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How to use test_sl method in hypothesis
Best Python code snippet using hypothesis
test_utils.py
Source:test_utils.py 
1from __future__ import division, print_function, absolute_import2from dipy.utils.six.moves import xrange3import numpy as np4import nose5from dipy.io.bvectxt import orientation_from_string6from dipy.tracking.utils import (affine_for_trackvis, connectivity_matrix,7 density_map, length, move_streamlines,8 ndbincount, reduce_labels,9 reorder_voxels_affine, seeds_from_mask,10 random_seeds_from_mask, target,11 target_line_based, _rmi, unique_rows, near_roi,12 reduce_rois, path_length, flexi_tvis_affine,13 get_flexi_tvis_affine, _min_at)14from dipy.tracking._utils import _to_voxel_coordinates15import dipy.tracking.metrics as metrix16from dipy.tracking.vox2track import streamline_mapping17import numpy.testing as npt18from numpy.testing import assert_array_almost_equal, assert_array_equal19from nose.tools import assert_equal, assert_raises, assert_true20def make_streamlines():21 streamlines = [np.array([[0, 0, 0],22 [1, 1, 1],23 [2, 2, 2],24 [5, 10, 12]], 'float'),25 np.array([[1, 2, 3],26 [3, 2, 0],27 [5, 20, 33],28 [40, 80, 120]], 'float')]29 return streamlines30def test_density_map():31 # One streamline diagonal in volume32 streamlines = [np.array([np.arange(10)] * 3).T]33 shape = (10, 10, 10)34 x = np.arange(10)35 expected = np.zeros(shape)36 expected[x, x, x] = 1.37 dm = density_map(streamlines, vol_dims=shape, voxel_size=(1, 1, 1))38 assert_array_equal(dm, expected)39 # add streamline, make voxel_size smaller. Each streamline should only be40 # counted once, even if multiple points lie in a voxel41 streamlines.append(np.ones((5, 3)))42 shape = (5, 5, 5)43 x = np.arange(5)44 expected = np.zeros(shape)45 expected[x, x, x] = 1.46 expected[0, 0, 0] += 147 dm = density_map(streamlines, vol_dims=shape, voxel_size=(2, 2, 2))48 assert_array_equal(dm, expected)49 # should work with a generator50 dm = density_map(iter(streamlines), vol_dims=shape, voxel_size=(2, 2, 2))51 assert_array_equal(dm, expected)52 # Test passing affine53 affine = np.diag([2, 2, 2, 1.])54 affine[:3, 3] = 1.55 dm = density_map(streamlines, shape, affine=affine)56 assert_array_equal(dm, expected)57 # Shift the image by 2 voxels, ie 4mm58 affine[:3, 3] -= 4.59 expected_old = expected60 new_shape = [i + 2 for i in shape]61 expected = np.zeros(new_shape)62 expected[2:, 2:, 2:] = expected_old63 dm = density_map(streamlines, new_shape, affine=affine)64 assert_array_equal(dm, expected)65def test_to_voxel_coordinates_precision():66 # To simplify tests, use an identity affine. This would be the result of67 # a call to _mapping_to_voxel with another identity affine.68 transfo = np.array([[1.0, 0.0, 0.0],69 [0.0, 1.0, 0.0],70 [0.0, 0.0, 1.0]])71 # Offset is computed by _mapping_to_voxel. With a 1x1x1 dataset72 # having no translation, the offset is half the voxel size, i.e. 0.5.73 offset = np.array([0.5, 0.5, 0.5])74 # Without the added tolerance in _to_voxel_coordinates, this streamline75 # should raise an Error in the call to _to_voxel_coordinates.76 failing_strl = [np.array([[-0.5000001, 0.0, 0.0], [0.0, 1.0, 0.0]],77 dtype=np.float32)]78 indices = _to_voxel_coordinates(failing_strl, transfo, offset)79 expected_indices = np.array([[[0, 0, 0], [0, 1, 0]]])80 assert_array_equal(indices, expected_indices)81def test_connectivity_matrix():82 label_volume = np.array([[[3, 0, 0],83 [0, 0, 0],84 [0, 0, 4]]])85 streamlines = [np.array([[0, 0, 0], [0, 0, 0], [0, 2, 2]], 'float'),86 np.array([[0, 0, 0], [0, 1, 1], [0, 2, 2]], 'float'),87 np.array([[0, 2, 2], [0, 1, 1], [0, 0, 0]], 'float')]88 expected = np.zeros((5, 5), 'int')89 expected[3, 4] = 290 expected[4, 3] = 191 # Check basic Case92 matrix = connectivity_matrix(streamlines, label_volume, (1, 1, 1),93 symmetric=False)94 assert_array_equal(matrix, expected)95 # Test mapping96 matrix, mapping = connectivity_matrix(streamlines, label_volume, (1, 1, 1),97 symmetric=False, return_mapping=True)98 assert_array_equal(matrix, expected)99 assert_equal(mapping[3, 4], [0, 1])100 assert_equal(mapping[4, 3], [2])101 assert_equal(mapping.get((0, 0)), None)102 # Test mapping and symmetric103 matrix, mapping = connectivity_matrix(streamlines, label_volume, (1, 1, 1),104 symmetric=True, return_mapping=True)105 assert_equal(mapping[3, 4], [0, 1, 2])106 # When symmetric only (3,4) is a key, not (4, 3)107 assert_equal(mapping.get((4, 3)), None)108 # expected output matrix is symmetric version of expected109 expected = expected + expected.T110 assert_array_equal(matrix, expected)111 # Test mapping_as_streamlines, mapping dict has lists of streamlines112 matrix, mapping = connectivity_matrix(streamlines, label_volume, (1, 1, 1),113 symmetric=False,114 return_mapping=True,115 mapping_as_streamlines=True)116 