skrebate

library0.62pypypi✓ verified 84d ago

A scikit-learn-compatible implementation of Relief-based feature selection algorithms for classification and regression. Current version 0.62; development is infrequent (last release 2020).

ai-ml

pip install skrebate

github ↗package ↗

INSTALL

IMPORT

SIG · SKREBATE

skrebate

ai-mlpythonv0.62

Install

10.7s avg

Import

3540ms

Disk

281MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.62 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 10.7s · import 3.540s · 271MB

281MB installed

● package 281MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

ReliefF

✓ from skrebate import ReliefF

✗ from skrebate import Relief

Old import path from pre-0.5 versions, raises ImportError

SURF

✓ from skrebate import SURF

✗ from skrebate.skmethods import SURF

Wrong submodule; direct import from skrebate is correct

MultiSURF

✓ from skrebate import MultiSURF

Basic ReliefF feature selection on the Iris dataset

from skrebate import ReliefF from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Feature selection with ReliefF fs = ReliefF(n_neighbors=10, n_features_to_select=2) fs.fit(X_train, y_train) X_train_selected = fs.transform(X_train) X_test_selected = fs.transform(X_test) print(X_train_selected.shape)

Debug

Known issues

gotchaThe 'n_features_to_select' parameter is required and must be specified. If omitted, a warning is issued and no features are selected (transform returns empty array).

fix

Always pass n_features_to_select explicitly, even if you want all features (use n_features_to_select=X.shape[1]).

affects: all

gotchaReliefF and SURF algorithms expect continuous features. They do not handle categorical features natively; encoding (e.g., one-hot) is required.

fix

Apply one-hot encoding or label encoding before fitting. Note that one-hot may increase dimensionality and affect neighbor distances.

affects: all

deprecatedThe 'verbose' parameter in fit() is deprecated and may be removed in future versions.

fix

Remove verbose flag or set verbose=False.

affects: >=0.6

gotchaskrebate uses 'discrete_threshold' parameter for binarizing continuous features into discrete states for some algorithms. Incorrect setting can lead to poor feature rankings.

fix

Set discrete_threshold to the number of discrete values for categorical targets or leave as default (10).

affects: all

Errors

Common errors & fixes

ImportError: cannot import name 'ReliefF' from 'skrebate'

Attempted import from the wrong package or wrong spelling. Possibly confused with 'sklearn' or old version.

fix

Ensure you installed skrebate (pip install skrebate) and import from 'skrebate' (not 'sklearn' or 'scikit-rebate').

ModuleNotFoundError: No module named 'skrebate'

Package not installed or installed in a different environment.

fix

Run 'pip install skrebate'. If using Anaconda, try 'conda install -c conda-forge skrebate'.

ValueError: n_neighbors should be >= 1, got 0

Instantiating ReliefF with n_neighbors=0 or negative.

fix

Set n_neighbors to a positive integer (e.g., 10).

TypeError: fit() got an unexpected keyword argument 'verbose'

Using verbose parameter in newer versions where it was removed.

fix

Remove the verbose argument from fit().

Upgrade

Version history

0.62latest on PyPI · released Feb 15, 2021

Audit

Dependencies

numpyrequiredNumerical operations

scikit-learnrequiredBase estimator and transformer interface

scipyrequiredSparse matrix support

Agent activity

13 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/EpistasisLab/scikit-rebate ↗

packagehttps://pypi.org/project/skrebate/ ↗