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laywerrobot/lib/python3.6/site-packages/gensim/models/doc2vec_inner.pyx

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2020-08-27 21:55:39 +02:00
#!/usr/bin/env cython
# cython: boundscheck=False
# cython: wraparound=False
# cython: cdivision=True
# cython: embedsignature=True
# coding: utf-8
#
# Copyright (C) 2013 Radim Rehurek <me@radimrehurek.com>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
"""Optimized cython functions for training :class:`~gensim.models.doc2vec.Doc2Vec` model."""
import cython
import numpy as np
from numpy import zeros, float32 as REAL
cimport numpy as np
from libc.string cimport memset, memcpy
# scipy <= 0.15
try:
from scipy.linalg.blas import fblas
except ImportError:
# in scipy > 0.15, fblas function has been removed
import scipy.linalg.blas as fblas
from word2vec_inner cimport bisect_left, random_int32, sscal, REAL_t, EXP_TABLE, our_dot, our_saxpy
DEF MAX_DOCUMENT_LEN = 10000
cdef int ONE = 1
cdef REAL_t ONEF = <REAL_t>1.0
DEF EXP_TABLE_SIZE = 1000
DEF MAX_EXP = 6
cdef void fast_document_dbow_hs(
const np.uint32_t *word_point, const np.uint8_t *word_code, const int codelen,
REAL_t *context_vectors, REAL_t *syn1, const int size,
const np.uint32_t context_index, const REAL_t alpha, REAL_t *work, int learn_context, int learn_hidden,
REAL_t *context_locks) nogil:
cdef long long a, b
cdef long long row1 = context_index * size, row2
cdef REAL_t f, g
memset(work, 0, size * cython.sizeof(REAL_t))
for b in range(codelen):
row2 = word_point[b] * size
f = our_dot(&size, &context_vectors[row1], &ONE, &syn1[row2], &ONE)
if f <= -MAX_EXP or f >= MAX_EXP:
continue
f = EXP_TABLE[<int>((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
g = (1 - word_code[b] - f) * alpha
our_saxpy(&size, &g, &syn1[row2], &ONE, work, &ONE)
if learn_hidden:
our_saxpy(&size, &g, &context_vectors[row1], &ONE, &syn1[row2], &ONE)
if learn_context:
our_saxpy(&size, &context_locks[context_index], work, &ONE, &context_vectors[row1], &ONE)
cdef unsigned long long fast_document_dbow_neg(
const int negative, np.uint32_t *cum_table, unsigned long long cum_table_len,
REAL_t *context_vectors, REAL_t *syn1neg, const int size, const np.uint32_t word_index,
const np.uint32_t context_index, const REAL_t alpha, REAL_t *work,
unsigned long long next_random, int learn_context, int learn_hidden, REAL_t *context_locks) nogil:
cdef long long a
cdef long long row1 = context_index * size, row2
cdef unsigned long long modulo = 281474976710655ULL
cdef REAL_t f, g, label
cdef np.uint32_t target_index
cdef int d
memset(work, 0, size * cython.sizeof(REAL_t))
for d in range(negative+1):
if d == 0:
target_index = word_index
label = ONEF
else:
target_index = bisect_left(cum_table, (next_random >> 16) % cum_table[cum_table_len-1], 0, cum_table_len)
next_random = (next_random * <unsigned long long>25214903917ULL + 11) & modulo
if target_index == word_index:
continue
label = <REAL_t>0.0
row2 = target_index * size
f = our_dot(&size, &context_vectors[row1], &ONE, &syn1neg[row2], &ONE)
if f <= -MAX_EXP or f >= MAX_EXP:
continue
f = EXP_TABLE[<int>((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
g = (label - f) * alpha
our_saxpy(&size, &g, &syn1neg[row2], &ONE, work, &ONE)
