def svmModel(x_train,x_test,y_train,y_test,type): if type=='rbf': svmmodel=svm.SVC(C=15,kernel='rbf',gamma=10,decision_function_shape='ovr') else: svmmodel=svm.SVC(C=0.1,kernel='linear',decision_function_shape='ovr') svmmodel.fit(x_train,y_train.ravel()) print('SVM模型:',svmmodel) train_accscore=svmmodel.score(x_train,y_train) test_accscore=svmmodel.score(x_test,y_test) n_support_numbers=svmmodel.n_support_ return svmmodel,train_accscore,test_accscore,n_support_numbers if __name__=='__main__': iris_feature='花萼长度','花萼宽度','花瓣长度','花瓣宽度' path="D:\data\iris(1).data" data=pd.read_csv(path,header=None) x,y=data[[0,1]],pd.Categorical(data[4]).codes x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=3,train_size=0.6) type='linear' svmmodel,train_accscore,test_accscore,n_support_numbers=svmModel(x_train,x_test,y_train,y_test,type) print('训练集准确率:',train_accscore) print('测试机准确率:',test_accscore) print('支持向量的数目:',n_support_numbers) print('-' * 50) if __name__=='__main__': path='D:/data/iris1-100.data' data=pd.read_csv(path,header=None) x=data[list(range(2,4))] y=data[4].replace(['Iris-versicolor','Iris-virginica'],[0,1]) svmmodel_param=(('linear',0.1),('rbf',1,0.1),('rbf',5,5),('rbf',10,10)) for i, param in enumerate(svmmodel_param): svmmodel,title,accuracyscore=svmModel(x,y,param) y_predict=svmmodel.predict(x) print(title) print('准确率:',accuracyscore) print('支持向量的数目:',svmmodel.n_support_)
时间: 2024-04-28 11:25:55 浏览: 21
这是一个使用 SVM 进行分类的 Python 代码。其中使用了 iris 数据集来进行测试,通过调整 SVM 模型的参数,来比较不同模型在数据集上的准确率和支持向量的数目。其中 SVM 模型的参数包括 C 值和 kernel 值,C 值是惩罚系数,kernel 值决定了 SVM 模型的核函数。代码中使用了线性核函数和径向基函数(RBF)核函数。
相关问题
import numpy as npimport pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.svm import SVCfrom sklearn.metrics import accuracy_score, confusion_matriximport matplotlib.pyplot as pltimport xlrd# 加载数据集并进行预处理def load_data(filename): data = pd.read_excel(filename) data.dropna(inplace=True) X = data.drop('label', axis=1) X = (X - X.mean()) / X.std() y = data['label'] return X, y# 训练SVM分类器def train_svm(X_train, y_train, kernel='rbf', C=1, gamma=0.1): clf = SVC(kernel=kernel, C=C, gamma=gamma) clf.fit(X_train, y_train) return clf# 预测新的excel文件并输出预测结果excel、精度和混淆矩阵图def predict_svm(clf, X_test, y_test, filename): y_pred = clf.predict(X_test) accuracy = accuracy_score(y_test, y_pred) cm = confusion_matrix(y_test, y_pred) # 输出预测结果excel data = pd.read_excel(filename) data['predicted_label'] = pd.Series(y_pred, index=data.index) data.to_excel('predicted_result.xlsx', index=False) # 绘制混淆矩阵图 plt.imshow(cm, cmap=plt.cm.Blues) plt.title('Confusion matrix') plt.colorbar() tick_marks = np.arange(len(set(y_test))) plt.xticks(tick_marks, sorted(set(y_test)), rotation=45) plt.yticks(tick_marks, sorted(set(y_test))) plt.xlabel('Predicted Label') plt.ylabel('True Label') plt.show() return accuracy# 加载数据集并划分训练集和验证集data = pd.read_excel('data.xlsx')data.dropna(inplace=True)X = data.drop('label', axis=1)X = (X - X.mean()) / X.std()y = data['label']X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 训练SVM分类器clf = train_svm(X_train, y_train)# 预测新的excel文件accuracy = predict_svm(clf, X_test, y_test, 'test_data.xlsx')# 输出精度print('Accuracy:', accuracy)改进,预测新的结果输出在新表中
