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How to use test_positive method in tempest

Best Python code snippet using tempest_python

data_split.py

Source:data_split.py

1import os2import pandas as pd3import numpy as np4import argparse5import random67parser = argparse.ArgumentParser(description='data_split')8parser.add_argument('--data_path', default='./Amazon-office-raw/ratings.csv', type=str,9                    help='datapath')10parser.add_argument('--save_path', default='./Amazon-office-raw', type=str,11                    help='savepath')12parser.add_argument('--negative_sampling', default=True, type=bool,13                    help='test negative sampling')14args = parser.parse_args()15random.seed(1)1617ratings=pd.read_csv(args.data_path)18type=['ratio-split','leave-one-out']19# indexing user20user_id=ratings[['userid']].drop_duplicates().reset_index(drop=True)21user_id['useridx']=pd.DataFrame(np.arange(len(user_id)))22userdict=dict(zip(user_id['userid'],user_id['useridx']))23ratings=ratings.replace({'userid':userdict})2425#indexing item26item_id=ratings[['itemid']].drop_duplicates().reset_index(drop=True)27item_id['itemidx']=pd.DataFrame(np.arange(len(item_id)))28itemdict=dict(zip(item_id['itemid'],item_id['itemidx']))29ratings=ratings.replace({'itemid':itemdict})3031# group items according to user index32items_set=set(item_id['itemidx'])33#ratings['rate'][ratings['rate']>0]=1.034rating1=ratings.groupby('userid')['itemid'].apply(list).reset_index()35rating2=ratings.groupby('userid')['timestamp'].apply(list).reset_index()3637# split38for split_type in type:39    ratings = pd.concat([rating1[['userid', 'itemid']], rating2['timestamp']], axis=1)40    if split_type=='leave-one-out':41        ratings['test_positive_index']=ratings['timestamp'].apply(lambda x: np.array(x).argmax())42        ratings['test_positive']=ratings.apply(lambda x: x['itemid'][x['test_positive_index']], axis=1)43        ratings['negative']=ratings['itemid'].apply(lambda x: list(items_set - set(x)))44        ratings['test_negative']=ratings['negative'].apply(lambda x: random.sample(x, 99))45        ratings['train_negative']=ratings.apply(lambda x: list(items_set - set(x['itemid']) - set(x['test_negative'])), axis=1)46        ratings.apply(lambda x: x['itemid'].remove(x['test_positive']), axis=1)474849    elif split_type=='ratio-split':50        ratings['test_positive']=ratings['itemid'].apply(lambda x: random.sample(x, round(len(x)*0.2)))51        ratings['itemid']=ratings.apply(lambda x: list(set(x['itemid'])-set(x['test_positive'])), axis=1)52        ratings['train_negative']=ratings.apply(lambda x: list(items_set - set(x['itemid'])),axis=1)53        if args.negative_sampling:54            ratings['test_negative']=ratings.apply(lambda x: random.sample(list(items_set - set(x['itemid'])- set(x['test_positive'])),len(x['test_positive']*10))55                                                if len(x['test_positive'])*10 <= len(list(items_set-set(x['itemid'])-set(x['test_positive']))) else list(items_set - set(x['itemid'])- set(x['test_positive'])), axis=1)5657        else :58            ratings['test_negative']=ratings.apply(lambda x: list(items_set - set(x['itemid'])- set(x['test_positive'])), axis=1)59        ratings['train_negative'] = ratings.apply(lambda x: list(set(x['train_negative'])-set(x['test_negative'])), axis=1)6061    ratings.rename(columns = {'itemid':'train_positive'}, inplace = True)62    ratings = ratings[['userid','train_positive','test_positive','train_negative', 'test_negative']].reset_index(drop=True)63    train_positive=ratings.join(ratings['train_positive'].apply(lambda x:pd.Series(x)).stack().reset_index(1,name='train_pos').drop('level_1',axis=1))64    test_positive=ratings.join(ratings['test_positive'].apply(lambda x:pd.Series(x)).stack().reset_index(1,name='test_pos').drop('level_1',axis=1))6566    # save67    if not os.path.exists(os.path.join(args.save_path, split_type)):68        os.makedirs(os.path.join(args.save_path, split_type))69    train_positive[['userid','train_pos']].reset_index(drop=True).to_feather(os.path.join(args.save_path,split_type, 'train_positive.ftr'))70    test_positive[['userid','test_pos']].reset_index(drop=True).to_feather(os.path.join(args.save_path,split_type, 'test_positive.ftr'))71    ratings[['userid','train_negative']].reset_index(drop=True).to_feather(os.path.join(args.save_path,split_type, 'train_negative.ftr'))72    ratings[['userid','test_negative']].reset_index(drop=True).to_feather(os.path.join(args.save_path,split_type, 'test_negative.ftr'))7374if not os.path.exists(os.path.join(args.save_path,'index-info')):75    os.makedirs(os.path.join(args.save_path,'index-info'))76user_id.to_csv(os.path.join(args.save_path,'index-info', 'user_index.csv'),index=False)
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evaluation_random_classifier.py

