Należy regularnie przesyłać dane zwrotne do systemu Watson OpenScale , aby zapewnić, że model będzie wskazywac jakiekolwiek zmiany w predykatach modelowych.

Dane zwrotne są niezbędne do utrzymania modelu beztendencyjnego. Dzięki pętli sprzężenia zwrotnego system uczy się w sposób ciągły, monitorując efektywność przewidywań i przekwalifikowania w razie potrzeby. Monitorowanie i korzystanie z uzyskanych informacji zwrotnych jest w centrum uczenia maszynowego. Poniższe informacje pomagają w formatowaniu i przesyłaniu danych dotyczących opinii.

Formatowanie danych opinii

Poprawne odczytywanie danych zwrotnych musi być poprawnie sformatowane. W ramach usługi oceny modelu akceptowane są następujące formaty:

• Formaty plików CSV, które można przesyłać za pomocą interfejsu użytkownika lub interfejsu REST API
• Formaty plików JSON, które można przesłać tylko za pomocą interfejsu REST API

Te formaty plików są definiowane przez schemat training_data_schema, który jest dostępny w szczegółach subskrypcji. Aby wyświetlić training_data_schema, należy uruchomić następującą komendę przy użyciu interfejsu API Python :

details= wos_client.subscriptions.get(subscription_id).result.to_dict()
details["entity"]["asset_properties"]["training_data_schema"]

format CSV

Zwykle w przypadku pliku CSV należy podać dane w kolumnach i wierszach z wierszem dla nazw kolumn.

Brak podwójnych cudzysłowów (") są potrzebne, gdy nazwy kolumn są pisane wielkimi literami, co sprawia, że są one niewrażliwe na Db2. Jednak w przypadku innych baz danych oraz w przypadku, gdy nazwy kolumn są małe, wielkość liter musi być zgodna.

Oczekuje się, że plik CSV sprzężenia zwrotnego będzie miał wszystkie wartości składników, a ręcznie przypisana wartość docelowa/etykieta. Na przykład: dane uczących modelu narkotykowego zawierają wartości opcji AGE, SEX, BP, CHOLESTEROL,NA,Koraz wartość docelową/etykietę DRUG. Plik CSV sprzężenia zwrotnego musi zawierać wartości dla tych pól; przykład wyglądałby jak [43, M, HIGH, NORMAL, 0.6345, 1.4587, DrugX]. Jeśli dla pliku CSV został udostępniony nagłówek, nazwy pól są odwzorowywane przy użyciu nagłówka. W przeciwnym razie kolejność pól MUSI być taka sama, jak w schemacie szkolenia. Więcej informacji na temat danych uczących zawiera sekcja Dlaczego system Watson OpenScale musi mieć dostęp do danych szkoleniowych?

Należy zwrócić uwagę, że typy predykcji zwracane przez model i kolumnę etykiety/celu w danych opinii muszą być zgodne.

Wielkość plików jest obecnie ograniczona do 8 MB.

Jeśli plik zawiera nazwy kolumn, kolumny nie muszą być zgodne z porządkiem tabeli, jednak jeśli w pliku nie ma nazw kolumn, należy je dopasować do kolejności tabel. Możliwe jest posiadanie kolumn, które nie znajdują się w oryginalnych danych uczących. Te kolumny są ignorowane podczas przetwarzania. W poniższym przykładzie przedstawiono poprawny sformatowany plik w formacie CSV, w którym znajdują się znaki cudzysłowu (") są używane dla nazw kolumn:

