使用KNN、SVM和BP进行红酒分类

红酒分类问题某研究获取了若干红酒的类别数据，存放于 wine数据.txt 中。每个样本的第一个属性是类别（1或2或3），其余按顺序均有如下13个属性1) Alcohol2) Malic acid3) Ash4) Alcalinity of ash5) Magnesium6) Total phenols7) Flavanoids8) Nonf...

YiRano_0

6178人浏览 · 2022-01-22 22:53:58

YiRano_0 · 2022-01-22 22:53:58 发布

红酒分类问题

某研究获取了若干红酒的类别数据，存放于 wine数据.txt 中。

每个样本的第一个属性是类别（1或2或3），其余按顺序均有如下13个属性

        1) Alcohol
        2) Malic acid
        3) Ash
        4) Alcalinity of ash
        5) Magnesium
        6) Total phenols
        7) Flavanoids
        8) Nonflavanoid phenols
        9) Proanthocyanins
        10)Color intensity
        11)Hue
        12)OD280/OD315 of diluted wines
        13)Proline

要求：自行选出训练样本和测试样本，如有必要可先对数据做预处理（如离散化、降维等）

说明：由于sklearn自带的红酒数据集（wine）与提供的 wine数据.txt 数据内容一样，所以本实验直接导入相应包以及红酒数据集。

一、实验流程

二、实验代码

import numpy as np
from sklearn import svm
from sklearn.datasets import load_wine
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
from sklearn.metrics import accuracy_score, classification_report
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier


def pca_show(pca_data, target):  # 绘制PCA降维后图像
    color = ['r', 'g', 'b']  # 图像点颜色
    marker = ['s', 'x', 'o']  # 图像点样式
    for lb, c, m in zip(np.unique(target), color, marker):  # 绘制数据点
        plt.scatter(pca_data[target == lb, 0],
                    pca_data[target == lb, 1],
                    c=c, label=lb, marker=m)
    plt.title('Result')
    plt.xlabel('PC1')
    plt.ylabel('PC2')
    plt.legend(loc='upper right')
    plt.show()


def data_split(data, target):
    """
    数据集前59个样本全是第1类，中间71个样本为第2类，最后48个样本是第3类
    分布区间为 0:58 , 59:129, 130:177 左右都是闭区间
    按照100：178比例划分数据集，第1类数据量为59*100/178 = 33，同理第2类为40，第3类为27
    故训练集区间为 0:33,59:99,130:157  左闭右开
    测试集区间为 33:59,99:130,157:177  左闭右开
    """

    def cell_concatenate(data_tuple):  # data_tuple内的数据连接
        return np.concatenate(data_tuple, axis=0)

    # 数据分解
    train_data = cell_concatenate((data[0:33, :], data[59:99, :], data[130:157, :]))  # 训练集
    train_target = cell_concatenate((target[0:33], target[59:99], target[130:157]))  # 样本类别
    test_data = cell_concatenate((data[33:59, :], data[99:130, :], data[157:, :]))  # 测试集
    test_target = cell_concatenate((target[33:59], target[99:130], target[157:]))  # 样本类别
    # print(train_data.shape)
    # print(test_data.shape)
    return train_data, train_target, test_data, test_target


def svm_classifier(train_X, train_Y, test_X, test_Y, title):
    print("SVM分类器", title)
    svm_clf = svm.SVC(kernel='linear', C=1000.)
    svm_clf.fit(train_X, train_Y) # 训练数据集
    predict_Y = svm_clf.predict(test_X) # 预测
    print("训练准确率为{:.2f}%".format(accuracy_score(test_Y, predict_Y) * 100))
    print(classification_report(test_Y, predict_Y))

def bp_classifier(train_X, train_Y, test_X, test_Y, title):
    print("人工神经网络分类器", title)
    # 构建神经网络，其中三个隐藏层，分别有100，50，20个神经元，最大训练次数400
    mlp = MLPClassifier(hidden_layer_sizes=(100, 50, 20), max_iter=400)
    mlp.fit(train_X, train_Y)# 训练
    predict = mlp.predict(test_X)# 预测
    # 打印测试结果和真实标签的准确率
    print("训练准确率为{:.2f}%".format(accuracy_score(test_Y, predict) * 100))
    print(classification_report(predict, test_Y))

