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How to use test_long_click method in uiautomator
Best Python code snippet using uiautomator
load_data_cikm.py
Source:load_data_cikm.py 
1import pandas as pd2import numpy as np3import random4from sklearn.model_selection import train_test_split5from scipy.sparse import csr_matrix6#df1 names=['queryId','sessionId','userId','timeframe','duration','eventdate','searchstring.tokens','categoryId','items','is.test'],7#df2 queryId;timeframe;itemId8######################################9df_click=pd.read_csv('/ext/czh-190/DeepRec-master/data/dataset-train-diginetica/train-clicks.csv',sep=';', engine='python')10df_test_click=pd.read_csv('/ext/czh-190/DeepRec-master/data/dataset-train-diginetica/test-cikm_1.csv',sep=';', engine='python')11click_sum={}12for i in df_click.iterrows():13 if i[1][2] not in click_sum:14 click_sum[i[1][2]]=115 else:16 click_sum[i[1][2]]+=117click_list=[]18for key in click_sum:19 click_list.append((click_sum[key], key))20long_tail = sorted(click_list, reverse=True)21hot=[x[1] for x in long_tail[0:2000]]22long_item=[x[1] for x in long_tail[50000:52000]]23test_long_click={}24test_hot_click={}25hot_i={}26long_i={}27for i in df_test_click.iterrows():28 if i[1][2] in hot:29 if i[1][0] not in test_hot_click:30 test_hot_click[i[1][0]] = [i[1][2]]31 else:32 test_hot_click[i[1][0]].append(i[1][2])33 if i[1][2] in long_item:34 if i[1][0] not in test_long_click:35 test_long_click[i[1][0]] = [i[1][2]]36 else:37 test_long_click[i[1][0]].append(i[1][2])38output = open('test_clicks_cikm_hot.csv', 'w')39for key in test_hot_click:40 output.write(str(key))41 output.write(';')42 for j in test_hot_click[key]:43 output.write(str(j))44 output.write(',')45 output.write('\n')46output.close()47output = open('test_clicks_cikm_long.csv', 'w')48for key in test_long_click:49 output.write(str(key))50 output.write(';')51 for j in test_long_click[key]:52 output.write(str(j))53 output.write(',')54 output.write('\n')55output.close()56######################################57df1 = pd.read_csv('/ext/czh-190/DeepRec-master/data/dataset-train-diginetica/train-queries.csv',sep=';', engine='python')58df2=pd.read_csv('/ext/czh-190/DeepRec-master/data/dataset-train-diginetica/train_clicks_cikm_1.csv',sep=';',engine='python')59###########################################################60df_test=pd.read_csv('/ext/czh-190/DeepRec-master/data/dataset-train-diginetica/test_clicks_cikm_1.csv',sep=';',engine='python')61df_combine_temp_test=pd.merge(df1,df_test,how='inner',on='queryId')[['queryId','userId','categoryId','items','test_click_items']]62df_clicks_all=pd.read_csv('/ext/czh-190/DeepRec-master/data/dataset-train-diginetica/train_clicks_all.csv',sep=';',engine='python')63df_combine_test=pd.merge(df_combine_temp_test,df2,how='inner',on='queryId')[['queryId','userId','categoryId','items','test_click_items','click_items']]64# filter out rows where Rating < 165df_combine_test = df_combine_test[~(df_combine_test['categoryId']==0)]66df_combine_test=df_combine_test.dropna(axis=0,how='any')67n_qids = df1.queryId.unique()68test_cikm={}69for i in df_combine_test.iterrows():70 test_cikm[i[1][0]]={}71 test_cikm[i[1][0]]['user_id']=int(i[1][1])72 test_cikm[i[1][0]]['cate_id']=i[1][2]73 test=i[1][4].split(',')[0:-1]74 test_cikm[i[1][0]]['test'] = test75 test_cikm[i[1][0]]['neg']=list(set(i[1][3].split(',')[0:-1])-set(i[1][5].split(',')[0:-1]))76# test_clicks={}77# # m=078# for i in df_test.iterrows():79# if i[1][0] not in test_clicks:80# test_clicks[i[1][0]]=[i[1][2]]81# else:82# test_clicks[i[1][0]].append(i[1][2])83#84#85# output = open('test_clicks_cikm_1.csv', 'w')86# for key in test_clicks:87# output.write(str(key))88# output.write(';')89# for j in test_clicks[key]:90# output.write(str(j))91# output.write(',')92# output.write('\n')93# output.close()94###########################################################95# train_data, test_data = train_test_split(df2, test_size=0.2)96# train_data = pd.DataFrame(train_data)97# test_data = pd.DataFrame(test_data)98# train_data[['queryId','timeframe','itemId']].to_csv('/ext/czh-190/DeepRec-master/data/dataset-train-diginetica/train-cikm_1.csv',sep=';',index=False)99# test_data[['queryId','timeframe','itemId']].to_csv('/ext/czh-190/DeepRec-master/data/dataset-train-diginetica/test-cikm_1.csv',sep=';',index=False)100#101# dfcombine=df.groupby(df['queryId'])102# clicks={}103# # m=0104# for i