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ENH Optimize runtime for IsolationForest#23149

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ogrisel merged 6 commits intoscikit-learn:mainfrom
MaxwellLZH:fet/optimize-isoforest
Apr 29, 2022
Merged

ENH Optimize runtime for IsolationForest#23149
ogrisel merged 6 commits intoscikit-learn:mainfrom
MaxwellLZH:fet/optimize-isoforest

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@MaxwellLZH MaxwellLZH commented Apr 17, 2022 •
edited by ogrisel
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Reference Issues/PRs

Towards #19275

What does this implement/fix? Explain your changes.

As shown here in the original issue discussion, the check_input argument is set to False for Forest classes, while for bagging estimators it's left as default value True, therefore we're validating the data repeatedly.

The proposed fix adds a check_input argument to the ensemble._bagging._parallel_build_estimators, which can be set to False when the base estimator actually supports that argument during the fit process.

Performance Impact

Code used for profiling:

from sklearn.datasets import make_classification
from scipy.sparse import csc_matrix, csr_matrix
from sklearn.ensemble import IsolationForest

X, y = make_classification(n_samples=50000, n_features=1000)
X = csc_matrix(X)
X.sort_indices()
IsolationForest(n_estimators=10, max_samples=256, n_jobs=1).fit(X)

Before (total time: 6.78s)
image

After (total time: 5.017s)
image

thomasjpfan
thomasjpfan reviewed Apr 17, 2022
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Thank you for the PR!

This looks reasonable to do. What does the benchmark look like for dense data?

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MaxwellLZH commented Apr 18, 2022

The benchmark result for dense data shows smaller improvement compared to sparse inputs

Before (8.29s):
image

After (7.92s):
image

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MaxwellLZH commented Apr 23, 2022

After more profiling, it seems like the indexing operation of estimator.fit(X[:, features, y) in _bagging.py::_parallel_build_estimators is quite expensive, I tried removing the indexing operation when max_features is equal to feature numbers using the following logic:

# only index arrays when needed
X_ = X if max_features == X.shape[1] else X[:, features]
estimator_fit(X_, y, sample_weight=curr_sample_weight)

and see improvements for both sparse and dense inputs.

While the problem will still exist when max_features is not set to 1.0 for IsolationForest. I'm wondering since we're randomly pick a single feature to split anyway, can we always set the max_features to be 1.0 for IsolationForest so we can get a faster run time.

Sparse Input:
Before (4.97s)
image

After (0.241s)
image

Dense Input:
Before (7.99s)
image

After (5.19s)
image

thomasjpfan
thomasjpfan reviewed Apr 23, 2022
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sklearn/ensemble/_bagging.py Outdated

estimator.fit(X[:, features], y, sample_weight=curr_sample_weight)
# only index arrays when needed
X_ = X if max_features == X.shape[1] else X[:, features]
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I would do this in a separate follow up PR. The check_input change is already an improvement that can be merged by itself.

As for this change, we can only do this optimization if bootstrap_features is False. bootstrap_features is always False for IsolationForest, but not for Bagging*. (If bootstrap_features is True, then the features are sampled with replacement which can result in repeated features.)

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@MaxwellLZH MaxwellLZH Apr 24, 2022 •
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Got it! I've reverted the changes in this PR, and will draft another PR once this got merged :)

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Revert "skip array indexing when possible"
26f7e84 
This reverts commit ff4e429.

revert last commit
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Please add an entry to the change log at doc/whats_new/v1.1.rst with tag |Efficiency|. Like the other entries there, please reference this pull request with :pr: and credit yourself (and other contributors if applicable) with :user:.

@MaxwellLZH MaxwellLZH requested a review from thomasjpfan April 28, 2022 13:10
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LGTM

@thomasjpfan thomasjpfan added the Quick Review For PRs that are quick to review label Apr 28, 2022
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ogrisel commented Apr 29, 2022

Merging this one. The real fix for the sparse case should happen in a follow-up PR probably targeting 1.2.

@ogrisel ogrisel merged commit 767e9ae into scikit-learn:main Apr 29, 2022
@ogrisel ogrisel added the To backport PR merged in master that need a backport to a release branch defined based on the milestone. label Apr 29, 2022
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ogrisel commented Apr 29, 2022

@jeremiedbb I added the "to backport" label to not forget to include this one in the 1.1.X branch before the final 1.1.0 release.

jjerphan pushed a commit to jjerphan/scikit-learn that referenced this pull request Apr 29, 2022
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ENH Optimize runtime for IsolationForest (scikit-learn#23149)
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ENH Optimize runtime for IsolationForest (scikit-learn#23149)
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ENH Optimize runtime for IsolationForest (scikit-learn#23149)
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glemaitre pushed a commit that referenced this pull request Aug 5, 2022
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ENH Optimize runtime for IsolationForest (#23149)
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