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Moved the AI tests into the AI folder. (#315)
This commit is contained in:
GitHub
@ -1,283 +0,0 @@
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"""Tests for checking NER Pipeline."""
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from unittest.mock import Mock, patch
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import pytest
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from aki_prj23_transparenzregister.ai.ner_pipeline import EntityPipeline
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from aki_prj23_transparenzregister.config.config_template import MongoConnection
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@pytest.fixture()
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def mock_mongo_connection() -> MongoConnection:
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"""Mock MongoConnector class.
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Args:
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mocker (any): Library mocker
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Returns:
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Mock: Mocked MongoConnector
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"""
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return MongoConnection("", "", None, "" "", "")
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@pytest.fixture()
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def mock_mongo_connector(mocker: Mock) -> Mock:
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"""Mock MongoConnector class.
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Args:
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mocker (any): Library mocker
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Returns:
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Mock: Mocked MongoConnector
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"""
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mock = Mock()
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mocker.patch(
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"aki_prj23_transparenzregister.utils.mongo.connector.MongoConnector",
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return_value=mock,
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)
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mock.database = {"news": Mock()}
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return mock
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@pytest.fixture()
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def mock_spacy(mocker: Mock) -> Mock:
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"""Mock MongoConnector class.
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Args:
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mocker (any): Library mocker
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Returns:
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Mock: Mocked MongoConnector
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"""
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mock = Mock()
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mocker.patch(
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"aki_prj23_transparenzregister.ai.ner_service.NerAnalysisService.init_spacy",
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return_value=mock,
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)
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return mock
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# Mocking the NerAnalysisService methods
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@patch("aki_prj23_transparenzregister.ai.ner_service.NerAnalysisService.ner_spacy")
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def test_entity_pipeline_with_spacy(
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mock_ner_spacy: Mock,
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mock_mongo_connector: Mock,
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mock_mongo_connection: MongoConnection,
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mock_spacy: Mock,
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) -> None:
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# Configure the mock to return a specific NER result
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mock_ner_spacy.return_value = {"ORG": 2, "PERSON": 1}
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# Create an instance of the EntityPipeline
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entity_pipeline = EntityPipeline(mock_mongo_connection)
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# Mock the news collection and documents for testing
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mock_collection = Mock()
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mock_documents = [{"_id": "document1", "title": "Apple Inc is a tech company."}]
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# Set the collection to the mock_collection
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entity_pipeline.news_obj.collection = mock_collection
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# Mock the find method of the collection to return the mock documents
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mock_collection.find.return_value = mock_documents
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# Call the process_documents method with spaCy NER
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entity_pipeline.process_documents(
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entity="ORG", doc_attrib="title", ner_selection="use_spacy_ner"
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)
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# Ensure that ner_spacy was called with the correct parameters
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mock_ner_spacy.assert_called_once_with(mock_documents[0], "ORG", "title")
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# Ensure that the document in the collection was updated with the NER results
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mock_collection.update_one.assert_called_once_with(
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{"_id": "document1"},
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{"$set": {"companies": {"ORG": 2, "PERSON": 1}}},
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)
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@patch("aki_prj23_transparenzregister.ai.ner_service.NerAnalysisService.ner_spacy")
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def test_entity_pipeline_with_spacy_no_docs(
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mock_ner_spacy: Mock,
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mock_mongo_connector: Mock,
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mock_mongo_connection: MongoConnection,
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mock_spacy: Mock,
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) -> None:
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# Configure the mock to return a specific NER result
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mock_ner_spacy.return_value = {"ORG": 2, "PERSON": 1}
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# Create an instance of the EntityPipeline
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entity_pipeline = EntityPipeline(mock_mongo_connection)
