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"""MLLM (Maui-like Lexical Matchin) model for Annif""" |
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import collections |
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import math |
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from statistics import mean |
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from enum import IntEnum |
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import numpy as np |
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from rdflib import URIRef |
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from rdflib.namespace import SKOS |
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from scipy.sparse import lil_matrix |
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from sklearn.feature_extraction.text import CountVectorizer |
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from sklearn.ensemble import BaggingClassifier |
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from sklearn.tree import DecisionTreeClassifier |
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import annif.util |
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from annif.lexical.tokenset import TokenSet, TokenSetIndex |
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Term = collections.namedtuple('Term', 'subject_id label is_pref') |
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Match = collections.namedtuple( |
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'Match', 'subject_id is_pref n_tokens pos ambiguity') |
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Candidate = collections.namedtuple( |
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'Candidate', |
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'doc_length subject_id freq is_pref n_tokens ambiguity ' + |
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'first_occ last_occ spread') |
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Feature = IntEnum( |
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'Feature', |
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'freq doc_freq subj_freq tfidf is_pref n_tokens ambiguity ' + |
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'first_occ last_occ spread doc_length ' + |
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'broader narrower related', |
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start=0) |
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class MLLMModel: |
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"""Maui-like Lexical Matching model""" |
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def _conflate_matches(self, matches, doc_length): |
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subj_matches = collections.defaultdict(list) |
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for match in matches: |
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subj_matches[match.subject_id].append(match) |
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return [ |
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Candidate( |
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doc_length=doc_length, |
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subject_id=subject_id, |
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freq=len(matches) / doc_length, |
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is_pref=mean((float(m.is_pref) for m in matches)), |
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n_tokens=mean((m.n_tokens for m in matches)), |
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ambiguity=mean((m.ambiguity for m in matches)), |
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first_occ=matches[0].pos / doc_length, |
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last_occ=matches[-1].pos / doc_length, |
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spread=(matches[-1].pos - matches[0].pos) / doc_length |
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) |
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for subject_id, matches in subj_matches.items()] |
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def generate_candidates(self, text, analyzer): |
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sentences = analyzer.tokenize_sentences(text) |
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sent_tokens = self._vectorizer.transform(sentences) |
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matches = [] |
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for sent_idx, token_matrix in enumerate(sent_tokens): |
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tset = TokenSet(token_matrix.nonzero()[1]) |
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for ts, ambiguity in self._index.search(tset): |
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matches.append(Match(subject_id=ts.subject_id, |
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is_pref=ts.is_pref, |
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n_tokens=len(ts), |
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pos=sent_idx, |
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ambiguity=ambiguity)) |
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return self._conflate_matches(matches, len(sentences)) |
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def _candidates_to_features(self, candidates): |
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"""Convert a list of Candidates to a NumPy feature matrix""" |
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matrix = np.zeros((len(candidates), len(Feature)), dtype=np.float32) |
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c_ids = [c.subject_id for c in candidates] |
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c_vec = np.zeros(self._related_matrix.shape[0], dtype=np.bool) |
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c_vec[c_ids] = True |
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broader = self._broader_matrix.multiply(c_vec).sum(axis=1) |
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narrower = self._narrower_matrix.multiply(c_vec).sum(axis=1) |
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related = self._related_matrix.multiply(c_vec).sum(axis=1) |
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for idx, c in enumerate(candidates): |
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subj = c.subject_id |
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matrix[idx, Feature.freq] = c.freq |
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matrix[idx, Feature.doc_freq] = self._doc_freq[subj] |
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matrix[idx, Feature.subj_freq] = self._subj_freq.get(subj, 1) - 1 |
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matrix[idx, Feature.tfidf] = c.freq * self._idf[subj] |
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matrix[idx, Feature.is_pref] = c.is_pref |
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matrix[idx, Feature.n_tokens] = c.n_tokens |
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matrix[idx, Feature.ambiguity] = c.ambiguity |
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matrix[idx, Feature.first_occ] = c.first_occ |
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matrix[idx, Feature.last_occ] = c.last_occ |
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matrix[idx, Feature.spread] = c.spread |
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matrix[idx, Feature.doc_length] = c.doc_length |
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matrix[idx, Feature.broader] = broader[subj, 0] / len(c_ids) |
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matrix[idx, Feature.narrower] = narrower[subj, 0] / len(c_ids) |
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matrix[idx, Feature.related] = related[subj, 0] / len(c_ids) |
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return matrix |
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def _prepare_terms(self, graph, vocab, params): |
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terms = [] |
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subject_ids = [] |
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for subj_id, (uri, pref, _) in enumerate(vocab.subjects): |
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if pref is None: |
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continue # deprecated subject |
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subject_ids.append(subj_id) |
