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) 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
2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
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