1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998
|
import os
import warnings
from unittest import TestCase
import numpy
import pytest
from numpy.testing import assert_allclose, assert_equal
from cogent3 import (
DNA,
PROTEIN,
RNA,
load_aligned_seqs,
make_aligned_seqs,
make_unaligned_seqs,
)
from cogent3.evolve.distance import EstimateDistances
from cogent3.evolve.fast_distance import (
DistanceMatrix,
HammingPair,
JC69Pair,
LogDetPair,
ParalinearPair,
ProportionIdenticalPair,
TN93Pair,
_calculators,
_fill_diversity_matrix,
_hamming,
_jc69_from_matrix,
available_distances,
get_distance_calculator,
get_moltype_index_array,
seq_to_indices,
)
from cogent3.evolve.models import F81, HKY85, JC69
from cogent3.evolve.pairwise_distance_numba import (
fill_diversity_matrix as numba_fill_diversity_matrix,
)
warnings.filterwarnings("ignore", "Not using MPI as mpi4py not found")
# hides the warning from taking log of -ve determinant
numpy.seterr(invalid="ignore")
class TestPair(TestCase):
dna_char_indices = get_moltype_index_array(DNA)
rna_char_indices = get_moltype_index_array(RNA)
alignment = make_aligned_seqs(
data=[("s1", "ACGTACGTAC"), ("s2", "GTGTACGTAC")], moltype=DNA
)
ambig_alignment = make_aligned_seqs(
data=[("s1", "RACGTACGTACN"), ("s2", "AGTGTACGTACA")], moltype=DNA
)
diff_alignment = make_aligned_seqs(
data=[("s1", "ACGTACGTTT"), ("s2", "GTGTACGTAC")], moltype=DNA
)
def test_char_to_index(self):
"""should correctly recode a DNA & RNA seqs into indices"""
seq = "TCAGRNY?-"
expected = [0, 1, 2, 3, -9, -9, -9, -9, -9]
indices = seq_to_indices(seq, self.dna_char_indices)
assert_equal(indices, expected)
seq = "UCAGRNY?-"
indices = seq_to_indices(seq, self.rna_char_indices)
assert_equal(indices, expected)
def test_fill_diversity_matrix_all(self):
"""make correct diversity matrix when all chars valid"""
s1 = seq_to_indices("ACGTACGTAC", self.dna_char_indices)
s2 = seq_to_indices("GTGTACGTAC", self.dna_char_indices)
matrix = numpy.zeros((4, 4), float)
# self-self should just be an identity matrix
_fill_diversity_matrix(matrix, s1, s1)
assert_equal(matrix.sum(), len(s1))
assert_equal(
matrix,
numpy.array(
[[2, 0, 0, 0], [0, 3, 0, 0], [0, 0, 3, 0], [0, 0, 0, 2]], float
),
)
# small diffs
matrix.fill(0)
_fill_diversity_matrix(matrix, s1, s2)
assert_equal(
matrix,
numpy.array(
[[2, 0, 0, 0], [1, 2, 0, 0], [0, 0, 2, 1], [0, 0, 0, 2]], float
),
)
def test_fill_diversity_matrix_some(self):
"""make correct diversity matrix when not all chars valid"""
s1 = seq_to_indices("RACGTACGTACN", self.dna_char_indices)
s2 = seq_to_indices("AGTGTACGTACA", self.dna_char_indices)
matrix = numpy.zeros((4, 4), float)
# small diffs
matrix.fill(0)
_fill_diversity_matrix(matrix, s1, s2)
assert_equal(
matrix,
numpy.array(
[[2, 0, 0, 0], [1, 2, 0, 0], [0, 0, 2, 1], [0, 0, 0, 2]], float
),
)
def test_python_vs_numba_fill_matrix(self):
"""python & cython fill_diversity_matrix give same answer"""
s1 = seq_to_indices("RACGTACGTACN", self.dna_char_indices)
s2 = seq_to_indices("AGTGTACGTACA", self.dna_char_indices)
matrix1 = numpy.zeros((4, 4), float)
_fill_diversity_matrix(matrix1, s1, s2)
matrix2 = numpy.zeros((4, 4), float)
numba_fill_diversity_matrix(matrix2, s1, s2)
assert_allclose(matrix1, matrix2)
def test_hamming_from_matrix(self):
"""compute hamming from diversity matrix"""
s1 = seq_to_indices("ACGTACGTAC", self.dna_char_indices)
s2 = seq_to_indices("GTGTACGTAC", self.dna_char_indices)
matrix = numpy.zeros((4, 4), float)
_fill_diversity_matrix(matrix, s1, s2)
total, p, dist, var = _hamming(matrix)
self.assertEqual(total, 10.0)
