年薪30W进阶指南-【聚类分析】王者荣耀英雄数据

# -*- coding: utf-8 -*-
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.mixture import GaussianMixture
from sklearn.preprocessing import StandardScaler

In [2]:

# 数据加载，避免中文乱码问题
data_ori = pd.read_csv('/home/kesci/input/hero9310/heros.csv', encoding = 'gb18030')
features = [u'最大生命',u'生命成长',u'初始生命',u'最大法力', u'法力成长',u'初始法力',u'最高物攻',u'物攻成长',u'初始物攻',u'最大物防',u'物防成长',u'初始物防', u'最大每5秒回血', u'每5秒回血成长', u'初始每5秒回血', u'最大每5秒回蓝', u'每5秒回蓝成长', u'初始每5秒回蓝', u'最大攻速', u'攻击范围']
data = data_ori[features]

In [3]:

# 对英雄属性之间的关系进行可视化分析
# 用热力图呈现 features_mean 字段之间的相关性
corr = data[features].corr()
plt.figure(figsize=(14,14))
# annot=True 显示每个方格的数据
sns.heatmap(corr, annot=True)
plt.show()

年薪30W进阶指南-【聚类分析】王者荣耀英雄数据

In [4]:

# 相关性大的属性保留一个，因此可以对属性进行降维
features_remain = [u'最大生命', u'初始生命', u'最大法力', u'最高物攻', u'初始物攻', u'最大物防', u'初始物防', u'最大每5秒回血', u'最大每5秒回蓝', u'初始每5秒回蓝', u'最大攻速', u'攻击范围']
data = data_ori[features_remain]
data[u'最大攻速'] = data[u'最大攻速'].apply(lambda x: float(x.strip('%'))/100)
data[u'攻击范围']=data[u'攻击范围'].map({'远程':1,'近战':0})
# 采用 Z-Score 规范化数据，保证每个特征维度的数据均值为 0，方差为 1
ss = StandardScaler()
data = ss.fit_transform(data)
# 构造 GMM 聚类
gmm = GaussianMixture(n_components=30, covariance_type='full')
gmm.fit(data)
# 训练数据
prediction = gmm.predict(data)
print(prediction)

[ 2 15  7 26 16 16 13  7  9 15  1 15 26  6 29  1 28  9  3 10 19 12 10 12
 12 12 10  4 11  0  6  0  0 25  0  0 11  5 24 18 18 11 25  0 25 18 15 24
 15 25 27  8  3 23 23  2 22 15 20 16 22  3 17  2 14  8  8 21  4]

/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.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
  after removing the cwd from sys.path.
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:5: 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
  """

In [5]:

# 将分组结果输出到 CSV 文件中
#data_ori.insert(0, '分组', prediction)
#data_ori.to_csv('./hero_out.csv', index=False, sep=',')
#输出得分
from sklearn.metrics import calinski_harabaz_score
print(calinski_harabaz_score(data, prediction))

21.5401587794009

/opt/conda/lib/python3.6/site-packages/sklearn/utils/deprecation.py:85: DeprecationWarning: Function calinski_harabaz_score is deprecated; Function 'calinski_harabaz_score' has been renamed to 'calinski_harabasz_score' and will be removed in version 0.23.
  warnings.warn(msg, category=DeprecationWarning)

年薪30W进阶指南-【聚类分析】王者荣耀英雄数据

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