Merge pull request #22 from reginabarzilaygroup/v1.0.3_dev

V1.0.3 dev

README.md

@@ -4,6 +4,15 @@
 
 Lung Cancer Risk Prediction
 
+## Run a regression test
+
+```shell
+python tests/regression_test.py
+```
+
+This will download the`sybil_ensemble` model and sample data, and compare the results to what has previously been calculated.
+
+
 ## Run the model
 
 You can load our pretrained model trained on the NLST dataset, and score a given DICOM serie as follows:

docs/requirements.txt

@@ -1,26 +1,6 @@
 # Requirements file for ReadTheDocs, check .readthedocs.yml.
 # To build the module reference correctly, make sure every external package
 # under `install_requires` in `setup.cfg` is also listed here!
-# sphinx_rtd_theme                                                                                        
---find-links https://download.pytorch.org/whl/cu113/torch_stable.html
+# sphinx_rtd_theme
 recommonmark
 sphinx>=3.2.1
-# deep learning
-torch==1.10.1+cu113
-torchvision==0.11.2+cu113
-pytorch_lightning==1.5.6
-# math
-scikit-learn==1.0.2
-# utils
-tqdm
-lifelines==0.26.4
-# loading
-opencv-python==4.5.4.60                                                                                   
-opencv-python-headless==4.5.4.60
-albumentations==1.1.0
-pydicom==2.2.2
-# logging
-#comet-ml
-torchio==0.18.74
-# downloading snapshots                                                                                   
-gdown==4.6.0

setup.cfg

@@ -7,12 +7,12 @@ author_email =
 license_file = LICENSE.txt
 long_description = file: README.md
 long_description_content_type = text/markdown; charset=UTF-8; variant=GFM
-version = 1.0.1
+version = 1.0.3
 # url = 
 project_urls =
     ; Documentation = https://.../docs
-    Source = https://github.com/pgmikhael/Sybil/
-    Tracker = https://github.com/pgmikhael/Sybil/issues
+    Source = https://github.com/reginabarzilaygroup/sybil
+    Tracker = https://github.com/reginabarzilaygroup/sybil/issues
 
 
 # Change if running only on Windows, Mac or Linux (comma-separated)
@@ -28,13 +28,28 @@ zip_safe = False
 packages = find:
 include_package_data = True
 python_requires = >=3.8
-
 # Add here dependencies of your project (line-separated), e.g. requests>=2.2,<3.0.
 # Version specifiers like >=2.2,<3.0 avoid problems due to API changes in
 # new major versions. This works if the required packages follow Semantic Versioning.
 # For more information, check out https://semver.org/.
+# Use --find-links https://download.pytorch.org/whl/cu113/torch_stable.html for torch libraries
 install_requires =
-    importlib-metadata; python_version<"3.8"
+    importlib-metadata; python_version>="3.8"
+    numpy==1.24.1
+    torch==1.11.0+cu113; sys_platform != "darwin"
+    torch==1.11.0; sys_platform == "darwin"
+    torchvision==0.12.0+cu113; sys_platform != "darwin"
+    torchvision==0.12.0; sys_platform == "darwin"
+    pytorch_lightning==1.5.6
+    scikit-learn==1.0.2
+    tqdm==4.62.3
+    lifelines==0.26.4
+    opencv-python==4.5.4.60
+    opencv-python-headless==4.5.4.60
+    albumentations==1.1.0
+    pydicom==2.2.2
+    torchio==0.18.74
+    gdown==4.6.0
 
 
 [options.packages.find]
@@ -76,7 +91,6 @@ norecursedirs =
     build
     .tox
 addopts =
-    --cov sybil --cov-report term-missing
     --verbose
 testpaths = tests
 # Use pytest markers to select/deselect specific tests

tests/regression_test.py

@@ -0,0 +1,93 @@
+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():
+    # Note that this function is named so that pytest will not automatically discover it
+    # It takes a long time to run and potentially a lot of disk space
+
+    # 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()

tests/test_create_sybilnet.py

@@ -0,0 +1,18 @@
+import argparse
+import datetime
+import os
+
+from sybil import Serie, Sybil
+
+def test_create_sybilnet():
+    from sybil.models.sybil import SybilNet
+
+    fake_args = argparse.Namespace(
+        dropout=0.1,
+        max_followup=5,
+        )
+
+    sybil_net = SybilNet(fake_args)
+
+    assert sybil_net.hidden_dim == 512
+    assert sybil_net.prob_of_failure_layer is not None

tox.ini

@@ -14,14 +14,15 @@ deps =
     setuptools
     pytest
     pytest-cov
-    flake8
-    mypy
-    black
+    # flake8
+    # mypy
+    # black
+install_command = pip install --pre --find-links https://download.pytorch.org/whl/cu113/torch_stable.html {opts} {packages}
 commands =
     pytest {posargs}
-    black {toxinidir}/sybil --check
-    flake8 {toxinidir}/sybil
-    mypy {toxinidir}/sybil
+    # black {toxinidir}/sybil --check
+    # flake8 {toxinidir}/sybil
+    # mypy {toxinidir}/sybil
 
 
 [testenv:{clean,build}]