---
title: Kmeans
description: "Kmeans为ML API。"
url: https://www.hikunpeng.com/document/detail/zh/kunpengboostkithistory/2003/bds/kunpengbdssparkml_20_0027.html
sourcePath: /source/zh/kunpengboostkithistory/2003/bds/kunpengbdssparkml_20_0027.html
indexId: 58c5f89d9b304b51ae1ba733e26f41d8055f36b79fba6358d00f55817eef24c073
---
# Kmeans

Kmeans为ML API。

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


#### ML API

- 功能描述
  传入Dataset格式的样本数据，调用fit接口，输出KMeans聚类模型。

- 输入输出

  1. 包名：package org.apache.spark.ml.clustering
  2. 类名：KMeans
  3. 方法名：fit
  4. 输入：Dataset[_]，训练样本数据，必须字段如下。| Param name | Type(s) | Default | Description |
| --- | --- | --- | --- |
| featuresCol | Vector | "features" | 特征标签 |


  5. 算法参数| 算法参数 |
| --- |
| def setFeaturesCol(value: String): KMeans.this.type def setPredictionCol(value: String): KMeans.this.type def setK(value: Int): KMeans.this.type def setInitMode(value: String): KMeans.this.type def setInitSteps(value: Int): KMeans.this.type def setMaxIter(value: Int): KMeans.this.type def setThreshold(value: Double): KMeans.this.type def setTol(value: Double): KMeans.this.type def setSeed(value: Long): KMeans.this.type |


  6. 新增算法参数。| 参数名称 | 参数含义 | 取值类型 |
| --- | --- | --- |
| sampleRate | 每一轮迭代使用的数据占全量数据集的比例 | 0~1[Double] |
| optMethod | 样本数据采样触发开关 | default/allData[String] |


    参数及fit代码接口示例： import org.apache.spark.ml.param.{ParamMap, ParamPair} val kmeans = new MlKMeans() //定义def fit(dataset: Dataset[_], paramMap: ParamMap) 接口参数 val paramMap = ParamMap(kmeans.initSteps -> initSteps) .put(kmeans.maxIter, maxIter) // 定义def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): 接口参数 val paramMaps: Array[ParamMap] = new Array[ParamMap](2) for (i <- 0 to 2) { paramMaps(i) = ParamMap(kmeans.initSteps -> initSteps) .put(kmeans.maxIter, maxIter) }//对paramMaps进行赋值 // 定义def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*) 接口参数 val initStepsParamPair = ParamPair(kmeans.initSteps, initSteps) val maxIterParamPair = ParamPair(kmeans.maxIter, maxIter) val tolParamPair = ParamPair(kmeans.tol, tol) // 调用各个fit接口 model = kmeans.fit(trainingData) model = kmeans.fit(trainingData, paramMap) models = kemans.fit(trainingData, paramMaps) model = kemans.fit(trainingData, initStepsParamPair, maxIterParamPair, tolParamPair)

  7. 输出：KMeansModel、KMeans聚类模型，模型预测时的输出字段如下。| Param name | Type(s) | Default | Description |
| --- | --- | --- | --- |
| predictionCol | Int | "prediction" | predictionCol |


- 使用样例

```
import org.apache.spark.ml.clustering.KMeans
import org.apache.spark.ml.evaluation.ClusteringEvaluator
// Loads data.
val dataset = spark.read.format("libsvm").load("data/mllib/sample_kmeans_data.txt")
// Trains a k-means model.
val kmeans = new KMeans().setK(2).setSeed(1L)
val model = kmeans.fit(dataset)
// Make predictions
val predictions = model.transform(dataset)
// Evaluate clustering by computing Silhouette score
val evaluator = new ClusteringEvaluator()
val silhouette = evaluator.evaluate(predictions)
println(s"Silhouette with squared euclidean distance = $silhouette")
// Shows the result.
println("Cluster Centers: ")
model.clusterCenters.foreach(println)
```