110 lines
3.8 KiB
Python
110 lines
3.8 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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from pyspark.ml.util import JavaMLReadable, JavaMLWritable
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from pyspark.ml.wrapper import JavaEstimator, JavaParams, JavaModel
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from pyspark.ml.param.shared import HasPredictionCol
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from pyspark.sql.dataframe import DataFrame
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from pyspark.ml.param import Param
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class _FPGrowthParams(HasPredictionCol):
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itemsCol: Param[str]
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minSupport: Param[float]
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numPartitions: Param[int]
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minConfidence: Param[float]
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def __init__(self, *args: Any): ...
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def getItemsCol(self) -> str: ...
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def getMinSupport(self) -> float: ...
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def getNumPartitions(self) -> int: ...
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def getMinConfidence(self) -> float: ...
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class FPGrowthModel(
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JavaModel, _FPGrowthParams, JavaMLWritable, JavaMLReadable[FPGrowthModel]
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):
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def setItemsCol(self, value: str) -> FPGrowthModel: ...
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def setMinConfidence(self, value: float) -> FPGrowthModel: ...
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def setPredictionCol(self, value: str) -> FPGrowthModel: ...
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@property
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def freqItemsets(self) -> DataFrame: ...
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@property
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def associationRules(self) -> DataFrame: ...
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class FPGrowth(
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JavaEstimator[FPGrowthModel],
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_FPGrowthParams,
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JavaMLWritable,
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JavaMLReadable[FPGrowth],
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):
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def __init__(
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self,
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*,
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minSupport: float = ...,
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minConfidence: float = ...,
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itemsCol: str = ...,
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predictionCol: str = ...,
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numPartitions: Optional[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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minSupport: float = ...,
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minConfidence: float = ...,
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itemsCol: str = ...,
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predictionCol: str = ...,
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numPartitions: Optional[int] = ...
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) -> FPGrowth: ...
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def setItemsCol(self, value: str) -> FPGrowth: ...
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def setMinSupport(self, value: float) -> FPGrowth: ...
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def setNumPartitions(self, value: int) -> FPGrowth: ...
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def setMinConfidence(self, value: float) -> FPGrowth: ...
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def setPredictionCol(self, value: str) -> FPGrowth: ...
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class PrefixSpan(JavaParams):
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minSupport: Param[float]
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maxPatternLength: Param[int]
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maxLocalProjDBSize: Param[int]
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sequenceCol: Param[str]
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def __init__(
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self,
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*,
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minSupport: float = ...,
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maxPatternLength: int = ...,
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maxLocalProjDBSize: int = ...,
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sequenceCol: str = ...
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) -> None: ...
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def setParams(
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self,
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*,
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minSupport: float = ...,
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maxPatternLength: int = ...,
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maxLocalProjDBSize: int = ...,
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sequenceCol: str = ...
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) -> PrefixSpan: ...
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def setMinSupport(self, value: float) -> PrefixSpan: ...
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def getMinSupport(self) -> float: ...
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def setMaxPatternLength(self, value: int) -> PrefixSpan: ...
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def getMaxPatternLength(self) -> int: ...
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def setMaxLocalProjDBSize(self, value: int) -> PrefixSpan: ...
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def getMaxLocalProjDBSize(self) -> int: ...
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def setSequenceCol(self, value: str) -> PrefixSpan: ...
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def getSequenceCol(self) -> str: ...
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def findFrequentSequentialPatterns(self, dataset: DataFrame) -> DataFrame: ...
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