IndexToString预测 (IndexToStringPredictStreamOp)

Java 类名：com.alibaba.alink.operator.stream.dataproc.IndexToStringPredictStreamOp
Python 类名：IndexToStringPredictStreamOp

功能介绍

基于 StringIndexer 模型，将一列整数映射为字符串。
在流式预测中，IndexToStringPredictStreamOp 在创建对象时，需要指定模型数据
（StringIndexer的getModelData()获取，或者直接输入StringIndexerTrainBatchOp）。
在LinkFrom中指定流式数据。

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 | | —- | —- | —- | —- | —- | —- | —- |

| modelName | 模型名字 | 模型名字 | String | ✓ | | |

| selectedCol | 选中的列名 | 计算列对应的列名 | String | ✓ | 所选列类型为 [LONG] | |

| 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 |

| 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 |

| modelStreamFilePath | 模型流的文件路径 | 模型流的文件路径 | String | | | null |

| modelStreamScanInterval | 扫描模型路径的时间间隔 | 描模型路径的时间间隔，单位秒 | Integer | | | 10 |

| modelStreamStartTime | 模型流的起始时间 | 模型流的起始时间。默认从当前时刻开始读。使用yyyy-mm-dd hh:mm:ss.fffffffff格式，详见Timestamp.valueOf(String s) | String | | | null |

代码示例

Python 代码

from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df_data = pd.DataFrame([
    ["football"],
    ["football"],
    ["football"],
    ["basketball"],
    ["basketball"],
    ["tennis"],
])
train_data = BatchOperator.fromDataframe(df_data, schemaStr='f0 string')
data = StreamOperator.fromDataframe(df_data, schemaStr='f0 string')
stringIndexer = StringIndexer() \
    .setModelName("string_indexer_model") \
    .setSelectedCol("f0") \
    .setOutputCol("f0_indexed") \
    .setStringOrderType("frequency_asc").fit(train_data)
indexed = stringIndexer.transform(data)
indexToStrings = IndexToStringPredictStreamOp(stringIndexer.getModelData()) \
    .setSelectedCol("f0_indexed") \
    .setOutputCol("f0_indxed_unindexed")
indexToStrings.linkFrom(indexed).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.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.dataproc.IndexToStringPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import com.alibaba.alink.pipeline.dataproc.StringIndexer;
import com.alibaba.alink.pipeline.dataproc.StringIndexerModel;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class IndexToStringPredictStreamOpTest {
    @Test
    public void testIndexToStringPredictStreamOp() throws Exception {
        List <Row> df_data = Arrays.asList(
            Row.of("football"),
            Row.of("football"),
            Row.of("football"),
            Row.of("basketball"),
            Row.of("basketball"),
            Row.of("tennis")
        );
        BatchOperator <?> train_data = new MemSourceBatchOp(df_data, "f0 string");
        StreamOperator <?> data = new MemSourceStreamOp(df_data, "f0 string");
        StringIndexerModel stringIndexer = new StringIndexer()
            .setModelName("string_indexer_model")
            .setSelectedCol("f0")
            .setOutputCol("f0_indexed")
            .setStringOrderType("frequency_asc").fit(train_data);
        StreamOperator indexed = stringIndexer.transform(data);
        StreamOperator <?> indexToStrings = new IndexToStringPredictStreamOp(stringIndexer.getModelData())
            .setSelectedCol("f0_indexed")
            .setOutputCol("f0_indxed_unindexed");
        indexToStrings.linkFrom(indexed).print();
        StreamOperator.execute();
    }
}

运行结果

| f0 | f0_indexed | f0_indxed_unindexed | | —- | —- | —- |

| football | 2 | football |

| football | 2 | football |

| football | 2 | football |

| basketball | 1 | basketball |

| basketball | 1 | basketball |

| tennis | 0 | tennis |