Java 类名：com.alibaba.alink.operator.batch.feature.FeatureHasherBatchOp
Python 类名：FeatureHasherBatchOp
功能介绍
将多个特征组合成一个特征向量。
参数说明
|
名称 |
中文名称 |
描述 |
类型 |
是否必须？ |
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[] |
? |
|
|
categoricalCols |
离散特征列名 |
离散特征列名 |
String[] |
||
|
numFeatures |
向量维度 |
生成向量长度 |
Integer |
262144 |
|
|
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 |
代码示例
Python 代码
from pyalink.alink import * import pandas as pd useLocalEnv(1) df = pd.DataFrame([ [1.1, True, "2", "A"], [1.1, False, "2", "B"], [1.1, True, "1", "B"], [2.2, True, "1", "A"] ]) inOp1 = BatchOperator.fromDataframe(df, schemaStr=‘double double, bool boolean, number int, str string‘) inOp2 = StreamOperator.fromDataframe(df, schemaStr=‘double double, bool boolean, number int, str string‘) hasher = FeatureHasherBatchOp().setSelectedCols(["double", "bool", "number", "str"]).setOutputCol("output").setNumFeatures(200) hasher.linkFrom(inOp1).print() hasher = FeatureHasherStreamOp().setSelectedCols(["double", "bool", "number", "str"]).setOutputCol("output").setNumFeatures(200) hasher.linkFrom(inOp2).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.feature.FeatureHasherBatchOp; import com.alibaba.alink.operator.batch.source.MemSourceBatchOp; import com.alibaba.alink.operator.stream.StreamOperator; import com.alibaba.alink.operator.stream.feature.FeatureHasherStreamOp; import com.alibaba.alink.operator.stream.source.MemSourceStreamOp; import org.junit.Test; import java.util.Arrays; import java.util.List; public class FeatureHasherBatchOpTest { @Test public void testFeatureHasherBatchOp() throws Exception { List <Row> df = Arrays.asList( Row.of(1.1, true, 2, "A"), Row.of(1.1, false, 2, "B"), Row.of(1.1, true, 1, "B"), Row.of(2.2, true, 1, "A") ); BatchOperator <?> inOp1 = new MemSourceBatchOp(df, "double double, bool boolean, number int, str string"); StreamOperator <?> inOp2 = new MemSourceStreamOp(df, "double double, bool boolean, number int, str string"); BatchOperator <?> hasher = new FeatureHasherBatchOp().setSelectedCols("double", "bool", "number", "str") .setOutputCol("output").setNumFeatures(200); hasher.linkFrom(inOp1).print(); StreamOperator <?> hasher2 = new FeatureHasherStreamOp().setSelectedCols("double", "bool", "number", "str") .setOutputCol("output").setNumFeatures(200); hasher2.linkFrom(inOp2).print(); StreamOperator.execute(); } }
运行结果
输出数据
|
double |
bool |
number |
str |
output |
|
1.1000 |
true |
2 |
A |
$200$13:2.0 38:1.1 45:1.0 195:1.0 |
|
1.1000 |
false |
2 |
B |
$200$13:2.0 30:1.0 38:1.1 76:1.0 |
|
1.1000 |
true |
1 |
B |
$200$13:1.0 38:1.1 76:1.0 195:1.0 |
|
2.2000 |
true |
1 |
A |
$200$13:1.0 38:2.2 45:1.0 195:1.0 |