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How to use merge_data method in autotest
Best Python code snippet using autotest_python
location_predict.py
Source:location_predict.py 
1# coding: utf-82# In[133]:3import numpy as np4import pandas as pd5import re6import xgboost as xgb7from sklearn import svm8from collections import defaultdict9from sklearn.feature_extraction.text import TfidfVectorizer10from sklearn.linear_model import LogisticRegression11from sklearn.cross_validation import StratifiedShuffleSplit12from sklearn.preprocessing import LabelEncoder13from sklearn.cross_validation import StratifiedKFold14#读å
¥è®ç»æµè¯æ°æ®ï¼å并15train_data = pd.read_csv('./data/train/train_labels.txt',sep=u'|',header=None).dropna(1)16train_data.columns = ['uid','sex','age','location']17test_data = pd.read_csv('./data/valid/valid_nolabel.txt',sep=u'|',header=None).dropna(1)18test_data.columns = ['uid']19total_data = pd.concat([train_data,test_data],axis=0)20#读å
¥è®ç»æµè¯infoæ°æ®ï¼å并21train_data_info = pd.read_csv('./data/train/train_info.txt',sep=u'|',header=None).dropna(1)22train_data_info.columns = ['uid','name','image']23train_data_info = train_data_info.drop_duplicates()24test_data_info = pd.read_csv('./data/valid/valid_info.txt',sep=u'|',header=None).dropna(1)25test_data_info.columns = ['uid','name','image']26test_data_info = test_data_info.drop_duplicates()27total_data_info = pd.concat([train_data_info,test_data_info],axis=0)28total_data_info = total_data_info.drop_duplicates('uid')29#读å
¥è®ç»æµè¯linksæ°æ®ï¼å并30links = []31for i, line in enumerate(open('./data/train/train_links.txt')):32 line = line.split()33 row = {'uid':int(line[0]),'sum_fans':len(line)-1,'fans':' '.join(line[1:])}34 links.append(row)35train_data_links = pd.DataFrame(links)36train_data_links = train_data_links.drop_duplicates()37links = []38for i, line in enumerate(open('./data/valid/valid_links.txt')):39 line = line.split()40 row = {'uid':int(line[0]),'sum_fans':len(line)-1,'fans':' '.join(line[1:])}41 links.append(row)42test_data_links = pd.DataFrame(links)43test_data_links = test_data_links.drop_duplicates()44total_data_links = pd.concat([train_data_links,test_data_links],axis=0)45#读å
¥è®ç»æµè¯statusæ°æ®ï¼å并46status = []47for i, line in enumerate(open('./data/train/train_status.txt')):48 49 l = re.search(',',line).span()[0]50 r = re.search(',',line).span()[1]51 row = {'uid':int(line[:l]),'sta':line[r:]}52 status.append(row)53train_data_status = pd.DataFrame(status)54status = []55for i, line in enumerate(open('./data/valid/valid_status.txt')):56 57 l = re.search(',',line).span()[0]58 r = re.search(',',line).span()[1]59 row = {'uid':int(line[:l]),'sta':line[r:]}60 status.append(row)61test_data_status = pd.DataFrame(status)62total_data_status = pd.concat([train_data_status,test_data_status],axis=0)63#å并é¢ç®ç»çå 个表æ°æ®64merge_data = pd.merge(total_data,total_data_info,on='uid',how='left')65merge_data = pd.merge(merge_data,total_data_links,on='uid',how='left')66merge_data.index = range(len(merge_data))67##################################################################################68total_data_status['retweet'] = total_data_status.sta.map(lambda s:int(s.split(',')[0]))69total_data_status['review'] = total_data_status.sta.map(lambda s:int(s.split(',')[1]))70total_data_status['source'] = total_data_status.sta.map(lambda s:s.split(',')[2])71total_data_status['time'] = total_data_status.sta.map(lambda s:s.split(',')[3])72total_data_status['content'] = total_data_status.sta.map(lambda s:','.join(s.split(',')[4:]))73contents = total_data_status.groupby('uid')['content'].agg(lambda lst:' '.join(lst))74merge_data['contents'] = merge_data.uid.map(contents)75merge_data['sum_content'] = merge_data.uid.map(total_data_status.groupby('uid').size())76#ç»è®¡ç¹å¾77merge_data['max_retweet'] = merge_data.uid.map(total_data_status.groupby('uid')['retweet'].agg('max'))78merge_data['max_review'] = merge_data.uid.map(total_data_status.groupby('uid')['review'].agg('max'))79merge_data['min_retweet'] = merge_data.uid.map(total_data_status.groupby('uid')['retweet'].agg('min'))80merge_data['min_review'] = merge_data.uid.map(total_data_status.groupby('uid')['review'].agg('min'))81merge_data['median_retweet'] = merge_data.uid.map(total_data_status.groupby('uid')['retweet'].agg('median'))82merge_data['median_review'] = merge_data.uid.map(total_data_status.groupby('uid')['review'].agg('median'))83merge_data['mean_retweet'] = merge_data.uid.map(total_data_status.groupby('uid')['retweet'].agg('mean'))84merge_data['mean_review'] = merge_data.uid.map(total_data_status.groupby('uid')['review'].agg('mean'))85merge_data['std_retweet'] = merge_data.uid.map(total_data_status.groupby('uid')['retweet'].agg('std'))86merge_data['std_review'] = merge_data.uid.map(total_data_status.groupby('uid')['review'].agg('std'))87#locationå°åºæ å°è¯è¡¨88d = {'ç³å®¶åº': 'åå',89 'åéµåº': 'åä¸',90 'æ·±å³': 'åå',91 '广å·': 'åå',92 'å®å®': 'åå',93 'ååº': 'åä¸',94 'æ²å¸': 'åä¸',95 'æ¦æ±': 'åä¸',96 'è¥é³': 'åä¸',97 'å®é': 'åä¸',98 'èé¨': 'åä¸',99 '西å®': '西å',100 'é¶å·': '西å',101 'æé½': '西å',102 '绵é³': '西å',103 'ä¸æµ·': 'åä¸',104 'äºå': '西å',105 'å
èå¤': 'åå',106 'å京': 'åå',107 'å°æ¹¾': 'åä¸',108 'åæ': 'ä¸å',109 'åå·': '西å',110 '天津': 'åå',111 'å®å¤': '西å',112 'å®å¾½': 'åä¸',113 'å±±ä¸': 'åä¸',114 '山西': 'åå',115 'è¾½å®': 'ä¸å',116 'éåº': '西å',117 'é西': '西å',118 'éæµ·': '西å',119 'é¦æ¸¯': 'åå',120 'é»é¾æ±': 'ä¸å',121 'é¿ç½': 'ä¸å',122 '丹ä¸': 'ä¸å',123 '大庸桥': 'ä¸å',124 'æ²é³': 'ä¸å',125 '大è¿': 'ä¸å',126 'æ顺': 'ä¸å',127 'ç³å®¶åº': 'åå',128 'æé³': 'åå',129 '广ä¸': 'åå',130 '广西': 'åå',131 'æ°ç': '西å',132 'æ±è': 'åä¸',133 'æ±è¥¿': 'åä¸',134 'æ²³å': 'åå',135 'æ²³å': 'åä¸',136 'æµæ±': 'åä¸',137 'æµ·å': 'åå',138 'æ¹å': 'åä¸',139 'æ¹å': 'åä¸',140 'æ¾³é¨': 'åå',141 'çè': '西å',142 'ç¦å»º': 'åä¸',143 '西è': '西å',144 'è´µå·': '西å',145}146#å°locationåage转åæéè¦æ交çèå´147def trans_loc(s):148 if pd.isnull(s):149 return s150 s = s.split(' ')[0]151 if s == 'None':152 return 'åå'153 if s == 'æµ·å¤':154 return 