KNN（近邻）算法
KNN算法可能是标准数据挖掘算法中最为直观的一种。为了对新个体进行分类，它查找训练集，找到与新个体最相似的那些个体，看看这些个体大多属于哪个类别，就把新个体分到哪个类别
KNN算法几乎可以对任何数据集进行分类，但是，要计算数据集中每两个个体之间的距离，计算量很大
数据集选取
本次数据集选用电离数据，该数据集每行有35个值，前34个为天线采集的数据，最后一个值不是“g”，就是“b”，表示数据的好与坏，即是否提供了有价值的信息
我们要做的就是使用KNN算法，自动判断这些数据的好坏
以下为部分数据视图
1,0,0.47337,0.19527,0.06213,-0.18343,0.62316,0.01006,0.45562,-0.04438,0.56509,0.01775,0.44675,0.27515,0.71598,-0.03846,0.55621,0.12426,0.41420,0.11538,0.52767,0.02842,0.51183,-0.10651,0.47929,-0.02367,0.46514,0.03259,0.53550,0.25148,0.31953,-0.14497,0.34615,-0.00296,g
1,0,0.59887,0.14689,0.69868,-0.13936,0.85122,-0.13936,0.80979,0.02448,0.50471,0.02825,0.67420,-0.04520,0.80791,-0.13748,0.51412,-0.24482,0.81544,-0.14313,0.70245,-0.00377,0.33333,0.06215,0.56121,-0.33145,0.61444,-0.16837,0.52731,-0.02072,0.53861,-0.31262,0.67420,-0.22034,g
1,0,0.84713,-0.03397,0.86412,-0.08493,0.81953,0,0.73673,-0.07643,0.71975,-0.13588,0.74947,-0.11677,0.77495,-0.18684,0.78132,-0.21231,0.61996,-0.10191,0.79193,-0.15711,0.89384,-0.03397,0.84926,-0.26115,0.74115,-0.23312,0.66242,-0.22293,0.72611,-0.37792,0.65817,-0.24841,g
1,0,0.87772,-0.08152,0.83424,0.07337,0.84783,0.04076,0.77174,-0.02174,0.77174,-0.05707,0.82337,-0.10598,0.67935,-0.00543,0.88043,-0.20924,0.83424,0.03261,0.86413,-0.05978,0.97283,-0.27989,0.85054,-0.18750,0.83705,-0.10211,0.85870,-0.03261,0.78533,-0.10870,0.79076,-0.00543,g
1,0,0.74704,-0.13241,0.53755,0.16996,0.72727,0.09486,0.69565,-0.11067,0.66798,-0.23518,0.87945,-0.19170,0.73715,0.04150,0.63043,-0.00395,0.63636,-0.11858,0.79249,-0.25296,0.66403,-0.28656,0.67194,-0.10474,0.61847,-0.12041,0.60079,-0.20949,0.37549,0.06917,0.61067,-0.01383,g
1,0,0.46785,0.11308,0.58980,0.00665,0.55432,0.06874,0.47894,-0.13969,0.52993,0.01330,0.63858,-0.16186,0.67849,-0.03326,0.54545,-0.13525,0.52993,-0.04656,0.47894,-0.19512,0.50776,-0.13525,0.41463,-0.20177,0.53930,-0.11455,0.59867,-0.02882,0.53659,-0.11752,0.56319,-0.04435,g
1,0,0.88116,0.27475,0.72125,0.42881,0.61559,0.63662,0.38825,0.90502,0.09831,0.96128,-0.20097,0.89200,-0.35737,0.77500,-0.65114,0.62210,-0.78768,0.45535,-0.81856,0.19095,-0.83943,-0.08079,-0.78334,-0.26356,-0.67557,-0.45511,-0.54732,-0.60858,-0.30512,-0.66700,-0.19312,-0.75597,g
1,0,0.93147,0.29282,0.79917,0.55756,0.59952,0.71596,0.26203,0.92651,0.04636,0.96748,-0.23237,0.95130,-0.55926,0.81018,-0.73329,0.62385,-0.90995,0.36200,-0.92254,0.06040,-0.93618,-0.19838,-0.83192,-0.46906,-0.65165,-0.69556,-0.41223,-0.85725,-0.13590,-0.93953,0.10007,-0.94823,g
