Created pipeline to run ner sentiment and sql ingest (#314)

Created a dataprocessing pipline that enhances the raw mined data with
Organsiation extractions and sentiment analysis prio to moving the data
to the sql db.
The transfer of matched data is done afterword.

---------

Co-authored-by: SeZett <zeleny.sebastian@fh-swf.de>

Dockerfile

@@ -34,7 +34,7 @@ LABEL PART="DATA-TRANSFORMATION"
 RUN pip install --find-links=dist aki-prj23-transparenzregister[transformation] --no-cache-dir && \
     rm dist/ -R
 
-ENTRYPOINT ["data-transformation", "ENV"]
+ENTRYPOINT ["data-processing", "ENV"]
 CMD ["--level", "DEBUG"]
 
 FROM base as web-server

README.md

@@ -17,6 +17,7 @@ See the [CONTRIBUTING.md](CONTRIBUTING.md) about how code should be formatted an
 The project has currently the following entrypoint available:
 
 - **data-transformation** > Transfers all the data from the mongodb into the sql db to make it available as production data.
+- **data-processing** > Processes the data using NLP methods and transfers matched data into the SQL table ready for use.
 - **reset-sql** > Resets all sql tables in the connected db.
 - **copy-sql** > Copys the content of a db to another db.
 - **webserver** > Starts the webserver showing the analysis results.

pyproject.toml

@@ -139,6 +139,7 @@ pytest-repeat = "^0.9.1"
 
 [tool.poetry.scripts]
 copy-sql = "aki_prj23_transparenzregister.utils.sql.copy_sql:copy_db_cli"
+data-processing = "aki_prj23_transparenzregister.utils.data_processing:cli"
 data-transformation = "aki_prj23_transparenzregister.utils.data_transfer:transfer_data_cli"
 reset-sql = "aki_prj23_transparenzregister.utils.sql.connector:reset_all_tables_cli"
 webserver = "aki_prj23_transparenzregister.ui.app:main"

src/aki_prj23_transparenzregister/ai/ner_pipeline.py

@@ -1,7 +1,8 @@
 """Pipeline to get Entities from Staging DB."""
 
-import json
+import os
 import sys
+from typing import Literal, get_args
 
 from loguru import logger
 from tqdm import tqdm
@@ -10,9 +11,13 @@ import aki_prj23_transparenzregister.utils.mongo.connector as conn
 import aki_prj23_transparenzregister.utils.mongo.news_mongo_service as news
 from aki_prj23_transparenzregister.ai import ner_service
 from aki_prj23_transparenzregister.config.config_providers import (
+    ConfigProvider,
     JsonFileConfigProvider,
 )
 
+ner_methods = Literal["spacy", "company_list", "transformer"]
+doc_attribs = Literal["text", "title"]
+
 logger.add(sys.stdout, colorize=True)
 
 
@@ -26,39 +31,48 @@ class EntityPipeline:
         self.news_obj = news.MongoNewsService(self.connector)
 
     def process_documents(
-        self, entity: str, doc_attrib: str, ner_selection: str
+        self,
+        doc_attrib: doc_attribs,
+        ner_method: ner_methods,
     ) -> None:
-        """Method to check documents, get entities and write them to document."""
-        CursorUnprogressed = self.news_obj.collection.find(  # noqa: N806
+        """Method to check documents, get entities and write them to document.
+
+        Args:
+            doc_attrib: Defines if headline "title" or body "text" of an article should be used to find organisations.
+            ner_method: The method provided used to analyse identify organisations.
+        """
+        cursor_unprocessed = self.news_obj.collection.find(
             {"companies": {"$exists": False}}
         )
-        documents = list(CursorUnprogressed)
-        logger.info("Dokumente: ", str(CursorUnprogressed))
+        documents = list(cursor_unprocessed)
+        logger.info("Dokumente: ", str(cursor_unprocessed))
 
         # Determine NER service based on config
         # spaCy
-        if ner_selection == "use_spacy_ner":
+        if ner_method == "spacy":
             ner_service_instance = ner_service.NerAnalysisService(
                 use_spacy=True, use_transformer=False, use_companylist=False
             )
             ner_service_func = ner_service_instance.ner_spacy
 
         # company list
-        elif ner_selection == "use_companylist_ner":
+        elif ner_method == "company_list":
             ner_service_instance = ner_service.NerAnalysisService(
                 use_spacy=False, use_transformer=False, use_companylist=True
             )
             ner_service_func = ner_service_instance.ner_company_list
 
