Add regression test

tests/regression_test.py

@@ -0,0 +1,90 @@
+import datetime
+import math
+import os
+import requests
+import zipfile
+
+from sybil import Serie, Sybil
+
+script_directory = os.path.dirname(os.path.abspath(__file__))
+project_directory = os.path.dirname(script_directory)
+
+
+def myprint(instr):
+    print(f"{datetime.datetime.now()} - {instr}")
+
+
+def download_and_extract_zip(zip_file_name, cache_dir, url, demo_data_dir):
+    # Check and construct the full path of the zip file
+    zip_file_path = os.path.join(cache_dir, zip_file_name)
+
+    # 1. Check if the zip file exists
+    if not os.path.exists(zip_file_path):
+        # myprint(f"Zip file not found at {zip_file_path}. Downloading from {url}...")
+        # 2. Download the file
+        response = requests.get(url)
+        with open(zip_file_path, 'wb') as file:
+            file.write(response.content)
+        # myprint(f"Downloaded zip file to {zip_file_path}")
+
+    # 3. Check if the output directory exists
+    if not os.path.exists(demo_data_dir):
+        # myprint(f"Output directory {demo_data_dir} does not exist. Creating and extracting...")
+        # 4. Extract the zip file
+        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+            zip_ref.extractall(demo_data_dir)
+        # myprint(f"Extracted zip file to {demo_data_dir}")
+    else:
+        pass
+        # myprint(f"Output directory {demo_data_dir} already exists. No extraction needed.")
+
+
+def main():
+    # Download demo data
+    demo_data_url = "https://www.dropbox.com/sh/addq480zyguxbbg/AACJRVsKDL0gpq-G9o3rfCBQa?dl=1"
+    expected_scores = [
+        0.021628819563619374,
+        0.03857256315036462,
+        0.07191945816622261,
+        0.07926975188037134,
+        0.09584583525781108,
+        0.13568094038444453
+    ]
+
+    zip_file_name = "SYBIL.zip"
+    cache_dir = os.path.expanduser("~/.sybil")
+    demo_data_dir = os.path.join(cache_dir, "SYBIL")
+    image_data_dir = os.path.join(demo_data_dir, "sybil_demo_data")
+    os.makedirs(cache_dir, exist_ok=True)
+    download_and_extract_zip(zip_file_name, cache_dir, demo_data_url, demo_data_dir)
+
+    dicom_files = os.listdir(image_data_dir)
+    dicom_files = [os.path.join(image_data_dir, x) for x in dicom_files]
+    num_files = len(dicom_files)
+
+    # Load a trained model
+    model = Sybil("sybil_ensemble")
+
+    # myprint(f"Beginning prediction using {num_files} from {image_data_dir}")
+
+    # Get risk scores
+    serie = Serie(dicom_files)
+    prediction = model.predict([serie])[0]
+    actual_scores = prediction[0]
+    count = len(actual_scores)
+
+    # myprint(f"Prediction finished. Results\n{actual_scores}")
+
+    assert len(expected_scores) == len(actual_scores), f"Unexpected score length {count}"
+
+    all_elements_match = True
+    for exp_score, act_score in zip(expected_scores, actual_scores):
+        does_match = math.isclose(exp_score, act_score, rel_tol=1e-6)
+        assert does_match, f"Mismatched scores. {exp_score} != {act_score}"
+        all_elements_match &= does_match
+
+    print(f"Data URL: {demo_data_url}\nAll {count} elements match: {all_elements_match}")
+
+
+if __name__ == "__main__":
+    main()