#!/usr/bin/env python """Unit tests for distance_transform.py functions. """ from __future__ import division from cogent.util.unit_test import TestCase, main from cogent.maths.distance_transform import * from numpy import array, sqrt, shape, ones, diag __author__ = "Justin Kuczynski" __copyright__ = "Copyright 2007-2012, The Cogent Project" __contributors__ = ["Justin Kuczynski", "Zongzhi Liu", "Greg Caporaso"] __license__ = "GPL" __version__ = "1.5.3" __maintainer__ = "Justin Kuczynski" __email__ = "justinak@gmail.com" __status__ = "Prototype" class functionTests(TestCase): """Tests of top-level functions.""" def setUp(self): self.mat_test = asmatrix([[10, 10, 20], [10, 15, 10], [15, 5, 5]], 'float') self.emptyarray = array([], 'd') self.mtx1 = array([[1, 3], [0.0, 23.1]],'d') self.dense1 = array([[1, 3], [5, 2], [0.1, 22]],'d') self.zeromtx = array([[ 0.0, 0.0, 0.0], [ 0.0, 0.0 , 0.0], [ 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0 ]],'d') self.sparse1 = array([[ 0.0, 0.0, 5.33], [ 0.0, 0.0 , 0.4], [ 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0 ]],'d') self.input_binary_dist_otu_gain1 = array([[2,1,0,0], [1,0,0,1], [0,0,3,0], [0,0,0,1]]) def get_sym_mtx_from_uptri(self, mtx): """helper fn, only for square matrices""" numrows, numcols = shape(mtx) for i in range(numrows): for j in range(i): if i==j: break mtx[i,j] = mtx[j,i] # j < i, so row upper triangle return mtx def test_dist_canberra(self): """tests dist_canberra tests inputs of empty mtx, zeros, and results compared with calcs done by hand""" self.assertFloatEqual(dist_canberra(self.zeromtx), zeros((4,4),'d')) mtx1expected = array([[ 0.0, 46.2/52.2], [ 46.2/52.2, 0.0 ]],'d') self.assertFloatEqual(dist_canberra(self.mtx1), mtx1expected) sparse1exp = ones((self.sparse1.shape[0],self.sparse1.shape[0])) # remove diagonal sparse1exp[0,0] = sparse1exp[1,1] = sparse1exp[2,2] = sparse1exp[3,3]\ = 0.0 sparse1exp[0,1] = sparse1exp[1,0] = ( (5.33-.4) / (5.33 + .4) ) self.assertFloatEqual(dist_canberra(self.sparse1), sparse1exp) def test_dist_euclidean(self): """tests dist_euclidean tests inputs of empty mtx, zeros, and dense1 compared with calcs done by hand""" self.assertFloatEqual(dist_euclidean(self.zeromtx), zeros((4,4),'d')) dense1expected = array([[ 0.0, sqrt(17.), sqrt(.9**2 + 19**2)], [ sqrt(17.), 0.0 , sqrt(4.9**2 + 20**2)], [ sqrt(.9**2 + 19**2), sqrt(4.9**2 + 20**2), 0.0 ]],'d') self.assertFloatEqual(dist_euclidean(self.dense1), dense1expected) def test_dist_gower(self): """tests dist_gower tests inputs of empty mtx, zeros, and results compared with calcs done by hand""" self.assertFloatEqual(dist_gower(self.zeromtx), zeros((4,4),'d')) mtx1expected = array([[ 0.0, 2.], [ 2., 0.0 ]],'d') self.assertFloatEqual(dist_gower(self.mtx1), mtx1expected) sparse1expected = array([[ 0.0, 4.93/5.33, 2, 1], [ 4.93/5.33 , 0.0 , 1 + .4/5.33, .4/5.33], [ 2, 1 + .4/5.33, 0,1], [1, .4/5.33, 1, 0.0]],'d') self.assertFloatEqual(dist_gower(self.sparse1), sparse1expected) def test_dist_manhattan(self): """tests dist_manhattan tests inputs of empty mtx, zeros, and dense1 compared with calcs