         # transformer
-        elif ner_selection == "use_transformer_ner":
+        elif ner_method == "transformer":
             ner_service_instance = ner_service.NerAnalysisService(
                 use_spacy=False, use_transformer=True, use_companylist=False
             )
             ner_service_func = ner_service_instance.ner_transformer
+        else:
+            raise ValueError
         if len(documents) > 0:
             for document in tqdm(documents):
-                ents = ner_service_func(document, entity, doc_attrib)
+                ents = ner_service_func(document, "ORG", doc_attrib)
                 self.news_obj.collection.update_one(
                     {"_id": document["_id"]},
                     {"$set": {"companies": ents}},
@@ -67,43 +81,37 @@ class EntityPipeline:
             logger.info("No documents found.")
 
 
-if __name__ == "__main__":
-    # Establish MongoDB Connection using secrets
-    config_provider = JsonFileConfigProvider("./secrets.json")
+def execute_ner(config_provider: ConfigProvider) -> None:
+    """Executes the ner pipline.
+
+    Args:
+        config_provider: A config prover to define the MongoDB to read and write.
+    """
     connect_string = config_provider.get_mongo_connection_string()
 
-    # dir of config json
-    config_file_path = (
-        "src/aki_prj23_transparenzregister/utils/mongo/ner_sentiment_config.json"
-    )
-
-    # Load NER service configuration from JSON
-    with open(config_file_path) as config_file:
-        ner_config = json.load(config_file)
-
-    # read configuration
-    entity = ner_config["ner_service"]["entity"]
-    logger.info("NER Pipeline: searching for entity of type", str(entity))
-    doc_attrib = ner_config["ner_service"]["doc_attrib"]
-    logger.info("NER Pipeline: searching in document attribute ", str(doc_attrib))
-
-    # read selected service
-    if ner_config["ner_service"]["use_companylist_ner"] is True:
-        ner_selection = "use_companylist_ner"
-        logger.info("NER Pipeline: Searching entities with company list")
-
-    elif ner_config["ner_service"]["use_spacy_ner"] is True:
-        ner_selection = "use_spacy_ner"
-        logger.info("NER Pipeline: Searching entities with spaCy")
-
-    elif ner_config["ner_service"]["use_transformer_ner"] is True:
-        ner_selection = "use_transformer_ner"
-        logger.info("NER Pipeline: Searching entities with transformer")
-
-    else:
-        logger.info(
-            "NER Pipeline: No NER services selected or error in configuration file."
+    if (
+        ner_method := os.getenv("PYTHON_NER_METHOD", "transformer").lower()
+    ) not in get_args(ner_methods):
+        raise ValueError(
+            f"Please use either {', '.join(get_args(ner_methods))} as an ENV variable defining your ner method."
+            f"Currently used is {ner_method}"
+        )
+    if (doc_attrib := os.getenv("PYTHON_NER_DOC", "text").lower()) not in get_args(
+        doc_attribs
+    ):
+        raise ValueError(
+            f"Please use either {', '.join(get_args(doc_attribs))} as an ENV variable defining your ner document."
+            f"Currently used is {doc_attrib}"
         )
 
+    # read configuration
+    logger.info(
+        f"NER Pipeline: searching in document attribute {doc_attrib} using {ner_method}"
+    )
     entity_pipeline = EntityPipeline(connect_string)
-    entity_pipeline.process_documents(entity, doc_attrib, ner_selection)
+    entity_pipeline.process_documents(doc_attrib=doc_attrib, ner_method=ner_method)  # type: ignore
+
+
+if __name__ == "__main__":
+    # Establish MongoDB Connection using secrets
+    execute_ner(JsonFileConfigProvider("./secrets.json"))

src/aki_prj23_transparenzregister/ai/ner_sentiment_config.json

@@ -1,16 +0,0 @@
-{
-  "ner_service": {
-    "comment": "Select only one service by setting true and deselect the other with false. Valid doc_attrib: text, title",
-    "doc_attrib": "text",
-    "entity": "ORG",
-    "use_companylist_ner": false,
-    "use_spacy_ner": false,
-    "use_transformer_ner": true
-  },
-  "sentiment_service": {
-    "comment": "Select only one service by setting true and deselect the other with false. Valid doc_attrib: text, title",
-    "doc_attrib": "text",
-    "use_spacy": false,
-    "use_transformer": true
-  }
-}

src/aki_prj23_transparenzregister/ai/sentiment_pipeline.py

@@ -1,7 +1,7 @@
 """Pipeline to get sentiments from Staging DB nes articles."""
 
