Java 类名:com.alibaba.alink.operator.batch.clustering.KModesPredictBatchOp
Python 类名:KModesPredictBatchOp
KModes是一种用于离散数据/分类数据(categorical data)的聚类算法。 基本思想是:把n个对象分为k个簇,使簇内具有较小的的相异度(或者称距离)。 距离计算方法:两个字符串比较,相同为0,不同为1。
名称 | 中文名称 | 描述 | 类型 | 是否必须? | 取值范围 | 默认值 |
---|---|---|---|---|---|---|
predictionCol | 预测结果列名 | 预测结果列名 | String | ✓ | ||
modelFilePath | 模型的文件路径 | 模型的文件路径 | String | null | ||
predictionDetailCol | 预测详细信息列名 | 预测详细信息列名 | String | |||
reservedCols | 算法保留列名 | 算法保留列 | String[] | null | ||
numThreads | 组件多线程线程个数 | 组件多线程线程个数 | Integer | 1 |
from pyalink.alink import * import pandas as pd useLocalEnv(1) df = pd.DataFrame([ ["pc", "Hp.com"], ["camera", "Hp.com"], ["digital camera", "Hp.com"], ["camera", "BestBuy.com"], ["digital camera", "BestBuy.com"], ["tv", "BestBuy.com"], ["flower", "Teleflora.com"], ["flower", "Orchids.com"] ]) inOp1 = BatchOperator.fromDataframe(df, schemaStr='f0 string, f1 string') inOp2 = StreamOperator.fromDataframe(df, schemaStr='f0 string, f1 string') kmodes = KModesTrainBatchOp()\ .setFeatureCols(["f0", "f1"])\ .setK(2)\ .linkFrom(inOp1) predict = KModesPredictBatchOp()\ .setPredictionCol("pred")\ .linkFrom(kmodes, inOp1) kmodes.lazyPrint(10) predict.print() predict = KModesPredictStreamOp(kmodes)\ .setPredictionCol("pred")\ .linkFrom(inOp2) predict.print() StreamOperator.execute()
import org.apache.flink.types.Row; import com.alibaba.alink.operator.batch.source.MemSourceBatchOp; import com.alibaba.alink.operator.stream.StreamOperator; import com.alibaba.alink.operator.stream.clustering.KModesPredictStreamOp; import com.alibaba.alink.operator.batch.clustering.KModesPredictBatchOp; import com.alibaba.alink.operator.batch.clustering.KModesTrainBatchOp; import com.alibaba.alink.operator.stream.source.MemSourceStreamOp; import org.junit.Test; import java.util.Arrays; import java.util.List; public class KModesPredictBatchOpTest { @Test public void testKModesPredictBatchOp() throws Exception { List <Row> dataPoints = Arrays.asList( Row.of("pc", "Hp.com"), Row.of("camera", "Hp.com"), Row.of("digital camera", "Hp.com"), Row.of("camera", "BestBuy.com"), Row.of("digital camera", "BestBuy.com"), Row.of("tv", "BestBuy.com"), Row.of("flower", "Teleflora.com"), Row.of("flower", "Orchids.com") ); MemSourceBatchOp inOp1 = new MemSourceBatchOp(dataPoints, "f0 string, f1 string"); MemSourceStreamOp inOp2 = new MemSourceStreamOp(dataPoints, "f0 string, f1 string"); KModesTrainBatchOp kmodes = new KModesTrainBatchOp() .setFeatureCols(new String[]{"f0", "f1"}) .setK(2) .linkFrom(inOp1); KModesPredictBatchOp kModesPredictBatchOp = new KModesPredictBatchOp() .setPredictionCol("pred") .linkFrom(kmodes, inOp1); kmodes.lazyPrint(10); kModesPredictBatchOp.print(); KModesPredictStreamOp kModesPredictStreamOp = new KModesPredictStreamOp(kmodes) .setPredictionCol("pred") .linkFrom(inOp2); kModesPredictStreamOp.print(); StreamOperator.execute(); } }
f0 | f1 | pred |
---|---|---|
pc | Hp.com | 1 |
flower | Teleflora.com | 0 |
digital camera | BestBuy.com | 1 |
digital camera | Hp.com | 1 |
flower | Orchids.com | 0 |
tv | BestBuy.com | 0 |
camera | BestBuy.com | 0 |
camera | Hp.com | 1 |