if learn_hidden:
our_saxpy(&size, &g, &context_vectors[row1], &ONE, &syn1neg[row2], &ONE)
if learn_context:
our_saxpy(&size, &context_locks[context_index], work, &ONE, &context_vectors[row1], &ONE)
return next_random
cdef void fast_document_dm_hs(
const np.uint32_t *word_point, const np.uint8_t *word_code, int word_code_len,
REAL_t *neu1, REAL_t *syn1, const REAL_t alpha, REAL_t *work,
const int size, int learn_hidden) nogil:
cdef long long b
cdef long long row2
cdef REAL_t f, g
# l1 already composed by caller, passed in as neu1
# work (also passed in) will accumulate l1 error
for b in range(word_code_len):
row2 = word_point[b] * size
f = our_dot(&size, neu1, &ONE, &syn1[row2], &ONE)
if f <= -MAX_EXP or f >= MAX_EXP:
continue
f = EXP_TABLE[<int>((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
g = (1 - word_code[b] - f) * alpha
our_saxpy(&size, &g, &syn1[row2], &ONE, work, &ONE)
if learn_hidden:
our_saxpy(&size, &g, neu1, &ONE, &syn1[row2], &ONE)
cdef unsigned long long fast_document_dm_neg(
const int negative, np.uint32_t *cum_table, unsigned long long cum_table_len, unsigned long long next_random,
REAL_t *neu1, REAL_t *syn1neg, const int predict_word_index, const REAL_t alpha, REAL_t *work,
const int size, int learn_hidden) nogil:
cdef long long row2
cdef unsigned long long modulo = 281474976710655ULL
cdef REAL_t f, g, label
cdef np.uint32_t target_index
cdef int d
# l1 already composed by caller, passed in as neu1
# work (also passsed in) will accumulate l1 error for outside application
for d in range(negative+1):
if d == 0:
target_index = predict_word_index
label = ONEF
else:
target_index = bisect_left(cum_table, (next_random >> 16) % cum_table[cum_table_len-1], 0, cum_table_len)
next_random = (next_random * <unsigned long long>25214903917ULL + 11) & modulo
if target_index == predict_word_index:
continue
label = <REAL_t>0.0
row2 = target_index * size
f = our_dot(&size, neu1, &ONE, &syn1neg[row2], &ONE)
if f <= -MAX_EXP or f >= MAX_EXP:
continue
f = EXP_TABLE[<int>((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
g = (label - f) * alpha
our_saxpy(&size, &g, &syn1neg[row2], &ONE, work, &ONE)
if learn_hidden:
our_saxpy(&size, &g, neu1, &ONE, &syn1neg[row2], &ONE)
return next_random
cdef void fast_document_dmc_hs(
const np.uint32_t *word_point, const np.uint8_t *word_code, int word_code_len,
REAL_t *neu1, REAL_t *syn1, const REAL_t alpha, REAL_t *work,
const int layer1_size, const int vector_size, int learn_hidden) nogil:
cdef long long a, b
cdef long long row2
cdef REAL_t f, g
cdef int m
# l1 already composed by caller, passed in as neu1
# work accumulates net l1 error; eventually applied by caller
for b in range(word_code_len):
row2 = word_point[b] * layer1_size
f = our_dot(&layer1_size, neu1, &ONE, &syn1[row2], &ONE)
if f <= -MAX_EXP or f >= MAX_EXP:
continue
f = EXP_TABLE[<int>((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
g = (1 - word_code[b] - f) * alpha
our_saxpy(&layer1_size, &g, &syn1[row2], &ONE, work, &ONE)
if learn_hidden:
our_saxpy(&layer1_size, &g, neu1, &ONE, &syn1[row2], &ONE)
cdef unsigned long long fast_document_dmc_neg(
const int negative, np.uint32_t *cum_table, unsigned long long cum_table_len, unsigned long long next_random,