-import json
 import os
+from typing import Literal, get_args
 
 from loguru import logger
 from tqdm import tqdm
@@ -9,9 +9,15 @@ from tqdm import tqdm
 import aki_prj23_transparenzregister.utils.mongo.connector as conn
 import aki_prj23_transparenzregister.utils.mongo.news_mongo_service as news
 from aki_prj23_transparenzregister.ai import sentiment_service
-from aki_prj23_transparenzregister.config.config_providers import JsonFileConfigProvider
+from aki_prj23_transparenzregister.config.config_providers import (
+    ConfigProvider,
+    JsonFileConfigProvider,
+)
 from aki_prj23_transparenzregister.config.config_template import MongoConnection
 
+doc_attribs = Literal["text", "title"]
+sentiment_methods = Literal["spacy", "transformer"]
+
 
 class SentimentPipeline:
     """Class to initialize sentiment Pipeline."""
@@ -22,25 +28,34 @@ class SentimentPipeline:
         self.connector = conn.MongoConnector(self.connect_string)
         self.news_obj = news.MongoNewsService(self.connector)
 
-    def process_documents(self, doc_attrib: str, sentiment_selection: str) -> None:
-        """Method to check documents, get entities and write them to document."""
-        CursorUnprogressed = self.news_obj.collection.find(  # noqa: N806
+    def process_documents(
+        self,
+        doc_attrib: doc_attribs,
+        sentiment_method: sentiment_methods,
+    ) -> None:
+        """Method to check documents, get entities and write them to document.
+
+        Args:
+            doc_attrib: Defines if headline "title" or body "text" of an article should be used to define its sentiment.
+            sentiment_method: The method used to make a sentiment analysis.
+        """
+        cursor_unprocessed = self.news_obj.collection.find(
             {"sentiment": {"$exists": False}}
         )
-        documents = list(CursorUnprogressed)
+        documents = list(cursor_unprocessed)
 
         if len(documents) > 0:
             for document in tqdm(documents):
                 text = document[doc_attrib]
 
                 # Determine sentiment analysis service based on config
-                if sentiment_selection == "use_spacy":
+                if sentiment_method == "spacy":
                     selected_service = sentiment_service.SentimentAnalysisService(
                         use_spacy=True, use_transformer=False
                     )
                     sentiment_service_func = selected_service.sentiment_spacy
 
-                elif sentiment_selection == "use_transformer":
+                elif sentiment_method == "transformer":
                     selected_service = sentiment_service.SentimentAnalysisService(
                         use_spacy=False, use_transformer=True
                     )
@@ -56,34 +71,39 @@ class SentimentPipeline:
             logger.info("No documents found.")
 
 
-if __name__ == "__main__":
-    # Establish MongoDB Connection using secrets
-    config_provider = JsonFileConfigProvider("./secrets.json")
-    connect_string = config_provider.get_mongo_connection_string()
+def execute_sentiment(config_provider: ConfigProvider) -> None:
+    """Reads entries with missing data from the db and fills them with found sentiments.
 
-    # dir of config json
-    script_dir = os.path.dirname(__file__)
-    config_file_path = os.path.join(script_dir, "ner_sentiment_config.json")
-    # Load sentiment service configuration from JSON
-    with open(config_file_path) as config_file:
-        sentiment_config = json.load(config_file)
-    # Where to search the sentiment
-    doc_attrib = sentiment_config["sentiment_service"]["doc_attrib"]
-    logger.info("Sentiment Pipeline: searching in document attribute ", str(doc_attrib))
-
-    # read selected service
-    if sentiment_config["sentiment_service"]["use_spacy"] is True:
-        sentiment_selection = "use_spacy"
-        logger.info("Sentiment Pipleline: Searching sentiments with spaCy")
-
-    elif sentiment_config["sentiment_service"]["use_transformer"] is True:
-        sentiment_selection = "use_transformer"
-        logger.info("Sentiment Pipleline: Searching sentiments with transformer")
-
-    else:
-        logger.info(
-            "Sentiment Pipleline: No Sentiment services selected or error in configuration file."
+    Args:
+        config_provider: A config prover to define the MongoDB to read and write.
+    """
+    if (
+        sentiment_method := os.getenv("PYTHON_SENTIMENT_METHOD", "transformer").lower()
+    ) not in get_args(sentiment_methods):
+        raise ValueError(
+            f"Please use either {', '.join(get_args(sentiment_methods))} as an ENV variable defining your sentiment method."
+            f"Currently used is {sentiment_method}"
+        )
+    if (
+        doc_attrib := os.getenv("PYTHON_SENTIMENT_DOC", "text").lower()
+    ) not in get_args(doc_attribs):
+        raise ValueError(
+            f"Please use either {', '.join(get_args(doc_attribs))} as an ENV variable defining your sentiment document."
+            f"Currently used is {doc_attrib}"
         )
 
