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[[package]] name = "scipy" @@ -5076,4 +5053,4 @@ testing = ["coverage[toml]", "zope.event", "zope.testing"] [metadata] lock-version = "2.1" python-versions = ">=3.12,<3.13" -content-hash = "23d070500a913096d46cf53a2761d9c0e4f4de931ab0872363bc65c795f5eab8" +content-hash = "5fb3f1831c3bd4d52446e4f0020ad9f0b36c163338e194c9c41b05189331924e" diff --git a/pyproject.toml b/pyproject.toml index 670cd53..0f09326 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,7 +43,7 @@ xx_sent_ud_sm = {url = "https://github.com/explosion/spacy-models/releases/downl lingua-language-detector = "^2.1.1" psycopg2-binary = "^2.9.10" brotli = "^1.2.0" -scikit-learn = "~=1.6.1" +scikit-learn = "~=1.7.0" optimum = {extras = ["onnxruntime"], version = "^2.1.0"} azure-storage-blob = "^12.28.0" welearn-database = "^1.3.0" diff --git a/tests/document_classifier/test_sdgs_classifiers.py b/tests/document_classifier/test_sdgs_classifiers.py index 7b33151..4c7bbf8 100644 --- a/tests/document_classifier/test_sdgs_classifiers.py +++ b/tests/document_classifier/test_sdgs_classifiers.py @@ -44,7 +44,7 @@ def test_should_classify_slices_n_with_force_sdg(self, mock_load): self.assertEqual(result.sdg_number, 4) @patch("joblib.load") - def test_should_not_classify_slices_n_with_force_sdg(self, mock_load): + def test_should_classify_slices_n(self, mock_load): mock_load.return_value.predict_proba.return_value = [ numpy.array( [0.3, 0.2, 0.99, 0.562, 0.2, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] @@ -54,17 +54,18 @@ def test_should_not_classify_slices_n_with_force_sdg(self, mock_load): result = n_classify_slice( slice, "model_name", - forced_sdg=[1, 11, 12], n_classifier_id=uuid.uuid4(), bi_classifier_id=uuid.uuid4(), ) - self.assertEqual(result, None) + self.assertEqual(result.sdg_number, 3) + self.assertIsNotNone(result.bi_classifier_model_id) + self.assertIsNone(result.n_classifier_model_id) @patch("joblib.load") - def test_should_classify_slices_n(self, mock_load): + def test_should_classify_slices_n_with_forced_corpus(self, mock_load): mock_load.return_value.predict_proba.return_value = [ numpy.array( - [0.3, 0.2, 0.99, 0.562, 0.2, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + [0.3, 0.2, 0.3, 0.462, 0.2, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ) ] slice = DocumentSlice(id=1, embedding=b"\x00" * 128) @@ -73,8 +74,9 @@ def test_should_classify_slices_n(self, mock_load): "model_name", n_classifier_id=uuid.uuid4(), bi_classifier_id=uuid.uuid4(), + is_forced_corpus=True, ) - self.assertEqual(result.sdg_number, 3) + self.assertEqual(result.sdg_number, 4) self.assertIsNotNone(result.bi_classifier_model_id) self.assertIsNone(result.n_classifier_model_id) diff --git a/welearn_datastack/constants.py b/welearn_datastack/constants.py index 7616d45..edf08cf 100644 --- a/welearn_datastack/constants.py +++ b/welearn_datastack/constants.py @@ -355,3 +355,5 @@ ] QDRANT_MULTI_LINGUAL_CODE = "mul" + +FORCED_CORPUS_CLASSIFIED = ["uved"] diff --git a/welearn_datastack/modules/retrieve_data_from_database.py b/welearn_datastack/modules/retrieve_data_from_database.py index f76bf65..2e53028 100644 --- a/welearn_datastack/modules/retrieve_data_from_database.py +++ b/welearn_datastack/modules/retrieve_data_from_database.py @@ -26,6 +26,7 @@ URLRetrievalType, WeighedScope, ) +from welearn_datastack.exceptions import NoModelFoundError from welearn_datastack.types import QuerySizeLimitDocument, QuerySizeLimitSlice logger = logging.getLogger(__name__) @@ -416,3 +417,40 @@ def retrieve_slices_sdgs( ) return {s[0]: s[1] for s in slices_sdgs} + + +def get_model_classification_model_by_id( + db_session, model_id: UUID +) -> BiClassifierModel | NClassifierModel | EmbeddingModel: + """ + Retrieve a model from the database by its ID, and return it as the correct type (BiClassifierModel, NClassifierModel or EmbeddingModel) + + :param db_session: Database session + :param model_id: Model ID to retrieve + :return: The model retrieved from the database, as the correct type + """ + model = ( + db_session.query(BiClassifierModel) + .filter(BiClassifierModel.id == model_id) + .first() + ) + if model: + return model + + model = ( + db_session.query(NClassifierModel) + .filter(NClassifierModel.id == model_id) + .first() + ) + if model: + return model + + model = ( + db_session.query(EmbeddingModel).filter(EmbeddingModel.id == model_id).first() + ) + if model: + return model + + raise NoModelFoundError( + f"Model not found in the database according this id : {model_id}" + ) diff --git a/welearn_datastack/modules/sdgs_classifiers.py b/welearn_datastack/modules/sdgs_classifiers.py index bd0cd7f..19b5c41 100644 --- a/welearn_datastack/modules/sdgs_classifiers.py +++ b/welearn_datastack/modules/sdgs_classifiers.py @@ -51,10 +51,36 @@ def n_classify_slice( bi_classifier_id: uuid.UUID, n_classifier_id: uuid.UUID, forced_sdg: None | list = None, + is_forced_corpus: bool = False, ) -> Sdg | None: + """ + n classifier for welearn sliced containers to classify them into one of the 17 SDGs + :param classifier_model_name: The name of the classifier model, which also the name of the file + :param _slice: Input of welearn sliced container + :param bi_classifier_id: The id of the bi-classifier model used to classify the slice as SDG or not, to keep track of the models used for classification + :param n_classifier_id: The id of the n-classifier model used to classify the slice into one of the 17 SDGs, to keep track of the models used for classification + :param forced_sdg: If not None, list of SDG numbers to force the classification on, if None, all SDGs are possible + :param is_forced_corpus: If True, the classification is forced even if the bi-classifier does not classify the slice as SDG, to keep track of the corpus that are forced classified + + :return: Sdg object if classified as one of the SDGs, None otherwise + :raises ValueError: If the embedding of the slice is not of type bytes + """ + # By default every SDGs are equally possible + is_forced_sdg_classif = bool(forced_sdg) if not forced_sdg: forced_sdg = [sdg_n + 1 for sdg_n in range(0, 17)] + # If there is only one forced sdg return it + if len(forced_sdg) == 1: + [sdg_number] = forced_sdg + return Sdg( + slice_id=_slice.id, + sdg_number=sdg_number, + id=uuid.uuid4(), + bi_classifier_model_id=bi_classifier_id, + n_classifier_model_id=n_classifier_id if not forced_sdg else None, + ) + logger.debug("Loading multiclass classifier model %s", classifier_model_name) classifier_path = generate_ml_models_path( model_type=MLModelsType.N_CLASSIFIER, model_name=classifier_model_name @@ -78,19 +104,19 @@ def n_classify_slice( ] proba_lst.sort(key=lambda x: x[1], reverse=True) - # If the score is superior to 0.5 - sdg_number = proba_lst[0][0] if proba_lst[0][1] > 0.5 else None - if sdg_number: - logger.debug( - f"Slice {_slice.id} is labelized with SDG {proba_lst[0][0]} with {proba_lst[0][1]} score" - ) - # Create Sdg object, associating it with the slice and classifiers except if forced_sdg is provided because - # in this case we assume classification was done outside the pipeline - return Sdg( - slice_id=_slice.id, - sdg_number=sdg_number, - id=uuid.uuid4(), - bi_classifier_model_id=bi_classifier_id, - n_classifier_model_id=n_classifier_id if not forced_sdg else None, - ) - return None + best_sdg, best_score = proba_lst[0] + + # If there is no forced SDG and no SDGs with more than 0.5 