【Python学习系列二十九】scikit-learn库实现天池平台智慧交通预测赛

1、背景：https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.3f6e7d83RQgWEL&raceId=231598

                分数是0.58比较弱，代码这里参考。

2、通过比赛提取的特征如下：

特征	类型	说明
link_ID	string	每条路段(link)唯一标识
link_seq	int	132条路段从1-132编号；
length	int	link长度(米)
width	int	link宽度(米)
link_class	int	link道路等级，如1代表主干道
date	string	日期，如2015-10-01
week	int	星期，根据日期映射到星期，从1到7
time_interval	string	时间段，如[2015-09-01 00:00:00,2015-09-01 00:02:00)
time_slot	int	时间片，根据时间段映射一天24小时，从1到720，每段2分
avg_travel_time	float	该时间片平均旅行时间的均值，反应集中趋势
inlinks_atl_1	float	车辆在该路段上（timeslot-上游平均旅行时间）时间段的平均旅行时间，上游最多4个路段汇入，如果小于4，则大于4的为0。 测试集中，该值是通过决策树回归预测出来的。
inlinks_atl_2	float
inlinks_atl_3	float
inlinks_atl_4	float
inlinks_atl_1	float	车辆在该路段上（timeslot+平均旅行时间）时间段的平均旅行时间，下游最多4个路段汇出，如果小于4，则大于4的为0。 测试集中，该值是通过决策树回归预测出来的。
inlinks_atl_2	float
inlinks_atl_3	float
inlinks_atl_4	float
travel_time	float	车辆在该路段上的平均旅行时间(秒)

