Mulit-Class Softmax log loss custom objective for LightGBM Classifier

Custom_Multi-Class_Softmax_Log_Loss.py

import numpy as np
import lightgbm as lgb
from sklearn.datasets import load_iris
from sklearn.metrics import confusion_matrix
import time

# Load Iris dataset
iris = load_iris()

# Separate features (X) and target (y)
X = iris.data
y = iris.target

# Print the shape of the data
print("Shape of features (X):", X.shape)
print("Shape of target (y):", y.shape)


# Define softmax function
def softmax(x):
    e_x = np.exp(x.T - np.max(x, axis=1)).T
    return ( e_x.T / e_x.sum(axis=1) ).T

# Custom softmax cross-entropy loss
def softmax_cross_entropy(preds, train_data):
    labels = train_data.get_label()
    num_class = len(np.unique(labels))
    labels = np.eye(num_class)[labels.astype(int)]
    preds = softmax(preds)
    grad = preds - labels
    hess = preds*(1 - preds)
    return grad, hess

lgb_train = lgb.Dataset(X, y)

params = {
    'max_depth': 5,
    'num_leaves': 16,
    'n_estimators': 50,
    'objective': 'multiclass',
    'num_class': len(np.unique(y)),
    'verbosity':-1
}
tic = time.time()
model = lgb.train(params, lgb_train)
toc = time.time()

print('Default')
print( confusion_matrix( y, np.argmax( model.predict(X), axis=1 ) ) )
print("Elapsed Time: ", (toc-tic) )

params = {
    'max_depth': 5,
    'num_leaves': 16,
    'n_estimators': 50,
    'objective': softmax_cross_entropy,
    'num_class': len(np.unique(y)),
    'verbosity':-1
}
tic = time.time()
model = lgb.train(params, lgb_train)
toc = time.time()
print('Custom')
print( confusion_matrix( y, np.argmax( model.predict(X), axis=1 ) ) )
print("Elapsed Time: ", (toc-tic) )