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# Mock the news collection and documents for testing
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mock_collection = Mock()
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mock_documents: list[dict] = []
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# Set the collection to the mock_collection
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entity_pipeline.news_obj.collection = mock_collection
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# Mock the find method of the collection to return the mock documents
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mock_collection.find.return_value = mock_documents
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# Call the process_documents method with spaCy NER
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entity_pipeline.process_documents(
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entity="ORG", doc_attrib="title", ner_selection="use_spacy_ner"
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)
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# Ensure that sentiment_spacy was not called
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mock_ner_spacy.assert_not_called()
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# Ensure that the document in the collection was not updated
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mock_collection.assert_not_called()
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@patch(
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"aki_prj23_transparenzregister.ai.ner_service.NerAnalysisService.ner_company_list"
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)
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def test_entity_pipeline_with_companylist_ner(
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mock_ner_companylist: Mock,
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mock_mongo_connector: Mock,
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mock_mongo_connection: MongoConnection,
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mock_spacy: Mock,
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) -> None:
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# Konfigurieren Sie das Mock-Objekt, um ein spezifisches NER-Ergebnis zurückzugeben
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mock_ner_companylist.return_value = {"ORG": 3, "LOCATION": 2}
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# Create an instance of the EntityPipeline
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entity_pipeline = EntityPipeline(mock_mongo_connection)
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# Mock die News-Sammlung und Dokumente für Tests
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mock_collection = Mock()
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mock_documents = [
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{"_id": "document2", "title": "Siemens ist ein deutsches Unternehmen."}
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]
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# Set the collection to the mock_collection
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entity_pipeline.news_obj.collection = mock_collection
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# Mock the find method of the collection to return the mock documents
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mock_collection.find.return_value = mock_documents
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# Call the process_documents method with Company List NER
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entity_pipeline.process_documents(
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entity="ORG", doc_attrib="title", ner_selection="use_companylist_ner"
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)
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# Überprüfen Sie, ob ner_company_list mit den richtigen Parametern aufgerufen wurde
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mock_ner_companylist.assert_called_once_with(mock_documents[0], "ORG", "title")
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# Überprüfen Sie, ob das Dokument in der Sammlung mit den NER-Ergebnissen aktualisiert wurde
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mock_collection.update_one.assert_called_once_with(
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{"_id": "document2"},
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{"$set": {"companies": {"ORG": 3, "LOCATION": 2}}},
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)
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@patch(
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"aki_prj23_transparenzregister.ai.ner_service.NerAnalysisService.ner_company_list"
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)
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def test_entity_pipeline_with_companylist_ner_no_docs(
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mock_ner_companylist: Mock,
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mock_mongo_connector: Mock,
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mock_mongo_connection: MongoConnection,
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mock_spacy: Mock,
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) -> None:
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# Configure the mock to return a specific NER result
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mock_ner_companylist.return_value = {"ORG": 3, "LOCATION": 2}
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# Create an instance of the EntityPipeline
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entity_pipeline = EntityPipeline(mock_mongo_connection)
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# Mock die News-Sammlung und Dokumente für Tests
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mock_collection = Mock()
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mock_documents: list[dict] = []
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# Set the collection to the mock_collection
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entity_pipeline.news_obj.collection = mock_collection
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# Mock the find method of the collection to return the mock documents
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mock_collection.find.return_value = mock_documents
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# Call the process_documents method with Company List NER
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entity_pipeline.process_documents(
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entity="ORG", doc_attrib="title", ner_selection="use_companylist_ner"
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)
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# Ensure that ner_company_list is not called
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mock_ner_companylist.assert_not_called()
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# Ensure that the document in the collection was not updated
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mock_collection.update_one.assert_not_called()
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# Add more test cases for other NER methods (e.g., use_companylist_ner, use_transformer_ner) following a similar pattern.
