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 pickledef 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 thelist is an integer when that dimension is statically known otherwise it isthe n-th element of `tf.shape(x)`."""x = tf.convert_to_tensor(x)# If unknown rank, return dynamic shapeif 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 retdef 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 sequenceprotein['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 sequenceprotein['cluster_deletion_mean'] = del_sum / mask_countsdel del_sumreturn proteinwith 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'])