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>
826 lines
27 KiB
Python
826 lines
27 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from typing import Any, List, Optional
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from pyspark.ml._typing import JM, M, T
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import abc
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from pyspark.ml import PredictionModel, Predictor
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from pyspark.ml.base import _PredictorParams
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from pyspark.ml.param.shared import (
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HasAggregationDepth,
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HasMaxBlockSizeInMB,
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HasElasticNetParam,
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HasFeaturesCol,
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HasFitIntercept,
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HasLabelCol,
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HasLoss,
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HasMaxIter,
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HasPredictionCol,
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HasRegParam,
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HasSeed,
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HasSolver,
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HasStandardization,
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HasStepSize,
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HasTol,
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HasVarianceCol,
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HasWeightCol,
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)
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from pyspark.ml.tree import (
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_DecisionTreeModel,
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_DecisionTreeParams,
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_GBTParams,
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_RandomForestParams,
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_TreeEnsembleModel,
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_TreeRegressorParams,
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)
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from pyspark.ml.util import (
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GeneralJavaMLWritable,
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HasTrainingSummary,
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JavaMLReadable,
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JavaMLWritable,
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)
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from pyspark.ml.wrapper import (
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JavaEstimator,
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JavaModel,
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JavaPredictionModel,
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JavaPredictor,
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JavaWrapper,
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)
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from pyspark.ml.linalg import Matrix, Vector
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from pyspark.ml.param import Param
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from pyspark.sql.dataframe import DataFrame
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class Regressor(Predictor[M], _PredictorParams, metaclass=abc.ABCMeta): ...
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class RegressionModel(PredictionModel[T], _PredictorParams, metaclass=abc.ABCMeta): ...
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class _JavaRegressor(Regressor, JavaPredictor[JM], metaclass=abc.ABCMeta): ...
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class _JavaRegressionModel(
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RegressionModel, JavaPredictionModel[T], metaclass=abc.ABCMeta
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): ...
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class _LinearRegressionParams(
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_PredictorParams,
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HasRegParam,
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HasElasticNetParam,
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HasMaxIter,
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HasTol,
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HasFitIntercept,
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HasStandardization,
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HasWeightCol,
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HasSolver,
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HasAggregationDepth,
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HasLoss,
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HasMaxBlockSizeInMB,
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):
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solver: Param[str]
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loss: Param[str]
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epsilon: Param[float]
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def __init__(self, *args: Any): ...
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def getEpsilon(self) -> float: ...
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class LinearRegression(
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_JavaRegressor[LinearRegressionModel],
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_LinearRegressionParams,
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JavaMLWritable,
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JavaMLReadable[LinearRegression],
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):
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def __init__(
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self,
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*,
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featuresCol: str = ...,
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labelCol: str = ...,
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predictionCol: str = ...,
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maxIter: int = ...,
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regParam: float = ...,
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elasticNetParam: float = ...,
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tol: float = ...,
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fitIntercept: bool = ...,
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standardization: bool = ...,
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solver: str = ...,
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weightCol: Optional[str] = ...,
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aggregationDepth: int = ...,
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epsilon: float = ...,
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maxBlockSizeInMB: float = ...
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) -> None: ...
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def setParams(
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self,
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*,
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featuresCol: str = ...,
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labelCol: str = ...,
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predictionCol: str = ...,
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maxIter: int = ...,
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regParam: float = ...,
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elasticNetParam: float = ...,
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tol: float = ...,
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fitIntercept: bool = ...,
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standardization: bool = ...,
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solver: str = ...,
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weightCol: Optional[str] = ...,
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aggregationDepth: int = ...,
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epsilon: float = ...,
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maxBlockSizeInMB: float = ...
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) -> LinearRegression: ...
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def setEpsilon(self, value: float) -> LinearRegression: ...
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def setMaxIter(self, value: int) -> LinearRegression: ...
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def setRegParam(self, value: float) -> LinearRegression: ...
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def setTol(self, value: float) -> LinearRegression: ...
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def setElasticNetParam(self, value: float) -> LinearRegression: ...
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def setFitIntercept(self, value: bool) -> LinearRegression: ...
