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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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) |
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