Merge branch 'refs/heads/main' into refactor/135-refactor-db-results

tracex_project/extraction/fixtures/prompts_fixture.json

@@ -558,10 +558,10 @@
           }
         ],
         [
-          "gender",
+          "sex",
           {
             "role": "system",
-            "content": "You are an expert in text understanding and your job is to take a given text about an illness             and to extract the gender of the author. If the gender isn't clear, you should take the context into             account. Only if the context doesn't help, you should return 'N/A'."
+            "content": "You are an expert in text understanding and your job is to take a given text about an illness             and to extract the sex of the author. If the sex isn't clear, you should take the context into             account. Only if the context doesn't help, you should return 'N/A'."
           },
           {
             "role": "user",
@@ -608,7 +608,7 @@
           "age",
           {
             "role": "system",
-            "content": "You are an expert in text understanding and your job is to take a given text about an illness             and to extract the age of the author. If the gender isn't clear, you should take the context into             account. Young means 25, middle aged 50 and old 75. Only if the context doesn't help, you should             return 'N/A'."
+            "content": "You are an expert in text understanding and your job is to take a given text about an illness             and to extract the age of the author. If the sex isn't clear, you should take the context into             account. Young means 25, middle aged 50 and old 75. Only if the context doesn't help, you should             return 'N/A'."
           },
           {
             "role": "user",

tracex_project/extraction/logic/orchestrator.py

@@ -243,7 +243,7 @@ def set_default_values(self):
         if "cohort_tagging" not in config_modules:
             cohort_default_values = {
                 "age": None,
-                "gender": None,
+                "sex": None,
                 "origin": None,
                 "condition": None,
                 "preexisting_condition": None,

tracex_project/extraction/tests/test_modules.py

@@ -0,0 +1,129 @@
+"""Test cases for the modules in the extraction app."""
+from django.test import TestCase
+import pandas as pd
+
+from extraction.logic.modules import (
+    ActivityLabeler,
+    TimeExtractor,
+    EventTypeClassifier,
+    LocationExtractor,
+)
+
+
+class ActivityLabelerTests(TestCase):
+    """Test cases for the ActivityLabeler."""
+
+    fixtures = ["tracex_project/extraction/fixtures/prompts_fixture.json"]
+
+    def test_execute_return_value(self):
+        """Test if the return value of the execute method is a DataFrame and if the column name is as expected."""
+        test_data = ["I fell ill yesterday.", "I went to the doctor today."]
+        activity_labeler = ActivityLabeler()
+        result = activity_labeler.execute(patient_journey_sentences=test_data)
+
+        self.assertIsInstance(result, pd.DataFrame)
+        self.assertIn("activity", result.columns)
+        self.assertIn("sentence_id", result.columns)
+
+
+class TimeExtractorTests(TestCase):
+    """Test cases for the TimeExtractor."""
+
+    fixtures = ["tracex_project/extraction/fixtures/prompts_fixture.json"]
+
+    def test_execute_return_value(self):
+        """Test if the return value of the execute method is a DataFrame and if the column names are as expected."""
+        data = {"activity": ["fell ill"], "sentence_id": ["1"]}
+        patient_journey = ["I fell ill on June 5 and recovered on June 7."]
+        input_dataframe = pd.DataFrame(data)
+        time_extractor = TimeExtractor()
+        result = time_extractor.execute(
+            df=input_dataframe, patient_journey_sentences=patient_journey
+        )
+
+        self.assertIsInstance(result, pd.DataFrame)
+        self.assertIn("time:timestamp", result.columns)
+        self.assertIn("time:end_timestamp", result.columns)
+        self.assertIn("time:duration", result.columns)
+
+    def test_return_value_is_datetime(self):
+        """Test if the returned DataFrame columns are of type datetime."""
+        data = {"activity": ["fell ill"], "sentence_id": ["1"]}
+        patient_journey = ["I fell ill on June 5 and recovered on June 7."]
+        input_dataframe = pd.DataFrame(data)
+        time_extractor = TimeExtractor()
+        result = time_extractor.execute(
+            df=input_dataframe, patient_journey_sentences=patient_journey
+        )
+
+        self.assertTrue((result["time:timestamp"].apply(lambda x: isinstance(x, pd.Timestamp))).all())
+        self.assertTrue((result["time:end_timestamp"].apply(lambda x: isinstance(x, pd.Timestamp))).all())
+
+    def test_processing_downwards(self):
+        """Test if the post-processing function is correctly applied to the DataFrame downwards."""
+        data = {"activity": ["fell ill", "had fever"], "sentence_id": ["1", "2"]}
+        patient_journey = ["I fell ill on June 5 and recovered on June 7. After that I had fever."]
+        input_dataframe = pd.DataFrame(data)
+        time_extractor = TimeExtractor()
+        result = time_extractor.execute(
+            df=input_dataframe, patient_journey_sentences=patient_journey
+        )
+
+        self.assertTrue((result["time:timestamp"].apply(lambda x: isinstance(x, pd.Timestamp))).all())
+        self.assertTrue((result["time:end_timestamp"].apply(lambda x: isinstance(x, pd.Timestamp))).all())
+
+    def test_post_processing_upwards(self):
+        """Test if the post-processing function is correctly applied to the DataFrame upwards."""
+        data = {"activity": ["had fever", "fell ill",], "sentence_id": ["1", "2"]}
+        patient_journey = ["I had fever. After that I fell ill on June 5 and recovered on June 7."]
+        input_dataframe = pd.DataFrame(data)
+        time_extractor = TimeExtractor()
+        result = time_extractor.execute(
+            df=input_dataframe, patient_journey_sentences=patient_journey
+        )
+
+        self.assertTrue((result["time:timestamp"].apply(lambda x: isinstance(x, pd.Timestamp))).all())
+        self.assertTrue((result["time:end_timestamp"].apply(lambda x: isinstance(x, pd.Timestamp))).all())
+
+
+class EventTypeClassifierTests(TestCase):
+    """Test cases for the EventTypeClassifier."""
+
+    fixtures = ["tracex_project/extraction/fixtures/prompts_fixture.json"]
+
+    def test_execute_return_value(self):
+        """Test if the return value of the execute method is a DataFrame and if the column name is as expected."""
+        test_data = {
+            "activity": "fell ill",
+            "time:timestamp": "20220601T0000",
+            "time:end_timestamp": "20220605T0000",
+            "time:duration": "96:00:00",
+        }
+        input_dataframe = pd.DataFrame([test_data])
+        event_type_classifier = EventTypeClassifier()
+        result = event_type_classifier.execute(input_dataframe)
+
+        self.assertIsInstance(result, pd.DataFrame)
+        self.assertIn("event_type", result.columns)
+
+
+class LocationExtractorTests(TestCase):
+    """Test cases for the LocationExtractor."""
+
+    fixtures = ["tracex_project/extraction/fixtures/prompts_fixture.json"]
+
+    def test_execute_return_value(self):
+        """Test if the return value of the execute method is a DataFrame and if the column name is as expected."""
+        test_data = {
+            "activity": "fell ill",
+            "time:timestamp": "20220601T0000",
+            "time:end_timestamp": "20220605T0000",
+            "time:duration": "96:00:00",
+            "event_type": "Symptom Onset",
+        }
+        input_dataframe = pd.DataFrame([test_data])
+        location_extractor = LocationExtractor()
+        result = location_extractor.execute(input_dataframe)
+
+        self.assertIsInstance(result, pd.DataFrame)
+        self.assertIn("attribute_location", result.columns)