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【实验目的】
掌握Pthon/R语言进行大数据分析，包括分类任务和聚类任务。掌握kNN、决策树、SVM分类器、kmeans聚类算法的Python或R语言编程方法。
【实验内容】
使用Python或R语言完成大数据分析任务
1、使用kNN、决策树、SVM模型，对iris数据集进行分类
2、使用kmeans聚类算法对iris数据集进行聚类
- Python导入iris数据集方法
from sklearn.datasets import load_iris
iris=load_iris()
attributes=iris.data #获取属性数据
#获取类别数据，这里注意的是已经经过了处理，target里0、1、2分别代表三种类别
target=iris.target
labels=iris.feature_names#获取类别名字
print(labels)
print(attributes)
print(target)
- R语言导入iris数据集
data("iris")
summary(iris)
我选择使用Python语言完成实验。
1.kNN算法
import randomimport numpy as np
import operator
from sklearn.datasets import load_irisiris = load_iris()
attributes=iris.data
target=iris.target
labels = iris.feature_namesf1 = attributes.tolist()
f2 = target.tolist()
i=0
dataset=[]
while i < len(attributes):f1[i].append(f2[i])dataset.append(f1[i])i = i+1
library = []
n = int(len(f1)*0.3)
samples = random.sample(f1, n)
for x in dataset:if x not in samples:library.append(x);def createDataSet():#四组二维特征group = np.array(library)#四组特征的标签labels = f2return group, labelsdef classify0(inX, dataSet, labels, k):''':param inX: 测试样本(arr):param dataSet: 训练数据集(arr):param labels: 类别(list):param k:(int):return: 类别'''#计算距离dataSetSize = dataSet.shape[0] # 样本数量diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet #tile(inX{数组},(dataSetSize{倍数},1{竖向}))：将数组(inX)竖向(1)复制dataSetSize倍sqDiffMat = diffMat ** 2 #先求平方sqDistances = sqDiffMat.sum(axis=1) #再求平方和distances = sqDistances ** 0.5 #开根号,欧式距离sortedDistIndicies = distances.argsort() #距离从小到大排序的索引classCount = {}for i in range(k):voteIlabel = labels[sortedDistIndicies[i]] #用索引得到相应的类别classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1return max(classCount, key=lambda k: classCount[k]) # 返回频数最大的类别if __name__ == '__main__':#创建数据集group, labels = createDataSet()#测试集i=0;while i<len(samples):test_class = classify0(samples[i], group, labels, 3)print("测试用例：",samples[i],"所属类别: ",test_class)i+=1#打印分类结果
2.决策树算法
# tree.py
import copy
import random
from sklearn.datasets import load_iris# 找到出现次数最多的分类名称
import operator
# 计算给定数据集的熵
from math import logdef calShannonEnt(dataSet):numEntries = len(dataSet)labelCounts = {}# 为所有可能的分类创建字典for featVec in dataSet:currentLabel = featVec[-1]if currentLabel not in labelCounts.keys():labelCounts[currentLabel] = 0labelCounts[currentLabel] += 1shannonEnt = 0.0for key in labelCounts:# 计算熵，先求pprob = float(labelCounts[key]) / numEntriesshannonEnt -= prob * log(prob, 2)return shannonEntiris = load_iris()
attributes=iris.data
target=iris.target
labels = iris.feature_names
labels1=copy.deepcopy(labels)f1 = attributes.tolist()
f2 = target.tolist()
i=0
dataset=[]
while i < len(attributes):f1[i].append(f2[i])dataset.append(f1[i])i = i+1library = []
n = int(len(f1)*0.3)
samples = random.sample(dataset,n)
for x in dataset:if x not in samples:library.append(x)while i<len(samples):del samples[i][4]i+=1# 构造数据集
def creatDataSet():dataSet1 = librarylabels1 = labelsreturn dataSet1, labels1# 根据属性及其属性值划分数据集
