昇思25天学习打卡营第14天|K近邻算法实现红酒聚类

红酒Wine数据集

类别(13类属性)：Alcohol，酒精；Malic acid，苹果酸

Ash，灰；Alcalinity of ash，灰的碱度；

Magnesium，镁；Total phenols，总酚；

Flavanoids，类黄酮；Nonflavanoid phenols，非黄酮酚；

Proanthocyanins，原花青素；Color intensity，色彩强度；

Hue，色调；OD280/OD315 of diluted wines，稀释酒的OD280/OD315；

Proline，脯氨酸。

Wine Data Set：{
	Alcohol,
	Malic acid,
	Ash,
	Alcalinity of ash,
	Magnesium,
	Total phenols,
	Flavanoids,
	Nonflavanoid phenols,
	Proanthocyanins,
	Color intensity,
	Hue,
	OD280/OD315 of diluted wines,
	Proline,
}

1.直接下载：wine数据集下载。

2.程序下载：pip install download

from download import download
# 下载红酒数据集
url = "https://ascend-professional-construction-dataset.obs.cn-north-4.myhuaweicloud.com:443/MachineLearning/wine.zip"  
path = download(url, "./", kind="zip", replace=True)

读取Wine数据集wine.data，并查看部分数据

取三类样本（共178条），将数据集的13个属性作为自变量 𝑋 。将数据集的3个类别作为因变量 𝑌 。

import os
import csv
import numpy as np
import matplotlib.pyplot as plt
import mindspore as ms
from mindspore import nn, ops
ms.set_context(device_target="CPU")
X = np.array([[float(x) for x in s[1:]] for s in data[:178]], np.float32)
Y = np.array([s[0] for s in data[:178]], np.int32)
attrs = ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols',
         'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue',
         'OD280/OD315 of diluted wines', 'Proline']
plt.figure(figsize=(10, 8))
for i in range(0, 4):
    plt.subplot(2, 2, i+1)
    a1, a2 = 2 * i, 2 * i + 1
    plt.scatter(X[:59, a1], X[:59, a2], label='1')
    plt.scatter(X[59:130, a1], X[59:130, a2], label='2')
    plt.scatter(X[130:, a1], X[130:, a2], label='3')
    plt.xlabel(attrs[a1])
    plt.ylabel(attrs[a2])
    plt.legend()
plt.show()

k临近算法：模型构建–计算距离

K近邻算法的基本原理是：对于一个新的输入实例，算法在训练数据集中找到与该实例最邻近的K个实例（即K个“邻居”），并基于这K个邻居的信息来进行预测。对于分类任务，通常采用多数表决的方式，即选择K个邻居中出现次数最多的类别作为预测结果；对于回归任务，则通常取K个邻居的实际值的平均值作为预测结果。

将数据集按128:50划分为训练集（已知类别样本）和验证集（待验证样本）

train_idx = np.random.choice(178, 128, replace=False)
test_idx = np.array(list(set(range(178)) - set(train_idx)))
X_train, Y_train = X[train_idx], Y[train_idx]
X_test, Y_test = X[test_idx], Y[test_idx]

利用MindSpore提供的tile, square, ReduceSum, sqrt, TopK等算子，通过矩阵运算的方式同时计算输入样本x和已明确分类的其他样本X_train的距离，并计算出top k近邻

class KnnNet(nn.Cell):
    def __init__(self, k):
        super(KnnNet, self).__init__()
        self.k = k
    def construct(self, x, X_train):
        #平铺输入x以匹配X_train中的样本数
        x_tile = ops.tile(x, (128, 1))
        square_diff = ops.square(x_tile - X_train)
        square_dist = ops.sum(square_diff, 1)
        dist = ops.sqrt(square_dist)
        #-dist表示值越大，样本就越接近
        values, indices = ops.topk(-dist, self.k)
        return indices
def knn(knn_net, x, X_train, Y_train):
    x, X_train = ms.Tensor(x), ms.Tensor(X_train)
    indices = knn_net(x, X_train)
    topk_cls = [0]*len(indices.asnumpy())
    for idx in indices.asnumpy():
        topk_cls[Y_train[idx]] += 1
    cls = np.argmax(topk_cls)
    return cls

模型预测

acc = 0
knn_net = KnnNet(5)
for x, y in zip(X_test, Y_test):
    pred = knn(knn_net, x, X_train, Y_train)
    acc += (pred == y)
    print('label: %d, prediction: %s' % (y, pred))
print('Validation accuracy is %f' % (acc/len(Y_test)))

输出：

label: 1, prediction: 1
label: 2, prediction: 2
label: 1, prediction: 3
label: 3, prediction: 3
label: 1, prediction: 1
label: 3, prediction: 3
label: 3, prediction: 1
label: 1, prediction: 3
label: 1, prediction: 1
label: 3, prediction: 2
label: 1, prediction: 3
label: 3, prediction: 3
label: 3, prediction: 3
label: 1, prediction: 1
label: 1, prediction: 1
label: 1, prediction: 1
label: 1, prediction: 1
label: 1, prediction: 1
label: 1, prediction: 1
label: 3, prediction: 3
label: 1, prediction: 3
label: 3, prediction: 3
label: 3, prediction: 3
label: 1, prediction: 1
label: 3, prediction: 2
label: 1, prediction: 1
label: 1, prediction: 1
label: 1, prediction: 1
label: 2, prediction: 3
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 1
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 3
label: 2, prediction: 2
label: 2, prediction: 3
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 2
label: 2, prediction: 2
Validation accuracy is 0.780000