def test_voxel_selection():
fake_raw_data = [create_epoch() for i in range(8)]
labels = [0, 1, 0, 1, 0, 1, 0, 1]
# 2 subjects, 4 epochs per subject
vs = VoxelSelector(labels, 4, 2, fake_raw_data, voxel_unit=1)
# test scipy normalization
fake_corr = prng.rand(1, 4, 5).astype(np.float32)
fake_corr = vs._correlation_normalization(fake_corr)
if MPI.COMM_WORLD.Get_rank() == 0:
expected_fake_corr = [[[1.06988919, 0.51641309, -0.46790636,
-1.31926763, 0.2270218],
[-1.22142744, -1.39881694, -1.2979387,
1.05702305, -0.6525566],
[0.89795232, 1.27406132, 0.36460185,
0.87538344, 1.5227468],
[-0.74641371, -0.39165771, 1.40124381,
-0.61313909, -1.0972116]]]
assert np.allclose(fake_corr, expected_fake_corr), \
'within-subject normalization does not provide correct results'
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [7, 4, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via SVM does not provide correct results'
# for cross validation, use logistic regression
clf = LogisticRegression()
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [6, 3, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), (
"voxel selection via logistic regression does not provide correct "
"results")
def test_voxel_selection():
fake_raw_data = [create_epoch() for i in range(8)]
labels = [0, 1, 0, 1, 0, 1, 0, 1]
# 2 subjects, 4 epochs per subject
vs = VoxelSelector(labels, 4, 2, fake_raw_data, voxel_unit=1)
# test scipy normalization
fake_corr = prng.rand(1, 4, 5).astype(np.float32)
fake_corr = vs._correlation_normalization(fake_corr)
if MPI.COMM_WORLD.Get_rank() == 0:
expected_fake_corr = [[[1.06988919, 0.51641309, -0.46790636,
-1.31926763, 0.2270218],
[-1.22142744, -1.39881694, -1.2979387,
1.05702305, -0.6525566],
[0.89795232, 1.27406132, 0.36460185,
0.87538344, 1.5227468],
[-0.74641371, -0.39165771, 1.40124381,
-0.61313909, -1.0972116]]]
assert np.allclose(fake_corr, expected_fake_corr), \
'within-subject normalization does not provide correct results'
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [7, 4, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via SVM does not provide correct results'
# for cross validation, use logistic regression
clf = LogisticRegression()
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [6, 3, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), (
"voxel selection via logistic regression does not provide correct "
"results")
def test_voxel_selection():
fake_raw_data = [create_epoch() for i in range(8)]
labels = [0, 1, 0, 1, 0, 1, 0, 1]
# 2 subjects, 4 epochs per subject
vs = VoxelSelector(labels, 4, 2, fake_raw_data, voxel_unit=1)
# test scipy normalization
fake_corr = prng.rand(1, 4, 5).astype(np.float32)
fake_corr = vs._correlation_normalization(fake_corr)
if MPI.COMM_WORLD.Get_rank() == 0:
expected_fake_corr = [[[1.19203866, 0.18862808, -0.54350245,
-1.18334889, -0.16860008],
[-1.06594729, -1.08742261, -1.19447124,
1.14114654, -0.67860204],
[0.7839641, 1.53981364, 0.24948341,
0.82626557, 1.67902875],
[-0.91005552, -0.64101928, 1.48848987,
-0.78406328, -0.83182675]]]
assert np.allclose(fake_corr, expected_fake_corr), \
'within-subject normalization does not provide correct results'
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [7, 4, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via SVM does not provide correct results'
# for cross validation, use logistic regression
clf = LogisticRegression()
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [6, 3, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via logistic regression does not provide correct results'
def test_voxel_selection():
fake_raw_data = [create_epoch() for i in range(8)]
labels = [0, 1, 0, 1, 0, 1, 0, 1]
# 2 subjects, 4 epochs per subject
vs = VoxelSelector(labels, 4, 2, fake_raw_data, voxel_unit=1)
# test scipy normalization
fake_corr = prng.rand(1, 4, 5).astype(np.float32)
fake_corr = vs._correlation_normalization(fake_corr)
if MPI.COMM_WORLD.Get_rank() == 0:
expected_fake_corr = [
[[1.19203866, 0.18862808, -0.54350245, -1.18334889, -0.16860008],
[-1.06594729, -1.08742261, -1.19447124, 1.14114654, -0.67860204],
[0.7839641, 1.53981364, 0.24948341, 0.82626557, 1.67902875],
[-0.91005552, -0.64101928, 1.48848987, -0.78406328, -0.83182675]]
]
assert np.allclose(fake_corr, expected_fake_corr), \
'within-subject normalization does not provide correct results'
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8 * tuple[1])
expected_output = [7, 4, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via SVM does not provide correct results'
# for cross validation, use logistic regression
clf = LogisticRegression()
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8 * tuple[1])
expected_output = [6, 3, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), (
"voxel selection via logistic regression does not provide correct "
"results")
