Feature inversion task (#81) に関わるPR内のテストコードの作成

[myaokai]

latent / task (core/inversion) / domain / datasetのテストケースの実装
encoder / generatorについてdomainまわりのパターンのテストを追加

• latent

• task core

• task inversion

• domain core

• domain image_domain

• domain feature_domain

• dataset (相談の上ひとまずなし）

• encoder

• generator

[github-actions]

Coverage Report

File	Stmts	Miss	Cover	Missing
bdpy/bdata
bdata.py	399	224	44%	79, 104, 109, 113, 118, 122, 132–134, 190, 233–239, 252–262, 276–277, 310, 314, 318–356, 405–411, 414–436, 443–450, 468–469, 475, 508, 539, 548, 560, 589–598, 606–614, 625, 648, 660–664, 671–676, 683–691, 696–729, 738, 750–757, 761–767, 771–799, 803–824, 828–862, 866–868, 872–874, 878–887
featureselector.py	64	12	81%	62–67, 69–74
metadata.py	67	1	99%	84
utils.py	113	39	65%	71, 82, 85–86, 95, 107–108, 127–173, 201, 246, 258, 263
bdpy/dataform
datastore.py	107	85	21%	59–75, 90–93, 97–98, 102–113, 116–119, 122–127, 131–132, 137–158, 190–197, 222–259, 262–265
features.py	298	165	45%	31–32, 43–46, 90–92, 101–103, 107, 111, 115, 119, 152–153, 157–161, 168–197, 214–215, 224–225, 232–236, 274, 288, 305–319, 323, 327, 331, 335, 339, 343, 347, 351, 355, 359, 364–394, 398–418, 422–462, 465, 470–477, 491–493, 496–499, 502–505, 508–512, 515–516, 536–549
pd.py	9	5	44%	25–27, 43–44
sparse.py	67	7	90%	29, 52–58, 74, 109, 123
utils.py	12	12	0%	3–18
bdpy/dataset
utils.py	45	45	0%	3–98
bdpy/distcomp
distcomp.py	92	18	80%	33, 35, 49, 53, 55, 66–70, 74, 76, 81–82, 89–93, 97
bdpy/dl
caffe.py	60	60	0%	4–129
bdpy/dl/torch
base.py	43	24	44%	31–41, 48, 54, 60, 63, 73–83, 90, 96, 102, 105
dataset.py	74	74	0%	1–195
models.py	334	226	32%	149–170, 298–317, 328–332, 346–351, 443–495, 516–518, 529–588, 612–615, 626–685, 709–712, 723–772, 791–794, 805–854, 873–876
torch.py	112	55	51%	175–212, 215, 218–230, 233–268
bdpy/dl/torch/domain
core.py	46	18	61%	47, 63, 89–90, 99, 102, 137–138, 141–143, 146–148, 164–165, 168, 173
feature_domain.py	24	24	0%	1–44
image_domain.py	64	37	42%	26, 31, 36, 41, 84–108, 111, 114, 121, 124, 137, 140–150, 179, 211–216, 223, 229
bdpy/evals
metrics.py	95	67	29%	49–53, 59–61, 82–112, 118–159, 172–179
bdpy/feature
feature.py	30	2	93%	69–70
bdpy/fig
__init__.py	4	4	0%	6–9
draw_group_image_set.py	90	90	0%	3–182
fig.py	88	88	0%	16–164
makeplots.py	336	336	0%	1–729
tile_images.py	59	59	0%	1–193
bdpy/ml
crossvalidation.py	59	27	54%	47–48, 113–114, 117–118, 138, 164–196
learning.py	308	96	69%	43–44, 48, 52, 59, 91–104, 109–125, 128, 158–170, 184–209, 293, 309, 313–315, 318–319, 329, 339–340, 345–346, 356–364, 367–368, 376, 411–418, 439, 452, 460, 469, 501–503, 542, 554, 557, 565, 573, 578, 599
model.py	140	120	14%	29–39, 54–70, 86–144, 156–169, 184–222, 225, 230–250, 254–258, 271–285
searchlight.py	16	13	19%	32–51
bdpy/mri
fmriprep.py	497	451	9%	25–34, 38, 44–62, 65–75, 78–89, 92–160, 163–194, 230–360, 367–380, 384, 388–390, 394, 398–400, 410–434, 437–454, 457–464, 471–472, 475–491, 494, 498, 502–815, 819–831, 842–862
glm.py	40	36	10%	46–95
image.py	24	19	21%	29–54
load_epi.py	28	18	36%	36–50, 56–63, 82–88
load_mri.py	19	16	16%	16–36
roi.py	248	234	6%	37–100, 122–148, 165–235, 241–314, 320–387, 399–466, 473–499
spm.py	158	139	12%	26–155, 162–166, 170, 174–179, 183–300
bdpy/opendata
__init__.py	1	1	0%	1
openneuro.py	210	210	0%	1–329
bdpy/pipeline
config.py	36	2	94%	37–38
bdpy/preproc
interface.py	52	16	69%	111–123, 148–157
preprocessor.py	129	69	47%	35, 44, 112–114, 121–128, 138–189, 196–227
select_top.py	22	1	95%	56
bdpy/recon
utils.py	55	55	0%	4–146
bdpy/recon/torch
icnn.py	161	161	0%	15–478
bdpy/recon/torch/modules
critic.py	44	2	95%	58, 132
encoder.py	29	1	97%	29
generator.py	72	5	93%	47, 52, 68, 128, 309
latent.py	34	13	62%	16, 21, 32, 42, 49, 52, 55, 80–83, 87, 97
bdpy/recon/torch/task
__init__.py	1	1	0%	1
inversion.py	82	82	0%	1–220
bdpy/stats
corr.py	43	3	93%	57, 68, 102
bdpy/task
callback.py	71	4	94%	114, 161, 166, 234
core.py	14	3	79%	41, 46, 56
bdpy/util
info.py	47	36	23%	19–79
utils.py	36	8	78%	60, 116–121, 140–142
TOTAL	5492	3619	34%

Tests	Skipped	Failures	Errors	Time
151	0 💤	18 ❌	0 🔥	16.443s ⏱️

[ganow]

いくつかマイナーコメントをいれました

[ganow]

Thank you very much for the implementations of the test cases for the domain module. I think basically the current implementations already cover the minimum test cases. But I would apppreciate it if you could add test cases which check the invertibility of the function domain.send . domain.receive over several example inputs including edge cases. I anticipate the tests like following:

domain = ...  # instantiate some subclass of the reversible domain class
input_candidates = [...]  # candidate of possible inputs including typical & edge-case examples
for x in input_candidates:
    assert x == domain.send(domain.receive(x))
    assert x == domain.receive(domain.send(x))

[ganow]

perhaps we should first test the type of the return value generated_image otherwise we cannot guarantee it has the instance variable like shape