mirror of
https://github.com/explosion/spaCy.git
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eae35e9b27
- add files to specify rules for German tokenization - change generate_specials.py to generate from an external file (abbrev.de.tab) - copy gazetteer.json from lang_data/en/ - init_model.py - change doc freq threshold to 0 - add train_german_tagger.py - expects conll09-formatted input
240 lines
7.5 KiB
Python
240 lines
7.5 KiB
Python
"""Set up a model directory.
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Requires:
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lang_data --- Rules for the tokenizer
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* prefix.txt
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* suffix.txt
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* infix.txt
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* morphs.json
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* specials.json
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corpora --- Data files
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* WordNet
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* words.sgt.prob --- Smoothed unigram probabilities
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* clusters.txt --- Output of hierarchical clustering, e.g. Brown clusters
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* vectors.tgz --- output of something like word2vec
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"""
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from __future__ import unicode_literals
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from ast import literal_eval
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import math
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import gzip
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import json
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import plac
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from pathlib import Path
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from shutil import copyfile
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from shutil import copytree
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from collections import defaultdict
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import io
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from spacy.vocab import Vocab
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from spacy.vocab import write_binary_vectors
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from spacy.strings import hash_string
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from preshed.counter import PreshCounter
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from spacy.parts_of_speech import NOUN, VERB, ADJ
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import spacy.en
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import spacy.de
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import spacy.fi
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import spacy.it
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try:
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unicode
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except NameError:
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unicode = str
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def setup_tokenizer(lang_data_dir, tok_dir):
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if not tok_dir.exists():
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tok_dir.mkdir()
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for filename in ('infix.txt', 'morphs.json', 'prefix.txt', 'specials.json',
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'suffix.txt'):
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src = lang_data_dir / filename
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dst = tok_dir / filename
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copyfile(str(src), str(dst))
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def _read_clusters(loc):
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if not loc.exists():
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print("Warning: Clusters file not found")
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return {}
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clusters = {}
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for line in io.open(str(loc), 'r', encoding='utf8'):
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try:
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cluster, word, freq = line.split()
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except ValueError:
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continue
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# If the clusterer has only seen the word a few times, its cluster is
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# unreliable.
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if int(freq) >= 3:
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clusters[word] = cluster
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else:
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clusters[word] = '0'
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# Expand clusters with re-casing
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for word, cluster in list(clusters.items()):
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if word.lower() not in clusters:
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clusters[word.lower()] = cluster
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if word.title() not in clusters:
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clusters[word.title()] = cluster
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if word.upper() not in clusters:
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clusters[word.upper()] = cluster
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return clusters
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def _read_probs(loc):
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if not loc.exists():
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print("Probabilities file not found. Trying freqs.")
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return {}, 0.0
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probs = {}
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for i, line in enumerate(io.open(str(loc), 'r', encoding='utf8')):
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prob, word = line.split()
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prob = float(prob)
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probs[word] = prob
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return probs, probs['-OOV-']
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def _read_freqs(loc, max_length=100, min_doc_freq=0, min_freq=200):
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if not loc.exists():
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print("Warning: Frequencies file not found")
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return {}, 0.0
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counts = PreshCounter()
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total = 0
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if str(loc).endswith('gz'):
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file_ = gzip.open(str(loc))
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else:
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file_ = loc.open()
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for i, line in enumerate(file_):
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freq, doc_freq, key = line.split('\t', 2)
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freq = int(freq)
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counts.inc(i+1, freq)
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total += freq
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counts.smooth()
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log_total = math.log(total)
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if str(loc).endswith('gz'):
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file_ = gzip.open(str(loc))
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else:
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file_ = loc.open()
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probs = {}
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for line in file_:
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freq, doc_freq, key = line.split('\t', 2)
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doc_freq = int(doc_freq)
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freq = int(freq)
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if doc_freq >= min_doc_freq and freq >= min_freq and len(key) < max_length:
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# word = literal_eval(key)
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word = key
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smooth_count = counts.smoother(int(freq))
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log_smooth_count = math.log(smooth_count)
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probs[word] = math.log(smooth_count) - log_total
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oov_prob = math.log(counts.smoother(0)) - log_total
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return probs, oov_prob
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def _read_senses(loc):
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lexicon = defaultdict(lambda: defaultdict(list))
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if not loc.exists():
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print("Warning: WordNet senses not found")
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return lexicon
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sense_names = dict((s, i) for i, s in enumerate(spacy.senses.STRINGS))
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pos_ids = {'noun': NOUN, 'verb': VERB, 'adjective': ADJ}
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for line in codecs.open(str(loc), 'r', 'utf8'):
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sense_strings = line.split()
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word = sense_strings.pop(0)
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for sense in sense_strings:
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pos, sense = sense[3:].split('.')
