From 18e12db6aca143da33e05be8de1470230dd1d5d3 Mon Sep 17 00:00:00 2001 From: Anton Kolonin Date: Mon, 2 Sep 2019 19:07:22 +0700 Subject: [PATCH] Update reputation buying AND debugging info in Java RS --- reputation/bin/Aigents.jar | Bin 12916464 -> 12920460 bytes reputation/reputation_buying.py | 38 +++++++++++++++++++++++---- reputation/reputation_buying_test.py | 30 ++++++++++++--------- 3 files changed, 51 insertions(+), 17 deletions(-) diff --git a/reputation/bin/Aigents.jar b/reputation/bin/Aigents.jar index 957e2cf984b75d2a524ab13235a7f30d5cb170f5..2f294bbda8fbe820950ab9305c5cbe260aa83fc1 100644 GIT binary patch delta 143062 zcmZtNb8uxryD0qFnAo0RV%wY~I}=+I+vd*1wr$(C?TKyM$=v;(bH2LYt$Y9Y)#|5L z*Q!;!yLxx^v-+kg5hijf5e7v;77_{*3=R$s3_gWiJ`RNk>Oa>yS$J@8#e`Y{tOiSP z34CzHhFXG9Jczc*giK!C1|^Q4(BQusGI*a!!POcPv>VXCgBzr5au~rg8;&zd$sjt9 z8sfV4S)gD*j|yo ziNO_HlK()mk%B9w)b&7Ox9sFW&Juwuw19#rGVrDrp0r>$! z3ql7%55fS#2*L!y48j7!3c?1$4#EM#3Bm=!4Z;J$3&ID&4JnhxRb~IA+rrJU}?^n 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zUXM}=23b7p2yR_dBy6>0kW8r|43dziq+3tLB*9e00}@{jSuVFGU%aE3dCRKCuaK5Q zFe~Yj+1rzcuuBnDkn5G!>s1OxIX* zA@Vek`l!x-CpL+yKpDXheM2&x$RZ>}ifD^@+am>Yol+_CU5Y40;fM&WBMQ?~U#-WT z@H>8j)Vey1PE9+{?bSZ_6)qT-fZ&32&G87qu>NYB6;JrM{~I~v+B&e`^8|SapBj*1 zw*{GRUxVz50)mLHIreNO;QWsTxcV!)A9Gu4XI~An3~o`fZbgKo!AkY(oid8bZ`~kx z&Oqc=eC!Cg0B0}H|Fa@oh= threshold: + good_seller_qualities.append(quality) + else: + bad_seller_qualities.append(quality) + + test_good_products = 0 costs = {} qualities = {} + budgets = {} for product in all_products: - costs[product] = 10 - qualities[product] = rand_list(bad_agent['qualities']) if product in bad_agents else rand_list(good_agent['qualities']) + cost = 10 #TODO price categories + costs[product] = cost + quality = rand_list(bad_seller_qualities) if product in bad_products else rand_list(good_seller_qualities) + qualities[product] = quality + entire_volume += cost * good_consumers_count + if product in good_products: + expected_good_volume += cost * good_consumers_count + for consumer in good_consumers: + budgets[consumer] = budgets[consumer] + cost if consumer in budgets else cost + #print(qualities) + #print(budgets) + #print(test_good_products,expected_good_volume,bad_seller_qualities,good_seller_qualities) #if verbose: # print('Good:',len(good_agents),good_agents_values[0],good_agents_transactions,len(good_agents)*good_agents_values[0]*good_agents_transactions) @@ -308,10 +332,14 @@ def reputation_simulate(good_agent,bad_agent,since,sim_days,ratings,threshold=40 for agent in good_consumers: daily_selections = {} for t in range(0, good_agents_transactions): + budget = budgets[agent] + if budget <= 0: # out of cash already + continue other = pick_product(ranks,all_products,agent,memories,bad_agents,encounters,threshold) if other is None: continue cost = costs[other] + budgets[agent] = budget - cost # subtract from budget buys += 1 actual_good_volume += cost # while ratings range is [0.0, 0.25, 0.5, 0.75, 1.0], we rank good agents as [0.25, 0.5, 0.75, 1.0] @@ -374,4 +402,4 @@ def ratio_str(x,y,round_digits = None): if silent is not True: print('PLRo='+str(plro),'agents='+str(len(good_consumers))+'/'+str(len(bad_consumers))+'/'+str(len(good_products))+'/'+str(len(bad_products)),'days='+str(sim_days),end =" ") - print('Organic:',str(actual_good_volume),'Sponsored:',str(actual_bad_volume),'Organic2Sponsored:',actual_good_to_bad_volume,'Organic/Sponsored:',ratio_str(actual_good_volume,actual_bad_volume,2),'LTS:',ratio_str(actual_good_to_bad_volume,actual_good_volume,2),'PFS:',ratio_str(actual_good_to_bad_volume,actual_bad_volume,2)) + print('Expect:',str(expected_good_volume),'Organ:',str(actual_good_volume),'Spons:',str(actual_bad_volume),'Organ2Spons:',actual_good_to_bad_volume,'Organ/Spons:',ratio_str(actual_good_volume,actual_bad_volume,2),'OMU:',ratio_str(actual_good_volume,expected_good_volume,2),'LTS:',ratio_str(actual_good_to_bad_volume,actual_good_volume,2),'PFS:',ratio_str(actual_good_to_bad_volume,actual_bad_volume*commission,2)) diff --git a/reputation/reputation_buying_test.py b/reputation/reputation_buying_test.py index d535979..547156b 100644 --- a/reputation/reputation_buying_test.py +++ b/reputation/reputation_buying_test.py @@ -39,16 +39,10 @@ def dict_sorted(d): return s -rs = AigentsAPIReputationService('http://localtest.com:1180/', 'john@doe.org', 'q', 'a', False, 'test', True) -#rs = PythonReputationService() -rs.set_parameters({'fullnorm':True,'weighting':False,'logratings':False,'denomination':False,'unrated':False,'default':0.5,'decayed':0.5,'conservatism':0.5,'ratings':1.0,'spendings':0.0}) - -verbose = False - good_transactions = 1 bad_transactions = 1 threshold = 40 # 3-4-5 stars -threshold = 60 # 4-5 stars +#threshold = 60 # 4-5 stars #threshold = 80 # 5 stars good_agent = {"buyers":[1,800], "products":[1001,1800], "qualities":[0.5,0.75,1.0], "transactions": good_transactions} @@ -75,8 +69,13 @@ def dict_sorted(d): #bad_agent = {"buyers":[4,5], "products":[9,10], "qualities":[0.0,0.25], "transactions": bad_transactions} #days = 5 +#rs = AigentsAPIReputationService('http://localtest.com:1180/', 'john@doe.org', 'q', 'a', False, 'test', True) +rs = PythonReputationService() +rs.set_parameters({'fullnorm':True,'weighting':False,'logratings':False,'denomination':False,'unrated':False,'default':0.5,'decayed':0.5,'conservatism':0.5,'ratings':1.0,'spendings':0.0}) +verbose = False +""" # Blending Anti-biased with Predictiveness print("RS=Anti-biased_def5_dec5_c9", end =" ") @@ -114,7 +113,7 @@ def dict_sorted(d): print("RS=Pred+A-B_def5_dec5_c1", end =" ") rs.set_parameters({'rating_bias':True,'predictiveness':1.0,'fullnorm':True,'weighting':True ,'logratings':False,'denomination':True ,'unrated':False ,'default':0.5,'decayed':0.5,'conservatism':0.1,'ratings':1.0,'spendings':0.0}) reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), days, True, threshold, 30, rs, verbose) - +""" """ # Exploring Anti-biased @@ -132,16 +131,23 @@ def dict_sorted(d): """ -""" + # Exploring threshold and PLRo + +#print("RS=A-B_def5_dec5_c9", end =" ") +#rs.set_parameters({'rating_bias':True,'predictiveness':0.0,'fullnorm':True,'weighting':True ,'logratings':False,'denomination':True ,'unrated':False,'default':0.5,'decayed':0.5,'conservatism':0.9,'ratings':1.0,'spendings':0.0}) +#print("RS=Pred_def5_dec5_c1", end =" ") +#rs.set_parameters({'rating_bias':False,'predictiveness':1.0,'fullnorm':True,'weighting':True ,'logratings':False,'denomination':True ,'unrated':False ,'default':0.5,'decayed':0.5,'conservatism':0.1,'ratings':1.0,'spendings':0.0}) + for threshold in [40,60,80]: print('threshold='+str(threshold)) + reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), days, True, threshold, 0, None, verbose) reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), days, True, threshold, 0, rs, verbose) - reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), days, True, threshold, 25, rs, verbose) + reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), days, True, threshold, 30, rs, verbose) reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), days, True, threshold, 50, rs, verbose) - reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), days, True, threshold, 75, rs, verbose) + reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), days, True, threshold, 70, rs, verbose) reputation_simulate(good_agent,bad_agent, datetime.date(2018, 1, 1), days, True, threshold, 100, rs, verbose) -""" + """ # Exploring PLRo-s and different RS-es except Predictive so far