用交叉验证调整参数，以测试集AUC为评价指标，利用随机森林预测二分类任务的代码

.substring(2), 0);
if (!set.contains(newItem) && !newItems.contains(newItem)) {
newItems.add好的，以下是利用交叉验证调整参数，以测试集AUC为评价指标，利用随机(newItem);
hasNewItem = true;
}
}
}
}
set.addAll(newItems);
} while (has森林预测二分类任务的Python代码：

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearnNewItem);
        return set;
    }

    // 查找所有包含指定符号的文法规则
    private List<String.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.metrics import roc_auc_score

# 读入数据> rulesWithSymbol(String symbol) {
        List<String> result = new ArrayList<>();
        for (String rule : rules) {
            if
data = pd.read_csv("data.csv")

# 将特征和标签分离
X = data.drop("label", axis= (rule.contains(symbol)) {
                result.add(rule);
            }
        }
        return result;
    }

    // 查找符号在1)
y = data["label"]

# 定义随机森林分类器
rfc = RandomForestClassifier(random_state=42)

#列表中的位置
    private int symbolIndex(String symbol) {
        for (int i = 0; i < rules.size(); i 定义需要测试的参数范围
param_grid = {
    "n_estimators": [100, 200, 300],
    "++) {
            if (rules.get(i).startsWith(symbol + " ")) {
                return i;
            }
        }
        return -1;
max_depth": [5, 10, 15, None],
    "min_samples_split": [2, 5, 10],
    }

    // 将 DFA 输出到文件
    private void outputDFA(String filename) throws IOException {
        PrintWriter writer = new PrintWriter    "min_samples_leaf": [1, 2, 4],
}

# 利用GridSearchCV进行交叉验证调参
grid_search = GridSearchCV(rfc, param_grid, cv=5, scoring="roc_auc", n_jobs=-1)
grid_search.fit(new FileWriter(filename));
        for (int i = 0; i < rules.size(); i++) {
            writer.print("\t" + rules(X, y)

# 输出最优参数
print("Best parameters: {}".format(grid_search.best_params_))

# 利用最优参数.get(i));
        }
        writer.println();
        for (int i = 0; i < itemSets.size(); i++) {
            writer构建随机森林分类器
rfc_best = RandomForestClassifier(n_estimators=grid_search.best_params_["n_estimators"],
                                  .print(i);
            Set<Item> set = itemSets.get(i);
            for (int j = 0; j < rules.size(); j max_depth=grid_search.best_params_["max_depth"],
                                   min_samples_split=grid_search.best_params_["min_samples_split"],
                                   min_samples_leaf=grid_search.best_params_["min_samples_leaf"],
                                   random_state=42)

# 利用交叉验证计算++) {
                if (table[i][j] >= 0) {
                    writer.print("\t" + table[i][j]);
               AUC得分
auc_scores = cross_val_score(rfc_best, X, y, cv=5, scoring="roc_auc", n } else {
                    writer.print("\t-");
                }
            }
            if (set.contains(new Item("S'", "S", _jobs=-1)

# 输出AUC得分
print("AUC scores: {}".format(auc_scores))
print("Mean AUC score1))) {
                writer.print("\t1");
            } else {
                writer.print("\t0");
            }
            for (Item item: {}".format(auc_scores.mean()))

其中，`data.csv`是二分类任务的数据集，包含多个特 : set) {
writer.println();
writer.print("\t\t" + item);
}
writer.println();
}
writer征和标签列。首先将特征和标签分离，然后定义随机森林分类器和需要测试.close();
}

// 构造函数，读取文件并构造DFA
public LR0DFA(String inputFilename,的参数范围。接着利用GridSearchCV进行交叉验证调参，以测试集AUC为评价指标 String outputFilename) throws IOException {
initRules(inputFilename);
initItemSets();
initDFA();
outputDFA(output。最后利用最优参数构建随机森林分类器，利用交叉验证计算AUC得分，输出AUC得分和平均AUC得分。