trainval,test = train_test_split(names,test_size=0.2,shuffle=10)

trainval,test = train_test_split(names, test_size=0.2, shuffle=10) 是用于划分数据集的函数。它将给定的数据集 `names` 分割成两个部分：`trainval` `test`。

- `trainval` 是训练和验证集的组合，用于模型的训练和调优。
- `test` 是测试集，用于评估模型的性能。

这个函数的参数解释如下：
- `names`：要划分的数据集。
- `test_size`：测试集的比例。在这个例子中，设置为0.2表示将20%的数据划分为测试集，剩下的80%作为训练和验证集。
- `shuffle`：是否对数据进行洗牌。在这个例子中，设置为10表示使用随机种子10进行洗牌操作，以确保每次划分的结果一致。

请注意，这里的 `train_test_split` 函数是一个示例函数，实际上可能是你自己定义的函数或者来自某个库。具体的实现可能会有所不同。

相关问题

帮我优化这段代码;import numpy as np from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from linear_model_03.closed_form_sol.LineaRegression_CFSol import LinearRegressionClosedFormSol boston = load_boston() # 加载数据 X, y = boston.data, boston.target # 样本数据和目标值 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, shuffle=True) lr_cfs = LinearRegressionClosedFormSol(fit_intercept=True, normalized=True) # 默认训练偏置项和进行标准化 lr_cfs.fit(X_train, y_train) # 训练模型 theta = lr_cfs.get_params() feature_names = boston.feature_names # 样本的名称 for i, fn in enumerate(feature_names): print(fn, ":", theta[0][i]) print("bias:", theta[1]) y_test_pred = lr_cfs.predict(x_test=X_test) mse, r2, r2c = lr_cfs.cal_mse_r2(y_test, y_test_pred) print("均方误差：%.5f,判决系数：%.5f,修正判决系数：%.5f" % (mse, r2, r2c)) # lr_cfs.plt_predict(y_test, y_test_pred, is_sort=False) lr_cfs.plt_predict(y_test, y_test_pred, is_sort=True)

好的，我来帮你优化代码。首先，你可以将导入的库放在代码开头，以便更好地组织代码。另外，命名规范也需要注意，例如，类名应该使用大写字母开头，函数名和变量名应该使用小写字母和下划线。下面是优化后的代码：

import numpy as np
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from linear_model_03.closed_form_sol.LinearRegression_CFSol import LinearRegressionClosedFormSol

boston = load_boston()
X, y = boston.data, boston.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, shuffle=True)

lr_cfs = LinearRegressionClosedFormSol(fit_intercept=True, normalized=True)
lr_cfs.fit(X_train, y_train)

theta = lr_cfs.get_params()
feature_names = boston.feature_names

for i, fn in enumerate(feature_names):
    print(fn, ":", theta[0][i])
print("bias:", theta[1])

y_test_pred = lr_cfs.predict(x_test=X_test)
mse, r2, r2c = lr_cfs.cal_mse_r2(y_test, y_test_pred)
print("均方误差：%.5f,判决系数：%.5f,修正判决系数：%.5f" % (mse, r2, r2c))

lr_cfs.plt_predict(y_test, y_test_pred, is_sort=True)

这样，代码更加清晰易读，同时也符合 PEP8 规范。

修改完善下列代码，得到十折交叉验证三分类的平均每一折的分类报告，三分类的每一类的平均每一折的混淆矩阵，平均每一折的“micro”和“macro”auc值和roc曲线。min_max_scaler = MinMaxScaler() X_train1, X_test1 = x[train_id], x[test_id] y_train1, y_test1 = y[train_id], y[test_id] # apply the same scaler to both sets of data X_train1 = min_max_scaler.fit_transform(X_train1) X_test1 = min_max_scaler.transform(X_test1) # convert to numpy arrays X_train1 = np.array(X_train1) X_test1 = np.array(X_test1) # train gcForest config = get_config() tree = gcForest(config) tree.fit(X_train1, y_train1)

这是一个使用gcForest进行分类的代码，需要补充一些内容才能完成您的要求。

首先，您需要导入必要的库：

from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve
import numpy as np
from gcforest.gcforest import GCForest
from gcforest.utils.config_utils import load_json, get_config

然后，您需要定义一个函数，该函数将返回每一折的分类报告，混淆矩阵，micro和macro auc值，以及roc曲线。

def evaluate_model(X_train, y_train, X_test, y_test):
    # apply the same scaler to both sets of data
    min_max_scaler = MinMaxScaler()
    X_train = min_max_scaler.fit_transform(X_train)
    X_test = min_max_scaler.transform(X_test)

    # train gcForest
    config = get_config()
    tree = GCForest(config)
    tree.fit(X_train, y_train)

    # predict on test set
    y_pred = tree.predict(X_test)

    # calculate classification report and confusion matrix for each class
    class_names = np.unique(y_train)
    reports = []
    matrices = []
    for class_name in class_names:
        mask_train = y_train == class_name
        mask_test = y_test == class_name
        y_train_class = np.zeros_like(y_train)
        y_train_class[mask_train] = 1
        y_test_class = np.zeros_like(y_test)
        y_test_class[mask_test] = 1
        y_pred_class = np.zeros_like(y_pred)
        y_pred_class[y_pred == class_name] = 1
        reports.append(classification_report(y_test_class, y_pred_class))
        matrices.append(confusion_matrix(y_test_class, y_pred_class))

    # calculate micro and macro AUC
    y_scores = tree.predict_proba(X_test)
    micro_auc = roc_auc_score(y_test, y_scores, multi_class='ovo', average='micro')
    macro_auc = roc_auc_score(y_test, y_scores, multi_class='ovo', average='macro')

    # calculate ROC curve
    fpr = dict()
    tpr = dict()
    roc_auc = dict()
    for i in range(len(class_names)):
        fpr[i], tpr[i], _ = roc_curve(y_test_class[:, i], y_scores[:, i])
        roc_auc[i] = auc(fpr[i], tpr[i])

    # return evaluation results
    return reports, matrices, micro_auc, macro_auc, fpr, tpr, roc_auc

最后，您需要将数据分成10折，依次对每一折进行评估，并计算平均值。

# load data
X = np.load('X.npy')
y = np.load('y.npy')

# split data into 10 folds
from sklearn.model_selection import KFold
kf = KFold(n_splits=10, shuffle=True, random_state=42)
reports_list = []
matrices_list = []
micro_auc_list = []
macro_auc_list = []
fpr_list = []
tpr_list = []
roc_auc_list = []
for train_id, test_id in kf.split(X):
    X_train, X_test = X[train_id], X[test_id]
    y_train, y_test = y[train_id], y[test_id]
    reports, matrices, micro_auc, macro_auc, fpr, tpr, roc_auc = evaluate_model(X_train, y_train, X_test, y_test)
    reports_list.append(reports)
    matrices_list.append(matrices)
    micro_auc_list.append(micro_auc)
    macro_auc_list.append(macro_auc)
    fpr_list.append(fpr)
    tpr_list.append(tpr)
    roc_auc_list.append(roc_auc)

# calculate average evaluation results
reports_avg = np.mean(reports_list, axis=0)
matrices_avg = np.mean(matrices_list, axis=0)
micro_auc_avg = np.mean(micro_auc_list)
macro_auc_avg = np.mean(macro_auc_list)
fpr_avg = np.mean(fpr_list, axis=0)
tpr_avg = np.mean(tpr_list, axis=0)
roc_auc_avg = np.mean(roc_auc_list, axis=0)

请注意，上面的代码是示例代码，需要根据您的数据进行适当修改。