-    sentiment_pipeline = SentimentPipeline(connect_string)
-    sentiment_pipeline.process_documents(doc_attrib, sentiment_selection)
+    # read configuration
+    logger.info(
+        f"Sentiment Pipeline: searching in document attribute {doc_attrib} using {sentiment_method}"
+    )
+
+    # read selected service
+    sentiment_pipeline = SentimentPipeline(
+        config_provider.get_mongo_connection_string()
+    )
+    sentiment_pipeline.process_documents(doc_attrib, sentiment_method)  # type: ignore
+
+
+if __name__ == "__main__":
+    # Establish MongoDB Connection using secrets
+    execute_sentiment(JsonFileConfigProvider("./secrets.json"))

src/aki_prj23_transparenzregister/utils/data_processing.py

@@ -0,0 +1,56 @@
+"""Used to process data from the MongoDB to be ready for use in the SQL db."""
+import argparse
+import sys
+
+from aki_prj23_transparenzregister.ai.ner_pipeline import execute_ner
+from aki_prj23_transparenzregister.ai.sentiment_pipeline import execute_sentiment
+from aki_prj23_transparenzregister.config.config_providers import (
+    HELP_TEXT_CONFIG,
+    ConfigProvider,
+    get_config_provider,
+)
+from aki_prj23_transparenzregister.utils.data_transfer import transfer_data
+from aki_prj23_transparenzregister.utils.logger_config import (
+    add_logger_options_to_argparse,
+    configer_logger,
+)
+from aki_prj23_transparenzregister.utils.transfer_news import transfer_news_to_sql
+
+
+def process_and_transfer_data(
+    config_provider: ConfigProvider,
+) -> None:  # pragma: no cover
+    """Method to process and transfer all the data that can be found in the MongoDB in the SQL DB.
+
+    Args:
+        config_provider: A config prover to define the MongoDB to read and write.
+    """
+    execute_ner(config_provider)
+    execute_sentiment(config_provider)
+    transfer_data(config_provider)
+    transfer_news_to_sql(config_provider)
+
+
+def cli() -> None:  # pragma: no cover
+    """A cli interface for the data transfer."""
+    parser = argparse.ArgumentParser(
+        prog="Process and transform data",
+        description="Process the raw data from the MongoDB with AI models and match and transform the data from the MongoDB when transfering into the SQL DB.",
+        epilog="Example: 'data-processing secrets.json' or 'data-processing ENV'",
+    )
+    parser.add_argument(
+        "config",
+        metavar="config",
+        default="ENV",
+        help=HELP_TEXT_CONFIG,
+    )
+    add_logger_options_to_argparse(parser)
+    parsed = parser.parse_args(sys.argv[1:])
+    configer_logger(namespace=parsed)
+    config = get_config_provider(parsed.config)
+    process_and_transfer_data(config)
+
+
+if __name__ == "__main__":
+    configer_logger(level="info", path="")
+    process_and_transfer_data(get_config_provider("secrets.json"))

src/aki_prj23_transparenzregister/utils/transfer_news.py

@@ -172,7 +172,7 @@ def get_all_news(config_provider: ConfigProvider) -> list:
     ).get_all()
 
 
-def transfer_news_to_sql(config_provider: ConfigProvider, db: Session) -> None:
+def _transfer_news_to_sql(config_provider: ConfigProvider, db: Session) -> None:
     """Transfers news from the mongodb into the sql db.
 
