|
1
|
|
|
"""Annif backend using the fastText classifier""" |
|
2
|
|
|
|
|
3
|
|
|
import collections |
|
4
|
|
|
import os.path |
|
5
|
|
|
import annif.util |
|
6
|
|
|
from annif.suggestion import SubjectSuggestion, ListSuggestionResult |
|
7
|
|
|
from annif.exception import NotInitializedException |
|
8
|
|
|
import fastText |
|
9
|
|
|
from . import backend |
|
10
|
|
|
from . import mixins |
|
11
|
|
|
|
|
12
|
|
|
|
|
13
|
|
|
class FastTextBackend(mixins.ChunkingBackend, backend.AnnifBackend): |
|
14
|
|
|
"""fastText backend for Annif""" |
|
15
|
|
|
|
|
16
|
|
|
name = "fasttext" |
|
17
|
|
|
needs_subject_index = True |
|
18
|
|
|
|
|
19
|
|
|
FASTTEXT_PARAMS = { |
|
20
|
|
|
'lr': float, |
|
21
|
|
|
'lrUpdateRate': int, |
|
22
|
|
|
'dim': int, |
|
23
|
|
|
'ws': int, |
|
24
|
|
|
'epoch': int, |
|
25
|
|
|
'minCount': int, |
|
26
|
|
|
'neg': int, |
|
27
|
|
|
'wordNgrams': int, |
|
28
|
|
|
'loss': str, |
|
29
|
|
|
'bucket': int, |
|
30
|
|
|
'minn': int, |
|
31
|
|
|
'maxn': int, |
|
32
|
|
|
'thread': int, |
|
33
|
|
|
't': float |
|
34
|
|
|
} |
|
35
|
|
|
|
|
36
|
|
|
MODEL_FILE = 'fasttext-model' |
|
37
|
|
|
TRAIN_FILE = 'fasttext-train.txt' |
|
38
|
|
|
|
|
39
|
|
|
# defaults for uninitialized instances |
|
40
|
|
|
_model = None |
|
41
|
|
|
|
|
42
|
|
|
def initialize(self): |
|
43
|
|
|
if self._model is None: |
|
44
|
|
|
path = os.path.join(self.datadir, self.MODEL_FILE) |
|
45
|
|
|
self.debug('loading fastText model from {}'.format(path)) |
|
46
|
|
|
if os.path.exists(path): |
|
47
|
|
|
self._model = fastText.load_model(path) |
|
48
|
|
|
self.debug('loaded model {}'.format(str(self._model))) |
|
49
|
|
|
self.debug('dim: {}'.format(self._model.get_dimension())) |
|
50
|
|
|
else: |
|
51
|
|
|
raise NotInitializedException( |
|
52
|
|
|
'model {} not found'.format(path), |
|
53
|
|
|
backend_id=self.backend_id) |
|
54
|
|
|
|
|
55
|
|
|
@staticmethod |
|
56
|
|
|
def _id_to_label(subject_id): |
|
57
|
|
|
return "__label__{:d}".format(subject_id) |
|
58
|
|
|
|
|
59
|
|
|
@staticmethod |
|
60
|
|
|
def _label_to_subject(project, label): |
|
61
|
|
|
labelnum = label.replace('__label__', '') |
|
62
|
|
|
subject_id = int(labelnum) |
|
63
|
|
|
return project.subjects[subject_id] |
|
64
|
|
|
|
|
65
|
|
|
def _write_train_file(self, doc_subjects, filename): |
|
66
|
|
|
with open(filename, 'w', encoding='utf-8') as trainfile: |
|
67
|
|
|
for doc, subject_ids in doc_subjects.items(): |
|
68
|
|
|
labels = [self._id_to_label(sid) for sid in subject_ids |
|
69
|
|
|
if sid is not None] |
|
70
|
|
|
if labels: |
|
71
|
|
|
print(' '.join(labels), doc, file=trainfile) |
|
72
|
|
|
else: |
|
73
|
|
|
self.warning('no labels for document "{}"'.format(doc)) |