assert_true(mapping[3, 4][0] is streamlines[0])117 assert_true(mapping[3, 4][1] is streamlines[1])118 assert_true(mapping[4, 3][0] is streamlines[2])119 # Test passing affine to connectivity_matrix120 expected = matrix121 affine = np.diag([-1, -1, -1, 1.])122 streamlines = [-i for i in streamlines]123 matrix = connectivity_matrix(streamlines, label_volume, affine=affine)124 # In the symmetrical case, the matrix should be, well, symmetric:125 assert_equal(matrix[4, 3], matrix[4, 3])126def test_ndbincount():127 def check(expected):128 assert_equal(bc[0, 0], expected[0])129 assert_equal(bc[0, 1], expected[1])130 assert_equal(bc[1, 0], expected[2])131 assert_equal(bc[2, 2], expected[3])132 x = np.array([[0, 0], [0, 0], [0, 1], [0, 1], [1, 0], [2, 2]]).T133 expected = [2, 2, 1, 1]134 # count occurrences in x135 bc = ndbincount(x)136 assert_equal(bc.shape, (3, 3))137 check(expected)138 # pass in shape139 bc = ndbincount(x, shape=(4, 5))140 assert_equal(bc.shape, (4, 5))141 check(expected)142 # pass in weights143 weights = np.arange(6.)144 weights[-1] = 1.23145 expeceted = [1., 5., 4., 1.23]146 bc = ndbincount(x, weights=weights)147 check(expeceted)148 # raises an error if shape is too small149 assert_raises(ValueError, ndbincount, x, None, (2, 2))150def test_reduce_labels():151 shape = (4, 5, 6)152 # labels from 100 to 220153 labels = np.arange(100, np.prod(shape) + 100).reshape(shape)154 # new labels form 0 to 120, and lookup maps range(0,120) to range(100, 220)155 new_labels, lookup = reduce_labels(labels)156 assert_array_equal(new_labels, labels - 100)157 assert_array_equal(lookup, labels.ravel())158def test_move_streamlines():159 streamlines = make_streamlines()160 affine = np.eye(4)161 new_streamlines = move_streamlines(streamlines, affine)162 for i, test_sl in enumerate(new_streamlines):163 assert_array_equal(test_sl, streamlines[i])164 affine[:3, 3] += (4, 5, 6)165 new_streamlines = move_streamlines(streamlines, affine)166 for i, test_sl in enumerate(new_streamlines):167 assert_array_equal(test_sl, streamlines[i] + (4, 5, 6))168 affine = np.eye(4)169 affine = affine[[2, 1, 0, 3]]170 new_streamlines = move_streamlines(streamlines, affine)171 for i, test_sl in enumerate(new_streamlines):172 assert_array_equal(test_sl, streamlines[i][:, [2, 1, 0]])173 affine[:3, 3] += (4, 5, 6)174 new_streamlines = move_streamlines(streamlines, affine)175 undo_affine = move_streamlines(new_streamlines, np.eye(4),176 input_space=affine)177 for i, test_sl in enumerate(undo_affine):178 assert_array_almost_equal(test_sl, streamlines[i])179 # Test that changing affine does affect moving streamlines180 affineA = affine.copy()181 affineB = affine.copy()182 streamlinesA = move_streamlines(streamlines, affineA)183 streamlinesB = move_streamlines(streamlines, affineB)184 affineB[:] = 0185 for (a, b) in zip(streamlinesA, streamlinesB):186 assert_array_equal(a, b)187def test_target():188 streamlines = [np.array([[0., 0., 0.],189 [1., 0., 0.],190 [2., 0., 0.]]),191 np.array([[0., 0., 0],192 [0, 1., 1.],193 [0, 2., 2.]])]194 _target(target, streamlines, (0, 0, 0), (1, 0, 0), True)195def test_target_lb():196 streamlines = [np.array([[0., 1., 1.],197 [3., 1., 1.]]),198 np.array([[0., 0., 0.],199 [2., 2., 2.]]),200 np.array([[1., 1., 1.]])] # Single-point streamline201 _target(target_line_based, streamlines, (1, 1, 1), (2, 1, 1), False)202def _target(target_f, streamlines, voxel_both_true, voxel_one_true,203 test_bad_points):204 affine = np.eye(4)205 mask = np.zeros((4, 4, 4), dtype=bool)206 # Both pass though207 mask[voxel_both_true] = True208 new = list(target_f(streamlines, mask, affine=affine))209 assert_equal(len(new), 2)210 new = list(target_f(streamlines, mask, affine=affine, include=False))211 assert_equal(len(new), 0)212 # only first213 mask[:] = False214 mask[voxel_one_true] = True215 new = list(target_f(streamlines, mask, affine=affine))216 assert_equal(len(new), 1)217 assert_true(new[0] is streamlines[0])218 new = list(target_f(streamlines, mask, affine=affine, include=False))219 assert_equal(len(new), 1)220 assert_true(new[0] is streamlines[1])221 # Test that bad points raise a value error222 if test_bad_points:223 bad_sl = streamlines + [np.array([[10.0, 10.0, 10.0]])]224 new = target_f(bad_sl, mask, affine=affine)225 assert_raises(ValueError, list, new)226 bad_sl = streamlines + [-np.array([[10.0, 10.0, 10.0]])]227 new = target_f(bad_sl, mask, affine=affine)228 assert_raises(ValueError, list, new)229 # Test smaller voxels230 affine = np.random.random((4, 4)) - .5231 affine[3] = [0, 0, 0, 1]232 streamlines = list(move_streamlines(streamlines, affine))233 new = list(target_f(streamlines, mask, affine=affine))234 assert_equal(len(new), 1)235 assert_true(new[0] is streamlines[0])236 new = list(target_f(streamlines, mask, affine=affine, include=False))237 assert_equal(len(new), 