def embedding(tag, labels, hot_vectors, step, labels_meta=None, walltime=None):
"""Package data to one embedding.
Args:
tag (string): Data identifier
labels (list): A list of labels.
hot_vectors (np.array or list): A matrix which each row is
feature of labels.
step (int): Step of embeddings.
walltime (int): Wall time of embeddings.
Return:
Package with format of record_pb2.Record
"""
embeddings = Record.Embeddings()
if labels_meta:
embeddings.label_meta.extend(labels_meta)
if isinstance(labels[0], list):
temp = []
for index in range(len(labels[0])):
temp.append([label[index] for label in labels])
labels = temp
for label, hot_vector in zip(labels, hot_vectors):
if not isinstance(label, list):
label = [label]
embeddings.embeddings.append(
Record.Embedding(label=label, vectors=hot_vector))
return Record(values=[
Record.Value(
id=step, tag=tag, timestamp=walltime, embeddings=embeddings)
])
def embedding(tag, labels, hot_vectors, step, walltime=None):
"""Package data to one embedding.
Args:
tag (string): Data identifier
labels (numpy.array or list): A list of labels.
hot_vectors (numpy.array or list): A matrix which each row is
feature of labels.
step (int): Step of embeddings.
walltime (int): Wall time of embeddings.
Return:
Package with format of record_pb2.Record
"""
embeddings = Record.Embeddings()
for index in range(len(hot_vectors)):
embeddings.embeddings.append(
Record.Embedding(label=labels[index], vectors=hot_vectors[index]))
return Record(values=[
Record.Value(
id=step, tag=tag, timestamp=walltime, embeddings=embeddings)
])