459 lines
18 KiB
Python
459 lines
18 KiB
Python
|
#!/usr/bin/env python
|
||
|
|
# -*- coding: utf-8 -*-
|
||
|
|
#
|
||
|
|
# Author: Jayant Jain <jayantjain1992@gmail.com>
|
||
|
|
# Copyright (C) 2017 Radim Rehurek <me@radimrehurek.com>
|
||
|
|
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
|
||
|
|
|
||
|
|
|
||
|
|
"""
|
||
|
|
Warnings
|
||
|
|
--------
|
||
|
|
.. deprecated:: 3.2.0
|
||
|
|
Use :mod:`gensim.models.fasttext` instead.
|
||
|
|
|
||
|
|
|
||
|
|
Python wrapper around word representation learning from FastText, a library for efficient learning
|
||
|
|
of word representations and sentence classification [1].
|
||
|
|
|
||
|
|
This module allows training a word embedding from a training corpus with the additional ability
|
||
|
|
to obtain word vectors for out-of-vocabulary words, using the fastText C implementation.
|
||
|
|
|
||
|
|
The wrapped model can NOT be updated with new documents for online training -- use gensim's
|
||
|
|
`Word2Vec` for that.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
>>> from gensim.models.wrappers import FastText
|
||
|
|
>>> model = FastText.train('/Users/kofola/fastText/fasttext', corpus_file='text8')
|
||
|
|
>>> print model['forests'] # prints vector for given out-of-vocabulary word
|
||
|
|
|
||
|
|
.. [1] https://github.com/facebookresearch/fastText#enriching-word-vectors-with-subword-information
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
|
||
|
|
import logging
|
||
|
|
import tempfile
|
||
|
|
import os
|
||
|
|
import struct
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from numpy import float32 as REAL, sqrt, newaxis
|
||
|
|
from gensim import utils
|
||
|
|
from gensim.models.deprecated.keyedvectors import KeyedVectors, Vocab
|
||
|
|
from gensim.models.deprecated.word2vec import Word2Vec
|
||
|
|
|
||
|
|
logger = logging.getLogger(__name__)
|
||
|
|
|
||
|
|
try:
|
||
|
|
FileNotFoundError
|
||
|
|
except NameError:
|
||
|
|
FileNotFoundError = IOError
|
||
|
|
|
||
|
|
FASTTEXT_FILEFORMAT_MAGIC = 793712314
|
||
|
|
|
||
|
|
|
||
|
|
class FastTextKeyedVectors(KeyedVectors):
|
||
|
|
"""
|
||
|
|
Class to contain vectors, vocab and ngrams for the FastText training class and other methods not directly
|
||
|
|
involved in training such as most_similar().
|
||
|
|
Subclasses KeyedVectors to implement oov lookups, storing ngrams and other FastText specific methods
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
super(FastTextKeyedVectors, self).__init__()
|
||
|
|
self.syn0_vocab = None
|
||
|
|
self.syn0_vocab_norm = None
|
||
|
|
self.syn0_ngrams = None
|
||
|
|
self.syn0_ngrams_norm = None
|
||
|
|
self.ngrams = {}
|
||
|
|
self.hash2index = {}
|
||
|
|
self.ngrams_word = {}
|
||
|
|
self.min_n = 0
|
||
|
|
self.max_n = 0
|
||
|
|
|
||
|
|
def save(self, *args, **kwargs):
|
||
|
|
# don't bother storing the cached normalized vectors
|
||
|
|
kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_vocab_norm', 'syn0_ngrams_norm'])
|
||
|
|
super(FastTextKeyedVectors, self).save(*args, **kwargs)
|
||
|
|
|
||
|
|
def word_vec(self, word, use_norm=False):
|
||
|
|
"""
|
||
|
|
Accept a single word as input.
|
||
|
|
Returns the word's representations in vector space, as a 1D numpy array.
|
||
|
|
|
||
|
|
The word can be out-of-vocabulary as long as ngrams for the word are present.
|
||
|
|
For words with all ngrams absent, a KeyError is raised.