'å¢å¤'155 return d[s]156def trans_age(age):157 if pd.isnull(age):158 return age159 if age <=1979:160 return "-1979"161 elif age<=1989:162 return "1980-1989"163 else:164 return "1990+"165merge_data['location2'] = merge_data['location'].map(trans_loc)166merge_data['age2'] = merge_data['age'].map(trans_age)167src_lst = total_data_status.groupby('uid')['source'].agg(lambda lst:' '.join(lst))168merge_data['source_content'] = merge_data['uid'].map(src_lst) 169keys = '|'.join(d.keys())170merge_data['source_province'] = merge_data['source_content'].map(lambda s:' '.join(re.findall(keys,s)))171merge_data['num_province'] = merge_data['contents'].map(lambda s:' '.join(re.findall(keys,s)))172d = defaultdict(lambda :'空',d)173tokenizer = lambda line: [d[w] for w in line.split(' ')]174tfv = TfidfVectorizer(tokenizer=tokenizer,norm=False, use_idf=False, smooth_idf=False, sublinear_tf=False)175X_all_sp = tfv.fit_transform(merge_data['num_province'])176sum_province = X_all_sp.toarray()177for i in range(sum_province.shape[1]):178 merge_data['sum_province_%d'%i] = sum_province[:,i]179length = total_data_status.groupby('uid')['content'].agg(lambda lst:np.mean([len(s.split(' ')) for s in lst]))180merge_data['max_content_len'] = merge_data['uid'].map(length)181length = total_data_status.groupby('uid')['content'].agg(lambda lst:np.min([len(s.split(' ')) for s in lst]))182merge_data['min_content_len'] = merge_data['uid'].map(length)183length = total_data_status.groupby('uid')['content'].agg(lambda lst:np.max([len(s.split(' ')) for s in lst]))184merge_data['mean_content_len'] = merge_data['uid'].map(length)185merge_data['name_len'] = merge_data.name.map(lambda s:s if pd.isnull(s) else len(re.sub(r'[\u4e00-\u9fff]+','',s)))186def num_missing(x): 187 return sum(x.isnull()) 188merge_data['num_missing'] = merge_data.apply(num_missing, axis=1) 189#rankç¹å¾190merge_data['rank_sum_content'] = merge_data['sum_content'].rank(method='max')191merge_data['rank_sum_fans'] = merge_data['sum_fans'].rank(method='max')192merge_data['rank_mean_retweet'] = merge_data['mean_retweet'].rank(method='max')193merge_data['rank_mean_review'] = merge_data['mean_review'].rank(method='max')194merge_data['rank_num_missing'] = merge_data['num_missing'].rank(method='max')195# In[134]:196#导å
¥ä½¿ç¨tfidfç¹å¾è®ç»ç模åçé¢æµç»æï¼éç¨stackingèåï¼æé¢æµç»æä½ä¸ºæ°ç¹å¾å è¿æ¨¡åï¼197tfidf_stacking = pd.read_csv('./data/newfeat/stack_new.csv')198merge_data = pd.concat([merge_data,tfidf_stacking],axis=1)199feat_time_3hour = pd.read_csv('./data/newfeat/feat_time_3hour.csv')200merge_data = pd.merge(merge_data,feat_time_3hour,on='uid',how='left')201#导å
