research/active_learning/active_learning_methods/informative_diverse.py [33:101]:
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class InformativeClusterDiverseSampler(SamplingMethod):
  """Selects batch based on informative and diverse criteria.

    Returns highest uncertainty lowest margin points while maintaining
    same distribution over clusters as entire dataset.
  """

  def __init__(self, X, y, seed):
    self.name = 'informative_and_diverse'
    self.X = X
    self.flat_X = self.flatten_X()
    # y only used for determining how many clusters there should be
    # probably not practical to assume we know # of classes before hand
    # should also probably scale with dimensionality of data
    self.y = y
    self.n_clusters = len(list(set(y)))
    self.cluster_model = MiniBatchKMeans(n_clusters=self.n_clusters)
    self.cluster_data()

  def cluster_data(self):
    # Probably okay to always use MiniBatchKMeans
    # Should standardize data before clustering
    # Can cluster on standardized data but train on raw features if desired
    self.cluster_model.fit(self.flat_X)
    unique, counts = np.unique(self.cluster_model.labels_, return_counts=True)
    self.cluster_prob = counts/sum(counts)
    self.cluster_labels = self.cluster_model.labels_

  def select_batch_(self, model, already_selected, N, **kwargs):
    """Returns a batch of size N using informative and diverse selection.

    Args:
      model: scikit learn model with decision_function implemented
      already_selected: index of datapoints already selected
      N: batch size

    Returns:
      indices of points selected to add using margin active learner
    """
    # TODO(lishal): have MarginSampler and this share margin function
    try:
      distances = model.decision_function(self.X)
    except:
      distances = model.predict_proba(self.X)
    if len(distances.shape) < 2:
      min_margin = abs(distances)
    else:
      sort_distances = np.sort(distances, 1)[:, -2:]
      min_margin = sort_distances[:, 1] - sort_distances[:, 0]
    rank_ind = np.argsort(min_margin)
    rank_ind = [i for i in rank_ind if i not in already_selected]
    new_batch_cluster_counts = [0 for _ in range(self.n_clusters)]
    new_batch = []
    for i in rank_ind:
      if len(new_batch) == N:
        break
      label = self.cluster_labels[i]
      if new_batch_cluster_counts[label] / N < self.cluster_prob[label]:
        new_batch.append(i)
        new_batch_cluster_counts[label] += 1
    n_slot_remaining = N - len(new_batch)
    batch_filler = list(set(rank_ind) - set(already_selected) - set(new_batch))
    new_batch.extend(batch_filler[0:n_slot_remaining])
    return new_batch

  def to_dict(self):
    output = {}
    output['cluster_membership'] = self.cluster_labels
    return output
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research/active_learning/sampling_methods/informative_diverse.py [33:101]:
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class InformativeClusterDiverseSampler(SamplingMethod):
  """Selects batch based on informative and diverse criteria.

    Returns highest uncertainty lowest margin points while maintaining
    same distribution over clusters as entire dataset.
  """

  def __init__(self, X, y, seed):
    self.name = 'informative_and_diverse'
    self.X = X
    self.flat_X = self.flatten_X()
    # y only used for determining how many clusters there should be
    # probably not practical to assume we know # of classes before hand
    # should also probably scale with dimensionality of data
    self.y = y
    self.n_clusters = len(list(set(y)))
    self.cluster_model = MiniBatchKMeans(n_clusters=self.n_clusters)
    self.cluster_data()

  def cluster_data(self):
    # Probably okay to always use MiniBatchKMeans
    # Should standardize data before clustering
    # Can cluster on standardized data but train on raw features if desired
    self.cluster_model.fit(self.flat_X)
    unique, counts = np.unique(self.cluster_model.labels_, return_counts=True)
    self.cluster_prob = counts/sum(counts)
    self.cluster_labels = self.cluster_model.labels_

  def select_batch_(self, model, already_selected, N, **kwargs):
    """Returns a batch of size N using informative and diverse selection.

    Args:
      model: scikit learn model with decision_function implemented
      already_selected: index of datapoints already selected
      N: batch size

    Returns:
      indices of points selected to add using margin active learner
    """
    # TODO(lishal): have MarginSampler and this share margin function
    try:
      distances = model.decision_function(self.X)
    except:
      distances = model.predict_proba(self.X)
    if len(distances.shape) < 2:
      min_margin = abs(distances)
    else:
      sort_distances = np.sort(distances, 1)[:, -2:]
      min_margin = sort_distances[:, 1] - sort_distances[:, 0]
    rank_ind = np.argsort(min_margin)
    rank_ind = [i for i in rank_ind if i not in already_selected]
    new_batch_cluster_counts = [0 for _ in range(self.n_clusters)]
    new_batch = []
    for i in rank_ind:
      if len(new_batch) == N:
        break
      label = self.cluster_labels[i]
      if new_batch_cluster_counts[label] / N < self.cluster_prob[label]:
        new_batch.append(i)
        new_batch_cluster_counts[label] += 1
    n_slot_remaining = N - len(new_batch)
    batch_filler = list(set(rank_ind) - set(already_selected) - set(new_batch))
    new_batch.extend(batch_filler[0:n_slot_remaining])
    return new_batch

  def to_dict(self):
    output = {}
    output['cluster_membership'] = self.cluster_labels
    return output
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