def test_ward_agglomeration():
"""
Check that we obtain the correct solution in a simplistic case
"""
rnd = np.random.RandomState(0)
mask = np.ones([10, 10], dtype=np.bool)
X = rnd.randn(50, 100)
connectivity = grid_to_graph(*mask.shape)
ward = WardAgglomeration(n_clusters=5, connectivity=connectivity)
ward.fit(X)
assert_true(np.size(np.unique(ward.labels_)) == 5)
Xred = ward.transform(X)
assert_true(Xred.shape[1] == 5)
Xfull = ward.inverse_transform(Xred)
assert_true(np.unique(Xfull[0]).size == 5)
def test_ward_agglomeration():
"""
Check that we obtain the correct solution in a simplistic case
"""
rnd = np.random.RandomState(0)
mask = np.ones([10, 10], dtype=np.bool)
X = rnd.randn(50, 100)
connectivity = grid_to_graph(*mask.shape)
ward = WardAgglomeration(n_clusters=5, connectivity=connectivity)
ward.fit(X)
assert_true(np.size(np.unique(ward.labels_)) == 5)
Xred = ward.transform(X)
assert_true(Xred.shape[1] == 5)
Xfull = ward.inverse_transform(Xred)
assert_true(np.unique(Xfull[0]).size == 5)
first_epi = nifti_masker.inverse_transform(fmri_masked[0]).get_data()
first_epi = np.ma.masked_array(first_epi, first_epi == 0)
# Outside the mask: a uniform value, smaller than inside the mask
first_epi[np.logical_not(mask)] = 0.9 * first_epi[mask].min()
vmax = first_epi[..., 20].max()
vmin = first_epi[..., 20].min()
pl.imshow(np.rot90(first_epi[..., 20]),
interpolation='nearest', cmap=pl.cm.spectral, vmin=vmin, vmax=vmax)
pl.axis('off')
pl.title('Original (%i voxels)' % fmri_masked.shape[1])
# A reduced data can be create by taking the parcel-level average:
# Note that, as many objects in the scikit-learn, the ward object exposes
# a transform method that modifies input features. Here it reduces their
# dimension
fmri_reduced = ward.transform(fmri_masked)
# Display the corresponding data compressed using the parcellation
fmri_compressed = ward.inverse_transform(fmri_reduced)
compressed = nifti_masker.inverse_transform(
fmri_compressed[0]).get_data()
compressed = np.ma.masked_equal(compressed, 0)
pl.figure()
pl.imshow(np.rot90(compressed[:, :, 20]),
interpolation='nearest', cmap=pl.cm.spectral, vmin=vmin, vmax=vmax)
pl.title('Compressed representation (2000 parcels)')
pl.axis('off')
pl.show()
first_plot = plot_roi(labels_img,
mean_func_img,
title="Ward parcellation",
display_mode='xz')
# labels_img is a Nifti1Image object, it can be saved to file with the
# following code:
labels_img.to_filename('parcellation.nii')
# Display the original data
plot_epi(nifti_masker.inverse_transform(fmri_masked[0]),
cut_coords=first_plot.cut_coords,
title='Original (%i voxels)' % fmri_masked.shape[1],
display_mode='xz')
# A reduced data can be create by taking the parcel-level average:
# Note that, as many objects in the scikit-learn, the ward object exposes
# a transform method that modifies input features. Here it reduces their
# dimension
fmri_reduced = ward.transform(fmri_masked)
# Display the corresponding data compressed using the parcellation
fmri_compressed = ward.inverse_transform(fmri_reduced)
compressed_img = nifti_masker.inverse_transform(fmri_compressed[0])
plot_epi(compressed_img,
cut_coords=first_plot.cut_coords,
title='Compressed representation (2000 parcels)',
display_mode='xz')
plt.show()
# the example_time_idx timepoint.
plot_epi(nifti_masker.inverse_transform(fmri_masked[example_time_idx]),
cut_coords=cut_coords,
axes=figure1.add_subplot(2, 2, 1),
title='Original (%i voxels)' % fmri_masked.shape[1],
display_mode='xz',
colorbar=True)
# A reduced data can be create by taking the parcel-level average:
# Note that, as many objects in the scikit-learn, the ward object exposes
# a transform method that modifies input features. Here it reduces their
# dimension by averaging data within a cluster.
fmri_reduced = ward.transform(fmri_masked)
# Plot an image of the data compressed using the parcellation
fmri_clustered = ward.inverse_transform(fmri_reduced)
clustered_img = nifti_masker.inverse_transform(fmri_clustered[0])
if 1 in plots:
plot_epi(clustered_img,
cut_coords=cut_coords,
axes=figure1.add_subplot(2, 2, 2),
title='Clustered representation (%d clusters)' % n_clusters,
display_mode='xz',
colorbar=True)
# To visualize the centroids, compute an image of the distance
# from each voxel's associated centroid to that voxel's position.
# Now, do true compression--reduce the number of samples.
centroids = compute_centroids(nifti_masker,
labels=ward.labels_,
labels[mask] = ward.labels_
cut = labels[:, :, 20].astype(np.int)
colors = np.random.random(size=(ward.n_clusters + 1, 3))
colors[-1] = 0
pl.axis('off')
pl.imshow(colors[cut], interpolation='nearest')
pl.title('Ward parcellation')
# Display the original data
pl.figure()
first_epi_img = epi_img[..., 0].copy()
first_epi_img[np.logical_not(mask)] = 0
pl.imshow(first_epi_img[..., 20], interpolation='nearest',
cmap=pl.cm.spectral)
pl.axis('off')
pl.title('Original')
# Display the corresponding data compressed using the parcellation
X_r = ward.transform(epi_masked.T)
X_c = ward.inverse_transform(X_r)
compressed_img = np.zeros(mask.shape)
compressed_img[mask] = X_c[0]
pl.figure()
pl.imshow(compressed_img[:, :, 20], interpolation='nearest',
cmap=pl.cm.spectral)
pl.title('Compressed representation')
pl.axis('off')
pl.show()