¥ä½¿ç¨word2vecç¹å¾è®ç»ç模åçé¢æµç»æ202w2v_stacking = pd.read_csv('./data/newfeat/w2v_prob1.csv')203merge_data = pd.merge(merge_data,w2v_stacking,on='uid',how='left')204newmerge_feat1 = pd.read_csv('./data/newfeat/newmerge_feat.csv')205merge_data = pd.merge(merge_data,newmerge_feat1,on='uid',how='left')206feat_area1 = pd.read_csv('./data/newfeat/feat_area.csv')207merge_data = pd.merge(merge_data,feat_area1,on='uid',how='left')208# In[135]:209#########################################################################################210cols = '|'.join(['twts_len','name_len','sum_province','sum_fans',211 'age_','sex_','loc_',212 'mean_retweet','sum_content','mean_review','num_missing',213 'w2v_f_prob','w2v_m_prob','w2v_young_prob','w2v_old_prob','w2v_mid_prob',214 'max_retweet','min_retweet','max_review','min_review',215 'rank_sum_content','rank_sum_fans','rank_mean_retweet','rank_mean_review','rank_num_missing',216 'timePeriod_3hour_0','timePeriod_3hour_1','timePeriod_3hour_2','timePeriod_3hour_3',217 'timePeriod_3hour_4','timePeriod_3hour_5','timePeriod_3hour_6','timePeriod_3hour_7',218 'name_isnull','image_isnull','fans_isnull','retweet_isnull','review_isnull',219 'area_0','area_1','area_2','area_3','area_4','area_5','area_6','area_7'220 ])221cols = [c for c in merge_data.columns if re.match(cols,c)]222age_le = LabelEncoder()223ys = {}224ys['age'] = age_le.fit_transform(merge_data.iloc[:3200]['age2'])225loc_le = LabelEncoder()226ys['loc'] = loc_le.fit_transform(merge_data.iloc[:3200]['location2'])227sex_le = LabelEncoder()228ys['sex'] = sex_le.fit_transform(merge_data.iloc[:3200]['sex'])229merge_data = merge_data.fillna(0)230# In[136]:231task = ['tr']232TR = 3200233TE = 1240234X_all = merge_data[cols]235X = X_all[:TR]236prds = []237# In[137]:238from sklearn.preprocessing import PolynomialFeatures239poly=PolynomialFeatures(2)240X_poly=poly.fit_transform(X_all)241from sklearn.feature_selection import VarianceThreshold242vt=VarianceThreshold(0.001)243X_poly=vt.fit_transform(X_poly)244from sklearn.preprocessing import StandardScaler245ss=StandardScaler()246X_poly=ss.fit_transform(X_poly)247# In[138]:248X_poly=pd.DataFrame(X_poly)249X_poly.columns='Poly_'+X_poly.columns.astype(str)250# In[144]:251X_train=X_poly[:TR]252X_test=X_poly[TR:]253# In[145]:254#label=pd.read_csv('newlabel.csv',header=None,index_col=0)255#label.columns=['uid','age','gender','province']256# In[146]:257merge_data.iloc[:3200]['location2'].value_counts()258# In[416]:259########################260#å°åºé¢æµé¨å261label = 'loc'262print('='*20)263print(label)264print('='*20)265y = ys[label]266dtrain=xgb.DMatrix(X_train,y)267dtest=xgb.DMatrix(X_test)268# In[424]:269params = {270 "objective": "multi:softprob",271 "booster": "gbtree",272 "eval_metric": "merror",273 "num_class":8,274 'max_depth':4,275 #'min_child_weight':2.5,276 'subsample':0.65,277 'colsample_bytree':1.0,278 'gamma':2.5,279 "eta": 0.006,280 #"lambda":1,281 #'alpha':0,282 "silent": 1,283 #'seed':1123284}285xgb1=xgb.train(params,dtrain,num_boost_round=25)286# In[425]:287pre=xgb1.predict(dtest,ntree_limit=25)288pre_loc=[loc_le.classes_[idx] for idx in pre.argmax(1)]289sub = pd.DataFrame()290sub['uid'] = merge_data.iloc[TR:]['uid']291sub['province'] = pre_loc292sub.to_csv('./data/location_sub.csv',index=False)293# In[426]:294#loc_pro2=pd.DataFrame(pre,columns=loc_le.classes_,index=test_data.uid)295# In[427]:...
