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