mirror of https://github.com/explosion/spaCy.git
138 lines
4.5 KiB
Python
138 lines
4.5 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import bz2
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import gzip
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import math
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from ast import literal_eval
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from pathlib import Path
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import numpy as np
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import spacy
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from preshed.counter import PreshCounter
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from .. import util
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from ..compat import fix_text
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def model(cmd, lang, model_dir, freqs_data, clusters_data, vectors_data,
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min_doc_freq=5, min_word_freq=200):
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model_path = Path(model_dir)
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freqs_path = Path(freqs_data)
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clusters_path = Path(clusters_data) if clusters_data else None
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vectors_path = Path(vectors_data) if vectors_data else None
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check_dirs(freqs_path, clusters_path, vectors_path)
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vocab = util.get_lang_class(lang).Defaults.create_vocab()
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nlp = spacy.blank(lang)
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vocab = nlp.vocab
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probs, oov_prob = read_probs(
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freqs_path, min_doc_freq=int(min_doc_freq), min_freq=int(min_doc_freq))
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clusters = read_clusters(clusters_path) if clusters_path else {}
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populate_vocab(vocab, clusters, probs, oov_prob)
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add_vectors(vocab, vectors_path)
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create_model(model_path, nlp)
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def add_vectors(vocab, vectors_path):
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with bz2.BZ2File(vectors_path.as_posix()) as f:
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num_words, dim = next(f).split()
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vocab.clear_vectors(int(dim))
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for line in f:
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word_w_vector = line.decode("utf8").strip().split(" ")
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word = word_w_vector[0]
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vector = np.array([float(val) for val in word_w_vector[1:]])
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if word in vocab:
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vocab.set_vector(word, vector)
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def create_model(model_path, model):
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if not model_path.exists():
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model_path.mkdir()
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model.to_disk(model_path.as_posix())
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def read_probs(freqs_path, max_length=100, min_doc_freq=5, min_freq=200):
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counts = PreshCounter()
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total = 0
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freqs_file = check_unzip(freqs_path)
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for i, line in enumerate(freqs_file):
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freq, doc_freq, key = line.rstrip().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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freqs_file = check_unzip(freqs_path)
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probs = {}
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for line in freqs_file:
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freq, doc_freq, key = line.rstrip().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(
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key) < max_length:
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word = literal_eval(key)
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smooth_count = counts.smoother(int(freq))
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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_clusters(clusters_path):
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clusters = {}
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with clusters_path.open() as f:
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for line in f:
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try:
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cluster, word, freq = line.split()
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word = fix_text(word)
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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
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# cluster is 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 populate_vocab(vocab, clusters, probs, oov_prob):
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for word, prob in reversed(
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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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def check_unzip(file_path):
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file_path_str = file_path.as_posix()
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if file_path_str.endswith('gz'):
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return gzip.open(file_path_str)
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else:
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return file_path.open()
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def check_dirs(freqs_data, clusters_data, vectors_data):
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if not freqs_data.is_file():
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util.sys_exit(freqs_data.as_posix(), title="No frequencies file found")
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if clusters_data and not clusters_data.is_file():
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util.sys_exit(
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clusters_data.as_posix(), title="No Brown clusters file found")
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if vectors_data and not vectors_data.is_file():
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util.sys_exit(
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vectors_data.as_posix(), title="No word vectors file found")
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