Java 类名:com.alibaba.alink.operator.stream.dataproc.StandardScalerPredictStreamOp
Python 类名:StandardScalerPredictStreamOp
功能介绍
- 标准化流式预测是对数据进行按正态化处理的组件
- 需要加载StandardScalerTrainBatchOp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 |
| outputCols | 输出结果列列名数组 | 输出结果列列名数组,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([
["a", 10.0, 100],
["b", -2.5, 9],
["c", 100.2, 1],
["d", -99.9, 100],
["a", 1.4, 1],
["b", -2.2, 9],
["c", 100.9, 1]
])
colnames = ["col1", "col2", "col3"]
selectedColNames = ["col2", "col3"]
inOp = BatchOperator.fromDataframe(df, schemaStr='col1 string, col2 double, col3 long')
# train
trainOp = StandardScalerTrainBatchOp()\
.setSelectedCols(selectedColNames)
trainOp.linkFrom(inOp)
# batch predict
predictOp = StandardScalerPredictBatchOp()
predictOp.linkFrom(trainOp, inOp).print()
# stream predict
sinOp = StreamOperator.fromDataframe(df, schemaStr='col1 string, col2 double, col3 long')
predictStreamOp = StandardScalerPredictStreamOp(trainOp)
predictStreamOp.linkFrom(sinOp).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.StandardScalerPredictBatchOp;
import com.alibaba.alink.operator.batch.dataproc.StandardScalerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.dataproc.StandardScalerPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class StandardScalerPredictStreamOpTest {
@Test
public void testStandardScalerPredictStreamOp() throws Exception {
List <Row> df = Arrays.asList(
Row.of("a", 10.0, 100),
Row.of("b", -2.5, 9),
Row.of("c", 100.2, 1),
Row.of("d", -99.9, 100),
Row.of("a", 1.4, 1),
Row.of("b", -2.2, 9),
Row.of("c", 100.9, 1)
);
String[] selectedColNames = new String[] {"col2", "col3"};
BatchOperator <?> inOp = new MemSourceBatchOp(df, "col1 string, col2 double, col3 int");
BatchOperator <?> trainOp = new StandardScalerTrainBatchOp()
.setSelectedCols(selectedColNames);
trainOp.linkFrom(inOp);
BatchOperator <?> predictOp = new StandardScalerPredictBatchOp();
predictOp.linkFrom(trainOp, inOp).print();
StreamOperator <?> sinOp = new MemSourceStreamOp(df, "col1 string, col2 double, col3 int");
StreamOperator <?> predictStreamOp = new StandardScalerPredictStreamOp(trainOp);
predictStreamOp.linkFrom(sinOp).print();
StreamOperator.execute();
}
}
运行结果
| col1 | col2 | col3 | | —- | —- | —- |
| a | -0.0784 | 1.4596 |
| b | -0.2592 | -0.4814 |
| c | 1.2270 | -0.6521 |
| d | -1.6687 | 1.4596 |
| a | -0.2028 | -0.6521 |
| b | -0.2549 | -0.4814 |
| c | 1.2371 | -0.6521 |
| col1 | col2 | col3 | | —- | —- | —- |
| b | -0.2592 | -0.4814 |
| d | -1.6687 | 1.4596 |
| c | 1.2270 | -0.6521 |
| b | -0.2549 | -0.4814 |
| c | 1.2371 | -0.6521 |
| a | -0.2028 | -0.6521 |
| a | -0.0784 | 1.4596 |