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https://github.com/crflynn/sklearn-instrumentation
Generalized scikit-learn machine learning model instrumentation library
https://github.com/crflynn/sklearn-instrumentation
instrumentation machine-learning scikit-learn
Last synced: about 2 months ago
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Generalized scikit-learn machine learning model instrumentation library
- Host: GitHub
- URL: https://github.com/crflynn/sklearn-instrumentation
- Owner: crflynn
- License: mit
- Created: 2020-11-11T04:59:44.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-07-21T16:02:26.000Z (over 2 years ago)
- Last Synced: 2024-10-07T11:48:07.939Z (3 months ago)
- Topics: instrumentation, machine-learning, scikit-learn
- Language: Python
- Homepage: https://sklearn-instrumentation.readthedocs.io/en/stable/
- Size: 296 KB
- Stars: 5
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGELOG.rst
- License: LICENSE.txt
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README
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"})