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#!/usr/bin/env python
# File created on 21 Feb 2010
from __future__ import division
__author__ = "Greg Caporaso"
__copyright__ = "Copyright 2011, The QIIME Project"
__credits__ = ["Greg Caporaso"]
__license__ = "GPL"
__version__ = "1.4.0"
__maintainer__ = "Greg Caporaso"
__email__ = "gregcaporaso@gmail.com"
__status__ = "Release"
from numpy import array
from cogent.util.unit_test import TestCase, main
from qiime.parse import parse_coords
from qiime.transform_coordinate_matrices import map_sample_ids, reorder_coords,\
filter_coords_matrix, pad_coords_matrix, pad_coords_matrices,\
get_procrustes_results, procrustes_monte_carlo
class ProcrustesTests(TestCase):
""" Tests of the Procrustes wrapper code """
def setUp(self):
""" """
self.pcoa1_f = pcoa1_f.split('\n')
self.sample_ids1, self.coords1, self.eigvals1, self.pct_var1 =\
parse_coords(self.pcoa1_f)
self.pcoa2_f = pcoa2_f.split('\n')
self.sample_ids2, self.coords2, self.eigvals2, self.pct_var2 =\
parse_coords(self.pcoa2_f)
self.pcoa3_f = pcoa3_f.split('\n')
self.sample_ids3, self.coords3, self.eigvals3, self.pct_var3 =\
parse_coords(self.pcoa3_f)
self.pcoa4_f = pcoa4_f.split('\n')
self.sample_ids4, self.coords4, self.eigvals4, self.pct_var4 =\
parse_coords(self.pcoa3_f)
self.sample_id_map1 = sample_id_map1
def test_map_sample_ids(self):
"""Mapping and reordering of sample IDs functions as expected
"""
expected = ['s4','s1','s2','s3']
actual = map_sample_ids(self.sample_ids1,self.sample_id_map1)
self.assertEqual(actual,expected)
expected = ['s1','s2','s3','s4']
actual = map_sample_ids(self.sample_ids2,self.sample_id_map1)
self.assertEqual(actual,expected)
expected = ['s1','s2','s3','s4']
actual = map_sample_ids(self.sample_ids3,self.sample_id_map1)
self.assertEqual(actual,expected)
# bad sample id raises KeyError
self.assertRaises(KeyError,map_sample_ids,['abcd','aaa','ccc'],\
self.sample_id_map1)
def test_reorder_coords(self):
"""Reordering of columns functions as expected """
m = [[1,2,3],[4,5,6],[7,8,9]]
in_sids = ['A','B','C']
order = ['A','B','C']
expected = m
self.assertEqual(reorder_coords(m,in_sids,order),expected)
in_sids = ['C','B','A']
expected = [[7,8,9],[4,5,6],[1,2,3]]
self.assertEqual(reorder_coords(m,in_sids,order),expected)
in_sids = ['C','B','A']
expected = [[7,8,9],[4,5,6],[1,2,3]]
self.assertEqual(reorder_coords(m,in_sids,order),expected)
# order leaves some samples out
m = [[1,2,3],[4,5,6],[7,8,9]]
in_sids = ['A','B','C']
order = ['A','B']
expected = [[1,2,3],[4,5,6]]
self.assertEqual(reorder_coords(m,in_sids,order),expected)
def test_reorder_coords_errors(self):
"""Reordering of columns handles errors """
m = [[1,2,3],[4,5,6],[7,8,9]]
in_sids = ['A','B','C']
order = ['A','B','C','D']
self.assertRaises(ValueError,reorder_coords,m,in_sids,order)
def test_filter_coords_matrix(self):
""" Removing coordinate from each sample functions as expected
"""
m = array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
expected = array([[1],[4],[7],[10]])
self.assertEqual(filter_coords_matrix(m,1),expected)
expected = array([[1,2],[4,5],[7,8],[10,11]])
self.assertEqual(filter_coords_matrix(m,2),expected)
def test_pad_coords_matrix(self):
""" padding a coordinates matrix with zeros functions as expected
"""
m = array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
actual = pad_coords_matrix(m,0)
self.assertEqual(actual,m)
m = array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
expected = array([[1,2,3,0.,0.],[4,5,6,0.,0.],\
[7,8,9,0.,0.],[10,11,12,0.,0.]])
actual = pad_coords_matrix(m,2)
self.assertEqual(actual,expected)
m = array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
self.assertRaises(ValueError,pad_coords_matrix,m,-3)
def test_pad_coords_matrices(self):
""" padding the correct choice of a coords matrix with zeros functions
"""
# first is longer
m1 = array([[1,0,3,9,4],[4,5,6,94,5],[7,8,9,6,6],[10,11,12,4,6]])
m2 = array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
m2_expected = array([[1,2,3,0.,0.],[4,5,6,0.,0.],\
[7,8,9,0.,0.],[10,11,12,0.,0.]])
