问题描述
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正常模型，则可以正常获取每个混淆矩阵:
I'm trying to get 10 fold confusion matrix for any models (Random forest, Decision tree, Naive Bayes. etc)I can able to get each confusion matrix normally if I run for normal model as below shown:
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.我应该如何处理或做到这一点.我试过了但是没用.
But, now I want to get confusion matrix for 10 cv fold. How should I approach or do it. I tried this but not working.
# 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)
数据集包含此数据帧dp
Dataset consists of this dataframe 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
推荐答案
来自 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(分层)KFold访问索引:
From the help page for cross_validate it doesn't return the indexes used for cross-validation. You need to access the indices from the (Stratified)KFold, using an example dataset:
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 来获取所有预测:
We apply cross_val_predict to get all the predictions:
y_pred = cross_val_predict(rfc, X, y, cv=skf)
然后使用索引将该y_pred拆分为每个混淆矩阵:
Then use the indices to split this y_pred to each confusion matrix:
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和是否相同:
Check that the addition of the matrices list and total sum is the same:
np.add.reduce(mats)
array([[130, 14],
[ 6, 225]])
confusion_matrix(y_train,y_pred)
array([[130, 14],
[ 6, 225]])
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