|
1
|
|
|
from __future__ import annotations |
|
2
|
|
|
|
|
3
|
|
|
import os |
|
4
|
|
|
from typing import TYPE_CHECKING, Any |
|
5
|
|
|
|
|
6
|
|
|
from stwfsapy.predictor import StwfsapyPredictor |
|
7
|
|
|
|
|
8
|
|
|
from annif.exception import NotInitializedException, NotSupportedException |
|
9
|
|
|
from annif.suggestion import SubjectSuggestion |
|
10
|
|
|
from annif.util import atomic_save, boolean |
|
11
|
|
|
|
|
12
|
|
|
from . import backend |
|
13
|
|
|
|
|
14
|
|
|
if TYPE_CHECKING: |
|
15
|
|
|
from annif.corpus import Document, DocumentCorpus |
|
16
|
|
|
|
|
17
|
|
|
_KEY_CONCEPT_TYPE_URI = "concept_type_uri" |
|
18
|
|
|
_KEY_SUBTHESAURUS_TYPE_URI = "sub_thesaurus_type_uri" |
|
19
|
|
|
_KEY_THESAURUS_RELATION_TYPE_URI = "thesaurus_relation_type_uri" |
|
20
|
|
|
_KEY_THESAURUS_RELATION_IS_SPECIALISATION = "thesaurus_relation_is_specialisation" |
|
21
|
|
|
_KEY_REMOVE_DEPRECATED = "remove_deprecated" |
|
22
|
|
|
_KEY_HANDLE_TITLE_CASE = "handle_title_case" |
|
23
|
|
|
_KEY_EXTRACT_UPPER_CASE_FROM_BRACES = "extract_upper_case_from_braces" |
|
24
|
|
|
_KEY_EXTRACT_ANY_CASE_FROM_BRACES = "extract_any_case_from_braces" |
|
25
|
|
|
_KEY_EXPAND_AMPERSAND_WITH_SPACES = "expand_ampersand_with_spaces" |
|
26
|
|
|
_KEY_EXPAND_ABBREVIATION_WITH_PUNCTUATION = "expand_abbreviation_with_punctuation" |
|
27
|
|
|
_KEY_SIMPLE_ENGLISH_PLURAL_RULES = "simple_english_plural_rules" |
|
28
|
|
|
_KEY_USE_TXT_VEC = "use_txt_vec" |
|
29
|
|
|
|
|
30
|
|
|
|
|
31
|
|
|
class StwfsaBackend(backend.AnnifBackend): |
|
32
|
|
|
name = "stwfsa" |
|
33
|
|
|
|
|
34
|
|
|
STWFSA_PARAMETERS = { |
|
35
|
|
|
_KEY_CONCEPT_TYPE_URI: str, |
|
36
|
|
|
_KEY_SUBTHESAURUS_TYPE_URI: str, |
|
37
|
|
|
_KEY_THESAURUS_RELATION_TYPE_URI: str, |
|
38
|
|
|
_KEY_THESAURUS_RELATION_IS_SPECIALISATION: boolean, |
|
39
|
|
|
_KEY_REMOVE_DEPRECATED: boolean, |
|
40
|
|
|
_KEY_HANDLE_TITLE_CASE: boolean, |
|
41
|
|
|
_KEY_EXTRACT_UPPER_CASE_FROM_BRACES: boolean, |
|
42
|
|
|
_KEY_EXTRACT_ANY_CASE_FROM_BRACES: boolean, |
|
43
|
|
|
_KEY_EXPAND_AMPERSAND_WITH_SPACES: boolean, |
|
44
|
|
|
_KEY_EXPAND_ABBREVIATION_WITH_PUNCTUATION: boolean, |
|
45
|
|
|
_KEY_SIMPLE_ENGLISH_PLURAL_RULES: boolean, |
|
46
|
|
|
_KEY_USE_TXT_VEC: bool, |
|
47
|
|
|
} |
|
48
|
|
|
|
|
49
|
|
|
DEFAULT_PARAMETERS = { |
|
50
|
|
|
_KEY_CONCEPT_TYPE_URI: "http://www.w3.org/2004/02/skos/core#Concept", |
|
51
|
|
|
_KEY_SUBTHESAURUS_TYPE_URI: "http://www.w3.org/2004/02/skos/core#Collection", |
|
52
|
|
|
_KEY_THESAURUS_RELATION_TYPE_URI: "http://www.w3.org/2004/02/skos/core#member", |
|
53
|
|
|
_KEY_THESAURUS_RELATION_IS_SPECIALISATION: True, |
|
54
|
|
|
_KEY_REMOVE_DEPRECATED: True, |
|
55
|
|
|
_KEY_HANDLE_TITLE_CASE: True, |
|
56
|
|
|
_KEY_EXTRACT_UPPER_CASE_FROM_BRACES: True, |
|
57
|
|
|
_KEY_EXTRACT_ANY_CASE_FROM_BRACES: False, |
|
58
|
|
|
_KEY_EXPAND_AMPERSAND_WITH_SPACES: True, |
|
59
|
|
|
_KEY_EXPAND_ABBREVIATION_WITH_PUNCTUATION: True, |
|
60
|
|
|
_KEY_SIMPLE_ENGLISH_PLURAL_RULES: False, |
