665817bd4f
### 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>
152 lines
5.1 KiB
Python
152 lines
5.1 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.
|
|
|
|
from typing import overload
|
|
from typing import Optional, Union
|
|
|
|
from pyspark.context import SparkContext
|
|
from pyspark.rdd import RDD
|
|
from pyspark.mllib._typing import VectorLike
|
|
from pyspark.mllib.linalg import Vector
|
|
from pyspark.mllib.regression import LabeledPoint, LinearModel, StreamingLinearAlgorithm
|
|
from pyspark.mllib.util import Saveable, Loader
|
|
from pyspark.streaming.dstream import DStream
|
|
|
|
from numpy import float64, ndarray # type: ignore[import]
|
|
|
|
class LinearClassificationModel(LinearModel):
|
|
def __init__(self, weights: Vector, intercept: float) -> None: ...
|
|
def setThreshold(self, value: float) -> None: ...
|
|
@property
|
|
def threshold(self) -> Optional[float]: ...
|
|
def clearThreshold(self) -> None: ...
|
|
@overload
|
|
def predict(self, test: VectorLike) -> Union[int, float, float64]: ...
|
|
@overload
|
|
def predict(self, test: RDD[VectorLike]) -> RDD[Union[int, float]]: ...
|
|
|
|
class LogisticRegressionModel(LinearClassificationModel):
|
|
def __init__(
|
|
self, weights: Vector, intercept: float, numFeatures: int, numClasses: int
|
|
) -> None: ...
|
|
@property
|
|
def numFeatures(self) -> int: ...
|
|
@property
|
|
def numClasses(self) -> int: ...
|
|
@overload
|
|
def predict(self, x: VectorLike) -> Union[int, float]: ...
|
|
@overload
|
|
def predict(self, x: RDD[VectorLike]) -> RDD[Union[int, float]]: ...
|
|
def save(self, sc: SparkContext, path: str) -> None: ...
|
|
@classmethod
|
|
def load(cls, sc: SparkContext, path: str) -> LogisticRegressionModel: ...
|
|
|
|
class LogisticRegressionWithSGD:
|
|
@classmethod
|
|
def train(
|
|
cls,
|
|
data: RDD[LabeledPoint],
|
|
iterations: int = ...,
|
|
step: float = ...,
|
|
miniBatchFraction: float = ...,
|
|
initialWeights: Optional[VectorLike] = ...,
|
|
regParam: float = ...,
|
|
regType: str = ...,
|
|
intercept: bool = ...,
|
|
validateData: bool = ...,
|
|
convergenceTol: float = ...,
|
|
) -> LogisticRegressionModel: ...
|
|
|
|
class LogisticRegressionWithLBFGS:
|
|
@classmethod
|
|
def train(
|
|
cls,
|
|
data: RDD[LabeledPoint],
|
|
iterations: int = ...,
|
|
initialWeights: Optional[VectorLike] = ...,
|
|
regParam: float = ...,
|
|
regType: str = ...,
|
|
intercept: bool = ...,
|
|
corrections: int = ...,
|
|
tolerance: float = ...,
|
|
validateData: bool = ...,
|
|
numClasses: int = ...,
|
|
) -> LogisticRegressionModel: ...
|
|
|
|
class SVMModel(LinearClassificationModel):
|
|
def __init__(self, weights: Vector, intercept: float) -> None: ...
|
|
@overload
|
|
def predict(self, x: VectorLike) -> float64: ...
|
|
@overload
|
|
def predict(self, x: RDD[VectorLike]) -> RDD[float64]: ...
|
|
def save(self, sc: SparkContext, path: str) -> None: ...
|
|
@classmethod
|
|
def load(cls, sc: SparkContext, path: str) -> SVMModel: ...
|
|
|
|
class SVMWithSGD:
|
|
@classmethod
|
|
def train(
|
|
cls,
|
|
data: RDD[LabeledPoint],
|
|
iterations: int = ...,
|
|
step: float = ...,
|
|
regParam: float = ...,
|
|
miniBatchFraction: float = ...,
|
|
initialWeights: Optional[VectorLike] = ...,
|
|
regType: str = ...,
|
|
intercept: bool = ...,
|
|
validateData: bool = ...,
|
|
convergenceTol: float = ...,
|
|
) -> SVMModel: ...
|
|
|
|
class NaiveBayesModel(Saveable, Loader[NaiveBayesModel]):
|
|
labels: ndarray
|
|
pi: ndarray
|
|
theta: ndarray
|
|
def __init__(self, labels: ndarray, pi: ndarray, theta: ndarray) -> None: ...
|
|
@overload
|
|
def predict(self, x: VectorLike) -> float64: ...
|
|
@overload
|
|
def predict(self, x: RDD[VectorLike]) -> RDD[float64]: ...
|
|
def save(self, sc: SparkContext, path: str) -> None: ...
|
|
@classmethod
|
|
def load(cls, sc: SparkContext, path: str) -> NaiveBayesModel: ...
|
|
|
|
class NaiveBayes:
|
|
@classmethod
|
|
def train(cls, data: RDD[VectorLike], lambda_: float = ...) -> NaiveBayesModel: ...
|
|
|
|
class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm):
|
|
stepSize: float
|
|
numIterations: int
|
|
regParam: float
|
|
miniBatchFraction: float
|
|
convergenceTol: float
|
|
def __init__(
|
|
self,
|
|
stepSize: float = ...,
|
|
numIterations: int = ...,
|
|
miniBatchFraction: float = ...,
|
|
regParam: float = ...,
|
|
convergenceTol: float = ...,
|
|
) -> None: ...
|
|
def setInitialWeights(
|
|
self, initialWeights: VectorLike
|
|
) -> StreamingLogisticRegressionWithSGD: ...
|
|
def trainOn(self, dstream: DStream[LabeledPoint]) -> None: ...
|