1)238 assert_true(new[0] is streamlines[1])239 # Test that changing mask or affine does not break target/target_line_based240 include = target_f(streamlines, mask, affine=affine)241 exclude = target_f(streamlines, mask, affine=affine, include=False)242 affine[:] = np.eye(4)243 mask[:] = False244 include = list(include)245 exclude = list(exclude)246 assert_equal(len(include), 1)247 assert_true(include[0] is streamlines[0])248 assert_equal(len(exclude), 1)249 assert_true(exclude[0] is streamlines[1])250def test_near_roi():251 streamlines = [np.array([[0., 0., 0.9],252 [1.9, 0., 0.],253 [3, 2., 2.]]),254 np.array([[0.1, 0., 0],255 [0, 1., 1.],256 [0, 2., 2.]]),257 np.array([[2, 2, 2],258 [3, 3, 3]])]259 affine = np.eye(4)260 mask = np.zeros((4, 4, 4), dtype=bool)261 mask[0, 0, 0] = True262 mask[1, 0, 0] = True263 assert_array_equal(near_roi(streamlines, mask, tol=1),264 np.array([True, True, False]))265 assert_array_equal(near_roi(streamlines, mask),266 np.array([False, True, False]))267 # If there is an affine, we need to use it:268 affine[:, 3] = [-1, 100, -20, 1]269 # Transform the streamlines:270 x_streamlines = [sl + affine[:3, 3] for sl in streamlines]271 assert_array_equal(near_roi(x_streamlines, mask, affine=affine, tol=1),272 np.array([True, True, False]))273 assert_array_equal(near_roi(x_streamlines, mask, affine=affine,274 tol=None),275 np.array([False, True, False]))276 # Test for use of the 'all' mode:277 assert_array_equal(near_roi(x_streamlines, mask, affine=affine, tol=None,278 mode='all'), np.array([False, False, False]))279 mask[0, 1, 1] = True280 mask[0, 2, 2] = True281 # Test for use of the 'all' mode, also testing that setting the tolerance282 # to a very small number gets overridden:283 assert_array_equal(near_roi(x_streamlines, mask, affine=affine, tol=0.1,284 mode='all'), np.array([False, True, False]))285 mask[2, 2, 2] = True286 mask[3, 3, 3] = True287 assert_array_equal(near_roi(x_streamlines, mask, affine=affine,288 tol=None,289 mode='all'),290 np.array([False, True, True]))291 # Test for use of endpoints as selection criteria:292 mask = np.zeros((4, 4, 4), dtype=bool)293 mask[0, 1, 1] = True294 mask[3, 2, 2] = True295 assert_array_equal(near_roi(streamlines, mask, tol=0.87,296 mode="either_end"),297 np.array([True, False, False]))298 assert_array_equal(near_roi(streamlines, mask, tol=0.87,299 mode="both_end"),300 np.array([False, False, False]))301 mask[0, 0, 0] = True302 mask[0, 2, 2] = True303 assert_array_equal(near_roi(streamlines, mask, mode="both_end"),304 np.array([False, True, False]))305 # Test with a generator input:306 def generate_sl(streamlines):307 for sl in streamlines:308 yield sl309 assert_array_equal(near_roi(generate_sl(streamlines),310 mask, mode="both_end"),311 np.array([False, True, False]))312def test_voxel_ornt():313 sh = (40, 40, 40)314 sz = (1, 2, 3)315 I4 = np.eye(4)316 ras = orientation_from_string('ras')317 sra = orientation_from_string('sra')318 lpi = orientation_from_string('lpi')319 srp = orientation_from_string('srp')320 affine = reorder_voxels_affine(ras, ras, sh, sz)321 assert_array_equal(affine, I4)322 affine = reorder_voxels_affine(sra, sra, sh, sz)323 assert_array_equal(affine, I4)324 affine = reorder_voxels_affine(lpi, lpi, sh, sz)325 assert_array_equal(affine, I4)326 affine = reorder_voxels_affine(srp, srp, sh, sz)327 assert_array_equal(affine, I4)328 streamlines = make_streamlines()329 box = np.array(sh) * sz330 sra_affine = reorder_voxels_affine(ras, sra, sh, sz)331 toras_affine = reorder_voxels_affine(sra, ras, sh, sz)332 assert_array_equal(np.dot(toras_affine, sra_affine), I4)333 expected_sl = (sl[:, [2, 0, 1]] for sl in streamlines)334 test_sl = move_streamlines(streamlines, sra_affine)335 for ii in xrange(len(streamlines)):336 assert_array_equal(next(test_sl), next(expected_sl))337 lpi_affine = reorder_voxels_affine(ras, lpi, sh, sz)338 toras_affine = reorder_voxels_affine(lpi, ras, sh, sz)339 assert_array_equal(np.dot(toras_affine, lpi_affine), I4)340 expected_sl = (box - sl for sl in streamlines)341 test_sl = move_streamlines(streamlines, lpi_affine)342 for ii in xrange(len(streamlines)):343 assert_array_equal(next(test_sl), next(expected_sl))344 srp_affine = reorder_voxels_affine(ras, srp, sh, sz)345 toras_affine = reorder_voxels_affine(srp, ras, (40, 40, 40), (3, 1, 2))346 assert_array_equal(np.dot(toras_affine, srp_affine), I4)347 expected_sl = [sl.copy() for sl in streamlines]348 for sl in expected_sl:349 sl[:, 1] = box[1] - sl[:, 1]350 expected_sl = (sl[:, [2, 0, 1]] for sl in expected_sl)351 test_sl = move_streamlines(streamlines, srp_affine)352 for ii in xrange(len(streamlines)):353 assert_array_equal(next(test_sl), next(expected_sl))354def test_streamline_mapping():355 streamlines = [np.array([[0, 0, 0], [0, 0, 0], [0, 2, 2]], 'float'),356 np.array([[0, 0, 