ClassSeparation.py 文件源码

def computePerformance(self, instances):
        X = instances.features
        labels = instances.true_labels
        # For unsupervised projection methods, the performance is always computed with the labels (not the families).
        if hasattr(self.projection.conf, 'families_supervision'):
            if self.projection.conf.families_supervision:
                labels = instances.true_families
        unique_labels, label_inds = np.unique(labels, return_inverse = True)
        ratio = 0
        for li in xrange(len(unique_labels)):
            Xc  = X[label_inds == li]
            Xnc = X[label_inds != li]
            ratio += pairwise_distances(Xc).mean() / pairwise_distances(Xc, Xnc).mean()
        self.class_separation = ratio / len(unique_labels)