使用sklearn.metrics.pairwise_distances或scipy.spatial.distance.cdist估计大型daraset的成对距离

如何解决使用sklearn.metrics.pairwise_distances或scipy.spatial.distance.cdist估计大型daraset的成对距离

我正在尝试估计约300,000张图像的数据集的特征到数据子集的特征之间的成对距离，以计算其最小值。但是，计算出的距离矩阵太大，无法容纳在内存中，从而导致OOM错误。

对于上下文，我正在尝试采用kcenter_greedy方法进行主动学习，如此处所示（https://github.com/google/active-learning/blob/master/sampling_methods/kcenter_greedy.py）

人们对此进行评估的大多数学术数据集都很小，因此代码可以正常工作。就我而言，我想使用一个更大的数据集，而该数据集的sklearn.metrics.pairwise_distances函数没有那么有用。

这是代码的相关部分

def update_distances(self,cluster_centers,only_new=True,reset_dist=False):
    """Update min distances given cluster centers.
    Args:
      cluster_centers: indices of cluster centers
      only_new: only calculate distance for newly selected points and update
        min_distances.
      rest_dist: whether to reset min_distances.
    """

    if reset_dist:
      self.min_distances = None
    if only_new:
      cluster_centers = [d for d in cluster_centers
                         if d not in self.already_selected]
    if cluster_centers:
      # Update min_distances for all examples given new cluster center.
      x = self.features[cluster_centers]
      ################ This is the section causing OOM issues######################################
      dist = pairwise_distances(self.features,x,metric=self.metric)

      if self.min_distances is None:
        self.min_distances = np.min(dist,axis=1).reshape(-1,1)
      else:
        self.min_distances = np.minimum(self.min_distances,dist)

  def select_batch_(self,model,already_selected,N,**kwargs):
    """
    diversity promoting active learning method that greedily forms a batch
    to minimize the maximum distance to a cluster center among all unlabeled
    datapoints.
    Args:
      model: model with scikit-like API with decision_function implemented
      already_selected: index of datapoints already selected
      N: batch size
    Returns:
      indices of points selected to minimize distance to cluster centers
    """

    try:
      # Assumes that the transform function takes in original data and not
      # flattened data.
      print('Getting transformed features...')
      self.features = model.transform(self.X)
      print('Calculating distances...')
      self.update_distances(already_selected,only_new=False,reset_dist=True)
    except:
      print('Using flat_X as features.')
      self.update_distances(already_selected,reset_dist=False)

    new_batch = []

    for _ in range(N):
      if self.already_selected is None:
        # Initialize centers with a randomly selected datapoint
        ind = np.random.choice(np.arange(self.n_obs))
      else:
        ind = np.argmax(self.min_distances)
      # New examples should not be in already selected since those points
      # should have min_distance of zero to a cluster center.
      assert ind not in already_selected

      self.update_distances([ind],reset_dist=False)
      new_batch.append(ind)
    print('Maximum distance from cluster centers is %0.2f'
            % max(self.min_distances))


    self.already_selected = already_selected

    return new_batch

我也尝试使用scipy.spatial.distance.cdist函数，但这对OOM问题没有帮助。

是否有更好的方法可以更有效地找到最小距离以存储内存？过去，scikit-learn库中的优化为我提供了帮助，但在这种情况下，它似乎不适用于大型数据集。