Source:evaluation_random_classifier.py

1import json2import subprocess3import sys4import os5import time6from PIL import Image7from numpy import average, dot, linalg8import cv29import skimage.measure10import os11import subprocess12import random13import numpy14# give a list for psc2code video dealing time15# def getlist_psc2code():16#     output = {}17#     rightlist = []18#     faultlist = []19#     database = "data_copy/test"20#     for category in os.listdir(database):21#         # print(category) 22#         if category == "non-screencast":23#             for video in os.listdir(os.path.join(database,category)):24#                 rightlist.append(video)25#                 for img in os.listdir(os.path.join(database,category,video)):26#                     # if "frame" not in img:27#                         # print(img)28#                     output[video] = int(1.6*len(os.listdir(os.path.join(database,category,video))))29#         else:30#             for video in os.listdir(os.path.join(database,category)):31#                 faultlist.append(video)32#                 for img in os.listdir(os.path.join(database,category,video)):33#                     # if "frame" not in img:34#                         # print(img)35#                     output[video] = len(os.listdir(os.path.join(database,category,video)))36#     return output,rightlist,faultlist37# psc2codelist,nonlist,yeslist = getlist_psc2code()38# count = 039# for video in psc2codelist.keys():40#     psc2codelist[video] = int(psc2codelist[video]*1.88)41#     count+= psc2codelist[video]42# print(len(nonlist))43# print(len(yeslist))44# # wrong one: MkJ1jY8ubKs45# # 2.5å46# def random1():47#     count = 048#     test_positive = random.sample(list(psc2codelist), k=13)49#     test_negative = list(set(list(psc2codelist)).difference(set(test_positive)))50#     # print(len(list(psc2codelist)))51#     # print(len(test_positive))52#     # print(len(test_negative))53#     tp = 054#     fp = 055#     fn = 056#     for item in test_positive:57#         if item in yeslist:58#             tp += 159#         if item in nonlist:60#             fp+=161#     for item in test_negative:62#         if item in yeslist:63#             fn+=164#     return tp,fp,fn65# allpre = []66# allrecall = []67# allfi = []68# for i in range(20):69#     tp,fp,fn = random1()70#     pre = float(tp)/(tp+fp)71#     allpre.append(pre)72#     recall = float(tp)/(tp+fn)73#     allrecall.append(recall)74#     allfi.append(2*recall*pre/(recall+pre))75#     # print("%d,%d,%d"%(tp,fp,fn))76# print("the pre is %.3f, the recall is %.3f, the F-1 is %.3f"%(numpy.mean(allpre),numpy.mean(allrecall),numpy.mean(allfi))) 77def random1():78    count = 079    psc2codelist = [i for i in range(22)]80    print(psc2codelist)81    yeslist = [i for i in range(14)]82    nonlist = [i for i in range(14,22)]83    print(yeslist)84    print(nonlist)85    test_positive = random.sample(list(psc2codelist), k=11)86    test_negative = list(set(list(psc2codelist)).difference(set(test_positive)))87    # print(len(list(psc2codelist)))88    # print(len(test_positive))89    # print(len(test_negative))90    tp = 091    fp = 092    fn = 093    for item in test_positive:94        if item in yeslist:95            tp += 196        if item in nonlist:97            fp+=198    for item in test_negative:99        if item in yeslist:100            fn+=1101    return tp,fp,fn102allpre = []103allrecall = []104allfi = []105for i in range(20):106    tp,fp,fn = random1()107    pre = float(tp)/(tp+fp)108    allpre.append(pre)109    recall = float(tp)/(tp+fn)110    allrecall.append(recall)111    allfi.append(2*recall*pre/(recall+pre))112    # print("%d,%d,%d"%(tp,fp,fn))...

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