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terms.append(Term(subject_id=subj_id, label=pref, is_pref=True)) |
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if annif.util.boolean(params['use_hidden_labels']): |
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label_props = [SKOS.altLabel, SKOS.hiddenLabel] |
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else: |
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label_props = [SKOS.altLabel] |
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for prop in label_props: |
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for label in graph.objects(URIRef(uri), prop): |
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if label.language != params['language']: |
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continue |
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terms.append(Term(subject_id=subj_id, |
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label=str(label), |
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is_pref=False)) |
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return (terms, subject_ids) |
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def _prepare_relations(self, graph, vocab): |
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n_subj = len(vocab.subjects) |
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self._broader_matrix = lil_matrix((n_subj, n_subj), dtype=np.bool) |
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self._narrower_matrix = lil_matrix((n_subj, n_subj), dtype=np.bool) |
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self._related_matrix = lil_matrix((n_subj, n_subj), dtype=np.bool) |
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prop_matrix = [ |
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(SKOS.broader, self._broader_matrix), |
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(SKOS.narrower, self._narrower_matrix), |
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(SKOS.related, self._related_matrix) |
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] |
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for subj_id, (uri, pref, _) in enumerate(vocab.subjects): |
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if pref is None: |
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continue # deprecated subject |
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for prop, matrix in prop_matrix: |
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for other in graph.objects(URIRef(uri), prop): |
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other_id = vocab.subjects.by_uri(str(other), |
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warnings=False) |
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if other_id is not None: |
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matrix[subj_id, other_id] = True |
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def _prepare_train_index(self, vocab, analyzer, params): |
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graph = vocab.as_graph() |
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terms, subject_ids = self._prepare_terms(graph, vocab, params) |
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self._prepare_relations(graph, vocab) |
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self._vectorizer = CountVectorizer( |
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binary=True, |
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tokenizer=analyzer.tokenize_words |
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) |
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label_corpus = self._vectorizer.fit_transform((t.label for t in terms)) |
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self._index = TokenSetIndex() |
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for term, label_matrix in zip(terms, label_corpus): |
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tokens = label_matrix.nonzero()[1] |
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tset = TokenSet(tokens, term.subject_id, term.is_pref) |
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self._index.add(tset) |
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return subject_ids |
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def prepare_train(self, corpus, vocab, analyzer, params): |
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subject_ids = self._prepare_train_index(vocab, analyzer, params) |
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# frequency of subjects (by id) in the generated candidates |
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self._doc_freq = collections.Counter() |
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# frequency of manually assigned subjects ("domain keyphraseness") |
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self._subj_freq = collections.Counter() |
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doc_count = 0 |
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train_x = [] |
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train_y = [] |
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for idx, doc in enumerate(corpus.documents): |
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doc_subject_ids = [vocab.subjects.by_uri(uri) |
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for uri in doc.uris] |
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self._subj_freq.update(doc_subject_ids) |
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candidates = self.generate_candidates(doc.text, analyzer) |
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self._doc_freq.update([c.subject_id for c in candidates]) |
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train_x.append(candidates) |
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train_y += [(c.subject_id in doc_subject_ids) for c in candidates] |
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doc_count += 1 |
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# precalculate idf values for candidate subjects |
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self._idf = collections.defaultdict(float) |
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for subj_id in subject_ids: |
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self._idf[subj_id] = math.log((doc_count + 1) / |
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(self._doc_freq[subj_id] + 1)) + 1 |
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return (np.vstack([self._candidates_to_features(candidates) |
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for candidates in train_x]), np.array(train_y)) |
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def _create_classifier(self, params): |
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return BaggingClassifier( |
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DecisionTreeClassifier( |
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min_samples_leaf=int(params['min_samples_leaf']), |
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max_leaf_nodes=int(params['max_leaf_nodes']) |
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), max_samples=float(params['max_samples'])) |
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def train(self, train_x, train_y, params): |
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# fit the model on the training corpus |
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self._classifier = self._create_classifier(params) |
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self._classifier.fit(train_x, train_y) |
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def _prediction_to_list(self, scores, candidates): |
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subj_scores = [(score[1], c.subject_id) |
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for score, c in zip(scores, candidates)] |
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return sorted(subj_scores, reverse=True) |
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def predict(self, candidates): |
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if not candidates: |
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return [] |
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features = self._candidates_to_features(candidates) |
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scores = self._classifier.predict_proba(features) |
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return self._prediction_to_list(scores, candidates) |
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NatLibFi /
Annif