self.assertEqual(dist, 2)
self.assertEqual(p, 0.2)
def test_hamming_pair(self):
"""get distances dict"""
calc = HammingPair(DNA, alignment=self.alignment)
calc.run(show_progress=False)
dists = calc.get_pairwise_distances()
dists = dists.to_dict()
dist = 2.0
expect = {("s1", "s2"): dist, ("s2", "s1"): dist}
self.assertEqual(list(dists.keys()), list(expect.keys()))
assert_allclose(list(dists.values()), list(expect.values()))
def test_prop_pair(self):
"""get distances dict"""
calc = ProportionIdenticalPair(DNA, alignment=self.alignment)
calc.run(show_progress=False)
dists = calc.get_pairwise_distances()
dists = dists.to_dict()
dist = 0.2
expect = {("s1", "s2"): dist, ("s2", "s1"): dist}
self.assertEqual(list(dists.keys()), list(expect.keys()))
assert_allclose(list(dists.values()), list(expect.values()))
def test_jc69_from_matrix(self):
"""compute JC69 from diversity matrix"""
s1 = seq_to_indices("ACGTACGTAC", self.dna_char_indices)
s2 = seq_to_indices("GTGTACGTAC", self.dna_char_indices)
matrix = numpy.zeros((4, 4), float)
_fill_diversity_matrix(matrix, s1, s2)
total, p, dist, var = _jc69_from_matrix(matrix)
self.assertEqual(total, 10.0)
self.assertEqual(p, 0.2)
def test_wrong_moltype(self):
"""specifying wrong moltype raises ValueError"""
with self.assertRaises(ValueError):
_ = JC69Pair(PROTEIN, alignment=self.alignment)
def test_jc69_from_alignment(self):
"""compute JC69 dists from an alignment"""
calc = JC69Pair(DNA, alignment=self.alignment)
calc.run(show_progress=False)
self.assertEqual(calc.lengths["s1", "s2"], 10)
self.assertEqual(calc.proportions["s1", "s2"], 0.2)
# value from OSX MEGA 5
assert_allclose(calc.dists["s1", "s2"], 0.2326161962)
# value**2 from OSX MEGA 5
assert_allclose(calc.variances["s1", "s2"], 0.029752066125078681)
# value from OSX MEGA 5
assert_allclose(calc.stderr["s1", "s2"], 0.1724878724)
# same answer when using ambiguous alignment
calc.run(self.ambig_alignment, show_progress=False)
assert_allclose(calc.dists["s1", "s2"], 0.2326161962)
# but different answer if subsequent alignment is different
calc.run(self.diff_alignment, show_progress=False)
self.assertTrue(calc.dists["s1", "s2"] != 0.2326161962)
def test_tn93_from_matrix(self):
"""compute TN93 distances"""
calc = TN93Pair(DNA, alignment=self.alignment)
calc.run(show_progress=False)
self.assertEqual(calc.lengths["s1", "s2"], 10)
self.assertEqual(calc.proportions["s1", "s2"], 0.2)
# value from OSX MEGA 5
assert_allclose(calc.dists["s1", "s2"], 0.2554128119)
# value**2 from OSX MEGA 5
assert_allclose(calc.variances["s1", "s2"], 0.04444444445376601)
# value from OSX MEGA 5
assert_allclose(calc.stderr["s1", "s2"], 0.2108185107)
# same answer when using ambiguous alignment
calc.run(self.ambig_alignment, show_progress=False)
assert_allclose(calc.dists["s1", "s2"], 0.2554128119)
# but different answer if subsequent alignment is different
calc.run(self.diff_alignment, show_progress=False)
self.assertTrue(calc.dists["s1", "s2"] != 0.2554128119)
def test_distance_pair(self):
"""get distances dict"""
calc = TN93Pair(DNA, alignment=self.alignment)
calc.run(show_progress=False)
dists = calc.get_pairwise_distances()
dists = dists.to_dict()
dist = 0.2554128119
expect = {("s1", "s2"): dist, ("s2", "s1"): dist}
self.assertEqual(list(dists.keys()), list(expect.keys()))
assert_allclose(list(dists.values()), list(expect.values()))
def test_logdet_pair_dna(self):
"""logdet should produce distances that match MEGA"""
aln = load_aligned_seqs("data/brca1_5.paml", moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
