zero323 commented on a change in pull request #34510:
URL: https://github.com/apache/spark/pull/34510#discussion_r746405189
##########
File path: python/pyspark/mllib/stat/test.py
##########
@@ -15,68 +15,72 @@
# limitations under the License.
#
+from typing import Generic, Tuple, TypeVar
+
from pyspark.mllib.common import inherit_doc, JavaModelWrapper
__all__ = ["ChiSqTestResult", "KolmogorovSmirnovTestResult"]
+DF = TypeVar("DF", int, float, Tuple[int, ...], Tuple[float, ...])
+
-class TestResult(JavaModelWrapper):
+class TestResult(JavaModelWrapper, Generic[DF]):
"""
Base class for all test results.
"""
@property
- def pValue(self):
+ def pValue(self) -> float:
"""
The probability of obtaining a test statistic result at least as
extreme as the one that was actually observed, assuming that the
null hypothesis is true.
"""
- return self._java_model.pValue()
+ return self._java_model.pValue() # type: ignore[attr-defined]
Review comment:
Let's not ignore this. If, at the moment, this is not exposed by
`JavaModelWrapper` is it better to add it to the current
https://github.com/apache/spark/blob/16e26049afc8ba92b06bdc58c47b211ea87e0d2b/python/pyspark/mllib/common.pyi#L30-L33
i.e.
```python
class JavaModelWrapper:
_java_model: JavaObject
...
```
Also, general advice ‒ it might be a good idea to look at the dependency
tree of modules, before picking next one to annotate. Cyclic dependencies
between annotations are rare (and non-existent in code), so it should make
remaining migration process smoother.
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