贝叶斯分类之旧金山犯罪类型分类预测
学习七月算法朴素贝叶斯分类器中项目的一个例子，这也是一个Kaggle比赛的例子。通过训练来预测犯罪类型。
环境： win7 64位 python3.5
1、加载数据
该数据是旧金山12年的犯罪记录，数据文件是一个csv文件可以使用pandas来加载数据，数据内容摘录：
Dates,Category,Descript,DayOfWeek,PdDistrict,Resolution,Address,X,Y
2015-05-13 23:53:00,WARRANTS,WARRANT ARREST,Wednesday,NORTHERN,”ARREST, BOOKED”,OAK ST / LAGUNA ST,-122.425891675136,37.7745985956747
2015-05-13 23:53:00,OTHER OFFENSES,TRAFFIC VIOLATION ARREST,Wednesday,NORTHERN,”ARREST, BOOKED”,OAK ST / LAGUNA ST,-122.425891675136,37.7745985956747
2015-05-13 23:33:00,OTHER OFFENSES,TRAFFIC VIOLATION ARREST,Wednesday,NORTHERN,”ARREST, BOOKED”,VANNESS AV / GREENWICH ST,-122.42436302145,37.8004143219856
2015-05-13 23:30:00,LARCENY/THEFT,GRAND THEFT FROM LOCKED AUTO,Wednesday,NORTHERN,NONE,1500 Block of LOMBARD ST,-122.42699532676599,37.80087263276921
2015-05-13 23:30:00,LARCENY/THEFT,GRAND THEFT FROM LOCKED AUTO,Wednesday,PARK,NONE,100 Block of BRODERICK ST,-122.438737622757,37.771541172057795
2015-05-13 23:30:00,LARCENY/THEFT,GRAND THEFT FROM UNLOCKED AUTO,Wednesday,INGLESIDE,NONE,0 Block of TEDDY AV,-122.40325236121201,37.713430704116
从上面的摘录可以看出有一下特征
Dates:犯罪的日期
Category:犯罪类型
Descript:犯罪描述
DayOfWeek:星期几
PdDistrict:所属警区
Resolution:处理结果
Address:发生街区
X and Y:GPS坐标
import pandas as pd
import numpy as np
train = pd.read_csv("C:\\data\\SanFrancisco\\train.csv",parse_dates=['Dates'])
test = pd.read_csv("C:\\data\\SanFrancisco\\test.csv",parse_dates=['Dates'])train[0:6] Dates Category Descript \
0 2015-05-13 23:53:00 WARRANTS WARRANT ARREST
1 2015-05-13 23:53:00 OTHER OFFENSES TRAFFIC VIOLATION ARREST
2 2015-05-13 23:33:00 OTHER OFFENSES TRAFFIC VIOLATION ARREST
3 2015-05-13 23:30:00 LARCENY/THEFT GRAND THEFT FROM LOCKED AUTO
4 2015-05-13 23:30:00 LARCENY/THEFT GRAND THEFT FROM LOCKED AUTO
5 2015-05-13 23:30:00 LARCENY/THEFT GRAND THEFT FROM UNLOCKED AUTO
DayOfWeek PdDistrict Resolution Address \
0 Wednesday NORTHERN ARREST, BOOKED OAK ST / LAGUNA ST
1 Wednesday NORTHERN ARREST, BOOKED OAK ST / LAGUNA ST
2 Wednesday NORTHERN ARREST, BOOKED VANNESS AV / GREENWICH ST
3 Wednesday NORTHERN NONE 1500 Block of LOMBARD ST
4 Wednesday PARK NONE 100 Block of BRODERICK ST
5 Wednesday INGLESIDE NONE 0 Block of TEDDY AV
X Y
0 -122.425892 37.774599
1 -122.425892 37.774599
2 -122.424363 37.800414
3 -122.426995 37.800873
4 -122.438738 37.771541
5 -122.403252 37.713431
2、特征预处理
上述数据中类别和文本类型非常多，所以要进行特征处理。因为我们要预测的是犯罪类型，
所以要尽可能的将犯罪相关因素的特征量化。
日期Dates：前5条记录发现几乎犯罪时间都是23点以后，这也符合常理。
犯罪类型Category：这个target，是需要量化的。
罪状Descript：这个特征都是犯罪以后的事了，没什么意义。
星期几DayOfWeek：这个与时间Dates关系还是挺强的，毕竟周末或者节假日户外活动的人多的话，也很容易招贼。
所属警区PdDistrict和处理结果Resolution：这两个特征与犯罪动因也没什么太大关系。
发生街区位置Address：对美国街区有一定了解的话，就知道美国有一些街区比如是低收入、非法移民等聚居的街区治安不是太好，犯罪比例也相对比较高。
接下来将对日期、犯罪类型、星期几、街区等特征进行预处理。
使用pandas的get_dummies()可以直接拿到一个二值化的01向量
使用pandas的LabelEncoder可以对类别编号
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing
# pd.set_option('display.notebook_repr_html',False)
# pd.set_option('display.max_columns',None)
# pd.set_option('display.max_rows',150)
# pd.set_option('display.max_seq_items',None)
#用LabelEncoder对不同的犯罪类型编号
leCrime = preprocessing.LabelEncoder()
crime = leCrime.fit_transform(train.Category)
#因子化星期几，街区，小时等特征
days = pd.get_dummies(train.DayOfWeek)
district = pd.get_dummies(train.PdDistrict)
hour = train.Dates.dt.hour
hour = pd.get_dummies(hour)
#组合特征
trainData = pd.concat([hour, days, district], axis=1)
trainData['crime']=crime
#对于测试数据做同样的处理
days = pd.get_dummies(test.DayOfWeek)
district = pd.get_dummies(test.PdDistrict)
hour = test.Dates.dt.hour