"CheckingStatus","LoanDuration","CreditHistory","LoanPurpose","LoanAmount","ExistingSavings","EmploymentDuration","InstallmentPercent","Sex","OthersOnLoan","CurrentResidenceDuration","OwnsProperty","Age","InstallmentPlans","Housing","ExistingCreditsCount","Job","Dependents","Telephone","ForeignWorker","Risk"
no_checking,28,outstanding_credit,appliances,5990,500_to_1000,greater_7,5,male,co-applicant,3,car_other,55,none,free,2,skilled,2,yes,yes,Risk
greater_200,22,all_credits_paid_back,car_used,3376,less_100,less_1,3,female,none,2,car_other,32,none,own,1,skilled,1,none,yes,No Risk
no_checking,39,credits_paid_to_date,vacation,6434,unknown,greater_7,5,male,none,4,car_other,39,none,own,2,skilled,2,yes,yes,Risk
0_to_200,20,credits_paid_to_date,furniture,2442,less_100,unemployed,3,female,none,1,real_estate,42,none,own,1,skilled,1,none,yes,No Risk
greater_200,4,all_credits_paid_back,education,4206,less_100,unemployed,1,female,none,3,savings_insurance,27,none,own,1,management_self-employed,1,none,yes,No Risk
greater_200,23,credits_paid_to_date,car_used,2963,greater_1000,greater_7,4,male,none,4,car_other,46,none,own,2,skilled,1,none,yes,Risk
no_checking,31,prior_payments_delayed,vacation,2673,500_to_1000,1_to_4,3,male,none,2,real_estate,35,stores,rent,1,skilled,2,none,yes,Risk
no_checking,37,prior_payments_delayed,other,6971,500_to_1000,1_to_4,3,male,none,3,savings_insurance,54,none,own,2,skilled,1,yes,yes,Risk
no_checking,14,all_credits_paid_back,car_new,1525,500_to_1000,4_to_7,3,male,none,4,real_estate,33,none,own,1,skilled,1,none,yes,No Risk
less_0,10,prior_payments_delayed,furniture,4037,less_100,4_to_7,3,male,none,3,savings_insurance,31,none,rent,1,skilled,1,none,yes,Risk
0_to_200,28,credits_paid_to_date,retraining,1152,less_100,less_1,2,female,none,2,savings_insurance,20,stores,own,1,skilled,1,none,yes,No Risk
less_0,17,credits_paid_to_date,car_new,1880,less_100,less_1,3,female,co-applicant,2,savings_insurance,41,none,own,1,skilled,1,none,yes,No Risk
0_to_200,39,prior_payments_delayed,appliances,5685,100_to_500,1_to_4,4,female,none,2,unknown,37,none,own,2,skilled,1,yes,yes,Risk
no_checking,32,prior_payments_delayed,radio_tv,5105,500_to_1000,1_to_4,4,male,none,5,savings_insurance,44,none,own,2,management_self-employed,1,none,yes,Risk
no_checking,38,prior_payments_delayed,appliances,4990,500_to_1000,greater_7,4,male,none,4,car_other,50,bank,own,2,unemployed,2,yes,yes,Risk
less_0,17,credits_paid_to_date,furniture,1017,less_100,less_1,2,female,none,1,car_other,30,none,own,1,skilled,1,none,yes,No Risk
less_0,33,all_credits_paid_back,car_new,3618,500_to_1000,4_to_7,2,male,none,3,unknown,31,stores,own,2,unskilled,1,none,yes,No Risk
less_0,12,no_credits,car_new,3037,less_100,less_1,1,female,none,2,car_other,31,stores,own,1,skilled,1,none,yes,No Risk
no_checking,23,prior_payments_delayed,furniture,1440,100_to_500,1_to_4,3,female,none,3,real_estate,39,stores,own,1,unskilled,1,yes,yes,No Risk
less_0,18,prior_payments_delayed,retraining,4032,less_100,1_to_4,2,female,none,2,car_other,36,none,rent,1,skilled,1,none,yes,No Risk

Format JSON

Format JSON składa się z kolekcji obiektów o polach odpowiadających naziełom kolumn. W poniższym przykładzie przedstawiono kompletny, poprawnie sformatowany plik formatu JSON:

{ "data":
[
["less_0",10,"all_credits_paid_back","car_new",250,"500_to_1000","4_to_7",3,"male","none",2,"real_estate",23,"none","rent",1,"skilled",1,"none","yes","No Risk"],
["no_checking",23,"prior_payments_delayed","appliances",6964,"100_to_500","4_to_7",4,"female","none",3,"car_other",39,"none","own",1,"skilled",1,"none","yes","Risk"],
["0_to_200",30,"outstanding_credit","appliances",3464,"100_to_500","greater_7",3,"male","guarantor",4,"savings_insurance",51,"stores","free",1,"skilled",1,"yes","yes","Risk"],
["no_checking",23,"outstanding_credit","car_used",2681,"500_to_1000","greater_7",4,"male","none",3,"car_other",33,"stores","free",1,"unskilled",1,"yes","yes","No Risk"],
["0_to_200",18,"prior_payments_delayed","furniture",1673,"less_100","1_to_4",2,"male","none",3,"car_other",30,"none","own",2,"skilled",1,"none","yes","Risk"],
["no_checking",44,"outstanding_credit","radio_tv",3476,"unknown","greater_7",4,"male","co-applicant",4,"unknown",60,"none","free",2,"skilled",2,"yes","yes","Risk"],
["less_0",8,"no_credits","education",803,"less_100","unemployed",1,"male","none",1,"savings_insurance",19,"stores","rent",1,"skilled",1,"none","yes","No Risk"],
["0_to_200",7,"all_credits_paid_back","car_new",250,"less_100","unemployed",1,"male","none",1,"real_estate",19,"stores","rent",1,"skilled",1,"none","yes","No Risk"],
["0_to_200",33,"credits_paid_to_date","radio_tv",3548,"100_to_500","1_to_4",3,"male","none",4,"car_other",28,"none","own",2,"skilled",1,"yes","yes","Risk"],
["no_checking",24,"prior_payments_delayed","retraining",4158,"100_to_500","greater_7",3,"female","none",2,"savings_insurance",35,"stores","own",1,"unskilled",2,"none","yes","Risk"],
["no_checking",30,"prior_payments_delayed","appliances",5796,"unknown","4_to_7",4,"male","none",4,"car_other",49,"none","own",2,"management_self-employed",2,"none","yes","No Risk"],
["0_to_200",6,"prior_payments_delayed","furniture",2079,"500_to_1000","less_1",3,"female","none",3,"savings_insurance",35,"none","rent",1,"skilled",1,"none","yes","No Risk"],
["0_to_200",16,"credits_paid_to_date","car_new",4172,"less_100","4_to_7",3,"male","none",3,"savings_insurance",30,"none","own",1,"skilled",1,"yes","yes","No Risk"],
["no_checking",31,"outstanding_credit","appliances",9149,"unknown","greater_7",5,"male","co-applicant",3,"unknown",61,"none","own",3,"skilled",2,"yes","yes","Risk"],
["no_checking",28,"outstanding_credit","appliances",7653,"unknown","4_to_7",4,"male","none",5,"car_other",42,"none","own",2,"skilled",1,"none","yes","No Risk"],
["0_to_200",14,"credits_paid_to_date","furniture",2056,"less_100","4_to_7",3,"male","none",3,"real_estate",20,"none","rent",1,"skilled",1,"none","yes","No Risk"],
["no_checking",29,"prior_payments_delayed","appliances",4321,"greater_1000","4_to_7",4,"male","none",4,"savings_insurance",39,"none","free",2,"management_self-employed",1,"yes","yes","Risk"],
["no_checking",8,"prior_payments_delayed","car_new",3332,"greater_1000","greater_7",3,"male","none",2,"savings_insurance",43,"none","free",2,"skilled",1,"yes","yes","Risk"],
["0_to_200",28,"credits_paid_to_date","furniture",3021,"less_100","4_to_7",3,"male","none",2,"savings_insurance",32,"none","own",2,"skilled",1,"none","yes","No Risk"],
["0_to_200",4,"prior_payments_delayed","car_new",250,"less_100","4_to_7",2,"male","none",2,"car_other",52,"bank","rent",1,"unemployed",1,"yes","yes","No Risk"],
["0_to_200",24,"outstanding_credit","business",5245,"100_to_500","greater_7",4,"male","none",4,"unknown",40,"none","own",2,"management_self-employed",1,"none","yes","Risk"],
["less_0",4,"all_credits_paid_back","radio_tv",1104,"less_100","1_to_4",2,"male","none",1,"savings_insurance",21,"none","rent",1,"skilled",1,"none","yes","No Risk"],
["less_0",10,"prior_payments_delayed","car_new",250,"less_100","4_to_7",3,"male","none",2,"real_estate",26,"none","own",1,"skilled",1,"none","yes","No Risk"],
["less_0",6,"all_credits_paid_back","radio_tv",250,"less_100","less_1",1,"female","none",2,"real_estate",19,"none","own",1,"skilled",1,"none","yes","No Risk"],
["no_checking",40,"outstanding_credit","education",7277,"unknown","greater_7",4,"male","co-applicant",5,"car_other",49,"none","own",2,"skilled",2,"yes","yes","Risk"],