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def setStandardization(self, value: bool) -> LinearRegression: ...
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def setWeightCol(self, value: str) -> LinearRegression: ...
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def setSolver(self, value: str) -> LinearRegression: ...
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def setAggregationDepth(self, value: int) -> LinearRegression: ...
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def setLoss(self, value: str) -> LinearRegression: ...
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def setMaxBlockSizeInMB(self, value: float) -> LinearRegression: ...
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class LinearRegressionModel(
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_JavaRegressionModel[Vector],
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_LinearRegressionParams,
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GeneralJavaMLWritable,
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JavaMLReadable[LinearRegressionModel],
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HasTrainingSummary[LinearRegressionSummary],
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):
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@property
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def coefficients(self) -> Vector: ...
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@property
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def intercept(self) -> float: ...
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@property
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def summary(self) -> LinearRegressionTrainingSummary: ...
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def evaluate(self, dataset: DataFrame) -> LinearRegressionSummary: ...
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class LinearRegressionSummary(JavaWrapper):
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@property
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def predictions(self) -> DataFrame: ...
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@property
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def predictionCol(self) -> str: ...
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@property
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def labelCol(self) -> str: ...
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@property
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def featuresCol(self) -> str: ...
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@property
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def explainedVariance(self) -> float: ...
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@property
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def meanAbsoluteError(self) -> float: ...
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@property
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def meanSquaredError(self) -> float: ...
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@property
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def rootMeanSquaredError(self) -> float: ...
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@property
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def r2(self) -> float: ...
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@property
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def r2adj(self) -> float: ...
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@property
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def residuals(self) -> DataFrame: ...
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@property
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def numInstances(self) -> int: ...
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@property
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def devianceResiduals(self) -> List[float]: ...
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@property
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def coefficientStandardErrors(self) -> List[float]: ...
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@property
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def tValues(self) -> List[float]: ...
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@property
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def pValues(self) -> List[float]: ...
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class LinearRegressionTrainingSummary(LinearRegressionSummary):
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@property
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def objectiveHistory(self) -> List[float]: ...
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@property
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def totalIterations(self) -> int: ...
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class _IsotonicRegressionParams(
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HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol
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):
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isotonic: Param[bool]
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featureIndex: Param[int]
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def getIsotonic(self) -> bool: ...
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def getFeatureIndex(self) -> int: ...
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class IsotonicRegression(
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JavaEstimator[IsotonicRegressionModel],
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_IsotonicRegressionParams,
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HasWeightCol,
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JavaMLWritable,
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JavaMLReadable[IsotonicRegression],
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):
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def __init__(
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self,
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*,
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featuresCol: str = ...,
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labelCol: str = ...,
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predictionCol: str = ...,
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weightCol: Optional[str] = ...,
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isotonic: bool = ...,
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featureIndex: int = ...
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) -> None: ...
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def setParams(
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self,
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*,
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featuresCol: str = ...,
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labelCol: str = ...,
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predictionCol: str = ...,
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weightCol: Optional[str] = ...,
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isotonic: bool = ...,
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featureIndex: int = ...
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) -> IsotonicRegression: ...
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def setIsotonic(self, value: bool) -> IsotonicRegression: ...
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def setFeatureIndex(self, value: int) -> IsotonicRegression: ...
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def setFeaturesCol(self, value: str) -> IsotonicRegression: ...
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def setPredictionCol(self, value: str) -> IsotonicRegression: ...
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def setLabelCol(self, value: str) -> IsotonicRegression: ...
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def setWeightCol(self, value: str) -> IsotonicRegression: ...
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class IsotonicRegressionModel(
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JavaModel,
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_IsotonicRegressionParams,
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JavaMLWritable,
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JavaMLReadable[IsotonicRegressionModel],
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):
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def setFeaturesCol(self, value: str) -> IsotonicRegressionModel: ...
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def setPredictionCol(self, value: str) -> IsotonicRegressionModel: ...
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def setFeatureIndex(self, value: int) -> IsotonicRegressionModel: ...
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@property
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def boundaries(self) -> Vector: ...
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@property
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def predictions(self) -> Vector: ...
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@property
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def numFeatures(self) -> int: ...