|
74
|
|
|
|
|
75
|
|
|
@staticmethod |
|
76
|
|
|
def _normalize_text(project, text): |
|
77
|
|
|
return ' '.join(project.analyzer.tokenize_words(text)) |
|
78
|
|
|
|
|
79
|
|
|
def _create_train_file(self, corpus, project): |
|
80
|
|
|
self.info('creating fastText training file') |
|
81
|
|
|
|
|
82
|
|
|
doc_subjects = collections.defaultdict(set) |
|
83
|
|
|
|
|
84
|
|
|
for doc in corpus.documents: |
|
85
|
|
|
text = self._normalize_text(project, doc.text) |
|
86
|
|
|
if text == '': |
|
87
|
|
|
continue |
|
88
|
|
|
doc_subjects[text] = [project.subjects.by_uri(uri) |
|
89
|
|
|
for uri in doc.uris] |
|
90
|
|
|
|
|
91
|
|
|
annif.util.atomic_save(doc_subjects, |
|
92
|
|
|
self.datadir, |
|
93
|
|
|
self.TRAIN_FILE, |
|
94
|
|
|
method=self._write_train_file) |
|
95
|
|
|
|
|
96
|
|
|
def _create_model(self): |
|
97
|
|
|
self.info('creating fastText model') |
|
98
|
|
|
trainpath = os.path.join(self.datadir, self.TRAIN_FILE) |
|
99
|
|
|
modelpath = os.path.join(self.datadir, self.MODEL_FILE) |
|
100
|
|
|
params = {param: self.FASTTEXT_PARAMS[param](val) |
|
101
|
|
|
for param, val in self.params.items() |
|
102
|
|
|
if param in self.FASTTEXT_PARAMS} |
|
103
|
|
|
self._model = fastText.train_supervised(trainpath, **params) |
|
104
|
|
|
self._model.save_model(modelpath) |
|
105
|
|
|
|
|
106
|
|
|
def train(self, corpus, project): |
|
107
|
|
|
self._create_train_file(corpus, project) |
|
108
|
|
|
self._create_model() |
|
109
|
|
|
|
|
110
|
|
|
def _predict_chunks(self, chunktexts, project, limit): |
|
111
|
|
|
return self._model.predict(list( |
|
112
|
|
|
filter(None, [self._normalize_text(project, chunktext) |
|
113
|
|
|
for chunktext in chunktexts])), limit) |
|
114
|
|
|
|
|
115
|
|
|
def _suggest_chunks(self, chunktexts, project): |
|
116
|
|
|
limit = int(self.params['limit']) |
|
117
|
|
|
chunklabels, chunkscores = self._predict_chunks( |
|
118
|
|
|
chunktexts, project, limit) |
|
119
|
|
|
label_scores = collections.defaultdict(float) |
|
120
|
|
|
for labels, scores in zip(chunklabels, chunkscores): |
|
121
|
|
|
for label, score in zip(labels, scores): |
|
122
|
|
|
label_scores[label] += score |
|
123
|
|
|
best_labels = sorted([(score, label) |
|
124
|
|
|
for label, score in label_scores.items()], |
|
125
|
|
|
reverse=True) |
|
126
|
|
|
|
|
127
|
|
|
results = [] |
|
128
|
|
|
for score, label in best_labels[:limit]: |
|
129
|
|
|
subject = self._label_to_subject(project, label) |
|
130
|
|
|
results.append(SubjectSuggestion( |
|
131
|
|
|
uri=subject[0], |
|
132
|
|
|
label=subject[1], |
|
133
|
|
|
score=score / len(chunktexts))) |
|
134
|
|
|
return ListSuggestionResult(results, project.subjects) |
|
135
|
|
|
|
NatLibFi /
Annif
