Java 类名:com.alibaba.alink.operator.batch.dataproc.StandardScalerPredictBatchOp
Python 类名:StandardScalerPredictBatchOp
功能介绍
标准化是对数据进行按正态化处理的组件
使用标准化训练组件训练的模型,对数据做标准化处理
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 |
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()
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 org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class StandardScalerPredictBatchOpTest {
@Test
public void testStandardScalerPredictBatchOp() 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();
}
}
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 |