绝对值最大化流预测 (MaxAbsScalerPredictStreamOp)
Java 类名:com.alibaba.alink.operator.stream.dataproc.MaxAbsScalerPredictStreamOp
Python 类名:MaxAbsScalerPredictStreamOp
功能介绍
- 绝对值最大标准化是对数据按照最大值和最小值进行标准化的组件, 将数据归一到-1和1之间。
- 需要读入MaxAbsScalerTrainBatchOp生成的模型
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 = MaxAbsScalerTrainBatchOp()\
.setSelectedCols(selectedColNames)
trainOp.linkFrom(inOp)
# batch predict
predictOp = MaxAbsScalerPredictBatchOp()
predictOp.linkFrom(trainOp, inOp).print()
# stream predict
sinOp = StreamOperator.fromDataframe(df, schemaStr='col1 string, col2 double, col3 long')
predictStreamOp = MaxAbsScalerPredictStreamOp(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.MaxAbsScalerPredictBatchOp;
import com.alibaba.alink.operator.batch.dataproc.MaxAbsScalerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.dataproc.MaxAbsScalerPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class MaxAbsScalerPredictStreamOpTest {
@Test
public void testMaxAbsScalerPredictStreamOp() 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 MaxAbsScalerTrainBatchOp()
.setSelectedCols(selectedColNames);
trainOp.linkFrom(inOp);
BatchOperator <?> predictOp = new MaxAbsScalerPredictBatchOp();
predictOp.linkFrom(trainOp, inOp).print();
StreamOperator <?> sinOp = new MemSourceStreamOp(df, "col1 string, col2 double, col3 int");
StreamOperator <?> predictStreamOp = new MaxAbsScalerPredictStreamOp(trainOp);
predictStreamOp.linkFrom(sinOp).print();
StreamOperator.execute();
}
}
运行结果
col1 |
col2 |
col3 |
a |
0.0991 |
1.0000 |
b |
-0.0248 |
0.0900 |
c |
0.9931 |
0.0100 |
d |
-0.9901 |
1.0000 |
a |
0.0139 |
0.0100 |
b |
-0.0218 |
0.0900 |
c |
1.0000 |
0.0100 |
col1 |
col2 |
col3 |
b |
-0.0248 |
0.0900 |
d |
-0.9901 |
1.0000 |
a |
0.0139 |
0.0100 |
c |
0.9931 |
0.0100 |
c |
1.0000 |
0.0100 |
a |
0.0991 |
1.0000 |
b |
-0.0218 |
0.0900 |