REAL_t *neu1, REAL_t *syn1neg, const int predict_word_index, const REAL_t alpha, REAL_t *work,
const int layer1_size, const int vector_size, int learn_hidden) nogil:
cdef long long a
cdef long long row2
cdef unsigned long long modulo = 281474976710655ULL
cdef REAL_t f, g, label
cdef np.uint32_t target_index
cdef int d, m
# l1 already composed by caller, passed in as neu1
# work accumulates net l1 error; eventually applied by caller
for d in range(negative+1):
if d == 0:
target_index = predict_word_index
label = ONEF
else:
target_index = bisect_left(cum_table, (next_random >> 16) % cum_table[cum_table_len-1], 0, cum_table_len)
next_random = (next_random * <unsigned long long>25214903917ULL + 11) & modulo
if target_index == predict_word_index:
continue
label = <REAL_t>0.0
row2 = target_index * layer1_size
f = our_dot(&layer1_size, neu1, &ONE, &syn1neg[row2], &ONE)
if f <= -MAX_EXP or f >= MAX_EXP:
continue
f = EXP_TABLE[<int>((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
g = (label - f) * alpha
our_saxpy(&layer1_size, &g, &syn1neg[row2], &ONE, work, &ONE)
if learn_hidden:
our_saxpy(&layer1_size, &g, neu1, &ONE, &syn1neg[row2], &ONE)
return next_random
def train_document_dbow(model, doc_words, doctag_indexes, alpha, work=None,
train_words=False, learn_doctags=True, learn_words=True, learn_hidden=True,
word_vectors=None, word_locks=None, doctag_vectors=None, doctag_locks=None):
"""Update distributed bag of words model ("PV-DBOW") by training on a single document.
Called internally from :meth:`~gensim.models.doc2vec.Doc2Vec.train` and
:meth:`~gensim.models.doc2vec.Doc2Vec.infer_vector`.
Parameters
----------
model : :class:`~gensim.models.doc2vec.Doc2Vec`
The model to train.
doc_words : list of str
The input document as a list of words to be used for training. Each word will be looked up in
the model's vocabulary.
doctag_indexes : list of int
Indices into `doctag_vectors` used to obtain the tags of the document.
alpha : float
Learning rate.
work : list of float, optional
Updates to be performed on each neuron in the hidden layer of the underlying network.
train_words : bool, optional
Word vectors will be updated exactly as per Word2Vec skip-gram training only if **both** `learn_words`
and `train_words` are set to True.
learn_doctags : bool, optional
Whether the tag vectors should be updated.
learn_words : bool, optional
Word vectors will be updated exactly as per Word2Vec skip-gram training only if **both**
`learn_words` and `train_words` are set to True.
learn_hidden : bool, optional
Whether or not the weights of the hidden layer will be updated.
word_vectors : numpy.ndarray, optional
The vector representation for each word in the vocabulary. If None, these will be retrieved from the model.
word_locks : numpy.ndarray, optional
A learning lock factor for each weight in the hidden layer for words, value 0 completely blocks updates,
a value of 1 allows to update word-vectors.
doctag_vectors : numpy.ndarray, optional
Vector representations of the tags. If None, these will be retrieved from the model.
doctag_locks : numpy.ndarray, optional
The lock factors for each tag, same as `word_locks`, but for document-vectors.
Returns
-------
int
Number of words in the input document that were actually used for training.
