完整的python 用BP网络实现的iris数据处理#coding:utf-8from __future__ import divisionimport pandas as pdimport matplotlib.pyplot as pltimport numpy as npfrom sklearn.metrics import roc_auc_scorefrom 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_rateself.maxepochs = int(maxepochs)self.convergence_thres = 1e-5self.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hinner = 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l2l2 = 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.shapeself.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 + 1counter = 0for 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_rateself.theta0 += X.T.dot(l1_delta)[:, 1:] / nobs * self.learning_ratecounter += 1costprev = costcost = self._multiplecost(X, y) # get next costself.costs.append(cost)if np.abs(costprev - cost) <self.convergence_thres and counter > 500:breakif __name__ == '__main__':###################################################数据处理##########################################读取数据##################iris = pd.read_csv("iris.csv")# 打乱数据顺序################shuffled_rows = np.random.permutation(iris.index) iris = iris.iloc[shuffled_rows]print(iris.species.unique())# 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0y = (iris.species == 'Iris-versicolor').values.astype(int)# First 70 rows to X_train and y_train# Last 30 rows to X_train and y_trainX_train = X[:70]y_train = y[:70]X_test = X[-30:]y_test = y[-30:]###################################################训练模型################################### Set a learning ratelearning_rate = 0.5# Maximum number of iterations for gradientdescentmaxepochs = 10000# Costs convergence threshold, ie. (prevcost - cost) > convergence_thresconvergence_thres = 0.00001# Number of hidden unitshidden_units = 8# Initialize modelmodel = 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=0for 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=pd.DataFrame(result[:,1:])result.columns = ['sepal_length','sepal_width','petal_length', 'petal_width','species', 'predict']print(result)#########################写出数据到excel################summaryDataFrame = pd.DataFrame(result)# summaryDataFrame.to_excel(filePath,summaryDataFrame.to_excel('iris_test.xlsx', encoding='utf-8', index=False)# Plot costsplt.plot(model.costs,color="red")plt.title("Convergence of the Cost Function") plt.ylabel("J($\Theta$)")plt.xlabel("Iteration")plt.show()。