3、代码参考：

# -*- coding: utf-8 -*-import pandas as pd import time import numpy as np from sklearn import metrics from sklearn import tree from sklearn.linear_model import LinearRegressiondef main():#加载标记数据label_ds=pd.read_csv(r"link_train_0801.txt",sep='\t',encoding='utf8',\names=['link_id','link_seq','length','width','link_class','start_date','week','time_interval','time_slot','travel_time',\'avg_travel_time','sd_travel_time','inlinks_num','outlinks_num','inlinks_avg_travel_time','outlinks_avg_travel_time',\'inlinks_atl_1','inlinks_atl_2','inlinks_atl_3','inlinks_atl_4','outlinks_atl_1','outlinks_atl_2','outlinks_atl_3','outlinks_atl_4']) label_ds["link_id"] = label_ds["link_id"].astype("string")label_ds["link_seq"] = label_ds["link_seq"].astype("int")label_ds["length"] = label_ds["length"].astype("int")label_ds["width"] = label_ds["width"].astype("int")label_ds["link_class"] = label_ds["link_class"].astype("int")label_ds["start_date"] = label_ds["start_date"].astype("string")label_ds["week"] = label_ds["week"].astype("int")label_ds["time_interval"] = label_ds["time_interval"].astype("string")label_ds["time_slot"] = label_ds["time_slot"].astype("int")label_ds["travel_time"] = label_ds["travel_time"].astype("float")label_ds["avg_travel_time"] = label_ds["avg_travel_time"].astype("float")label_ds["sd_travel_time"] = label_ds["sd_travel_time"].astype("float")label_ds["inlinks_num"] = label_ds["inlinks_num"].astype("int")label_ds["outlinks_num"] = label_ds["outlinks_num"].astype("int")label_ds["inlinks_avg_travel_time"] = label_ds["inlinks_avg_travel_time"].astype("float")label_ds["outlinks_avg_travel_time"] = label_ds["outlinks_avg_travel_time"].astype("float")label_ds["inlinks_atl_1"] = label_ds["inlinks_atl_1"].astype("float")label_ds["inlinks_atl_2"] = label_ds["inlinks_atl_2"].astype("float")label_ds["inlinks_atl_3"] = label_ds["inlinks_atl_3"].astype("float")label_ds["inlinks_atl_4"] = label_ds["inlinks_atl_4"].astype("float")label_ds["outlinks_atl_1"] = label_ds["outlinks_atl_1"].astype("float")label_ds["outlinks_atl_2"] = label_ds["outlinks_atl_2"].astype("float")label_ds["outlinks_atl_3"] = label_ds["outlinks_atl_3"].astype("float")label_ds["outlinks_atl_4"] = label_ds["outlinks_atl_4"].astype("float")#加载预测数据 unlabel_ds=pd.read_csv(r"link_test_0801.txt",sep='\t',encoding='utf8',\names=['link_id','link_seq','length','width','link_class','start_date','week','time_interval','time_slot',\'avg_travel_time','sd_travel_time','inlinks_num','outlinks_num','inlinks_avg_travel_time','outlinks_avg_travel_time',\'inlinks_atl_1','inlinks_atl_2','inlinks_atl_3','inlinks_atl_4','outlinks_atl_1','outlinks_atl_2','outlinks_atl_3','outlinks_atl_4']) unlabel_ds["link_id"] = unlabel_ds["link_id"].astype("string")unlabel_ds["link_seq"] = unlabel_ds["link_seq"].astype("int")unlabel_ds["length"] = unlabel_ds["length"].astype("int")unlabel_ds["width"] = unlabel_ds["width"].astype("int")unlabel_ds["link_class"] = unlabel_ds["link_class"].astype("int")unlabel_ds["start_date"] = unlabel_ds["start_date"].astype("string")unlabel_ds["week"] = unlabel_ds["week"].astype("int")unlabel_ds["time_interval"] = unlabel_ds["time_interval"].astype("string")unlabel_ds["time_slot"] = unlabel_ds["time_slot"].astype("int")unlabel_ds["avg_travel_time"] = unlabel_ds["avg_travel_time"].astype("float")unlabel_ds["sd_travel_time"] = unlabel_ds["sd_travel_time"].astype("float")unlabel_ds["inlinks_num"] = unlabel_ds["inlinks_num"].astype("int")unlabel_ds["outlinks_num"] = unlabel_ds["outlinks_num"].astype("int")unlabel_ds["inlinks_avg_travel_time"] = unlabel_ds["inlinks_avg_travel_time"].astype("float")unlabel_ds["outlinks_avg_travel_time"] = unlabel_ds["outlinks_avg_travel_time"].astype("float")unlabel_ds["inlinks_atl_1"] = unlabel_ds["inlinks_atl_1"].astype("float")unlabel_ds["inlinks_atl_2"] = unlabel_ds["inlinks_atl_2"].astype("float")unlabel_ds["inlinks_atl_3"] = unlabel_ds["inlinks_atl_3"].astype("float")unlabel_ds["inlinks_atl_4"] = unlabel_ds["inlinks_atl_4"].astype("float")unlabel_ds["outlinks_atl_1"] = unlabel_ds["outlinks_atl_1"].astype("float")unlabel_ds["outlinks_atl_2"] = unlabel_ds["outlinks_atl_2"].astype("float")unlabel_ds["outlinks_atl_3"] = unlabel_ds["outlinks_atl_3"].astype("float")unlabel_ds["outlinks_atl_4"] = unlabel_ds["outlinks_atl_4"].astype("float")outit=pd.DataFrame()#输出结果mr_df=pd.DataFrame()#输出link的mape和rmsemape=0;rmse=0;train_df=label_ds.loc[(pd.to_datetime(label_ds["start_date"])<'2016-06-01')]#训练集valid_df=label_ds.loc[(pd.to_datetime(label_ds["start_date"])>='2016-06-01')]#验证集train_df.sample(frac=0.2) for linkid in range(1,133):#提取训练集、验证集、测试集 train_df_id=train_df.loc[(train_df["link_seq"]==linkid)]print "训练集，有", train_df_id.shape[0], "行", train_df_id.shape[1], "列" valid_df_id=valid_df.loc[(valid_df["link_seq"]==linkid)] print "验证集，有", valid_df_id.shape[0], "行", valid_df_id.shape[1], "列"test_df=unlabel_ds.loc[(unlabel_ds["link_seq"]==linkid)]#测试集print "测试集，有", test_df.shape[0], "行", test_df.shape[1], "列"#特征选择#模型训练train_X=train_df_id[['link_seq','time_slot','length','avg_travel_time',\'inlinks_atl_1','inlinks_atl_2','inlinks_atl_3','inlinks_atl_4','outlinks_atl_1','outlinks_atl_2','outlinks_atl_3','outlinks_atl_4']]train_X=train_X.fillna(0)#空值替换为0train_y = train_df_id['travel_time']#标记model_it=LinearRegression()#tree.DecisionTreeRegressor()model_it.fit(train_X, train_y) #模型验证valid_X=valid_df_id[['link_seq','time_slot','length','avg_travel_time',\'inlinks_atl_1','inlinks_atl_2','inlinks_atl_3','inlinks_atl_4','outlinks_atl_1','outlinks_atl_2','outlinks_atl_3','outlinks_atl_4']]valid_X=valid_X.fillna(0)#空值替换为0valid_y=valid_df_id['travel_time']pre_valid_y=model_it.predict(valid_X)abs_y=abs(pre_valid_y-valid_y)abs_error=abs_y.sum()#求和mape_id=abs_error/valid_df_id.shape[0]rmse_id=np.sqrt(metrics.mean_squared_error(valid_y, pre_valid_y))#均方差，模型评估print "linkseq="+str(linkid)+"的mape=",mape_idprint "linkseq="+str(linkid)+"的RMSE=",rmse_idmr_list=[[linkid,mape_id,rmse_id]]mr_df=mr_df.append(mr_list)mape=mape+mape_idrmse=rmse+rmse_id#模型预测test_X = test_df[['link_seq','time_slot','length','avg_travel_time',\'inlinks_atl_1','inlinks_atl_2','inlinks_atl_3','inlinks_atl_4','outlinks_atl_1','outlinks_atl_2','outlinks_atl_3','outlinks_atl_4']] test_X=test_X.fillna(0)#空值替换为0test_info = test_df[['link_id','start_date','time_interval']]test_y=model_it.predict(test_X) test_info["travel_time"]=test_youtit=outit.append(test_info)#追加到输出结果print "all mape:",mape/132print "all RMSE:",rmse/132mr_df.to_csv('linkmape.txt',sep='#',index=False,header=None)outit.to_csv('outit.txt',sep='#',index=False,header=None)#输出预测数据 #执行 if __name__ == '__main__': start = time.clock() main()end = time.clock() print('finish all in %s' % str(end - start))

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