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def predict(self, value: float) -> float: ...
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class _DecisionTreeRegressorParams(
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_DecisionTreeParams, _TreeRegressorParams, HasVarianceCol
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):
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def __init__(self, *args: Any): ...
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class DecisionTreeRegressor(
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_JavaRegressor[DecisionTreeRegressionModel],
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_DecisionTreeRegressorParams,
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JavaMLWritable,
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JavaMLReadable[DecisionTreeRegressor],
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):
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def __init__(
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self,
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*,
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featuresCol: str = ...,
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labelCol: str = ...,
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predictionCol: str = ...,
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maxDepth: int = ...,
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maxBins: int = ...,
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minInstancesPerNode: int = ...,
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minInfoGain: float = ...,
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maxMemoryInMB: int = ...,
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cacheNodeIds: bool = ...,
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checkpointInterval: int = ...,
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impurity: str = ...,
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seed: Optional[int] = ...,
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varianceCol: Optional[str] = ...,
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weightCol: Optional[str] = ...,
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leafCol: str = ...,
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minWeightFractionPerNode: float = ...
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) -> None: ...
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def setParams(
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self,
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*,
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featuresCol: str = ...,
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labelCol: str = ...,
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predictionCol: str = ...,
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maxDepth: int = ...,
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maxBins: int = ...,
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minInstancesPerNode: int = ...,
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minInfoGain: float = ...,
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maxMemoryInMB: int = ...,
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cacheNodeIds: bool = ...,
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checkpointInterval: int = ...,
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impurity: str = ...,
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seed: Optional[int] = ...,
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varianceCol: Optional[str] = ...,
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weightCol: Optional[str] = ...,
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leafCol: str = ...,
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minWeightFractionPerNode: float = ...
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) -> DecisionTreeRegressor: ...
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def setMaxDepth(self, value: int) -> DecisionTreeRegressor: ...
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def setMaxBins(self, value: int) -> DecisionTreeRegressor: ...
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def setMinInstancesPerNode(self, value: int) -> DecisionTreeRegressor: ...
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def setMinWeightFractionPerNode(self, value: float) -> DecisionTreeRegressor: ...
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def setMinInfoGain(self, value: float) -> DecisionTreeRegressor: ...
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def setMaxMemoryInMB(self, value: int) -> DecisionTreeRegressor: ...
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def setCacheNodeIds(self, value: bool) -> DecisionTreeRegressor: ...
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def setImpurity(self, value: str) -> DecisionTreeRegressor: ...
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def setCheckpointInterval(self, value: int) -> DecisionTreeRegressor: ...
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def setSeed(self, value: int) -> DecisionTreeRegressor: ...
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def setWeightCol(self, value: str) -> DecisionTreeRegressor: ...
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def setVarianceCol(self, value: str) -> DecisionTreeRegressor: ...
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class DecisionTreeRegressionModel(
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_JavaRegressionModel[Vector],
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_DecisionTreeModel,
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_DecisionTreeRegressorParams,
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JavaMLWritable,
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JavaMLReadable[DecisionTreeRegressionModel],
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):
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def setVarianceCol(self, value: str) -> DecisionTreeRegressionModel: ...
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@property
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def featureImportances(self) -> Vector: ...
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class _RandomForestRegressorParams(_RandomForestParams, _TreeRegressorParams):
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def __init__(self, *args: Any): ...
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class RandomForestRegressor(
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_JavaRegressor[RandomForestRegressionModel],
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_RandomForestRegressorParams,
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JavaMLWritable,
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JavaMLReadable[RandomForestRegressor],
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):
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def __init__(
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self,
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*,
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featuresCol: str = ...,
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labelCol: str = ...,
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predictionCol: str = ...,
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maxDepth: int = ...,
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maxBins: int = ...,
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minInstancesPerNode: int = ...,
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minInfoGain: float = ...,
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maxMemoryInMB: int = ...,
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cacheNodeIds: bool = ...,
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checkpointInterval: int = ...,
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impurity: str = ...,
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subsamplingRate: float = ...,
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seed: Optional[int] = ...,
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numTrees: int = ...,
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featureSubsetStrategy: str = ...,
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leafCol: str = ...,
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minWeightFractionPerNode: float = ...,
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weightCol: Optional[str] = ...,
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bootstrap: Optional[bool] = ...
