kaggle之旧金山犯罪
kaggle地址
github地址
特点：
1. 离散特征
2. 离散特征二值化处理
import pandas as pd
import numpy as np
# 载入数据
train = pd.read_csv('~/kaggle/dataset/San_Francisco_Crime_Classification/train.csv', parse_dates = ['Dates'])
test = pd.read_csv('~/kaggle/dataset/San_Francisco_Crime_Classification/test.csv', parse_dates = ['Dates'])print train.head(10)print test.head(10)from sklearn import preprocessing
label = preprocessing.LabelEncoder()
label.fit([1, 2, 2, 6])
print label.transform([1, 1, 2, 6])crime = label.fit_transform(train.Category)pd.get_dummies(pd.Series(list('abca')))| a | b | c | |
|---|---|---|---|
| 0 | 1.0 | 0.0 | 0.0 |
| 1 | 0.0 | 1.0 | 0.0 |
| 2 | 0.0 | 0.0 | 1.0 |
| 3 | 1.0 | 0.0 | 0.0 |
接下来对Dates、DayOfWeek、PdDistrict三个特征进行二值化处理：
days = pd.get_dummies(train.DayOfWeek)
district = pd.get_dummies(train.PdDistrict)
hour = pd.get_dummies(train.Dates.dt.hour)接下来重新组合训练集，并把类别附加上：
train_data = pd.concat([days, district, hour], axis=1)
train_data['crime'] = crime针对测试集做同样的处理：
days = pd.get_dummies(test.DayOfWeek)
district = pd.get_dummies(test.PdDistrict)
hour = pd.get_dummies(test.Dates.dt.hour)
test_data = pd.concat([days, district, hour], axis=1)预览新的训练集和测试集：
print train_data.head(10)
print test_data.head(10) Friday Monday Saturday Sunday Thursday Tuesday Wednesday BAYVIEW \
0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
1 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
4 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
5 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
6 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
7 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0
8 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
9 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
CENTRAL INGLESIDE ... 15 16 17 18 19 20 21 22 23 \
0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
1 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
2 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
3 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
4 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
5 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
6 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
7 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
8 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
9 1.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
crime
0 37
1 21
2 21
3 16
4 16
5 16
6 36
7 36
8 16
9 16
[10 rows x 42 columns]
Friday Monday Saturday Sunday Thursday Tuesday Wednesday BAYVIEW \
0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0
1 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0
2 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
5 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
6 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
7 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
8 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
9 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
CENTRAL INGLESIDE ... 14 15 16 17 18 19 20 21 22 23
0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
1 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
2 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
3 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
4 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
5 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
6 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
7 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
8 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
9 1.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
[10 rows x 41 columns]
分割训练集和验证集(70%训练,30%验证)准备建模：
from sklearn.cross_validation import train_test_split
training, validation = train_test_split(train_data, train_size=0.6)贝叶斯训练
from sklearn.metrics import log_loss
from sklearn.naive_bayes import BernoulliNB
model = BernoulliNB()
feature_list = training.columns.tolist()
feature_list = feature_list[:len(feature_list) - 1]
print '选取的特征列：', feature_list
model.fit(training[feature_list], training['crime'])
predicted = np.array(model.predict_proba(validation[feature_list]))
print "朴素贝叶斯log损失为 %f" % (log_loss(validation['crime'], predicted))选取的特征列： ['Friday', 'Monday', 'Saturday', 'Sunday', 'Thursday', 'Tuesday', 'Wednesday', 'BAYVIEW', 'CENTRAL', 'INGLESIDE', 'MISSION', 'NORTHERN', 'PARK', 'RICHMOND', 'SOUTHERN', 'TARAVAL', 'TENDERLOIN', 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
朴素贝叶斯log损失为 2.581561
逻辑回归
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(C=0.1)
model.fit(training[feature_list], training['crime'])
predicted = np.array(model.predict_proba(validation[feature_list]))
print "逻辑回归log损失为 %f" %(log_loss(validation['crime'], predicted))逻辑回归log损失为 2.580102
在测试集上运行：
test_predicted = np.array(model.predict_proba(test_data[feature_list]))保存结果：
col_names = np.sort(train['Category'].unique())
print col_names
result = pd.DataFrame(data=test_predicted, columns=col_names)
result['Id'] = test['Id'].astype(int)
result.to_csv('output.csv', index=False)['ARSON' 'ASSAULT' 'BAD CHECKS' 'BRIBERY' 'BURGLARY' 'DISORDERLY CONDUCT'
'DRIVING UNDER THE INFLUENCE' 'DRUG/NARCOTIC' 'DRUNKENNESS' 'EMBEZZLEMENT'
'EXTORTION' 'FAMILY OFFENSES' 'FORGERY/COUNTERFEITING' 'FRAUD' 'GAMBLING'
'KIDNAPPING' 'LARCENY/THEFT' 'LIQUOR LAWS' 'LOITERING' 'MISSING PERSON'
'NON-CRIMINAL' 'OTHER OFFENSES' 'PORNOGRAPHY/OBSCENE MAT' 'PROSTITUTION'
'RECOVERED VEHICLE' 'ROBBERY' 'RUNAWAY' 'SECONDARY CODES'
'SEX OFFENSES FORCIBLE' 'SEX OFFENSES NON FORCIBLE' 'STOLEN PROPERTY'
'SUICIDE' 'SUSPICIOUS OCC' 'TREA' 'TRESPASS' 'VANDALISM' 'VEHICLE THEFT'
'WARRANTS' 'WEAPON LAWS']