|
||
|
|
|
||
|
|
Example::
|
||
|
|
|
||
|
|
>>> trained_model['office']
|
||
|
|
array([ -1.40128313e-02, ...])
|
||
|
|
|
||
|
|
"""
|
||
|
|
if word in self.vocab:
|
||
|
|
return super(FastTextKeyedVectors, self).word_vec(word, use_norm)
|
||
|
|
else:
|
||
|
|
word_vec = np.zeros(self.syn0_ngrams.shape[1], dtype=np.float32)
|
||
|
|
ngrams = compute_ngrams(word, self.min_n, self.max_n)
|
||
|
|
ngrams = [ng for ng in ngrams if ng in self.ngrams]
|
||
|
|
if use_norm:
|
||
|
|
ngram_weights = self.syn0_ngrams_norm
|
||
|
|
else:
|
||
|
|
ngram_weights = self.syn0_ngrams
|
||
|
|
for ngram in ngrams:
|
||
|
|
word_vec += ngram_weights[self.ngrams[ngram]]
|
||
|
|
if word_vec.any():
|
||
|
|
return word_vec / len(ngrams)
|
||
|
|
else: # No ngrams of the word are present in self.ngrams
|
||
|
|
raise KeyError('all ngrams for word %s absent from model' % word)
|
||
|
|
|
||
|
|
def init_sims(self, replace=False):
|
||
|
|
"""
|
||
|
|
Precompute L2-normalized vectors.
|
||
|
|
|
||
|
|
If `replace` is set, forget the original vectors and only keep the normalized
|
||
|
|
ones = saves lots of memory!
|
||
|
|
|
||
|
|
Note that you **cannot continue training** after doing a replace. The model becomes
|
||
|
|
effectively read-only = you can only call `most_similar`, `similarity` etc.
|
||
|
|
|
||
|
|
"""
|
||
|
|
super(FastTextKeyedVectors, self).init_sims(replace)
|
||
|
|
if getattr(self, 'syn0_ngrams_norm', None) is None or replace:
|
||
|
|
logger.info("precomputing L2-norms of ngram weight vectors")
|
||
|
|
if replace:
|
||
|
|
for i in range(self.syn0_ngrams.shape[0]):
|
||
|
|
self.syn0_ngrams[i, :] /= sqrt((self.syn0_ngrams[i, :] ** 2).sum(-1))
|
||
|
|
self.syn0_ngrams_norm = self.syn0_ngrams
|
||
|
|
else:
|
||
|
|
self.syn0_ngrams_norm = \
|
||
|
|
(self.syn0_ngrams / sqrt((self.syn0_ngrams ** 2).sum(-1))[..., newaxis]).astype(REAL)
|
||
|
|
|
||
|
|
def __contains__(self, word):
|
||
|
|
"""
|
||
|
|
Check if `word` or any character ngrams in `word` are present in the vocabulary.
|
||
|
|
A vector for the word is guaranteed to exist if `__contains__` returns True.
|
||
|
|
"""
|
||
|
|
if word in self.vocab:
|
||
|
|
return True
|
||
|
|
else:
|
||
|
|
char_ngrams = compute_ngrams(word, self.min_n, self.max_n)
|
||
|
|
return any(ng in self.ngrams for ng in char_ngrams)
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def load_word2vec_format(cls, *args, **kwargs):
|
||
|
|
"""Not suppported. Use gensim.models.KeyedVectors.load_word2vec_format instead."""
|
||
|
|
raise NotImplementedError("Not supported. Use gensim.models.KeyedVectors.load_word2vec_format instead.")
|
||
|
|
|
||
|
|
|
||
|
|
class FastText(Word2Vec):
|
||
|
|
"""
|
||
|
|
Class for word vector training using FastText. Communication between FastText and Python
|
||
|
|
takes place by working with data files on disk and calling the FastText binary with
|
||
|
|
subprocess.call().
|
||
|
|
Implements functionality similar to [fasttext.py](https://github.com/salestock/fastText.py),
|
||
|
|
improving speed and scope of functionality like `most_similar`, `similarity` by extracting vectors
|
||
|
|
into numpy matrix.
