def writefile2(X, y, model, filerw):
ts = tsp(X, y, model)
a1, a2, a3, a4, a5, a6, a7, a8, a9, a10 = ts.fit()
filerw.write('True Positive: ' + str(a1) + "\n")
filerw.write('True Negative: ' + str(a2) + "\n")
filerw.write('False Positive ' + str(a3) + "\n")
filerw.write('False Negative ' + str(a4) + "\n")
filerw.write('Sensitivity: ' + str(a5) + "\n")
filerw.write('Specificity: ' + str(a6) + "\n")
filerw.write('Accuracy: ' + str(a7) + "\n")
filerw.write('f1_score: ' + str(a8) + "\n")
#filer.write('Recall score: '+str(recall_score(self.y,ypred))
filerw.write('MCC: ' + str(a9) + "\n")
filerw.write('ROC_AUC: ' + str(a10) + "\n")
def writefile(X,y,model,filerw):
ts=tsp(X,y,model)
a1,a2,a3,a4,a5,a6,a7,a8,a9,a10=ts.fit()
filerw.write('True Positive: '+str(a1)+"\n")
filerw.write('True Negative: '+str(a2)+"\n")
filerw.write('False Positive '+str(a3)+"\n")
filerw.write('False Negative '+str(a4)+"\n")
filerw.write('Sensitivity: '+str(a5)+"\n")
filerw.write('Specificity: '+str(a6)+"\n")
filerw.write('Accuracy: '+str(a7)+"\n")
filerw.write('f1_score: '+str(a8)+"\n")
#filer.write('Recall score: '+str(recall_score(self.y,ypred))
filerw.write('MCC: '+str(a9)+"\n")
filerw.write('ROC_AUC: '+str(a10)+"\n")
ypred=pd.DataFrame(model.predict(X))
ypred.columns=['Pred']
ypr2=pd.DataFrame(model.predict_proba(X))
ypr2.columns=['%Prob(-1)','%Prob(+1)']
dfsvd=pd.concat([ypred,ypr2],axis=1)
dfsvd['Diff']=abs(dfsvd['%Prob(-1)']-dfsvd['%Prob(+1)'])
dfsvd['Outlier_info(Confidence estimation approach, Threshold 0.5)']=['In' if x>=0.5 else 'Outlier' for x in dfsvd['Diff']]
return dfsvd