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sense_name = '%s_%s' % (pos[0].upper(), sense.lower())
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if sense_name != 'N_tops':
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sense_id = sense_names[sense_name]
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lexicon[word][pos_ids[pos]].append(sense_id)
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return lexicon
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def setup_vocab(get_lex_attr, tag_map, src_dir, dst_dir):
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if not dst_dir.exists():
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dst_dir.mkdir()
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vectors_src = src_dir / 'vectors.tgz'
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if vectors_src.exists():
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write_binary_vectors(str(vectors_src), str(dst_dir / 'vec.bin'))
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else:
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print("Warning: Word vectors file not found")
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vocab = Vocab(get_lex_attr=get_lex_attr, tag_map=tag_map)
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clusters = _read_clusters(src_dir / 'clusters.txt')
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probs, oov_prob = _read_probs(src_dir / 'words.sgt.prob')
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if not probs:
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probs, oov_prob = _read_freqs(src_dir / 'freqs.txt')
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if not probs:
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oov_prob = -20
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else:
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oov_prob = min(probs.values())
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for word in clusters:
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if word not in probs:
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probs[word] = oov_prob
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lexicon = []
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for word, prob in reversed(sorted(list(probs.items()), key=lambda item: item[1])):
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# First encode the strings into the StringStore. This way, we can map
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# the orth IDs to frequency ranks
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orth = vocab.strings[word]
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# Now actually load the vocab
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for word, prob in reversed(sorted(list(probs.items()), key=lambda item: item[1])):
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lexeme = vocab[word]
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lexeme.prob = prob
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lexeme.is_oov = False
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# Decode as a little-endian string, so that we can do & 15 to get
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# the first 4 bits. See _parse_features.pyx
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if word in clusters:
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lexeme.cluster = int(clusters[word][::-1], 2)
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else:
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lexeme.cluster = 0
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vocab.dump(str(dst_dir / 'lexemes.bin'))
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with (dst_dir / 'strings.json').open('w') as file_:
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vocab.strings.dump(file_)
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with (dst_dir / 'oov_prob').open('w') as file_:
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file_.write('%f' % oov_prob)
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def main(lang_id, lang_data_dir, corpora_dir, model_dir):
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languages = {
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'en': spacy.en.English.default_lex_attrs(),
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'de': spacy.de.German.default_lex_attrs(),
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'fi': spacy.fi.Finnish.default_lex_attrs(),
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'it': spacy.it.Italian.default_lex_attrs(),
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}
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model_dir = Path(model_dir)
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lang_data_dir = Path(lang_data_dir) / lang_id
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corpora_dir = Path(corpora_dir) / lang_id
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assert corpora_dir.exists()
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assert lang_data_dir.exists()
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if not model_dir.exists():
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model_dir.mkdir()
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tag_map = json.load((lang_data_dir / 'tag_map.json').open())
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setup_tokenizer(lang_data_dir, model_dir / 'tokenizer')
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setup_vocab(languages[lang_id], tag_map, corpora_dir, model_dir / 'vocab')
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if (lang_data_dir / 'gazetteer.json').exists():
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copyfile(str(lang_data_dir / 'gazetteer.json'),
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str(model_dir / 'vocab' / 'gazetteer.json'))
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copyfile(str(lang_data_dir / 'tag_map.json'),
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str(model_dir / 'vocab' / 'tag_map.json'))
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if (lang_data_dir / 'lemma_rules.json').exists():
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copyfile(str(lang_data_dir / 'lemma_rules.json'),
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str(model_dir / 'vocab' / 'lemma_rules.json'))
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if not (model_dir / 'wordnet').exists() and (corpora_dir / 'wordnet').exists():
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copytree(str(corpora_dir / 'wordnet' / 'dict'), str(model_dir / 'wordnet'))
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if __name__ == '__main__':
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plac.call(main)
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