Java 类名:com.alibaba.alink.operator.stream.dataproc.vector.VectorImputerPredictStreamOp
Python 类名:VectorImputerPredictStreamOp
功能介绍
- 使用 Vecotor 缺失值填充模型对流Vector数据进行数据填充
- 读取VectorImputerTrainBatchOp训练的模型
参数说明
| 名称 | 中文名称 | 描述 | 类型 | 是否必须? | 取值范围 | 默认值 | | —- | —- | —- | —- | —- | —- | —- |
| modelFilePath | 模型的文件路径 | 模型的文件路径 | String | | | null |
| outputCol | 输出结果列 | 输出结果列列名,可选,默认null | String | | | null |
| numThreads | 组件多线程线程个数 | 组件多线程线程个数 | Integer | | | 1 |
| modelStreamFilePath | 模型流的文件路径 | 模型流的文件路径 | String | | | null |
| modelStreamScanInterval | 扫描模型路径的时间间隔 | 描模型路径的时间间隔,单位秒 | Integer | | | 10 |
| modelStreamStartTime | 模型流的起始时间 | 模型流的起始时间。默认从当前时刻开始读。使用yyyy-mm-dd hh:mm:ss.fffffffff格式,详见Timestamp.valueOf(String s) | String | | | null |
代码示例
Python 代码
from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
["1:3 2:4 4:7", 1],
["0:3 5:5", 3],
["2:4 4:5", 4]
])
dataStream = StreamOperator.fromDataframe(df, schemaStr="vec string, id bigint")
data = BatchOperator.fromDataframe(df, schemaStr="vec string, id bigint")
vecFill = VectorImputerTrainBatchOp().setSelectedCol("vec")
model = data.link(vecFill)
VectorImputerPredictStreamOp(model).setOutputCol("vec1").linkFrom(dataStream).print()
StreamOperator.execute()
Java 代码
import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.dataproc.vector.VectorImputerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.dataproc.vector.VectorImputerPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class VectorImputerPredictStreamOpTest {
@Test
public void testVectorImputerPredictStreamOp() throws Exception {
List <Row> df = Arrays.asList(
Row.of("1:3 2:4 4:7", 1),
Row.of("0:3 5:5", 3),
Row.of("2:4 4:5", 4)
);
StreamOperator <?> dataStream = new MemSourceStreamOp(df, "vec string, id int");
BatchOperator <?> data = new MemSourceBatchOp(df, "vec string, id int");
BatchOperator <?> vecFill = new VectorImputerTrainBatchOp().setSelectedCol("vec");
BatchOperator <?> model = data.link(vecFill);
new VectorImputerPredictStreamOp(model).setOutputCol("vec1").linkFrom(dataStream).print();
StreamOperator.execute();
}
}
运行结果
| vec | id | vec1 | | —- | —- | —- |
| 1:3,2:4,4:7 | 1 | 1:3.0 2:4.0 4:7.0 |
| 1:3,2:NaN | 3 | 1:3.0 2:4.0 |
| 2:4,4:5 | 4 | 2:4.0 4:5.0 |