["less_0",23,"credits_paid_to_date","repairs",1348,"500_to_1000","1_to_4",3,"male","none",2,"savings_insurance",35,"bank","own",2,"unskilled",1,"yes","yes","No Risk"],
["no_checking",31,"prior_payments_delayed","car_used",5011,"100_to_500","1_to_4",3,"male","none",3,"savings_insurance",41,"stores","own",1,"unskilled",1,"yes","no","No Risk"],
["0_to_200",21,"credits_paid_to_date","car_new",1658,"less_100","less_1",2,"male","none",3,"savings_insurance",27,"none","own",2,"skilled",1,"yes","yes","Risk"],
["no_checking",43,"prior_payments_delayed","appliances",6744,"greater_1000","4_to_7",3,"male","co-applicant",5,"savings_insurance",35,"none","own",2,"skilled",1,"none","yes","No Risk"],
["less_0",4,"all_credits_paid_back","car_used",250,"less_100","less_1",1,"female","none",2,"real_estate",22,"none","rent",1,"skilled",1,"none","yes","No Risk"],
["0_to_200",24,"credits_paid_to_date","car_used",3294,"less_100","less_1",3,"male","none",4,"real_estate",25,"none","own",1,"management_self-employed",1,"none","no","No Risk"],
["greater_200",17,"credits_paid_to_date","car_new",3581,"less_100","1_to_4",2,"male","none",4,"savings_insurance",25,"none","own",1,"skilled",1,"yes","yes","No Risk"],
["no_checking",52,"outstanding_credit","appliances",9249,"unknown","greater_7",5,"male","co-applicant",4,"unknown",52,"none","free",3,"skilled",2,"yes","yes","Risk"],
["0_to_200",15,"prior_payments_delayed","retraining",398,"less_100","less_1",2,"male","co-applicant",2,"savings_insurance",44,"none","own",1,"skilled",1,"yes","yes","No Risk"],
["0_to_200",26,"credits_paid_to_date","car_used",3650,"less_100","1_to_4",3,"male","none",4,"savings_insurance",32,"stores","own",1,"unskilled",1,"none","yes","Risk"],
["less_0",4,"credits_paid_to_date","car_new",250,"100_to_500","less_1",1,"female","none",1,"real_estate",19,"bank","rent",1,"unskilled",1,"none","yes","No Risk"],
["0_to_200",16,"credits_paid_to_date","furniture",1694,"100_to_500","4_to_7",3,"female","none",3,"savings_insurance",37,"none","own",2,"skilled",1,"none","yes","No Risk"],
["no_checking",48,"outstanding_credit","education",10197,"500_to_1000","4_to_7",4,"male","co-applicant",3,"unknown",36,"none","free",2,"skilled",1,"yes","yes","Risk"],
["no_checking",43,"prior_payments_delayed","repairs",5648,"unknown","greater_7",4,"male","co-applicant",5,"unknown",49,"none","own",3,"skilled",2,"yes","yes","Risk"],
["0_to_200",13,"prior_payments_delayed","radio_tv",2217,"greater_1000","1_to_4",3,"male","co-applicant",4,"unknown",46,"bank","own",1,"unemployed",1,"yes","no","Risk"],
["greater_200",24,"credits_paid_to_date","car_used",6428,"less_100","4_to_7",4,"female","none",4,"car_other",54,"none","own",1,"skilled",1,"yes","yes","Risk"],
["0_to_200",12,"no_credits","car_new",250,"less_100","less_1",2,"female","none",1,"real_estate",20,"bank","rent",1,"unskilled",1,"none","yes","No Risk"],
["0_to_200",24,"outstanding_credit","radio_tv",9282,"500_to_1000","1_to_4",4,"male","none",5,"car_other",32,"none","own",2,"management_self-employed",1,"none","yes","No Risk"],
["less_0",8,"no_credits","radio_tv",250,"less_100","unemployed",1,"male","none",1,"real_estate",19,"bank","rent",1,"unemployed",1,"none","yes","No Risk"],
["no_checking",25,"prior_payments_delayed","radio_tv",5159,"100_to_500","1_to_4",3,"male","none",4,"car_other",53,"none","own",1,"management_self-employed",2,"yes","yes","Risk"],
["less_0",4,"credits_paid_to_date","car_new",250,"less_100","unemployed",1,"male","co-applicant",2,"real_estate",31,"stores","own",1,"skilled",1,"none","yes","No Risk"],
["no_checking",25,"outstanding_credit","business",6725,"500_to_1000","4_to_7",3,"male","none",4,"unknown",50,"none","own",2,"skilled",2,"yes","yes","Risk"],
["no_checking",19,"outstanding_credit","car_used",5092,"500_to_1000","4_to_7",4,"male","none",5,"savings_insurance",38,"none","own",1,"management_self-employed",1,"yes","yes","No Risk"],
["0_to_200",26,"all_credits_paid_back","furniture",3635,"less_100","4_to_7",4,"male","co-applicant",2,"car_other",35,"none","free",2,"skilled",1,"yes","yes","Risk"],