smp_merge_data.py
Source:smp_merge_data.py
1import numpy as np2import pandas as pd3import re4import xgboost as xgb5from sklearn import svm6from collections import defaultdict7from sklearn.feature_extraction.text import TfidfVectorizer8from sklearn.linear_model import LogisticRegression9from sklearn.cross_validation import StratifiedShuffleSplit10from sklearn.preprocessing import LabelEncoder11path = './data/'12#â¡»Ãⵡââ¤â â ËæâºÂ£Â¨â«Åâ¤Â¢13train_data = pd.read_csv( path + 'train/train_labels.txt',sep=u'|',header=None).dropna(1)14train_data.columns = ['uid','sex','age','location']15test_data = pd.read_csv(path + 'valid/valid_nolabel.txt',sep=u'|',header=None).dropna(1)16test_data.columns = ['uid']17total_data = pd.concat([train_data,test_data],axis=0)18#â¡»Ãⵡââ¤â âinfo ËæâºÂ£Â¨â«Åâ¤Â¢19train_data_info = pd.read_csv( path + 'train/train_info.txt',sep=u'|',header=None).dropna(1)20train_data_info.columns = ['uid','name','image']21train_data_info = train_data_info.drop_duplicates('uid')22test_data_info = pd.read_csv(path + 'valid/valid_info.txt',sep=u'|',header=None).dropna(1)23test_data_info.columns = ['uid','name','image']24test_data_info = test_data_info.drop_duplicates('uid')25total_data_info = pd.concat([train_data_info,test_data_info],axis=0)26#â¡»Ãⵡââ¤â âlinks ËæâºÂ£Â¨â«Åâ¤Â¢27links = []28for i, line in enumerate(open(path + 'train/train_links.txt')):29 line = line.split()30 row = {'uid':int(line[0]),'sum_fans':len(line)-1,'fans':' '.join(line[1:])}31 links.append(row)32train_data_links = pd.DataFrame(links)33train_data_links = train_data_links.drop_duplicates()34links = []35for i, line in enumerate(open(path + 'valid/valid_links.txt')):36 line = line.split()37 row = {'uid':int(line[0]),'sum_fans':len(line)-1,'fans':' '.join(line[1:])}38 links.append(row)39test_data_links = pd.DataFrame(links)40test_data_links = test_data_links.drop_duplicates()41total_data_links = pd.concat([train_data_links,test_data_links],axis=0)42#â¡»Ãⵡââ¤â âstatus ËæâºÂ£Â¨â«Åâ¤Â¢43status = []44for i, line in enumerate(open(path + 'train/train_status.txt')):45 46 l = re.search(',',line).span()[0]47 r = re.search(',',line).span()[1]48 row = {'uid':int(line[:l]),'sta':line[r:]}49 status.append(row)50train_data_status = pd.DataFrame(status)51status = []52for i, line in enumerate(open(path + 'valid/valid_status.txt')):53 54 l = re.search(',',line).span()[0]55 r = re.search(',',line).span()[1]56 row = {'uid':int(line[:l]),'sta':line[r:]}57 status.append(row)58test_data_status = pd.DataFrame(status)59total_data_status = pd.concat([train_data_status,test_data_status],axis=0)60#â«Åâ¤Â¢ÃâÆø⯵ƺââ˱àËæâº61merge_data = pd.merge(total_data,total_data_info,on='uid',how='left')62merge_data = pd.merge(merge_data,total_data_links,on='uid',how='left')63merge_data.index = range(len(merge_data))64##################################################################################65total_data_status['retweet'] = total_data_status.sta.map(lambda s:int(s.split(',')[0]))66total_data_status['review'] = total_data_status.sta.map(lambda s:int(s.split(',')[1]))67total_data_status['source'] = total_data_status.sta.map(lambda s:s.split(',')[2])68total_data_status['time'] = total_data_status.sta.map(lambda s:s.split(',')[3])69total_data_status['content'] = total_data_status.sta.map(lambda s:','.join(s.split(',')[4:]))70contents = total_data_status.groupby('uid')['content'].agg(lambda lst:' '.join(lst))71merge_data['contents'] = merge_data.uid.map(contents)72merge_data['sum_content'] = merge_data.uid.map(total_data_status.groupby('uid').size())73#Ãâ¥ÂºâÃÿâË74merge_data['max_retweet'] = merge_data.uid.map(total_data_status.groupby('uid')['retweet'].agg('max'))75merge_data['max_review'] = merge_data.uid.map(total_data_status.groupby('uid')['review'].agg('max'))76merge_data['min_retweet'] = merge_data.uid.map(total_data_status.groupby('uid')['retweet'].agg('min'))77merge_data['min_review'] = merge_data.uid.map(total_data_status.groupby('uid')['review'].agg('min'))78merge_data['median_retweet'] = merge_data.uid.map(total_data_status.groupby('uid')['retweet'].agg('median'))79merge_data['median_review'] = merge_data.uid.map(total_data_status.groupby('uid')['review'].agg('median'))80merge_data['mean_retweet'] = merge_data.uid.map(total_data_status.groupby('uid')['retweet'].agg('mean'))81merge_data['mean_review'] = merge_data.uid.map(total_data_status.groupby('uid')['review'].agg('mean'))82merge_data['std_retweet'] = merge_data.uid.map(total_data_status.groupby('uid')['retweet'].agg('std'))83merge_data['std_review'] = merge_data.uid.map(total_data_status.groupby('uid')['review'].agg('std'))84#locationµÿ«¯ââ¥â¦â°Â¥Â ±Ã85d = {' ÃºââÃ': 'ªâ¢Â±Â±',86 'ââ¡ÃâÃ': 'ªâ¢â´',87 'â¦Ãâ¬â': 'ªâ¢ÆÅ',88 'Ïâ÷âº': 'ªâ¢ÆÅ',89 '±¶ââ¤': 'ªâ¢ÆÅ',90 '¡ıâÃ': 'ªâ¢Ã·â',91 'â¦â¥Â â': 'ªâ¢Ã·â',92 'Åâ°â«â«': 'ªâ¢Ã·â',93 'ÅÃâÃ': 'ªâ¢Ã·â',94 'ââ¤Â¬Î©': 'ªâ¢Ã·â',95 'æ£ââ': 'ªâ¢Ã·â',96 'ÅËââ¤': 'Å˱±',97 '⯥®': 'Å˱±',98 'â¥â¦âº': 'ÅËÆÅ',99 'ââ¡âÃ': 'ÅËÆÅ',100 'â¦Åâ«Â£': 'ªâ¢â´',101 'ââÆÅ': 'ÅËÆÅ',102 'Æâââ¦Ïâ': 'ªâ¢Â±Â±',103 '±±æ©': 'ªâ¢Â±Â±',104 'îÃÃ': 'ªâ¢â´',105 'ºâ¢Â¡Ã·': 'â´±±',106 'ÃÆ¥®': 'ÅËÆÅ',107 'ÃÃΩÃ': 'ªâ¢Â±Â±',108 'ÆËÅÆ': 'Å˱±',109 'ââ¤Âªâ': 'ªâ¢â´',110 'â¦Î©â´': 'ªâ¢â´',111 'â¦Î©ÅË': 'ªâ¢Â±Â±',112 '¡â¦ÆË': 'â´±±',113 '÷ÿ«Ã': 'ÅËÆÅ',114 'â¦Â¬ÅË': 'Å˱±',115 '«â¡â«Â£': 'Å˱±',116 'Åâââ¬': 'ªâ¢ÆÅ',117 'â«â¡ËΩâ ': 'â´±±',118 'â¥Â§ââ': 'â´±±',119 'µ§â´': 'â´±±',120 'Â¥ÃâÏ«â': 'â´±±',121 'â¦ÃâÃ': 'â´±±',122 '¥á¨': 'â´±±',123 'âÃÃâ¥': 'â´±±',124 ' ÃºââÃ': 'ªâ¢Â±Â±',125 'â¥ÃâÃ': 'ªâ¢Â±Â±',126 'Ïââ´': 'ªâ¢ÆÅ',127 