dists = logdet_calc.get_pairwise_distances().to_dict()
all_expected = {
("Human", "NineBande"): 0.075336929999999996,
("NineBande", "DogFaced"): 0.0898575452,
("DogFaced", "Human"): 0.1061747919,
("HowlerMon", "DogFaced"): 0.0934480008,
("Mouse", "HowlerMon"): 0.26422862920000001,
("NineBande", "Human"): 0.075336929999999996,
("HowlerMon", "NineBande"): 0.062202897899999998,
("DogFaced", "NineBande"): 0.0898575452,
("DogFaced", "HowlerMon"): 0.0934480008,
("Human", "DogFaced"): 0.1061747919,
("Mouse", "Human"): 0.26539976700000001,
("NineBande", "HowlerMon"): 0.062202897899999998,
("HowlerMon", "Human"): 0.036571181899999999,
("DogFaced", "Mouse"): 0.2652555144,
("HowlerMon", "Mouse"): 0.26422862920000001,
("Mouse", "DogFaced"): 0.2652555144,
("NineBande", "Mouse"): 0.22754789210000001,
("Mouse", "NineBande"): 0.22754789210000001,
("Human", "Mouse"): 0.26539976700000001,
("Human", "HowlerMon"): 0.036571181899999999,
}
for pair in dists:
got = dists[pair]
expected = all_expected[pair]
assert_allclose(got, expected)
def test_slice_dmatrix(self):
data = {
("ABAYE2984", "Atu3667"): 0.25,
("ABAYE2984", "Avin_42730"): 0.638,
("ABAYE2984", "BAA10469"): None,
("Atu3667", "ABAYE2984"): 0.25,
("Atu3667", "Avin_42730"): 2.368,
("Atu3667", "BAA10469"): 0.25,
("Avin_42730", "ABAYE2984"): 0.638,
("Avin_42730", "Atu3667"): 2.368,
("Avin_42730", "BAA10469"): 1.85,
("BAA10469", "ABAYE2984"): 0.25,
("BAA10469", "Atu3667"): 0.25,
("BAA10469", "Avin_42730"): 1.85,
}
darr = DistanceMatrix(data)
names = darr.template.names[0][:3]
got = darr[:3, :3]
self.assertEqual(list(got.template.names[0]), names)
def test_logdet_tk_adjustment(self):
"""logdet using tamura kumar differs from classic"""
aln = load_aligned_seqs("data/brca1_5.paml", moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
tk = logdet_calc.get_pairwise_distances()
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
not_tk = logdet_calc.get_pairwise_distances()
self.assertNotEqual(tk, not_tk)
def test_logdet_pair_aa(self):
"""logdet shouldn't fail to produce distances for aa seqs"""
aln = load_aligned_seqs("data/brca1_5.paml", moltype=DNA)
aln = aln.get_translation()
logdet_calc = LogDetPair(moltype=PROTEIN, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
logdet_calc.get_pairwise_distances()
def test_logdet_missing_states(self):
"""should calculate logdet measurement with missing states"""
data = [
(
"seq1",
"GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
),
(
"seq2",
"TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTNTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
),
]
aln = make_aligned_seqs(data=data, moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
dists = logdet_calc.get_pairwise_distances().to_dict()
self.assertTrue(list(dists.values())[0] is not None)
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
dists = logdet_calc.get_pairwise_distances().to_dict()
self.assertTrue(list(dists.values())[0] is not None)
def test_logdet_variance(self):
"""calculate logdet variance consistent with hand calculation"""
data = [
(
"seq1",
"GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
),
(
"seq2",
"TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
),
]
aln = make_aligned_seqs(data=data, moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
self.assertEqual(logdet_calc.variances[1, 1], None)
index = dict(list(zip("ACGT", list(range(4)))))
J = numpy.zeros((4, 4))
for p in zip(data[0][1], data[1][1]):
J[index[p[0]], index[p[1]]] += 1
for i in range(4):
if J[i, i] == 0:
J[i, i] += 0.5
J /= J.sum()
