def test_nti(self):
"test that nti returns the correct value when shuffle seeded at 0"
# define nec components
seed(0)
datamtx = array([[0.0, 0.4, 2.0, 1.3],
[0.4, 0.0, 1.6, 0.2],
[2.0, 1.6, 0.0, 1.1],
[1.3, 0.2, 1.1, 0.0]])
all_ids = [0,1,2,3]
ids_to_keep = [0,1,2]
iters = 3
expected_out = 0.08151391459392235
actual_out = nti(datamtx, all_ids, ids_to_keep, iters)
self.assertFloatEqual(expected_out, actual_out)
# test that an unsorted list of ids returns same result
unsorted_ids_to_keep = [2,0,1]
seed(0)
actual_out = nti(datamtx, all_ids, ids_to_keep, iters)
self.assertFloatEqual(expected_out, actual_out)
# test for situations where standard deviation would be close to zero
# but not actually zero due to floating point arithmetic errors
datamtx = array([[0.0, 0.1, 0.1, 0.1],
[0.1, 0.0, 0.1, 0.1],
[0.1, 0.1, 0.0, 0.1],
[0.1, 0.1, 0.1, 0.0]])
all_ids = [0,1,2,3]
ids_to_keep = [0,1,2,3]
self.assertRaises(ValueError, nti, datamtx, all_ids, ids_to_keep, iters)
def test_nti(self):
"test that nti returns the correct value when shuffle seeded at 0"
# define nec components
seed(0)
datamtx = array([[0.0, 0.4, 2.0, 1.3], [0.4, 0.0, 1.6, 0.2],
[2.0, 1.6, 0.0, 1.1], [1.3, 0.2, 1.1, 0.0]])
all_ids = [0, 1, 2, 3]
ids_to_keep = [0, 1, 2]
iters = 3
expected_out = 0.08151391459392235
actual_out = nti(datamtx, all_ids, ids_to_keep, iters)
self.assertFloatEqual(expected_out, actual_out)
# test that an unsorted list of ids returns same result
unsorted_ids_to_keep = [2, 0, 1]
seed(0)
actual_out = nti(datamtx, all_ids, ids_to_keep, iters)
self.assertFloatEqual(expected_out, actual_out)
# test for situations where standard deviation would be close to zero
# but not actually zero due to floating point arithmetic errors
datamtx = array([[0.0, 0.1, 0.1, 0.1], [0.1, 0.0, 0.1, 0.1],
[0.1, 0.1, 0.0, 0.1], [0.1, 0.1, 0.1, 0.0]])
all_ids = [0, 1, 2, 3]
ids_to_keep = [0, 1, 2, 3]
self.assertRaises(ValueError, nti, datamtx, all_ids, ids_to_keep,
iters)
def test_nti(self):
"""Test that nti works correctly."""
# using the input of the distance matrix generated from this tree
# '(((sp1:.06,sp2:.1)A:.031,(sp3:.001,sp4:.01)B:.2)AB:.4,((sp5:.03,sp6:.02)C:.13,(sp7:.01,sp8:.005)D:.1)CD:.3)root;'
# using this tree and this clumping we get almost exact agreement
# between phylocom and relatedness.py
distmat = array([[ 0. , 0.16 , 0.292, 0.301, 0.951, 0.941, 0.901, 0.896],
[ 0.16 , 0. , 0.332, 0.341, 0.991, 0.981, 0.941, 0.936],
[ 0.292, 0.332, 0. , 0.011, 1.061, 1.051, 1.011, 1.006],
[ 0.301, 0.341, 0.011, 0. , 1.07 , 1.06 , 1.02 , 1.015],
[ 0.951, 0.991, 1.061, 1.07 , 0. , 0.05 , 0.27 , 0.265],
[ 0.941, 0.981, 1.051, 1.06 , 0.05 , 0. , 0.26 , 0.255],
[ 0.901, 0.941, 1.011, 1.02 , 0.27 , 0.26 , 0. , 0.015],
[ 0.896, 0.936, 1.006, 1.015, 0.265, 0.255, 0.015, 0. ]])
seed(0)
obs_nti = nti(distmat, list(arange(8)), [0,1,3,6],1000)
self.assertFloatEqual(-1.2046544711672049, obs_nti)
def test_nti(self):
"""Test that nti works correctly."""
# using the input of the distance matrix generated from this tree
# '(((sp1:.06,sp2:.1)A:.031,(sp3:.001,sp4:.01)B:.2)AB:.4,((sp5:.03,sp6:.02)C:.13,(sp7:.01,sp8:.005)D:.1)CD:.3)root;'
# using this tree and this clumping we get almost exact agreement
# between phylocom and relatedness.py
distmat = array([[0., 0.16, 0.292, 0.301, 0.951, 0.941, 0.901, 0.896],
[0.16, 0., 0.332, 0.341, 0.991, 0.981, 0.941, 0.936],
[0.292, 0.332, 0., 0.011, 1.061, 1.051, 1.011, 1.006],
[0.301, 0.341, 0.011, 0., 1.07, 1.06, 1.02, 1.015],
[0.951, 0.991, 1.061, 1.07, 0., 0.05, 0.27, 0.265],
[0.941, 0.981, 1.051, 1.06, 0.05, 0., 0.26, 0.255],
[0.901, 0.941, 1.011, 1.02, 0.27, 0.26, 0., 0.015],
[0.896, 0.936, 1.006, 1.015, 0.265, 0.255, 0.015,
0.]])
seed(0)
obs_nti = nti(distmat, list(arange(8)), [0, 1, 3, 6], 1000)
assert_almost_equal(-1.2046544711672049, obs_nti)