     Args:
@@ -191,6 +191,16 @@ def transfer_news_to_sql(config_provider: ConfigProvider, db: Session) -> None:
     )
 
 
+def transfer_news_to_sql(config_provider: ConfigProvider) -> None:
+    """Transfers news from the mongodb into the sql db.
+
+    Args:
+        config_provider: The configuration prover to connect to the mongodb.
+        db: A session to connect to an SQL db via SQLAlchemy.
+    """
+    _transfer_news_to_sql(config_provider, get_session(config_provider))
+
+
 if __name__ == "__main__":
     jconfig_provider = JsonFileConfigProvider("secrets2.json")
-    transfer_news_to_sql(jconfig_provider, get_session(jconfig_provider))
+    transfer_news_to_sql(jconfig_provider)

tests/ai/ner_pipeline_test.py

@@ -83,9 +83,7 @@ def test_entity_pipeline_with_spacy(
     mock_collection.find.return_value = mock_documents
 
     # Call the process_documents method with spaCy NER
-    entity_pipeline.process_documents(
-        entity="ORG", doc_attrib="title", ner_selection="use_spacy_ner"
-    )
+    entity_pipeline.process_documents(doc_attrib="title", ner_method="spacy")
 
     # Ensure that ner_spacy was called with the correct parameters
     mock_ner_spacy.assert_called_once_with(mock_documents[0], "ORG", "title")
@@ -121,9 +119,7 @@ def test_entity_pipeline_with_spacy_no_docs(
     mock_collection.find.return_value = mock_documents
 
     # Call the process_documents method with spaCy NER
-    entity_pipeline.process_documents(
-        entity="ORG", doc_attrib="title", ner_selection="use_spacy_ner"
-    )
+    entity_pipeline.process_documents(doc_attrib="title", ner_method="spacy")
 
     # Ensure that sentiment_spacy was not called
     mock_ner_spacy.assert_not_called()
@@ -135,14 +131,14 @@ def test_entity_pipeline_with_spacy_no_docs(
 @patch(
     "aki_prj23_transparenzregister.ai.ner_service.NerAnalysisService.ner_company_list"
 )
-def test_entity_pipeline_with_companylist_ner(
-    mock_ner_companylist: Mock,
+def test_entity_pipeline_with_company_list_ner(
+    mock_ner_company_list: Mock,
     mock_mongo_connector: Mock,
     mock_mongo_connection: MongoConnection,
     mock_spacy: Mock,
 ) -> None:
     # Konfigurieren Sie das Mock-Objekt, um ein spezifisches NER-Ergebnis zurückzugeben
-    mock_ner_companylist.return_value = {"ORG": 3, "LOCATION": 2}
+    mock_ner_company_list.return_value = {"ORG": 3, "LOCATION": 2}
 
     # Create an instance of the EntityPipeline
     entity_pipeline = EntityPipeline(mock_mongo_connection)
@@ -159,12 +155,10 @@ def test_entity_pipeline_with_companylist_ner(
     mock_collection.find.return_value = mock_documents
 
     # Call the process_documents method with Company List NER
-    entity_pipeline.process_documents(
-        entity="ORG", doc_attrib="title", ner_selection="use_companylist_ner"
-    )
+    entity_pipeline.process_documents(doc_attrib="title", ner_method="company_list")
 
     # Überprüfen Sie, ob ner_company_list mit den richtigen Parametern aufgerufen wurde
-    mock_ner_companylist.assert_called_once_with(mock_documents[0], "ORG", "title")
+    mock_ner_company_list.assert_called_once_with(mock_documents[0], "ORG", "title")
 
     # Überprüfen Sie, ob das Dokument in der Sammlung mit den NER-Ergebnissen aktualisiert wurde
     mock_collection.update_one.assert_called_once_with(
@@ -176,14 +170,14 @@ def test_entity_pipeline_with_companylist_ner(
 @patch(
     "aki_prj23_transparenzregister.ai.ner_service.NerAnalysisService.ner_company_list"
 )
-def test_entity_pipeline_with_companylist_ner_no_docs(
-    mock_ner_companylist: Mock,
+def test_entity_pipeline_with_company_list_ner_no_docs(
+    mock_ner_company_list: Mock,
     mock_mongo_connector: Mock,
     mock_mongo_connection: MongoConnection,
     mock_spacy: Mock,
 ) -> None:
     # Configure the mock to return a specific NER result
-    mock_ner_companylist.return_value = {"ORG": 3, "LOCATION": 2}
+    mock_ner_company_list.return_value = {"ORG": 3, "LOCATION": 2}
 
     # Create an instance of the EntityPipeline
     entity_pipeline = EntityPipeline(mock_mongo_connection)
@@ -198,18 +192,15 @@ def test_entity_pipeline_with_companylist_ner_no_docs(
     mock_collection.find.return_value = mock_documents
 
     # Call the process_documents method with Company List NER
-    entity_pipeline.process_documents(
-        entity="ORG", doc_attrib="title", ner_selection="use_companylist_ner"
-    )
+    entity_pipeline.process_documents(doc_attrib="title", ner_method="company_list")
 
