SVM分类.rar_SVM_iris_sheps3_svm python

#coding:utf-8 from __future__ import division import pandas import matplotlib.pyplot as plt import numpy as np from sklearn.metrics import roc_auc_score from sklearn.metrics import confusion_matrix ################定义激活函数~########################################## def sigmoid_activation(x, theta): x = np.asarray(x) theta = np.asarray(theta) return 1 / (1 + np.exp(-np.dot(theta.T, x))) # 将模型的函数凝结为一个类，这是很好的一种编程习惯 class NNet3: # 初始化必要的几个参数 def __init__(self, learning_rate=0.5, maxepochs=1e4, convergence_thres=1e-5, hidden_layer=4): self.learning_rate = learning_rate self.maxepochs = int(maxepochs) self.convergence_thres = 1e-5 self.hidden_layer = int(hidden_layer) # 计算最终的误差 def _multiplecost(self, X, y): # l1是中间层的输出，l2是输出层的结果 l1, l2 = self._feedforward(X) # 计算误差，这里的l2是前面的h inner = y * np.log(l2) + (1-y) * np.log(1-l2) # 添加符号，将其转换为正值 return -np.mean(inner) # 前向传播函数计算每层的输出结果 def _feedforward(self, X): # l1是中间层的输出 l1 = sigmoid_activation(X.T, self.theta0).T # 为中间层添加一个常数列 l1 = np.column_stack([np.ones(l1.shape[0]), l1]) # 中间层的输出作为输出层的输入产生结果l2 l2 = sigmoid_activation(l1.T, self.theta1) return l1, l2 # 传入一个结果未知的样本，返回其属于1的概率 def predict(self, X): _, y = self._feedforward(X) return y # 学习参数，不断迭代至参数收敛，误差最小化 def learn(self, X, y): nobs, ncols = X.shape self.theta0 = np.random.normal(0,0.01,size=(ncols,self.hidden_layer)) self.theta1 = np.random.normal(0,0.01,size=(self.hidden_layer+1,1)) self.costs = [] cost = self._multiplecost(X, y) self.costs.append(cost) costprev = cost + self.convergence_thres+1 counter = 0 for counter in range(self.maxepochs): # 计算中间层和输出层的输出 l1, l2 = self._feedforward(X) # 首先计算输出层的梯度，再计算中间层的梯度 l2_delta = (y-l2) * l2 * (1-l2) l1_delta = l2_delta.T.dot(self.theta1.T) * l1 * (1-l1) # 更新参数 self.theta1 += l1.T.dot(l2_delta.T) / nobs * self.learning_rate self.theta0 += X.T.dot(l1_delta)[:,1:] / nobs * self.learning_rate counter += 1 costprev = cost cost = self._multiplecost(X, y) # get next cost self.costs.append(cost) if np.abs(costprev-cost) < self.convergence_thres and counter > 500: break if __name__ == '__main__': ###################################################数据处理############################# #############读取数据################## iris = pandas.read_csv("c:/pic2/iris.csv") # 打乱数据顺序################ shuffled_rows = np.random.permutation(iris.index) iris = iris.iloc[shuffled_rows] print(iris.species()) # 添加一个值全为1的属性iris["ones"]，截距 iris["ones"] = np.ones(iris.shape[0]) X = iris[['ones', 'sepal_length', 'sepal_width', 'petal_length', 'petal_width']].values # 将Iris-versicolor类标签设置为1，Iris-virginica设置为0 y = (iris.species == 'Iris-versicolor').values.astype(int) # First 70 rows to X_train and y_train # Last 30 rows to X_train and y_train X_train = X[:70] y_train = y[:70] X_test = X[-30:] y_test = y[-30:] ###################################################训练模型################################## # Set a learning rate learning_rate = 0.5 # Maximum number of iterations for gradient descent maxepochs = 10000 # Costs convergence threshold, ie. (prevcost - cost) > convergence_thres convergence_thres = 0.00001 # Number of hidden units hidden_units = 8 # Initialize model model = NNet3(learning_rate=learning_rate, maxepochs=maxepochs, convergence_thres=convergence_thres, hidden_layer=hidden_units) model.learn(X_train, y_train) train_yhat = model.predict(X_train)[0] print(y_train) print(train_yhat) train_auc = roc_auc_score(y_train, train_yhat) print(train_auc) ########################################预测数据############################## # 因为predict返回的是一个二维数组，此处是(1,30)，取第一列作为一个列向量 yhat = model.predict(X_test)[0] print(y_test) print(yhat) predict=[] for each in yhat: if each>0.5: predict.append(1) else: predict.append(0) print(predict) auc = roc_auc_score(y_test, yhat) print(auc) #################################写出数据########################## ######################合并各列数据######################### result=np.column_stack([X_test,y_test,predict]) print(result) count=0 for i in range(0,len(result)): if result[i,5]==result[i,6]: count+=1 ################计算准确率############################# print(count),len(result) acurate=count/len(result) print("分类正确率是：%.2f%%" % (acurate * 100)) labels = list(set(predict)) print(labels) conf_mat = confusion_matrix(y_test, predict, labels=labels) print(conf_mat) #####################数组转换为数据框########################## result=pandas.DataFrame(result[:,1:]) result.columns = ['sepal_length', 'sepal_width','petal_length', 'petal_width', 'species', 'predict'] print(result) #########################写出数据到excel################ pandas.DataFrame.to_excel(result,"c:/pic2/iris_test.xlsx",index=False) # Plot costs plt.plot(model.costs,color="red") plt.title("Convergence of the Cost Function") plt.ylabel("J($\Theta$)") plt.xlabel("Iteration") plt.show()