spark-instrumented-optimizer/python/pyspark/mllib/stat/_statistics.pyi
zero323 665817bd4f [SPARK-33457][PYTHON] Adjust mypy configuration
### What changes were proposed in this pull request?

This pull request:

- Adds following flags to the main mypy configuration:
  - [`strict_optional`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-strict_optional)
  - [`no_implicit_optional`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-no_implicit_optional)
  - [`disallow_untyped_defs`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-disallow_untyped_calls)

These flags are enabled only for public API and disabled for tests and internal modules.

Additionally, these PR fixes missing annotations.

### Why are the changes needed?

Primary reason to propose this changes is to use standard configuration as used by typeshed project. This will allow us to be more strict, especially when interacting with JVM code. See for example https://github.com/apache/spark/pull/29122#pullrequestreview-513112882

Additionally, it will allow us to detect cases where annotations have unintentionally omitted.

### Does this PR introduce _any_ user-facing change?

Annotations only.

### How was this patch tested?

`dev/lint-python`.

Closes #30382 from zero323/SPARK-33457.

Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-11-25 09:27:04 +09:00

70 lines
2.4 KiB
Python

#
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# to you under the Apache License, Version 2.0 (the
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# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from typing import List, Optional, overload, Union
from typing_extensions import Literal
from numpy import ndarray # type: ignore[import]
from pyspark.mllib.common import JavaModelWrapper
from pyspark.mllib.linalg import Vector, Matrix
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.stat.test import ChiSqTestResult, KolmogorovSmirnovTestResult
from pyspark.rdd import RDD
CorrelationMethod = Union[Literal["spearman"], Literal["pearson"]]
class MultivariateStatisticalSummary(JavaModelWrapper):
def mean(self) -> ndarray: ...
def variance(self) -> ndarray: ...
def count(self) -> int: ...
def numNonzeros(self) -> ndarray: ...
def max(self) -> ndarray: ...
def min(self) -> ndarray: ...
def normL1(self) -> ndarray: ...
def normL2(self) -> ndarray: ...
class Statistics:
@staticmethod
def colStats(rdd: RDD[Vector]) -> MultivariateStatisticalSummary: ...
@overload
@staticmethod
def corr(
x: RDD[Vector], *, method: Optional[CorrelationMethod] = ...
) -> Matrix: ...
@overload
@staticmethod
def corr(
x: RDD[float], y: RDD[float], method: Optional[CorrelationMethod] = ...
) -> float: ...
@overload
@staticmethod
def chiSqTest(observed: Matrix) -> ChiSqTestResult: ...
@overload
@staticmethod
def chiSqTest(
observed: Vector, expected: Optional[Vector] = ...
) -> ChiSqTestResult: ...
@overload
@staticmethod
def chiSqTest(observed: RDD[LabeledPoint]) -> List[ChiSqTestResult]: ...
@staticmethod
def kolmogorovSmirnovTest(
data: RDD[float], distName: Literal["norm"] = ..., *params: float
) -> KolmogorovSmirnovTestResult: ...