python-3.x - 使用LightGBM的网格搜索示例

Question

我正在尝试使用lightgbm中的GridSearchCV为sklearn.model_selection模型找到最佳参数。我还没有找到实际可行的解决方案。

我设法建立了部分工作的代码:

import numpy as np
import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold

np.random.seed(1)

train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
y = pd.read_csv('y.csv')
y = y.values.ravel()
print(train.shape, test.shape, y.shape)

categoricals = ['COL_A','COL_B']
indexes_of_categories = [train.columns.get_loc(col) for col in categoricals]

gkf = KFold(n_splits=5, shuffle=True, random_state=42).split(X=train, y=y)

param_grid = {
    'num_leaves': [31, 127],
    'reg_alpha': [0.1, 0.5],
    'min_data_in_leaf': [30, 50, 100, 300, 400],
    'lambda_l1': [0, 1, 1.5],
    'lambda_l2': [0, 1]
    }

lgb_estimator = lgb.LGBMClassifier(boosting_type='gbdt',  objective='binary', num_boost_round=2000, learning_rate=0.01, metric='auc',categorical_feature=indexes_of_categories)

gsearch = GridSearchCV(estimator=lgb_estimator, param_grid=param_grid, cv=gkf)
lgb_model = gsearch.fit(X=train, y=y)

print(lgb_model.best_params_, lgb_model.best_score_)

这似乎正在工作，但是使用了 UserWarning:

categorical_feature keyword has been found in params and will be ignored. Please use categorical_feature argument of the Dataset constructor to pass this parameter.

我正在寻找一个可行的解决方案，或者是关于如何确保lightgbm接受上述代码中的分类参数的建议

Answer 1

如警告状态所示，categorical_feature不是LGBMModel参数之一。它与lgb.Dataset实例化相关，在sklearn API的情况下，直接在fit()方法see the doc中完成。因此，为了在GridSearchCV优化中传递这些参数，在sklearn v0.19.1的情况下，必须将其作为GridSearchCV.fit()方法的参数提供，或者在较旧的sklearn版本中作为fit_params实例化的附加GridSearchCV参数提供