done by hand""" self.assertFloatEqual(dist_manhattan(self.zeromtx), zeros((4,4),'d')) dense1expected = array([[ 0.0, 5.0, 019.9], [ 5.0, 0.0 , 24.9], [ 19.9, 24.90, 0.0 ]],'d') self.assertFloatEqual(dist_manhattan(self.dense1), dense1expected) def test_dist_abund_jaccard(self): """dist_abund_jaccard should compute distances for dense1 and mtx1""" mtx1_expected = array([[0, 0.25], [0.25, 0]], 'd') self.assertEqual(dist_abund_jaccard(self.mtx1), mtx1_expected) dense1_expected = zeros((3,3), 'd') self.assertEqual(dist_abund_jaccard(self.dense1), dense1_expected) sparse1_expected = array([ [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [1.0, 1.0, 0.0, 1.0], [1.0, 1.0, 1.0, 0.0]], 'd') self.assertEqual(dist_abund_jaccard(self.sparse1), sparse1_expected) def test_dist_morisita_horn(self): """tests dist_morisita_horn tests inputs of empty mtx, zeros, and dense1 compared with calcs done by hand""" self.assertFloatEqual(dist_morisita_horn(self.zeromtx), zeros((4,4),'d')) a = 1 - 2*69.3/(26/16. * 23.1 * 4) mtx1expected = array([[0, a], [a,0]],'d') self.assertFloatEqual(dist_morisita_horn(self.mtx1), mtx1expected) def test_dist_bray_curtis(self): """tests dist_bray_curtis tests inputs of empty mtx, zeros, and mtx1 compared with calcs done by hand""" self.assertFloatEqual(dist_manhattan(self.zeromtx), zeros((4,4)*1,'d')) mtx1expected = array([[0, 21.1/27.1], [21.1/27.1, 0]],'d') self.assertFloatEqual(dist_bray_curtis(self.mtx1), mtx1expected) def test_dist_bray_curtis_faith(self): """tests dist_bray_curtis_faith tests inputs of empty mtx, zeros, and mtx1 compared with calcs done by hand""" self.assertFloatEqual(dist_manhattan(self.zeromtx), zeros((4,4)*1,'d')) mtx1expected = array([[0, 21.1/27.1], [21.1/27.1, 0]],'d') self.assertFloatEqual(dist_bray_curtis_faith(self.mtx1), mtx1expected) def test_dist_soergel(self): """tests dist_soergel tests inputs of empty mtx, zeros, and dense1 compared with calcs done by hand/manhattan dist""" self.assertFloatEqual(dist_soergel(self.zeromtx), zeros((4,4)*1,'d')) dense1expected = dist_manhattan(self.dense1) dense1norm = array([[ 1, 8, 23], [8,1,27], [23,27,1]],'d') dense1expected /= dense1norm self.assertFloatEqual(dist_soergel(self.dense1), dense1expected) def test_dist_kulczynski(self): """tests dist_kulczynski tests inputs of empty mtx, zeros, and mtx1 compared with calcs done by hand""" self.assertFloatEqual(dist_kulczynski(self.zeromtx), zeros((4,4)*1,'d')) mtx1expected = array([[0, 1.-1./2.*(3./4. + 3./23.1)], [1.-1./2.*(3./4. + 3./23.1), 0]],'d') self.assertFloatEqual(dist_kulczynski(self.mtx1), mtx1expected) def test_dist_pearson(self): """tests dist_pearson tests inputs of empty mtx, zeros, mtx compared with calcs done by hand, and an example from http://davidmlane.com/hyperstat/A56626.html """ self.assertFloatEqual(dist_pearson(self.zeromtx), zeros((4,4),'d')) mtx1expected = array([[0, 0], [0, 0]],'d') self.assertFloatEqual(dist_pearson(self.mtx1), mtx1expected) # example 1 from http://davidmlane.com/hyperstat/A56626.html ex1 = array([[1, 2, 3, ], [2,5,6]],'d') ex1res = 1 - 4./sqrt(2.