in df2.iterrows():105# if i[1][0] not in clicks:106# clicks[i[1][0]]=[i[1][2]]107# else:108# clicks[i[1][0]].append(i[1][2])109#110#111# output = open('train_clicks_all.csv', 'w')112# for key in clicks:113# output.write(str(key))114# output.write(';')115# for j in clicks[key]:116# output.write(str(j))117# output.write(',')118# output.write('\n')119# output.close()120df_combine_temp=pd.merge(df1,df2,how='inner',on='queryId')[['queryId','userId','categoryId','items','click_items']]121df_clicks_all=pd.read_csv('/ext/czh-190/DeepRec-master/data/dataset-train-diginetica/train_clicks_all.csv',sep=';',engine='python')122df_combine=pd.merge(df_combine_temp,df_clicks_all,how='inner',on='queryId')[['queryId','userId','categoryId','items','click_items','click_items_all']]123# filter out rows where Rating < 1124df_combine = df_combine[~(df_combine['categoryId']==0)]125df_combine=df_combine.dropna(axis=0,how='any')126# print(df_combine)127# n_users = df_combine.userId.unique().shape[0]128# print(n_users)129cate_item={}130all_items=[]131df3=pd.read_csv('/ext/czh-190/DeepRec-master/data/dataset-train-diginetica/product-categories.csv',sep=';',engine='python')132for i in df3.iterrows():133 all_items.append(i[1][0])134 if i[1][1] not in cate_item:135 cate_item[i[1][1]]=[i[1][0]]136 else:137 cate_item[i[1][1]].append(i[1][0])138for k in cate_item:139 if len(cate_item[k])<10:140 cate_item[k]+=random.sample(all_items,10)141#142#143# output = open('train_cate_item.csv', 'w')144# for key in cate_item:145# output.write(str(key))146# output.write(';')147# for j in cate_item[key]:148# output.write(str(j))149# output.write(',')150# output.write('\n')151# output.close()152df4=pd.read_csv('/ext/czh-190/DeepRec-master/data/dataset-train-diginetica/train_cate_item.csv',sep=';',engine='python')153df_combine_1=pd.merge(df_combine,df4,how='left',on='categoryId')154print(df_combine_1)155m=0156n=0157# output = open('train_cikm_1.csv', 'w')158# output.write('queryId;userId;categoryId;source;label;target')159# output.write('\n')160# for i in df_combine_1.iterrows():161# n+=1162# output.write(str(i[1][0]))163# output.write(';')164# output.write(str(int(i[1][1])))165# output.write(';')166# output.write(str(i[1][2]))167# output.write(';')168# train=i[1][4].split(',')[0:-1]169#170# if len(train)>=10:171# m+=1172# print('filter'+str(len(train)))173# continue174# label=list(np.ones((len(train),)))175# label+=list(np.zeros(10-len(train)))176# neg=[]177# temp=list(set(i[1][3].split(',')[0:-1]) - set(i[1][4].split(',')[0:-1]))178# # print(len(temp))179# if len(temp)<10-len(train):180# neg+=random.sample(cate_item[i[1][2]],10-len(train)-len(temp))181# neg+=list(temp)182# else:183# # print("=====")184# # print(len(temp))185# # print(len(train))186# neg+=random.sample(temp, 10-len(train))187# train+=neg188# if len(train)!=10:189# print("error")190# print(i[1][0])191# break192# for j in train:193# output.write(str(j))194# output.write(',')195# output.write(';')196# for j in label:197# output.write(str(int(j)))198# output.write(',')199# output.write(';')200# target=random.sample(cate_item[i[1][2]],10)201# for j in target:202# output.write(str(j))203# output.write(',')204#205#206# output.write('\n')207# output.close()208n_qids = df_combine_1.queryId.unique()209train_cikm={}210for i in df_combine_1.iterrows():211 n+=1212 train_cikm[i[1][0]]={}213 train_cikm[i[1][0]]['user_id']=int(i[1][1])214 train_cikm[i[1][0]]['cate_id']=i[1][2]215 train=i[1][4].split(',')[0:-1]216 pos=i[1][5].split(',')[0:-1]217 if len(train)>=10:218 m+=1219 print('filter'+str(len(pos)))220 continue221 label=list(np.ones((len(train),)))222 label+=list(np.zeros(10-len(train)))223 train_cikm[i[1][0]]['label']=label224 neg=[]225 temp=list(set(i[1][3].split(',')[0:-1]) - set(i[1][5].split(',')[0:-1]))226 # print(len(temp))227 if len(temp)<10-len(train):228 neg+=random.sample(list(set(cate_item[i[1][2]])-set(i[1][3].split(',')[0:-1])),10-len(train)-len(temp))229 neg+=list(temp)230 else:231 # print("=====")232 # print(len(temp))233 # print(len(train))234 neg+=random.sample(temp, 10-len(train))235 train+=neg236 if len(train)!