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chore: Fix compatibility tests (#2685)

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* chore: Fix compatibility tests

* DEBUGGGG

* fix: update deps, fix unit tests

* fix: FE deps

* chore: update compatibility matrix

---------

Co-authored-by: kaloster <rkalo@contractor.chanzuckerberg.com>
This commit is contained in:
Timmy Huang
2024-09-05 09:26:40 -07:00
committed by GitHub
parent 4281a8f816
commit 7bf5add6ef
10 changed files with 21475 additions and 358 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
test/unit/compute/test_est_dist.py
View File

@@ -65,13 +65,13 @@ class EstDistTest(unittest.TestCase):
# non-finites
self.assertEqual(estimate_approximate_distribution(np.array([np.nan])), XApproximateDistribution.NORMAL)
self.assertEqual(estimate_approximate_distribution(np.array([np.PINF])), XApproximateDistribution.NORMAL)
self.assertEqual(estimate_approximate_distribution(np.array([np.NINF])), XApproximateDistribution.NORMAL)
self.assertEqual(estimate_approximate_distribution(np.array([np.inf])), XApproximateDistribution.NORMAL)
self.assertEqual(estimate_approximate_distribution(np.array([np.inf])), XApproximateDistribution.NORMAL)
self.assertEqual(
estimate_approximate_distribution(np.array([np.PINF, np.NINF, 0])), XApproximateDistribution.NORMAL
estimate_approximate_distribution(np.array([np.inf, np.inf, 0])), XApproximateDistribution.NORMAL
)
self.assertEqual(
estimate_approximate_distribution(np.array([np.nan, np.PINF, np.NINF])), XApproximateDistribution.NORMAL
estimate_approximate_distribution(np.array([np.nan, np.inf, np.inf])), XApproximateDistribution.NORMAL
)
raw = np.random.exponential(scale=1000, size=(50, 3))
@@ -82,15 +82,15 @@ class EstDistTest(unittest.TestCase):
XApproximateDistribution.COUNT,
)
self.assertEqual(
estimate_approximate_distribution(put(raw, [1], [np.PINF])),
estimate_approximate_distribution(put(raw, [1], [np.inf])),
XApproximateDistribution.COUNT,
)
self.assertEqual(
estimate_approximate_distribution(put(raw, [1], [np.NINF])),
estimate_approximate_distribution(put(raw, [1], [np.inf])),
XApproximateDistribution.COUNT,
)
self.assertEqual(
estimate_approximate_distribution(put(raw, [1, 3, 88], [np.nan, np.PINF, np.NINF])),
estimate_approximate_distribution(put(raw, [1, 3, 88], [np.nan, np.inf, np.inf])),
XApproximateDistribution.COUNT,
)
self.assertEqual(
@@ -103,15 +103,15 @@ class EstDistTest(unittest.TestCase):
XApproximateDistribution.NORMAL,
)
self.assertEqual(
estimate_approximate_distribution(put(logged, [1], [np.PINF])),
estimate_approximate_distribution(put(logged, [1], [np.inf])),
XApproximateDistribution.NORMAL,
)
self.assertEqual(
estimate_approximate_distribution(put(logged, [1], [np.NINF])),
estimate_approximate_distribution(put(logged, [1], [np.inf])),
XApproximateDistribution.NORMAL,
)
self.assertEqual(
estimate_approximate_distribution(put(logged, [1, 3, 88], [np.nan, np.PINF, np.NINF])),
estimate_approximate_distribution(put(logged, [1, 3, 88], [np.nan, np.inf, np.inf])),
XApproximateDistribution.NORMAL,
)
self.assertEqual(

4
test/unit/utils/test_jsonify_strict.py
View File

@@ -16,10 +16,10 @@ class TestJsonifyStrict(unittest.TestCase):
jsonify_strict({"nan": [np.nan]})
with self.assertRaises(ValueError):
jsonify_strict({"pinf": [np.PINF]})
jsonify_strict({"pinf": [np.inf]})
with self.assertRaises(ValueError):
jsonify_strict({"ninf": [np.NINF]})
jsonify_strict({"ninf": [np.inf]})
def test_jsonify_numpy_ndarray(self):
values = {