1,0,0.88241,0.30634,0.73232,0.57816,0.34109,0.58527,0.05717,1,-0.09238,0.92118,-0.62403,0.71996,-0.69767,0.32558,-0.81422,0.41195,-1,-0.00775,-0.78973,-0.41085,-0.76901,-0.45478,-0.57242,-0.67605,-0.31610,-0.81876,-0.02979,-0.86841,0.25392,-0.82127,0.00194,-0.81686,g
1,0,0.83479,0.28993,0.69256,0.47702,0.49234,0.68381,0.21991,0.86761,-0.08096,0.85011,-0.35558,0.77681,-0.52735,0.58425,-0.70350,0.31291,-0.75821,0.03939,-0.71225,-0.15317,-0.58315,-0.39168,-0.37199,-0.52954,-0.16950,-0.60863,0.08425,-0.61488,0.25164,-0.48468,0.40591,-0.35339,g
1,0,0.92870,0.33164,0.76168,0.62349,0.49305,0.84266,0.21592,0.95193,-0.13956,0.96167,-0.47202,0.83590,-0.70747,0.65490,-0.87474,0.36750,-0.91814,0.05595,-0.89824,-0.26173,-0.73969,-0.54069,-0.50757,-0.74735,-0.22323,-0.86122,0.07810,-0.87159,0.36021,-0.78057,0.59407,-0.60270,g
运行算法
import numpy as np
import csv
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
# 新建特征数据，默认为float类型
X = np.zeros((351, 34), dtype="float")
# 新建类别数据
y = np.zeros((351,), dtype="bool")
# 读取数据集并且赋值
with open("ionosphere.data", "r") as input_file:
reader = csv.reader(input_file)
for i, row in enumerate(reader):
data = [float(datum) for datum in row[:-1]]
X[i] = data
y[i] = row[-1] == 'g'
# 对数据进行分割，创建训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=14)
# 建立KNN模型
estimator = KNeighborsClassifier()
# 使用训练数据进行训练
estimator.fit(X_train, y_train)
# 用测试集测试算法，评估它在测试集上的表现
y_predicted = estimator.predict(X_test)
# 查看正确率
accuracy = np.mean(y_test == y_predicted) * 100
print("{:.1f}".format(accuracy))
运行结果如下：
86.4%
交叉检验
在上述代码中，我们把数据集随机切分为训练集和测试集，用训练集训练算法，在测试集上评估效果。但是由于是随机分配，且只测试一次，所以会存在偶然性。
交叉检验能解决上述一次性测试带来的问题。他的实现方式就是对数据集进行多次切分，且每次切分都要保证与上次切分的不一样。还要确保每条数据都只能用来测试一次算法描述如下：
- 将整个数据集分为几个部分
- 对每一部分执行以下操作
(1)、将其中一部分用作当前测试集
(2)、用剩余部分训练算法
(3)、在当前测试集上测试算法 - 记录每次得分及平均分
- 在上述过程中，每条数据只能在测试集出现一次，以减少（但不能完全规避）运气成分
sklearn库中提供了一个辅助函数 cross_val_score 实现了上述交叉检验步骤
from sklearn.model_selection import cross_val_score
# 进行交叉检验
scores = cross_val_score(estimator, X, y, scoring="accuracy")
# 求平均得分
average_accurancy = np.mean(scores) * 100
print("{:.1f}".format(average_accurancy))注：此段代码接上段代码，运行结果如下：
82.3%
结果为82.3%，说明训练集和测试集的切分对于算法的精确度有一定影响，接下来我们通过调整参数来提高算法的精确度
设置参数
几乎所有的数据挖掘算法都可以设置参数，这样做的好处是可以增强算法的泛化能力。参数的设置可直接影响算法的精确度，选取参数与数据集的特征有关。
KNN算法有多个参数，最重要的是选取多少个近邻作为预测依据，接下来通过设置不同的参数来进行多次试验,观察不同的参数值所带来的结果之间的差异。
# 保存不同参数下的平均分
avg_socre = []
# 参数从1-20
para = list(range(1, 21))
# 对每个参数进行交叉检验，将结果保存到列表
for o in para:
estimator = KNeighborsClassifier(n_neighbors=o)
score = cross_val_score(estimator, X, y, scoring="accuracy")
avg_socre.append(np.mean(score))
from matplotlib import pyplot as plt
# 可视化处理
plt.plot(para, avg_socre, "-o")
plt.show()结果如图：
从上图可以看出，随着参数的数值增大，算法的正确率却在不断下降