|
61
|
|
|
_KEY_USE_TXT_VEC: False, |
|
62
|
|
|
} |
|
63
|
|
|
|
|
64
|
|
|
MODEL_FILE = "stwfsa_predictor.zip" |
|
65
|
|
|
|
|
66
|
|
|
_model = None |
|
67
|
|
|
|
|
68
|
|
|
def initialize(self, parallel: bool = False) -> None: |
|
69
|
|
|
if self._model is None: |
|
70
|
|
|
path = os.path.join(self.datadir, self.MODEL_FILE) |
|
71
|
|
|
self.debug(f"Loading STWFSA model from {path}.") |
|
72
|
|
|
if os.path.exists(path): |
|
73
|
|
|
self._model = StwfsapyPredictor.load(path) |
|
74
|
|
|
self.debug("Loaded model.") |
|
75
|
|
|
else: |
|
76
|
|
|
raise NotInitializedException( |
|
77
|
|
|
f"Model not found at {path}", backend_id=self.backend_id |
|
78
|
|
|
) |
|
79
|
|
|
|
|
80
|
|
|
def _load_data(self, corpus: DocumentCorpus) -> tuple[list[str], list[list[str]]]: |
|
81
|
|
|
if corpus == "cached": |
|
82
|
|
|
raise NotSupportedException( |
|
83
|
|
|
"Training stwfsa project from cached data not supported." |
|
84
|
|
|
) |
|
85
|
|
|
if corpus.is_empty(): |
|
86
|
|
|
raise NotSupportedException( |
|
87
|
|
|
"Cannot train stwfsa project with no documents." |
|
88
|
|
|
) |
|
89
|
|
|
self.debug("Transforming training data.") |
|
90
|
|
|
X = [] |
|
91
|
|
|
y = [] |
|
92
|
|
|
for doc in corpus.documents: |
|
93
|
|
|
X.append(doc.text) |
|
94
|
|
|
y.append( |
|
95
|
|
|
[ |
|
96
|
|
|
self.project.subjects[subject_id].uri |
|
97
|
|
|
for subject_id in doc.subject_set |
|
98
|
|
|
] |
|
99
|
|
|
) |
|
100
|
|
|
return X, y |
|
101
|
|
|
|
|
102
|
|
|
def _train( |
|
103
|
|
|
self, |
|
104
|
|
|
corpus: DocumentCorpus, |
|
105
|
|
|
params: dict[str, Any], |
|
106
|
|
|
jobs: int = 0, |
|
107
|
|
|
) -> None: |
|
108
|
|
|
X, y = self._load_data(corpus) |
|
109
|
|
|
new_params = { |
|
110
|
|
|
key: self.STWFSA_PARAMETERS[key](val) |
|
111
|
|
|
for key, val in params.items() |
|
112
|
|
|
if key in self.STWFSA_PARAMETERS |
|
113
|
|
|
} |
|
114
|
|
|
p = StwfsapyPredictor( |
|
115
|
|
|
graph=self.project.vocab.as_graph(), |
|
116
|
|
|
langs=frozenset([params["language"]]), |
|
117
|
|
|
**new_params, |
|
118
|
|
|
) |
|
119
|
|
|
p.fit(X, y) |
|
120
|
|
|
self._model = p |
|
121
|
|
|
atomic_save( |
|
122
|
|
|
p, |
|
123
|
|
|
self.datadir, |
|
124
|
|
|
self.MODEL_FILE, |
|
125
|
|
|
lambda model, store_path: model.store(store_path), |
|
126
|
|
|
) |
|
127
|
|
|
|
|
128
|
|
|
def _suggest( |
|
129
|
|
|
self, doc: Document, params: dict[str, Any] |
|
130
|
|
|
) -> list[SubjectSuggestion]: |
|
131
|
|
|
self.debug( |
|
132
|
|
|
f'Suggesting subjects for text "{doc.text[:20]}..." (len={len(doc.text)})' |
|
133
|
|
|
) |
|
134
|
|
|
result = self._model.suggest_proba([doc.text])[0] |
|
135
|
|
|
suggestions = [] |
|
136
|
|
|
for uri, score in result: |
|
137
|
|
|
subject_id = self.project.subjects.by_uri(uri) |
|
138
|
|
|
if subject_id is not None: |
|
139
|
|
|
suggestions.append( |
|
140
|
|
|
SubjectSuggestion(subject_id=subject_id, score=score) |
|
141
|
|
|
) |
|
142
|
|
|
return suggestions |
|
143
|
|
|
|
NatLibFi /
Annif
