机器学习之K-means聚类分析NBA球员案例

import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt

nba = pd.read_csv("D:\\test\\machineLearning\\nba_2013.csv")
print nba.head(3)

player pos age bref_team_id g gs mp fg fga fg. \ 0 Quincy Acy SF 23 TOT 63 0 847 66 141 0.468 1 Steven Adams C 20 OKC 81 20 1197 93 185 0.503 2 Jeff Adrien PF 27 TOT 53 12 961 143 275 0.520 … drb trb ast stl blk tov pf pts season season_end 0 … 144 216 28 23 26 30 122 171 2013-2014 2013 1 … 190 332 43 40 57 71 203 265 2013-2014 2013 2 … 204 306 38 24 36 39 108 362 2013-2014 2013 [3 rows x 31 columns]

#取出后卫的数据
point_guards = nba[nba["pos"]=="PG"]
#pts代表是总得分，g代表是打了多少场球
point_guards["ppg"] = point_guards['pts']/point_guards['g']
point_guards[['pts','g','ppg']].head(5)

C:\Users\qiujiahao\Anaconda2\lib\site-packages\ipykernel\__main__.py:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

	pts	g	ppg
24	930	71	13.098592
29	150	20	7.500000
30	660	79	8.354430
38	666	72	9.250000
50	378	55	6.872727

#tov代表失误的次数,ast代表助攻的次数
point_guards=point_guards[point_guards['tov']!=0]
point_guards['atr'] = point_guards['ast']/point_guards['tov']

plt.scatter(point_guards['ppg'],point_guards['atr'],c='y')
plt.title("Point Guards")
plt.xlabel("Points Per Game",fontsize=13)
plt.ylabel("Assist Turnover Ratio",fontsize=13)
plt.show()

#使用K-means聚类时，
#第一步：当k=5时，他会随机选取5个点作为中心点，然后计算所有点到这5个点的距离
#第二步：将每一个点划到不同的簇
#第三步：将每一个簇中的点计算横纵坐标的均值，计算出新的中心点(可以是不是实际的点)
#第四步：重新计算每一个点到中心点的距离，重新划分属于不同的簇
#第五步：不断的更新中心点，不断的重新划分簇，直到再怎么更新中心点，簇里的元素都不再发生变化了
num_clusters=5
#随机的选出5个点
random_initial_points=np.random.choice(point_guards.index,size=num_clusters)
#选出中心点
centroids = point_guards.loc[random_initial_points]

#红色的即为初始化后的中心点
plt.scatter(point_guards['ppg'],point_guards['atr'],c='yellow')
plt.scatter(centroids['ppg'],centroids['atr'],c='red')
plt.title("Centroids")
plt.xlabel('Points Per Game',fontsize=13)
plt.ylabel('Assist Turnover Ratio',fontsize=13)
plt.show()

#将中心点的信息保存再字典里
def centroids_to_dict(centroids):
    dictionary = dict()
    counter = 0
#iterrows代表逐行遍历
for index,row in centroids.iterrows():
        coordinates = [row['ppg'],row['atr']]
        dictionary[counter]=coordinates
        counter += 1

return dictionary 

centroids_dict = centroids_to_dict(centroids)
print centroids_dict

{0: [7.5, 1.6842105263157894], 1: [17.936708860759495, 3.020618556701031], 2: [9.5, 2.43609022556391], 3: [1.125, 2.5555555555555554], 4: [1.0, 3.4]}

#计算距离的公式
def calculate_distance(centroid,player_values):
    root_distance=0

for x in range(0,len(centroid)):
        difference = centroid[x]-player_values[x]
        squared_difference = difference**2
        root_distance += squared_difference

    euclid_distance = math.sqrt(root_distance)
return euclid_distance

q = [5,2]
p = [3,1]
print (calculate_distance(q,p))

2.2360679775

#对所有数据选择自己的中心点,进行分类
def assign_to_cluster(row):
    lowest_distance=-1
    closest_cluster=-1
    euclidean_distance=-1

#对每行的数据进行比较，得出这一行数据最近的中心点，属于这一簇
for cluster_id,centroid in centroids_dict.items():
        df_row = [row['ppg'],row['atr']]
        euclidean_distance = calculate_distance(centroid,df_row)
if lowest_distance == -1:
            lowest_distance = euclidean_distance
            closest_cluster = cluster_id
elif euclidean_distance < lowest_distance:
            lowest_distance = euclidean_distance
            closest_cluster = cluster_id
return closest_cluster

point_guards['cluster'] = point_guards.apply(lambda row:assign_to_cluster(row),axis=1)

#将结果显示出来
def visualize_clusters(df,num_clusters):
    colors = ['b','g','r','c','m','y','k']

for n in range(num_clusters):
        clustered_df = df[df['cluster'] == n]
        plt.scatter(clustered_df['ppg'],clustered_df['atr'],c=colors[n-1])
        plt.xlabel('Points Per Game',fontsize=13)
        plt.ylabel('Assist Turnover Ratio',fontsize=13)

    plt.show()

#将分类的数据显示出来 
visualize_clusters(point_guards,5)

#重新计算中心点
def recalculate_centroids(df):
    new_centroids_dict = dict()
#0..1..2..3..4
for cluster_id in range(0,num_clusters):
#取出每一中类型的所有点
        class_all=df[df['cluster']==cluster_id]
        mean_ppg = class_all['ppg'].mean()
        mean_atr = class_all['atr'].mean()
        new_centroids_dict[cluster_id]= [mean_ppg,mean_atr]
return new_centroids_dict

#重新计算中心点
centroids_dict = recalculate_centroids(point_guards)
#对所有点进行重新划分中心点
point_guards["cluster"] = point_guards.apply(lambda row:assign_to_cluster(row),axis=1)
#将分类的数据显示出来 
visualize_clusters(point_guards,5)

#以上代码是自己实现,以下是利用库函数实现
from sklearn.cluster import KMeans
#调用sklearn的库函数，只需指定需要分类的个数
kmeans = KMeans(n_clusters=num_clusters)
kmeans.fit(point_guards[['ppg','atr']])
point_guards['cluster'] = kmeans.labels_
visualize_clusters(point_guards,num_clusters)