*(8+2./3.)) ex1expected = array([[0, ex1res], [ex1res, 0]],'d') self.assertFloatEqual(dist_pearson(ex1), ex1expected) def test_dist_spearman_approx(self): """tests dist_spearman_approx tests inputs of empty mtx, zeros, and an example from wikipedia """ self.assertFloatEqual(dist_spearman_approx(self.zeromtx), zeros((4,4)*1,'d')) # ex1 from wikipedia Spearman's_rank_correlation_coefficient 20jan2009 ex1 = array([[106 ,86 ,100 ,101 ,99 ,103 ,97 ,113 ,112 ,110], [7,0,27,50,28,29,20,12,6,17]],'d') ex1res = 6.*194./(10.*99.) ex1expected = array([[0, ex1res], [ex1res, 0]],'d') self.assertFloatEqual(dist_spearman_approx(ex1), ex1expected) # now binary fns def test_binary_dist_otu_gain(self): """ binary OTU gain functions as expected """ actual = binary_dist_otu_gain(self.input_binary_dist_otu_gain1) expected = array([[0, 1, 2, 2], [1, 0, 2, 1], [1, 1, 0, 1], [1, 0, 1, 0]]) self.assertEqual(actual,expected) def test_binary_dist_chisq(self): """tests binary_dist_chisq tests inputs of empty mtx, zeros, and mtx1 compared with calcs done by hand""" self.assertFloatEqual(binary_dist_chisq(self.zeromtx), zeros((4,4),'d')) mtx1expected = array([[0,sqrt(9/8.)], [ sqrt(9/8.),0]],'d') self.assertFloatEqual(binary_dist_chisq(self.mtx1), mtx1expected) def test_binary_dist_chord(self): """tests binary_dist_chord tests inputs of empty mtx, zeros, and results compared with calcs done by hand""" self.assertFloatEqual(binary_dist_chord(self.zeromtx), zeros((4,4),'d')) mtx1expected = array([[0,sqrt( 1/2. + (1./sqrt(2.) -1.)**2)], [ sqrt( 1/2. + (1./sqrt(2.) -1.)**2),0]],'d') self.assertFloatEqual(binary_dist_chord(self.mtx1), mtx1expected) def test_binary_dist_lennon(self): """tests binary_dist_lennon tests inputs of empty mtx, zeros, and results compared with calcs done by hand""" self.assertFloatEqual(binary_dist_lennon(self.zeromtx), zeros((4,4),'d')) mtxa = array([[5.2,9,0.2], [0,99,1], [0,0.0,8233.1]],'d') self.assertFloatEqual(binary_dist_lennon(mtxa), zeros((3,3),'d') ) mtxb = array([[5.2,0,0.2, 9.2], [0,0,0,1], [0,3.2,0,8233.1]],'d') mtxbexpected = array([[0,0,0.5], [0,0,0], [0.5,0,0]],'d') self.assertFloatEqual(binary_dist_lennon(mtxb), mtxbexpected) def test_binary_dist_pearson(self): """tests binary_dist_pearson tests inputs of empty mtx, zeros, and dense1 compared with calcs done by hand""" self.assertFloatEqual(binary_dist_pearson(self.zeromtx), zeros((4,4),'d')) self.assertFloatEqual(binary_dist_pearson(self.dense1), zeros((3,3))) def test_binary_dist_jaccard(self): """tests binary_dist_jaccard tests inputs of empty mtx, zeros, and sparse1 compared with calcs done by hand""" self.assertFloatEqual(binary_dist_jaccard(self.zeromtx), zeros((4,4),'d')) sparse1expected = array([[0, 0, 1., 1.], [0, 0, 1, 1], [1,1,0,1], [1,1,1,0]],'d') self.assertFloatEqual(binary_dist_jaccard(self.sparse1), sparse1expected) sparse1expected = dist_manhattan(self.sparse1.astype(bool)) sparse1norm = array([[ 1, 1,2,1], [1,1,2,1], [2,2,1,1], [1,1,1,100]],'d') sparse1expected /= sparse1norm self.assertFloatEqual(binary_dist_jaccard(self.sparse1), sparse1expected) def test_binary_dist_ochiai(self): """tests