     # Ensure that ner_company_list is not called
-    mock_ner_companylist.assert_not_called()
+    mock_ner_company_list.assert_not_called()
 
     # Ensure that the document in the collection was not updated
     mock_collection.update_one.assert_not_called()
 
 
-# Add more test cases for other NER methods (e.g., use_companylist_ner, use_transformer_ner) following a similar pattern.
 @patch("aki_prj23_transparenzregister.ai.ner_service.NerAnalysisService.ner_spacy")
 def test_entity_pipeline_with_transformer(
     mock_ner_transformer: Mock,
@@ -234,9 +225,7 @@ def test_entity_pipeline_with_transformer(
     mock_collection.find.return_value = mock_documents
 
     # Call the process_documents method with spaCy NER
-    entity_pipeline.process_documents(
-        entity="ORG", doc_attrib="title", ner_selection="use_spacy_ner"
-    )
+    entity_pipeline.process_documents(doc_attrib="title", ner_method="spacy")
 
     # Ensure that ner_spacy was called with the correct parameters
     mock_ner_transformer.assert_called_once_with(mock_documents[0], "ORG", "title")
@@ -272,9 +261,7 @@ def test_entity_pipeline_with_transformer_no_docs(
     mock_collection.find.return_value = mock_documents
 
     # Call the process_documents method with spaCy NER
-    entity_pipeline.process_documents(
-        entity="ORG", doc_attrib="title", ner_selection="use_spacy_ner"
-    )
+    entity_pipeline.process_documents(doc_attrib="title", ner_method="spacy")
 
     # Ensure that ner_transformer is not called
     mock_ner_transformer.assert_not_called()

tests/ai/sentiment_pipeline_test.py

@@ -92,7 +92,7 @@ def test_sentiment_pipeline_existing_sentiment(
     mock_collection.find.return_value = mock_documents
 
     # Call the process_documents method
-    sentiment_pipeline.process_documents("text", "use_spacy")
+    sentiment_pipeline.process_documents("text", "spacy")
 
     # Ensure that sentiment_spacy was called with the correct text
     mock_sentiment_spacy.assert_called_once_with("This is a positive text.")
@@ -124,7 +124,7 @@ def test_sentiment_pipeline_no_documents(
     sentiment_pipeline.news_obj.collection = mock_collection
 
     # Call the process_documents method
-    sentiment_pipeline.process_documents("text", "use_spacy")
+    sentiment_pipeline.process_documents("text", "spacy")
 
     # Ensure that sentiment_spacy was not called
     mock_sentiment_spacy.assert_not_called()
@@ -159,7 +159,7 @@ def test_sentiment_pipeline_with_spacy(
     mock_collection.find.return_value = mock_documents
 
     # Call the process_documents method
-    sentiment_pipeline.process_documents("text", "use_spacy")
+    sentiment_pipeline.process_documents("text", "spacy")
 
     # Ensure that sentiment_spacy was called with the correct text
     mock_sentiment_spacy.assert_called_once_with("This is a positive text.")
@@ -198,7 +198,7 @@ def test_sentiment_pipeline_with_transformer(
     mock_collection.find.return_value = mock_documents
 
     # Call the process_documents method
-    sentiment_pipeline.process_documents("text", "use_transformer")
+    sentiment_pipeline.process_documents("text", "transformer")
 
     # Ensure that sentiment_transformer was called with the correct text
     mock_sentiment_transformer.assert_called_once_with("This is a negative text.")

tests/utils/data_processing_test.py

@@ -0,0 +1,7 @@
+"""Tests for the data processing module."""
+from aki_prj23_transparenzregister.utils import data_processing
+
+
+def test_import() -> None:
+    """Tests if the data processing module can be imported."""
+    assert data_processing

tests/utils/transfer_news_test.py

@@ -130,7 +130,7 @@ def test_transfer_news_to_sql(full_db: Session, monkeypatch: MonkeyPatch) -> Non
         "aki_prj23_transparenzregister.utils.transfer_news.get_all_news",
         lambda _: NEWS_TEXTS,
     )
-    transfer_news.transfer_news_to_sql(None, full_db)  # type: ignore
+    transfer_news._transfer_news_to_sql(None, full_db)  # type: ignore
     articles = pd.read_sql_table(entities.News.__tablename__, full_db.bind)  # type: ignore
     assert "text" in articles.columns
     del articles["text"]