585205 seconds
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.fit elapsed time: 0.16404390335083008 seconds
    classification_model.predict(X)
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.predict starting.
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.predict starting.
    # INFO:sklearn_instrumentation.instruments.logging:FeatureUnion.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:FeatureUnion.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform elapsed time: 0.0001049041748046875 seconds
    # INFO:sklearn_instrumentation.instruments.logging:StandardScaler.transform elapsed time: 0.00017309188842773438 seconds
    # INFO:sklearn_instrumentation.instruments.logging:_BasePCA.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:_BasePCA.transform starting.
    # INFO:sklearn_instrumentation.instruments.logging:_BasePCA.transform elapsed time: 0.0001690387725830078 seconds
    # INFO:sklearn_instrumentation.instruments.logging:_BasePCA.transform elapsed time: 0.00023698806762695312 seconds
    # INFO:sklearn_instrumentation.instruments.logging:FeatureUnion.transform elapsed time: 0.0008630752563476562 seconds
    # INFO:sklearn_instrumentation.instruments.logging:FeatureUnion.transform elapsed time: 0.0009222030639648438 seconds
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict starting.
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict starting.
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict_proba starting.
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict_proba starting.
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict_proba elapsed time: 0.01138925552368164 seconds
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict_proba elapsed time: 0.011497974395751953 seconds
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict elapsed time: 0.011577844619750977 seconds
    # INFO:sklearn_instrumentation.instruments.logging:ForestClassifier.predict elapsed time: 0.011635780334472656 seconds
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.predict elapsed time: 0.012682199478149414 seconds
    # INFO:sklearn_instrumentation.instruments.logging:Pipeline.predict elapsed time: 0.012733936309814453 seconds

    instrumentor.uninstrument_instance_classes(classification_model)

    classification_model.predict(X)

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

.. code-block:: python

    from functools import wraps


    def my_instrumentation(estimator, 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, use a class with the ``__call__`` method for implementing the decorator:

.. code-block:: python

    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__(self, estimator, 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 kwargs for different ml models:

.. code-block:: python

    instrumentor = SklearnInstrumentor(instrument=my_instrument)

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