["0_to_200",30,"credits_paid_to_date","radio_tv",1638,"500_to_1000","1_to_4",2,"female","none",1,"savings_insurance",21,"stores","rent",1,"skilled",1,"none","yes","Risk"],
["no_checking",30,"outstanding_credit","furniture",7499,"unknown","greater_7",5,"male","co-applicant",5,"car_other",60,"none","free",2,"skilled",2,"yes","yes","Risk"],
["greater_200",5,"credits_paid_to_date","car_new",1674,"less_100","less_1",3,"female","co-applicant",3,"real_estate",31,"none","own",1,"skilled",1,"yes","yes","No Risk"],
["less_0",16,"credits_paid_to_date","car_new",250,"less_100","4_to_7",2,"male","none",1,"real_estate",29,"stores","free",1,"unskilled",1,"none","yes","No Risk"],
["less_0",32,"outstanding_credit","appliances",3511,"100_to_500","4_to_7",4,"male","none",2,"car_other",24,"none","own",1,"skilled",1,"yes","yes","No Risk"],
["0_to_200",24,"outstanding_credit","appliances",4511,"100_to_500","greater_7",4,"male","none",4,"unknown",46,"none","own",1,"skilled",1,"none","no","No Risk"],
["less_0",4,"prior_payments_delayed","car_used",1394,"less_100","1_to_4",3,"female","none",1,"savings_insurance",28,"none","own",1,"skilled",1,"none","yes","No Risk"],
["0_to_200",23,"credits_paid_to_date","radio_tv",2795,"less_100","1_to_4",3,"male","none",3,"car_other",29,"none","own",1,"skilled",1,"none","yes","No Risk"],
["no_checking",17,"credits_paid_to_date","retraining",4331,"less_100","less_1",3,"male","none",3,"savings_insurance",32,"none","free",1,"management_self-employed",1,"none","yes","No Risk"],
["0_to_200",16,"prior_payments_delayed","radio_tv",2373,"less_100","1_to_4",3,"female","none",1,"savings_insurance",32,"none","free",2,"skilled",1,"none","yes","No Risk"],
["no_checking",38,"prior_payments_delayed","furniture",8742,"500_to_1000","4_to_7",3,"male","co-applicant",4,"unknown",46,"none","free",2,"skilled",1,"yes","yes","Risk"],
["greater_200",28,"prior_payments_delayed","radio_tv",3936,"100_to_500","1_to_4",4,"male","none",4,"car_other",36,"none","free",2,"management_self-employed",1,"yes","yes","No Risk"],
["less_0",24,"prior_payments_delayed","furniture",5098,"less_100","4_to_7",4,"female","none",2,"car_other",39,"none","own",2,"skilled",1,"none","yes","Risk"],
["no_checking",27,"prior_payments_delayed","furniture",5250,"100_to_500","1_to_4",4,"male","none",2,"car_other",53,"none","own",2,"skilled",1,"yes","yes","Risk"],
["less_0",4,"all_credits_paid_back","car_used",250,"less_100","1_to_4",1,"female","none",2,"real_estate",25,"stores","rent",1,"skilled",1,"none","yes","No Risk"],
["less_0",14,"prior_payments_delayed","vacation",4398,"less_100","1_to_4",3,"male","none",3,"car_other",31,"none","own",2,"skilled",1,"none","yes","No Risk"],
["0_to_200",4,"all_credits_paid_back","car_new",250,"less_100","1_to_4",2,"female","none",1,"real_estate",42,"none","own",1,"skilled",1,"none","yes","No Risk"],
["less_0",16,"credits_paid_to_date","furniture",2291,"less_100","less_1",3,"male","none",2,"savings_insurance",32,"stores","rent",1,"unskilled",1,"none","yes","No Risk"],
["no_checking",31,"prior_payments_delayed","furniture",7079,"100_to_500","less_1",4,"female","none",3,"car_other",43,"none","own",2,"skilled",2,"yes","yes","No Risk"],
["0_to_200",14,"credits_paid_to_date","car_new",4366,"100_to_500","1_to_4",3,"female","none",1,"car_other",37,"none","own",1,"skilled",1,"none","yes","No Risk"],
["no_checking",17,"credits_paid_to_date","repairs",3418,"100_to_500","less_1",2,"male","none",4,"car_other",43,"stores","own",2,"unskilled",1,"none","yes","Risk"],
["less_0",20,"credits_paid_to_date","furniture",902,"less_100","4_to_7",2,"male","none",1,"savings_insurance",26,"stores","own",1,"unskilled",1,"none","no","No Risk"],
["less_0",4,"credits_paid_to_date","car_used",250,"500_to_1000","less_1",1,"female","none",1,"real_estate",19,"stores","own",1,"unskilled",1,"none","yes","No Risk"],
["0_to_200",17,"no_credits","car_new",1662,"less_100","less_1",2,"female","none",2,"savings_insurance",32,"none","own",2,"skilled",1,"yes","yes","No Risk"],
["0_to_200",27,"credits_paid_to_date","education",1272,"greater_1000","1_to_4",3,"male","none",2,"savings_insurance",37,"stores","own",2,"unskilled",1,"none","no","No Risk"],
