Merge pull request #26 from spirosmaggioros/fixing_issues

Fixing python 3.12 issues for MacOS

.github/workflows/macos-tests-3.12.yml

@@ -0,0 +1,24 @@
+name: macos build
+
+# workflow dispatch has been added for testing purposes
+on: [push, pull_request, workflow_dispatch]
+
+jobs:
+  build:
+    runs-on: ["macos-13"]
+
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+      - name: Install spare scores
+        run: |
+          python -m pip cache purge
+          pip install -r requirements.txt
+          pip install setuptools twine wheel
+          python -m pip install .
+      - name: Run unit tests
+        run: |
+          pip install pytest pytest-cov
+          cd tests/unit && pytest --cov=../../

.github/workflows/macos_test_cases_p3-8.yml → .github/workflows/macos-tests-3.8.yml

@@ -1,4 +1,4 @@
-name: spare_scores test cases on macos for python 3.8
+name: macos tests python=3.8
 
 # workflow dispatch has been added for testing purposes
 on: [push, pull_request, workflow_dispatch]

.github/workflows/macos_test_cases_p3-12.yml → .github/workflows/ubuntu-tests-3.12.yml

@@ -1,29 +1,21 @@
-name: spare_scores test cases on macos for python 3.12
+name: ubuntu build
 
 # workflow dispatch has been added for testing purposes
 on: [push, pull_request, workflow_dispatch]
 
 jobs:
   build:
-    runs-on: ["macos-13"]
+    runs-on: ["ubuntu-latest"]
 
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
           python-version: "3.12"
-      - name: Set-up miniconda for macos and ubuntu
-        uses: conda-incubator/setup-miniconda@v2
-        with:
-          auto-update-conda: true
-          python-version: 3.12
-          miniconda-version: "latest"
-      - name: Create conda env
-        run: conda create -n spare python=3.12
-      - name: Install pip
-        run: conda run -n spare conda install pip
       - name: Install spare scores
         run: |
+          python -m pip cache purge
+          pip install -r requirements.txt
           pip install setuptools twine wheel
           python -m pip install .
       - name: Run unit tests

.github/workflows/ubuntu_test_cases_p3-8.yml → .github/workflows/ubuntu-tests-3.8.yml

@@ -1,4 +1,4 @@
-name: spare_scores test cases on ubuntu for python 3.8
+name: ubuntu build python=3.8
 
 # workflow dispatch has been added for testing purposes
 on: [push, pull_request, workflow_dispatch]
@@ -12,25 +12,15 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: "3.8"
-      - name: Set-up miniconda for macos and ubuntu
-        uses: conda-incubator/setup-miniconda@v2
-        with:
-          auto-update-conda: true
-          python-version: 3.8
-          miniconda-version: "latest"
-      - name: Create conda env
-        run: conda create -n spare python=3.8
-      - name: Install pip
-        run: conda run -n spare conda install pip
       - name: Install spare scores
         run: |
+          python -m pip cache purge
+          pip install -r requirements.txt
           pip install setuptools twine wheel
           python setup.py bdist_wheel
           cd dist
           WHEEL_FILE=$(ls spare_scores*)
           pip install "$WHEEL_FILE"
-      - name: Download dependencies
-        run: pip install setuptools && pip install .
       - name: Run unit tests
         run: |
           cd tests/unit && python -m unittest discover -s . -p "*.py"

README.md

@@ -1,4 +1,6 @@
 [![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)
 
 # Compute SPARE Scores for Your Case
 
@@ -38,9 +40,9 @@ Please open an issue if you find any bugs for the newer versions of spare_score
     # for python 3.12
     git clone https://github.com/CBICA/spare_score.git
     cd spare_score
-    python -m pip install . 
+    python -m pip install .
 
-    # for python 3.8...
+    # for python 3.8
     python setup.py bdist_wheel
     cd dist && pip install ...
 

dev-dependencies.txt → requirements.txt

@@ -16,11 +16,11 @@ iniconfig==2.0.0
 joblib==1.3.1
 jsonschema==4.17.3
 kiwisolver==1.4.4
-matplotlib==3.9.0
+matplotlib==3.7.5
 msgpack==1.0.5
-numpy==1.22.0
+numpy
 packaging==23.1
-pandas==2.2.0
+pandas==2.0.3
 Pillow==9.5.0
 pkgutil_resolve_name==1.3.10
 pluggy==1.5.0
@@ -31,14 +31,13 @@ pytest==8.2.2
 python-dateutil==2.8.2
 pytz==2023.3
 PyYAML==6.0.2
-ray==2.34.0
 requests==2.31.0
-scikit-learn==1.0.1
-scipy==1.14.0
+scikit-learn==1.3.2
+scipy
 six==1.16.0
 threadpoolctl==3.1.0
 tomli==2.0.1
-torch==2.2.0
+torch==2.2.1
 torchvision==0.17.1
 typing_extensions==4.8.0
 tzdata==2023.3

