我想在PySpark MLlib中构建一个简单的自定义Estimator.我有here可以写一个自定义的Transformer,但我不知道如何在Estimator上做.我也不明白@keyword_only是做什么的,为什么我需要这么多的setter和getter. Scikit-learn似乎有适合自定义模型的文档(see here但PySpark没有.

示例模型的伪代码：

class NormalDeviation():
    def __init__(self, threshold = 3):
    def fit(x, y=None):
       self.model = {'mean': x.mean(), 'std': x.std()]
    def predict(x):
       return ((x-self.model['mean']) > self.threshold * self.model['std'])
    def decision_function(x): # does ml-lib support this?

解决方法:

一般来说没有文档,因为对于Spark 1.6 / 2.0,大多数相关的API并不是公开的.它应该在Spark 2.1.0中更改(参见SPARK-7146).

API相对复杂,因为它必须遵循特定的约定才能使给定的Transformer或Estimator与Pipeline API兼容.读取和写入或网格搜索等功能可能需要其中一些方法.其他,比如keyword_only只是一个简单的帮助者,并不是严格要求的.

假设您已为平均参数定义了以下混合：

from pyspark.ml.pipeline import Estimator, Model, Pipeline
from pyspark.ml.param.shared import *
from pyspark.sql.functions import avg, stddev_samp


class HasMean(Params):

    mean = Param(Params._dummy(), "mean", "mean", 
        typeConverter=TypeConverters.toFloat)

    def __init__(self):
        super(HasMean, self).__init__()

    def setMean(self, value):
        return self._set(mean=value)

    def getMean(self):
        return self.getOrDefault(self.mean)

标准差参数：

class HasStandardDeviation(Params):

    stddev = Param(Params._dummy(), "stddev", "stddev", 
        typeConverter=TypeConverters.toFloat)

    def __init__(self):
        super(HasStandardDeviation, self).__init__()

    def setStddev(self, value):
        return self._set(stddev=value)

    def getStddev(self):
        return self.getOrDefault(self.stddev)

和门槛：

class HasCenteredThreshold(Params):

    centered_threshold = Param(Params._dummy(),
            "centered_threshold", "centered_threshold",
            typeConverter=TypeConverters.toFloat)

    def __init__(self):
        super(HasCenteredThreshold, self).__init__()

    def setCenteredThreshold(self, value):
        return self._set(centered_threshold=value)

    def getCenteredThreshold(self):
        return self.getOrDefault(self.centered_threshold)

你可以创建基本的Estimator,如下所示：

class NormalDeviation(Estimator, HasInputCol, 
        HasPredictionCol, HasCenteredThreshold):

    def _fit(self, dataset):
        c = self.getInputCol()
        mu, sigma = dataset.agg(avg(c), stddev_samp(c)).first()
        return (NormalDeviationModel()
            .setInputCol(c)
            .setMean(mu)
            .setStddev(sigma)
            .setCenteredThreshold(self.getCenteredThreshold())
            .setPredictionCol(self.getPredictionCol()))

class NormalDeviationModel(Model, HasInputCol, HasPredictionCol,
        HasMean, HasStandardDeviation, HasCenteredThreshold):

    def _transform(self, dataset):
        x = self.getInputCol()
        y = self.getPredictionCol()
        threshold = self.getCenteredThreshold()
        mu = self.getMean()
        sigma = self.getStddev()

        return dataset.withColumn(y, (dataset[x] - mu) > threshold * sigma)

最后它可以使用如下：

df = sc.parallelize([(1, 2.0), (2, 3.0), (3, 0.0), (4, 99.0)]).toDF(["id", "x"])

normal_deviation = NormalDeviation().setInputCol("x").setCenteredThreshold(1.0)
model  = Pipeline(stages=[normal_deviation]).fit(df)

model.transform(df).show()
## +---+----+----------+
## | id|   x|prediction|
## +---+----+----------+
## |  1| 2.0|     false|
## |  2| 3.0|     false|
## |  3| 0.0|     false|
## |  4|99.0|      true|
## +---+----+----------+