0], [0, 1, 1], [0, 2, 2]], 'float'),357 np.array([[0, 2, 2], [0, 1, 1], [0, 0, 0]], 'float')]358 mapping = streamline_mapping(streamlines, (1, 1, 1))359 expected = {(0, 0, 0): [0, 1, 2], (0, 2, 2): [0, 1, 2],360 (0, 1, 1): [1, 2]}361 assert_equal(mapping, expected)362 mapping = streamline_mapping(streamlines, (1, 1, 1),363 mapping_as_streamlines=True)364 expected = dict((k, [streamlines[i] for i in indices])365 for k, indices in expected.items())366 assert_equal(mapping, expected)367 # Test passing affine368 affine = np.eye(4)369 affine[:3, 3] = .5370 mapping = streamline_mapping(streamlines, affine=affine,371 mapping_as_streamlines=True)372 assert_equal(mapping, expected)373 # Make the voxel size smaller374 affine = np.diag([.5, .5, .5, 1.])375 affine[:3, 3] = .25376 expected = dict((tuple(i * 2 for i in key), value)377 for key, value in expected.items())378 mapping = streamline_mapping(streamlines, affine=affine,379 mapping_as_streamlines=True)380 assert_equal(mapping, expected)381def test_rmi():382 I1 = _rmi([3, 4], [10, 10])383 assert_equal(I1, 34)384 I1 = _rmi([0, 0], [10, 10])385 assert_equal(I1, 0)386 assert_raises(ValueError, _rmi, [10, 0], [10, 10])387 try:388 from numpy import ravel_multi_index389 except ImportError:390 raise nose.SkipTest()391 # Dtype of random integers is system dependent392 A, B, C, D = np.random.randint(0, 1000, size=[4, 100])393 I1 = _rmi([A, B], dims=[1000, 1000])394 I2 = ravel_multi_index([A, B], dims=[1000, 1000])395 assert_array_equal(I1, I2)396 I1 = _rmi([A, B, C, D], dims=[1000] * 4)397 I2 = ravel_multi_index([A, B, C, D], dims=[1000] * 4)398 assert_array_equal(I1, I2)399 # Check for overflow with small int types400 indices = np.random.randint(0, 255, size=(2, 100))401 dims = (1000, 1000)402 I1 = _rmi(indices, dims=dims)403 I2 = ravel_multi_index(indices, dims=dims)404 assert_array_equal(I1, I2)405def test_affine_for_trackvis():406 voxel_size = np.array([1., 2, 3.])407 affine = affine_for_trackvis(voxel_size)408 origin = np.dot(affine, [0, 0, 0, 1])409 assert_array_almost_equal(origin[:3], voxel_size / 2)410def test_length():411 # Generate a simulated bundle of fibers:412 n_streamlines = 50413 n_pts = 100414 t = np.linspace(-10, 10, n_pts)415 bundle = []416 for i in np.linspace(3, 5, n_streamlines):417 pts = np.vstack((np.cos(2 * t / np.pi), np.zeros(t.shape) + i, t)).T418 bundle.append(pts)419 start = np.random.randint(10, 30, n_streamlines)420 end = np.random.randint(60, 100, n_streamlines)421 bundle = [10 * streamline[start[i]:end[i]] for (i, streamline) in422 enumerate(bundle)]423 bundle_lengths = length(bundle)424 for idx, this_length in enumerate(bundle_lengths):425 assert_equal(this_length, metrix.length(bundle[idx]))426def test_seeds_from_mask():427 mask = np.random.random_integers(0, 1, size=(10, 10, 10))428 seeds = seeds_from_mask(mask, density=1)429 assert_equal(mask.sum(), len(seeds))430 assert_array_equal(np.argwhere(mask), seeds)431 mask[:] = False432 mask[3, 3, 3] = True433 seeds = seeds_from_mask(mask, density=[3, 4, 5])434 assert_equal(len(seeds), 3 * 4 * 5)435 assert_true(np.all((seeds > 2.5) & (seeds < 3.5)))436 mask[4, 4, 4] = True437 seeds = seeds_from_mask(mask, density=[3, 4, 5])438 assert_equal(len(seeds), 2 * 3 * 4 * 5)439 assert_true(np.all((seeds > 2.5) & (seeds < 4.5)))440 in_333 = ((seeds > 2.5) & (seeds < 3.5)).all(1)441 assert_equal(in_333.sum(), 3 * 4 * 5)442 in_444 = ((seeds > 3.5) & (seeds < 4.5)).all(1)443 assert_equal(in_444.sum(), 3 * 4 * 5)444def test_random_seeds_from_mask():445 mask = np.random.random_integers(0, 1, size=(4, 6, 3))446 seeds = random_seeds_from_mask(mask,447 seeds_count=24,448 seed_count_per_voxel=True)449 assert_equal(mask.sum() * 24, len(seeds))450 seeds = random_seeds_from_mask(mask,451 seeds_count=0,452 seed_count_per_voxel=True)453 assert_equal(0, len(seeds))454 mask[:] = False455 mask[2, 2, 2] = True456 seeds = random_seeds_from_mask(mask,457 seeds_count=8,458 seed_count_per_voxel=True)459 assert_equal(mask.sum() * 8, len(seeds))460 assert_true(np.all((seeds > 1.5) & (seeds < 2.5)))461 seeds = random_seeds_from_mask(mask,462 seeds_count=24,463 seed_count_per_voxel=False)464 assert_equal(24, len(seeds))465 seeds = random_seeds_from_mask(mask,466 seeds_count=0,467 seed_count_per_voxel=False)468 assert_equal(0, len(seeds))469 mask[:] = False470 mask[2, 2, 2] = True471 seeds = random_seeds_from_mask(mask,472 seeds_count=100,473 seed_count_per_voxel=False)474 assert_equal(100, len(seeds))475 assert_true(np.all((seeds > 1.5) & (seeds < 2.5)))476def test_connectivity_matrix_shape():477 # Labels: z-planes have labels 0,1,2478 labels = np.zeros((3, 3, 3), dtype=int)479 labels[:, :, 1] = 1480 labels[:, :, 2] = 2481 # Streamline set, only moves between first two z-planes.482 streamlines = [np.array([[0., 0., 0.],483 [0., 0., 0.5],484 [0., 0., 1.]]),485 np.array([[0., 