'ÏâÅË': 'ªâ¢ÆÅ',128 'â¬ΩÃ': 'Å˱±',129 'Ωâ Ãâ': 'ªâ¢â´',130 'Ωâ ÅË': 'ªâ¢â´',131 'â«â±±': 'ªâ¢Â±Â±',132 'â«âÆÅ': 'ªâ¢Ã·â',133 'ââΩâ ': 'ªâ¢â´',134 'â«Â£ÆÅ': 'ªâ¢ÆÅ',135 'â«Ë±±': 'ªâ¢Ã·â',136 'â«ËÆÅ': 'ªâ¢Ã·â',137 'âÆââ': 'ªâ¢ÆÅ',138 'â Ãâ¡': 'Å˱±',139 'â£Ω®': 'ªâ¢â´',140 'ÅËâ¤Ã¿': 'ÅËÆÅ',141 'ÏÃ÷âº': 'ÅËÆÅ',142}143#Ω´locationâ«Ãageââ¢ÂªÃâ¥â¦âÃââ¢Ã·ΩªµÆââÅÃ144def trans_loc(s):145 if pd.isnull(s):146 return s147 s = s.split(' ')[0]148 if s == 'None':149 return 'ªâ¢Â±Â±'150 if s == 'â«Â£Ãâ':151 return s152 return d[s]153def trans_age(age):154 if pd.isnull(age):155 return age156 if age <=1979:157 return "-1979"158 elif age<=1989:159 return "1980-1989"160 else:161 return "1990+"162merge_data['location2'] = merge_data['location'].map(trans_loc)163merge_data['age2'] = merge_data['age'].map(trans_age)164src_lst = total_data_status.groupby('uid')['source'].agg(lambda lst:' '.join(lst))165merge_data['source_content'] = merge_data['uid'].map(src_lst) 166keys = '|'.join(d.keys())167merge_data['source_province'] = merge_data['source_content'].map(lambda s:' '.join(re.findall(keys,s)))168merge_data['num_province'] = merge_data['contents'].map(lambda s:' '.join(re.findall(keys,s)))169d = defaultdict(lambda :'øâ',d)170tokenizer = lambda line: [d[w] for w in line.split(' ')]171tfv = TfidfVectorizer(tokenizer=tokenizer,norm=False, use_idf=False, smooth_idf=False, sublinear_tf=False)172X_all_sp = tfv.fit_transform(merge_data['num_province'])173sum_province = X_all_sp.toarray()174for i in range(sum_province.shape[1]):175 merge_data['sum_province_%d'%i] = sum_province[:,i]176length = total_data_status.groupby('uid')['content'].agg(lambda lst:np.mean([len(s.split(' ')) for s in lst]))177merge_data['max_content_len'] = merge_data['uid'].map(length)178length = total_data_status.groupby('uid')['content'].agg(lambda lst:np.min([len(s.split(' ')) for s in lst]))179merge_data['min_content_len'] = merge_data['uid'].map(length)180length = total_data_status.groupby('uid')['content'].agg(lambda lst:np.max([len(s.split(' ')) for s in lst]))181merge_data['mean_content_len'] = merge_data['uid'].map(length)182merge_data['name_len'] = merge_data.name.map(lambda s:s if pd.isnull(s) else len(re.sub(r'[\u4e00-\u9fff]+','',s)))183def num_missing(x): 184 return sum(x.isnull()) 185merge_data['num_missing'] = merge_data.apply(num_missing, axis=1) 186#rankÃÿâË187merge_data['rank_sum_content'] = merge_data['sum_content'].rank(method='max')188merge_data['rank_sum_fans'] = merge_data['sum_fans'].rank(method='max')189merge_data['rank_mean_retweet'] = merge_data['mean_retweet'].rank(method='max')190merge_data['rank_mean_review'] = merge_data['mean_review'].rank(method='max')191merge_data['rank_num_missing'] = merge_data['num_missing'].rank(method='max')...
