Kaggle 入门 Crime

对犯罪案件进行分类
提交每个类别概率
xgboost成绩 2.35461

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feature：

Category用preprocessing.labelEncoder()转成int型有序变量
时间抽出年月日时分,分钟根据时间对称性abs(m-30)
地址判断是否包含’/’
XY进行标准化
区域和DOW变为哑变量

xgboost参数:

“booster”: “gbtree”,
“objective”: “multi:softprob”,
“num_class”: 39,
“max_delta_step”: 1,
“max_depth”: 6

import csvfrom datetime import datetimeimport numpyimport pandasfrom sklearn import preprocessingimport xgboostdef address(data):    if data.find('/') != -1:        return 1    else:        return 0def parse_time(data):    DD = datetime.strptime(data, "%Y-%m-%d %H:%M:%S")    minute = DD.minute    hour = DD.hour    day = DD.day    month = DD.month    year = DD.year    hour = abs(hour - 30)    return minute, hour, day, month, yeardataset = pandas.read_csv("data\\train.csv")testset = pandas.read_csv("data\\test.csv")# labellabelset = pandas.DataFrame(data=dataset['Category'], columns=['Category'])le = preprocessing.LabelEncoder()le.fit(labelset['Category'].unique().tolist())labelData = le.transform(labelset['Category'].values)cList = le.classes_.tolist()myId = testset['Id']# Datesdataset['minute'], dataset['hour'], dataset['day'], dataset['month'], dataset['year'] = zip(    *dataset['Dates'].apply(func=parse_time))testset['minute'], testset['hour'], testset['day'], testset['month'], testset['year'] = zip(    *testset['Dates'].apply(func=parse_time))# Addressdataset['Address'] = dataset['Address'].apply(func=address)testset['Address'] = testset['Address'].apply(func=address)# XYx = preprocessing.scale(dataset['X'])dataset = pandas.DataFrame(data=x, columns=['xx']).join(dataset)y = preprocessing.scale(dataset['Y'])dataset = pandas.DataFrame(data=y, columns=['yy']).join(dataset)x = preprocessing.scale(testset['X'])testset = pandas.DataFrame(data=x, columns=['xx']).join(testset)y = preprocessing.scale(testset['Y'])testset = pandas.DataFrame(data=y, columns=['yy']).join(testset)# PdDistrict DayOfWeekdataset = pandas.get_dummies(dataset['DayOfWeek']).join(dataset)dataset = pandas.get_dummies(dataset['PdDistrict']).join(dataset)testset = pandas.get_dummies(testset['DayOfWeek']).join(testset)testset = pandas.get_dummies(testset['PdDistrict']).join(testset)dataset.drop(['Dates', 'Descript', 'Resolution', 'Category',              'X', 'Y', 'DayOfWeek', 'PdDistrict'], inplace=True, axis=1)testset.drop(['Dates', 'X', 'Y', 'DayOfWeek', 'PdDistrict', 'Id'], inplace=True, axis=1)trainData = dataset.iloc[:, :].valuestestData = testset.iloc[:, :].valuesoffset = 600000xgtrain = xgboost.DMatrix(trainData[:offset, :], label=labelData[:offset])xgeval = xgboost.DMatrix(trainData[offset:, :], label=labelData[offset:])xgtest = xgboost.DMatrix(testData)params = {"booster": "gbtree", "objective": "multi:softprob", "num_class": 39, "max_delta_step": 1, "max_depth": 6}watchlist = [(xgtrain, 'train'), (xgeval, 'val')]model = xgboost.train(list(params.items()), xgtrain, 150, watchlist, early_stopping_rounds=100)preds = numpy.column_stack((myId, model.predict(xgtest, ntree_limit=model.best_iteration))).tolist()preds = [[int(i[0])] + i[1:] for i in preds]cList.insert(0, 'Id')with open("result/sub_xgb_softprob.csv", "w") as output:    writer = csv.writer(output, lineterminator='\n')    writer.writerow(cList)    writer.writerows(preds)