Java 类名:com.alibaba.alink.pipeline.dataproc.format.KvToVector
Python 类名:KvToVector
功能介绍
将数据格式从 Kv 转成 Vector
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 |
---|---|---|---|---|---|---|
kvCol | KV列名 | KV列的列名 | String | ✓ | ||
vectorCol | 向量列名 | 向量列对应的列名 | String | ✓ | ||
handleInvalid | 解析异常处理策略 | 解析异常处理策略,可选为ERROR(抛出异常)或者SKIP(输出NULL) | String | “ERROR”, “SKIP” | “ERROR” | |
kvColDelimiter | 分隔符 | 当输入数据为稀疏格式时,key-value对之间的分隔符 | String | “,” | ||
kvValDelimiter | 分隔符 | 当输入数据为稀疏格式时,key和value的分割符 | String | “:” | ||
reservedCols | 算法保留列名 | 算法保留列 | String[] | null | ||
vectorSize | 向量长度 | 向量长度 | Long | -1 |
代码示例
Python 代码
from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
['1', '{"f0":"1.0","f1":"2.0"}', '$3$0:1.0 1:2.0', '0:1.0,1:2.0', '1.0,2.0', 1.0, 2.0],
['2', '{"f0":"4.0","f1":"8.0"}', '$3$0:4.0 1:8.0', '0:4.0,1:8.0', '4.0,8.0', 4.0, 8.0]])
data = BatchOperator.fromDataframe(df, schemaStr="row string, json string, vec string, kv string, csv string, f0 double, f1 double")
op = KvToVector()\
.setKvCol("kv")\
.setReservedCols(["row"])\
.setVectorCol("vec")\
.setVectorSize(5)\
.transform(data)
op.print()
Java 代码
import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.pipeline.dataproc.format.KvToVector;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class KvToVectorTest {
@Test
public void testKvToVector() throws Exception {
List <Row> df = Arrays.asList(
Row.of("1", "{\"f0\":\"1.0\",\"f1\":\"2.0\"}", "$3$0:1.0 1:2.0", "0:1.0,1:2.0", "1.0,2.0", 1.0, 2.0)
);
BatchOperator <?> data = new MemSourceBatchOp(df,
"row string, json string, vec string, kv string, csv string, f0 double, f1 double");
BatchOperator op = new KvToVector()
.setKvCol("kv")
.setReservedCols("row")
.setVectorCol("vec")
.setVectorSize(5)
.transform(data);
op.print();
}
}
运行结果
| row | vec | | —- | —- |
| 1 | $5$1.0 2.0 |
| 2 | $5$4.0 8.0 |