scikit-gbm 0.1.0

scikit-learn compatible tools to work with GBM models

scikit-gbm

scikit-learn compatible tools to work with GBM models

Installation

pip install scikit-gbm

# or 

pip install git+https://github.com/krzjoa/scikit-gbm.git

Usage

For the moment, the only available class is GBMFeaturezier. It's a wrapper around scikit-learn GBMs, XGBoost, LightGBM and CatBoost models.

# Classification
from sklearn.datasets import make_classification
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

from skgbm.preprocessing import GBMFeaturizer
from lightgbm import LGBMRegressor
from xgboost import XGBClassifier

X, y = make_classification()
# train_test_split

pipeline = \
    Pipeline([
        ('gbm_featurizer', GBMFeaturizer(XGBClassifier())),
        ('logistic_regression', LogisticRegression())
    ])

# Try also:
# ('gbm_featurizer', GBMFeaturizer(GradientBoostingClassifier())),
# ('gbm_featurizer', GBMFeaturizer(LGBMClassifier())),
# ('gbm_featurizer', GBMFeaturizer(CatBoostClassifier())),


# Regression
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)

# Try also:
# ('gbm_featurizer', GBMFeaturizer(GradientBoostingClassifier())),
# ('gbm_featurizer', GBMFeaturizer(LGBMClassifier())),
# ('gbm_featurizer', GBMFeaturizer(CatBoostClassifier())),

pipeline = \
    Pipeline([
        ('gbm_featurizer', GBMFeaturizer(XGBClassifier())),
        ('logistic_regression', LogisticRegression())
    ])

# Training
pipeline.fit(X_train, y_train)

# Predictions for the test set
pipeline_pred = pipeline.predict(X_test)