BOGA NIE MA!!

library(GA)
library(caret)
library(ROCR)
Dane_treningowe <- read.csv("C:/Users/Maciek/Desktop/SMUwAP-lab3/3/Dane_treningowe.csv",header = T, sep = ",");
regress_all<-glm(Dane_treningowe$Status.kredytu~Dane_treningowe[,2]+Dane_treningowe[,3]+ Dane_treningowe[,4]+Dane_treningowe[,5]+Dane_treningowe[,6]+Dane_treningowe[,7]+Dane_treningowe[,8]+Dane_treningowe[,9]+ Dane_treningowe[,10]+Dane_treningowe[,11]+Dane_treningowe[,12], family = binomial(logit))
for (i in (2:12))
{
  regress<-glm(Status.kredytu~Dane_treningowe[,i] , data=Dane_treningowe, family = binomial(logit))
  print(i)
  print(summary(regress)$coefficient)
}

precision <- function(matrix) {
  # True positive
  tp <- matrix[2, 2]
  # false positive
  fp <- matrix[1, 2]
  return (tp / (tp + fp))
}

recall <- function(matrix) {
  # true positive
  tp <- matrix[2, 2]
  # false positive
  fn <- matrix[2, 1]
  return (tp / (tp + fn))
}

 x = 0
while(x < 0.8)
{
  x = x + 0.05
  print("próg=")
  print(x)
  predict <- predict(regress_all, Dane_treningowe, type='response')

  table_mat <- table(Dane_treningowe$Status.kredytu, predict > x)
  print(table_mat)

  accuracy_Test <- sum(diag(table_mat)) / sum(table_mat)
  print("acc = ")
  print(accuracy_Test)

  print("prec = ")
  prec <- precision(table_mat)
  print(prec)

  print("rec = ")
  rec <- recall(table_mat)
  print(rec)

  F1 <- (2*(prec*rec)/(prec+rec))
  print("F1")
  print(F1)

}

 temp <- Dane_treningowe[,2:12]

FitFun<-function(string)
{
  formula <- Dane_treningowe$Status.kredytu~.
  inc <- which(string == 1)
  X <- as.data.frame(cbind(temp[,inc]))
  #print(X[1:5,])
  reg<-glm(formula, data =  X, family = binomial(logit))
  BIC.f<-BIC(reg)
  return(-BIC.f)
}
GA <- ga("binary",fitness = FitFun, nBits = 11, maxiter= 20, popSize= 75, pcrossover= 0.8, pmutation= 0.2, monitor = plot)

summary(GA)


temp <- temp[,10:11]
reg_po_ga<-glm(Dane_treningowe$Status.kredytu~., data =  temp, family = binomial(logit))
summary(reg_po_ga)

x = 0
while(x < 0.8)
{
  x = x + 0.1
  print("próg=")
  print(x)
  predict <- predict(reg_po_ga, Dane_treningowe, type='response')

  table_mat <- table(Dane_treningowe$Status.kredytu, predict > x)
  print(table_mat)

  accuracy_Test <- sum(diag(table_mat)) / sum(table_mat)
  print("acc = ")
  print(accuracy_Test)

  print("prec = ")
  prec <- precision(table_mat)
  print(prec)

  print("rec = ")
  rec <- recall(table_mat)
  print(rec)

  F1 <- (2*(prec*rec)/(prec+rec))
  print("F1")
  print(F1)
}


rm(Dane_treningowe)
Dane_treningowe <- read.csv("C:/Users/Maciek/Desktop/SMUwAP-lab3/3/Dane_testowe.csv",header = T, sep = ",")
x = 0
while(x == 0){
  x = 0.5
print("próg=");
print(x);
predict <- predict(reg_po_ga, Dane_treningowe, type='response');

table_mat <- table(Dane_treningowe$Status.kredytu, predict > 0.5);
print(table_mat);

accuracy_Test <- sum(diag(table_mat)) / sum(table_mat);
print("acc = ");
print(accuracy_Test);

print("prec = ");
prec <- precision(table_mat);
print(prec);

print("rec = ");
rec <- recall(table_mat);
print(rec);

F1 <- (2*(prec*rec)/(prec+rec));
print("F1");
print(F1)}