binary_dist_ochiai tests inputs of empty mtx, zeros, and mtx1 compared with calcs done by hand""" self.assertFloatEqual(binary_dist_ochiai(self.zeromtx), zeros((4,4),'d')) mtx1expected = array([[0,1-1/sqrt(2.)], [1-1/sqrt(2.), 0,]],'d') self.assertFloatEqual(binary_dist_ochiai(self.mtx1),mtx1expected) def test_binary_dist_hamming(self): """tests binary_dist_hamming tests inputs of empty mtx, zeros, and mtx1 compared with calcs done by hand""" self.assertFloatEqual(binary_dist_hamming(self.zeromtx), zeros((4,4),'d')) mtx1expected = array([[0,1], [1, 0,]],'d') self.assertFloatEqual(binary_dist_hamming(self.mtx1),mtx1expected) def test_binary_dist_sorensen_dice(self): """tests binary_dist_sorensen_dice tests inputs of empty mtx, zeros, and mtx1 compared with calcs done by hand""" self.assertFloatEqual(binary_dist_sorensen_dice(self.zeromtx), zeros((4,4),'d')) mtx1expected = array([[0,1/3.], [1/3., 0,]],'d') self.assertFloatEqual(binary_dist_sorensen_dice(self.mtx1), mtx1expected) sparse1expected = array([[0, 0, 1., 1.], [0, 0, 1, 1], [1,1,0,1], [1,1,1,0]],'d') self.assertFloatEqual(binary_dist_sorensen_dice(self.sparse1), sparse1expected) def test_binary_dist_euclidean(self): """tests binary_dist_euclidean tests two inputs compared with calculations by hand, and runs zeros and an empty input""" dense1expected = array([[ 0.0, 0.0, 0.0], [ 0.0, 0.0 , 0.0], [ 0.0, 0.0, 0.0 ]],'d') sparse1expected = zeros((4,4),'d') sparse1expected[0,2] = sqrt(2) sparse1expected[0,3] = 1.0 sparse1expected[1,2] = sqrt(2) sparse1expected[1,3] = 1.0 sparse1expected[2,3] = 1.0 sparse1expected = self.get_sym_mtx_from_uptri(sparse1expected) self.assertFloatEqual(binary_dist_euclidean(self.dense1), dense1expected) self.assertFloatEqual(binary_dist_euclidean(self.sparse1), sparse1expected) self.assertFloatEqual(binary_dist_euclidean(self.zeromtx), zeros((4,4),'d')) #zj's stuff def test_chord_transform(self): """trans_chord should return the exp result in the ref paper.""" exp = [[ 0.40824829, 0.40824829, 0.81649658], [ 0.48507125, 0.72760688, 0.48507125], [ 0.90453403, 0.30151134, 0.30151134]] res = trans_chord(self.mat_test) self.assertFloatEqual(res, exp) def test_chord_dist(self): """dist_chord should return the exp result.""" self.assertFloatEqual(dist_chord(self.zeromtx), zeros((4,4),'d')) exp = [[ 0. , 0.46662021, 0.72311971], [ 0.46662021, 0. , 0.62546036], [ 0.72311971, 0.62546036, 0. ]] dist = dist_chord(self.mat_test) self.assertFloatEqual(dist, exp) def test_chisq_transform(self): """trans_chisq should return the exp result in the ref paper.""" exp_m = [[ 0.42257713, 0.45643546, 0.84515425], [ 0.48294529, 0.7824608 , 0.48294529], [ 1.01418511, 0.36514837, 0.3380617 ]] res_m = trans_chisq(self.mat_test) self.assertFloatEqual(res_m, exp_m) def test_chisq_distance(self): """dist_chisq should return the exp result.""" self.assertFloatEqual(dist_chisq(self.zeromtx), zeros((4,4),'d')) exp_d = [[ 0. , 0.4910521 , 0.78452291], [ 0.4910521 , 0. , 0.69091002], [ 0.78452291, 0.69091002, 0. ]] res_d = dist_chisq(self.mat_test) self.assertFloatEqual(res_d, exp_d) def