["less_0",14,"prior_payments_delayed","radio_tv",1640,"500_to_1000","less_1",2,"male","none",2,"real_estate",29,"none","rent",2,"skilled",1,"none","yes","No Risk"],
["greater_200",20,"credits_paid_to_date","car_new",250,"100_to_500","1_to_4",2,"male","none",3,"savings_insurance",44,"none","own",1,"skilled",1,"yes","yes","No Risk"],
["no_checking",33,"all_credits_paid_back","education",6126,"less_100","4_to_7",5,"male","none",3,"savings_insurance",33,"stores","rent",1,"unskilled",1,"none","yes","Risk"],
["0_to_200",8,"prior_payments_delayed","car_new",731,"greater_1000","1_to_4",3,"female","none",2,"savings_insurance",33,"bank","rent",1,"unemployed",2,"none","yes","No Risk"],
["no_checking",31,"prior_payments_delayed","vacation",5086,"100_to_500","greater_7",5,"male","none",3,"savings_insurance",44,"none","free",2,"management_self-employed",1,"none","yes","Risk"],
["less_0",15,"prior_payments_delayed","furniture",3986,"less_100","less_1",2,"male","none",2,"car_other",19,"bank","rent",1,"unskilled",1,"none","yes","No Risk"],
["0_to_200",13,"credits_paid_to_date","car_new",1700,"less_100","4_to_7",3,"female","none",2,"car_other",39,"none","own",1,"skilled",1,"none","yes","Risk"],
["0_to_200",4,"all_credits_paid_back","car_new",250,"less_100","1_to_4",2,"female","none",1,"real_estate",29,"none","own",1,"skilled",1,"none","yes","No Risk"],
["less_0",16,"prior_payments_delayed","car_new",2056,"less_100","4_to_7",2,"male","none",2,"unknown",34,"stores","own",1,"skilled",1,"none","yes","No Risk"],
["no_checking",32,"prior_payments_delayed","car_used",5145,"500_to_1000","4_to_7",4,"male","none",4,"savings_insurance",40,"none","own",1,"skilled",1,"none","yes","No Risk"],
["greater_200",24,"outstanding_credit","business",8570,"100_to_500","4_to_7",4,"male","none",4,"unknown",47,"none","own",2,"skilled",1,"yes","yes","Risk"],
["no_checking",16,"all_credits_paid_back","vacation",2928,"less_100","1_to_4",2,"female","none",1,"real_estate",29,"none","own",1,"management_self-employed",1,"none","yes","No Risk"],
["less_0",25,"credits_paid_to_date","car_new",2049,"less_100","less_1",2,"male","none",1,"real_estate",33,"none","own",1,"skilled",1,"none","yes","No Risk"],
["no_checking",24,"prior_payments_delayed","car_used",3838,"500_to_1000","1_to_4",4,"female","none",3,"savings_insurance",41,"none","own",2,"skilled",1,"none","yes","No Risk"],
["0_to_200",21,"all_credits_paid_back","car_new",1653,"100_to_500","less_1",1,"female","none",1,"car_other",33,"bank","rent",1,"unemployed",1,"none","yes","No Risk"],
["less_0",7,"credits_paid_to_date","car_new",250,"less_100","1_to_4",1,"female","none",2,"savings_insurance",19,"stores","rent",1,"skilled",1,"none","yes","No Risk"],
["0_to_200",20,"credits_paid_to_date","retraining",2553,"less_100","1_to_4",3,"female","none",3,"savings_insurance",27,"stores","rent",1,"unskilled",2,"none","yes","No Risk"],
["less_0",39,"prior_payments_delayed","appliances",820,"100_to_500","4_to_7",2,"male","none",1,"savings_insurance",26,"stores","own",1,"skilled",1,"none","yes","No Risk"],
["0_to_200",20,"credits_paid_to_date","retraining",2810,"less_100","1_to_4",2,"female","none",3,"car_other",38,"bank","rent",2,"unemployed",1,"yes","yes","No Risk"],
["greater_200",4,"prior_payments_delayed","furniture",3262,"less_100","1_to_4",2,"male","none",3,"savings_insurance",31,"none","free",1,"management_self-employed",1,"yes","yes","No Risk"],
["less_0",28,"outstanding_credit","business",2891,"100_to_500","4_to_7",4,"male","co-applicant",2,"savings_insurance",40,"none","own",1,"skilled",1,"yes","yes","Risk"],
["0_to_200",28,"credits_paid_to_date","education",1531,"less_100","4_to_7",2,"male","none",2,"car_other",28,"stores","own",1,"skilled",1,"yes","yes","Risk"],
["no_checking",41,"prior_payments_delayed","repairs",7507,"unknown","4_to_7",5,"male","co-applicant",5,"car_other",41,"none","own",2,"skilled",2,"none","yes","No Risk"],
["0_to_200",4,"prior_payments_delayed","vacation",250,"500_to_1000","4_to_7",2,"male","none",3,"savings_insurance",27,"none","own",1,"skilled",1,"none","yes","No Risk"]
]
}

