Merge branch 'master' into development

.github/workflows/ci.yaml

@@ -0,0 +1,37 @@
+name: Python package
+
+on:
+  push:
+    branches:
+      - main
+    tags: '*'
+  pull_request:
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.7", "3.8", "3.9", "3.10"]
+
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install flake8 pytest torch
+          python -m pip install .
+      - name: Lint with flake8
+        run: |
+          # stop the build if there are Python syntax errors or undefined names
+          flake8 . --exclude=manuscript --count --select=E9,F63,F7,F82 --show-source --statistics
+          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+          flake8 . --exclude=manuscript --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+      - name: Test with pytest
+        run: |
+          pytest

setup.py

@@ -40,9 +40,7 @@
         "numpy",
         "matplotlib",
         "torch",
-        "scipy",
         "attrs>=21.1.0",
-        "sklearn",
     ],
     tests_requires=["pytest", "pytest-cov"],
     extras_require={},

tests/test_integration.py

@@ -23,8 +23,8 @@ def test_quickstart():
     # build a LANTERN model based on the dataset, using an upper-bound
     # of K latent dimensions
     model = Model(
-        VariationalBasis.fromDataset(ds, 3),
-        Phenotype.fromDataset(ds, 3, Ni=50),
+        VariationalBasis.fromDataset(ds, 1),
+        Phenotype.fromDataset(ds, 1, Ni=50),
         GaussianLikelihood(),
     )
 
@@ -38,7 +38,7 @@ def test_quickstart():
     lss = loss(yhat, y)
     baseline = sum(lss.values())
 
-    for i in range(20):
+    for i in range(100):
         optimizer.zero_grad()
         yhat = model(X)
         lss = loss(yhat, y)