actual = pad_coords_matrices(m1,m2)
expected = (m1,m2_expected)
self.assertEqual(actual,expected)
# second is longer
m1 = array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
m2 = array([[1,2,3,9],[4,5,6,94],[7,8,9,6],[10,11,12,4]])
m1_expected = array([[1,2,3,0.],[4,5,6,0.],[7,8,9,0.],[10,11,12,0.]])
actual = pad_coords_matrices(m1,m2)
expected = (m1_expected,m2)
self.assertEqual(actual,expected)
# equal length, so no change
m1 = array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
m2 = array([[0,2,3],[4,5,6],[7,8,9],[10,11,12]])
actual = pad_coords_matrices(m1,m2)
expected = (m1,m2)
self.assertEqual(actual,expected)
def test_get_procrustes_results(self):
sample_id_map = {'CP3A1':'S1','CC1A1':'S2','CC2A1':'S3','CP1A1':'S4'}
actual = get_procrustes_results(self.pcoa1_f,self.pcoa1_f,\
sample_id_map=sample_id_map,randomize=None,max_dimensions=None)
# just some sanity checks as the individual componenets are
# already tested -- these are based on looking at the output of the
# run, and testing to ensure that it hasn't changed
self.assertEqual(set(actual[0].split('\n')),set('pc vector number\t1\t2\t3\t4\t5\t6\nS1\t0.116737988534\t0.414627960015\t0.201315243115\t0.113769076804\t-0.283025353088\t-0.144278863311\nS2\t-0.238263544222\t-0.37724227779\t-0.169458651217\t0.0305157004776\t0.112181007345\t0.0677415967093\nS3\t-0.199225958574\t-0.250846540029\t-0.119813087305\t-0.155652031006\t0.18495315824\t-0.160875399364\nS4\t0.320751514262\t0.213460857804\t0.0879564954067\t0.0113672537238\t-0.0141088124974\t0.237412665966\n\n\neigvals\t8976580.24393\t6044862.67619\t4372581.39431\t3161360.10319\t2583594.45275\t2407555.39787\n% variation explained\t23.1764657118\t15.6071186064\t11.2894866423\t8.16225689998\t6.67053450426\t6.21602253997'.split('\n')))
self.assertEqual(set(actual[1].split('\n')),set('pc vector number\t1\t2\t3\t4\t5\t6\nS1\t0.116737988534\t0.414627960015\t0.201315243115\t0.113769076804\t-0.283025353088\t-0.144278863311\nS2\t-0.238263544222\t-0.37724227779\t-0.169458651217\t0.0305157004776\t0.112181007345\t0.0677415967093\nS3\t-0.199225958574\t-0.250846540029\t-0.119813087305\t-0.155652031006\t0.18495315824\t-0.160875399364\nS4\t0.320751514262\t0.213460857804\t0.0879564954067\t0.0113672537238\t-0.0141088124974\t0.237412665966\n\n\neigvals\t8976580.24393\t6044862.67619\t4372581.39431\t3161360.10319\t2583594.45275\t2407555.39787\n% variation explained\t23.1764657118\t15.6071186064\t11.2894866423\t8.16225689998\t6.67053450426\t6.21602253997'.split('\n')))
self.assertTrue(actual[2] < 6e-30)
def test_get_procrustes_results_imprefect_sample_overlap(self):
sample_id_map = {'aaa':'S0','bbb':'S1','ccc':'S2','ddd':'S3','eee':'S4'}
actual = get_procrustes_results(self.pcoa3_f,self.pcoa4_f,\
sample_id_map=sample_id_map,randomize=None,max_dimensions=None)
# Confirm that only the sample ids that are in both procrustes results
# show up in the output
for a in actual[:2]:
self.assertTrue('S1' in a)
self.assertTrue('S2' in a)
self.assertTrue('S3' in a)
self.assertTrue('S0' not in a)
self.assertTrue('S4' not in a)
def test_procrustes_monte_carlo(self):
""" sanity test of procrustes_monte_carlo wrapper function
THIS TEST MAY RANDOMLY FAIL BECAUSE IT IS BASED ON TESTING
RANDOM PERMUTATIONS, BUT THAT SHOULD BE RARE.