prepare_data.py
Source:prepare_data.py
1import numpy as np2import pandas as pd3from sklearn.linear_model import LinearRegression4import sys56args = sys.argv7order_file_name = args[1]8group_file_name = args[2]9airline_file_name = args[3]10train_set_file_name = args[4]11total_dataset = args[5]121314order = pd.read_csv(order_file_name)15group = pd.read_csv(group_file_name)16airline = pd.read_csv(airline_file_name)1718group['product_name_price_min'] = group['price'].groupby(group['product_name']).transform('min')19merge_data = order.merge(group, on=['group_id'], how='left')2021#cpå¼:å¹é¢/天æ¸22merge_data["cp"] = merge_data["price"] / merge_data['days']2324#source25source_1_dummy = pd.get_dummies(merge_data["source_1"] )26source_2_dummy = pd.get_dummies(merge_data["source_2"] )27merge_data = pd.concat([merge_data , source_1_dummy] , axis = 1)28merge_data = pd.concat([merge_data , source_2_dummy] , axis = 1)29303132#åä¸è¡ç¨æ¯å¦çºæä½å¹33merge_data['price-min'] = merge_data['price'] - merge_data['product_name_price_min']34merge_data['is-min-price'] = 035merge_data.loc[merge_data['price-min'] == 0 , ['is-min-price']] = 13637#åä¸è¡ç¨ææå¤å°è¨å®38merge_data['num_same_group'] = merge_data[['order_id']].groupby(merge_data['group_id']).transform('count')394041#åæè¡ç¨ç¸½å
±å¤å°äºº42merge_data['total_people_amount'] = merge_data[['people_amount']].groupby(merge_data['group_id']).transform('sum')434445#è¡ç¨ åªæ 46merge_data["discount"] = 047merge_data.loc[merge_data["product_name"].str.contains("ç") == True , ["discount"]] = 148merge_data.loc[merge_data["product_name"].str.contains("æ") == True , ["discount"]] = 149merge_data.loc[merge_data["product_name"].str.contains("è´") == True , ["discount"]] = 150merge_data.loc[merge_data["product_name"].str.contains("é") == True , ["discount"]] = 151merge_data.loc[merge_data["product_name"].str.contains("æ¸") == True , ["discount"]] = 152merge_data.loc[merge_data["product_name"].str.contains("åªæ ") == True , ["discount"]] = 153merge_data.drop(['product_name'], axis=1, inplace=True)545556#æéæ ¼å¼è½æï¼ä»¥åæéèç57merge_data['begin_date'] = pd.to_datetime(merge_data['begin_date'])58merge_data['order_date'] = pd.to_datetime(merge_data['order_date'])59merge_data['begin_date_month'] = merge_data["begin_date"].dt.month60merge_data['order_date_month'] = merge_data["order_date"].dt.month61merge_data['order_date_dayofweek'] = merge_data['order_date'].dt.dayofweek62merge_data['begin_date_dayofweek'] = merge_data['begin_date'].dt.dayofweek63merge_data['order_date_isweekend'] = 064merge_data['begin_date_isweekend'] = 065merge_data.loc[merge_data['order_date_dayofweek'] == 5 , ['order_date_isweekend']] = 166merge_data.loc[merge_data['order_date_dayofweek'] == 6 , ['order_date_isweekend']] = 167merge_data.loc[merge_data['begin_date_dayofweek'] == 5 , ['order_date_isweekend']] = 168merge_data.loc[merge_data['begin_date_dayofweek'] == 6 , ['order_date_isweekend']] = 16970717273# èªçèç74#å»ç¨èµ·é£æéï¼åç¨æµéæé75go_fly = airline[["group_id" , "fly_time" , "arrive_time"]]76go_fly['fly_time'] = airline['fly_time'].groupby(airline['group_id']).transform('min')77go_fly['fly_time'] = pd.to_datetime(go_fly['fly_time'])78go_fly['arrive_time'] = airline['arrive_time'].groupby(airline['group_id']).transform('max')79go_fly['arrive_time'] = pd.to_datetime(go_fly['arrive_time'])80go_fly = go_fly.drop_duplicates()81merge_data = merge_data.merge(go_fly, on=['group_id'], how='left')8283#æ´åè¡ç¨æäºå¹¾æ¬¡é£æ©84count = airline.groupby(['group_id']).size().to_frame("fly_count")85merge_data = merge_data.merge(count, on=['group_id'], how='left')8687#åªé¤æ²ç¨å°çæ¬ä½88merge_data.drop(['source_1'], axis=1, inplace=True)89merge_data.drop(['source_2'], axis=1, inplace=True)90merge_data.drop(['unit'], axis=1, inplace=True)91merge_data.drop(['area'], axis=1, inplace=True)92merge_data.drop(['sub_line'], axis=1, inplace=True)93merge_data.drop(['promotion_prog'], axis=1, inplace=True)949596training_set = pd.read_csv(train_set_file_name)97merge_data = merge_data.merge(training_set , on=['order_id'], how='left')98merge_data = merge_data.dropna() #åªé¤æ缺å¼çå99100print(merge_data.info())101merge_data.to_csv(total_dataset , index = False)102
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