I am applying Benders decomposition that solves the primal of the
sub-problem. The objective function of this primal sub-problem is +ve value
and all the dual variables, given by AMPL, are zeros. How this could be
possible?

If you run this files, you will find the primal sub-problem is solved with
an objective value equals 12. I asked AMPL to display the dual variables
associated with the constraints of this primal and it gives me zeros.

Here is the model file
--------------------------------------------------------------------------- --------------------------------------------------------
#parameters
param num_attributes_plus1;
param num_attributes;
param double_num_attributes;
param num_observations;
param step default 1;
param start default 1;
set nols := 1..10000;
set nols2 := 1..10000;

param M_Pos default 0; # number of +ve opservations
param M_Neg default 0; # number of -ve opservations

# sets

set N = start .. num_attributes by step; # set of attributes
set S = start .. num_observations by step;# set of observations
set NN = start .. double_num_attributes by step; # double the set of
attributes
set S_dot;
set S_dot_bar;
set N1= start .. num_attributes_plus1; # set of attributes +1 column

param a{S,N1} binary;
param c{S} default 0; # = 0 if the +ve observation is not covered, =1 if
the +ve observation is covered
param dual_coverage_pos {nols2};
param dual_a_pos{S, nols2};
param dual_y_pos{S, nols2};
param nsoln default 0;
param nsoln2 default 0;
param x_inf{nols,NN};

### variables

var d integer; # degree of the generated pattern
var x{NN} binary;
var y{S} >= 0, integer;
var Gamma;

# Master problem-positive pattern generation

minimize Benders_master : Gamma ;

# Note, in the binarized data, the entity a(i,1) is reserved to show
whether observation i is positive or negative

#### constraints

subject to constraint_b_pos {i in S: a[i,1] = 0}: sum{ j in N} a[i,j+1]*
x[j] + sum{j in N} (1-a[i,j+1]) * x[num_attributes+j]<= d-1;

subject to constraint_c {j in N}: x[j] + x[num_attributes+j] <= 1;

subject to constraint_d: sum{j in NN} x[j] = d;

subject to constraint_e_1: d >= 1;

subject to constraint_e_2: d <= num_attributes;

subject to feasibility_cut {n in nols: n <= nsoln}: if nsoln > 0 then  

     sum {j in NN: x_inf[n,j]=0} x[j] + sum {j in NN: x_inf[n,j]=1}
(1-x[j]) >=1 ;

subject to optimality_cut_pos {m in nols2: m <= nsoln2}: if nsoln2 > 0 then

     Gamma >=  sum {i in S: c[i] = 0 and a[i,1] = 1} dual_a_pos[i, m] * d +
dual_coverage_pos[m] * ((M_Pos- sum{i in S:a[i,1] = 1 and c[i]=0} c[i]) -
1) + sum {i in S: c[i] = 0 and a[i,1] = 1} dual_y_pos[i, m];

 # Sub problem-positive pattern generation

 minimize objective_z_pos: sum{i in S: a[i,1] = 1 and c[i]=0} y[i]; # the
model tells that when y = 1 it refers to an uncovered observation

 subject to constraint_a_pos {i in S: c[i] = 0 and a[i,1] = 1}: sum{j in N}
a[i,j+1] * x[j] + sum{j in N} (1-a[i,j+1]) * x[num_attributes+j] +
num_attributes * y[i] >= d;

 subject to coverage_pos: sum{i in S:a[i,1] = 1 and c[i]=0} y[i] <= (M_Pos-
sum{i in S:a[i,1] = 1 and c[i]=0} c[i]) - 1; # the generated pattern should
cover at least one observation

 subject to y_pos {i in S: c[i] = 0 and a[i,1] = 1}: y[i] <= 1;
--------------------------------------------------------------------------- --------------------------------------------------------------------

and here is the algorithm

--------------------------------------------------------------------------- --------------------
model bender3.mod;
data  ryoo09.txt;
param UB default 0;
param sub_sol_time;
param mas_sol_time;

param Num_pos_patterns default 0;
param Num_neg_patterns default 0;
param Num_covered default 0; #to tell the number of observations covered by
the generated pattern.
option solver_msg 0;
option solver cplexamp;
option eexit -500;
option display_precision 0;
option presolve 0;
options cplex_options "presolve 0";

suffix up OUT;
suffix down OUT;
suffix dunbdd;

problem master_1_problem_pos: x,d,constraint_b_pos, constraint_c,
constraint_d, constraint_e_1, constraint_e_2;  

problem master_problem_pos: x,d,Gamma, constraint_b_pos, constraint_c,
constraint_d, constraint_e_1, constraint_e_2, feasibility_cut,
optimality_cut_pos, Benders_master;  

problem master_problem_feasible_pos: x,d,constraint_b_pos, constraint_c,
constraint_d, constraint_e_1, constraint_e_2, feasibility_cut;  

problem sub_problem_pos: y,constraint_a_pos, coverage_pos, y_pos,
objective_z_pos;

let double_num_attributes := 2 * num_attributes;
let num_attributes_plus1 := num_attributes + 1;

for {i in S}
{
 if a[i,1] = 1 then let M_Pos := M_Pos + 1 ;
 else if a[i,1] = 0 then let M_Neg := M_Neg +1 ;

param a:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17:=
1 1 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0
2 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0
3 1 0 0 0 0 1 0 1 0 1 1 1 0 1 1 1 0
4 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
5 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
6 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
7 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
8 1 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0
9 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0
10 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0
11 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
12 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
13 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
14 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
15 1 1 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0
16 1 1 1 0 0 1 0 1 0 1 0 0 0 1 0 0 0
17 1 1 1 0 0 1 0 1 0 1 0 0 0 1 0 0 0
18 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 0
19 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1
20 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1
21 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1
22 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 0 0
23 1 1 1 1 0 1 0 1 0 1 0 0 0 1 0 0 0
24 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0
25 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1
26 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1
27 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1
28 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0
29 1 1 1 1 1 1 0 1 0 1 0 0 0 1 0 0 0
30 1 1 1 1 1 1 0 1 0 1 0 0 0 1 0 0 0
31 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
32 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
33 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
34 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1;