1., 1.],486 [0., 1., 0.5],487 [0., 1., 0.]])]488 matrix = connectivity_matrix(streamlines, labels, affine=np.eye(4))489 assert_equal(matrix.shape, (3, 3))490def test_unique_rows():491 """492 Testing the function unique_coords493 """494 arr = np.array([[1, 2, 3], [1, 2, 3], [2, 3, 4], [3, 4, 5]])495 arr_w_unique = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])496 assert_array_equal(unique_rows(arr), arr_w_unique)497 # Should preserve order:498 arr = np.array([[2, 3, 4], [1, 2, 3], [1, 2, 3], [3, 4, 5]])499 arr_w_unique = np.array([[2, 3, 4], [1, 2, 3], [3, 4, 5]])500 assert_array_equal(unique_rows(arr), arr_w_unique)501 # Should work even with longer arrays:502 arr = np.array([[2, 3, 4], [1, 2, 3], [1, 2, 3], [3, 4, 5],503 [6, 7, 8], [0, 1, 0], [1, 0, 1]])504 arr_w_unique = np.array([[2, 3, 4], [1, 2, 3], [3, 4, 5],505 [6, 7, 8], [0, 1, 0], [1, 0, 1]])506 assert_array_equal(unique_rows(arr), arr_w_unique)507def test_reduce_rois():508 roi1 = np.zeros((4, 4, 4), dtype=np.bool)509 roi2 = np.zeros((4, 4, 4), dtype=np.bool)510 roi1[1, 1, 1] = 1511 roi2[2, 2, 2] = 1512 include_roi, exclude_roi = reduce_rois([roi1, roi2], [True, True])513 npt.assert_equal(include_roi, roi1 + roi2)514 npt.assert_equal(exclude_roi, np.zeros((4, 4, 4)))515 include_roi, exclude_roi = reduce_rois([roi1, roi2], [True, False])516 npt.assert_equal(include_roi, roi1)517 npt.assert_equal(exclude_roi, roi2)518 # Array input:519 include_roi, exclude_roi = reduce_rois(np.array([roi1, roi2]),520 [True, True])521 npt.assert_equal(include_roi, roi1 + roi2)522 npt.assert_equal(exclude_roi, np.zeros((4, 4, 4)))523def test_flexi_tvis_affine():524 sl_vox_order = 'RPI'525 grid_affine = np.array(526 [[-1.08566022e+00, 1.42664334e-03, 2.43463114e-01, 1.34783203e+02],527 [2.43251352e-03, 1.09376717e+00, 1.48301506e-02, -1.07367630e+02],528 [1.33170187e-01, -8.34854878e-03, 1.98454463e+00, -9.98151169e+01],529 [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])530 dim = (256, 256, 86)531 voxel_size = np.array([1.09379995, 1.09379995, 1.99947774])532 affine = flexi_tvis_affine(sl_vox_order, grid_affine, dim, voxel_size)533 origin = np.dot(affine, [0, 0, 0, 1])534 assert_array_almost_equal(origin[:3],535 np.multiply(dim, voxel_size) - voxel_size / 2)536def test_get_flexi_tvis_affine():537 tvis_hdr = {'voxel_order': 'RPI', 'dim': (30, 40, 50),538 'voxel_size': [2, 3, 4]}539 grid_affine = np.array([[-2, 0, 0, 0],540 [0, 3, 0, 0],541 [0, 0, 4, 0],542 [0, 0, 0, 1.]])543 affine = get_flexi_tvis_affine(tvis_hdr, grid_affine)544 origin = np.dot(affine, [0, 0, 0, 1])545 vsz = np.array(tvis_hdr['voxel_size'])546 assert_array_almost_equal(origin[:3],547 np.multiply(tvis_hdr['dim'], vsz) - vsz / 2)548 # grid_affine =549 tvis_hdr['voxel_order'] = 'ASL'550 vsz = tvis_hdr['voxel_size'] = np.array([3, 4, 2.])551 affine = get_flexi_tvis_affine(tvis_hdr, grid_affine)552 vox_point = np.array([9, 8, 7])553 trk_point = np.dot(affine, np.append(vox_point, 1))554 assert_array_almost_equal(trk_point[:3],555 (vox_point[[1, 2, 0]] + 0.5) * vsz)556def test_path_length():557 aoi = np.zeros((20, 20, 20), dtype=bool)558 aoi[0, 0, 0] = 1559 # A few tests for basic usage560 x = np.arange(20)561 streamlines = [np.array([x, x, x]).T]562 pl = path_length(streamlines, aoi, affine=np.eye(4))563 expected = x.copy() * np.sqrt(3)564 # expected[0] = np.inf565 npt.assert_array_almost_equal(pl[x, x, x], expected)566 aoi[19, 19, 19] = 1567 pl = path_length(streamlines, aoi, affine=np.eye(4))568 expected = np.minimum(expected, expected[::-1])569 npt.assert_array_almost_equal(pl[x, x, x], expected)570 aoi[19, 19, 19] = 0571 aoi[1, 1, 1] = 1572 pl = path_length(streamlines, aoi, affine=np.eye(4))573 expected = (x - 1) * np.sqrt(3)574 expected[0] = 0575 npt.assert_array_almost_equal(pl[x, x, x], expected)576 z = np.zeros(x.shape, x.dtype)577 streamlines.append(np.array([x, z, z]).T)578 pl = path_length(streamlines, aoi, affine=np.eye(4))579 npt.assert_array_almost_equal(pl[x, x, x], expected)580 npt.assert_array_almost_equal(pl[x, 0, 0], x)581 # Only streamlines that pass through aoi contribute to path length so if582 # all streamlines are duds, plm will be all inf.583 aoi[:] = 0584 aoi[0, 0, 0] = 1585 streamlines = []586 for i in range(1000):587 rando = np.random.random(size=(100, 3)) * 19 + .5588 assert (rando > .5).all()589 assert (rando < 19.5).all()590 streamlines.append(rando)591 pl = path_length(streamlines, aoi, affine=np.eye(4))592 npt.assert_array_almost_equal(pl, -1)593 pl = path_length(streamlines, aoi, affine=np.eye(4), fill_value=-12.)594 npt.assert_array_almost_equal(pl, -12.)595def test_min_at():596 k = np.array([3, 2, 2, 2, 1, 1, 1])597 values = np.array([10., 1, 2, 3, 31, 21, 11])598 i = np.zeros(k.shape, int)599 j = np.zeros(k.shape, int)600 a = np.zeros([1, 1, 4]) + 100.601 _min_at(a, (i, j, k), values)...