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@patch("aki_prj23_transparenzregister.ai.ner_service.NerAnalysisService.ner_spacy")
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def test_entity_pipeline_with_transformer(
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mock_ner_transformer: Mock,
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mock_mongo_connector: Mock,
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mock_mongo_connection: MongoConnection,
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mock_spacy: Mock,
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) -> None:
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# Configure the mock to return a specific NER result
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mock_ner_transformer.return_value = {"ORG": 2, "PERSON": 1}
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# Create an instance of the EntityPipeline
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entity_pipeline = EntityPipeline(mock_mongo_connection)
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# Mock the news collection and documents for testing
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mock_collection = Mock()
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mock_documents = [{"_id": "document1", "title": "Apple Inc is a tech company."}]
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# Set the collection to the mock_collection
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entity_pipeline.news_obj.collection = mock_collection
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# Mock the find method of the collection to return the mock documents
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mock_collection.find.return_value = mock_documents
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# Call the process_documents method with spaCy NER
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entity_pipeline.process_documents(
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entity="ORG", doc_attrib="title", ner_selection="use_spacy_ner"
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)
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# Ensure that ner_spacy was called with the correct parameters
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mock_ner_transformer.assert_called_once_with(mock_documents[0], "ORG", "title")
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# Ensure that the document in the collection was updated with the NER results
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mock_collection.update_one.assert_called_once_with(
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{"_id": "document1"},
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{"$set": {"companies": {"ORG": 2, "PERSON": 1}}},
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)
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@patch("aki_prj23_transparenzregister.ai.ner_service.NerAnalysisService.ner_spacy")
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def test_entity_pipeline_with_transformer_no_docs(
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mock_ner_transformer: Mock,
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mock_mongo_connector: Mock,
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mock_mongo_connection: MongoConnection,
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mock_spacy: Mock,
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) -> None:
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# Configure the mock to return a specific NER result
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mock_ner_transformer.return_value = {"ORG": 2, "PERSON": 1}
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# Create an instance of the EntityPipeline
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entity_pipeline = EntityPipeline(mock_mongo_connection)
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# Mock the news collection and documents for testing
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mock_collection = Mock()
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mock_documents: list[dict] = []
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# Set the collection to the mock_collection
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entity_pipeline.news_obj.collection = mock_collection
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# Mock the find method of the collection to return the mock documents
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mock_collection.find.return_value = mock_documents
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# Call the process_documents method with spaCy NER
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entity_pipeline.process_documents(
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entity="ORG", doc_attrib="title", ner_selection="use_spacy_ner"
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)
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# Ensure that ner_transformer is not called
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mock_ner_transformer.assert_not_called()
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# Ensure that the document in the collection was not updated
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mock_collection.update_one.assert_not_called()
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@ -1,54 +0,0 @@
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"""Tests for checking NER Services."""
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from aki_prj23_transparenzregister.ai.ner_service import NerAnalysisService
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def test_ner_spacy() -> None:
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"""Mock TestNerService."""
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# Create instance of NerAnalysisService with use_spacy=True
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ner_service = NerAnalysisService(
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use_spacy=True, use_transformer=False, use_companylist=False
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)
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# 1st testing
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doc = {"title": "Siemens ist ein Unternehmen."}
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result = ner_service.ner_spacy(doc, ent_type="ORG", doc_attrib="title")
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assert result == {"Siemens": 1}
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# 2nd testing
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doc = {"text": "BASF ist ein großes Unternehmen."}
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result = ner_service.ner_spacy(doc, ent_type="ORG", doc_attrib="text")
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assert result == {"BASF": 1}
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def test_ner_company_list() -> None:
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"""Mock test_ner_company."""
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# Create instance of NerAnalysisService with use_companylist=True
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ner_service = NerAnalysisService(
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use_spacy=False, use_transformer=False, use_companylist=True
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)
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doc = {"title": "Siemens ist ein Unternehmen."}
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result = ner_service.ner_company_list(doc, ent_type="ORG", doc_attrib="title")
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assert result == {"siemens": 1}
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# 2nd testing
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doc = {"text": "BASF ist ein großes Unternehmen."}
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result = ner_service.ner_company_list(doc, ent_type="ORG", doc_attrib="text")
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assert result == {"basf": 1}
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def test_ner_transformer() -> None:
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"""Mock test_ner_company."""
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# Create instance of NerAnalysisService with use_use_companylist=True
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ner_service = NerAnalysisService(
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use_spacy=False, use_transformer=True, use_companylist=False
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)
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doc = {"title": "Siemens ist ein Unternehmen."}
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result = ner_service.ner_transformer(doc, ent_type="ORG", doc_attrib="title")
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assert result == {"Siemens": 1}
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# 2nd testing
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doc = {"text": "BASF ist ein großes Unternehmen."}
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result = ner_service.ner_transformer(doc, ent_type="ORG", doc_attrib="text")
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assert result == {"BASF": 1}
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@ -1,210 +0,0 @@
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"""Unit test for sentiment pipeline."""
