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85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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"""Example of multi-processing with Joblib. Here, we're exporting
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part-of-speech-tagged, true-cased, (very roughly) sentence-separated text, with
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each "sentence" on a newline, and spaces between tokens. Data is loaded from
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the IMDB movie reviews dataset and will be loaded automatically via Thinc's
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built-in dataset loader.
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Compatible with: spaCy v2.0.0+
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Last tested with: v2.1.0
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Prerequisites: pip install joblib
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"""
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from __future__ import print_function, unicode_literals
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from pathlib import Path
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from joblib import Parallel, delayed
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from functools import partial
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import thinc.extra.datasets
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import plac
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import spacy
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from spacy.util import minibatch
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@plac.annotations(
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output_dir=("Output directory", "positional", None, Path),
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model=("Model name (needs tagger)", "positional", None, str),
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n_jobs=("Number of workers", "option", "n", int),
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batch_size=("Batch-size for each process", "option", "b", int),
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limit=("Limit of entries from the dataset", "option", "l", int),
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)
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def main(output_dir, model="en_core_web_sm", n_jobs=4, batch_size=1000, limit=10000):
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nlp = spacy.load(model) # load spaCy model
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print("Loaded model '%s'" % model)
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if not output_dir.exists():
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output_dir.mkdir()
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# load and pre-process the IMBD dataset
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print("Loading IMDB data...")
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data, _ = thinc.extra.datasets.imdb()
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texts, _ = zip(*data[-limit:])
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print("Processing texts...")
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partitions = minibatch(texts, size=batch_size)
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executor = Parallel(n_jobs=n_jobs, backend="multiprocessing", prefer="processes")
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do = delayed(partial(transform_texts, nlp))
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tasks = (do(i, batch, output_dir) for i, batch in enumerate(partitions))
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executor(tasks)
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def transform_texts(nlp, batch_id, texts, output_dir):
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print(nlp.pipe_names)
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out_path = Path(output_dir) / ("%d.txt" % batch_id)
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if out_path.exists(): # return None in case same batch is called again
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return None
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print("Processing batch", batch_id)
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with out_path.open("w", encoding="utf8") as f:
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for doc in nlp.pipe(texts):
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f.write(" ".join(represent_word(w) for w in doc if not w.is_space))
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f.write("\n")
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print("Saved {} texts to {}.txt".format(len(texts), batch_id))
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def represent_word(word):
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text = word.text
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# True-case, i.e. try to normalize sentence-initial capitals.
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# Only do this if the lower-cased form is more probable.
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if (
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text.istitle()
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and is_sent_begin(word)
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and word.prob < word.doc.vocab[text.lower()].prob
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):
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text = text.lower()
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return text + "|" + word.tag_
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def is_sent_begin(word):
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if word.i == 0:
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return True
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elif word.i >= 2 and word.nbor(-1).text in (".", "!", "?", "..."):
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return True
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else:
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return False
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if __name__ == "__main__":
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plac.call(main)
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