标准化流预测 (StandardScalerPredictStreamOp)

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 |