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starplus_decoding_example.py
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starplus_decoding_example.py
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"""
================= Supervised Clustering =====================
Decoding example using supervised clustering on starplus data
"""
print __doc__
import numpy as np
from sklearn.feature_extraction.image import grid_to_graph
from sklearn.cross_val import StratifiedKFold, cross_val_score
from sklearn.svm import SVC
from datasets import fetch_star_plus_data
from supervised_clustering import SupervisedClusteringClassifier
# Loading data
data = fetch_star_plus_data()
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)