Java 类名：com.alibaba.alink.operator.batch.evaluation.EvalClusterBatchOp
Python 类名：EvalClusterBatchOp
功能介绍
聚类评估是对聚类算法的预测结果进行效果评估，支持下列评估指标。
参数说明
|
名称 |
中文名称 |
描述 |
类型 |
是否必须？ |
默认值 |
|
predictionCol |
预测结果列名 |
预测结果列名 |
String |
? |
|
|
labelCol |
标签列名 |
输入表中的标签列名 |
String |
null |
|
|
vectorCol |
向量列名 |
输入表中的向量列名 |
String |
null |
|
|
distanceType |
距离度量方式 |
距离类型 |
String |
"EUCLIDEAN" |
代码示例
Python 代码
from pyalink.alink import * import pandas as pd useLocalEnv(1) df = pd.DataFrame([ [0, "0 0 0"], [0, "0.1,0.1,0.1"], [0, "0.2,0.2,0.2"], [1, "9 9 9"], [1, "9.1 9.1 9.1"], [1, "9.2 9.2 9.2"] ]) inOp = BatchOperator.fromDataframe(df, schemaStr=‘id int, vec string‘) metrics = EvalClusterBatchOp().setVectorCol("vec").setPredictionCol("id").linkFrom(inOp).collectMetrics() print("Total Samples Number:", metrics.getCount()) print("Cluster Number:", metrics.getK()) print("Cluster Array:", metrics.getClusterArray()) print("Cluster Count Array:", metrics.getCountArray()) print("CP:", metrics.getCp()) print("DB:", metrics.getDb()) print("SP:", metrics.getSp()) print("SSB:", metrics.getSsb()) print("SSW:", metrics.getSsw()) print("CH:", metrics.getVrc())
Java 代码
import org.apache.flink.types.Row; import com.alibaba.alink.operator.batch.BatchOperator; import com.alibaba.alink.operator.batch.evaluation.EvalClusterBatchOp; import com.alibaba.alink.operator.batch.source.MemSourceBatchOp; import com.alibaba.alink.operator.common.evaluation.ClusterMetrics; import org.junit.Test; import java.util.Arrays; import java.util.List; public class EvalClusterBatchOpTest { @Test public void testEvalClusterBatchOp() throws Exception { List <Row> df = Arrays.asList( Row.of(0, "0 0 0"), Row.of(0, "0.1,0.1,0.1"), Row.of(0, "0.2,0.2,0.2"), Row.of(1, "9 9 9"), Row.of(1, "9.1 9.1 9.1"), Row.of(1, "9.2 9.2 9.2") ); BatchOperator <?> inOp = new MemSourceBatchOp(df, "id int, vec string"); ClusterMetrics metrics = new EvalClusterBatchOp().setVectorCol("vec").setPredictionCol("id").linkFrom(inOp) .collectMetrics(); System.out.println("Total Samples Number:" + metrics.getCount()); System.out.println("Cluster Number:" + metrics.getK()); System.out.println("Cluster Array:" + Arrays.toString(metrics.getClusterArray())); System.out.println("Cluster Count Array:" + Arrays.toString(metrics.getCountArray())); System.out.println("CP:" + metrics.getCp()); System.out.println("DB:" + metrics.getDb()); System.out.println("SP:" + metrics.getSp()); System.out.println("SSB:" + metrics.getSsb()); System.out.println("SSW:" + metrics.getSsw()); System.out.println("CH:" + metrics.getVrc()); } }
运行结果
Total Samples Number: 6 Cluster Number: 2 Cluster Array: [‘0‘, ‘1‘] Cluster Count Array: [3.0, 3.0] CP: 0.11547005383792497 DB: 0.014814814814814791 SP: 15.588457268119896 SSB: 364.5 SSW: 0.1199999999999996 CH: 12150.000000000042