shap-hypetune

library0.2.7pypypiunverified

A Python package for simultaneous hyperparameters tuning and feature selection for gradient boosting models (LightGBM, XGBoost, CatBoost, Random Forest). Current version: 0.2.7. Release cadence: occasional, last release Jun 2023.

ai-ml data

pip install shap-hypetune

github ↗package ↗

INSTALL

IMPORT

SIG · SHAP-HYPETUNE

shap-hypetune

ai-mlpythonv0.2.7

Install

19.8s avg

Import

—

Disk

569MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.2.7 · 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 19.8s · import 0.000s · 558MB

569MB installed

● package 569MB

Code

Verified usage

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

BoostingRFA

✓ from shap_hypetune import BoostingRFA

✗ from shap_hypetune import RFA

RFA was added in v0.2.4 but the class is actually named BoostingRFA; importing just 'RFA' fails

BoostingRFE

✓ from shap_hypetune import BoostingRFE

✗ from shap_hypetune.rfe import BoostingRFE

Top-level import works; no submodule needed

BoostingRFECV

✓ from shap_hypetune import BoostingRFECV

BoostingSelector

✓ from shap_hypetune import BoostingSelector

✗ from shap_hypetune.selector import BoostingSelector

Top-level import is correct; submodule not directly exposed

Basic usage: perform recursive feature addition with hyperparameter tuning using LightGBM. Note: estimator can be 'lgbm', 'xgb', 'catboost', or 'rf' (Random Forest, added in v0.2.4).

import pandas as pd from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from shap_hypetune import BoostingRFA X, y = make_classification(n_samples=100, n_features=20, random_state=0) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) model = BoostingRFA(estimator='lgbm', n_iter=5, early_stopping_rounds=10, random_state=0) model.fit(X_train, y_train) print('Train accuracy:', model.score(X_train, y_train)) print('Selected features:', model.get_support())

Debug

Known issues

breakingIn v0.2.7, numpy types np.bool, np.int, np.float were replaced by native Python types. If you rely on exact numpy dtypes in model outputs, upgrade numpy to >=1.20 and check your custom callbacks.

fix

Upgrade to v0.2.7 or cast numpy types explicitly.

affects: <=0.2.6

gotchaThe 'n_iter' parameter controls both tuning and feature selection iterations; setting it too low may result in poor convergence. In BoostingRFA and BoostingRFE, n_iter is the total number of trials (if hyperopt=True) or grid points (if grid=True).

fix

Use at least 10-20 iterations for tuning; increase if using hyperopt=True.

affects: all

gotchaFeature selection methods (RFA, RFE) require a fitted model with feature importance; if you pass a custom estimator that does not provide 'feature_importances_', it will raise an error.

fix

Use only LightGBM, XGBoost, CatBoost, or Random Forest as the base estimator, or ensure custom estimator has the attribute.

affects: all

deprecatedUsage of 'estimator' parameter as a string (e.g., 'lgbm') is stable, but passing a scikit-learn estimator object is experimental and may break in future versions.

fix

Stick to string names for now; if you need a custom estimator, test thoroughly.

affects: all

Upgrade

Version history

0.2.7latest on PyPI · released Feb 21, 2024

Audit

Dependencies

numpyrequiredCore numerical dependency

pandasrequiredData structure for input features

scikit-learnrequiredBase estimator and compatibility

shaprequiredSHAP importance for feature selection

lightgbmoptionalOne of the supported gradient boosting engines

xgboostoptionalOne of the supported gradient boosting engines

catboostoptionalOne of the supported gradient boosting engines

hyperoptoptionalAlternative tuning method (used with hyperopt=True)

Agent activity

24 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/cerlymarco/shap-hypetune ↗

packagehttps://pypi.org/project/shap-hypetune/ ↗