Insufficient combination when filter function is specified?

Hello, thank you for providing a great library! I tried to use filter function to cut invalid combination, but it seems generated combinations are insufficient.

# Generate combinations of every supported Python + NumPy version combination.

from allpairspy import AllPairs

numpy = ['1.9', '1.10', '1.11', '1.12', '1.13', '1.14']
python = ['2.7', '3.4', '3.5', '3.6']

def _validate(numpy, python):
    valid = []
    if numpy == '1.9':
        valid = ['2.7', '3.4']
    elif numpy == '1.10':
        valid = ['2.7', '3.4']
    elif numpy == '1.11':
        valid = ['2.7', '3.4', '3.5']
    elif numpy == '1.12':
        valid = ['2.7', '3.4', '3.5', '3.6']
    elif numpy == '1.13':
        valid = ['2.7', '3.4', '3.5', '3.6']
    elif numpy == '1.14':
        valid = ['2.7', '3.4', '3.5', '3.6']
    return python in valid


def _filter_func(row):
    if len(row) == 2:
        stat = _validate(*row)
        #print('Valid? ', row, ' -> ', stat)
        return stat
    return True


if __name__ == '__main__':
    for i, pairs in enumerate(AllPairs([numpy, python], filter_func=_filter_func)):
        print(i, ':', pairs)

I got:

0 : ['1.9', '2.7']
1 : ['1.10', '2.7']
2 : ['1.11', '2.7']
3 : ['1.12', '2.7']
4 : ['1.13', '2.7']
5 : ['1.14', '2.7']
6 : ['1.14', '3.4']
7 : ['1.13', '3.4']
8 : ['1.12', '3.4']
9 : ['1.11', '3.4']
10 : ['1.10', '3.4']
11 : ['1.9', '3.4']

It seems Python 3.5/3.6 variations are totally ignored. Am I missing something? Any suggestions are welcome. Thanks in advance!

[thombashi]

@kmaehashi Thank you for your comment. That is unexpected behavior. I will check into it.

[ashfaq92]

Code:

parameters = OrderedDict(
    {"x1": [True, False],
     "x2": [True, False]})


def constraints(row):
    n = len(row)
    if n > 1:
        if row[0] is False and row[1] is False:
            return False
    return True


print("PAIRWISE:")
res = AllPairs(parameters, filter_func=constraints, n=2)

Desired Output:

0 Pairs(x1=True, x2=True)
1 Pairs(x1=False, x2=True)
2 Pairs(x1=True, x2=False)

Actual Output:

0 Pairs(x1=True, x2=True)
1 Pairs(x1=False, x2=True)