主成分分析预测 (PcaPredictStreamOp)

Java 类名：com.alibaba.alink.operator.stream.feature.PcaPredictStreamOp
Python 类名：PcaPredictStreamOp

功能介绍

主成分分析，是考察多个变量间相关性一种多元统计方法，研究如何通过少数几个主成分来揭示多个变量间的内部结构，即从原始变量中导出少数几个主成分，使它们尽可能多地保留原始变量的信息，且彼此间互不相关，作为新的综合指标。详细介绍请见维基百科链接wiki。
主成分分析功能包含主成分分析训练和主成分分析预测（批和流)。

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

| predictionCol | 预测结果列名 | 预测结果列名 | String | ✓ | | |

| modelFilePath | 模型的文件路径 | 模型的文件路径 | String | | | null |

| reservedCols | 算法保留列名 | 算法保留列 | String[] | | | null |

| vectorCol | 向量列名 | 向量列对应的列名，默认值是null | String | | 所选列类型为 [DENSE_VECTOR, SPARSE_VECTOR, STRING, VECTOR] | 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 = pd.DataFrame([
    ["a", 1, 1, 2.0, True],
    ["c", 1, 2, -3.0, True],
    ["a", 2, 2, 2.0, False],
    ["c", 0, 0, 0.0, False]
    ])
batchSource = BatchOperator.fromDataframe(df, schemaStr='f_string string, f_long long, f_int int, f_double double, f_boolean boolean')
streamSource = StreamOperator.fromDataframe(df, schemaStr='f_string string, f_long long, f_int int, f_double double, f_boolean boolean')
trainOp = QuantileDiscretizerTrainBatchOp()\
    .setSelectedCols(['f_double'])\
    .setNumBuckets(8)\
    .linkFrom(batchSource)
predictBatchOp = QuantileDiscretizerPredictBatchOp()\
    .setSelectedCols(['f_double'])
predictBatchOp.linkFrom(trainOp,batchSource).print()
predictStreamOp = QuantileDiscretizerPredictStreamOp(trainOp)\
    .setSelectedCols(['f_double'])
predictStreamOp.linkFrom(streamSource).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.QuantileDiscretizerPredictBatchOp;
import com.alibaba.alink.operator.batch.feature.QuantileDiscretizerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.feature.QuantileDiscretizerPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class PcaPredictStreamOpTest {
    @Test
    public void testPcaPredictStreamOp() throws Exception {
        List <Row> df = Arrays.asList(
            Row.of("a", 1, 1, 2.0, true),
            Row.of("c", 1, 2, -3.0, true),
            Row.of("a", 2, 2, 2.0, false),
            Row.of("c", 0, 0, 0.0, false)
        );
        BatchOperator <?> batchSource = new MemSourceBatchOp(df,
            "f_string string, f_long int, f_int int, f_double double, f_boolean boolean");
        StreamOperator <?> streamSource = new MemSourceStreamOp(df,
            "f_string string, f_long int, f_int int, f_double double, f_boolean boolean");
        BatchOperator <?> trainOp = new QuantileDiscretizerTrainBatchOp()
            .setSelectedCols("f_double")
            .setNumBuckets(8)
            .linkFrom(batchSource);
        BatchOperator <?> predictBatchOp = new QuantileDiscretizerPredictBatchOp()
            .setSelectedCols("f_double");
        predictBatchOp.linkFrom(trainOp, batchSource).print();
        StreamOperator <?> predictStreamOp = new QuantileDiscretizerPredictStreamOp(trainOp)
            .setSelectedCols("f_double");
        predictStreamOp.linkFrom(streamSource).print();
        StreamOperator.execute();
    }
}

运行结果

批预测结果

| f_string | f_long | f_int | f_double | f_boolean | | —- | —- | —- | —- | —- |

| a | 1 | 1 | 2 | true |

| c | 1 | 2 | 0 | true |

| a | 2 | 2 | 2 | false |

| c | 0 | 0 | 1 | false |

流预测结果

| f_string | f_long | f_int | f_double | f_boolean | | —- | —- | —- | —- | —- |

| a | 2 | 2 | 2 | false |

| c | 1 | 2 | 0 | true |

| c | 0 | 0 | 1 | false |

| a | 1 | 1 | 2 | true |