This loss function contains focal loss[1],now only support lightgbm for multi-class(classes > 3,it will support xgboost and binary class task later)

and alpha,gamma is the parameter of focal loss,which is:

alpha is used for imbalanced sample(It's no use while in multi-class task),and gamma is used for hard-to-learn sample,and in multi-class problem,it's seems that the alpha is no use.

in xgboost/lightgbm,we should provide the calculate formula of grad and hess.

while the first derivative of multi-class loss function is(From: https://zhuanlan.zhihu.com/p/149419189):

and the second derivative is:

To speed up the calculate of loss function,we calculate this formula by numpy.

import lightgbm as lgb
import lossfunction as lf
import numpy as np

focal_loss_lgb = lf.ComplexLoss(gamma = 0.5)
param_dist= {'objective':focal_loss_lgb.focal_loss}
param_dist['num_class'] = '3'
clf_lgb = lgb.LGBMClassifier(**param_dist,random_state=2021)

clf_lgb.fit(X_train, y_train)

Here val_data is your validation dataset.

lgb_prob = clf_lgb.predict_proba(val_data)
lgb_prob = np.exp(lgb_prob)
results_lgb_prob = np.multiply(lgb_prob, 1/np.sum(lgb_prob, axis=1)[:, np.newaxis])

and results_lgb_prob is the probability of your validation dataset.

Ref:

[1] Lin T Y , Goyal P , Girshick R , et al. Focal Loss for Dense Object Detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, PP(99):2999-3007.