---
title: ALS
description: "ALS为ML API类模型接口。"
url: https://www.hikunpeng.com/document/detail/zh/kunpengboostkithistory/2200/bds/kunpengbdssparkml_16_0030.html
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---
# ALS

ALS为ML API类模型接口。

| 模型接口类别 | 函数接口 |
| --- | --- |
| ML API | def fit(dataset: Dataset[\_]): ALSModel def fit(dataset: Dataset[\_], paramMaps: Array[ParamMap]): Seq[ALSModel] def fit(dataset: Dataset[\_], paramMap: ParamMap): ALSModel def fit(dataset: Dataset[\_], firstParamPair: ParamPair[\_], otherParamPairs: ParamPair[\_]\*): ALSModel |


#### ML API

- 功能描述
  传入dataSet格式的样本数据，调用fit接口，输出ALS推荐模型。


- 输入输出

  1. 包名：package org.apache.spark.ml.recommendation
  2. 类名：ALS
  3. 方法名：fit
  4. 输入：dataSet[_]，训练样本数据
  5. 基于原生算法优化的参数

```
def setRank(value: Int): ALS.this.type
def setNumUserBlocks(value: Int): ALS.this.type
def setNumItemBlocks(value: Int): ALS.this.type
def setImplicitPrefs(value: Boolean): ALS.this.type
def setAlpha(value: Double): ALS.this.type
def setUserCol(value: String): ALS.this.type
def setItemCol(value: String): ALS.this.type
def setRatingCol(value: String): ALS.this.type
def setPredictionCol(value: String): ALS.this.type
def setMaxIter(value: Int): ALS.this.type
def setRegParam(value: Double): ALS.this.type
def setNonnegative(value: Boolean): ALS.this.type
def setCheckpointInterval(value: Int): ALS.this.type
def setSeed(value: Long): ALS.this.type
def setIntermediateStorageLevel(value: String): ALS.this.type
def setFinalStorageLevel(value: String): ALS.this.type
def setColdStartStrategy(value: String): ALS.this.type
def setNumBlocks(value: Int): ALS.type
```


  6. 新增算法参数| 参数名称 | 取值类型 | 描述 |
| --- | --- | --- |
| spark.boostkit.ALS.blockMaxRow | 正整数 | 计算格莱姆矩阵行分块大小（不建议修改） |
| spark.boostkit.ALS.unpersistCycle | 正整数 | srcFactorRDD反持久化周期（不建议修改） |


    参数及fit代码接口示例： 1 2 3 4 5 6 7 8 9 10 11 12 val als = new ALS() .setMaxIter(numIterations) .setUserCol("user") .setItemCol("product") .setRatingCol("rating") .setNonnegative(nonnegative) .setImplicitPrefs(implicitPrefs) .setNumItemBlocks(numItemBlocks) .setNumUserBlocks(numUserBlocks) .setRegParam(regParam) .setAlpha(alpha) val model = als.fit(ratings)

  7. 输出：ALSModel，ALS推荐模型

- 使用样例
  1 2 3 val model = als.fit(ratings) val predictions = model.transform(ratings) val p = predictions.select("rating", "prediction").rdd .map { case Row(prediction: Float, label: Float) => (prediction, label) } .map{t => val err = (t._1 - t._2) err * err }.mean() println("Mean Squared Error = " + p)


- 结果样例
  1 Mean Squared Error = 0.9962046583156793