spaCy/examples/vectors_tensorboard.py
2018-03-24 17:15:48 +01:00

83 lines
3.2 KiB
Python

#!/usr/bin/env python
# coding: utf8
"""Visualize spaCy word vectors in Tensorboard.
Adapted from: https://gist.github.com/BrikerMan/7bd4e4bd0a00ac9076986148afc06507
"""
from __future__ import unicode_literals
from os import path
import math
import numpy
import plac
import spacy
import tensorflow as tf
import tqdm
from tensorflow.contrib.tensorboard.plugins.projector import visualize_embeddings, ProjectorConfig
@plac.annotations(
vectors_loc=("Path to spaCy model that contains vectors", "positional", None, str),
out_loc=("Path to output folder for tensorboard session data", "positional", None, str),
name=("Human readable name for tsv file and vectors tensor", "positional", None, str),
)
def main(vectors_loc, out_loc, name="spaCy_vectors"):
meta_file = "{}.tsv".format(name)
out_meta_file = path.join(out_loc, meta_file)
print('Loading spaCy vectors model: {}'.format(vectors_loc))
model = spacy.load(vectors_loc)
print('Finding lexemes with vectors attached: {}'.format(vectors_loc))
strings_stream = tqdm.tqdm(model.vocab.strings, total=len(model.vocab.strings), leave=False)
queries = [w for w in strings_stream if model.vocab.has_vector(w)]
vector_count = len(queries)
print('Building Tensorboard Projector metadata for ({}) vectors: {}'.format(vector_count, out_meta_file))
# Store vector data in a tensorflow variable
tf_vectors_variable = numpy.zeros((vector_count, model.vocab.vectors.shape[1]))
# Write a tab-separated file that contains information about the vectors for visualization
#
# Reference: https://www.tensorflow.org/programmers_guide/embedding#metadata
with open(out_meta_file, 'wb') as file_metadata:
# Define columns in the first row
file_metadata.write("Text\tFrequency\n".encode('utf-8'))
# Write out a row for each vector that we add to the tensorflow variable we created
vec_index = 0
for text in tqdm.tqdm(queries, total=len(queries), leave=False):
# https://github.com/tensorflow/tensorflow/issues/9094
text = '<Space>' if text.lstrip() == '' else text
lex = model.vocab[text]
# Store vector data and metadata
tf_vectors_variable[vec_index] = model.vocab.get_vector(text)
file_metadata.write("{}\t{}\n".format(text, math.exp(lex.prob) * vector_count).encode('utf-8'))
vec_index += 1
print('Running Tensorflow Session...')
sess = tf.InteractiveSession()
tf.Variable(tf_vectors_variable, trainable=False, name=name)
tf.global_variables_initializer().run()
saver = tf.train.Saver()
writer = tf.summary.FileWriter(out_loc, sess.graph)
# Link the embeddings into the config
config = ProjectorConfig()
embed = config.embeddings.add()
embed.tensor_name = name
embed.metadata_path = meta_file
# Tell the projector about the configured embeddings and metadata file
visualize_embeddings(writer, config)
# Save session and print run command to the output
print('Saving Tensorboard Session...')
saver.save(sess, path.join(out_loc, '{}.ckpt'.format(name)))
print('Done. Run `tensorboard --logdir={0}` to view in Tensorboard'.format(out_loc))
if __name__ == '__main__':
plac.call(main)