Java 类名:com.alibaba.alink.operator.batch.dataproc.vector.VectorAssemblerBatchOp
Python 类名:VectorAssemblerBatchOp
| 名称 | 中文名称 | 描述 | 类型 | 是否必须? | 取值范围 | 默认值 |
|---|---|---|---|---|---|---|
| outputCol | 输出结果列列名 | 输出结果列列名,必选 | String | ✓ | ||
| selectedCols | 选择的列名 | 计算列对应的列名列表 | String[] | ✓ | 所选列类型为 [BIGDECIMAL, BIGINTEGER, BYTE, DENSE_VECTOR, DOUBLE, FLOAT, INTEGER, LONG, SHORT, SPARSE_VECTOR, STRING, VECTOR] | |
| handleInvalidMethod | 处理无效值的方法 | 处理无效值的方法,可取 error, skip | String | “ERROR”, “SKIP” | “ERROR” | |
| reservedCols | 算法保留列名 | 算法保留列 | String[] | null | ||
| numThreads | 组件多线程线程个数 | 组件多线程线程个数 | Integer | 1 |
from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
[2, 1, 1],
[3, 2, 1],
[4, 3, 2],
[2, 4, 1],
[2, 2, 1],
[4, 3, 2],
[1, 2, 1],
[5, 3, 3]
])
data = BatchOperator.fromDataframe(df, schemaStr="f0 int, f1 int, f2 int")
colnames = ["f0","f1","f2"]
VectorAssemblerBatchOp().setSelectedCols(colnames)\
.setOutputCol("out").linkFrom(data).print()
import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.dataproc.vector.VectorAssemblerBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class VectorAssemblerBatchOpTest {
@Test
public void testVectorAssemblerBatchOp() throws Exception {
List <Row> df = Arrays.asList(
Row.of(2, 1, 1),
Row.of(3, 2, 1),
Row.of(4, 3, 2),
Row.of(2, 4, 1),
Row.of(2, 2, 1),
Row.of(4, 3, 2),
Row.of(1, 2, 1),
Row.of(5, 3, 3)
);
BatchOperator <?> data = new MemSourceBatchOp(df, "f0 int, f1 int, f2 int");
new VectorAssemblerBatchOp().setSelectedCols("f0", "f1", "f2")
.setOutputCol("out").linkFrom(data).print();
}
}
| f0 | f1 | f2 | out |
|---|---|---|---|
| 2 | 1 | 1 | 2.0 1.0 1.0 |
| 3 | 2 | 1 | 3.0 2.0 1.0 |
| 4 | 3 | 2 | 4.0 3.0 2.0 |
| 2 | 4 | 1 | 2.0 4.0 1.0 |
| 2 | 2 | 1 | 2.0 2.0 1.0 |
| 4 | 3 | 2 | 4.0 3.0 2.0 |
| 1 | 2 | 1 | 1.0 2.0 1.0 |
| 5 | 3 | 3 | 5.0 3.0 3.0 |