M = numpy.linalg.inv(J)
var = 0.0
for i in range(4):
for j in range(4):
var += M[j, i] ** 2 * J[i, j] - 1
var /= 16 * len(data[0][1])
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
logdet_calc.get_pairwise_distances()
assert_allclose(logdet_calc.variances[1, 1], var, atol=1e-3)
def test_logdet_for_determinant_lte_zero(self):
"""returns distance of None if the determinant is <= 0"""
data = dict(
seq1="AGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
seq2="TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
)
aln = make_aligned_seqs(data=data, moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
dists = logdet_calc.get_pairwise_distances().to_dict()
self.assertTrue(numpy.isnan(list(dists.values())[0]))
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
dists = logdet_calc.get_pairwise_distances().to_dict()
self.assertTrue(numpy.isnan(list(dists.values())[0]))
# but raises ArithmeticError if told to
logdet_calc = LogDetPair(moltype=DNA, alignment=aln, invalid_raises=True)
with self.assertRaises(ArithmeticError):
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
def test_paralinear_pair_aa(self):
"""paralinear shouldn't fail to produce distances for aa seqs"""
aln = load_aligned_seqs("data/brca1_5.paml", moltype=DNA)
aln = aln.get_translation()
paralinear_calc = ParalinearPair(moltype=PROTEIN, alignment=aln)
paralinear_calc.run(show_progress=False)
paralinear_calc.get_pairwise_distances()
def test_paralinear_distance(self):
"""calculate paralinear variance consistent with hand calculation"""
data = [
(
"seq1",
"GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
),
(
"seq2",
"TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
),
]
aln = make_aligned_seqs(data=data, moltype=DNA)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
index = dict(list(zip("ACGT", list(range(4)))))
J = numpy.zeros((4, 4))
for p in zip(data[0][1], data[1][1]):
J[index[p[0]], index[p[1]]] += 1
for i in range(4):
if J[i, i] == 0:
J[i, i] += 0.5
J /= J.sum()
numpy.linalg.inv(J)
f = J.sum(1), J.sum(0)
dist = -0.25 * numpy.log(
numpy.linalg.det(J) / numpy.sqrt(f[0].prod() * f[1].prod())
)
assert_allclose(paralinear_calc.dists["seq1", "seq2"], dist)
def test_paralinear_variance(self):
"""calculate paralinear variance consistent with hand calculation"""
data = [
(
"seq1",
"GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
),
(
"seq2",
"TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
),
]
aln = make_aligned_seqs(data=data, moltype=DNA)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
index = dict(list(zip("ACGT", list(range(4)))))
J = numpy.zeros((4, 4))
for p in zip(data[0][1], data[1][1]):
J[index[p[0]], index[p[1]]] += 1
for i in range(4):
if J[i, i] == 0:
J[i, i] += 0.5
J /= J.sum()
M = numpy.linalg.inv(J)
f = J.sum(1), J.sum(0)
var = 0.0
for i in range(4):
for j in range(4):
var += M[j, i] ** 2 * J[i, j]
var -= 1 / numpy.sqrt(f[0][i] * f[1][i])
var /= 16 * len(data[0][1])
assert_allclose(paralinear_calc.variances[1, 1], var, atol=1e-3)
def test_paralinear_for_determinant_lte_zero(self):
"""returns distance of None if the determinant is <= 0"""
data = dict(
seq1="AGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
seq2="TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
)
aln = make_aligned_seqs(data=data, moltype=DNA)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
dists = paralinear_calc.get_pairwise_distances().to_dict()
self.assertTrue(numpy.isnan(list(dists.values())[0]))
paralinear_calc.run(show_progress=False)
dists = paralinear_calc.get_pairwise_distances().to_dict()