Przesyłanie danych opinii

Dane opinii można przesłać z pliku CSV bezpośrednio w interfejsie użytkownika Watson OpenScale . W celu przesyłania pliku JSON można użyć produktu Watson Studio.

Przesyłanie pliku CSV

Aby przesłać plik CSV, należy użyć opcji Dodaj dane opinii. Aby wykonać następujące kroki w ramach kursu, należy otworzyć i skopiować zawartość pliku credit_feedback_data.csv .

1. W panelu kontrolnym Watson OpenScale kliknij kafel wdrażania.
2. W oknie wdrożenia modelu kliknij opcję Monitory konfiguracji .
3. Na panelu nawigacyjnym kliknij opcję Jakość.
4. Kliknij opcję Opinia , a następnie kliknij opcję Dodaj dane opinii.

5. Wybierz plik CSV zawierający dane opinii, a następnie kliknij przycisk Otwórz. Na potrzeby kursu wybierz pobrany plik credit_feedback_data.csv .

   Wielkość plików jest obecnie ograniczona do 8 MB.

6. W menu rozwijanym kliknij ogranicznik pola i kliknij przycisk Wybierz.

Dodanie pliku CSV udostępnia dane o informacji zwrotnej do modelu.

Przesyłanie pliku JSON

1. Uruchom produkt Watson Studio i przejdź do projektu, który zawiera model.
2. Pobierz plik JSON.

3. Na karcie Deployments (Wdrożenia) projektu Watson Studio kliknij odsyłacz model (Model), kliknij kartę Test (Test), a następnie wybierz ikonę wejścia JSON.

   

4. Teraz otwórz pobrany plik JSON i skopiuj jego zawartość do pola JSON na karcie Test . Kliknij opcję Predykt , aby wysłać i zaliczyć obciążenia szkoleniowe dla modelu.

Wyświetlanie wyników

Aby natychmiast sprawdzić wynik, na stronie Insights (Insights) wybierz wdrożenie, kliknij jedną z pomiarów Quality (Jakość), a następnie kliknij opcję Evaluate quality now(Wartościuj jakość teraz).

Dalsze kroki

Konfigurowanie oceny punktu końcowego

Temat nadrzędny: Zarządzanie danymi dla ocen modelu w produkcie Watson OpenScale