76 lines
2.7 KiB
Python
76 lines
2.7 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 List, Optional, Tuple, Union
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import array
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from collections import namedtuple
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from pyspark.context import SparkContext
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from pyspark.rdd import RDD
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from pyspark.mllib.common import JavaModelWrapper
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from pyspark.mllib.util import JavaLoader, JavaSaveable
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class Rating(namedtuple("Rating", ["user", "product", "rating"])):
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def __reduce__(self): ...
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class MatrixFactorizationModel(
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JavaModelWrapper, JavaSaveable, JavaLoader[MatrixFactorizationModel]
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):
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def predict(self, user: int, product: int) -> float: ...
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def predictAll(self, user_product: RDD[Tuple[int, int]]) -> RDD[Rating]: ...
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def userFeatures(self) -> RDD[Tuple[int, array.array]]: ...
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def productFeatures(self) -> RDD[Tuple[int, array.array]]: ...
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def recommendUsers(self, product: int, num: int) -> List[Rating]: ...
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def recommendProducts(self, user: int, num: int) -> List[Rating]: ...
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def recommendProductsForUsers(
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self, num: int
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) -> RDD[Tuple[int, Tuple[Rating, ...]]]: ...
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def recommendUsersForProducts(
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self, num: int
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) -> RDD[Tuple[int, Tuple[Rating, ...]]]: ...
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@property
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def rank(self) -> int: ...
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> MatrixFactorizationModel: ...
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class ALS:
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@classmethod
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def train(
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cls,
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ratings: Union[RDD[Rating], RDD[Tuple[int, int, float]]],
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rank: int,
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iterations: int = ...,
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lambda_: float = ...,
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blocks: int = ...,
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nonnegative: bool = ...,
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seed: Optional[int] = ...,
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) -> MatrixFactorizationModel: ...
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@classmethod
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def trainImplicit(
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cls,
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ratings: Union[RDD[Rating], RDD[Tuple[int, int, float]]],
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rank: int,
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iterations: int = ...,
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lambda_: float = ...,
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blocks: int = ...,
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alpha: float = ...,
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nonnegative: bool = ...,
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seed: Optional[int] = ...,
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) -> MatrixFactorizationModel: ...
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