|
||
|
|
|
||
|
|
Warnings
|
||
|
|
--------
|
||
|
|
.. deprecated:: 3.2.0
|
||
|
|
Use :class:`gensim.models.fasttext.FastText` instead of :class:`gensim.models.wrappers.fasttext.FastText`.
|
||
|
|
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
def initialize_word_vectors(self):
|
||
|
|
self.wv = FastTextKeyedVectors()
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def train(cls, ft_path, corpus_file, output_file=None, model='cbow', size=100, alpha=0.025, window=5, min_count=5,
|
||
|
|
word_ngrams=1, loss='ns', sample=1e-3, negative=5, iter=5, min_n=3, max_n=6, sorted_vocab=1, threads=12):
|
||
|
|
"""
|
||
|
|
`ft_path` is the path to the FastText executable, e.g. `/home/kofola/fastText/fasttext`.
|
||
|
|
|
||
|
|
`corpus_file` is the filename of the text file to be used for training the FastText model.
|
||
|
|
Expects file to contain utf-8 encoded text.
|
||
|
|
|
||
|
|
`model` defines the training algorithm. By default, cbow is used. Accepted values are
|
||
|
|
'cbow', 'skipgram'.
|
||
|
|
|
||
|
|
`size` is the dimensionality of the feature vectors.
|
||
|
|
|
||
|
|
`window` is the maximum distance between the current and predicted word within a sentence.
|
||
|
|
|
||
|
|
`alpha` is the initial learning rate.
|
||
|
|
|
||
|
|
`min_count` = ignore all words with total occurrences lower than this.
|
||
|
|
|
||
|
|
`word_ngram` = max length of word ngram
|
||
|
|
|
||
|
|
`loss` = defines training objective. Allowed values are `hs` (hierarchical softmax),
|
||
|
|
`ns` (negative sampling) and `softmax`. Defaults to `ns`
|
||
|
|
|
||
|
|
`sample` = threshold for configuring which higher-frequency words are randomly downsampled;
|
||
|
|
default is 1e-3, useful range is (0, 1e-5).
|
||
|
|
|
||
|
|
`negative` = the value for negative specifies how many "noise words" should be drawn
|
||
|
|
(usually between 5-20). Default is 5. If set to 0, no negative samping is used.
|
||
|
|
Only relevant when `loss` is set to `ns`
|
||
|
|
|
||
|
|
`iter` = number of iterations (epochs) over the corpus. Default is 5.
|
||
|
|
|
||
|
|
`min_n` = min length of char ngrams to be used for training word representations. Default is 3.
|
||
|
|
|
||
|
|
`max_n` = max length of char ngrams to be used for training word representations. Set `max_n` to be
|
||
|
|
lesser than `min_n` to avoid char ngrams being used. Default is 6.
|
||
|
|
|
||
|
|
`sorted_vocab` = if 1 (default), sort the vocabulary by descending frequency before
|
||
|
|
assigning word indexes.
|
||
|
|
|
||
|
|
`threads` = number of threads to use. Default is 12.
|
||
|
|
|
||
|
|
"""
|
||
|
|
ft_path = ft_path
|
||
|
|
output_file = output_file or os.path.join(tempfile.gettempdir(), 'ft_model')
|
||
|
|
ft_args = {
|
||
|
|
'input': corpus_file,
|
||
|
|
'output': output_file,
|
||
|
|
'lr': alpha,
|
||
|
|
'dim': size,
|
||
|
|
'ws': window,
|
||
|
|
'epoch': iter,
|
||
|
|
'minCount': min_count,
|
||
|
|
'wordNgrams': word_ngrams,
|
||
|
|
'neg': negative,
|
||
|
|
'loss': loss,
|
||
|
|
'minn': min_n,
|
||
|
|
'maxn': max_n,
|
||
|
|
'thread': threads,
|
||
|
|
't': sample
|
||
|
|
}
|
||
|
|
cmd = [ft_path, model]
|
||
|
|
for option, value in ft_args.items():
|
||
|
|
cmd.append("-%s" % option)
|
||
|
|
cmd.append(str(value))
|
||
|
|
|
||
|
|
utils.check_output(args=cmd)
|
||
|
|
model = cls.load_fasttext_format(output_file)
|
||
|
|
cls.delete_training_files(output_file)
|
||
|
|
return model
|
||
|
|
|
||
|
|
def save(self, *args, **kwargs):
|
||
|
|
# don't bother storing the cached normalized vectors
|
||
|
|
kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_vocab_norm', 'syn0_ngrams_norm'])
|
||
|
|
super(FastText, self).save(*args, **kwargs)
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def load_fasttext_format(cls, model_file, encoding='utf8'):
|
||
|
|
"""
|
||
|
|
Load the input-hidden weight matrix from the fast text output files.
