def setUp(self):
self.objs_num = 60
self.atts_num = 12
self.cxt_random = fca.make_random_context(self.objs_num, self.atts_num,
0.3)
self.cxt1 = fca.Context([[1, 1, 0], [0, 1, 1], [0, 0, 1]],
['g2', 'g1', 'g3'], ['m1', 'm3', 'm2'])
self.cxt2 = fca.Context([(0, 1, 1), (1, 0, 1), (0, 1, 0)],
['g1', 'g2', 'g3'], ['m1', 'm2', 'm3'])
def setUp(self):
self.objs_num = 60
self.atts_num = 12
self.cxt_random = fca.make_random_context(self.objs_num, self.atts_num, 0.3)
self.cxt1 = fca.Context([[1,1,0], [0,1,1], [0,0,1]],
['g2', 'g1', 'g3'],
['m1', 'm3', 'm2'])
self.cxt2 = fca.Context([(0,1,1), (1,0,1), (0,1,0)],
['g1', 'g2', 'g3'],
['m1', 'm2', 'm3'])
def test_random_cxt(self):
atts_num = 20
objs_num = 1500
cxt_random = fca.make_random_context(objs_num, atts_num, 0.3)
factors_iter = fca.algorithms.algorithm2_w_condition(
cxt_random,
fidelity=1,
allow_repeatitions=False,
min_atts_and_objs=3,
objs_ge_atts=True)
assert len(list(factors_iter)) > 1
def setUp(self):
atts_num = 20
objs_num = 1500
self.cxt_random = fca.make_random_context(objs_num, atts_num, 0.3)
self.cxt1 = fca.Context([(0, 1, 1), (1, 0, 1), (0, 1, 0)],
['g1', 'g2', 'g3'], ['m1', 'm2', 'm3'])
self.cxt2 = fca.Context([(0, 1, 1), (1, 0, 1), (0, 1, 1)],
['g1', 'g2', 'g3'], ['m1', 'm2', 'm3'])
self.cxt3 = fca.Context(
[[1, 0, 1, 0, 1, 1], [0, 0, 1, 0, 0, 0], [1, 1, 0, 1, 1, 1],
[0, 0, 1, 0, 0, 1], [0, 1, 1, 1, 0, 1]],
['g1', 'g2', 'g3', 'g4', 'g5'],
['m1', 'm2', 'm3', 'm4', 'm5', 'm6'])
def setUp(self):
atts_num = 20
objs_num = 1500
self.cxt_random = fca.make_random_context(objs_num, atts_num, 0.3)
self.cxt1 = fca.Context([(0,1,1), (1,0,1), (0,1,0)],
['g1', 'g2', 'g3'],
['m1', 'm2', 'm3'])
self.cxt2 = fca.Context([(0,1,1), (1,0,1), (0,1,1)],
['g1', 'g2', 'g3'],
['m1', 'm2', 'm3'])
self.cxt3 = fca.Context([[1,0,1,0,1,1],
[0,0,1,0,0,0],
[1,1,0,1,1,1],
[0,0,1,0,0,1],
[0,1,1,1,0,1]],
['g1', 'g2', 'g3', 'g4', 'g5'],
['m1', 'm2', 'm3', 'm4', 'm5', 'm6'])
def test_compare_aclosure_associative_aclosure(self):
setup = '''import fca
cxt_r = fca.make_random_context(2000, 200, 0.4)
'''
stmt = '''
for att in cxt_r.attributes:
cxt_r.aclosure([att])
'''
cxt_r = fca.make_random_context(1000, 100, 0.4)
print(timeit.timeit(stmt, setup, number=10))
cProfile.runctx(stmt, globals(), locals())
stmt = '''
for att in cxt_r.attributes:
cxt_r.associative_aclosure([att])
'''
print(timeit.timeit(stmt, setup, number=10))
cProfile.runctx(stmt, globals(), locals())
def test_compare_aclosure_associative_aclosure(self):
setup = '''import fca
cxt_r = fca.make_random_context(2000, 200, 0.4)
'''
stmt = '''
for att in cxt_r.attributes:
cxt_r.aclosure([att])
'''
cxt_r = fca.make_random_context(1000, 100, 0.4)
print(timeit.timeit(stmt, setup, number=10))
cProfile.runctx(stmt, globals(), locals())
stmt = '''
for att in cxt_r.attributes:
cxt_r.associative_aclosure([att])
'''
print(timeit.timeit(stmt, setup, number=10))
cProfile.runctx(stmt, globals(), locals())
D = cxt.oprime(C)
to_remove_U = set(itertools.product(C, D)) & U
V = len(to_remove_U)
if not allow_repeatitions:
to_remove = (set(itertools.product(C, cxt.attributes)) |
set(itertools.product(cxt.objects, D))) & U
else:
to_remove = to_remove_U
elif good_factor(cpt=fca.Concept(C, D)):
if len(to_remove) == 0:
raise Exception(
f'Algorithm stuck, something went wrong, pairs left '
f'{len(U)}')
U -= to_remove
# print(f'Factor out: {len(C)}, {len(D)}')
yield fca.Concept(C, D), len(to_remove) / len_initial, (len_initial - len(U)) / len_initial
break
else:
assert False
if __name__ == '__main__':
import cProfile
import numpy.linalg as lalg
from fca import make_random_context
r_cxt = make_random_context(1200, 1000, .3)
# r_cxt = r_cxt.reduce_attributes().reduce_objects()
cProfile.run('print(lalg.svd(r_cxt.np_table))')
cProfile.run('for x in algorithm2(r_cxt, .3): print(len(x[0].extent), len(x[0].intent), x[1])')
for j in set(cxt.attributes) - D
]
if ls_measures:
maxDj = max(ls_measures, key=lambda x: x[0])
else:
maxDj = [0,]
if maxDj[0] > V:
j = maxDj[1]
Dj = D | {j}
C = cxt.aprime(Dj)
D = cxt.oprime(C)
to_remove = set(itertools.product(C, D)) & U
V = len(to_remove)
else:
break
if len(to_remove) == 0:
print('Algorithm stuck, something went wrong, pairs left ', len(U))
assert False
U -= to_remove
yield fca.Concept(C, D), len(to_remove) / len_initial
if __name__ == '__main__':
import cProfile
import numpy.linalg as lalg
r_cxt = fca.make_random_context(1200, 1000, .3)
# r_cxt = r_cxt.reduce_attributes().reduce_objects()
cProfile.run('print(lalg.svd(r_cxt.np_table))')
cProfile.run('for x in algorithm2(r_cxt, .3): print(len(x[0].extent), len(x[0].intent), x[1])')
def setUp(self):
self.cxt_random = fca.make_random_context(60, 12, 0.3)
def test_random_dg_iter():
r_cxt = fca.make_random_context(40, 15, 0.3)
dg_basis = r_cxt.get_attribute_implications()
dg_basis_from_iter = [x for x in r_cxt.attribute_implications_iter()]
assert len(dg_basis) == len(dg_basis_from_iter)
def setUp(self):
self.cxt_random = fca.make_random_context(60, 12, 0.3)
def test_random_dg_iter():
r_cxt = fca.make_random_context(40, 15, 0.3)
dg_basis = r_cxt.get_attribute_implications()
dg_basis_from_iter = [x for x in r_cxt.attribute_implications_iter()]
assert len(dg_basis) == len(dg_basis_from_iter)