Java 类名:com.alibaba.alink.operator.batch.similarity.StringApproxNearestNeighborPredictBatchOp
Python 类名:StringApproxNearestNeighborPredictBatchOp
功能介绍
该功能由训练和预测组成,支持计算1. 求最近邻topN 2. 求radius范围内的邻居。该功能由预测时候的topN和radius参数控制, 如果填写了topN,则输出最近邻,如果填写了radius,则输出radius范围内的邻居。
SimhashHamming(SimHash_Hamming_Distance)相似度=1-距离/64.0,应选择metric的参数为SIMHASH_HAMMING_SIM。
MinHash应选择metric的参数为MINHASH_SIM。
Jaccard应选择metric的参数为JACCARD_SIM。
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 |
---|---|---|---|---|---|---|
selectedCol | 选中的列名 | 计算列对应的列名 | String | ✓ | ||
modelFilePath | 模型的文件路径 | 模型的文件路径 | String | null | ||
outputCol | 输出结果列 | 输出结果列列名,可选,默认null | String | null | ||
radius | radius值 | radius值 | Double | null | ||
reservedCols | 算法保留列名 | 算法保留列 | String[] | null | ||
topN | TopN的值 | TopN的值 | Integer | [1, +inf) | 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([
[0, "abcde", "aabce"],
[1, "aacedw", "aabbed"],
[2, "cdefa", "bbcefa"],
[3, "bdefh", "ddeac"],
[4, "acedm", "aeefbc"]
])
inOp = BatchOperator.fromDataframe(df, schemaStr='id long, text1 string, text2 string')
train = StringApproxNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("text1").setMetric("SIMHASH_HAMMING_SIM").linkFrom(inOp)
predict = StringApproxNearestNeighborPredictBatchOp().setSelectedCol("text2").setTopN(3).linkFrom(train, inOp)
predict.print()
Java 代码
import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.similarity.StringApproxNearestNeighborPredictBatchOp;
import com.alibaba.alink.operator.batch.similarity.StringApproxNearestNeighborTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class StringApproxNearestNeighborPredictBatchOpTest {
@Test
public void testStringApproxNearestNeighborPredictBatchOp() throws Exception {
List <Row> df = Arrays.asList(
Row.of(0, "abcde", "aabce"),
Row.of(1, "aacedw", "aabbed"),
Row.of(2, "cdefa", "bbcefa"),
Row.of(3, "bdefh", "ddeac"),
Row.of(4, "acedm", "aeefbc")
);
BatchOperator <?> inOp = new MemSourceBatchOp(df, "id int, text1 string, text2 string");
BatchOperator <?> train = new StringApproxNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("text1")
.setMetric("SIMHASH_HAMMING_SIM").linkFrom(inOp);
BatchOperator <?> predict = new StringApproxNearestNeighborPredictBatchOp().setSelectedCol("text2").setTopN(3)
.linkFrom(train, inOp);
predict.print();
}
}
运行结果
id | text1 | text2 |
---|---|---|
0 | abcde | {“ID”:”[0,1,2]”,”METRIC”:”[0.953125,0.921875,0… |
1 | aacedw | {“ID”:”[0,1,4]”,”METRIC”:”[0.9375,0.90625,0.85… |
2 | cdefa | {“ID”:”[0,1,4]”,”METRIC”:”[0.890625,0.859375,0… |
3 | bdefh | {“ID”:”[4,2,1]”,”METRIC”:”[0.9375,0.90625,0.89… |
4 | acedm | {“ID”:”[1,0,4]”,”METRIC”:”[0.921875,0.921875,0… |