Incorporate other changes:

* Update version to 1.1.0
* Download model from GitHub
* Update README to point to wiki
* Update default model to be "sybil_ensemble"

README.md

@@ -2,7 +2,9 @@
 
 # Sybil
 
-Lung Cancer Risk Prediction
+Lung Cancer Risk Prediction.
+
+Additional documentation can be found on the [GitHub Wiki](https://github.com/reginabarzilaygroup/Sybil/wiki).
 
 ## Run a regression test
 
@@ -21,7 +23,7 @@ You can load our pretrained model trained on the NLST dataset, and score a given
 from sybil import Serie, Sybil
 
 # Load a trained model
-model = Sybil("sybil_base")
+model = Sybil("sybil_ensemble")
 
 # Get risk scores
 serie = Serie([dicom_path_1, dicom_path_2, ...])
@@ -32,9 +34,9 @@ serie = Serie([dicom_path_1, dicom_path_2, ...], label=1)
 results = model.evaluate([serie])
 ```
 
-Models available include: `sybil_base` and `sybil_ensemble`.
+Models available include: `sybil_1`, `sybil_2`, `sybil_3`, `sybil_4`, `sybil_5` and `sybil_ensemble`.
 
-All model files are available [here](https://drive.google.com/drive/folders/1nBp05VV9mf5CfEO6W5RY4ZpcpxmPDEeR?usp=sharing).
+All model files are available on [GitHub releases](https://github.com/reginabarzilaygroup/Sybil/releases) as well as [here](https://drive.google.com/drive/folders/1nBp05VV9mf5CfEO6W5RY4ZpcpxmPDEeR?usp=sharing).
 
 ## Replicating results
 

setup.cfg

@@ -7,7 +7,7 @@ author_email =
 license_file = LICENSE.txt
 long_description = file: README.md
 long_description_content_type = text/markdown; charset=UTF-8; variant=GFM
-version = 1.0.4
+version = 1.1.0
 # url = 
 project_urls =
     ; Documentation = https://.../docs

sybil/model.py

@@ -1,7 +1,10 @@
-from typing import NamedTuple, Union, Dict, List, Optional
-import os
 from argparse import Namespace
-import gdown
+from io import BytesIO
+import os
+from typing import NamedTuple, Union, Dict, List, Optional, Tuple
+from urllib.request import urlopen
+from zipfile import ZipFile
+# import gdown
 
 import torch
 import numpy as np
@@ -12,6 +15,7 @@
 from sybil.utils.metrics import get_survival_metrics
 
 
+# Leaving this here for a bit; these are IDs to download the models from Google Drive
 NAME_TO_FILE = {
     "sybil_base": {
         "checkpoint": ["28a7cd44f5bcd3e6cc760b65c7e0d54d"],
@@ -62,6 +66,8 @@
     },
 }
 
+CHECKPOINT_URL = "https://github.com/reginabarzilaygroup/Sybil/releases/download/v1.0.3/sybil_checkpoints.zip"
+
 
 class Prediction(NamedTuple):
     scores: List[List[float]]
@@ -75,7 +81,7 @@ class Evaluation(NamedTuple):
     attentions: List[Dict[str, np.ndarray]] = None
 
 
-def download_sybil(name, cache):
+def download_sybil_gdrive(name, cache):
     """Download trained models and calibrator from Google Drive
 
     Parameters
@@ -118,10 +124,44 @@ def download_sybil(name, cache):
     return download_model_paths, download_calib_path
 
 
+def download_sybil(name, cache) -> Tuple[List[str], str]:
+    """Download trained models and calibrator"""
+    # Create cache folder if not exists
+    cache = os.path.expanduser(cache)
+    os.makedirs(cache, exist_ok=True)
+
+    # Download models
+    model_files = NAME_TO_FILE[name]
+    checkpoints = model_files["checkpoint"]
+    download_calib_path = os.path.join(cache, f"{name}.p")
+    have_all_files = os.path.exists(download_calib_path)
+
+    download_model_paths = []
+    for checkpoint in checkpoints:
+        cur_checkpoint_path = os.path.join(cache, f"{checkpoint}.ckpt")
+        have_all_files &= os.path.exists(cur_checkpoint_path)
+        download_model_paths.append(cur_checkpoint_path)
+
+    if not have_all_files:
+        print(f"Downloading models to {cache}")
+        download_and_extract(CHECKPOINT_URL, cache)
+
+    return download_model_paths, download_calib_path
+
+
+def download_and_extract(remote_model_url: str, local_model_dir) -> List[str]:
+    resp = urlopen(remote_model_url)
+    os.makedirs(local_model_dir, exist_ok=True)
+    with ZipFile(BytesIO(resp.read())) as zip_file:
+        all_files_and_dirs = zip_file.namelist()
+        zip_file.extractall(local_model_dir)
+    return all_files_and_dirs
+
+
 class Sybil:
     def __init__(
         self,
-        name_or_path: Union[List[str], str] = "sybil_base",
+        name_or_path: Union[List[str], str] = "sybil_ensemble",
         cache: str = "~/.sybil/",
         calibrator_path: Optional[str] = None,
         device: Optional[str] = None,

tests/regression_test.py

@@ -66,7 +66,8 @@ def main():
     num_files = len(dicom_files)
 
     # Load a trained model
-    model = Sybil("sybil_ensemble")
+    # model = Sybil("sybil_ensemble")
+    model = Sybil()
 
     myprint(f"Beginning prediction using {num_files} files from {image_data_dir}")