diff options
Diffstat (limited to 'backend/microservice/api')
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 48 |
1 files changed, 26 insertions, 22 deletions
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 85be0c2f..99e3cae5 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -179,6 +179,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): ###KATEGORIJSKE KOLONE kategorijskekolone=[] ###PRETVARANJE NUMERICKIH U KATREGORIJSKE AKO JE KORISNIK TAKO OZNACIO + columnInfo=paramsDataset['columnInfo'] columnTypes=paramsExperiment['columnTypes'] for i in range(len(columnInfo)): @@ -187,25 +188,27 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): data[col['columnName']]=data[col['columnName']].apply(str) kategorijskekolone.append(col['columnName']) #kategorijskekolone=data.select_dtypes(include=['object']).columns - print(kategorijskekolone) + #print(kategorijskekolone) ###NULL - null_value_options = paramsExperiment["nullValues"] - null_values_replacers = paramsExperiment["nullValuesReplacers"] + #null_value_options = paramsExperiment["nullValues"] # + null_values_replacers = paramsExperiment["nullValuesReplacers"] #{"column":"naziv","opt":"tip promene","value":"vrednost za zamenu"} - if(null_value_options=='replace'): + #if(null_value_options=='replace'): #print("replace null") - dict=null_values_replacers - while(len(dict)>0): - replace=dict.pop() - col=replace['column'] - opt=replace['option'] - if(opt=='replace'): - replacevalue=replace['value'] - data[col]=data[col].fillna(replacevalue) - elif(null_value_options=='delete_rows'): - data=data.dropna() - elif(null_value_options=='delete_columns'): - data=data.dropna(axis=1) + dict=null_values_replacers + while(len(dict)>0): + replace=dict.pop() + col=replace['column'] + opt=replace['option'] + + if(opt=='replace'): + replacevalue=replace['value'] + data[col]=data[col].fillna(replacevalue) + + data=data.dropna() + + #print(data) + #print(data.shape) # @@ -237,11 +240,12 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): elif(encoding=='onehot'): - category_columns=[] - for col in data.columns: - if(data[col].dtype==np.object_): - category_columns.append(col) - data=pd.get_dummies(data, columns=category_columns, prefix=category_columns) + if((len(pd.unique(data[kolona]))>20)or (kolona==output_column)): + encoder=LabelEncoder() + data[kolona]=encoder.fit_transform(data[kolona]) + + else: + data=pd.get_dummies(data, columns=kolona, prefix=kolona) elif(encoding=='ordinal'): encoder = OrdinalEncoder() @@ -296,7 +300,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): #x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test, random_state=random) #print(x_train,x_test) x, x_test, y, y_test = train_test_split(x, y, test_size=test, random_state=random, shuffle=True) - x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.15, shuffle=True) + x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=(1.0-paramsModel['validationSize'])) # Treniranje modela # # |