版权声明:本套技术专栏是做者(秦凯新)平时工做的总结和升华,经过从真实商业环境抽取案例进行总结和分享,并给出商业应用的调优建议和集群环境容量规划等内容,请持续关注本套博客。QQ邮箱地址:1120746959@qq.com,若有任何学术交流,可随时联系。app
头几行展现dom
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import KFold
# import data
filename= "C:\\ML\\MLData\\data.csv"
raw = pd.read_csv(filename)
print (raw.shape)
raw.head()
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尾几行展现 ide
去除空值测试
matplot列属性绘制分布编码
#plt.subplot(211) first is raw second Column
# 透明程度 (颜色深度和密度)
alpha = 0.02
# 指定图大概占用的区域
plt.figure(figsize=(10,10))
# loc_x and loc_y(一行两列第一个位置)
plt.subplot(121)
# scatter 散点图
plt.scatter(kobe.loc_x, kobe.loc_y, color='R', alpha=alpha)
plt.title('loc_x and loc_y')
# lat and lon(一行两列第二个位置)
plt.subplot(122)
plt.scatter(kobe.lon, kobe.lat, color='B', alpha=alpha)
plt.title('lat and lon')
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角度和极坐标预处理spa
raw['dist'] = np.sqrt(raw['loc_x']**2 + raw['loc_y']**2)
loc_x_zero = raw['loc_x'] == 0
#print (loc_x_zero)
raw['angle'] = np.array([0]*len(raw))
raw['angle'][~loc_x_zero] = np.arctan(raw['loc_y'][~loc_x_zero] / raw['loc_x'][~loc_x_zero])
raw['angle'][loc_x_zero] = np.pi / 2
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时间处理3d
raw['remaining_time'] = raw['minutes_remaining'] * 60 + raw['seconds_remaining']
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属性惟一值及分组统计打印出来rest
投篮方式
print(kobe.action_type.unique())
print(kobe.combined_shot_type.unique())
print(kobe.shot_type.unique())
分组统计
print(kobe.shot_type.value_counts())
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按列进行特殊符号处理code
kobe['season'].unique()
array(['2000-01', '2001-02', '2002-03', '2003-04', '2004-05', '2005-06',
'2006-07', '2007-08', '2008-09', '2009-10', '2010-11', '2011-12',
'2012-13', '2013-14', '2014-15', '2015-16', '1996-97', '1997-98',
'1998-99', '1999-00'], dtype=object)
raw['season'] = raw['season'].apply(lambda x: int(x.split('-')[1]) )
raw['season'].unique()
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 97,
98, 99, 0], dtype=int64)
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pd的DataFrame使用技巧(matchup两队对决,opponent对手是谁)orm
pd.DataFrame({'matchup':kobe.matchup, 'opponent':kobe.opponent})
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版权声明:本套技术专栏是做者(秦凯新)平时工做的总结和升华,经过从真实商业环境抽取案例进行总结和分享,并给出商业应用的调优建议和集群环境容量规划等内容,请持续关注本套博客。QQ邮箱地址:1120746959@qq.com,若有任何学术交流,可随时联系。
属性相关性展现是不是线性关系(位置和投篮位置)
plt.figure(figsize=(5,5))
plt.scatter(raw.dist, raw.shot_distance, color='blue')
plt.title('dist and shot_distance')
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pd的groupby对kebe的投射位置进行分组
gs = kobe.groupby('shot_zone_area')
print (kobe['shot_zone_area'].value_counts())
print (len(gs))
Center(C) 11289
Right Side Center(RC) 3981
Right Side(R) 3859
Left Side Center(LC) 3364
Left Side(L) 3132
Back Court(BC) 72
Name: shot_zone_area, dtype: int64
6
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区域划分拉链展现
import matplotlib.cm as cm
plt.figure(figsize=(20,10))
def scatter_plot_by_category(feat):
alpha = 0.1
gs = kobe.groupby(feat)
cs = cm.rainbow(np.linspace(0, 1, len(gs)))
for g, c in zip(gs, cs):
plt.scatter(g[1].loc_x, g[1].loc_y, color=c, alpha=alpha)
# shot_zone_area
plt.subplot(131)
scatter_plot_by_category('shot_zone_area')
plt.title('shot_zone_area')
# shot_zone_basic
plt.subplot(132)
scatter_plot_by_category('shot_zone_basic')
plt.title('shot_zone_basic')
# shot_zone_range
plt.subplot(133)
scatter_plot_by_category('shot_zone_range')
plt.title('shot_zone_range')
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去除某一列
drops = ['shot_id', 'team_id', 'team_name', 'shot_zone_area', 'shot_zone_range', 'shot_zone_basic', \