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from unittest.mock import Mock, patch
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import pytest
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from aki_prj23_transparenzregister.ai.sentiment_pipeline import (
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SentimentPipeline,
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)
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from aki_prj23_transparenzregister.config.config_template import MongoConnection
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@pytest.fixture()
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def mock_mongo_connection() -> MongoConnection:
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"""Mock MongoConnector class.
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Args:
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mocker (any): Library mocker
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Returns:
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Mock: Mocked MongoConnector
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"""
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return MongoConnection("", "", None, "" "", "")
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@pytest.fixture()
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def mock_mongo_connector(mocker: Mock) -> Mock:
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"""Mock MongoConnector class.
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|
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Args:
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mocker (any): Library mocker
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Returns:
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Mock: Mocked MongoConnector
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"""
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mock = Mock()
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mocker.patch(
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"aki_prj23_transparenzregister.utils.mongo.connector.MongoConnector",
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return_value=mock,
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)
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mock.database = {"news": Mock()}
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return mock
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@pytest.fixture()
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def mock_spacy(mocker: Mock) -> Mock:
|
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"""Mock MongoConnector class.
|
||||
|
||||
Args:
|
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mocker (any): Library mocker
|
||||
|
||||
Returns:
|
||||
Mock: Mocked MongoConnector
|
||||
"""
|
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mock = Mock()
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mocker.patch(
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"aki_prj23_transparenzregister.ai.sentiment_service.SentimentAnalysisService.init_spacy",
|
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return_value=mock,
|
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)
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return mock
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@patch(
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"aki_prj23_transparenzregister.ai.sentiment_service.SentimentAnalysisService.sentiment_spacy"
|
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)
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def test_sentiment_pipeline_existing_sentiment(
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mock_sentiment_spacy: Mock,
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mock_mongo_connector: Mock,
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||||
mock_mongo_connection: MongoConnection,
|
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mock_spacy: Mock,
|
||||
) -> None:
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# Configure the mock to return a specific sentiment result
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mock_sentiment_spacy.return_value = ("positive", 0.8)
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|
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# Create an instance of the SentimentPipeline
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sentiment_pipeline = SentimentPipeline(mock_mongo_connection)
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|
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# Mock the news collection and documents for testing
|
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mock_collection = Mock()
|
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mock_documents = [
|
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{
|
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"_id": "document1",
|
||||
"text": "This is a positive text.",
|
||||
"sentiment": {"label": "neutral", "score": 0.5},
|
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}
|
||||
]
|
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# Set the collection to the mock_collection
|
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sentiment_pipeline.news_obj.collection = mock_collection
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|
||||
# Mock the find method of the collection to return the mock documents
|
||||
mock_collection.find.return_value = mock_documents
|
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# Call the process_documents method
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sentiment_pipeline.process_documents("text", "use_spacy")
|
||||
|
||||
# Ensure that sentiment_spacy was called with the correct text
|
||||
mock_sentiment_spacy.assert_called_once_with("This is a positive text.")