self.assertTrue(numpy.isnan(list(dists.values())[0]))
def test_paralinear_pair_dna(self):
"""calculate paralinear distance consistent with logdet distance"""
data = [
(
"seq1",
"TAATTCATTGGGACGTCGAATCCGGCAGTCCTGCCGCAAAAGCTTCCGGAATCGAATTTTGGCA",
),
(
"seq2",
"AAAAAAAAAAAAAAAACCCCCCCCCCCCCCCCTTTTTTTTTTTTTTTTGGGGGGGGGGGGGGGG",
),
]
aln = make_aligned_seqs(data=data, moltype=DNA)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(show_progress=False)
self.assertEqual(logdet_calc.dists[1, 1], paralinear_calc.dists[1, 1])
self.assertEqual(paralinear_calc.variances[1, 1], logdet_calc.variances[1, 1])
def test_duplicated(self):
"""correctly identifies duplicates"""
def get_calc(data):
aln = make_aligned_seqs(data=data, moltype=DNA)
calc = ParalinearPair(moltype=DNA, alignment=aln)
calc(show_progress=False)
return calc
# no duplicates
data = [
(
"seq1",
"GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
),
(
"seq2",
"TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
),
]
calc = get_calc(data)
self.assertEqual(calc.duplicated, None)
data = [
(
"seq1",
"GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
),
(
"seq2",
"TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
),
(
"seq3",
"TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
),
]
calc = get_calc(data)
self.assertTrue(
{"seq2": ["seq3"]} == calc.duplicated
or {"seq3": ["seq2"]} == calc.duplicated
)
# default to get all pairwise distances
pwds = calc.get_pairwise_distances().to_dict()
self.assertEqual(pwds[("seq2", "seq3")], 0.0)
self.assertEqual(pwds[("seq2", "seq1")], pwds[("seq3", "seq1")])
# only unique seqs when using include_duplicates=False
pwds = calc.get_pairwise_distances(include_duplicates=False).to_dict()
present = list(calc.duplicated.keys())[0]
missing = calc.duplicated[present][0]
self.assertEqual(set([(present, missing)]), set([("seq2", "seq3")]))
self.assertTrue((present, "seq1") in pwds)
self.assertFalse((missing, "seq1") in pwds)
class TestGetDisplayCalculators(TestCase):
def test_get_calculator(self):
"""exercising getting specified calculator"""
for key in _calculators:
get_distance_calculator(key)
get_distance_calculator(key.upper())
with self.assertRaises(ValueError):
get_distance_calculator("blahblah")
def test_available_distances(self):
"""available_distances has correct content"""
content = available_distances()
self.assertEqual(content.shape, (6, 2))
self.assertEqual(content["tn93", 1], "dna, rna")
class TestDistanceMatrix(TestCase):
def test_to_dict(self):
"""distance matrix correctly produces a 1D dict"""
data = {("s1", "s2"): 0.25, ("s2", "s1"): 0.25}
dmat = DistanceMatrix(data)
got = dmat.to_dict()
self.assertEqual(got, data)
def test_matrix_dtype(self):
"""tests DistanceMatrix correctly accepts the data with proper dtype"""
data = {
("ABAYE2984", "Atu3667"): None,
("ABAYE2984", "Avin_42730"): 0.638,
("ABAYE2984", "BAA10469"): None,
("Atu3667", "ABAYE2984"): None,
("Atu3667", "Avin_42730"): 2.368,
("Atu3667", "BAA10469"): None,
("Avin_42730", "ABAYE2984"): 0.638,
("Avin_42730", "Atu3667"): 2.368,
("Avin_42730", "BAA10469"): 1.85,
("BAA10469", "ABAYE2984"): None,
("BAA10469", "Atu3667"): None,
("BAA10469", "Avin_42730"): 1.85,
}
names = set()
for p in data:
names.update(p)
# tests when data has None values and DistanceMatrix using dtype('float')
darr = DistanceMatrix(data)
self.assertEqual(darr.shape, (4, 4))
self.assertEqual(set(darr.names), names)
for (a, b), dist in data.items():
if dist is None:
assert numpy.isnan(darr[a, b])
else:
assert_allclose(dist, darr[a, b])
data = {