threshold + if not (is_forced_corpus or is_forced_sdg_classif) and best_score <= 0.5: + return None + + logger.debug( + f"Slice {_slice.id} is labelized with SDG {best_sdg} with {best_score} score" + ) + return Sdg( + slice_id=_slice.id, + sdg_number=best_sdg, + id=uuid.uuid4(), + bi_classifier_model_id=bi_classifier_id, + n_classifier_model_id=n_classifier_id if not forced_sdg else None, + ) diff --git a/welearn_datastack/nodes_workflow/DocumentClassifier/document_classifier.py b/welearn_datastack/nodes_workflow/DocumentClassifier/document_classifier.py index 6699a48..ee209ae 100644 --- a/welearn_datastack/nodes_workflow/DocumentClassifier/document_classifier.py +++ b/welearn_datastack/nodes_workflow/DocumentClassifier/document_classifier.py @@ -9,6 +9,7 @@ from welearn_database.data.enumeration import Step from welearn_database.data.models import DocumentSlice, ProcessState, Sdg +from welearn_datastack.constants import FORCED_CORPUS_CLASSIFIED from welearn_datastack.data.enumerations import MLModelsType from welearn_datastack.modules.retrieve_data_from_database import retrieve_models from welearn_datastack.modules.retrieve_data_from_files import retrieve_ids_from_csv @@ -84,7 +85,11 @@ def main() -> None: slices_per_docs, lambda x: x.document_id ): doc_slices: List[DocumentSlice] = list(group_doc_slices) # type: ignore - + corpus_name = doc_slices[0].document.corpus.source_name + is_forced_corpus = corpus_name in FORCED_CORPUS_CLASSIFIED + logger.info( + f"Classifying document {key_doc_id} from corpus {corpus_name} with {len(doc_slices)} slices" + ) bi_model_name = bi_model_by_docid.get(key_doc_id, dict()).get("model_name") bi_model_id: UUID = bi_model_by_docid.get(key_doc_id, dict()).get("model_id") if not bi_model_name and not isinstance(bi_model_name, str): @@ -118,7 +123,17 @@ def main() -> None: key_external_sdg in s.document.details and s.document.details[key_external_sdg] ) - if bi_classify_slice(slice_=s, classifier_model_name=bi_model_name): + if externaly_classified_flag: + logger.info(f"Document {key_doc_id} is externally classified ") + + if ( + externaly_classified_flag + or is_forced_corpus + or bi_classify_slice(slice_=s, classifier_model_name=bi_model_name) + ): + logger.info( + f"Document {key_doc_id} is classified as SDG by bi-classifier" + ) specific_sdg = n_classify_slice( _slice=s, classifier_model_name=n_model_name, @@ -129,9 +144,13 @@ def main() -> None: ), bi_classifier_id=bi_model_id, n_classifier_id=n_model_id, + is_forced_corpus=is_forced_corpus, ) if not specific_sdg: continue + logger.info( + f"Document {key_doc_id} is classified as SDG {specific_sdg.sdg_number} by n-classifier" + ) specific_sdgs.append(specific_sdg) sdg_docs_ids.add(key_doc_id) diff --git a/welearn_datastack/nodes_workflow/DocumentVectorizer/generate_to_vectorize_batch.py b/welearn_datastack/nodes_workflow/DocumentVectorizer/generate_to_vectorize_batch.py index bacdc23..d17b1a4 100644 --- a/welearn_datastack/nodes_workflow/DocumentVectorizer/generate_to_vectorize_batch.py +++ b/welearn_datastack/nodes_workflow/DocumentVectorizer/generate_to_vectorize_batch.py @@ -35,6 +35,7 @@ def main() -> None: parallelism_max: int = int(os.getenv("PARALLELISM_URL_MAX", 15)) batch_urls_directory: str = os.getenv("BATCH_URLS_DIRECTORY", "batch_urls") qty_max_str: str | None = os.getenv("PICK_QTY_MAX", None) + corpus_name: str = os.getenv("PICK_CORPUS_NAME", "*") size_limit_str: str | None = os.getenv("SIZE_TOTAL_LIMIT", None) qty_max: int | None = None @@ -69,6 +70,7 @@ def main() -> None: process_titles=[Step.DOCUMENT_SCRAPED], size_total_max=size_limit, weighed_scope=WeighedScope.DOCUMENT, + corpus_name=corpus_name, ) ) logger.info("'%s' Docsids were retrieved", len(ids_to_batch))