results.py
Source:results.py
1# Copyright 2020 Google LLC2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14from sklearn.metrics import roc_auc_score15from sklearn.metrics import average_precision_score16from sklearn.metrics import brier_score_loss17from sklearn.calibration import calibration_curve18import pandas as pd19import numpy as np20import os, sys21import json22np.random.seed(2020)23def config_get(name):24 FILEDIR = os.path.dirname(os.path.realpath(__file__))25 CONFIG_JSON = 'config.json'26 return json.load(open(os.path.join(FILEDIR, CONFIG_JSON)))[name]27WORKDIR = config_get('work-dir')28DATADIR = os.path.join(WORKDIR, 'data')29SLICE_SETS = list(config_get('partitions').values())30TASKS = config_get('labels')31PREDDIR = os.path.join(WORKDIR, 'predictions')32FEATDIR = os.path.join(DATADIR, 'fixedlen')33MODELS = config_get('models-fixedlen')34RESDIR = os.path.join(WORKDIR, 'results')35SLICE_NAMES = config_get('slice-desc')36SLICE_TESTS = { 'mimic-adult' : ['mimic-adult', 'mimic-neonate', 'picdb-paed'],37 'mimic-neonate' : ['mimic-neonate', 'mimic-adult', 'picdb-paed'],38 'picdb-paed' : ['picdb-paed', 'mimic-adult', 'mimic-neonate'],39 'mimic-male' : ['mimic-male', 'mimic-female'],40 'mimic-female' : ['mimic-female', 'mimic-male'],41 'mimic-lt50' : ['mimic-lt50', 'mimic-5060', 'mimic-6070', 'mimic-7080', 'mimic-gt80'] }42METRICS = [ ('AUC', 'ABAR', 'blue'),43 ('ECE', 'EBAR', 'brown'),44 ('OOD', 'OBAR', 'red'),45 ('POOD', 'POBAR', 'black')]46# Expected Calibration Error47def ECE(Y, P, n_bins=10):48 #return brier_score_loss(Y, P)49 P, Y = list(Y), list(P)50 l = 1. * len(Y)51 Y_buckets = [ [], [], [], [], [], [], [], [], [], [] ]52 P_buckets = [ [], [], [], [], [], [], [], [], [], [] ]53 for y, p in zip(Y, P):54 idx = int(p * 10)55 if idx == 10:56 idx = 957 Y_buckets[idx].append(y)58 P_buckets[idx].append(p)59 ece = sum([(len(y_l) / l) *abs(np.mean(y_l) - np.mean(p_l))60 for y_l, p_l in zip(Y_buckets, P_buckets) if len(y_l) > 0])61 return ece62def bootstrap(Y, P, scorefn):63 B = 10064 l = len(Y)65 try:66 Y0 = Y[Y == 0].values67 P0 = P[Y == 0].values68 Y1 = Y[Y == 1].values69 P1 = P[Y == 1].values70 except:71 Y = Y.flatten()72 P = P.flatten()73 Y0 = Y[Y == 0]74 P0 = P[Y == 0]75 Y1 = Y[Y == 1]76 P1 = P[Y == 1]77 l0 = len(Y0)78 l1 = len(Y1)79 choices0 = [np.random.choice(range(l0), l0, replace=True) for _ in range(B)]80 choices1 = [np.random.choice(range(l1), l1, replace=True) for _ in range(B)]81 scores = [scorefn(np.concatenate([Y0[choice0], Y1[choice1]]),82 np.concatenate([P0[choice0], P1[choice1]]))83 for choice0, choice1 in zip(choices0, choices1)]84 return np.mean(scores), 2 * np.std(scores)85def task_print(task):86 d = {'HOSPITAL_EXPIRE_FLAG' : 'Mortality',87 'REMAINING_LOS_3' : 'LoS 3+',88 'REMAINING_LOS_7' : 'LoS 7+' }89 if task in d:90 return d[task]91 return task92def task_desc(task):93 d = {'Mortality' : 'In Hospital Mortality',94 'LoS 3+' : 'Length of Stay 3+ days',95 'LoS 7+' : 'Length of Stay 7+ days'}96 if task in d:97 return d[task]98 return task99def task_BRNN(task):100 d = {'HOSPITAL_EXPIRE_FLAG' : 'mortality',101 'REMAINING_LOS_3' : 'los3',102 'REMAINING_LOS_7' : 'los7' }103 if task in d:104 return d[task]105 return task106def collect(fd, mname):107 for SLICES in SLICE_SETS:108 for train_sl in SLICES:109 try:110 Y_in = pd.read_csv('%s/train-%s/test/%s_Y.csv' % (FEATDIR, train_sl, train_sl))111 P_in = pd.read_csv('%s/%s/train-%s/test/%s_P.csv' % (PREDDIR, mname, train_sl, train_sl))112 except:113 continue114 Y_in['IO'] = 0115 for task in TASKS:116 for test_sl in SLICES:117 try:118 Y = pd.read_csv('%s/train-%s/test/%s_Y.csv' % (FEATDIR, train_sl, test_sl))119 P = pd.read_csv('%s/%s/train-%s/test/%s_P.csv' % (PREDDIR, mname, train_sl, test_sl))120 except:121 continue122 Y['IO'] = 1123 auc, abar = bootstrap(Y[task], P[task], roc_auc_score)124 ece, ebar = bootstrap(Y[task], P[task], ECE)125 prev = Y[task].mean()126 def ood_auroc(Y, P):127 Pfix = P * (1 - prev) / (P + prev - 2 * prev * P)128 return roc_auc_score(Y, Pfix * (1 - Pfix))129 Y_io = Y_in.append(Y)130 P_io = P_in.append(P)131 ood, obar = bootstrap(Y_io['IO'], P_io[task] * (1 - P_io[task]), roc_auc_score)132 pood, pobar = bootstrap(Y_io['IO'], P_io[task], ood_auroc)133 row = '%s,%s,%s,%s,%0.3f,%0.3f,%0.3f,%0.3f,%0.3f,%0.3f,%0.3f,%0.3f' % \134 (mname, train_sl, test_sl, task_print(task), auc, abar, ece, ebar, ood, obar, pood, pobar)135 print(row)136 fd.write(row + '\n')137 pass138def collect_BRNN(fd, basedir, ts='20201210_192808'):139 for