spark-instrumented-optimizer/python/pyspark/ml/param/__init__.pyi
zero323 31a16fbb40 [SPARK-32714][PYTHON] Initial pyspark-stubs port
### What changes were proposed in this pull request?

This PR proposes migration of [`pyspark-stubs`](https://github.com/zero323/pyspark-stubs) into Spark codebase.

### Why are the changes needed?

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

Yes. This PR adds type annotations directly to Spark source.

This can impact interaction with development tools for users, which haven't used `pyspark-stubs`.

### How was this patch tested?

- [x] MyPy tests of the PySpark source
    ```
    mypy --no-incremental --config python/mypy.ini python/pyspark
    ```
- [x] MyPy tests of Spark examples
    ```
   MYPYPATH=python/ mypy --no-incremental --config python/mypy.ini examples/src/main/python/ml examples/src/main/python/sql examples/src/main/python/sql/streaming
    ```
- [x] Existing Flake8 linter

- [x] Existing unit tests

Tested against:

- `mypy==0.790+dev.e959952d9001e9713d329a2f9b196705b028f894`
- `mypy==0.782`

Closes #29591 from zero323/SPARK-32681.

Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-09-24 14:15:36 +09:00

97 lines
3.2 KiB
Python

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# 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.
import abc
from typing import overload
from typing import Any, Callable, Generic, List, Optional
from pyspark.ml._typing import T
import pyspark.ml._typing
import pyspark.ml.util
from pyspark.ml.linalg import DenseVector, Matrix
class Param(Generic[T]):
parent: str
name: str
doc: str
typeConverter: Callable[[Any], T]
def __init__(
self,
parent: pyspark.ml.util.Identifiable,
name: str,
doc: str,
typeConverter: Optional[Callable[[Any], T]] = ...,
) -> None: ...
def __hash__(self) -> int: ...
def __eq__(self, other: Any) -> bool: ...
class TypeConverters:
@staticmethod
def identity(value: T) -> T: ...
@staticmethod
def toList(value: Any) -> List: ...
@staticmethod
def toListFloat(value: Any) -> List[float]: ...
@staticmethod
def toListInt(value: Any) -> List[int]: ...
@staticmethod
def toListString(value: Any) -> List[str]: ...
@staticmethod
def toVector(value: Any) -> DenseVector: ...
@staticmethod
def toMatrix(value: Any) -> Matrix: ...
@staticmethod
def toFloat(value: Any) -> float: ...
@staticmethod
def toInt(value: Any) -> int: ...
@staticmethod
def toString(value: Any) -> str: ...
@staticmethod
def toBoolean(value: Any) -> bool: ...
class Params(pyspark.ml.util.Identifiable, metaclass=abc.ABCMeta):
def __init__(self) -> None: ...
@property
def params(self) -> List[Param]: ...
def explainParam(self, param: str) -> str: ...
def explainParams(self) -> str: ...
def getParam(self, paramName: str) -> Param: ...
@overload
def isSet(self, param: str) -> bool: ...
@overload
def isSet(self, param: Param[Any]) -> bool: ...
@overload
def hasDefault(self, param: str) -> bool: ...
@overload
def hasDefault(self, param: Param[Any]) -> bool: ...
@overload
def isDefined(self, param: str) -> bool: ...
@overload
def isDefined(self, param: Param[Any]) -> bool: ...
def hasParam(self, paramName: str) -> bool: ...
@overload
def getOrDefault(self, param: str) -> Any: ...
@overload
def getOrDefault(self, param: Param[T]) -> T: ...
def extractParamMap(
self, extra: Optional[pyspark.ml._typing.ParamMap] = ...
) -> pyspark.ml._typing.ParamMap: ...
def copy(self, extra: Optional[pyspark.ml._typing.ParamMap] = ...) -> Params: ...
def set(self, param: Param, value: Any) -> None: ...
def clear(self, param: Param) -> None: ...