version

Signed-off-by: xadupre <xadupre@microsoft.com>

Author: xadupre

.github/workflows/ci.yml

@@ -87,14 +87,14 @@ jobs:
           python -c "from sklearn import __version__;print('sklearn', __version__)"
           python -c "from onnxruntime import __version__;print('onnxruntime', __version__)"
           python -c "from onnx import __version__;print('onnx', __version__)"
-          python -c "from catboost import __version__;print('catboost', __version__)"
           python -c "import onnx.defs;print('onnx_opset_version', onnx.defs.onnx_opset_version())"
   
-      - name: versions lightgbm
+      - name: versions lightgbm, xgboost, catboost
         if: matrix.os != 'macos-latest'
         run: |
           python -c "from lightgbm import __version__;print('lightgbm', __version__)"
           python -c "from xgboost import __version__;print('xgboost', __version__)"
+          python -c "from catboost import __version__;print('catboost', __version__)"
   
       - name: Run tests baseline
         run: pytest --maxfail=10 --durations=10 tests/baseline

tests/xgboost/test_xgboost_converters_base_score.py

@@ -37,7 +37,7 @@ def test_xgbregressor_sparse_base_score(self):
         expected = rf.predict(X).astype(np.float32).reshape((-1, 1))
         expected_sparse = rf.predict(X_sp).astype(np.float32).reshape((-1, 1))
         diff = np.abs(expected - expected_sparse)
-        self.assertNotEqual(diff.min(), diff.max())
+        self.assertNotEqual(diff.min(), diff.max(), atol=1e-5)
 
         onx = convert_xgboost(
             rf,
@@ -66,7 +66,7 @@ def test_xgbregressor_sparse_no_base_score(self):
         expected = rf.predict(X).astype(np.float32).reshape((-1, 1))
         expected_sparse = rf.predict(X_sp).astype(np.float32).reshape((-1, 1))
         diff = np.abs(expected - expected_sparse)
-        self.assertNotEqual(diff.min(), diff.max())
+        self.assertNotEqual(diff.min(), diff.max(), atol=1e-5)
 
         onx = convert_xgboost(
             rf,
@@ -96,7 +96,7 @@ def test_xgbclassifier_sparse_base_score(self):
         expected = rf.predict_proba(X).astype(np.float32).reshape((-1, 1))
         expected_sparse = rf.predict_proba(X_sp).astype(np.float32).reshape((-1, 1))
         diff = np.abs(expected - expected_sparse)
-        self.assertNotEqual(diff.min(), diff.max())
+        self.assertNotEqual(diff.min(), diff.max(), atol=1e-5)
 
         onx = convert_xgboost(
             rf,
@@ -126,7 +126,7 @@ def test_xgbclassifier_sparse_no_base_score(self):
         expected = rf.predict_proba(X).astype(np.float32).reshape((-1, 1))
         expected_sparse = rf.predict_proba(X_sp).astype(np.float32).reshape((-1, 1))
         diff = np.abs(expected - expected_sparse)
-        self.assertNotEqual(diff.min(), diff.max())
+        self.assertNotEqual(diff.min(), diff.max(), atol=1e-5)
 
         onx = convert_xgboost(
             rf,