("ABAYE2984", "Atu3667"): "None",
("ABAYE2984", "Avin_42730"): 0.638,
("ABAYE2984", "BAA10469"): None,
("Atu3667", "ABAYE2984"): None,
("Atu3667", "Avin_42730"): 2.368,
("Atu3667", "BAA10469"): "None",
("Avin_42730", "ABAYE2984"): 0.638,
("Avin_42730", "Atu3667"): 2.368,
("Avin_42730", "BAA10469"): 1.85,
("BAA10469", "ABAYE2984"): None,
("BAA10469", "Atu3667"): None,
("BAA10469", "Avin_42730"): 1.85,
}
# tests when data has str values and DistanceMatrix using dtype('float')
with self.assertRaises(ValueError):
darr = DistanceMatrix(data)
def test_dropping_from_matrix(self):
"""pairwise distances should have method for dropping invalid data"""
data = {
("ABAYE2984", "Atu3667"): None,
("ABAYE2984", "Avin_42730"): 0.638,
("ABAYE2984", "BAA10469"): None,
("Atu3667", "ABAYE2984"): None,
("Atu3667", "Avin_42730"): 2.368,
("Atu3667", "BAA10469"): None,
("Avin_42730", "ABAYE2984"): 0.638,
("Avin_42730", "Atu3667"): 2.368,
("Avin_42730", "BAA10469"): 1.85,
("BAA10469", "ABAYE2984"): None,
("BAA10469", "Atu3667"): None,
("BAA10469", "Avin_42730"): 1.85,
}
darr = DistanceMatrix(data)
new = darr.drop_invalid()
self.assertEqual(new, None)
data = {
("ABAYE2984", "Atu3667"): 0.25,
("ABAYE2984", "Avin_42730"): 0.638,
("ABAYE2984", "BAA10469"): None,
("Atu3667", "ABAYE2984"): 0.25,
("Atu3667", "Avin_42730"): 2.368,
("Atu3667", "BAA10469"): 0.25,
("Avin_42730", "ABAYE2984"): 0.638,
("Avin_42730", "Atu3667"): 2.368,
("Avin_42730", "BAA10469"): 1.85,
("BAA10469", "ABAYE2984"): 0.25,
("BAA10469", "Atu3667"): 0.25,
("BAA10469", "Avin_42730"): 1.85,
}
darr = DistanceMatrix(data)
new = darr.drop_invalid()
self.assertEqual(new.shape, (2, 2))
def test_take_dists(self):
"""subsets the distance matrix"""
data = {
("ABAYE2984", "Atu3667"): 0.25,
("ABAYE2984", "Avin_42730"): 0.638,
("ABAYE2984", "BAA10469"): None,
("Atu3667", "ABAYE2984"): 0.25,
("Atu3667", "Avin_42730"): 2.368,
("Atu3667", "BAA10469"): 0.25,
("Avin_42730", "ABAYE2984"): 0.638,
("Avin_42730", "Atu3667"): 2.368,
("Avin_42730", "BAA10469"): 1.85,
("BAA10469", "ABAYE2984"): 0.25,
("BAA10469", "Atu3667"): 0.25,
("BAA10469", "Avin_42730"): 1.85,
}
darr = DistanceMatrix(data)
got1 = darr.take_dists(["ABAYE2984", "Atu3667", "Avin_42730"])
got2 = darr.take_dists("BAA10469", negate=True)
assert_allclose(got1.array.astype(float), got2.array.astype(float))
def test_build_phylogeny(self):
"""build a NJ tree"""
from cogent3 import make_tree
dists = {
("DogFaced", "FlyingFox"): 0.05,
("DogFaced", "FreeTaile"): 0.14,
("DogFaced", "LittleBro"): 0.16,
("DogFaced", "TombBat"): 0.15,
("FlyingFox", "DogFaced"): 0.05,
("FlyingFox", "FreeTaile"): 0.12,
("FlyingFox", "LittleBro"): 0.13,
("FlyingFox", "TombBat"): 0.14,
("FreeTaile", "DogFaced"): 0.14,
("FreeTaile", "FlyingFox"): 0.12,
("FreeTaile", "LittleBro"): 0.09,
("FreeTaile", "TombBat"): 0.1,
("LittleBro", "DogFaced"): 0.16,
("LittleBro", "FlyingFox"): 0.13,
("LittleBro", "FreeTaile"): 0.09,
("LittleBro", "TombBat"): 0.12,
("TombBat", "DogFaced"): 0.15,
("TombBat", "FlyingFox"): 0.14,
("TombBat", "FreeTaile"): 0.1,
("TombBat", "LittleBro"): 0.12,
}
dists = DistanceMatrix(dists)
got = dists.quick_tree(show_progress=False)
expect = make_tree(
treestring="((TombBat,(DogFaced,FlyingFox)),LittleBro,FreeTaile)"
)
self.assertTrue(expect.same_topology(got))
def test_names(self):
"""names property works"""
data = {
("ABAYE2984", "Atu3667"): 0.25,
("ABAYE2984", "Avin_42730"): 0.638,
("ABAYE2984", "BAA10469"): None,
("Atu3667", "ABAYE2984"): 0.25,
("Atu3667", "Avin_42730"): 2.368,
("Atu3667", "BAA10469"): 0.25,
("Avin_42730", "ABAYE2984"): 0.638,
