构建每个聚类的profile和deletion_mean特征

通过summarize_clusters函数构建每个聚类的protein['cluster_profile']和protein['cluster_deletion_mean']特征。目的是把extra_msa信息反映到msa中。

集成函数数据处理流程： sample_msa ->make_masked_msa -> nearest_neighbor_clusters -> summarize_clusters-> ...

主要函数 tf.math.unsorted_segment_sum：用于沿指定轴对数据进行分段求和。
tf.math.unsorted_segment_sum(data, segment_ids, num_segments, name=None)

• data: 输入张量，包含待求和的数据。

• segment_ids: 用于指定每个元素属于哪个段的一维整数张量。

• num_segments: 整数，表示分段的总数。

• name: 可选参数，用于指定操作的名称。

  import tensorflow as tf
  import pickle

  def shape_list(x):
  """Return list of dimensions of a tensor, statically where possible.

  Like `x.shape.as_list()` but with tensors instead of `None`s.
  
  Args:
    x: A tensor.
  Returns:
    A list with length equal to the rank of the tensor. The n-th element of the
    list is an integer when that dimension is statically known otherwise it is
    the n-th element of `tf.shape(x)`.
  """
  x = tf.convert_to_tensor(x)
  
  # If unknown rank, return dynamic shape
  if x.get_shape().dims is None:
    return tf.shape(x)
  
  static = x.get_shape().as_list()
  shape = tf.shape(x)
  
  ret = []
  for i in range(len(static)):
    dim = static[i]
    if dim is None:
      dim = shape[i]
    ret.append(dim)
  return ret
  

  def data_transforms_curry1(f):
  """Supply all arguments but the first."""

  def fc(*args, **kwargs):
    return lambda x: f(x, *args, **kwargs)
  
  return fc
  

  @data_transforms_curry1
  def summarize_clusters(protein):
  """Produce profile and deletion_matrix_mean within each cluster."""
  num_seq = shape_list(protein['msa'])[0]
  def csum(x):
  return tf.math.unsorted_segment_sum(
  x, protein['extra_cluster_assignment'], num_seq)

  mask = protein['extra_msa_mask']
  mask_counts = 1e-6 + protein['msa_mask'] + csum(mask)  # Include center
  
  # 结果张量[num_seq, num_resi]，第一行表示和msa中的0号序列是最近邻序列的extr_msa之和，以此类推
  msa_sum = csum(mask[:, :, None] * tf.one_hot(protein['extra_msa'], 23))
  msa_sum += tf.one_hot(protein['msa'], 23)  # Original sequence
  protein['cluster_profile'] = msa_sum / mask_counts[:, :, None]
  
  del msa_sum
  
  # 每条msa序列的最近邻序列的extr_msa，在不同位置deletion数统计
  # del_sum [num_seq, num_resi]，第一行表示和msa中的0号序列是最近邻序列的extr_msa,不同位置deletion数，以此类推
  del_sum = csum(mask * protein['extra_deletion_matrix'])
  del_sum += protein['deletion_matrix']  # Original sequence
  protein['cluster_deletion_mean'] = del_sum / mask_counts
  del del_sum
  
  return protein
  

  with open('Human_HBB_tensor_dict_nnclusted.pkl','rb') as f:
  protein = pickle.load(f)

  print(protein.keys())

  protein = summarize_clusters()(protein)
  print(protein.keys())
  print(protein['cluster_profile'].shape)
  print(protein['cluster_profile'])

  print(protein['cluster_deletion_mean'].shape)
  print(protein['cluster_deletion_mean'])