def knn_classifier(train_X, train_Y, test_X, test_Y, title):
    print("KNN分类器", title)
    knn = KNeighborsClassifier(algorithm='auto', leaf_size=10, metric='minkowski',
                               metric_params=None, n_jobs=1, n_neighbors=2, p=2,
                               weights='uniform')
    knn.fit(train_X, train_Y) # 加载数据集
    predict_Y = knn.predict(test_X)
    print("训练准确率为{:.2f}%".format(accuracy_score(test_Y, predict_Y) * 100))
    print(classification_report(test_Y, predict_Y))
    # if "PCA" in title:
    #     pca_show(test_X,predict_Y)


if __name__ == '__main__':
    wine_dataset = load_wine()  # 导入红酒数据集，数据为字典形式，数据集在data键中，标签在target键中
    # print("初始化完成")
    sc = StandardScaler()  # 数据标准化处理
    wine_data_std = sc.fit_transform(wine_dataset['data'])
    pca = PCA(n_components=2)  # PCA降维降至2维
    pca.fit(wine_data_std)  # PCA训练
    wine_data_pca = pca.fit_transform(wine_data_std)
    pca_show(wine_data_pca, wine_dataset['target']) # 展示数据图像

    # 原始数据划分后的训练、测试数据集
    train_X, train_Y, test_X, test_Y = data_split(wine_dataset['data'], wine_dataset['target'])
    # 标准化后的训练、测试数据集
    train_X_std, train_Y_std, test_X_std, test_Y_std = data_split(wine_data_std, wine_dataset['target'])
    # 降维后的训练、测试数据集
    pca_train_X, pca_train_Y, pca_test_X, pca_test_Y = data_split(wine_data_pca, wine_dataset['target'])

    bp_classifier(train_X, train_Y, test_X, test_Y, title="原始数据")
    bp_classifier(train_X_std, train_Y_std, test_X_std, test_Y_std, title="标准化后数据")
    bp_classifier(pca_train_X, pca_train_Y, pca_test_X, pca_test_Y, title="PCA降维后数据")
    svm_classifier(train_X, train_Y, test_X, test_Y, title="原始数据")
    svm_classifier(train_X_std, train_Y_std, test_X_std, test_Y_std, title="标准化后数据")
    svm_classifier(pca_train_X, pca_train_Y, pca_test_X, pca_test_Y, title="PCA降维后数据")
    knn_classifier(train_X, train_Y, test_X, test_Y, title="原始数据")
    knn_classifier(train_X_std, train_Y_std, test_X_std, test_Y_std, title="标准化后数据")
    knn_classifier(pca_train_X, pca_train_Y, pca_test_X, pca_test_Y, title="PCA降维后数据")

三、实验结果

KNN

SVM

BP

四、结果分析

根据上面的实验结果截图，可得：

①原始数据下，KNN算法分类准确率为69.23%，SVM算法分类准确率为100%，人工神经网络算法分类准确率为97.44%。

②标准化后数据下，KNN算法分类准确率为94.87%，SVM算法分类准确率为98.72%，人工神经网络算法分类准确率为100%。

③PCA降维后数据下，KNN算法分类准确率为97.44%，SVM算法分类准确率为97.44%，人工神经网络算法分类准确率为94.87%。

可见，每种算法在不同的数据处理下效果也大不相同。SVM算法在不对数据进行预处理的情况下就能够达到100%的分类准确率，在数据进行标准化和PCA降维后，准确率反而下降了，而KNN和神经网络算法则是在数据预处理之后准确率有所提升，KNN算法提升效果尤为明显，原始数据下的准确率只有69.23%，而PCA降维后提高到了97.44%。因此，并非每种分析算法都适应全部的数据集，不一样数据集其特征不一样，最佳分析的算也会不一样，所以在进行数据分析时，一般会对比多种分析算法，再优化本身的实验和模型。

附 wine数据.txt

1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
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