您好,根据您的要求,我稍作修改,将输出预测结果的excel文件名改为新的文件名,并在新的文件中保存预测结果。修改后的代码如下:
```
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, confusion_matrix
import matplotlib.pyplot as plt
import xlrd
# 加载数据集并进行预处理
def load_data(filename):
data = pd.read_excel(filename)
data.dropna(inplace=True)
X = data.drop('label', axis=1)
X = (X - X.mean()) / X.std()
y = data['label']
return X, y
# 训练SVM分类器
def train_svm(X_train, y_train, kernel='rbf', C=1, gamma=0.1):
clf = SVC(kernel=kernel, C=C, gamma=gamma)
clf.fit(X_train, y_train)
return clf
# 预测新的excel文件并输出预测结果excel、精度和混淆矩阵图
def predict_svm(clf, X_test, y_test, filename, result_file):
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
cm = confusion_matrix(y_test, y_pred)
# 输出预测结果excel
data = pd.read_excel(filename)
data['predicted_label'] = pd.Series(y_pred, index=data.index)
data.to_excel(result_file, index=False)
# 绘制混淆矩阵图
plt.imshow(cm, cmap=plt.cm.Blues)
plt.title('Confusion matrix')
plt.colorbar()
tick_marks = np.arange(len(set(y_test)))
plt.xticks(tick_marks, sorted(set(y_test)), rotation=45)
plt.yticks(tick_marks, sorted(set(y_test)))
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.show()
return accuracy
# 加载数据集并划分训练集和验证集
data = pd.read_excel('data.xlsx')
data.dropna(inplace=True)
X = data.drop('label', axis=1)
X = (X - X.mean()) / X.std()
y = data['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 训练SVM分类器
clf = train_svm(X_train, y_train)
# 预测新的excel文件
accuracy = predict_svm(clf, X_test, y_test, 'test_data.xlsx', 'predicted_result.xlsx')
# 输出精度
print('Accuracy:', accuracy)
```
在这个修改后的代码中,`predict_svm` 函数中新增了一个参数 `result_file`,用于指定预测结果输出的文件名。在预测结果输出时,将预测结果保存为新的excel文件,文件名即为 `result_file` 参数所指定的文件名。
如何把下面这段函数的准确值评估改成交叉验证评估:def obj3(x): nu = x[0] gamma = x[1] clf = svm.OneClassSVM(kernel='rbf', gamma=gamma, nu=nu) clf.fit(X_train) pre = clf.predict(X1) pre = np.asarray(pre)+2 v = 1 if nu > 0.5 else -1 y_test = [v] * len(X1) y_test = np.asarray(y_test)+2 acc = accuracy_score(y_test, pre) return acc
可以使用交叉验证来评估该函数的准确值。具体来说,可以按照以下步骤进行交叉验证评估:
1. 将数据集分成 k 个折叠(例如,k=5)。
2. 对于每个折叠 i,将其作为测试集,其余折叠作为训练集。
3. 对于每个测试集,使用训练集上的参数训练模型,然后计算测试集上的准确率。
4. 将 k 个测试集的准确率取平均值作为最终的准确率评估结果。
下面是修改后的函数代码:
```
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score
def obj3(x):
nu = x[0]
gamma = x[1]
kf = KFold(n_splits=5)
accs = []
for train_index, test_index in kf.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train = [1 if nu > 0.5 else -1] * len(X_train)
y_test = [1 if nu > 0.5 else -1] * len(X_test)
clf = svm.OneClassSVM(kernel='rbf', gamma=gamma, nu=nu)
clf.fit(X_train)
pre = clf.predict(X_test)
acc = accuracy_score(y_test, pre)
accs.append(acc)
return np.mean(accs)
```
在上述代码中,我们使用了 KFold 函数来进行 k 折交叉验证,并且在每个测试集上计算了准确率。最终,我们返回 k 个准确率的平均值作为最终的准确率评估结果。
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