Merge pull request #65 from vkrithika25/main

#62 Added crosswalk table + unit tests

Author: woodthom2

src/harmony/matching/generate_crosswalk_table.py

@@ -0,0 +1,48 @@
+"""
+MIT License
+
+Copyright (c) 2023 Ulster University (https://www.ulster.ac.uk).
+Project: Harmony (https://harmonydata.ac.uk)
+Maintainer: Thomas Wood (https://fastdatascience.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import pandas as pd
+
+def generate_crosswalk_table(all_questions, similarity, threshold):
+    matching_pairs = []
+
+    # iterate through all pairs of questions
+    for i, q1 in enumerate(all_questions):
+        for j, q2 in enumerate(all_questions):
+            # check for non-dupe and similarity above inputted threshold
+            if j > i and similarity[i, j] > threshold:
+                # add to list of matches
+                matching_pairs.append({
+                    'pair_name': f"{i}_{j}",
+                    'question1_no': q1.question_no,
+                    'question1_text': q1.question_text,
+                    'question2_no': q2.question_no,
+                    'question2_text': q2.question_text,
+                    'match_score': similarity[i, j]
+                })
+
+    # convert list to dataframe
+    return pd.DataFrame(matching_pairs)

tests/test_crosswalk.py

@@ -0,0 +1,101 @@
+"""
+MIT License
+
+Copyright (c) 2023 Ulster University (https://www.ulster.ac.uk).
+Project: Harmony (https://harmonydata.ac.uk)
+Maintainer: Thomas Wood (https://fastdatascience.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+"""
+
+import sys
+import unittest
+import pandas as pd
+import numpy as np
+
+sys.path.append("../src")
+
+from harmony.matching.generate_crosswalk_table import generate_crosswalk_table
+from harmony import match_instruments
+from harmony.schemas.requests.text import Instrument, Question
+
+class TestGenerateCrosswalkTable(unittest.TestCase):
+    def setUp(self):
+        # Sample data
+        self.all_questions_dummy = [
+            Question(question_no="1", question_text="potato"),
+            Question(question_no="2", question_text="tomato"),
+            Question(question_no="3", question_text="radish"),
+        ]
+
+        self.instruments_dummy = Instrument(questions=self.all_questions_dummy)
+
+        self.similarity = np.array([
+            [1.0, 0.7, 0.9],
+            [0.7, 1.0, 0.8],
+            [0.9, 0.8, 1.0]
+        ])
+        self.all_questions_real = [Question(question_no="1", question_text="Feeling nervous, anxious, or on edge"),
+                        Question(question_no="2", question_text="Not being able to stop or control worrying")]
+        self.instruments = Instrument(questions=self.all_questions_real)
+
+        self.threshold = 0.6
+
+
+    def test_generate_crosswalk_table_dummy_data(self):
+        result = generate_crosswalk_table(self.instruments_dummy.questions, self.similarity, self.threshold)
+
+        expected_matches = [
+            {"pair_name": "0_1", "question1_no": "1", "question1_text": "potato",
+             "question2_no": "2", "question2_text": "tomato", "match_score": 0.7},
+            {"pair_name": "0_2", "question1_no": "1", "question1_text": "potato",
+             "question2_no": "3", "question2_text": "radish", "match_score": 0.9},
+            {"pair_name": "1_2", "question1_no": "2", "question1_text": "tomato",
+             "question2_no": "3", "question2_text": "radish", "match_score": 0.8},
+        ]
+
+        for _, row in pd.DataFrame(expected_matches).iterrows():
+            self.assertTrue(any(row.equals(result_row) for _, result_row in result.iterrows()))
+
+        self.assertEqual(len(result), len(expected_matches))
+
+    def test_generate_crosswalk_table_empty(self):
+        empty_similarity = np.eye(3)  # Identity matrix, no matches above threshold
+        result = generate_crosswalk_table(self.all_questions_dummy, empty_similarity, self.threshold)
+        self.assertTrue(result.empty)
+
+    def test_generate_crosswalk_table_real(self):
+        all_questions, similarity_with_polarity, _, _ = match_instruments([self.instruments])
+        result = generate_crosswalk_table(all_questions, similarity_with_polarity, self.threshold)
+        expected_matches = []
+
+        for _, row in pd.DataFrame(expected_matches).iterrows():
+            self.assertTrue(any(row.equals(result_row) for _, result_row in result.iterrows()))
+
+        self.assertEqual(len(result), len(expected_matches))
+
+        lower_threshold = 0.5
+        result = generate_crosswalk_table(all_questions, similarity_with_polarity, lower_threshold)
+
+        self.assertEqual(len(result), 1)
+
+
+if __name__ == '__main__':
+    unittest.main()