def splitDataSet(dataSet, axis, value):'''dataSet : 待划分的数据集axis : 属性及特征value : 属性值及特征的hasattr值'''retDataSet = []for featVet in dataSet:if featVet[axis] == value:reducedFeatVec = featVet[:axis]reducedFeatVec.extend(featVet[axis + 1:])retDataSet.append(reducedFeatVec)return retDataSet# 选择最好的数据集划分方式，及根绝信息增益选择划分属性
def chooseBestFeatureToSplit(dataSet):numFeatures = len(dataSet[0]) - 1baseEntropy = calShannonEnt(dataSet)bestInfoGain, bestFeature = 0, -1for i in range(numFeatures):featList = [example[i] for example in dataSet]uniqueVals = set(featList)newEntropy = 0.0# 计算每种划分方式的信息熵for value in uniqueVals:subDataSet = splitDataSet(dataSet, i, value)prob = len(subDataSet) / float(len(dataSet))newEntropy += prob * calShannonEnt(subDataSet)infoGain = baseEntropy - newEntropyif (infoGain > bestInfoGain):bestInfoGain = infoGainbestFeature = ireturn bestFeaturedef majorityCnt(classList):classCount = {}for vote in classList:if vote not in classCount.keys():classCount[vote] = 0classCount[vote] += 1sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)return sortedClassCount[0][0]# 创建树的函数
def creatTree(dataSet, labels):classList = [example[-1] for example in dataSet]# 类别完全相同停止划分if classList.count(classList[0]) == len(classList):return classList[0]if len(dataSet[0]) == 1:return majorityCnt(classList)bestFeat = chooseBestFeatureToSplit(dataSet)bestFeatLabel = labels[bestFeat]myTree = {bestFeatLabel: {}}del (labels[bestFeat])featValues = [example[bestFeat] for example in dataSet]uniqueVals = set(featValues)for value in uniqueVals:sublabels = labels[:]myTree[bestFeatLabel][value] = creatTree(splitDataSet(dataSet, bestFeat, value), sublabels)return myTreedef classify(inputTree,featLabels,testVec):global classLabelfirstStr = list(inputTree.keys())[0]secondDict = inputTree[firstStr]featIndex = featLabels.index(firstStr)for key in secondDict.keys():if testVec[featIndex] == key:if type(secondDict[key]).__name__=='dict':classLabel = classify(secondDict[key],featLabels,testVec)else: classLabel = secondDict[key]return classLabelif __name__ == '__main__':myData, labels = creatDataSet()print("数据集：{}\n 标签：{}".format(myData, labels))print("该数据集下的香农熵为:{}".format(calShannonEnt(myData)))#print("划分前的数据集：{}\n \n按照“离开水是否能生存”为划分属性，得到下一层待划分的结果为:\n{}--------{}".format(myData, splitDataSet(myData, 0, 0),#splitDataSet(myData, 0, 1)))chooseBestFeatureToSplit(myData)myTree = creatTree(myData, labels)i=0print("决策树:",myTree)while (i < len(samples)):f = classify(myTree, labels1, samples[i])print("测试用例：", samples[i], "测试结果： ", f)i = i + 1{'petal length (cm)': {1.7: 0, 1.4: 0, 1.6: 0, 1.3: 0, 1.5: 0, 1.1: 0, 1.2: 0, 1.0: 0, 1.9: 0, 4.7: 1,4.5: {'sepal length (cm)': {4.9: 2, 5.6: 1, 6.0: 1, 5.7: 1, 6.4: 1, 6.2: 1, 5.4: 1}},4.9: {'sepal width (cm)': {2.5: 1, 3.0: 2, 3.1: 1, 2.8: 2, 2.7: 2}}, 4.0: 1,5.0: {'sepal length (cm)': {6.3: 2, 5.7: 2, 6.7: 1, 6.0: 2}}, 6.0: 2, 3.5: 1, 3.0: 1, 4.6: 1, 4.4: 1, 4.1: 1,5.1: {'sepal length (cm)': {5.8: 2, 6.9: 2, 6.3: 2, 6.0: 1, 6.5: 2, 5.9: 2}}, 5.9: 2, 5.6: 2, 5.5: 2, 5.4: 2, 6.6: 2, 6.1: 2, 6.9: 2, 6.4: 2, 3.6: 1, 3.3: 1, 3.8: 1, 3.7: 1, 4.2: 1,4.8: {'sepal length (cm)': {6.0: 2, 5.9: 1, 6.8: 1, 6.2: 2}}, 4.3: 1, 5.8: 2, 5.3: 2, 5.7: 2, 5.2: 2, 6.3: 2, 6.7: 2, 3.9: 1}}