'matchup', 'lon', 'lat', 'seconds_remaining', 'minutes_remaining', \
'shot_distance', 'loc_x', 'loc_y', 'game_event_id', 'game_id', 'game_date']
for drop in drops:
raw = raw.drop(drop, 1)
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独热编码(one-hot编码)(一列变多列(0000000)prefix指定添加列前缀)
print (raw['combined_shot_type'].value_counts())
pd.get_dummies(raw['combined_shot_type'], prefix='combined_shot_type')[0:2]
Jump Shot 23485
Layup 5448
Dunk 1286
Tip Shot 184
Hook Shot 153
Bank Shot 141
Name: combined_shot_type, dtype: int64
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独热编码以后,拼接成1列后,删除对应列。
categorical_vars = ['action_type', 'combined_shot_type', 'shot_type', 'opponent', 'period', 'season']
for var in categorical_vars:
raw = pd.concat([raw, pd.get_dummies(raw[var], prefix=var)], 1)
raw = raw.drop(var, 1)
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版权声明:本套技术专栏是做者(秦凯新)平时工做的总结和升华,经过从真实商业环境抽取案例进行总结和分享,并给出商业应用的调优建议和集群环境容量规划等内容,请持续关注本套博客。QQ邮箱地址:1120746959@qq.com,若有任何学术交流,可随时联系。
1 测试集和训练集准备
train_kobe = raw[pd.notnull(raw['shot_made_flag'])]
train_kobe = train_kobe.drop('shot_made_flag', 1)
train_label = train_kobe['shot_made_flag']
test_kobe = raw[pd.isnull(raw['shot_made_flag'])]
test_kobe = test_kobe.drop('shot_made_flag', 1)
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2 随机森林分类
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import confusion_matrix,log_loss
import time
# find the best n_estimators for RandomForestClassifier
print('Finding best n_estimators for RandomForestClassifier...')
min_score = 100000
best_n = 0
scores_n = []
range_n = np.logspace(0,2,num=3).astype(int)
for n in range_n:
print("the number of trees : {0}".format(n))
t1 = time.time()
rfc_score = 0.
rfc = RandomForestClassifier(n_estimators=n)
for train_k, test_k in KFold(len(train_kobe), n_folds=10, shuffle=True):
rfc.fit(train_kobe.iloc[train_k], train_label.iloc[train_k])
#rfc_score += rfc.score(train.iloc[test_k], train_y.iloc[test_k])/10
pred = rfc.predict(train_kobe.iloc[test_k])
rfc_score += log_loss(train_label.iloc[test_k], pred) / 10
scores_n.append(rfc_score)
if rfc_score < min_score:
min_score = rfc_score
best_n = n
t2 = time.time()
print('Done processing {0} trees ({1:.3f}sec)'.format(n, t2-t1))
print(best_n, min_score)
# find best max_depth for RandomForestClassifier
print('Finding best max_depth for RandomForestClassifier...')
min_score = 100000
best_m = 0
scores_m = []
range_m = np.logspace(0,2,num=3).astype(int)
for m in range_m:
print("the max depth : {0}".format(m))
t1 = time.time()
rfc_score = 0.
rfc = RandomForestClassifier(max_depth=m, n_estimators=best_n)
for train_k, test_k in KFold(len(train_kobe), n_folds=10, shuffle=True):
rfc.fit(train_kobe.iloc[train_k], train_label.iloc[train_k])
#rfc_score += rfc.score(train.iloc[test_k], train_y.iloc[test_k])/10
pred = rfc.predict(train_kobe.iloc[test_k])
rfc_score += log_loss(train_label.iloc[test_k], pred) / 10
scores_m.append(rfc_score)
if rfc_score < min_score:
min_score = rfc_score
best_m = m
t2 = time.time()
print('Done processing {0} trees ({1:.3f}sec)'.format(m, t2-t1))
print(best_m, min_score)
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plt.figure(figsize=(10,5))
plt.subplot(121)
plt.plot(range_n, scores_n)
plt.ylabel('score')
plt.xlabel('number of trees')
plt.subplot(122)
plt.plot(range_m, scores_m)
plt.ylabel('score')
plt.xlabel('max depth')
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model = RandomForestClassifier(n_estimators=best_n, max_depth=best_m)
model.fit(train_kobe, train_label)
# 474241623
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综上所述, numpy与pandas与matplotlit与sklearn四剑客组成了强大的数据分析预处理支持。
秦凯新 于深圳 201812081439