("Avin_42730", "Atu3667"): 2.368,
("Avin_42730", "BAA10469"): 1.85,
("BAA10469", "ABAYE2984"): 0.25,
("BAA10469", "Atu3667"): 0.25,
("BAA10469", "Avin_42730"): 1.85,
}
names = set()
for p in data:
names.update(p)
darr = DistanceMatrix(data)
self.assertEqual(set(darr.names), names)
darr = darr.drop_invalid()
for n in ("ABAYE2984", "BAA10469"):
names.remove(n)
self.assertEqual(set(darr.names), names)
class DistancesTests(TestCase):
def setUp(self):
self.al = make_aligned_seqs(
data={
"a": "GTACGTACGATC",
"b": "GTACGTACGTAC",
"c": "GTACGTACGTTC",
"e": "GTACGTACTGGT",
}
)
self.collection = make_unaligned_seqs(
data={
"a": "GTACGTACGATC",
"b": "GTACGTACGTAC",
"c": "GTACGTACGTTC",
"e": "GTACGTACTGGT",
}
)
def assertDistsAlmostEqual(self, expected, observed, precision=4):
observed = dict([(frozenset(k), v) for (k, v) in list(observed.items())])
expected = dict([(frozenset(k), v) for (k, v) in list(expected.items())])
for key in expected:
self.assertAlmostEqual(expected[key], observed[key], precision)
def test_EstimateDistances(self):
"""testing (well, exercising at least), EstimateDistances"""
d = EstimateDistances(self.al, JC69())
d.run(show_progress=False)
canned_result = {
("b", "e"): 0.440840,
("c", "e"): 0.440840,
("a", "c"): 0.088337,
("a", "b"): 0.188486,
("a", "e"): 0.440840,
("b", "c"): 0.0883373,
}
result = d.get_pairwise_distances().to_dict()
self.assertDistsAlmostEqual(canned_result, result)
# excercise writing to file
d.write("junk.txt")
try:
os.remove("junk.txt")
except OSError:
pass # probably parallel
def test_EstimateDistancesWithMotifProbs(self):
"""EstimateDistances with supplied motif probs"""
motif_probs = {"A": 0.1, "C": 0.2, "G": 0.2, "T": 0.5}
d = EstimateDistances(self.al, HKY85(), motif_probs=motif_probs)
d.run(show_progress=False)
canned_result = {
("a", "c"): 0.07537,
("b", "c"): 0.07537,
("a", "e"): 0.39921,
("a", "b"): 0.15096,
("b", "e"): 0.39921,
("c", "e"): 0.37243,
}
result = d.get_pairwise_distances().to_dict()
self.assertDistsAlmostEqual(canned_result, result)
def test_EstimateDistances_fromThreeway(self):
"""testing (well, exercising at least), EsimateDistances fromThreeway"""
d = EstimateDistances(self.al, JC69(), threeway=True)
d.run(show_progress=False)
canned_result = {
("b", "e"): 0.495312,
("c", "e"): 0.479380,
("a", "c"): 0.089934,
("a", "b"): 0.190021,
("a", "e"): 0.495305,
("b", "c"): 0.0899339,
}
result = d.get_pairwise_distances(summary_function="mean").to_dict()
self.assertDistsAlmostEqual(canned_result, result)
def test_EstimateDistances_fromUnaligned(self):
"""Excercising estimate distances from unaligned sequences"""
d = EstimateDistances(
self.collection, JC69(), do_pair_align=True, rigorous_align=True
)
d.run(show_progress=False)
canned_result = {
("b", "e"): 0.440840,
("c", "e"): 0.440840,
("a", "c"): 0.088337,
("a", "b"): 0.188486,
("a", "e"): 0.440840,
("b", "c"): 0.0883373,
}
result = d.get_pairwise_distances().to_dict()
self.assertDistsAlmostEqual(canned_result, result)
d = EstimateDistances(
self.collection, JC69(), do_pair_align=True, rigorous_align=False
)
d.run(show_progress=False)
canned_result = {
("b", "e"): 0.440840,
("c", "e"): 0.440840,
("a", "c"): 0.088337,
("a", "b"): 0.188486,
("a", "e"): 0.440840,
("b", "c"): 0.0883373,
}
result = d.get_pairwise_distances().to_dict()
self.assertDistsAlmostEqual(canned_result, result)
def test_EstimateDistances_other_model_params(self):
"""test getting other model params from EstimateDistances"""
d = EstimateDistances(self.al, HKY85(), est_params=["kappa"])
d.run(show_progress=False)
