recursive-pietro
SHAP-based recursive feature elimination with cross-validation and early stopping.
Drop-in sklearn-compatible replacement for Probatus ShapRFECV — faster, cleaner, and works in pipelines.
Installation
pip install recursive-pietro
With optional boosting-library support:
pip install recursive-pietro[lightgbm] # LightGBM early stopping
pip install recursive-pietro[xgboost] # XGBoost early stopping
pip install recursive-pietro[catboost] # CatBoost early stopping
pip install recursive-pietro[plot] # matplotlib plotting
pip install recursive-pietro[all] # everything
Quick start
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from recursive_pietro import ShapFeatureElimination
X, y = make_classification(n_samples=200, n_features=15, n_informative=5, random_state=42)
selector = ShapFeatureElimination(
RandomForestClassifier(n_estimators=50, random_state=42),
step=0.2,
cv=3,
scoring="roc_auc",
random_state=42,
)
selector.fit(X, y)
# Selected features
print(selector.selected_features_)
# Use in transform
X_reduced = selector.transform(X)
Early stopping (LightGBM / XGBoost / CatBoost)
from lightgbm import LGBMClassifier
selector = ShapFeatureElimination(
LGBMClassifier(n_estimators=500, random_state=42),
step=0.2,
cv=5,
scoring="roc_auc",
early_stopping_rounds=50,
eval_metric="auc",
)
selector.fit(X, y)
Feature set selection strategies
After fitting, choose different feature sets from the elimination report:
selector.get_feature_set(method="best") # highest validation score
selector.get_feature_set(method="best_parsimonious") # fewest features within threshold
selector.get_feature_set(method="best_coherent") # lowest std within threshold
selector.get_feature_set(method=10) # exactly 10 features
sklearn pipeline support
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
pipe = Pipeline([
("feature_selection", ShapFeatureElimination(
RandomForestClassifier(n_estimators=50, random_state=42),
step=1, cv=3, scoring="roc_auc",
)),
("classifier", LogisticRegression()),
])
pipe.fit(X, y)
Plotting
pip install recursive-pietro[plot]
selector.plot()
License
MIT
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
recursive_pietro-0.2.0.tar.gz
(20.3 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file recursive_pietro-0.2.0.tar.gz.
File metadata
- Download URL: recursive_pietro-0.2.0.tar.gz
- Upload date:
- Size: 20.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via:
twine/6.1.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e01ac885fa9fd86d078651555915c079e62a57e7dc2306db83ac19506c91f984
|
|
| MD5 |
581d469b88a02e1eb3b4bd352dd68ccb
|
|
| BLAKE2b-256 |
4372d4b02b3d5621d1491072bc4a9c24a601909eefc37b30d5e10a3ef4743880
|
Provenance
The following attestation bundles were made for recursive_pietro-0.2.0.tar.gz:
Publisher:
publish.yml on ReinierKoops/Recursive_pietro
-
Statement:
-
Statement type:
https://in-toto.io/Statement/v1 -
Predicate type:
https://docs.pypi.org/attestations/publish/v1 -
Subject name:
recursive_pietro-0.2.0.tar.gz -
Subject digest:
e01ac885fa9fd86d078651555915c079e62a57e7dc2306db83ac19506c91f984 - Sigstore transparency entry: 995739526
- Sigstore integration time:
-
Permalink:
ReinierKoops/Recursive_pietro@99fa9263e12902d715ce12196f48a71e7310fd95 -
Branch / Tag:
refs/tags/0.2.0 - Owner: https://github.com/ReinierKoops
-
Access:
public
-
Token Issuer:
https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
Publication workflow:
publish.yml@99fa9263e12902d715ce12196f48a71e7310fd95 -
Trigger Event:
release
-
Statement type:
File details
Details for the file recursive_pietro-0.2.0-py3-none-any.whl.
File metadata
- Download URL: recursive_pietro-0.2.0-py3-none-any.whl
- Upload date:
- Size: 16.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via:
twine/6.1.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
5f875704f91a3f5a5fc3a9b0f97816edee9044e78ddd0f394cce29f4330a1c7e
|
|
| MD5 |
c7b9019e61472b7e4fedf34ab4ac296d
|
|
| BLAKE2b-256 |
2f04df42c8c6dd94ae69ae5584e2051cf9ef0df905c6ed0e9b8b05b0ab95ab4d
|
Provenance
The following attestation bundles were made for recursive_pietro-0.2.0-py3-none-any.whl:
Publisher:
publish.yml on ReinierKoops/Recursive_pietro
-
Statement:
-
Statement type:
https://in-toto.io/Statement/v1 -
Predicate type:
https://docs.pypi.org/attestations/publish/v1 -
Subject name:
recursive_pietro-0.2.0-py3-none-any.whl -
Subject digest:
5f875704f91a3f5a5fc3a9b0f97816edee9044e78ddd0f394cce29f4330a1c7e - Sigstore transparency entry: 995739603
- Sigstore integration time:
-
Permalink:
ReinierKoops/Recursive_pietro@99fa9263e12902d715ce12196f48a71e7310fd95 -
Branch / Tag:
refs/tags/0.2.0 - Owner: https://github.com/ReinierKoops
-
Access:
public
-
Token Issuer:
https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
Publication workflow:
publish.yml@99fa9263e12902d715ce12196f48a71e7310fd95 -
Trigger Event:
release
-
Statement type:
