prec, recall, _ = precision_recall_curve(GROUND_TRUTH[target_idx],
P[target_idx])
return auc(recall, prec)
if __name__ == '__main__':
# load data
st = dt.datetime.now()
mat = loadmat('E:/experiments/rescal-bilinear/data/countries_s2.mat')
#mat = loadmat('F:/experiment/rescal-bilinear/data/umls.mat')
#mat = loadmat('F:/experiment/rescal-bilinear/data/nations.mat')
K = array(mat['Rs'], np.float32)
#print(K[0],K.shape)
K = Tensor2Matrix(K)
#print(K[0],'\r',K.shape)
K = HimmingDistance(K, -0.0005)
#print(K)
e, k = K.shape[0], K.shape[2]
SZ = e * e * k
# copy ground truth before preprocessing
GROUND_TRUTH = K.copy()
# construct array for rescal
T = [lil_matrix(K[:, :, i]) for i in range(k)]
_log.info('Datasize: %d x %d x %d | No. of classes: %d' %
(T[0].shape + (len(T), ) + (k, )))
# Do cross-validation
FOLDS = 10
P[target_idx])
ac = accuracy_score(GROUND_TRUTH[target_idx], P[target_idx].round())
return auc(recall, prec), ac
if __name__ == '__main__':
# load data
#st = dt.datetime.now()
#mat = loadmat('F:/experiment/rescal-bilinear/data/alyawarradata.mat')
#mat = loadmat('E:/experiments/rescal-bilinear/data/alyawarradata.mat')
mat = loadmat('E:/experiments/rescal-bilinear/data/dnations.mat')
K = array(mat['R'], np.float32)
#print(K[0],K.shape)
K = Tensor2Matrix(K)
#print(K[0],'\r',K.shape)
K = HimmingDistance(K, 0.00000004)
#print(K)
st = dt.datetime.now()
e, k = K.shape[0], K.shape[2]
SZ = e * e * k
# copy ground truth before preprocessing
GROUND_TRUTH = K.copy()
# construct array for rescal
T = [lil_matrix(K[:, :, i]) for i in range(k)]
_log.info('Datasize: %d x %d x %d | No. of classes: %d' %
(T[0].shape + (len(T), ) + (k, )))
# Do cross-validation
FOLDS = 10