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) -> None: ...
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def setParams(
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self,
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*,
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featuresCol: str = ...,
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labelCol: str = ...,
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predictionCol: str = ...,
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maxDepth: int = ...,
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maxBins: int = ...,
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minInstancesPerNode: int = ...,
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minInfoGain: float = ...,
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maxMemoryInMB: int = ...,
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cacheNodeIds: bool = ...,
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checkpointInterval: int = ...,
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impurity: str = ...,
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subsamplingRate: float = ...,
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seed: Optional[int] = ...,
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numTrees: int = ...,
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featureSubsetStrategy: str = ...,
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leafCol: str = ...,
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minWeightFractionPerNode: float = ...,
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weightCol: Optional[str] = ...,
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bootstrap: Optional[bool] = ...
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) -> RandomForestRegressor: ...
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def setMaxDepth(self, value: int) -> RandomForestRegressor: ...
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def setMaxBins(self, value: int) -> RandomForestRegressor: ...
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def setMinInstancesPerNode(self, value: int) -> RandomForestRegressor: ...
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def setMinInfoGain(self, value: float) -> RandomForestRegressor: ...
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def setMaxMemoryInMB(self, value: int) -> RandomForestRegressor: ...
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def setCacheNodeIds(self, value: bool) -> RandomForestRegressor: ...
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def setImpurity(self, value: str) -> RandomForestRegressor: ...
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def setNumTrees(self, value: int) -> RandomForestRegressor: ...
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def setBootstrap(self, value: bool) -> RandomForestRegressor: ...
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def setSubsamplingRate(self, value: float) -> RandomForestRegressor: ...
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def setFeatureSubsetStrategy(self, value: str) -> RandomForestRegressor: ...
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def setCheckpointInterval(self, value: int) -> RandomForestRegressor: ...
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def setSeed(self, value: int) -> RandomForestRegressor: ...
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def setWeightCol(self, value: str) -> RandomForestRegressor: ...
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def setMinWeightFractionPerNode(self, value: float) -> RandomForestRegressor: ...
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class RandomForestRegressionModel(
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_JavaRegressionModel[Vector],
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_TreeEnsembleModel,
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_RandomForestRegressorParams,
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JavaMLWritable,
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JavaMLReadable[RandomForestRegressionModel],
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):
|
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@property
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def trees(self) -> List[DecisionTreeRegressionModel]: ...
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@property
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def featureImportances(self) -> Vector: ...
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class _GBTRegressorParams(_GBTParams, _TreeRegressorParams):
|
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supportedLossTypes: List[str]
|
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lossType: Param[str]
|
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def __init__(self, *args: Any): ...
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def getLossType(self) -> str: ...
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class GBTRegressor(
|
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_JavaRegressor[GBTRegressionModel],
|
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_GBTRegressorParams,
|
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JavaMLWritable,
|
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JavaMLReadable[GBTRegressor],
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):
|
|
def __init__(
|
|
self,
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|
*,
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|
featuresCol: str = ...,
|
|
labelCol: str = ...,
|
|
predictionCol: str = ...,
|
|
maxDepth: int = ...,
|
|
maxBins: int = ...,
|
|
minInstancesPerNode: int = ...,
|
|
minInfoGain: float = ...,
|
|
maxMemoryInMB: int = ...,
|
|
cacheNodeIds: bool = ...,
|
|
subsamplingRate: float = ...,
|
|
checkpointInterval: int = ...,
|
|
lossType: str = ...,
|
|
maxIter: int = ...,
|
|
stepSize: float = ...,
|
|
seed: Optional[int] = ...,
|
|
impurity: str = ...,
|
|
featureSubsetStrategy: str = ...,
|
|
validationTol: float = ...,
|
|
validationIndicatorCol: Optional[str] = ...,
|
|
leafCol: str = ...,
|
|
minWeightFractionPerNode: float = ...,
|
|
weightCol: Optional[str] = ...
|
|
) -> None: ...