"""
def shuffle_f(coords):
""" A fake shuffle function -- used to avoid random test failures
returns a re-ordered coords2
"""
return array(\
[[-0.16713783, 0.22321481, 0.33766418, 0.22785083, -0.23830446, -0.18754852],
[-0.14864343, 0.07290181, -0.06250315, 0.03201972, -0.0966749 , -0.10337987],
[ 0.35725269, -0.00761567, 0.09044279, -0.21006839, -0.01355589, -0.04590791],
[ 0.26535811, 0.09772598, 0.04339214, -0.21014987, 0.14089095, -0.10261849]])
actual = procrustes_monte_carlo(self.pcoa1_f,
self.pcoa2_f,
trials=100,
shuffle_f=shuffle_f)
# just some sanity checks as the individual componenets are
# already tested -- these are based on looking at the output of the
# run, and testing to ensure that it hasn't changed
expected_actual_m2 = 0.0211
expected_len_trial_m2 = 100
expected_count_better = 0
expected_p_value = 0.0
self.assertAlmostEqual(actual[0],expected_actual_m2,3)
self.assertEqual(len(actual[1]),expected_len_trial_m2)
self.assertEqual(actual[2], expected_count_better)
self.assertEqual(actual[3], expected_p_value)
pcoa1_f = """pc vector number 1 2 3 4 5 6
CP3A1 -322.585729836 938.204618621 -28.2137779927 490.569459399 -1046.48732174 -234.500487421
CC1A1 -1190.7802044 -998.399315723 -934.981261143 286.964362952 -79.9676118756 284.018377708
CC2A1 -1095.30958517 -689.284928862 -813.567688811 -168.328870745 98.0045191705 -275.089103439
CP1A1 176.351315302 446.228808693 -305.44480187 240.13477453 -388.822965062 698.967247585
eigvals 8976580.24393 6044862.67619 4372581.39431 3161360.10319 2583594.45275 2407555.39787
% variation explained 23.1764657118 15.6071186064 11.2894866423 8.16225689998 6.67053450426 6.21602253997
"""
pcoa2_f = """pc vector number 1 2 3 4 5 6
CC1A1 0.265358109258 0.0977259770776 0.0433921359913 -0.210149870291 0.140890951431 -0.102618488686
CC2A1 0.357252689812 -0.00761566770389 0.0904427944586 -0.210068386229 -0.0135558941951 -0.0459079084294
CP1A1 -0.148643434206 0.072901814482 -0.0625031523656 0.0320197230867 -0.0966748975994 -0.103379871571
CP3A1 -0.167137833152 0.223214806728 0.337664175642 0.227850833252 -0.238304458085 -0.187548520834
eigvals 1.03654365499 0.486727634877 0.436010533243 0.344489629748 0.325443839964 0.301867574234
% variation explained 17.9408597738 8.42445197874 7.54662266189 5.96254688461 5.63289569997 5.2248294546
"""
pcoa3_f = """pc vector number 1 2 3 4 5 6
aaa 0.265358109258 0.0977259770776 0.0433921359913 -0.210149870291 0.140890951431 -0.102618488686
bbb 0.357252689812 -0.00761566770389 0.0904427944586 -0.210068386229 -0.0135558941951 -0.0459079084294
ccc -0.148643434206 0.072901814482 -0.0625031523656 0.0320197230867 -0.0966748975994 -0.103379871571
ddd -0.167137833152 0.223214806728 0.337664175642 0.227850833252 -0.238304458085 -0.187548520834
eigvals 1.03654365499 0.486727634877 0.436010533243 0.344489629748 0.325443839964 0.301867574234
% variation explained 17.9408597738 8.42445197874 7.54662266189 5.96254688461 5.63289569997 5.2248294546
"""
pcoa4_f = """pc vector number 1 2 3 4 5 6
eee 0.265358109258 0.0977259770776 0.0433921359913 -0.210149870291 0.140890951431 -0.102618488686
bbb 0.357252689812 -0.00761566770389 0.0904427944586 -0.210068386229 -0.0135558941951 -0.0459079084294
ccc -0.148643434206 0.072901814482 -0.0625031523656 0.0320197230867 -0.0966748975994 -0.103379871571
ddd -0.167137833152 0.223214806728 0.337664175642 0.227850833252 -0.238304458085 -0.187548520834
eigvals 1.03654365499 0.486727634877 0.436010533243 0.344489629748 0.325443839964 0.301867574234
% variation explained 17.9408597738 8.42445197874 7.54662266189 5.96254688461 5.63289569997 5.2248294546
"""
sample_id_map1 = {'CC1A1':'s1','CC2A1':'s2','CP1A1':'s3','CP3A1':'s4',\
'aaa':'s1','bbb':'s2','ccc':'s3','ddd':'s4'}
if __name__ == "__main__":
main()
|
