Make tests fail gracefully if optional deps are missing

tests/python/hypothesis_util.py

@@ -1,7 +1,6 @@
 """Utility functions for hypothesis-based testing"""
 
 from math import ceil
-from sys import platform as _platform
 
 import numpy as np
 from hypothesis import assume

tests/python/test_gtil.py

@@ -5,25 +5,29 @@
 
 import numpy as np
 import pytest
-from hypothesis import given, settings
-from hypothesis.strategies import data as hypothesis_callback
-from hypothesis.strategies import integers, just, sampled_from
 
 import treelite
 
-from .hypothesis_util import (
-    standard_classification_datasets,
-    standard_regression_datasets,
-    standard_settings,
-)
+try:
+    from hypothesis import given, settings
+    from hypothesis.strategies import data as hypothesis_callback
+    from hypothesis.strategies import integers, just, sampled_from
+except ImportError:
+    pytest.skip("hypothesis not installed; skipping", allow_module_level=True)
 
 try:
     import xgboost as xgb
 except ImportError:
-    # skip this test suite if XGBoost is not installed
     pytest.skip("XGBoost not installed; skipping", allow_module_level=True)
 
 
+from .hypothesis_util import (
+    standard_classification_datasets,
+    standard_regression_datasets,
+    standard_settings,
+)
+
+
 @given(
     predict_kind=sampled_from(["leaf_id", "score_per_tree"]),
     dataset=standard_regression_datasets(),

tests/python/test_lightgbm_integration.py

@@ -5,33 +5,34 @@
 import numpy as np
 import pytest
 import scipy
-from hypothesis import given, settings
-from hypothesis.strategies import data as hypothesis_callback
-from hypothesis.strategies import integers, just, sampled_from
 
 import treelite
 
-from .hypothesis_util import (
-    standard_classification_datasets,
-    standard_regression_datasets,
-    standard_settings,
-)
-from .metadata import dataset_db
-from .util import TemporaryDirectory, load_txt, to_categorical
+try:
+    from hypothesis import given, settings
+    from hypothesis.strategies import data as hypothesis_callback
+    from hypothesis.strategies import integers, just, sampled_from
+except ImportError:
+    pytest.skip("hypothesis not installed; skipping", allow_module_level=True)
 
 try:
     import lightgbm as lgb
 except ImportError:
-    # skip this test suite if LightGBM is not installed
     pytest.skip("LightGBM not installed; skipping", allow_module_level=True)
 
-
 try:
     from sklearn.datasets import load_svmlight_file
 except ImportError:
-    # skip this test suite if scikit-learn is not installed
     pytest.skip("scikit-learn not installed; skipping", allow_module_level=True)
 
+from .hypothesis_util import (
+    standard_classification_datasets,
+    standard_regression_datasets,
+    standard_settings,
+)
+from .metadata import dataset_db
+from .util import TemporaryDirectory, load_txt, to_categorical
+
 
 @given(
     objective=sampled_from(["regression", "regression_l1", "huber"]),

tests/python/test_serializer.py

@@ -4,18 +4,15 @@
 
 import numpy as np
 import pytest
-from hypothesis import given, settings
-from hypothesis.strategies import data as hypothesis_callback
-from hypothesis.strategies import floats, integers, just, sampled_from
 
 import treelite
 
-from .hypothesis_util import (
-    standard_classification_datasets,
-    standard_regression_datasets,
-    standard_settings,
-)
-from .util import TemporaryDirectory
+try:
+    from hypothesis import given, settings
+    from hypothesis.strategies import data as hypothesis_callback
+    from hypothesis.strategies import floats, integers, just, sampled_from
+except ImportError:
+    pytest.skip("hypothesis not installed; skipping", allow_module_level=True)
 
 try:
     from sklearn.dummy import DummyClassifier, DummyRegressor
@@ -30,10 +27,17 @@
         RandomForestRegressor,
     )
 except ImportError:
-    # Skip this test suite if scikit-learn is not installed
     pytest.skip("scikit-learn not installed; skipping", allow_module_level=True)
 
 
+from .hypothesis_util import (
+    standard_classification_datasets,
+    standard_regression_datasets,
+    standard_settings,
+)
+from .util import TemporaryDirectory
+
+
 @given(
     clazz=sampled_from(
         [

tests/python/test_sklearn_integration.py

@@ -1,21 +1,17 @@
 """Tests for scikit-learn integration"""
 
 import numpy as np
-import pandas as pd
 import pytest
-from hypothesis import assume, given, settings
-from hypothesis.strategies import data as hypothesis_callback
-from hypothesis.strategies import floats, integers, just, sampled_from
 from packaging.version import parse as parse_version
 
 import treelite
 
-from .hypothesis_util import (
-    standard_classification_datasets,
-    standard_regression_datasets,
-    standard_settings,
-)
-from .util import has_pandas, to_categorical
+try:
+    from hypothesis import assume, given, settings
+    from hypothesis.strategies import data as hypothesis_callback
+    from hypothesis.strategies import floats, integers, just, sampled_from
+except ImportError:
+    pytest.skip("hypothesis not installed; skipping", allow_module_level=True)
 
 try:
     from sklearn import __version__ as sklearn_version
@@ -34,10 +30,20 @@
     )
     from sklearn.utils import shuffle
 except ImportError:
-    # Skip this test suite if scikit-learn is not installed
     pytest.skip("scikit-learn not installed; skipping", allow_module_level=True)
 
-pytestmark = pytest.mark.skipif(not has_pandas(), reason="Pandas required")
+try:
+    import pandas as pd
+except ImportError:
+    pytest.skip("Pandas required", allow_module_level=True)
+
+
+from .hypothesis_util import (
+    standard_classification_datasets,
+    standard_regression_datasets,
+    standard_settings,
+)
+from .util import to_categorical
 
 
 @given(

tests/python/test_xgboost_integration.py

@@ -6,12 +6,21 @@
 
 import numpy as np
 import pytest
-from hypothesis import given, settings
-from hypothesis.strategies import data as hypothesis_callback
-from hypothesis.strategies import integers, just, lists, sampled_from
 
 import treelite
 
+try:
+    from hypothesis import given, settings
+    from hypothesis.strategies import data as hypothesis_callback
+    from hypothesis.strategies import integers, just, lists, sampled_from
+except ImportError:
+    pytest.skip("hypothesis not installed; skipping", allow_module_level=True)
+
+try:
+    import xgboost as xgb
+except ImportError:
+    pytest.skip("XGBoost not installed; skipping", allow_module_level=True)
+
 from .hypothesis_util import (
     standard_classification_datasets,
     standard_multi_target_binary_classification_datasets,
@@ -20,12 +29,6 @@
 )
 from .util import TemporaryDirectory, to_categorical
 
-try:
-    import xgboost as xgb
-except ImportError:
-    # skip this test suite if XGBoost is not installed
-    pytest.skip("XGBoost not installed; skipping", allow_module_level=True)
-
 
 def generate_data_for_squared_log_error(n_targets: int = 1):
     """Generate data containing outliers."""

tests/python/util.py

@@ -9,6 +9,7 @@
 from typing import Any, Optional, Tuple, Union
 
 import numpy as np
+import pytest
 
 
 def load_txt(filename):
@@ -76,7 +77,7 @@ def to_categorical(
         Random seed or RandomState object, to control the behavior of randomness
     """
     if not has_pandas():
-        raise RuntimeError("Pandas is required for to_categorical()")
+        pytest.skip(reason="Pandas is required for to_categorical()")
     import pandas as pd
 
     features = features.copy()  # Avoid clobbering source matrix