3.SVM算法
from sklearn.datasets import load_iris
from sklearn import svm
import numpy as np
from sklearn import model_selection
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import colorsiris = load_iris()
attributes = iris.data # 获取属性数据 X
# 获取类别数据，这里注意的是已经经过了处理，target里0、1、2分别代表三种类别
target = iris.target # Y
labels = iris.feature_names # 获取类别名字
print(labels)
print(attributes)x = attributes[:, 0:2]
y = target
x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, random_state=1, test_size=0.3)clf = svm.SVC(kernel='linear')
clf.fit(x_train, y_train)acc = clf.predict(x_train) == y_train.flat
print('Accuracy:%f' % (np.mean(acc)))# print("SVM-训练集的准确率：", clf.score(x_train, y_train))
# # y_hat = clf.predict(x_train)
#
# print("SVM-测试集的准确率：", clf.score(x_test, y_test))
# # y_hat = clf.predict(x_test)x1_min, x1_max = x[:, 0].min(), x[:, 0].max()
x2_min, x2_max = x[:, 1].min(), x[:, 1].max()
x1, x2 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]
grid_test = np.stack((x1.flat, x2.flat), axis=1)print("grid_test = \n", grid_test)
grid_hat = clf.predict(grid_test)
print("grid_hat = \n", grid_hat)
grid_hat = grid_hat.reshape(x1.shape)# mpl.rcParams['font.sans-serif'] = [u'SimHei']
# mpl.rcParams['axes.unicode_minus'] = Falsecm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
# cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])plt.pcolormesh(x1, x2, grid_hat, cmap=cm_light)
plt.plot(x[:, 0], x[:, 1], 'o', alpha=0.5, color='blue', markeredgecolor='k')
plt.scatter(x_test[:, 0], x_test[:, 1], s=120, facecolors='none', zorder=10)
plt.xlabel(labels[0])
plt.ylabel(labels[1])
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.title("SVM")
plt.show()
4.Kmeans算法
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn.cluster import KMeansiris = load_iris()
attributes = iris.data # 获取属性数据 X
# 获取类别数据，这里注意的是已经经过了处理，target里0、1、2分别代表三种类别
target = iris.target # y
labels = iris.feature_names # 获取类别名字
print(labels)
print(attributes.shape)
print(attributes)
print(target)plt.style.use('seaborn') # 样式美化x = attributes[:, 0:2]
y = target
plt.scatter(attributes[:, 0], attributes[:, 1], s=50, marker='o', label='see')
plt.xlabel(labels[0])
plt.ylabel(labels[1])
plt.show()est = KMeans(n_clusters=3) # 选择聚为 x 类
est.fit(attributes)
y_kmeans = est.predict(attributes) # 预测类别，输出为含0、1、2、3数字的数组
x0 = attributes[y_kmeans == 0]
x1 = attributes[y_kmeans == 1]
x2 = attributes[y_kmeans == 2]# 为预测结果上色并可视化
x1_min, x1_max = x[:, 0].min(), x[:, 0].max()
x2_min, x2_max = x[:, 1].min(), x[:, 1].max()plt.scatter(x0[:, 0], x0[:, 1], s=50, c="red", marker='o', label='label0', cmap='viridis')
plt.scatter(x1[:, 0], x1[:, 1], s=50, c="green", marker='*', label='label1', cmap='viridis')
plt.scatter(x2[:, 0], x2[:, 1], s=50, c="blue", marker='+', label='label2', cmap='viridis')
plt.xlabel(labels[0])
plt.ylabel(labels[1])
centers = est.cluster_centers_ # 找出中心
plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5) # 绘制中心点
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.title("kmeans")
plt.legend(loc=2)
plt.show()
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