hour = pd.get_dummies(hour)
testData = pd.concat([hour, days, district], axis=1)
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RICHMOND SOUTHERN TARAVAL TENDERLOIN crime
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[878049 rows x 42 columns]
我们可以快速地筛出一部分重要的特征，搭建一个baseline系统，再考虑步步优化。比如我们这里
简单一点，就只取星期几和街区作为分类器输入特征，我们用scikit-learn中的train_test_split
函数拿到训练集和交叉验证集，用朴素贝叶斯和逻辑回归都建立模型，对比一下它们的表现：
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing
from sklearn.metrics import log_loss
from sklearn.naive_bayes import BernoulliNB
from sklearn.linear_model import LogisticRegression
import time
# 只取星期几和街区作为分类器输入特征
features = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday', 'BAYVIEW', 'CENTRAL', 'INGLESIDE', 'MISSION',
'NORTHERN', 'PARK', 'RICHMOND', 'SOUTHERN', 'TARAVAL', 'TENDERLOIN']
# 分割训练集(3/5)和测试集(2/5)
training, validation = train_test_split(trainData, train_size=.60)
# 朴素贝叶斯建模，计算log_loss
model = BernoulliNB()
nbStart = time.time()
model.fit(training[features], training['crime'])
nbCostTime = time.time() - nbStart
predicted = np.array(model.predict_proba(validation[features]))
print("朴素贝叶斯建模耗时 %f 秒" %(nbCostTime))
print("朴素贝叶斯log损失为 %f " %(log_loss(validation['crime'],predicted)))
#逻辑回归建模，计算log_loss
model = LogisticRegression(C=.01)
lrStart = time.time()
model.fit(training[features],training['crime'])
lrCostTime = time.time() - lrStart
predicted = np.array(model.predict_proba(validation[features]))
print("逻辑回归建模耗时 %f 秒" %(lrCostTime))
print("逻辑回归log损失为 %f" %(log_loss(validation['crime'], predicted)))朴素贝叶斯建模耗时 0.477027 秒
朴素贝叶斯log损失为 2.614108 秒
逻辑回归建模耗时 58.954372 秒
逻辑回归log损失为 2.621150
我们可以看到目前的特征和参数设定下，朴素贝叶斯的log损失还低一些，另外我们可以明显看到，
朴素贝叶斯建模消耗的时间远小于逻辑回归建模时间。
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing
from sklearn.metrics import log_loss
from sklearn.naive_bayes import BernoulliNB
from sklearn.linear_model import LogisticRegression
import time
features = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday', 'BAYVIEW', 'CENTRAL', 'INGLESIDE', 'MISSION',
'NORTHERN', 'PARK', 'RICHMOND', 'SOUTHERN', 'TARAVAL', 'TENDERLOIN']
hourFea = [x for x in range(0,24)]
features = features + hourFea
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing
from sklearn.metrics import log_loss
from sklearn.naive_bayes import BernoulliNB
from sklearn.linear_model import LogisticRegression
import time
# 只取星期几和街区作为分类器输入特征
features = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday', 'BAYVIEW', 'CENTRAL', 'INGLESIDE', 'MISSION',
'NORTHERN', 'PARK', 'RICHMOND', 'SOUTHERN', 'TARAVAL', 'TENDERLOIN']
# 分割训练集(3/5)和测试集(2/5)
training, validation = train_test_split(trainData, train_size=.60)
# 朴素贝叶斯建模，计算log_loss
model = BernoulliNB()
nbStart = time.time()
model.fit(training[features], training['crime'])
nbCostTime = time.time() - nbStart
predicted = np.array(model.predict_proba(validation[features]))
print("朴素贝叶斯建模耗时 %f 秒" %(nbCostTime))
print("朴素贝叶斯log损失为 %f 秒" %(log_loss(validation['crime'],predicted)))
#逻辑回归建模，计算log_loss
model = LogisticRegression(C=.01)
lrStart = time.time()
model.fit(training[features],training['crime'])
lrCostTime = time.time() - lrStart
predicted = np.array(model.predict_proba(validation[features]))
print("逻辑回归建模耗时 %f 秒" %(lrCostTime))
print("逻辑回归log损失为 %f" %(log_loss(validation['crime'], predicted)))朴素贝叶斯建模耗时 0.478027 秒
朴素贝叶斯log损失为 2.613777 秒
逻辑回归建模耗时 58.734359 秒
逻辑回归log损失为 2.621033
可以看到在这三个类别特征下，朴素贝叶斯相对于逻辑回归，依旧有一定的优势(log损失更小),
同时训练时间很短，这意味着模型虽然简单，但是效果依旧强大。
参考文献：
http://blog.csdn.net/han_xiaoyang/article/details/50629608