setup.cfg

@@ -17,7 +17,7 @@ per-file-ignores =
     __init__.py: F401, F403
 
 [mypy]
-exclude= tests
+exclude = tests
 # Import discovery
 namespace_packages = False
 ignore_missing_imports = True

spare_scores/mlp.py

@@ -100,7 +100,7 @@ def __init__(
                 "mlp__alpha": [0.001, 0.01, 0.05, 0.1],
                 "mlp__learning_rate": ["constant", "adaptive"],
                 "mlp__early_stopping": [True],
-                "mlp__max_iter": [500],
+                "mlp__max_iter": [1000],
             }
 
     def set_parameters(self, **parameters: Any) -> None:
@@ -113,11 +113,11 @@ def _fit(self, df: pd.DataFrame) -> None:
         y = df[self.to_predict].astype("float64")
 
         if self.task == "Regression":
-            mlp = MLPRegressor(early_stopping=True, max_iter=500)
+            mlp = MLPRegressor(early_stopping=True, max_iter=1000)
             scoring = "neg_mean_absolute_error"
             metrics = ["MAE", "RMSE", "R2"]
         else:
-            mlp = MLPClassifier(early_stopping=True, max_iter=500)
+            mlp = MLPClassifier(early_stopping=True, max_iter=1000)
             scoring = "balanced_accuracy"
             metrics = [
                 "AUC",

spare_scores/mlp_torch.py

@@ -49,7 +49,7 @@ class MLPDataset(Dataset):
 
     """
 
-    def __init__(self, X: list, y: list):
+    def __init__(self, X: list, y: list) -> None:
         self.X = np.array(X, dtype=np.float32)
         self.y = np.array(y, dtype=np.float32)
 
@@ -59,13 +59,15 @@ def __len__(self) -> int:
         """
         return len(self.y)
 
-    def __getitem__(self, idx: int) -> Tuple[np.float32, np.float32]:
+    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
         """
         (getter)returns the index of both X and y at index: idx(X[idx], y[idx])
         :param idx: the index
         :type idx: int
         """
-        return self.X[idx], self.y[idx]
+        return torch.tensor(self.X[idx], dtype=torch.float32), torch.tensor(
+            self.y[idx], dtype=torch.float32
+        )
 
 
 class SimpleMLP(nn.Module):
@@ -213,12 +215,12 @@ def __init__(
 
         # Model settings
         self.classification = True if self.task == "Classification" else False
-        self.mdl: Any
-        self.scaler: Any
-        self.stats: Any
-        self.param: Any
-        self.train_dl: Any
-        self.val_dl: Any
+        self.mdl: SimpleMLP
+        self.scaler: StandardScaler
+        self.stats: dict
+        self.param: dict
+        self.train_dl: DataLoader
+        self.val_dl: DataLoader
 
     def find_best_threshold(self, y_hat: list, y: list) -> Any:
         """
@@ -230,7 +232,7 @@ def find_best_threshold(self, y_hat: list, y: list) -> Any:
         :type y: list
 
         :return: the best threshold value
-        :rtype: Any
+        :rtype: List
 
         """
 

spare_scores/svm.py

@@ -249,7 +249,7 @@ def train_initialize(self, df: pd.DataFrame, to_predict: str) -> None:
             )
             self.to_predict, self.classify = to_predict, list(df[to_predict].unique())
             self.mdl = (
-                [LinearSVC(max_iter=100000, dual="auto")]
+                [LinearSVC(max_iter=100000, dual=False)]
                 if self.kernel == "linear"
                 else [SVC(max_iter=100000, kernel=self.kernel)]
             ) * len(self.folds)
@@ -260,7 +260,11 @@ def train_initialize(self, df: pd.DataFrame, to_predict: str) -> None:
                 ["MAE", "RMSE", "R2"],
             )
             self.to_predict, self.classify = to_predict, None
-            self.mdl = [LinearSVR(max_iter=100000, dual="auto")] * len(self.folds)
+            self.mdl = [
+                LinearSVR(
+                    max_iter=100000, loss="squared_epsilon_insensitive", dual=False
+                )
+            ] * len(self.folds)
             self.bias_correct = {
                 "slope": np.zeros((len(self.folds),)),
                 "int": np.zeros((len(self.folds),)),
@@ -320,6 +324,7 @@ def param_search(
             scoring=scoring,
             cv=self.k,
             return_train_score=True,
+            error_score=0.0,
             verbose=0,
         )
         gs.fit(X_train, y_train)