=10:237 print("error")238 print(i[1][0])239 break240 train_cikm[i[1][0]]['source']=train241 target=random.sample(cate_item[i[1][2]],10)242 train_cikm[i[1][0]]['target']=target243print('success')244print(m)245print(n)246# df = pd.read_csv(path, sep=sep, names=header, engine='python')247#248# n_users = 6040249# n_items = 3952250# train_row_all = []251# train_col_all = []252# train_rating_all = []253# ######################################################254# for line in df.itertuples():255# u = line[1] - 1256# # i_list=line[2].split('|')[0]257# i = int(line[2].split('|')[0]) - 1258# train_row_all.append(u)259# train_col_all.append(i)260# # print(max(train_row))261# # print(max(train_col))262# train_rating_all.append(1)263# train_matrix = csr_matrix((train_rating_all, (train_row_all, train_col_all)), shape=(n_users, n_items))264# train = train_matrix.A265# sum_m = np.sum(train, axis=0)266# cold = np.where(sum_m == 0)267# s = []268# for i, k in enumerate(sum_m):269# s.append((k, i))270# long_tail = sorted(s, reverse=True)271# hot = [x[1] for x in long_tail[0:200]]272# long_item = [x[1] for x in long_tail[200:400]]273# # hot=[x[1] for x in long_tail[0:500]]274# # long_item=[x[1] for x in long_tail[500:1000]]275# ######################################################276# train_data, test_data = train_test_split(df, test_size=test_size)277# train_data = pd.DataFrame(train_data)278# test_data = pd.DataFrame(test_data)279#280# train_row = []281# train_col = []282# train_rating = []283#284# for line in train_data.itertuples():285# u = line[1] - 1286# # i_list=line[2].split('|')[0]287# i = int(line[2].split('|')[0]) - 1288# train_row.append(u)289# train_col.append(i)290# train_rating.append(1)291# train_matrix = csr_matrix((train_rating, (train_row, train_col)), shape=(n_users, n_items))292#293# # all_items = set(np.arange(n_items))294# # neg_items = {}295# # for u in range(n_users):296# # neg_items[u] = list(all_items - set(train_matrix.getrow(u).nonzero()[1]))297#298# test_row = []299# test_col = []300# test_rating = []301# for line in test_data.itertuples():302# test_row.append(line[1] - 1)303# test_col.append(int(line[2].split('|')[0]) - 1)304# test_rating.append(1)305# test_matrix = csr_matrix((test_rating, (test_row, test_col)), shape=(n_users, n_items))306#307# test_dict = {}308# for u in range(n_users):309# test_dict[u] = test_matrix.getrow(u).nonzero()[1]310# ############################################################################################311# test_row_hot = []312# test_col_hot = []313# test_rating_hot = []314# for line in test_data.itertuples():315# if (int(line[2].split('|')[0]) - 1) in hot:316# test_row_hot.append(line[1] - 1)317# test_col_hot.append(int(line[2].split('|')[0]) - 1)318# test_rating_hot.append(1)319# test_matrix_hot = csr_matrix((test_rating_hot, (test_row_hot, test_col_hot)), shape=(n_users, n_items))320#321# test_dict_hot = {}322# for u in range(n_users):323# test_dict_hot[u] = test_matrix_hot.getrow(u).nonzero()[1]324# ############################################################################################325#326# ############################################################################################327# test_row_long = []328# test_col_long = []329# test_rating_long = []330# for line in test_data.itertuples():331# if (int(line[2].split('|')[0]) - 1) in long_item:332# test_row_long.append(line[1] - 1)333# test_col_long.append(int(line[2].split('|')[0]) - 1)334# test_rating_long.append(1)335# test_matrix_long = csr_matrix((test_rating_long, (test_row_long, test_col_long)), shape=(n_users, n_items))336#337# test_dict_long = {}338# for u in range(n_users):339# test_dict_long[u] = test_matrix_long.getrow(u).nonzero()[1]340# ############################################################################################341#342# print("Load data finished. Number of users:", n_users, "Number of items:", n_items)343# return train_matrix.todok(), test_dict, n_users, n_items, test_dict_hot, test_dict_long, hot, long_item344#345#346#...
share2.py
Source:share2.py
...86 self.get_screenshot("click_fail")87 sleep(2)88 self.get_screenshot("click")89 #é¿æ90 def test_long_click(self):91 try:92 a = self.driver.find_element_by_id("com.netease.cloudmusic:id/anm")93 b = a.find_element_by_class_name("android.widget.LinearLayout")94 c = b.find_elements_by_class_name("android.widget.FrameLayout")95 d = c[1].find_element_by_class_name("android.widget.TextView")96 actions = TouchAction(self.driver)97 actions.long_press(d)98 actions.perform()99 except:100 self.get_screenshot("long_click_fail")101 sleep(2)102 self.get_screenshot("long_click")103 #backé®104 def test_back_key(self):...
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