"""
cdef int hs = model.hs
cdef int negative = model.negative
cdef int sample = (model.vocabulary.sample != 0)
cdef int _train_words = train_words
cdef int _learn_words = learn_words
cdef int _learn_hidden = learn_hidden
cdef int _learn_doctags = learn_doctags
cdef REAL_t *_word_vectors
cdef REAL_t *_doctag_vectors
cdef REAL_t *_word_locks
cdef REAL_t *_doctag_locks
cdef REAL_t *_work
cdef REAL_t _alpha = alpha
cdef int size = model.trainables.layer1_size
cdef int codelens[MAX_DOCUMENT_LEN]
cdef np.uint32_t indexes[MAX_DOCUMENT_LEN]
cdef np.uint32_t _doctag_indexes[MAX_DOCUMENT_LEN]
cdef np.uint32_t reduced_windows[MAX_DOCUMENT_LEN]
cdef int document_len
cdef int doctag_len
cdef int window = model.window
cdef int i, j
cdef unsigned long long r
cdef long result = 0
# For hierarchical softmax
cdef REAL_t *syn1
cdef np.uint32_t *points[MAX_DOCUMENT_LEN]
cdef np.uint8_t *codes[MAX_DOCUMENT_LEN]
# For negative sampling
cdef REAL_t *syn1neg
cdef np.uint32_t *cum_table
cdef unsigned long long cum_table_len
cdef unsigned long long next_random
# default vectors, locks from syn0/doctag_syn0
if word_vectors is None:
word_vectors = model.wv.vectors
_word_vectors = <REAL_t *>(np.PyArray_DATA(word_vectors))
if doctag_vectors is None:
doctag_vectors = model.docvecs.vectors_docs
_doctag_vectors = <REAL_t *>(np.PyArray_DATA(doctag_vectors))
if word_locks is None:
word_locks = model.trainables.vectors_lockf
_word_locks = <REAL_t *>(np.PyArray_DATA(word_locks))
if doctag_locks is None:
doctag_locks = model.trainables.vectors_docs_lockf
_doctag_locks = <REAL_t *>(np.PyArray_DATA(doctag_locks))
if hs:
syn1 = <REAL_t *>(np.PyArray_DATA(model.trainables.syn1))
if negative:
syn1neg = <REAL_t *>(np.PyArray_DATA(model.trainables.syn1neg))
cum_table = <np.uint32_t *>(np.PyArray_DATA(model.vocabulary.cum_table))
cum_table_len = len(model.vocabulary.cum_table)
if negative or sample:
next_random = (2**24) * model.random.randint(0, 2**24) + model.random.randint(0, 2**24)
# convert Python structures to primitive types, so we can release the GIL
if work is None:
work = zeros(model.trainables.layer1_size, dtype=REAL)
_work = <REAL_t *>np.PyArray_DATA(work)
vlookup = model.wv.vocab
i = 0
for token in doc_words:
predict_word = vlookup[token] if token in vlookup else None
if predict_word is None: # shrink document to leave out word
continue # leaving i unchanged
if sample and predict_word.sample_int < random_int32(&next_random):
continue
indexes[i] = predict_word.index
if hs:
codelens[i] = <int>len(predict_word.code)
codes[i] = <np.uint8_t *>np.PyArray_DATA(predict_word.code)
points[i] = <np.uint32_t *>np.PyArray_DATA(predict_word.point)
result += 1
i += 1
if i == MAX_DOCUMENT_LEN:
break # TODO: log warning, tally overflow?
document_len = i
if _train_words:
# single randint() call avoids a big thread-synchronization slowdown
for i, item in enumerate(model.random.randint(0, window, document_len)):
reduced_windows[i] = item
doctag_len = <int>min(MAX_DOCUMENT_LEN, len(doctag_indexes))
for i in range(doctag_len):
_doctag_indexes[i] = doctag_indexes[i]
result += 1
# release GIL & train on the document
with nogil:
for i in range(document_len):
if _train_words: # simultaneous skip-gram wordvec-training
j = i - window + reduced_windows[i]
if j < 0:
j = 0
k = i + window + 1 - reduced_windows[i]
if k > document_len:
k = document_len
for j in range(j, k):
if j == i:
continue
if hs:
# we reuse the DBOW function, as it is equivalent to skip-gram for this purpose
fast_document_dbow_hs(points[i], codes[i], codelens[i], _word_vectors, syn1, size, indexes[j],
_alpha, _work, _learn_words, _learn_hidden, _word_locks)
if negative:
# we reuse the DBOW function, as it is equivalent to skip-gram for this purpose
next_random = fast_document_dbow_neg(negative, cum_table, cum_table_len, _word_vectors, syn1neg, size,
indexes[i], indexes[j], _alpha, _work, next_random,
_learn_words, _learn_hidden, _word_locks)
# docvec-training
for j in range(doctag_len):
if hs:
fast_document_dbow_hs(points[i], codes[i], codelens[i], _doctag_vectors, syn1, size, _doctag_indexes[j],
_alpha, _work, _learn_doctags, _learn_hidden, _doctag_locks)
if negative:
next_random = fast_document_dbow_neg(negative, cum_table, cum_table_len, _doctag_vectors, syn1neg, size,
indexes[i], _doctag_indexes[j], _alpha, _work, next_random,
_learn_doctags, _learn_hidden, _doctag_locks)
return result
def train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=None,
learn_doctags=True, learn_words=True, learn_hidden=True,
word_vectors=None, word_locks=None, doctag_vectors=None, doctag_locks=None):
"""Update distributed memory model ("PV-DM") by training on a single document.