SLICES in SLICE_SETS:140 for train_sl in SLICES:141 for task in TASKS:142 try:143 Y_in = np.load('%s/%s/predictions/train-%s/test-%s/%s/test/labels.npy' % \144 (basedir, task_BRNN(task), train_sl, train_sl, ts))145 L_in = np.load('%s/%s/predictions/train-%s/test-%s/%s/test/logits.npy' % \146 (basedir, task_BRNN(task), train_sl, train_sl, ts))147 except:148 continue149 P_in = 1/(1 + np.exp(-L_in))150 for test_sl in SLICES:151 try:152 Y = np.load('%s/%s/predictions/train-%s/test-%s/%s/test/labels.npy' % \153 (basedir, task_BRNN(task), train_sl, test_sl, ts))154 L = np.load('%s/%s/predictions/train-%s/test-%s/%s/test/logits.npy' % \155 (basedir, task_BRNN(task), train_sl, test_sl, ts))156 except:157 continue158 P = 1/(1 + np.exp(-L))159 auc, abar = bootstrap(Y, P, roc_auc_score)160 ece, ebar = bootstrap(Y, P, ECE)161 prev = Y.mean()162 def ood_auroc(Y, P):163 Pfix = P * (1 - prev) / (P + prev - 2 * prev * P)164 return roc_auc_score(Y, Pfix * (1 - Pfix))165 Y_io = np.append(np.zeros(len(Y_in)), np.ones(len(Y)))166 P_io = np.append(P_in, P)167 ood, obar = bootstrap(Y_io, P_io * (1 - P_io), roc_auc_score)168 pood, pobar = bootstrap(Y_io, P_io, ood_auroc)169 row = '%s,%s,%s,%s,%0.3f,%0.3f,%0.3f,%0.3f,%0.3f,%0.3f,%0.3f,%0.3f' % \170 ('BRNN', train_sl, test_sl, task_print(task), auc, abar, ece, ebar, ood, obar, pood, pobar)171 print(row)172 fd.write(row + '\n')173 pass174def create_results_df():175 brnn_dpath = '/tmp/medical_uncertainty/bayesian_rnn'176 brnn_rpath = '%s/BRNN.csv' % RESDIR177 TS = '20201210_192808'178 if not os.path.exists(brnn_rpath) or os.stat(brnn_rpath).st_size < 100:179 fd = open(brnn_rpath, 'w')180 fd.write('Model,Train,Test,Task,AUC,ABAR,ECE,EBAR,OOD,OBAR,POOD,POBAR\n')181 print('Collecting BRNN...')182 collect_BRNN(fd, brnn_dpath, TS)183 fd.close()184 stats = pd.read_csv(brnn_rpath)185 for mname in MODELS:186 rpath = '%s/%s.csv' % (RESDIR, mname)187 if not os.path.exists(rpath) or os.stat(rpath).st_size < 100:188 fd = open(rpath, 'w')189 fd.write('Model,Train,Test,Task,AUC,ABAR,ECE,EBAR,OOD,OBAR,POOD,POBAR\n')190 print('Collecting %s...' % mname)191 collect(fd, mname)192 fd.close()193 df = pd.read_csv(rpath)194 stats = stats.append(df)195 return stats196def gen_latex(df):197 os.makedirs(os.path.join(RESDIR, 'latex'), exist_ok=True)198 fd = open(os.path.join(RESDIR, 'latex', 'results.tex'), 'w')199 TASKNAMES = df['Task'].unique()200 def P(*args):201 line = ' '.join(str(w) for w in args) + '\n'202 fd.write(line)203 hdr = r'''\documentclass{article}204%%\usepackage[landscape]{geometry}205\usepackage{xcolor}206\usepackage{graphicx}207\usepackage{fullpage}208\usepackage{diagbox}209\usepackage{multirow}210\begin{document}211\centering212\tiny213'''214 P(hdr)215 for task in TASKNAMES:216 P(r'\section*{', task_desc(task), '}')217 P(r"\begin{tabular}{|l|l|l|" + ''.join([ 'c|' for _ in MODELS ]) + "}")218 P(r"\hline")219 P(r"Train & Test & Metric & " + ' & '.join(MODELS) + r'\\')220 P(r"\hline")221 for sl, SLICES in SLICE_TESTS.items():222 P(r"\multirow{", len(METRICS) * len(SLICES), "}{*}{\\rotatebox{90}{", SLICE_NAMES[sl], "}}")223 for test_sl in SLICES:224 P(r"& \multirow{", len(METRICS), "}{*}{", SLICE_NAMES[test_sl], "}")225 for i, (metric, bar, color) in enumerate(METRICS):226 if (i > 0):227 P('&')228 P(r'& \color{%s}{%s}' % (color, metric))229 # All stats (across models) to decide which model "wins" the row230 all_stats = []231 for model in MODELS:232 stats = df.query('Train=="%s" and Test=="%s" and Task=="%s" and Model=="%s"' % \233 (sl, test_sl, task, model))234 if stats.shape[0] != 1:235 continue236 all_stats.append(stats.iloc[0][metric])237 for model in MODELS:238 stats = df.query('Train=="%s" and Test=="%s" and Task=="%s" and Model=="%s"' % \239 (sl, test_sl, task, model))240 if stats.shape[0] != 1:241 P('& NA')242 elif sl == test_sl and (metric == 'OOD' or metric == 'POOD'):243 P('& NA')244 else:245 val = stats.iloc[0][metric]246 valbar = stats.iloc[0][bar]247 if metric == 'ECE':248 best = min(all_stats)249 else:250 best = max(all_stats)251 if val != best:252 P(r'& \color{%s}{%0.3f $\pm$ %0.3f}' % (color, val, valbar))253 else:254 P(r'& \textbf{\color{%s}{%0.3f $\pm$ %0.3f}}' % (color, val, valbar))255 P(r"\\")256 P(r"\cline{2-",len(MODELS)+3,"}")257 P(r"\hline")258 P(r"\hline")259 P(r"\end{tabular}")260 P(r"\end{document}")261def main(args):262 os.makedirs(RESDIR, exist_ok=True)263 df = create_results_df()264 df.to_csv('%s/results.csv' % RESDIR)265 gen_latex(df)266if __name__ == '__main__':...