# this will be a Number object with Mean, Median etc ..
kappa = d.get_param_values("kappa")
self.assertAlmostEqual(kappa.mean, 0.8939, 4)
# this will be a dict with pairwise instances, it's called by the above
# method, so the correctness of it's values is already checked
kappa = d.get_pairwise_param("kappa")
def test_EstimateDistances_modify_lf(self):
"""tests modifying the lf"""
def constrain_fit(lf):
lf.set_param_rule("kappa", is_constant=True)
lf.optimise(local=True, show_progress=False)
return lf
d = EstimateDistances(self.al, HKY85(), modify_lf=constrain_fit)
d.run(show_progress=False)
result = d.get_pairwise_distances().to_dict()
d = EstimateDistances(self.al, F81())
d.run(show_progress=False)
expect = d.get_pairwise_distances().to_dict()
self.assertDistsAlmostEqual(expect, result)
def test_get_raw_estimates(self):
"""correctly return raw result object"""
d = EstimateDistances(self.al, HKY85(), est_params=["kappa"])
d.run(show_progress=False)
expect = {
("a", "b"): {
"kappa": 1.0000226766004808e-06,
"length": 0.18232155856115662,
},
("a", "c"): {
"kappa": 1.0010380037049357e-06,
"length": 0.087070406623635604,
},
("a", "e"): {"kappa": 2.3965871843412687, "length": 0.4389176272584539},
("b", "e"): {"kappa": 2.3965871854366592, "length": 0.43891762729173389},
("b", "c"): {
"kappa": 1.0010380037049357e-06,
"length": 0.087070406623635604,
},
("c", "e"): {"kappa": 0.57046787478038707, "length": 0.43260232210282784},
}
got = d.get_all_param_values()
for pair in expect:
for param in expect[pair]:
self.assertAlmostEqual(got[pair][param], expect[pair][param], places=6)
def test_no_calc(self):
"""returns None if no calculation done"""
al = load_aligned_seqs("data/brca1_5.paml")
d = EstimateDistances(al, submodel=HKY85())
self.assertEqual(d.get_pairwise_distances(), None)
def test_to_table(self):
"""converts a distance matrix to a Table"""
data = {
("A", "B"): 2,
("A", "C"): 3,
("B", "C"): 1,
("B", "A"): 2,
("C", "A"): 3,
("C", "B"): 1,
}
darr = DistanceMatrix(data)
table = darr.to_table()
self.assertEqual(table.shape, (3, 4))
self.assertEqual(table.columns["names"].tolist(), list(darr.names))
self.assertEqual(table["A", "B"], 2)
self.assertEqual(table["A", "A"], 0)
@pytest.fixture
def min_working_example_dmat():
return DistanceMatrix(
{
("A", "B"): 1,
("A", "C"): 2,
("B", "C"): 3,
}
)
def test_max_pair_mwe(min_working_example_dmat):
assert min_working_example_dmat.max_pair() == ("B", "C")
def test_min_pair_mwe(min_working_example_dmat):
assert min_working_example_dmat.min_pair() == ("A", "B")
def test_max_pair_has_max_val():
aln = load_aligned_seqs("data/primate_brca1.fasta", moltype="dna")
dmat = aln.distance_matrix()
got = dmat[dmat.max_pair()]
expect = dmat.array.max()
assert got == expect
def test_min_pair_has_min_val():
aln = load_aligned_seqs("data/primate_brca1.fasta", moltype="dna")
dmat = aln.distance_matrix()
got = dmat[dmat.min_pair()]
numpy.fill_diagonal(dmat.array, numpy.inf)
expect = dmat.array.min()
assert got == expect
def test_min_max_pair_single_pair():
dmat = DistanceMatrix({("A", "B"): 2})
assert dmat.max_pair() == ("A", "B")
assert dmat.min_pair() == ("A", "B")
def test_max_pair_tied():
dmat = DistanceMatrix(
{
("A", "B"): 1,
("A", "C"): 1,
("A", "D"): 3,
("B", "C"): 3,
("B", "D"): 2,
("C", "D"): 2,
}
)
got = set(dmat.max_pair())
expect = {frozenset(("B", "C")), frozenset(("A", "D"))}
assert got in expect
def test_min_pair_tied():
dmat = DistanceMatrix(
{
("A", "B"): 1,
("A", "C"): 1,
("A", "D"): 3,
("B", "C"): 3,
("B", "D"): 2,
("C", "D"): 2,
}
)
got = set(dmat.min_pair())
expect = {frozenset(("A", "B")), frozenset(("A", "C"))}
assert got in expect
|