This method implements the DM model with a projection (input) layer that is either the sum or mean of the context
vectors, depending on the model's `dm_mean` configuration field.
Called internally from :meth:`~gensim.models.doc2vec.Doc2Vec.train` and
:meth:`~gensim.models.doc2vec.Doc2Vec.infer_vector`.
Parameters
----------
model : :class:`~gensim.models.doc2vec.Doc2Vec`
The model to train.
doc_words : list of str
The input document as a list of words to be used for training. Each word will be looked up in
the model's vocabulary.
doctag_indexes : list of int
Indices into `doctag_vectors` used to obtain the tags of the document.
alpha : float
Learning rate.
work : np.ndarray, optional
Private working memory for each worker.
neu1 : np.ndarray, optional
Private working memory for each worker.
learn_doctags : bool, optional
Whether the tag vectors should be updated.
learn_words : bool, optional
Word vectors will be updated exactly as per Word2Vec skip-gram training only if **both**
`learn_words` and `train_words` are set to True.
learn_hidden : bool, optional
Whether or not the weights of the hidden layer will be updated.
word_vectors : numpy.ndarray, optional
The vector representation for each word in the vocabulary. If None, these will be retrieved from the model.
word_locks : numpy.ndarray, optional
A learning lock factor for each weight in the hidden layer for words, value 0 completely blocks updates,
a value of 1 allows to update word-vectors.
doctag_vectors : numpy.ndarray, optional
Vector representations of the tags. If None, these will be retrieved from the model.
doctag_locks : numpy.ndarray, optional
The lock factors for each tag, same as `word_locks`, but for document-vectors.
Returns
-------
int
Number of words in the input document that were actually used for training.
"""
cdef int hs = model.hs
cdef int negative = model.negative
cdef int sample = (model.vocabulary.sample != 0)
cdef int _learn_doctags = learn_doctags
cdef int _learn_words = learn_words
cdef int _learn_hidden = learn_hidden
cdef int cbow_mean = model.cbow_mean
cdef REAL_t count, inv_count = 1.0
cdef REAL_t *_word_vectors
cdef REAL_t *_doctag_vectors
cdef REAL_t *_word_locks
cdef REAL_t *_doctag_locks
cdef REAL_t *_work
cdef REAL_t *_neu1
cdef REAL_t _alpha = alpha
cdef int size = model.trainables.layer1_size
cdef int codelens[MAX_DOCUMENT_LEN]
cdef np.uint32_t indexes[MAX_DOCUMENT_LEN]
cdef np.uint32_t _doctag_indexes[MAX_DOCUMENT_LEN]
cdef np.uint32_t reduced_windows[MAX_DOCUMENT_LEN]
cdef int document_len
cdef int doctag_len
cdef int window = model.window
cdef int i, j, k, m
cdef long result = 0
# For hierarchical softmax
cdef REAL_t *syn1
cdef np.uint32_t *points[MAX_DOCUMENT_LEN]
cdef np.uint8_t *codes[MAX_DOCUMENT_LEN]
# For negative sampling
cdef REAL_t *syn1neg
cdef np.uint32_t *cum_table
cdef unsigned long long cum_table_len
cdef unsigned long long next_random
# default vectors, locks from syn0/doctag_syn0
if word_vectors is None:
word_vectors = model.wv.vectors
_word_vectors = <REAL_t *>(np.PyArray_DATA(word_vectors))
if doctag_vectors is None:
doctag_vectors = model.docvecs.vectors_docs
_doctag_vectors = <REAL_t *>(np.PyArray_DATA(doctag_vectors))
if word_locks is None:
word_locks = model.trainables.vectors_lockf
_word_locks = <REAL_t *>(np.PyArray_DATA(word_locks))
if doctag_locks is None:
doctag_locks = model.trainables.vectors_docs_lockf
_doctag_locks = <REAL_t *>(np.PyArray_DATA(doctag_locks))
if hs:
syn1 = <REAL_t *>(np.PyArray_DATA(model.trainables.syn1))
if negative:
syn1neg = <REAL_t *>(np.PyArray_DATA(model.trainables.syn1neg))
cum_table = <np.uint32_t *>(np.PyArray_DATA(model.vocabulary.cum_table))
cum_table_len = len(model.vocabulary.cum_table)
if negative or sample:
next_random = (2**24) * model.random.randint(0, 2**24) + model.random.randint(0, 2**24)
# convert Python structures to primitive types, so we can release the GIL
if work is None:
work = zeros(model.trainables.layer1_size, dtype=REAL)
_work = <REAL_t *>np.PyArray_DATA(work)
if neu1 is None:
neu1 = zeros(model.trainables.layer1_size, dtype=REAL)
_neu1 = <REAL_t *>np.PyArray_DATA(neu1)
vlookup = model.wv.vocab
i = 0
for token in doc_words:
predict_word = vlookup[token] if token in vlookup else None
if predict_word is None: # shrink document to leave out word
continue # leaving i unchanged
if sample and predict_word.sample_int < random_int32(&next_random):
continue
indexes[i] = predict_word.index
if hs:
codelens[i] = <int>len(predict_word.code)
codes[i] = <np.uint8_t *>np.PyArray_DATA(predict_word.code)
points[i] = <np.uint32_t *>np.PyArray_DATA(predict_word.point)
result += 1
i += 1
if i == MAX_DOCUMENT_LEN:
break # TODO: log warning, tally overflow?
document_len = i
# single randint() call avoids a big thread-sync slowdown
for i, item in enumerate(model.random.randint(0, window, document_len)):
reduced_windows[i] = item
doctag_len = <int>min(MAX_DOCUMENT_LEN, len(doctag_indexes))
for i in range(doctag_len):
_doctag_indexes[i] = doctag_indexes[i]
result += 1
# release GIL & train on the document
with nogil:
for i in range(document_len):
j = i - window + reduced_windows[i]
if j < 0:
j = 0
k = i + window + 1 - reduced_windows[i]
if k > document_len:
k = document_len
# compose l1 (in _neu1) & clear _work
memset(_neu1, 0, size * cython.sizeof(REAL_t))
count = <REAL_t>0.0
for m in range(j, k):
if m == i:
continue
else:
count += ONEF
our_saxpy(&size, &ONEF, &_word_vectors[indexes[m] * size], &ONE, _neu1, &ONE)
for m in range(doctag_len):
count += ONEF
our_saxpy(&size, &ONEF, &_doctag_vectors[_doctag_indexes[m] * size], &ONE, _neu1, &ONE)
if count > (<REAL_t>0.5):
inv_count = ONEF/count
if cbow_mean:
sscal(&size, &inv_count, _neu1, &ONE) # (does this need BLAS-variants like saxpy?)
memset(_work, 0, size * cython.sizeof(REAL_t)) # work to accumulate l1 error
if hs:
fast_document_dm_hs(points[i], codes[i], codelens[i],
_neu1, syn1, _alpha, _work,
size, _learn_hidden)
if negative:
next_random = fast_document_dm_neg(negative, cum_table, cum_table_len, next_random,
_neu1, syn1neg, indexes[i], _alpha, _work,
size, _learn_hidden)
if not cbow_mean:
sscal(&size, &inv_count, _work, &ONE) # (does this need BLAS-variants like saxpy?)
