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import numpy as np
from sklearn.metrics import normalized_mutual_info_score, adjusted_rand_score
nmi = normalized_mutual_info_score
ari = adjusted_rand_score
def acc(y_true, y_pred):
Calculate clustering accuracy. Require scikit-learn installed
# Arguments
y: true labels, numpy.array with shape `(n_samples,)`
y_pred: predicted labels, numpy.array with shape `(n_samples,)`
# Return
accuracy, in [0,1]
y_true = y_true.astype(np.int64)
assert y_pred.size == y_true.size
D = max(y_pred.max(), y_true.max()) + 1
w = np.zeros((D, D), dtype=np.int64)
for i in range(y_pred.size):
w[y_pred[i], y_true[i]] += 1
from sklearn.utils.linear_assignment_ import linear_assignment
ind = linear_assignment(w.max() - w)
return sum([w[i, j] for i, j in ind]) * 1.0 / y_pred.size
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