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【BP神经网络】西瓜分类和鸢尾花分类

花匠小妙招
2025-01-11 02:22

文章目录一、数据集和资料二、鸢尾花分类三、西瓜分类四、一些笔记Sklearn部分资料pandas部分函数

一、数据集和资料

数据集，提取码：zmlc
大佬笔记，提取码：ytfa

二、鸢尾花分类

# -*- coding: utf-8 -* # 使用pandas读入.csv # 使用numpy计算k折平均 import pandas as pd import numpy as np # MLPClassifier是神经网络分类器 # train_test_split帮助划分测试训练集 # cross_val_score是计算K折平均 from sklearn.neural_network import MLPClassifier from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score ## 读取数据 path = 'iris.csv' # 数据文件路径 data = pd.read_csv(path, index_col=0,header=0,names=[0,1,2,3,4]) print(data) # 将类别装换成数字 mapping = {'setosa':0,'versicolor':1,'virginica':2} data = data.replace(mapping) x, y = data[list(range(4))], data[4] # 分割测试集和训练集 x_train,x_test,y_train,y_test = train_test_split(x,y,train_size=0.75,random_state=125) # 创建神经网络分类器对象 clf = MLPClassifier(solver='adam', alpha=1e-5,hidden_layer_sizes=(3,3), random_state=1,max_iter=100000,) # 训练模型 clf.fit(x_train, y_train) # 训练集数据模型评估 score1 = clf.score(x_train,y_train) print(u'测试集准确度：%.4lf' %score1) # 测试集数据模型评估 score2 = clf.score(x_test,y_test) print(u'训练集准确度：%.4lf' %score2) # 不划分测试集使用k折交叉验证 print("不划分测试集使用k折交叉验证") print(u'cross_val_score 准确率为 %.4lf' % np.mean(cross_val_score(clf, x, y, cv=10)))

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三、西瓜分类

# -*- coding: utf-8 -* # 使用pandas读入.csv import pandas as pd from sklearn.neural_network import MLPClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report df = pd.read_csv('watermelon_3.csv', encoding='utf-8') # 将类别变为数字 mapping = {'是':1,'否':0} df = df.replace(mapping) # 取出X和y X = pd.get_dummies(df.iloc[:,1:-1]).values y = df.iloc[:,-1].values # 切割数据集 X_train,X_test,y_train,y_test = train_test_split(X,y,train_size=0.8,random_state=125) # 建模并预测 BPNet = MLPClassifier(random_state=123) BPNet.fit(X_train,y_train) y_pred = BPNet.predict(X_test) #print(y_test,y_pred) # 输出预测结果报告 print('预测报告为：n',classification_report(y_test,y_pred))

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