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- def clustering_accuracy(y_true, y_pred):
- import numpy as np
- """
- Calculate clustering accuracy. Require scikit-learn installed
- See: Unsupervised Deep Embedding for Clustering Analysis
- # 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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