def test_with_and_without_js():
dir = os.path.join(os.path.dirname(__file__), 'data')
name = 'gaussian-2d-mean0-std1,2'
feats = Features.load_from_hdf5(os.path.join(dir, name + '.h5'))
specs = ['hellinger']
Ks = [3, 5]
with h5py.File(os.path.join(dir, name + '.divs.h5'), 'r') as f:
expected = load_divs(f, specs, Ks)
min_dist = f.attrs['min_dist']
est = partial(estimate_divs, feats, Ks=Ks, min_dist=min_dist,
status_fn=None)
with capture_output(True, True, merge=False):
ds = est(specs=specs + ['js'])
oth_with = ds[:, :, :-1, :]
js_with = ds[:, :, -1, :]
js_without = est(specs=['js'])[:, :, 0, :]
assert_close(oth_with, expected, atol=5e-5,
msg="others ({}) broke with JS".format(', '.join(specs)))
assert_close(js_with, js_without, atol=5e-5,
msg="JS different with/without others")
def test_with_and_without_js():
dir = os.path.join(os.path.dirname(__file__), 'data')
name = 'gaussian-2d-mean0-std1,2'
feats = Features.load_from_hdf5(os.path.join(dir, name + '.h5'))
specs = ['hellinger']
Ks = [3, 5]
with h5py.File(os.path.join(dir, name + '.divs.h5'), 'r') as f:
expected = load_divs(f, specs, Ks)
min_dist = f.attrs['min_dist']
est = partial(estimate_divs,
feats,
Ks=Ks,
min_dist=min_dist,
status_fn=None)
with capture_output(True, True, merge=False):
ds = est(specs=specs + ['js'])
oth_with = ds[:, :, :-1, :]
js_with = ds[:, :, -1, :]
js_without = est(specs=['js'])[:, :, 0, :]
assert_close(oth_with,
expected,
atol=5e-5,
msg="others ({}) broke with JS".format(', '.join(specs)))
assert_close(js_with,
js_without,
atol=5e-5,
msg="JS different with/without others")
def test_divs():
dir = os.path.join(os.path.dirname(__file__), 'data')
argses = [{'cores': cores, 'status_fn': status_fn}
for cores in [1, None]
for status_fn in [None]] # , True]]
# TODO: test a custom status_fn also
specs = ['hellinger', 'kl', 'l2', 'linear',
'renyi:0.5', 'renyi:0.7', 'renyi:0.9', 'renyi:0.99']
Ks = [1, 3, 5, 10]
for name in ['gaussian-2d-mean0-std1,2', 'gaussian-20d-mean0-std1,2']:
for dtype in [np.float64, np.float32]:
feats = Features.load_from_hdf5(
os.path.join(dir, name + '.h5'),
features_dtype=dtype)
with h5py.File(os.path.join(dir, name + '.divs.h5'), 'r') as f:
expected = load_divs(f, specs, Ks)
min_dist = f.attrs['min_dist']
tests = []
for args in argses:
tests.extend(check_div(feats, expected, specs, Ks, name,
min_dist=min_dist, **args))
for test in sorted(tests, key=lambda t: t[0].description):
yield test
def test_divs():
dir = os.path.join(os.path.dirname(__file__), 'data')
argses = [{
'cores': cores,
'status_fn': status_fn
} for cores in [1, None] for status_fn in [None]] # , True]]
# TODO: test a custom status_fn also
specs = [
'hellinger', 'kl', 'l2', 'linear', 'renyi:0.5', 'renyi:0.7',
'renyi:0.9', 'renyi:0.99'
]
Ks = [1, 3, 5, 10]
for name in ['gaussian-2d-mean0-std1,2', 'gaussian-20d-mean0-std1,2']:
for dtype in [np.float64, np.float32]:
feats = Features.load_from_hdf5(os.path.join(dir, name + '.h5'),
features_dtype=dtype)
with h5py.File(os.path.join(dir, name + '.divs.h5'), 'r') as f:
expected = load_divs(f, specs, Ks)
min_dist = f.attrs['min_dist']
tests = []
for args in argses:
tests.extend(
check_div(feats,
expected,
specs,
Ks,
name,
min_dist=min_dist,
**args))
for test in sorted(tests, key=lambda t: t[0].description):
yield test
