def _raw_fit(self, data):
group = self.group
sub_num = self.sub_num
if group:
data = MultiPCA._raw_fit(self, data).T
else:
data = data.reshape((3, self.n_components, -1))
data = data[sub_num - 1]
# plt.plot(data[:, :3])
# plt.tight_layout()
# plt.show()
self.hvmf_fit(data.T)
return self
def _raw_fit(self, data):
""" Fits the parcellation method on this reduced data.
Data are coming from a base decomposition estimator which computes
the mask and reduces the dimensionality of images using
randomized_svd.
Parameters
----------
data : ndarray
Shape (n_samples, n_features)
Returns
-------
labels_ : numpy.ndarray
Labels to each cluster in the brain.
connectivity_ : numpy.ndarray
voxel-to-voxel connectivity matrix computed from a mask.
Note that, this attribute is returned only for selected methods
such as 'ward', 'complete', 'average'.
"""
valid_methods = self.VALID_METHODS
if self.method is None:
raise ValueError("Parcellation method is specified as None. "
"Please select one of the method in "
"{0}".format(valid_methods))
if self.method is not None and self.method not in valid_methods:
raise ValueError("The method you have selected is not implemented "
"'{0}'. Valid methods are in {1}".format(
self.method, valid_methods))
# we delay importing Ward or AgglomerativeClustering and same
# time import plotting module before that.
# Because sklearn.cluster imports scipy hierarchy and hierarchy imports
# matplotlib. So, we force import matplotlib first using our
# plotting to avoid backend display error with matplotlib
# happening in Travis
try:
from nilearn import plotting
except:
pass
components = MultiPCA._raw_fit(self, data)
mask_img_ = self.masker_.mask_img_
if self.verbose:
print("[{0}] computing {1}".format(self.__class__.__name__,
self.method))
if self.method == 'kmeans':
from sklearn.cluster import MiniBatchKMeans
kmeans = MiniBatchKMeans(n_clusters=self.n_parcels,
init='k-means++',
random_state=self.random_state,
verbose=max(0, self.verbose - 1))
labels = self._cache(_estimator_fit,
func_memory_level=1)(components.T, kmeans)
else:
mask_ = _safe_get_data(mask_img_).astype(np.bool)
shape = mask_.shape
connectivity = image.grid_to_graph(n_x=shape[0],
n_y=shape[1],
n_z=shape[2],
mask=mask_)
# from data.new_agglo import NewAgglomerativeClustering as AgglomerativeClustering
from sklearn.cluster import AgglomerativeClustering
agglomerative = AgglomerativeClustering(n_clusters=self.n_parcels,
connectivity=connectivity,
linkage=self.method,
memory=self.memory,
compute_full_tree=True)
labels = self._cache(_estimator_fit,
func_memory_level=1)(components.T,
agglomerative)
self.agglomerative = agglomerative
self.connectivity_ = connectivity
# Avoid 0 label
labels = labels + 1
self.labels_img_ = self.masker_.inverse_transform(labels)
return self
# Avoid 0 label
labels = labels + 1
self.labels_img_ = self.masker_.inverse_transform(labels)
return self
