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"""Backend that returns most similar subjects based on similarity in sparse |
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TF-IDF normalized bag-of-words vector space""" |
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from __future__ import annotations |
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import os.path |
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import tempfile |
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from typing import TYPE_CHECKING, Any |
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from scipy.sparse import csr_array, load_npz, save_npz |
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from sklearn.preprocessing import normalize |
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import annif.util |
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from annif.exception import ( |
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NotInitializedException, |
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NotSupportedException, |
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OperationFailedException, |
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) |
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from annif.suggestion import SuggestionBatch |
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from . import backend, mixins |
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if TYPE_CHECKING: |
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from collections.abc import Iterator |
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from annif.corpus import Document, DocumentCorpus |
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class SubjectBuffer: |
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"""A file-backed buffer to store and retrieve subject text.""" |
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BUFFER_SIZE = 100 |
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def __init__(self, tempdir: str, subject_id: int) -> None: |
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filename = "{:08d}.txt".format(subject_id) |
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self._path = os.path.join(tempdir, filename) |
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self._buffer = [] |
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self._created = False |
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def flush(self) -> None: |
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if self._created: |
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mode = "a" |
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else: |
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mode = "w" |
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with open(self._path, mode, encoding="utf-8") as subjfile: |
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for text in self._buffer: |
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print(text, file=subjfile) |
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self._buffer = [] |
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self._created = True |
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def write(self, text: str) -> None: |
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self._buffer.append(text) |
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if len(self._buffer) >= self.BUFFER_SIZE: |
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self.flush() |
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def read(self) -> str: |
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if not self._created: |
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# file was never created - we can simply return the buffer content |
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return "\n".join(self._buffer) |
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else: |
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with open(self._path, "r", encoding="utf-8") as subjfile: |
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return subjfile.read() + "\n" + "\n".join(self._buffer) |
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class TFIDFBackend(mixins.TfidfVectorizerMixin, backend.AnnifBackend): |
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"""TF-IDF vector space similarity based backend for Annif""" |
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name = "tfidf" |
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# defaults for uninitialized instances |
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_tfidf_matrix = None |
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MATRIX_FILE = "tfidf-matrix.npz" |
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OLD_INDEX_FILE = "tfidf-index" |
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def _generate_subjects_from_documents( |
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self, corpus: DocumentCorpus |
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) -> Iterator[str]: |
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with tempfile.TemporaryDirectory() as tempdir: |
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subject_buffer = {} |
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for subject_id in range(len(self.project.subjects)): |
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subject_buffer[subject_id] = SubjectBuffer(tempdir, subject_id) |
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for doc in corpus.documents: |
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tokens = self.project.analyzer.tokenize_words(doc.text) |
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for subject_id in doc.subject_set: |
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subject_buffer[subject_id].write(" ".join(tokens)) |
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for sid in range(len(self.project.subjects)): |
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yield subject_buffer[sid].read() |
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def _initialize_index(self) -> None: |
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if self._tfidf_matrix is None: |
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path = os.path.join(self.datadir, self.MATRIX_FILE) |
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self.debug("loading tf-idf matrix from {}".format(path)) |
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if os.path.exists(path): |
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self._tfidf_matrix = load_npz(path) |
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elif os.path.exists(os.path.join(self.datadir, self.OLD_INDEX_FILE)): |
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raise OperationFailedException( |
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"TFIDF models trained on Annif versions older than 1.4 cannot be " |
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"loaded. Please retrain your project." |
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) |
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else: |
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raise NotInitializedException( |
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"tf-idf matrix {} not found".format(path), |
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backend_id=self.backend_id, |
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) |
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111
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def initialize(self, parallel: bool = False) -> None: |
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self.initialize_vectorizer() |
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self._initialize_index() |
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def _train( |
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self, |
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corpus: DocumentCorpus, |
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params: dict[str, Any], |
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jobs: int = 0, |
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) -> None: |
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if corpus == "cached": |
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raise NotSupportedException( |
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"Training tfidf project from cached data not supported." |
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) |
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if corpus.is_empty(): |
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raise NotSupportedException("Cannot train tfidf project with no documents") |
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self.info("transforming subject corpus") |
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subjects = self._generate_subjects_from_documents(corpus) |
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# Note: Intentionally don't pass a tokenizer to the vectorizer here. |
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# Instead the tokenization is done inside _generate_subjects_from_documents |
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# and in _suggest_batch. This way, the same train document doesn't have to be |
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# tokenized many times during training if it has many subjects. |
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self._tfidf_matrix = normalize(self.create_vectorizer(subjects)) |
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self.info("saving tf-idf matrix") |
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annif.util.atomic_save( |
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self._tfidf_matrix, |
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self.datadir, |
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self.MATRIX_FILE, |
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lambda obj, filename: save_npz(filename, obj), |
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) |
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def _suggest_batch( |
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self, documents: list[Document], params: dict[str, Any] |
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) -> SuggestionBatch: |
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query_vector = normalize( |
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self.vectorizer.transform( |
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[ |
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" ".join(self.project.analyzer.tokenize_words(doc.text)) |
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for doc in documents |
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] |
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) |
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) |
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153
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154
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# Compute cosine similarity between query and indexed corpus |
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similarities = query_vector @ self._tfidf_matrix.T |
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return SuggestionBatch(csr_array(similarities)).filter(int(params["limit"])) |
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NatLibFi /
Annif