|
||
|
|
|
||
|
|
Note that due to limitations in the FastText API, you cannot continue training
|
||
|
|
with a model loaded this way, though you can query for word similarity etc.
|
||
|
|
|
||
|
|
`model_file` is the path to the FastText output files.
|
||
|
|
FastText outputs two model files - `/path/to/model.vec` and `/path/to/model.bin`
|
||
|
|
|
||
|
|
Expected value for this example: `/path/to/model` or `/path/to/model.bin`,
|
||
|
|
as gensim requires only `.bin` file to load entire fastText model.
|
||
|
|
|
||
|
|
"""
|
||
|
|
model = cls()
|
||
|
|
if not model_file.endswith('.bin'):
|
||
|
|
model_file += '.bin'
|
||
|
|
model.file_name = model_file
|
||
|
|
model.load_binary_data(encoding=encoding)
|
||
|
|
return model
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def load(cls, *args, **kwargs):
|
||
|
|
model = super(FastText, cls).load(*args, **kwargs)
|
||
|
|
if hasattr(model.wv, 'syn0_all'):
|
||
|
|
setattr(model.wv, 'syn0_ngrams', model.wv.syn0_all)
|
||
|
|
delattr(model.wv, 'syn0_all')
|
||
|
|
return model
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def delete_training_files(cls, model_file):
|
||
|
|
"""Deletes the files created by FastText training"""
|
||
|
|
try:
|
||
|
|
os.remove('%s.vec' % model_file)
|
||
|
|
os.remove('%s.bin' % model_file)
|
||
|
|
except FileNotFoundError:
|
||
|
|
logger.debug('Training files %s not found when attempting to delete', model_file)
|
||
|
|
pass
|
||
|
|
|
||
|
|
def load_binary_data(self, encoding='utf8'):
|
||
|
|
"""Loads data from the output binary file created by FastText training"""
|
||
|
|
|
||
|
|
# TODO use smart_open again when https://github.com/RaRe-Technologies/smart_open/issues/207 will be fixed
|
||
|
|
with open(self.file_name, 'rb') as f:
|
||
|
|
self.load_model_params(f)
|
||
|
|
self.load_dict(f, encoding=encoding)
|
||
|
|
self.load_vectors(f)
|
||
|
|
|
||
|
|
def load_model_params(self, file_handle):
|
||
|
|
magic, version = self.struct_unpack(file_handle, '@2i')
|
||
|
|
if magic == FASTTEXT_FILEFORMAT_MAGIC: # newer format
|
||
|
|
self.new_format = True
|
||
|
|
dim, ws, epoch, min_count, neg, _, loss, model, bucket, minn, maxn, _, t = \
|
||
|
|
self.struct_unpack(file_handle, '@12i1d')
|
||
|
|
else: # older format
|
||
|
|
self.new_format = False
|
||
|
|
dim = magic
|
||
|
|
ws = version
|
||
|
|
epoch, min_count, neg, _, loss, model, bucket, minn, maxn, _, t = self.struct_unpack(file_handle, '@10i1d')
|
||
|
|
# Parameters stored by [Args::save](https://github.com/facebookresearch/fastText/blob/master/src/args.cc)
|
||
|
|
self.vector_size = dim
|
||
|
|
self.window = ws
|
||
|
|
self.iter = epoch
|
||
|
|
self.min_count = min_count
|
||
|
|
self.negative = neg
|
||
|
|
self.hs = loss == 1
|
||
|
|
self.sg = model == 2
|
||
|
|
self.bucket = bucket
|
||
|
|
self.wv.min_n = minn
|
||
|
|
self.wv.max_n = maxn
|
||
|
|
self.sample = t
|
||
|
|
|
||
|
|
def load_dict(self, file_handle, encoding='utf8'):
|
||
|
|
vocab_size, nwords, nlabels = self.struct_unpack(file_handle, '@3i')
|
||
|
|
# Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc)
