GornitzAnnotationQueries.py 文件源码

def computeNeighboursScores(self):
        all_instances = self.iteration.datasets.instances
        # Connectivity matrix
        pipeline = Pipeline([
            ('scaler', StandardScaler()),
            ('model', NearestNeighbors(self.num_neighbours, n_jobs = -1))])
        pipeline.fit(all_instances.getFeatures())
        # Labels
        labels = np.array([generateLabel(x) for x in all_instances.getLabels()])
        # Compute neighbour scores
        scores = []
        all_neighbours = pipeline.named_steps['model'].kneighbors(return_distance = False)
        for i, label in enumerate(labels):
            if label != 0:
                continue
            else:
                neighbours = all_neighbours[i]
                score = sum(labels[neighbours] + 1) / (2.0 * self.num_neighbours)
                scores.append(score)
        return np.array(scores)