|
|
def setParams(
|
|
self,
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|
*,
|
|
featuresCol: str = ...,
|
|
labelCol: str = ...,
|
|
predictionCol: str = ...,
|
|
maxDepth: int = ...,
|
|
maxBins: int = ...,
|
|
minInstancesPerNode: int = ...,
|
|
minInfoGain: float = ...,
|
|
maxMemoryInMB: int = ...,
|
|
cacheNodeIds: bool = ...,
|
|
subsamplingRate: float = ...,
|
|
checkpointInterval: int = ...,
|
|
lossType: str = ...,
|
|
maxIter: int = ...,
|
|
stepSize: float = ...,
|
|
seed: Optional[int] = ...,
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|
impuriy: str = ...,
|
|
featureSubsetStrategy: str = ...,
|
|
validationTol: float = ...,
|
|
validationIndicatorCol: Optional[str] = ...,
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|
leafCol: str = ...,
|
|
minWeightFractionPerNode: float = ...,
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|
weightCol: Optional[str] = ...
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|
) -> GBTRegressor: ...
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def setMaxDepth(self, value: int) -> GBTRegressor: ...
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|
def setMaxBins(self, value: int) -> GBTRegressor: ...
|
|
def setMinInstancesPerNode(self, value: int) -> GBTRegressor: ...
|
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def setMinInfoGain(self, value: float) -> GBTRegressor: ...
|
|
def setMaxMemoryInMB(self, value: int) -> GBTRegressor: ...
|
|
def setCacheNodeIds(self, value: bool) -> GBTRegressor: ...
|
|
def setImpurity(self, value: str) -> GBTRegressor: ...
|
|
def setLossType(self, value: str) -> GBTRegressor: ...
|
|
def setSubsamplingRate(self, value: float) -> GBTRegressor: ...
|
|
def setFeatureSubsetStrategy(self, value: str) -> GBTRegressor: ...
|
|
def setValidationIndicatorCol(self, value: str) -> GBTRegressor: ...
|
|
def setMaxIter(self, value: int) -> GBTRegressor: ...
|
|
def setCheckpointInterval(self, value: int) -> GBTRegressor: ...
|
|
def setSeed(self, value: int) -> GBTRegressor: ...
|
|
def setStepSize(self, value: float) -> GBTRegressor: ...
|
|
def setWeightCol(self, value: str) -> GBTRegressor: ...
|
|
def setMinWeightFractionPerNode(self, value: float) -> GBTRegressor: ...
|
|
|
|
class GBTRegressionModel(
|
|
_JavaRegressionModel[Vector],
|
|
_TreeEnsembleModel,
|
|
_GBTRegressorParams,
|
|
JavaMLWritable,
|
|
JavaMLReadable[GBTRegressionModel],
|
|
):
|
|
@property
|
|
def featureImportances(self) -> Vector: ...
|
|
@property
|
|
def trees(self) -> List[DecisionTreeRegressionModel]: ...
|
|
def evaluateEachIteration(self, dataset: DataFrame, loss: str) -> List[float]: ...
|
|
|
|
class _AFTSurvivalRegressionParams(
|
|
_PredictorParams,
|
|
HasMaxIter,
|
|
HasTol,
|
|
HasFitIntercept,
|
|
HasAggregationDepth,
|
|
HasMaxBlockSizeInMB,
|
|
):
|
|
censorCol: Param[str]
|
|
quantileProbabilities: Param[List[float]]
|
|
quantilesCol: Param[str]
|
|
def __init__(self, *args: Any): ...
|
|
def getCensorCol(self) -> str: ...
|
|
def getQuantileProbabilities(self) -> List[float]: ...
|
|
def getQuantilesCol(self) -> str: ...
|
|
|
|
class AFTSurvivalRegression(
|
|
_JavaRegressor[AFTSurvivalRegressionModel],
|
|
_AFTSurvivalRegressionParams,
|
|
JavaMLWritable,
|
|
JavaMLReadable[AFTSurvivalRegression],
|
|
):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
featuresCol: str = ...,
|
|
labelCol: str = ...,
|
|
predictionCol: str = ...,
|
|
fitIntercept: bool = ...,
|
|
maxIter: int = ...,
|
|
tol: float = ...,
|
|
censorCol: str = ...,
|
|
quantileProbabilities: List[float] = ...,
|
|
quantilesCol: Optional[str] = ...,
|
|
aggregationDepth: int = ...,
|
|
maxBlockSizeInMB: float = ...