|
||
|
|
if nlabels > 0:
|
||
|
|
raise NotImplementedError("Supervised fastText models are not supported")
|
||
|
|
logger.info("loading %s words for fastText model from %s", vocab_size, self.file_name)
|
||
|
|
|
||
|
|
self.struct_unpack(file_handle, '@1q') # number of tokens
|
||
|
|
if self.new_format:
|
||
|
|
pruneidx_size, = self.struct_unpack(file_handle, '@q')
|
||
|
|
for i in range(vocab_size):
|
||
|
|
word_bytes = b''
|
||
|
|
char_byte = file_handle.read(1)
|
||
|
|
# Read vocab word
|
||
|
|
while char_byte != b'\x00':
|
||
|
|
word_bytes += char_byte
|
||
|
|
char_byte = file_handle.read(1)
|
||
|
|
word = word_bytes.decode(encoding)
|
||
|
|
count, _ = self.struct_unpack(file_handle, '@qb')
|
||
|
|
|
||
|
|
self.wv.vocab[word] = Vocab(index=i, count=count)
|
||
|
|
self.wv.index2word.append(word)
|
||
|
|
|
||
|
|
assert len(self.wv.vocab) == nwords, (
|
||
|
|
'mismatch between final vocab size ({} words), '
|
||
|
|
'and expected number of words ({} words)'.format(len(self.wv.vocab), nwords))
|
||
|
|
if len(self.wv.vocab) != vocab_size:
|
||
|
|
# expecting to log this warning only for pretrained french vector, wiki.fr
|
||
|
|
logger.warning(
|
||
|
|
"mismatch between final vocab size (%s words), and expected vocab size (%s words)",
|
||
|
|
len(self.wv.vocab), vocab_size
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.new_format:
|
||
|
|
for j in range(pruneidx_size):
|
||
|
|
self.struct_unpack(file_handle, '@2i')
|
||
|
|
|
||
|
|
def load_vectors(self, file_handle):
|
||
|
|
if self.new_format:
|
||
|
|
self.struct_unpack(file_handle, '@?') # bool quant_input in fasttext.cc
|
||
|
|
num_vectors, dim = self.struct_unpack(file_handle, '@2q')
|
||
|
|
# Vectors stored by [Matrix::save](https://github.com/facebookresearch/fastText/blob/master/src/matrix.cc)
|
||
|
|
assert self.vector_size == dim, (
|
||
|
|
'mismatch between vector size in model params ({}) and model vectors ({})'
|
||
|
|
.format(self.vector_size, dim)
|
||
|
|
)
|
||
|
|
float_size = struct.calcsize('@f')
|
||
|
|
if float_size == 4:
|
||
|
|
dtype = np.dtype(np.float32)
|
||
|
|
elif float_size == 8:
|
||
|
|
dtype = np.dtype(np.float64)
|
||
|
|
|
||
|
|
self.num_original_vectors = num_vectors
|
||
|
|
self.wv.syn0_ngrams = np.fromfile(file_handle, dtype=dtype, count=num_vectors * dim)
|
||
|
|
self.wv.syn0_ngrams = self.wv.syn0_ngrams.reshape((num_vectors, dim))
|
||
|
|
assert self.wv.syn0_ngrams.shape == (self.bucket + len(self.wv.vocab), self.vector_size), \
|
||
|
|
'mismatch between actual weight matrix shape {} and expected shape {}'\
|
||
|
|
.format(
|
||
|
|
self.wv.syn0_ngrams.shape, (self.bucket + len(self.wv.vocab), self.vector_size)
|
||
|
|
)
|
||
|
|
|
||
|
|
self.init_ngrams()
|
||
|
|
|
||
|
|
def struct_unpack(self, file_handle, fmt):
|
||
|
|
num_bytes = struct.calcsize(fmt)
|
||
|
|
return struct.unpack(fmt, file_handle.read(num_bytes))
|
||
|
|
|
||
|
|
def init_ngrams(self):
|
||
|
|
"""
|
||
|
|
Computes ngrams of all words present in vocabulary and stores vectors for only those ngrams.
