Python 3.13.0 support for spare scores package

.github/dependabot.yml

@@ -0,0 +1,16 @@
+# The documentation for all configuration options:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+
+version: 2
+updates:
+  - package-ecosystem: "maven"
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"
+
+  - package-ecosystem: "github-actions"
+    # Workflow files stored in the
+    # default location of `.github/workflows`
+    directory: "/"
+    schedule:
+      interval: "weekly"

.github/workflows/macos-tests-3.8.yml → .github/workflows/macos-tests-3.9.yml

@@ -1,4 +1,4 @@
-name: macos tests python=3.8
+name: macos tests python=3.9
 
 # workflow dispatch has been added for testing purposes
 on: [push, pull_request, workflow_dispatch]
@@ -11,12 +11,12 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.8"
+          python-version: "3.9"
       - name: Set-up miniconda for macos and ubuntu
         uses: conda-incubator/setup-miniconda@v2
         with:
           auto-update-conda: true
-          python-version: 3.8
+          python-version: 3.9
           miniconda-version: "latest"
       - name: Create conda env
         run: conda create -n spare python=3.8

.github/workflows/pre-commit.yml

@@ -3,7 +3,7 @@ name: pre-commit
 on:
   pull_request:
   push:
-    branches: [main, fixing_issues]
+    branches: [main, spiros-dev]
 
 jobs:
   pre-commit:
@@ -13,5 +13,7 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: "3.12"
-      - run: pip3 install pre-commit
-      - run: pre-commit run --files spare_scores/*
+      - name: install pre commit
+        run: pip install pre-commit && pre-commit install
+      - name: pre-commit
+        uses: pre-commit/[email protected]

.github/workflows/ubuntu-tests-3.8.yml → .github/workflows/ubuntu-tests-3.9.yml

@@ -1,4 +1,4 @@
-name: ubuntu build python=3.8
+name: ubuntu build python=3.9
 
 # workflow dispatch has been added for testing purposes
 on: [push, pull_request, workflow_dispatch]
@@ -11,7 +11,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.8"
+          python-version: "3.9"
       - name: Install spare scores
         run: |
           python -m pip cache purge

README.md

@@ -1,6 +1,7 @@
 [![codecov](https://codecov.io/gh/CBICA/spare_score/graph/badge.svg?token=7yk7pkydHE)](https://codecov.io/gh/CBICA/spare_score)
 ![macos tests](https://github.com/CBICA/spare_score/actions/workflows/macos-tests-3.12.yml/badge.svg)
 ![ubuntu tests](https://github.com/CBICA/spare_score/actions/workflows/ubuntu-tests-3.12.yml/badge.svg)
+![PyPI Stable](https://img.shields.io/pypi/v/spare_scores)
 
 # Compute SPARE Scores for Your Case
 

setup.py

@@ -22,7 +22,7 @@
         "pandas",
         "setuptools",
         "scikit-learn",
-        "torch<=2.3.1",
+        "torch<=2.5.1",
         "matplotlib",
         "optuna",
     ],

spare_scores/data_prep.py

@@ -1,7 +1,7 @@
 import logging
 import os
 import random
-from typing import Any, Tuple, Union
+from typing import Any, Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -16,7 +16,7 @@ def check_train(
     to_predict: str,
     verbose: int = 1,  # this needs to be removed(non used)
     pos_group: str = "",
-) -> Union[str, Tuple[pd.DataFrame, list, str]]:
+) -> Union[Tuple[pd.DataFrame, list, str], str]:
     """
     Checks training dataframe for errors.
 
@@ -71,7 +71,7 @@ def check_train(
             logging.error("At least one of the groups to classify is too small (n<10).")
             return "At least one of the groups to classify is too small (n<10)."
         elif np.min(df[to_predict].value_counts()) < 100:
-            logging.warn(
+            logging.warning(
                 "At least one of the groups to classify may be too small (n<100)."
             )
         mdl_task = "Classification"
@@ -84,7 +84,7 @@ def check_train(
             logging.error("Sample size is too small (n<10).")
             return "Sample size is too small (n<10)."
         elif len(df.index) < 100:
-            logging.warn("Sample size may be too small (n<100).")
+            logging.warning("Sample size may be too small (n<100).")
         if pos_group != "":
             logging.info(
                 'SPARE regression does not need a "pos_group". This will be ignored.'
@@ -126,7 +126,7 @@ def check_test(
     #     logging.warn('Some participants fall outside the age range of the SPARE model.')
 
     if np.sum(np.sum(pd.isna(df[meta_data["predictors"]]))) > 0:
-        logging.warn(
+        logging.warning(
             "Some participants have invalid (missing or NaN values) predictor variables."
         )
 
@@ -221,7 +221,7 @@ def smart_unique(
 def age_sex_match(
     df1: pd.DataFrame,
     df2: Union[pd.DataFrame, None] = None,
-    to_match: str = "",
+    to_match: Optional[str] = "",
     p_threshold: float = 0.15,
     verbose: int = 1,
     age_out_percentage: float = 20,

spare_scores/mlp.py

@@ -253,9 +253,7 @@ def get_stats(self, y: np.ndarray, y_hat: np.ndarray) -> None:
             self.stats["F1"].append(2 * precision * recall / (precision + recall))
         else:
             self.stats["MAE"].append(metrics.mean_absolute_error(y, y_hat))
-            self.stats["RMSE"].append(
-                metrics.mean_squared_error(y, y_hat, squared=False)
-            )
+            self.stats["RMSE"].append(metrics.root_mean_squared_error(y, y_hat))
             self.stats["R2"].append(metrics.r2_score(y, y_hat))
 
     def output_stats(self) -> None:

spare_scores/svm.py

@@ -346,9 +346,7 @@ def get_stats(self, y_test: np.ndarray, y_score: np.ndarray) -> None:
             self.stats["F1"].append(2 * precision * recall / (precision + recall))
         else:
             self.stats["MAE"].append(metrics.mean_absolute_error(y_test, y_score))
-            self.stats["RMSE"].append(
-                metrics.mean_squared_error(y_test, y_score, squared=False)
-            )
+            self.stats["RMSE"].append(metrics.root_mean_squared_error(y_test, y_score))
             self.stats["R2"].append(metrics.r2_score(y_test, y_score))
         logging.debug(
             "   > "

spare_scores/util.py

@@ -2,7 +2,7 @@
 import logging
 import os
 import pickle
-from typing import Any, Union
+from typing import Any, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -43,7 +43,7 @@ def add_file_extension(filename: str, extension: str) -> str:
     return filename
 
 
-def check_file_exists(filename: str, logger: Any) -> Any:
+def check_file_exists(filename: Optional[str], logger: Any) -> Any:
     """
     Checks if file exists