我正在使用这个数据实现一个SVM项目
这是我提取特征的方式:
import itertoolsimport matplotlib.pyplot as pltimport pandas as pdimport numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn import svmfrom sklearn.metrics import classification_report, confusion_matrixdf = 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.dayofweekdf['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 = Featurey = 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 matrixplt.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
我查看了这个问题，它与我的情况类似，我将y从categorical更改为numerical，但错误仍然存在!
回答：
y中的值是0和1，但在confusion_matrix调用中:
cnf_matrix = confusion_matrix(y_test_svm, yhat_svm, labels=[2,4])标签是2和4。
confusion_matrix中的标签应该等于y向量中的标记，即:
cnf_matrix = confusion_matrix(y_test_svm, yhat_svm, labels=[0,1])