10 交叉折叠的混淆矩阵 - 如何做熊猫数据帧 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))

出[1]：

=== Confusion Matrix ===
    [[16243  1011]
     [  827 16457]]
    
    
    === Classification Report ===
                  precision    recall  f1-score   support
    
               0       0.95      0.94      0.95     17254
               1       0.94      0.95      0.95     17284
    
        accuracy                           0.95     34538
       macro avg       0.95      0.95      0.95     34538
    weighted avg       0.95      0.95      0.95     34538

但是，现在我想获得10 cv fold 的混淆矩阵。 我应该如何接近或去做。 我试过这个但没有用。

    # 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)

数据集由这个数据帧 dp 组成

Temperature Series  Parallel    Shading Number of cells Voltage(V)  Current(I)  I/V     Solar Panel Cell Shade Percentage   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

Answer 1

从cross_validate的帮助页面，它不会返回用于交叉验证的索引。 您需要使用示例数据集从 (Stratified)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)

我们应用cross_val_predict来获得所有预测：

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]])

Answer 2

对我来说，这里的问题在于kf的不正确解包。 事实上， cross_validate()默认返回一个包含 test_scores 和 fit/score 时间的数组字典。

您可以改用Kfold实例的split()方法，该方法可帮助您生成索引以将数据拆分为训练和测试（验证）集。 因此，通过改成

for train_index, test_index in kfold.split(X_train, y_train):

你应该得到你正在寻找的东西。