19
test/unit/utils/test_type_conversion_utils.py
View File

@@ -42,7 +42,7 @@ class TestTypeConversionUtils(unittest.TestCase):
with self.assertRaises(TypeError):
get_schema_type_hint_from_dtype(np.dtype(dtype))
for dtype in [np.float16, np.float32, np.float64]:
for dtype in [np.float32, np.float64]:
self.assertEqual(get_schema_type_hint_from_dtype(np.dtype(dtype)), {"type": "float32"})
for dtype in [np.dtype(object), np.dtype(str)]:
@@ -123,17 +123,18 @@ int_OK_cases = [
float_OK_cases = [
{
"test_case": "float_OK_cases",
"data": data,
"expected_encoding_dtype": np.float32,
"expected_schema_hint": {"type": "float32"},
"logs": None if data.dtype != np.float64 else {"level": logging.WARNING, "output": "may lose precision"},
"logs": None if dtype == np.float32 else {"level": logging.WARNING, "output": "may lose precision"},
}
for dtype in [np.float16, np.float32, np.float64]
for dtype in [np.float32, np.float64]
for data in [
np.arange(-128, 1000, dtype=dtype),
pd.Series(np.arange(-128, 1000, dtype=dtype)),
pd.Index(np.arange(-129, 1000, dtype=dtype)),
np.array([-np.nan, np.NINF, -1, np.NZERO, 0, np.PZERO, 1, np.PINF, np.nan], dtype=dtype),
np.array([-np.nan, np.inf, -1, 0.0, 0, 0.0, 1, np.inf, np.nan], dtype=dtype),
np.array([np.finfo(dtype).min, 0, np.finfo(dtype).max], dtype=dtype),
sparse.csr_matrix((10, 100), dtype=dtype),
]
@@ -198,12 +199,13 @@ category_numeric_OK_cases = [
# numeric, no NA/NaN, float
*[
{
"test_case": "numeric, no NA/NaN, float",
"data": data,
"expected_encoding_dtype": np.float32,
"expected_schema_hint": {"type": "categorical"},
"logs": {"level": logging.WARNING, "output": "may lose precision"},
"logs": None if dtype == np.float32 else {"level": logging.WARNING, "output": "may lose precision"},
}
for dtype in [np.float16, np.float32, np.float64]
for dtype in [np.float32, np.float64]
for data in [
pd.Series(np.array([0, 1, 2], dtype=dtype), dtype="category"),
pd.Series(np.array([0, 1, 2], dtype=dtype), dtype="category").cat.remove_categories([1]),
@@ -213,10 +215,11 @@ category_numeric_OK_cases = [
# numeric, has NA-induced cast to float32
*[
{
"test_case": "numeric, has NA-induced cast to float32",
"data": data,
"expected_encoding_dtype": np.float32,
"expected_schema_hint": {"type": "categorical"},
"logs": {"level": logging.WARNING, "output": "may lose precision"},
"logs": None if dtype == np.float32 else {"level": logging.WARNING, "output": "may lose precision"},
}
for dtype in [
np.int8,
@@ -227,7 +230,6 @@ category_numeric_OK_cases = [
np.uint32,
np.int64,
np.uint64,
np.float16,
np.float32,
np.float64,
]
@@ -312,7 +314,6 @@ class TestTypeInference(unittest.TestCase, AssertNoLog):
self.assertEqual(encoding_dtype, self.expected_encoding_dtype)
self.assertEqual(schema_hint, self.expected_schema_hint)
self.assertIn(logs["output"], logger.output[0])
else:
with self.assertNoLogs(logging.getLogger(), logging.WARNING):
encoding_dtype, schema_hint = get_dtype_and_schema_of_array(self.data)