mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-15 06:09:01 +03:00
108 lines
3.5 KiB
Python
108 lines
3.5 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
|
|
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"):
|
|
# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
|
|
import tqdm
|
|
|
|
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)
|