|
||||
|
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# Ensure that the document in the collection was not updated with sentiment
|
||||
# mock_collection.update_one.assert_not_called()
|
||||
|
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|
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@patch(
|
||||
"aki_prj23_transparenzregister.ai.sentiment_service.SentimentAnalysisService.sentiment_spacy"
|
||||
)
|
||||
def test_sentiment_pipeline_no_documents(
|
||||
mock_sentiment_spacy: Mock,
|
||||
mock_mongo_connector: Mock,
|
||||
mock_mongo_connection: MongoConnection,
|
||||
mock_spacy: Mock,
|
||||
) -> None:
|
||||
# Configure the mock to return a specific sentiment result
|
||||
mock_sentiment_spacy.return_value = ("positive", 0.8)
|
||||
|
||||
# Create an instance of the SentimentPipeline
|
||||
sentiment_pipeline = SentimentPipeline(mock_mongo_connection)
|
||||
|
||||
# Mock the news collection to return an empty result
|
||||
mock_collection = Mock()
|
||||
mock_collection.find.return_value = []
|
||||
|
||||
# Set the collection to the mock_collection
|
||||
sentiment_pipeline.news_obj.collection = mock_collection
|
||||
|
||||
# Call the process_documents method
|
||||
sentiment_pipeline.process_documents("text", "use_spacy")
|
||||
|
||||
# Ensure that sentiment_spacy was not called
|
||||
mock_sentiment_spacy.assert_not_called()
|
||||
|
||||
# Ensure that the document in the collection was not updated with sentiment
|
||||
mock_collection.update_one.assert_not_called()
|
||||
|
||||
|
||||
@patch(
|
||||
"aki_prj23_transparenzregister.ai.sentiment_service.SentimentAnalysisService.sentiment_spacy"
|
||||
)
|
||||
def test_sentiment_pipeline_with_spacy(
|
||||
mock_sentiment_spacy: Mock,
|
||||
mock_mongo_connector: Mock,
|
||||
mock_mongo_connection: MongoConnection,
|
||||
mock_spacy: Mock,
|
||||
) -> None:
|
||||
# Configure the mock to return a specific sentiment result
|
||||
mock_sentiment_spacy.return_value = ("positive", 0.8)
|
||||
|
||||
# Create an instance of the SentimentPipeline
|
||||
sentiment_pipeline = SentimentPipeline(mock_mongo_connection)
|
||||
|
||||
# Mock the news collection and documents for testing
|
||||
mock_collection = Mock()
|
||||
mock_documents = [{"_id": "document1", "text": "This is a positive text."}]
|
||||
|
||||
# Set the collection to the mock_collection
|
||||
sentiment_pipeline.news_obj.collection = mock_collection
|
||||
|
||||
# Mock the find method of the collection to return the mock documents
|
||||
mock_collection.find.return_value = mock_documents
|
||||
|
||||
# Call the process_documents method
|
||||
sentiment_pipeline.process_documents("text", "use_spacy")
|
||||
|
||||
# Ensure that sentiment_spacy was called with the correct text
|
||||
mock_sentiment_spacy.assert_called_once_with("This is a positive text.")
|
||||
|
||||
# Ensure that the document in the collection was updated with the sentiment result
|
||||
mock_collection.update_one.assert_called_once_with(
|
||||
{"_id": "document1"},
|
||||
{"$set": {"sentiment": {"label": "positive", "score": 0.8}}},
|
||||
)
|
||||
|
||||
|
||||
# Mocking the SentimentAnalysisService methods
|
||||
@patch(
|
||||
"aki_prj23_transparenzregister.ai.sentiment_service.SentimentAnalysisService.sentiment_transformer"
|
||||
)
|
||||
def test_sentiment_pipeline_with_transformer(
|
||||
mock_sentiment_transformer: Mock,
|
||||
mock_mongo_connector: Mock,
|
||||
mock_mongo_connection: MongoConnection,
|
||||
mock_spacy: Mock,
|
||||
) -> None:
|
||||
# Configure the mock to return a specific sentiment result
|
||||
mock_sentiment_transformer.return_value = ("negative", 0.6)
|
||||
|
||||
# Create an instance of the SentimentPipeline
|
||||
sentiment_pipeline = SentimentPipeline(mock_mongo_connection)
|
||||
|
||||
# Mock the news collection and documents for testing
|
||||
mock_collection = Mock()
|
||||
mock_documents = [{"_id": "document2", "text": "This is a negative text."}]
|
||||
|
||||
# Set the collection to the mock_collection
|
||||
sentiment_pipeline.news_obj.collection = mock_collection
|
||||
|
||||
# Mock the find method of the collection to return the mock documents
|
||||
mock_collection.find.return_value = mock_documents
|
||||
|
||||
# Call the process_documents method
|
||||
sentiment_pipeline.process_documents("text", "use_transformer")
|
||||
|
||||
# Ensure that sentiment_transformer was called with the correct text
|
||||
mock_sentiment_transformer.assert_called_once_with("This is a negative text.")
