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

Fo the moment, you can find the following tools in the library:

• GBMFeaturizer
• GBMDiscretizer
• trees_to_dataframe
• AXIL

Take a look at the documentation to learn more. A simple example, how to use GBMFeaturizer in a classification task.

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

from skgbm.preprocessing import GBMFeaturizer
from xgboost import XGBClassifier

X, y = make_classification()
X_train, X_test, y_train, y_test = train_test_split(X, y)

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

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

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