|
|
) -> None: ...
|
|
def setParams(
|
|
self,
|
|
*,
|
|
featuresCol: str = ...,
|
|
labelCol: str = ...,
|
|
predictionCol: str = ...,
|
|
fitIntercept: bool = ...,
|
|
maxIter: int = ...,
|
|
tol: float = ...,
|
|
censorCol: str = ...,
|
|
quantileProbabilities: List[float] = ...,
|
|
quantilesCol: Optional[str] = ...,
|
|
aggregationDepth: int = ...,
|
|
maxBlockSizeInMB: float = ...
|
|
) -> AFTSurvivalRegression: ...
|
|
def setCensorCol(self, value: str) -> AFTSurvivalRegression: ...
|
|
def setQuantileProbabilities(self, value: List[float]) -> AFTSurvivalRegression: ...
|
|
def setQuantilesCol(self, value: str) -> AFTSurvivalRegression: ...
|
|
def setMaxIter(self, value: int) -> AFTSurvivalRegression: ...
|
|
def setTol(self, value: float) -> AFTSurvivalRegression: ...
|
|
def setFitIntercept(self, value: bool) -> AFTSurvivalRegression: ...
|
|
def setAggregationDepth(self, value: int) -> AFTSurvivalRegression: ...
|
|
def setMaxBlockSizeInMB(self, value: float) -> AFTSurvivalRegression: ...
|
|
|
|
class AFTSurvivalRegressionModel(
|
|
_JavaRegressionModel[Vector],
|
|
_AFTSurvivalRegressionParams,
|
|
JavaMLWritable,
|
|
JavaMLReadable[AFTSurvivalRegressionModel],
|
|
):
|
|
def setQuantileProbabilities(
|
|
self, value: List[float]
|
|
) -> AFTSurvivalRegressionModel: ...
|
|
def setQuantilesCol(self, value: str) -> AFTSurvivalRegressionModel: ...
|
|
@property
|
|
def coefficients(self) -> Vector: ...
|
|
@property
|
|
def intercept(self) -> float: ...
|
|
@property
|
|
def scale(self) -> float: ...
|
|
def predictQuantiles(self, features: Vector) -> Vector: ...
|
|
def predict(self, features: Vector) -> float: ...
|
|
|
|
class _GeneralizedLinearRegressionParams(
|
|
_PredictorParams,
|
|
HasFitIntercept,
|
|
HasMaxIter,
|
|
HasTol,
|
|
HasRegParam,
|
|
HasWeightCol,
|
|
HasSolver,
|
|
HasAggregationDepth,
|
|
):
|
|
family: Param[str]
|
|
link: Param[str]
|
|
linkPredictionCol: Param[str]
|
|
variancePower: Param[float]
|
|
linkPower: Param[float]
|
|
solver: Param[str]
|
|
offsetCol: Param[str]
|
|
def __init__(self, *args: Any): ...
|
|
def getFamily(self) -> str: ...
|
|
def getLinkPredictionCol(self) -> str: ...
|
|
def getLink(self) -> str: ...
|
|
def getVariancePower(self) -> float: ...
|
|
def getLinkPower(self) -> float: ...
|
|
def getOffsetCol(self) -> str: ...
|
|
|
|
class GeneralizedLinearRegression(
|
|
_JavaRegressor[GeneralizedLinearRegressionModel],
|
|
_GeneralizedLinearRegressionParams,
|
|
JavaMLWritable,
|
|
JavaMLReadable[GeneralizedLinearRegression],
|
|
):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
labelCol: str = ...,
|
|
featuresCol: str = ...,
|
|
predictionCol: str = ...,
|
|
family: str = ...,
|
|
link: Optional[str] = ...,
|
|
fitIntercept: bool = ...,
|
|
maxIter: int = ...,
|
|
tol: float = ...,
|
|
regParam: float = ...,
|
|
weightCol: Optional[str] = ...,
|
|
solver: str = ...,
|
|
linkPredictionCol: Optional[str] = ...,
|
|
variancePower: float = ...,
|
|
linkPower: Optional[float] = ...,
|
|
offsetCol: Optional[str] = ...,
|
|
aggregationDepth: int = ...