# apply accumulated error in work
if _learn_doctags:
for m in range(doctag_len):
our_saxpy(&size, &_doctag_locks[_doctag_indexes[m]], _work,
&ONE, &_doctag_vectors[_doctag_indexes[m] * size], &ONE)
if _learn_words:
for m in range(j, k):
if m == i:
continue
else:
our_saxpy(&size, &_word_locks[indexes[m]], _work, &ONE,
&_word_vectors[indexes[m] * size], &ONE)
return result
def train_document_dm_concat(model, doc_words, doctag_indexes, alpha, work=None, neu1=None,
learn_doctags=True, learn_words=True, learn_hidden=True,
word_vectors=None, word_locks=None, doctag_vectors=None, doctag_locks=None):
"""Update distributed memory model ("PV-DM") by training on a single document, using a concatenation of the context
window word vectors (rather than a sum or average).
This might be slower since the input at each batch will be significantly larger.
Called internally from :meth:`~gensim.models.doc2vec.Doc2Vec.train` and
:meth:`~gensim.models.doc2vec.Doc2Vec.infer_vector`.
Parameters
----------
model : :class:`~gensim.models.doc2vec.Doc2Vec`
The model to train.
doc_words : list of str
The input document as a list of words to be used for training. Each word will be looked up in
the model's vocabulary.
doctag_indexes : list of int
Indices into `doctag_vectors` used to obtain the tags of the document.
alpha : float, optional
Learning rate.
work : np.ndarray, optional
Private working memory for each worker.
neu1 : np.ndarray, optional
Private working memory for each worker.
learn_doctags : bool, optional
Whether the tag vectors should be updated.
learn_words : bool, optional
Word vectors will be updated exactly as per Word2Vec skip-gram training only if **both**
`learn_words` and `train_words` are set to True.
learn_hidden : bool, optional
Whether or not the weights of the hidden layer will be updated.
word_vectors : numpy.ndarray, optional
The vector representation for each word in the vocabulary. If None, these will be retrieved from the model.
word_locks : numpy.ndarray, optional
A learning lock factor for each weight in the hidden layer for words, value 0 completely blocks updates,
a value of 1 allows to update word-vectors.
doctag_vectors : numpy.ndarray, optional
Vector representations of the tags. If None, these will be retrieved from the model.
doctag_locks : numpy.ndarray, optional
The lock factors for each tag, same as `word_locks`, but for document-vectors.
Returns
-------
int
Number of words in the input document that were actually used for training.
"""
cdef int hs = model.hs
cdef int negative = model.negative
cdef int sample = (model.vocabulary.sample != 0)
cdef int _learn_doctags = learn_doctags
cdef int _learn_words = learn_words
cdef int _learn_hidden = learn_hidden
cdef REAL_t *_word_vectors
cdef REAL_t *_doctag_vectors
cdef REAL_t *_word_locks
cdef REAL_t *_doctag_locks
cdef REAL_t *_work
cdef REAL_t *_neu1
cdef REAL_t _alpha = alpha
cdef int layer1_size = model.trainables.layer1_size
cdef int vector_size = model.docvecs.vector_size
cdef int codelens[MAX_DOCUMENT_LEN]
cdef np.uint32_t indexes[MAX_DOCUMENT_LEN]
cdef np.uint32_t _doctag_indexes[MAX_DOCUMENT_LEN]
cdef np.uint32_t window_indexes[MAX_DOCUMENT_LEN]
cdef int document_len
cdef int doctag_len
cdef int window = model.window
cdef int expected_doctag_len = model.dm_tag_count
cdef int i, j, k, m, n
cdef long result = 0
cdef int null_word_index = model.wv.vocab['\0'].index
# For hierarchical softmax
cdef REAL_t *syn1
cdef np.uint32_t *points[MAX_DOCUMENT_LEN]
cdef np.uint8_t *codes[MAX_DOCUMENT_LEN]
# For negative sampling
cdef REAL_t *syn1neg
cdef np.uint32_t *cum_table
cdef unsigned long long cum_table_len
cdef unsigned long long next_random
