如何解决10个交叉折叠的混淆矩阵-如何做熊猫数据框df
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 如果我运行如下所示的普通模型,则可以正常获取每个混淆矩阵:
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,现在我想得到10个cv折叠的混淆矩阵。我应该如何做或做。我试过了但是没用。
# 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,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)
数据集包含此数据帧dp
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
解决方法
对我来说,这里的问题在于kf
的错误包装。实际上,cross_validate()
返回的数组字典默认具有test_scores和fit / score时间。
您可以改为使用split()
实例的Kfold
方法,该方法可以帮助您生成索引以将数据分为训练和测试(验证)集。因此,通过更改为
for train_index,test_index in kfold.split(X_train,y_train):
您应该得到想要的东西。
,从help page for cross_validate不会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,cv=skf)
我们应用cross_val_predict
来获得所有预测:
y_pred = cross_val_predict(rfc,X,cv=skf)
然后使用索引将该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]]),23]])]
检查矩阵列表和总和的和是否相同:
np.add.reduce(mats)
array([[130,14],[ 6,225]])
confusion_matrix(y_train,y_pred)
array([[130,225]])
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