至少指定一个标签必须在y_true内，目标向量为数值型

Question

我正在用这个 data

实现一个 SVM 项目

以下是我提取特征的方法：

import itertools
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.metrics import classification_report, confusion_matrix

df = pd.read_csv('loan_train.csv')
df['due_date'] = pd.to_datetime(df['due_date'])
df['effective_date'] = pd.to_datetime(df['effective_date'])
df['dayofweek'] = df['effective_date'].dt.dayofweek
df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)
Feature = df[['Principal','terms','age','Gender','weekend']]
Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)
Feature.drop(['Master or Above'], axis = 1,inplace=True)

X = Feature
y = df['loan_status'].replace(to_replace=['PAIDOFF','COLLECTION'], value=[0,1],inplace=False)

创建模型和预测：

clf = svm.SVC(kernel='rbf')
clf.fit(X_train_svm, y_train_svm)
yhat_svm = clf.predict(X_test_svm)

评估阶段：

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show()


cnf_matrix = confusion_matrix(y_test_svm, yhat_svm, labels=[2,4])
np.set_printoptions(precision=2)

print (classification_report(y_test_svm, yhat_svm))

# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=['Benign(2)','Malignant(4)'],normalize= False,  title='Confusion matrix')

这里是错误：

Traceback (most recent call last):

File "E:/python/classification_project/classification.py", line 229,in

cnf_matrix = confusion_matrix(y_test_svm, yhat_svm, labels=[2,4])

File "C:\Program Files(x86)\Python38-32\lib\site-packages\sklearn\metrics_classification.py", line 277, in confusion_matrix

raise ValueError("At least one label specified must be in y_true")

ValueError: At least one label specified must be in y_true

我检查了这个，它和我的一样，我将 y 从 categorical 更改为 numerical，但错误仍然存在！

Answer 1

y 中的值是 0 和 1 但在 confusion_matrix 中调用：

cnf_matrix = confusion_matrix(y_test_svm, yhat_svm, labels=[2,4])

标签是 2 和 4。
confusion_matrix 中的标签应等于 y 向量中的标记，即：

cnf_matrix = confusion_matrix(y_test_svm, yhat_svm, labels=[0,1])

Answer 2

在计算矩阵步骤中，我使用 signs labels=['PAIDOFF','COLLECTION'] 定义了标签，而不是使用 labels=[2,4] 所以这是计算代码：

cnf_matrix = confusion_matrix(y_test, yhat, labels=['PAIDOFF','COLLECTION'])
np.set_printoptions(precision=2)

print (classification_report(y_test, yhat))

# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=['PAIDOFF','COLLECTION'],normalize= False,  title='Confusion matrix')

至少指定一个标签必须在y_true内，目标向量为数值型

At least one label specified must be in y_true, target vector is numerical

machine-learning

svm

python-3.x

scikit-learn