doctag_len = <int>min(MAX_DOCUMENT_LEN, len(doctag_indexes))
if doctag_len != expected_doctag_len:
return 0 # skip doc without expected number of tags
# default vectors, locks from syn0/doctag_syn0
if word_vectors is None:
word_vectors = model.wv.vectors
_word_vectors = <REAL_t *>(np.PyArray_DATA(word_vectors))
if doctag_vectors is None:
doctag_vectors = model.docvecs.vectors_docs
_doctag_vectors = <REAL_t *>(np.PyArray_DATA(doctag_vectors))
if word_locks is None:
word_locks = model.trainables.vectors_lockf
_word_locks = <REAL_t *>(np.PyArray_DATA(word_locks))
if doctag_locks is None:
doctag_locks = model.trainables.vectors_docs_lockf
_doctag_locks = <REAL_t *>(np.PyArray_DATA(doctag_locks))
if hs:
syn1 = <REAL_t *>(np.PyArray_DATA(model.trainables.syn1))
if negative:
syn1neg = <REAL_t *>(np.PyArray_DATA(model.trainables.syn1neg))
cum_table = <np.uint32_t *>(np.PyArray_DATA(model.vocabulary.cum_table))
cum_table_len = len(model.vocabulary.cum_table)
if negative or sample:
next_random = (2**24) * model.random.randint(0, 2**24) + model.random.randint(0, 2**24)
# convert Python structures to primitive types, so we can release the GIL
if work is None:
work = zeros(model.trainables.layer1_size, dtype=REAL)
_work = <REAL_t *>np.PyArray_DATA(work)
if neu1 is None:
neu1 = zeros(model.trainables.layer1_size, dtype=REAL)
_neu1 = <REAL_t *>np.PyArray_DATA(neu1)
vlookup = model.wv.vocab
i = 0
for token in doc_words:
predict_word = vlookup[token] if token in vlookup else None
if predict_word is None: # shrink document to leave out word
continue # leaving i unchanged
if sample and predict_word.sample_int < random_int32(&next_random):
continue
indexes[i] = predict_word.index
if hs:
codelens[i] = <int>len(predict_word.code)
codes[i] = <np.uint8_t *>np.PyArray_DATA(predict_word.code)
points[i] = <np.uint32_t *>np.PyArray_DATA(predict_word.point)
result += 1
i += 1
if i == MAX_DOCUMENT_LEN:
break # TODO: log warning, tally overflow?
document_len = i
for i in range(doctag_len):
_doctag_indexes[i] = doctag_indexes[i]
result += 1
# release GIL & train on the document
with nogil:
for i in range(document_len):
j = i - window # negative OK: will pad with null word
k = i + window + 1 # past document end OK: will pad with null word
# compose l1 & clear work
for m in range(doctag_len):
# doc vector(s)
memcpy(&_neu1[m * vector_size], &_doctag_vectors[_doctag_indexes[m] * vector_size],
vector_size * cython.sizeof(REAL_t))
n = 0
for m in range(j, k):
# word vectors in window
if m == i:
continue
if m < 0 or m >= document_len:
window_indexes[n] = null_word_index
else:
window_indexes[n] = indexes[m]
n += 1
for m in range(2 * window):
memcpy(&_neu1[(doctag_len + m) * vector_size], &_word_vectors[window_indexes[m] * vector_size],
vector_size * cython.sizeof(REAL_t))
memset(_work, 0, layer1_size * cython.sizeof(REAL_t)) # work to accumulate l1 error
if hs:
fast_document_dmc_hs(points[i], codes[i], codelens[i],
_neu1, syn1, _alpha, _work,
layer1_size, vector_size, _learn_hidden)
if negative:
next_random = fast_document_dmc_neg(negative, cum_table, cum_table_len, next_random,
_neu1, syn1neg, indexes[i], _alpha, _work,
layer1_size, vector_size, _learn_hidden)
if _learn_doctags:
for m in range(doctag_len):
our_saxpy(&vector_size, &_doctag_locks[_doctag_indexes[m]], &_work[m * vector_size],
&ONE, &_doctag_vectors[_doctag_indexes[m] * vector_size], &ONE)
if _learn_words:
for m in range(2 * window):
our_saxpy(&vector_size, &_word_locks[window_indexes[m]], &_work[(doctag_len + m) * vector_size],
&ONE, &_word_vectors[window_indexes[m] * vector_size], &ONE)
return result
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