153 lines
5.4 KiB
Python
153 lines
5.4 KiB
Python
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#
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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, Optional
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import sys # noqa: F401
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from pyspark import since, keyword_only # noqa: F401
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from pyspark.ml.param.shared import (
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HasBlockSize,
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HasCheckpointInterval,
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HasMaxIter,
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HasPredictionCol,
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HasRegParam,
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HasSeed,
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)
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from pyspark.ml.wrapper import JavaEstimator, JavaModel
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from pyspark.ml.common import inherit_doc # noqa: F401
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from pyspark.ml.param import Param
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from pyspark.ml.util import JavaMLWritable, JavaMLReadable
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from pyspark.sql.dataframe import DataFrame
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class _ALSModelParams(HasPredictionCol, HasBlockSize):
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userCol: Param[str]
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itemCol: Param[str]
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coldStartStrategy: Param[str]
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def getUserCol(self) -> str: ...
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def getItemCol(self) -> str: ...
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def getColdStartStrategy(self) -> str: ...
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class _ALSParams(
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_ALSModelParams, HasMaxIter, HasRegParam, HasCheckpointInterval, HasSeed
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):
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rank: Param[int]
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numUserBlocks: Param[int]
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numItemBlocks: Param[int]
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implicitPrefs: Param[bool]
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alpha: Param[float]
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ratingCol: Param[str]
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nonnegative: Param[bool]
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intermediateStorageLevel: Param[str]
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finalStorageLevel: Param[str]
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def __init__(self, *args: Any): ...
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def getRank(self) -> int: ...
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def getNumUserBlocks(self) -> int: ...
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def getNumItemBlocks(self) -> int: ...
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def getImplicitPrefs(self) -> bool: ...
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def getAlpha(self) -> float: ...
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def getRatingCol(self) -> str: ...
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def getNonnegative(self) -> bool: ...
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def getIntermediateStorageLevel(self) -> str: ...
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def getFinalStorageLevel(self) -> str: ...
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class ALS(JavaEstimator[ALSModel], _ALSParams, JavaMLWritable, JavaMLReadable[ALS]):
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def __init__(
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self,
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*,
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rank: int = ...,
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maxIter: int = ...,
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regParam: float = ...,
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numUserBlocks: int = ...,
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numItemBlocks: int = ...,
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implicitPrefs: bool = ...,
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alpha: float = ...,
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userCol: str = ...,
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itemCol: str = ...,
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seed: Optional[int] = ...,
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ratingCol: str = ...,
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nonnegative: bool = ...,
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checkpointInterval: int = ...,
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intermediateStorageLevel: str = ...,
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finalStorageLevel: str = ...,
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coldStartStrategy: str = ...,
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blockSize: 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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rank: int = ...,
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maxIter: int = ...,
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regParam: float = ...,
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numUserBlocks: int = ...,
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numItemBlocks: int = ...,
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implicitPrefs: bool = ...,
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alpha: float = ...,
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userCol: str = ...,
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itemCol: str = ...,
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seed: Optional[int] = ...,
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ratingCol: str = ...,
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nonnegative: bool = ...,
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checkpointInterval: int = ...,
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intermediateStorageLevel: str = ...,
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finalStorageLevel: str = ...,
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coldStartStrategy: str = ...,
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blockSize: int = ...
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) -> ALS: ...
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def setRank(self, value: int) -> ALS: ...
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def setNumUserBlocks(self, value: int) -> ALS: ...
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def setNumItemBlocks(self, value: int) -> ALS: ...
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def setNumBlocks(self, value: int) -> ALS: ...
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def setImplicitPrefs(self, value: bool) -> ALS: ...
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def setAlpha(self, value: float) -> ALS: ...
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def setUserCol(self, value: str) -> ALS: ...
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def setItemCol(self, value: str) -> ALS: ...
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def setRatingCol(self, value: str) -> ALS: ...
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def setNonnegative(self, value: bool) -> ALS: ...
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def setIntermediateStorageLevel(self, value: str) -> ALS: ...
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def setFinalStorageLevel(self, value: str) -> ALS: ...
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def setColdStartStrategy(self, value: str) -> ALS: ...
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def setMaxIter(self, value: int) -> ALS: ...
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def setRegParam(self, value: float) -> ALS: ...
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def setPredictionCol(self, value: str) -> ALS: ...
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def setCheckpointInterval(self, value: int) -> ALS: ...
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def setSeed(self, value: int) -> ALS: ...
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def setBlockSize(self, value: int) -> ALS: ...
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class ALSModel(JavaModel, _ALSModelParams, JavaMLWritable, JavaMLReadable[ALSModel]):
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def setUserCol(self, value: str) -> ALSModel: ...
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def setItemCol(self, value: str) -> ALSModel: ...
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def setColdStartStrategy(self, value: str) -> ALSModel: ...
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def setPredictionCol(self, value: str) -> ALSModel: ...
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def setBlockSize(self, value: int) -> ALSModel: ...
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@property
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def rank(self) -> int: ...
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@property
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def userFactors(self) -> DataFrame: ...
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@property
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def itemFactors(self) -> DataFrame: ...
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def recommendForAllUsers(self, numItems: int) -> DataFrame: ...
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def recommendForAllItems(self, numUsers: int) -> DataFrame: ...
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def recommendForUserSubset(
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self, dataset: DataFrame, numItems: int
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) -> DataFrame: ...
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def recommendForItemSubset(
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self, dataset: DataFrame, numUsers: int
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) -> DataFrame: ...
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