|
|
) -> None: ...
|
|
def setParams(
|
|
self,
|
|
*,
|
|
labelCol: str = ...,
|
|
featuresCol: str = ...,
|
|
predictionCol: str = ...,
|
|
family: str = ...,
|
|
link: Optional[str] = ...,
|
|
fitIntercept: bool = ...,
|
|
maxIter: int = ...,
|
|
tol: float = ...,
|
|
regParam: float = ...,
|
|
weightCol: Optional[str] = ...,
|
|
solver: str = ...,
|
|
linkPredictionCol: Optional[str] = ...,
|
|
variancePower: float = ...,
|
|
linkPower: Optional[float] = ...,
|
|
offsetCol: Optional[str] = ...,
|
|
aggregationDepth: int = ...
|
|
) -> GeneralizedLinearRegression: ...
|
|
def setFamily(self, value: str) -> GeneralizedLinearRegression: ...
|
|
def setLinkPredictionCol(self, value: str) -> GeneralizedLinearRegression: ...
|
|
def setLink(self, value: str) -> GeneralizedLinearRegression: ...
|
|
def setVariancePower(self, value: float) -> GeneralizedLinearRegression: ...
|
|
def setLinkPower(self, value: float) -> GeneralizedLinearRegression: ...
|
|
def setOffsetCol(self, value: str) -> GeneralizedLinearRegression: ...
|
|
def setMaxIter(self, value: int) -> GeneralizedLinearRegression: ...
|
|
def setRegParam(self, value: float) -> GeneralizedLinearRegression: ...
|
|
def setTol(self, value: float) -> GeneralizedLinearRegression: ...
|
|
def setFitIntercept(self, value: bool) -> GeneralizedLinearRegression: ...
|
|
def setWeightCol(self, value: str) -> GeneralizedLinearRegression: ...
|
|
def setSolver(self, value: str) -> GeneralizedLinearRegression: ...
|
|
def setAggregationDepth(self, value: int) -> GeneralizedLinearRegression: ...
|
|
|
|
class GeneralizedLinearRegressionModel(
|
|
_JavaRegressionModel[Vector],
|
|
_GeneralizedLinearRegressionParams,
|
|
JavaMLWritable,
|
|
JavaMLReadable[GeneralizedLinearRegressionModel],
|
|
HasTrainingSummary[GeneralizedLinearRegressionTrainingSummary],
|
|
):
|
|
def setLinkPredictionCol(self, value: str) -> GeneralizedLinearRegressionModel: ...
|
|
@property
|
|
def coefficients(self) -> Vector: ...
|
|
@property
|
|
def intercept(self) -> float: ...
|
|
@property
|
|
def summary(self) -> GeneralizedLinearRegressionTrainingSummary: ...
|
|
def evaluate(self, dataset: DataFrame) -> GeneralizedLinearRegressionSummary: ...
|
|
|
|
class GeneralizedLinearRegressionSummary(JavaWrapper):
|
|
@property
|
|
def predictions(self) -> DataFrame: ...
|
|
@property
|
|
def predictionCol(self) -> str: ...
|
|
@property
|
|
def rank(self) -> int: ...
|
|
@property
|
|
def degreesOfFreedom(self) -> int: ...
|
|
@property
|
|
def residualDegreeOfFreedom(self) -> int: ...
|
|
@property
|
|
def residualDegreeOfFreedomNull(self) -> int: ...
|
|
def residuals(self, residualsType: str = ...) -> DataFrame: ...
|
|
@property
|
|
def nullDeviance(self) -> float: ...
|
|
@property
|
|
def deviance(self) -> float: ...
|
|
@property
|
|
def dispersion(self) -> float: ...
|
|
@property
|
|
def aic(self) -> float: ...
|
|
|
|
class GeneralizedLinearRegressionTrainingSummary(GeneralizedLinearRegressionSummary):
|
|
@property
|
|
def numIterations(self) -> int: ...