test_hellinger_transform(self): """dist_hellinger should return the exp result in the ref paper.""" exp = [[ 0.5 , 0.5 , 0.70710678], [ 0.53452248, 0.65465367, 0.53452248], [ 0.77459667, 0.4472136 , 0.4472136 ]] res = trans_hellinger(self.mat_test) self.assertFloatEqual(res, exp) def test_hellinger_distance(self): """dist_hellinger should return the exp result.""" self.assertFloatEqual(dist_hellinger(self.zeromtx), zeros((4,4),'d')) exp = [[ 0. , 0.23429661, 0.38175149], [ 0.23429661, 0. , 0.32907422], [ 0.38175149, 0.32907422, 0. ]] dist = dist_hellinger(self.mat_test) self.assertFloatEqual(dist, exp) def test_species_profile_transform(self): """trans_specprof should return the exp result.""" exp = [[ 0.25 , 0.25 , 0.5 ], [ 0.28571429, 0.42857143, 0.28571429], [ 0.6 , 0.2 , 0.2 ]] res = trans_specprof(self.mat_test) self.assertFloatEqual(res, exp) def test_species_profile_distance(self): """dist_specprof should return the exp result.""" self.assertFloatEqual(dist_specprof(self.zeromtx), zeros((4,4),'d')) exp = [[ 0. , 0.28121457, 0.46368092], [ 0.28121457, 0. , 0.39795395], [ 0.46368092, 0.39795395, 0. ]] dist = dist_specprof(self.mat_test) self.assertFloatEqual(dist, exp) def test_dist_bray_curtis_magurran1(self): """ zero values should return zero dist, or 1 with nonzero samples""" res = dist_bray_curtis_magurran( numpy.array([[0,0,0], [0,0,0], [1,1,1], ])) self.assertFloatEqual(res,numpy.array([ [0,0,1], [0,0,1], [1,1,0], ])) def test_dist_bray_curtis_magurran2(self): """ should match hand-calculated values""" res = dist_bray_curtis_magurran( numpy.array([[1,4,3], [1,3,5], [0,2,0], ])) self.assertFloatEqual(res,numpy.array([ [0,1-14/17,1-(.4)], [1-14/17,0,1-4/11], [1-.4,1-4/11,0], ])) #def test_no_dupes(self): #""" here we check all distance functions in distance_transform for #duplicate #results. Uses an unsafe hack to get all distance functions, #thus disabled by default #The dataset is from Legendre 2001, Ecologically Meaningful... #also, doesn't actually raise an error on failing, just prints to #stdout #""" #import distance_transform ## L19 dataset #L19data = array( #[[7,1,0,0,0,0,0,0,0], #[4,2,0,0,0,1,0,0,0], #[2,4,0,0,0,1,0,0,0], #[1,7,0,0,0,0,0,0,0], #[0,8,0,0,0,0,0,0,0], #[0,7,1,0,0,0,0,0,0], #[0,4,2,0,0,0,2,0,0], #[0,2,4,0,0,0,1,0,0], #[0,1,7,0,0,0,0,0,0], #[0,0,8,0,0,0,0,0,0], #[0,0,7,1,0,0,0,0,0], #[0,0,4,2,0,0,0,3,0], #[0,0,2,4,0,0,0,1,0], #[0,0,1,7,0,0,0,0,0], #[0,0,0,8,0,0,0,0,0], #[0,0,0,7,1,0,0,0,0], #[0,0,0,4,2,0,0,0,4], #[0,0,0,2,4,0,0,0,1], #[0,0,0,1,7,0,0,0,0]], 'd') #distfns = [] #distfn_strs = dir(distance_transform) ## warning: dangerous eval, and might catch bad or not functions #for fnstr in distfn_strs: #if fnstr.find('dist') != -1: #distfns.append(eval('%s' % fnstr)) #dist_results = [] #for distfn in distfns: #dist_results.append(distfn(L19data)) #for i in range(len(dist_results)): #for j in range(i): #try: #self.assertFloatEqual(dist_results[i], dist_results[j]) #except: #pass # should not be equal, so catch error and proceed #else: #print "duplicates found: ", distfns[i], distfns[j] if __name__ == '__main__': main()