data_preparation.py
Source:data_preparation.py
1import pandas as pd2import random3from configuration import CONSTANTS as C4def data_cls_csv():5 train_sl = pd.read_csv("data/slang_train_10000_split.csv")6 train_st = pd.read_csv("data/standard_train_10000.csv")7 test_sl = pd.read_csv("data/slang_test_10000_split.csv")8 test_st = pd.read_csv("data/standard_test_10000.csv")9 train_sl["label"] = 110 train_st["label"] = 011 test_sl["label"] = 112 test_st["label"] = 013 train_sl = train_sl[['example', 'label']]14 train_st = train_st[['train', 'label']]15 test_sl = test_sl[['example', 'label']]16 test_st = test_st[['test', 'label']]17 train_st.columns = ['example', 'label']18 test_st.columns = ['example', 'label']19 eval_sl = test_sl[:5000]20 eval_st = test_st[:5000]21 test_sl = test_sl[5000:]22 test_st = test_st[5000:]23 trainset = pd.concat([train_sl,train_st], axis = 0).reset_index(drop = True)24 evalset = pd.concat([eval_sl,eval_st], axis = 0).reset_index(drop = True)25 testset = pd.concat([test_sl,test_st], axis = 0).reset_index(drop = True)26 trainset.to_csv('data/train_cls.csv')27 evalset.to_csv('data/eval_cls.csv')28 testset.to_csv('data/test_cls.csv')29def example_gener(word,wordlist):30 s= ""31# print("word",word)32# print("wordlist",wordlist)33 for i in range(len(wordlist)):34# print("i",i)35# print("wordlist[i]",wordlist[i])36 s = s+str(i+1) + ". " + str(word) + " : "+str(wordlist[i])+"\n"37 s = s + str(len(wordlist)+1) + ". " +str(word) + " : "38 return s39def data_trigger_csv():40 filedir = C.DATA_DIR + "slang_augment_50000_updated.csv"41 data_cleaned = pd.read_csv(filedir).sort_values(['word'])42 temp_list = []43 tempword = data_cleaned.iloc[0, 0]44 trigger_list = []45 trigger_word = []46 trigger_len = []47 for i in range(len(data_cleaned)):48 if (data_cleaned.iloc[i, 0] == tempword):49 temp_list.append(data_cleaned.iloc[i, 1])50 else:51 s = example_gener(tempword, temp_list)52 trigger_list.append(s)53 trigger_word.append(tempword)54 trigger_len.append(len(temp_list))55 tempword = data_cleaned.iloc[i, 0]56 temp_list = [data_cleaned.iloc[i, 1]]57 df_trigger = pd.DataFrame(columns = ['word', 'trigger', 'length'])58 df_trigger['word'] = trigger_word59 df_trigger['length'] = trigger_len60 df_trigger['trigger'] = trigger_list61 df_trigger = df_trigger.reset_index()62 df_trigger.to_csv(C.DATA_DIR+'trigger_data.csv', index = False)63def augment_split_csv():64 filedir = C.DATA_DIR + 'augment_result_06251547.csv'65 data_augment = pd.read_csv(filedir, index_col=0) 66 random.seed(122)67 sample_idx = random.sample(range(0, data_augment.shape[0]), k=data_augment.shape[0])68 train_cnt = int(data_augment.shape[0]* 0.8)69 train = data_augment.iloc[sample_idx[:train_cnt]]70 eval = data_augment.iloc[sample_idx[train_cnt:]]71 train.to_csv(C.DATA_DIR + 'augment_train.csv')72 eval.to_csv(C.DATA_DIR + 'augment_eval.csv')73def rsearch_trigger_csv():74 filedir = C.DATA_DIR + 'trigger_data.csv'75 trigger_new = pd.read_csv(filedir, index_col=0)76 random.seed(122)77 sample_idx = random.sample(range(0, trigger_new.shape[0]), k=1000)78 rsearch_trigger = trigger_new.iloc[sample_idx].reset_index(drop = True)79 rsearch_trigger.to_csv(C.DATA_DIR + 'rsearch_trigger.csv')80if __name__ == '__main__':81 data_cls_csv()82 data_trigger_csv()83 # augment_split_csv()...
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