|
|
@property
|
|
def solver(self) -> str: ...
|
|
@property
|
|
def coefficientStandardErrors(self) -> List[float]: ...
|
|
@property
|
|
def tValues(self) -> List[float]: ...
|
|
@property
|
|
def pValues(self) -> List[float]: ...
|
|
|
|
class _FactorizationMachinesParams(
|
|
_PredictorParams,
|
|
HasMaxIter,
|
|
HasStepSize,
|
|
HasTol,
|
|
HasSolver,
|
|
HasSeed,
|
|
HasFitIntercept,
|
|
HasRegParam,
|
|
HasWeightCol,
|
|
):
|
|
factorSize: Param[int]
|
|
fitLinear: Param[bool]
|
|
miniBatchFraction: Param[float]
|
|
initStd: Param[float]
|
|
solver: Param[str]
|
|
def __init__(self, *args: Any): ...
|
|
def getFactorSize(self) -> int: ...
|
|
def getFitLinear(self) -> bool: ...
|
|
def getMiniBatchFraction(self) -> float: ...
|
|
def getInitStd(self) -> float: ...
|
|
|
|
class FMRegressor(
|
|
_JavaRegressor[FMRegressionModel],
|
|
_FactorizationMachinesParams,
|
|
JavaMLWritable,
|
|
JavaMLReadable[FMRegressor],
|
|
):
|
|
factorSize: Param[int]
|
|
fitLinear: Param[bool]
|
|
miniBatchFraction: Param[float]
|
|
initStd: Param[float]
|
|
solver: Param[str]
|
|
def __init__(
|
|
self,
|
|
featuresCol: str = ...,
|
|
labelCol: str = ...,
|
|
predictionCol: str = ...,
|
|
factorSize: int = ...,
|
|
fitIntercept: bool = ...,
|
|
fitLinear: bool = ...,
|
|
regParam: float = ...,
|
|
miniBatchFraction: float = ...,
|
|
initStd: float = ...,
|
|
maxIter: int = ...,
|
|
stepSize: float = ...,
|
|
tol: float = ...,
|
|
solver: str = ...,
|
|
seed: Optional[int] = ...,
|
|
) -> None: ...
|
|
def setParams(
|
|
self,
|
|
featuresCol: str = ...,
|
|
labelCol: str = ...,
|
|
predictionCol: str = ...,
|
|
factorSize: int = ...,
|
|
fitIntercept: bool = ...,
|
|
fitLinear: bool = ...,
|
|
regParam: float = ...,
|
|
miniBatchFraction: float = ...,
|
|
initStd: float = ...,
|
|
maxIter: int = ...,
|
|
stepSize: float = ...,
|
|
tol: float = ...,
|
|
solver: str = ...,
|
|
seed: Optional[int] = ...,
|
|
) -> FMRegressor: ...
|
|
def setFactorSize(self, value: int) -> FMRegressor: ...
|
|
def setFitLinear(self, value: bool) -> FMRegressor: ...
|
|
def setMiniBatchFraction(self, value: float) -> FMRegressor: ...
|
|
def setInitStd(self, value: float) -> FMRegressor: ...
|
|
def setMaxIter(self, value: int) -> FMRegressor: ...
|
|
def setStepSize(self, value: float) -> FMRegressor: ...
|
|
def setTol(self, value: float) -> FMRegressor: ...
|
|
def setSolver(self, value: str) -> FMRegressor: ...
|
|
def setSeed(self, value: int) -> FMRegressor: ...
|
|
def setFitIntercept(self, value: bool) -> FMRegressor: ...
|
|
def setRegParam(self, value: float) -> FMRegressor: ...
|
|
|
|
class FMRegressionModel(
|
|
_JavaRegressionModel,
|
|
_FactorizationMachinesParams,
|
|
JavaMLWritable,
|
|
JavaMLReadable[FMRegressionModel],
|
|
):
|
|
@property
|
|
def intercept(self) -> float: ...
|
|
@property
|
|
def linear(self) -> Vector: ...
|
|
@property
|
|
def factors(self) -> Matrix: ...
|