|
||
|
|
Vectors for other ngrams are initialized with a random uniform distribution in FastText. These
|
||
|
|
vectors are discarded here to save space.
|
||
|
|
|
||
|
|
"""
|
||
|
|
self.wv.ngrams = {}
|
||
|
|
all_ngrams = []
|
||
|
|
self.wv.syn0 = np.zeros((len(self.wv.vocab), self.vector_size), dtype=REAL)
|
||
|
|
|
||
|
|
for w, vocab in self.wv.vocab.items():
|
||
|
|
all_ngrams += compute_ngrams(w, self.wv.min_n, self.wv.max_n)
|
||
|
|
self.wv.syn0[vocab.index] += np.array(self.wv.syn0_ngrams[vocab.index])
|
||
|
|
|
||
|
|
all_ngrams = set(all_ngrams)
|
||
|
|
self.num_ngram_vectors = len(all_ngrams)
|
||
|
|
ngram_indices = []
|
||
|
|
for i, ngram in enumerate(all_ngrams):
|
||
|
|
ngram_hash = ft_hash(ngram)
|
||
|
|
ngram_indices.append(len(self.wv.vocab) + ngram_hash % self.bucket)
|
||
|
|
self.wv.ngrams[ngram] = i
|
||
|
|
self.wv.syn0_ngrams = self.wv.syn0_ngrams.take(ngram_indices, axis=0)
|
||
|
|
|
||
|
|
ngram_weights = self.wv.syn0_ngrams
|
||
|
|
|
||
|
|
logger.info(
|
||
|
|
"loading weights for %s words for fastText model from %s",
|
||
|
|
len(self.wv.vocab), self.file_name
|
||
|
|
)
|
||
|
|
|
||
|
|
for w, vocab in self.wv.vocab.items():
|
||
|
|
word_ngrams = compute_ngrams(w, self.wv.min_n, self.wv.max_n)
|
||
|
|
for word_ngram in word_ngrams:
|
||
|
|
self.wv.syn0[vocab.index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]])
|
||
|
|
|
||
|
|
self.wv.syn0[vocab.index] /= (len(word_ngrams) + 1)
|
||
|
|
logger.info(
|
||
|
|
"loaded %s weight matrix for fastText model from %s",
|
||
|
|
self.wv.syn0.shape, self.file_name
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def compute_ngrams(word, min_n, max_n):
|
||
|
|
BOW, EOW = ('<', '>') # Used by FastText to attach to all words as prefix and suffix
|
||
|
|
extended_word = BOW + word + EOW
|
||
|
|
ngrams = []
|
||
|
|
for ngram_length in range(min_n, min(len(extended_word), max_n) + 1):
|
||
|
|
for i in range(0, len(extended_word) - ngram_length + 1):
|
||
|
|
ngrams.append(extended_word[i:i + ngram_length])
|
||
|
|
return ngrams
|
||
|
|
|
||
|
|
|
||
|
|
def ft_hash(string):
|
||
|
|
"""
|
||
|
|
Reproduces [hash method](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc)
|
||
|
|
used in fastText.
|
||
|
|
|
||
|
|
"""
|
||
|
|
# Runtime warnings for integer overflow are raised, this is expected behaviour. These warnings are suppressed.
|
||
|
|
old_settings = np.seterr(all='ignore')
|
||
|
|
h = np.uint32(2166136261)
|
||
|
|
for c in string:
|
||
|
|
h = h ^ np.uint32(ord(c))
|
||
|
|
h = h * np.uint32(16777619)
|
||
|
|
np.seterr(**old_settings)
|
||
|
|
return h
|