jarvis.ai.pkgs.lgbm package

Submodules

jarvis.ai.pkgs.lgbm.classification module

Module for LightGBM classification.

jarvis.ai.pkgs.lgbm.classification.classification(X=[], Y=[], tol=100, plot=False, preprocess=False, models=[], model_name='my_model', save_model=False)

Provide function for classification models.

jarvis.ai.pkgs.lgbm.regression module

Modules for LightGBM regression.

jarvis.ai.pkgs.lgbm.regression.get_lgbm(train_x, val_x, train_y, val_y, cv, n_jobs, scoring, n_iter, objective, alpha, random_state, param_dist={'est__learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object>, 'est__max_depth': <scipy.stats._distn_infrastructure.rv_frozen object>, 'est__min_data_in_leaf': <scipy.stats._distn_infrastructure.rv_frozen object>, 'est__n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object>, 'est__num_leaves': <scipy.stats._distn_infrastructure.rv_frozen object>})

Train a lightgbm model.

Args:

train_x: samples used for trainiing

val_x: validation set

train_y: train targets

val_y: validation targets

cv: # of cross-validations

n_jobs: for making the job parallel

scoring: scoring function to use such as MAE

Returns:

Best estimator.

jarvis.ai.pkgs.lgbm.regression.parameters_dict()

Give example optimized parameters.

jarvis.ai.pkgs.lgbm.regression.regression(X=[], Y=[], jid=[], test_size=0.1, plot=False, preprocess=True, feature_importance=True, save_model=False, feat_names=[], model_name='my_model', config={})

Get generic regression model.

Module contents

LighGBM applications.