mask = data[i].mask
img_shape = mask.shape
X = X[:, mask != 0]
# Binarizing y to perform classification
y = y.astype(np.bool)
# Computing connectivity matrix
A = grid_to_graph(n_x=img_shape[0],
n_y=img_shape[1],
n_z=img_shape[2],
mask=mask)
estimator = SVC(kernel='linear', C=1.)
sc = SupervisedClusteringClassifier(estimator=estimator,
n_jobs=1,
n_iterations=150,
cv=6,
connectivity=A,
verbose=0)
cv = StratifiedKFold(y, 10)
print "Computing score for the patient %d on 6" % i
cv_scores = cross_val_score(sc, X, y, cv=cv, n_jobs=8, verbose=0)
sc.fit(X, y)
print ". Classification score for patient %d : %f" % (i,
np.mean(cv_scores))
print ". Number of parcels : %d" % len(np.unique(sc.labels_))
scores.append(np.mean(cv_scores))
print "===================================="
print "Average score for the whole dataset : %f", np.mean(scores)
# We compute the score for each patient
for i in range(6):
# Using the data corresponding to the patient
X = data[i].data
y = data[i].target
mask = data[i].mask
img_shape = mask.shape
X = X[:, mask!=0]
# Binarizing y to perform classification
y = y.astype(np.bool)
# Computing connectivity matrix
A = grid_to_graph(n_x=img_shape[0], n_y=img_shape[1], n_z=img_shape[2],
mask=mask)
estimator = SVC(kernel='linear', C=1.)
sc = SupervisedClusteringClassifier(estimator=estimator, n_jobs=1,
n_iterations=150, cv=6, connectivity=A, verbose=0)
cv = StratifiedKFold(y, 10)
print "Computing score for the patient %d on 6" % i
cv_scores = cross_val_score(sc, X, y, cv=cv, n_jobs=8, verbose=0)
sc.fit(X, y)
print ". Classification score for patient %d : %f" % (i, np.mean(cv_scores))
print ". Number of parcels : %d" % len(np.unique(sc.labels_))
scores.append(np.mean(cv_scores))
print "===================================="
print "Average score for the whole dataset : %f", np.mean(scores)
data = data[0]
X = data.data
y = data.target
mask = data.mask
img_shape = mask.shape
X = X[:, mask!=0]
# Binarizing y
y = y.astype(np.bool)
# Connectivity matrix
print "computing connectivity matrix"
A = grid_to_graph(n_x=img_shape[0], n_y=img_shape[1], n_z=img_shape[2],
mask=mask)
estimator = SVC(kernel='linear', C=1.)
sc = SupervisedClusteringClassifier(estimator=estimator, n_jobs=1, n_iterations=30,
verbose=1, cv=5, connectivity=A)
cv = StratifiedKFold(y, 5)
print "computing score"
cv_scores = cross_val_score(sc, X, y, cv=cv, n_jobs=8, verbose=1)
sc.fit(X, y)
print "classification score : ", np.mean(cv_scores)
print "number of parcels : %d" % len(np.unique(sc.labels_))
coefs = np.zeros(img_shape)
coefs[mask!=0] = sc.inverse_transform()
pl.figure()
vminmax = np.max(np.abs(coefs))
vmin = -vminmax
vmax = +vminmax
for i in range(8):
pl.subplot(2, 4, i+1)