debug macos tests (#291)

* debug macos tests * update workflows * update tree_method * fix covnet test issue introduced * set macosx deployment target = 10.13 * clean up
vanderschaarlab · Sep 13, 2024 · a856d6b · a856d6b
1 parent abae817
commit a856d6b
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Showing 5 changed files with 26 additions and 6 deletions.
diff --git a/.github/workflows/test_pr.yml b/.github/workflows/test_pr.yml
@@ -12,7 +12,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.11"]
         os: [ubuntu-latest]
     steps:
       - uses: actions/checkout@v3
@@ -36,7 +36,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.11"]
         os: [macos-latest, ubuntu-latest, windows-latest]
     steps:
       - uses: actions/checkout@v3
@@ -47,19 +47,31 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           cache: "pip" # caching pip dependencies
-      - name: Install MacOS dependencies
+      - name: Set libomp environment variables (macOS only)
         run: |
           brew install libomp
-        if: ${{ matrix.os == 'macos-latest' }}
+          LIBOMP_PATH="/opt/homebrew/opt/libomp"
+          echo "LDFLAGS=-L${LIBOMP_PATH}/lib" >> $GITHUB_ENV
+          echo "CPPFLAGS=-I${LIBOMP_PATH}/include" >> $GITHUB_ENV
+          echo "DYLD_LIBRARY_PATH=${LIBOMP_PATH}/lib:\$DYLD_LIBRARY_PATH" >> $GITHUB_ENV
+          echo "OMP_PATH=${LIBOMP_PATH}/include" >> $GITHUB_ENV
+        if: ${{ runner.os == 'macOS' }}
       - name: Install dependencies
         run: |
           python -m pip install -U pip
           pip install -r prereq.txt
+      - name: Limit OpenMP threads (Critical for macOS else it can hang without error)
+        run: |
+          echo "OMP_NUM_THREADS=2" >> $GITHUB_ENV
       - name: Test Core
         run: |
           pip install .[testing]
           pip freeze
           pytest -vvvsx -m "not slow" --durations=50
+      - name: Set macOS deployment target
+        run: |
+          echo "MACOSX_DEPLOYMENT_TARGET=10.13" >> $GITHUB_ENV
+        if: ${{ runner.os == 'macOS' }}
       - name: Test GOGGLE
         run: |
           pip install .[testing,goggle]

diff --git a/src/synthcity/metrics/eval_performance.py b/src/synthcity/metrics/eval_performance.py
@@ -1064,6 +1064,7 @@ def evaluate(
 
         elif self._task_type == "classification":
             model = XGBClassifier(
+                tree_method="approx",
                 n_jobs=2,
                 verbosity=0,
                 depth=3,

diff --git a/src/synthcity/plugins/survival_analysis/_survival_pipeline.py b/src/synthcity/plugins/survival_analysis/_survival_pipeline.py
@@ -153,6 +153,7 @@ def _fit(
             raise ValueError(f"unsupported strategy {self.strategy}")
 
         xgb_params = {
+            "tree_method": "approx",
             "n_jobs": 2,
             "verbosity": 0,
             "depth": 3,

diff --git a/src/synthcity/utils/compression.py b/src/synthcity/utils/compression.py
@@ -45,7 +45,12 @@ def compress_dataset(
         y = df[column]
 
         if len(df[column].unique()) < cat_limit:
-            model = XGBClassifier()
+            model = model = XGBClassifier(
+                tree_method="approx",
+                n_jobs=2,
+                verbosity=0,
+                depth=3,
+            )
             try:
                 score = evaluate_classifier(model, X, y)["clf"]["aucroc"][0]
             except BaseException:

diff --git a/tests/metrics/test_detection.py b/tests/metrics/test_detection.py
@@ -155,10 +155,11 @@ def test_detect_synth_timeseries(test_plugin: Plugin, evaluator_t: Type) -> None
     assert evaluator.direction() == "minimize"
 
 
-@pytest.mark.skipif(sys.platform == "linux", reason="Linux only for faster results")
+@pytest.mark.skipif(sys.platform != "linux", reason="Linux only for faster results")
 @pytest.mark.slow_1
 @pytest.mark.slow
 def test_image_support_detection() -> None:
+
     dataset = datasets.MNIST(".", download=True)
 
     X1 = ImageDataLoader(dataset).sample(100)