python - 10个交叉折叠的混淆矩阵 - 如何做pandas dataframe df

Question

我正在尝试为任何模型（随机森林、决策树、朴素贝叶斯等）获得 10 倍混淆矩阵，如果我为正常模型运行，我可以正常获得每个混淆矩阵，如下所示：

    from sklearn.model_selection import train_test_split
    from sklearn import model_selection
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import roc_auc_score
    
    # implementing train-test-split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.34, random_state=66)
    
    # random forest model creation
    rfc = RandomForestClassifier(n_estimators=200, random_state=39, max_depth=4)
    rfc.fit(X_train,y_train)
    # predictions
    rfc_predict = rfc.predict(X_test)
    
    print("=== Confusion Matrix ===")
    print(confusion_matrix(y_test, rfc_predict))
    print('\n')
    print("=== Classification Report ===")
    print(classification_report(y_test, rfc_predict))

输出[1]：

    === 混淆矩阵 ===
    [[16243 1011]
     [ 827 16457]]
    
    
    === 分类报告 ===
                  精确召回 f1 分数支持
    
               0 0.95 0.94 0.95 17254
               1 0.94 0.95 0.95 17284
    
        精度 0.95 34538
       宏平均 0.95 0.95 0.95 34538
    加权平均 0.95 0.95 0.95 34538

但是，现在我想获得10 cv fold 的混淆矩阵。我应该如何接近或做到这一点。我试过这个但没有工作。

    # from sklearn import cross_validation
    from sklearn.model_selection import cross_validate
    kfold = KFold(n_splits=10)
    
    conf_matrix_list_of_arrays = []
    kf = cross_validate(rfc, X, y, cv=kfold)
    print(kf)
    for train_index, test_index in kf:
    
        X_train, X_test = X[train_index], X[test_index]
        y_train, y_test = y[train_index], y[test_index]
    
        rfc.fit(X_train, y_train)
        conf_matrix = confusion_matrix(y_test, rfc.predict(X_test))
        conf_matrix_list_of_arrays.append(conf_matrix)

数据集由这个数据框 dp 组成

温度系列 平行阴影 电池数量 电压(V) 电流(I) I/V 太阳能电池板 电池阴影百分比 IsShade
30 10 1 2 10 1.11 2.19 1.97 1985 1 20.0 1
27 5 2 10 10 2.33 4.16 1.79 1517 3 100.0 1
30 5 2 7 10 2.01 4.34 2.16 3532 1 70.0 1
40 2 4 3 8 1.13 -20.87 -18.47 6180 1 37.5 1
45 5 2 4 10 1.13 6.52 5.77 8812 3 40.0 1

标签： pythonpandasdataframecross-validationk-fold

Answer 1

从cross_validate 的帮助页面中，它不返回用于交叉验证的索引。您需要使用示例数据集从（分层）KFold 访问索引：

from sklearn import datasets, linear_model
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_predict
from sklearn.ensemble import RandomForestClassifier

data = datasets.load_breast_cancer()
X = data.data
y = data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.34, random_state=66)

skf = StratifiedKFold(n_splits=10,random_state=111,shuffle=True)
skf.split(X_train,y_train)

rfc = RandomForestClassifier(n_estimators=200, random_state=39, max_depth=4)
y_pred = cross_val_predict(rfc, X_train, y_train, cv=skf)

我们申请cross_val_predict得到所有的预测：

y_pred = cross_val_predict(rfc, X, y, cv=skf)

然后使用索引将此 y_pred 拆分为每个混淆矩阵：

mats = []
for train_index, test_index in skf.split(X_train,y_train):
    mats.append(confusion_matrix(y_train[test_index],y_pred[test_index]))
    

看起来像这样：

mats[:3]

[array([[13,  2],
        [ 0, 23]]),
 array([[14,  1],
        [ 1, 22]]),
 array([[14,  1],
        [ 0, 23]])]

检查矩阵列表和总和的相加是否相同：

np.add.reduce(mats)

array([[130,  14],
       [  6, 225]])

confusion_matrix(y_train,y_pred)

array([[130,  14],
       [  6, 225]])