sklearn-instrumentation 0.10.0

scikit-learn instrumentation tooling

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
• predict
• predict_log_proba
• predict_proba
• transform
• _fit
• _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

pip install sklearn-instrumentation

Package instrumentation

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

from sklearn_instrumentation import SklearnInstrumentor

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

Full example:

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)

Machine learning model instrumentation

Instrument any sklearn compatible trained estimator or metaestimator.

from sklearn_instrumentation import SklearnInstrumentor

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

Example:

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_estimator(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_estimator(rf)

# No more logging
rf.predict(X)

Instrumentation

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

from functools import wraps


def my_instrumentation(func, **dkwargs):
    """Wrap an estimator method with 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:

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, func, **dkwargs):
        """Wrap an estimator method with 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:

instrumentor = SklearnInstrumentor(instrument=my_instrument)

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