Moved the AI tests into the AI folder. (#315)

tests/ai/sentiment_pipeline_test.py

@@ -0,0 +1,210 @@
+"""Unit test for sentiment pipeline."""
+
+from unittest.mock import Mock, patch
+
+import pytest
+
+from aki_prj23_transparenzregister.ai.sentiment_pipeline import (
+    SentimentPipeline,
+)
+from aki_prj23_transparenzregister.config.config_template import MongoConnection
+
+
+@pytest.fixture()
+def mock_mongo_connection() -> MongoConnection:
+    """Mock MongoConnector class.
+
+    Args:
+        mocker (any): Library mocker
+
+    Returns:
+        Mock: Mocked MongoConnector
+    """
+    return MongoConnection("", "", None, "" "", "")
+
+
+@pytest.fixture()
+def mock_mongo_connector(mocker: Mock) -> Mock:
+    """Mock MongoConnector class.
+
+    Args:
+        mocker (any): Library mocker
+
+    Returns:
+        Mock: Mocked MongoConnector
+    """
+    mock = Mock()
+    mocker.patch(
+        "aki_prj23_transparenzregister.utils.mongo.connector.MongoConnector",
+        return_value=mock,
+    )
+    mock.database = {"news": Mock()}
+    return mock
+
+
+@pytest.fixture()
+def mock_spacy(mocker: Mock) -> Mock:
+    """Mock MongoConnector class.
+
+    Args:
+        mocker (any): Library mocker
+
+    Returns:
+        Mock: Mocked MongoConnector
+    """
+    mock = Mock()
+    mocker.patch(
+        "aki_prj23_transparenzregister.ai.sentiment_service.SentimentAnalysisService.init_spacy",
+        return_value=mock,
+    )
+    return mock
+
+
+@patch(
+    "aki_prj23_transparenzregister.ai.sentiment_service.SentimentAnalysisService.sentiment_spacy"
+)
+def test_sentiment_pipeline_existing_sentiment(
+    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.",
+            "sentiment": {"label": "neutral", "score": 0.5},
+        }
+    ]
+
+    # 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 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_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}}},
+    )