Merge pull request #4 from gmgeorg/cleanup-1

numpy/cython fix; adding workflow for testing flake8 and pytest

.github/workflows/python-app.yml

@@ -0,0 +1,39 @@
+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python application
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: "3.10"
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8 pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --ignore=C901
+    - name: Test with pytest
+      run: |
+        pytest

pylambertw/_version.py

@@ -1,3 +1,3 @@
 """Version."""
 
-__version__ = "0.0.2"
+__version__ = "0.0.3"

pylambertw/igmm.py

@@ -216,11 +216,11 @@ def fit(self, data: np.ndarray):
         self._initialize_params(data)
 
         tau_trace = np.zeros(shape=(self.max_iter + 1, 3))
-        tau_trace[0,] = (
+        tau_trace[0,] = np.array([
             self.tau_init.loc,
             self.tau_init.scale,
             self.tau_init.lambertw_params.delta,
-        )
+        ]).reshape(1, -1)
 
         for kk in range(self.max_iter):
             current = tau_trace[kk, :]

pylambertw/mle.py

@@ -29,7 +29,8 @@
 
 
 def _dict_torch_to_series(dict_torch: Dict[str, torch.Tensor]) -> pd.Series:
-    return pd.Series({k: float(v.detach().numpy()) for k, v in dict_torch.items()})
+    """Turns a dictionary of torch tensors (float) into a pandas Series."""
+    return pd.Series({k: float(v.detach().numpy().item()) for k, v in dict_torch.items()})
 
 
 class MLE(sklearn.base.BaseEstimator, sklearn.base.TransformerMixin):
@@ -49,8 +50,7 @@ def __init__(
         """Initializes the class."""
         self.distribution_name = distribution_name
         self.distribution_constructor = (
-            distribution_constructor
-            or lwd.utils.get_distribution_constructor(self.distribution_name)
+            distribution_constructor or lwd.utils.get_distribution_constructor(self.distribution_name)
         )
         self.lambertw_type = base.LambertWType(lambertw_type)
         self.max_iter = max_iter

requirements.txt

@@ -1,11 +1,12 @@
+cython>=3.0.10
 dataclasses>=0.6
 git+https://github.com/gmgeorg/torchlambertw.git
 matplotlib>=3.3.0
 numpy>=1.0.1
 pandas>=1.0.0
 pytest>=6.1.1
 scikit-learn>=1.0.1
-scipy~=1.6.0
+scipy>=1.7.0
 seaborn>=0.11.1
 statsmodels>=0.12.0
 torch>=2.0.1