向量标准化预测 (VectorStandardScalerPredictBatchOp)

Java 类名：com.alibaba.alink.operator.batch.dataproc.vector.VectorStandardScalerPredictBatchOp
Python 类名：VectorStandardScalerPredictBatchOp

功能介绍

标准化是对向量数据进行按正态化处理的组件
VectorStandardScalerTrainBatchOp 计算向量的每一列的均值和方差，组件可以指定默认均值为0，标准差为1。
生成向量标准化的模型，在 VectorStandardScalerPredictBatchOp 中加载，对数据做标准化处理。
输入的向量可以同时包含稀疏向量和稠密向量，向量维度也可以不相同。输入稠密向量维度不够时，没有的维度默认为0。

参数说明

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"]
])
data = BatchOperator.fromDataframe(df, schemaStr="col string, vector string")
trainOp = VectorStandardScalerTrainBatchOp().setSelectedCol("vector")
model = trainOp.linkFrom(data)
VectorStandardScalerPredictBatchOp().linkFrom(model, data).collectToDataframe()

Java 代码

import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.dataproc.vector.VectorStandardScalerPredictBatchOp;
import com.alibaba.alink.operator.batch.dataproc.vector.VectorStandardScalerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class VectorStandardScalerPredictBatchOpTest {
    @Test
    public void testVectorStandardScalerPredictBatchOp() 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")
        );
        BatchOperator <?> data = new MemSourceBatchOp(df, "col string, vector string");
        BatchOperator <?> trainOp = new VectorStandardScalerTrainBatchOp().setSelectedCol("vector");
        BatchOperator <?> model = trainOp.linkFrom(data);
        new VectorStandardScalerPredictBatchOp().linkFrom(model, data).print();
    }
}

运行结果

| col1 | vec | | —- | —- |

| a | -0.07835182408093559,1.4595814453461897 |

| c | 1.2269606224811418,-0.6520885789229323 |

| b | -0.2549018445693762,-0.4814485769617911 |

| a | -0.20280511721213143,-0.6520885789229323 |

| c | 1.237090541689495,-0.6520885789229323 |

| b | -0.25924323851581327,-0.4814485769617911 |

| d | -1.6687491397923802,1.4595814453461897 |