|
||||
|
||||
# Ensure that the document in the collection was updated with the sentiment result
|
||||
mock_collection.update_one.assert_called_once_with(
|
||||
{"_id": "document2"},
|
||||
{"$set": {"sentiment": {"label": "negative", "score": 0.6}}},
|
||||
)
|
||||
@ -1,78 +0,0 @@
|
||||
"""Tests for checking Sentiment Services."""
|
||||
|
||||
|
||||
from aki_prj23_transparenzregister.ai.sentiment_service import (
|
||||
SentimentAnalysisService,
|
||||
)
|
||||
|
||||
|
||||
def test_sentiment_service_with_spacy_pos() -> None:
|
||||
"""Mock testing spaCy Sentiment Service with positive sentiment."""
|
||||
# Init SentimentAnalysisService with spaCy
|
||||
sentiment_service = SentimentAnalysisService(use_spacy=True)
|
||||
|
||||
# run the test
|
||||
text = "Dies ist ein großartiger Test. Ich liebe es!"
|
||||
sentiment, score = sentiment_service.sentiment_spacy(text)
|
||||
assert sentiment == "positive"
|
||||
assert score > 0
|
||||
|
||||
|
||||
def test_sentiment_service_with_spacy_neg() -> None:
|
||||
"""Mock testing spaCy Sentiment Service with negative sentiment."""
|
||||
# Init SentimentAnalysisService with spaCy
|
||||
sentiment_service = SentimentAnalysisService(use_spacy=True)
|
||||
|
||||
# run the test
|
||||
text = "Dies ist ein wirklich schrecklicher Test. Ich hasse ihn!"
|
||||
sentiment, score = sentiment_service.sentiment_spacy(text)
|
||||
assert sentiment == "negative"
|
||||
assert score > 0
|
||||
|
||||
|
||||
def test_sentiment_service_with_spacy_neut() -> None:
|
||||
"""Mock testing spaCy Sentiment Service with neutral sentiment."""
|
||||
# Init SentimentAnalysisService with spaCy
|
||||
sentiment_service = SentimentAnalysisService(use_spacy=True)
|
||||
|
||||
# run the test
|
||||
text = "Dies ist ein Test."
|
||||
sentiment, score = sentiment_service.sentiment_spacy(text)
|
||||
assert sentiment == "neutral"
|
||||
assert score >= 0
|
||||
|
||||
|
||||
def test_sentiment_service_with_transformer_pos() -> None:
|
||||
"""Mock testing Transformer Sentiment Service with positive Sentiment."""
|
||||
# Init SentimentAnalysisService with Transformer
|
||||
sentiment_service = SentimentAnalysisService(use_transformer=True)
|
||||
|
||||
# run the test
|
||||
text = "Dies ist ein großartiger Test. Ich liebe es!"
|
||||
sentiment, score = sentiment_service.sentiment_transformer(text)
|
||||
assert sentiment == "positive"
|
||||
assert score > 0
|
||||
|
||||
|
||||
def test_sentiment_service_with_transformer_neg() -> None:
|
||||
"""Mock testing Transformer Sentiment Service with negative Sentiment."""
|
||||
# Init SentimentAnalysisService with Transformer
|
||||
sentiment_service = SentimentAnalysisService(use_transformer=True)
|
||||
|
||||
# run the test
|
||||
text = "Dies ist ein wirklich schrecklicher Test. Ich hasse ihn!"
|
||||
sentiment, score = sentiment_service.sentiment_transformer(text)
|
||||
assert sentiment == "negative"
|
||||
assert score > 0
|
||||
|
||||
|
||||
def test_sentiment_service_with_transformer_neut() -> None:
|
||||
"""Mock testing Transformer Sentiment Service with neutral Sentiment."""
|
||||
# Init SentimentAnalysisService with Transformer
|
||||
sentiment_service = SentimentAnalysisService(use_transformer=True)
|
||||
|
||||
# run the test
|
||||
text = "Das ist ein Text, ohne besondere Stimmung."
|
||||
sentiment, score = sentiment_service.sentiment_transformer(text)
|
||||
assert sentiment == "neutral"
|
||||
assert score >= 0
|
||||
Reference in New Issue
Block a user