Scikit-Bloom

An excuse to play with Rust, but also a neat trick for sklearn!

This package contains some bloom tricks for text pipelines in scikit-learn. To learn more about this trick, check out this blogpost.

You can install it via:

python -m pip install scikit-bloom

And you can import the components via:

from skbloom import BloomVectorizer, BloomishVectorizer, SlowBloomVectorizer

BloomVectorizer().fit(X).transform(X)
BloomishVectorizer().fit(X).transform(X)

The BloomVectorizer will use rust under the hood for the hashing to construct the bloom representation. The BloomishVectorizer will just run the HashingVectorizer from scikit-learn multiple times in sequence. The SlowBloomVectorizer is pretty much the same as the BloomVectizer in terms of features, but is implemented in Python.

Benchmarks

I ran a quick benchmark, which seems to suggest the approach is pretty speedy.

Show me the code 
import time
from datasets import load_dataset
from skbloom import BloomVectorizer, BloomishVectorizer, SlowBloomVectorizer
from sklearn.feature_extraction.text import HashingVectorizer

dataset = load_dataset("clinc_oos", "plus")
texts = dataset['train']['text'] * 10

trials = [BloomVectorizer(n_features=10_000), 
          BloomishVectorizer(n_features=10_000), 
          SlowBloomVectorizer(n_features=10_000), 
          HashingVectorizer(n_features=10_000)]

for trial in trials:
    tic = time.time()
    trial.fit_transform(texts)
    toc = time.time()
    print(f"{trial.__class_.__name__}: {toc - tic}")

In this benchmark we're creating a

Approach Time taken Description
BloomVectorizer 1.562 The speedy rust implementation
BloomishVectorizer 2.111 Using sklearn's implementation sequentially
SlowBloomVectorizer 5.259 A pure python implementation
HashingVectorizer 0.695 Using sklearn's hashing vectorizer to only hash once

Note that the HashingVectorizer is faster here because it only hashes each word once. The other implementations hash it three times.

An extra benchmark

Just as an extra, you can also choose to run the BloomVectorizer by just hashing once and when I do that ... it seems to be competative with the HashingVectorizer.

Show me the code 
import time
from datasets import load_dataset
from skbloom import BloomVectorizer, BloomishVectorizer, SlowBloomVectorizer
from sklearn.feature_extraction.text import HashingVectorizer

dataset = load_dataset("clinc_oos", "plus")
texts = dataset['train']['text'] * 10

for feats in [3000, 5000, 10000, 20000, 100_000]:
    trials = [BloomVectorizer(n_hash=1, n_features=feats), HashingVectorizer(n_features=feats)]
    for trial in trials:
        tic = time.time()
        trial.fit_transform(texts)
        toc = time.time()
        print(f"{feats}: {trial.__class__.__name__}: {toc - tic}")
Number of feats BloomVectorizer HashingVectorizer
3000 0.6071 0.6864
5000 0.6092 0.6947
10000 0.6123 0.6911
20000 0.6124 0.6918
100000 0.6108 0.6938

I want to be careful with suggesting that the BloomVectorizer is always faster because the HashingVectorizer comes with way more features. You can build n-gram representations, just to mention one example, which the BloomVectorizer does not do. But it does seem like we're in the same ballpark, which is neat consider the implementation was very little effort.

Important

In fairness, while this trick is interesting ... you might be fine just using the HashingVectorizer that just comes with sklearn. This project works, but it was also an excuse for me to try out rust.

It's a nice motivating example for me to learn a bit of rust, partially because it's a tangible example from a field that I am familiar with. But it's also been a relatively low investment to rewrite an expensive bit of code.

Development

These are mainly some notes for myself.

To install all of this locally;

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
python -m pip install uv
uv venv --python 3.12
uv pip install maturin
uv run maturin develop --uv
uv pip install -e .

If you want to make a release, remember to tag before pushing.

git tag v0.2.1
git push origin <branchname>
git push origin --tags

Key dates

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  • Released:
Latest release

1 maintainer

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Requires

Python >=3.10

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Metadata

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for koaning from gravatar.com koaning

Requires

Python >=3.10

Classifiers

Report project as malware

Release files for scikit-bloom 0.2.2

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scikit_bloom-0.2.2-cp312-cp312-musllinux_1_2_i686.whl 441.6 kB CPython 3.12 CPython 3.12 Linux musl 1.2+ x86-32 Details
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scikit_bloom-0.2.2-cp311-cp311-musllinux_1_2_x86_64.whl 417.9 kB CPython 3.11 CPython 3.11 Linux musl 1.2+ x86-64 Details
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scikit_bloom-0.2.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl 304.1 kB CPython 3.11 CPython 3.11 Linux glibc 2.17+ IBM System/390x Details
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scikit_bloom-0.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 246.2 kB CPython 3.11 CPython 3.11 Linux glibc 2.17+ ARM64 Details
scikit_bloom-0.2.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl 259.6 kB CPython 3.11 CPython 3.11 Linux glibc 2.5+ x86-32 Details
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scikit_bloom-0.2.2-cp310-cp310-win_amd64.whl 115.4 kB CPython 3.10 CPython 3.10 Windows x86-64 Details
scikit_bloom-0.2.2-cp310-cp310-win32.whl 110.4 kB CPython 3.10 CPython 3.10 Windows x86-32 Details
scikit_bloom-0.2.2-cp310-cp310-musllinux_1_2_x86_64.whl 417.9 kB CPython 3.10 CPython 3.10 Linux musl 1.2+ x86-64 Details
scikit_bloom-0.2.2-cp310-cp310-musllinux_1_2_i686.whl 441.4 kB CPython 3.10 CPython 3.10 Linux musl 1.2+ x86-32 Details
scikit_bloom-0.2.2-cp310-cp310-musllinux_1_2_armv7l.whl 515.6 kB CPython 3.10 CPython 3.10 Linux musl 1.2+ ARMv7l Details
scikit_bloom-0.2.2-cp310-cp310-musllinux_1_2_aarch64.whl 424.7 kB CPython 3.10 CPython 3.10 Linux musl 1.2+ ARM64 Details
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scikit_bloom-0.2.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl 283.3 kB CPython 3.10 CPython 3.10 Linux glibc 2.17+ PowerPC 64-le Details
scikit_bloom-0.2.2-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl 252.8 kB CPython 3.10 CPython 3.10 Linux glibc 2.17+ ARMv7l Details
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Total release size: 22.1 MB

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Release files / scikit_bloom-0.2.2-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl

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Release files / scikit_bloom-0.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

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Tags CPython 3.10 Linux glibc 2.17+ ARM64
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Release files / scikit_bloom-0.2.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl

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Size 259.4 kB
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This release

0.2.2 This release

72 release files

0.2.1

83 release files

0.2.0

70 release files

0.1.1

2 release files

0.1.0

3 release files

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