lightgbm使用multiclass训练二分类模型

由于lgbm.predict_proba输出的结果维度和样本的数据集不一致，导致dataframe进行列赋值的时候报错，下面可以通过修改num_class来验证

# -*- coding: utf-8 -*-
"""

@Time  : 2022/7/29 14:48
@Author: Breeze
@File  : 多分类测试输出.py
"""
import sys

from lightgbm import LGBMClassifier
import numpy as np
import os
print(sys.version)
clf_multiclass = LGBMClassifier()
n_estimators = 100
lr = 0.01
max_depth = 3
l1 = 0.1
l2 = 0.1
subsample_for_bin = 32
min_child_sample = 32
num_class = 3
wd_namelist_model = LGBMClassifier(
        objective='multiclass',  # 'multilogloss
        num_class=num_class,   # 'regression' for LGBMRegressor, 'binary' or 'multiclass' for LGBMClassifier, 'lambdarank' for LGBMRanker.
        n_estimators=n_estimators,
        learning_rate=lr,
        num_leaves=2**max_depth-1, # (0, 2^max_depth - 1]
        colsample_bytree=0.6,
        subsample=0.6,
        max_depth=max_depth,  # 10
        reg_alpha=l1,  # 0.04
        reg_lambda=l2,
        subsample_for_bin=subsample_for_bin,
        subsample_freq=32,  # 12
        min_split_gain=0.01,
        min_child_weight=0.01,
        min_child_sample=min_child_sample,
        silent=False,
        verbose=1,
        # importance_type='gain',
        random_state=2022)
train_data = np.random.rand(50000, 100)  # 500 entities, each contains 100 features
train_label = np.random.randint(num_class, size=50000)  # 5 targets
val_data = np.random.rand(5000, 100)
val_label = np.random.randint(num_class, size=5000)

# wd_namelist_model.fit(train_data,train_label)

wd_namelist_model.fit(train_data, train_label
                      # , sample_weight=train_weight

                      , eval_set=[(train_data, train_label),(val_data, val_label)]
                      # , eval_sample_weight=[train_weight, val_weight]
                      #                           , eval_metric=['auc']
                      , eval_metric=['auc_mu']  #  binary_logloss  multi_logloss,cross_entropy
                      , verbose=10
                      , early_stopping_rounds=50)
# val_pred = wd_namelist_model.predict(val_data)
val_pred = wd_namelist_model.predict_proba(val_data)# [0,:]

print(val_pred.shape)

如果想要用多分类，来训练二分类目标变量，可以通过如下方法获取概率值

wd_namelist_model.predict_proba(val_data)[0,:len(val_data)]