多层感知机分类预测 (MultilayerPerceptronPredictStreamOp)

Java 类名：com.alibaba.alink.operator.stream.classification.MultilayerPerceptronPredictStreamOp

Python 类名：MultilayerPerceptronPredictStreamOp

功能介绍

多层感知机（MLP，Multilayer Perceptron）也被称作人工神经网络（ANN，Artificial Neural Network），经常用来进行多分类问题的训练预测。

算法原理

多层感知机算法除了输入输出层外，它中间可以有多个隐层，最简单的MLP只含一个隐层，即三层的结构，如下图：

从上图可以看到，多层感知机层与层之间是全连接的。多层感知机最左边是输入层，中间是隐藏层，最后是输出层。 其中输出层对应的是各个分类标签，输出层
的每一个节点对应每一个标签的出现的概率。

算法使用

多层感知机主要用于多分类问题，类似文字识别，语音识别，文本分析等问题。

文献

[1]Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
MW Gardner, SR Dorling - Atmospheric environment, 1998 - Elsevier.

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
predictionDetailCol	预测详细信息列名	预测详细信息列名	String
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([
    [5,2,3.5,1,'Iris-versicolor'],
    [5.1,3.7,1.5,0.4,'Iris-setosa'],
    [6.4,2.8,5.6,2.2,'Iris-virginica'],
    [6,2.9,4.5,1.5,'Iris-versicolor'],
    [4.9,3,1.4,0.2,'Iris-setosa'],
    [5.7,2.6,3.5,1,'Iris-versicolor'],
    [4.6,3.6,1,0.2,'Iris-setosa'],
    [5.9,3,4.2,1.5,'Iris-versicolor'],
    [6.3,2.8,5.1,1.5,'Iris-virginica'],
    [4.7,3.2,1.3,0.2,'Iris-setosa'],
    [5.1,3.3,1.7,0.5,'Iris-setosa'],
    [5.5,2.4,3.8,1.1,'Iris-versicolor'],
])

train_data = BatchOperator.fromDataframe(df, schemaStr='sepal_length double, sepal_width double, petal_length double, petal_width double, category string')

test_data = StreamOperator.fromDataframe(df, schemaStr='sepal_length double, sepal_width double, petal_length double, petal_width double, category string')

mlpc = MultilayerPerceptronTrainBatchOp() \
    .setFeatureCols(["sepal_length", "sepal_width", "petal_length", "petal_width"]) \
    .setLabelCol("category") \
    .setLayers([4, 5, 3]) \
    .setMaxIter(10)

model = mlpc.linkFrom(train_data)

predictor = MultilayerPerceptronPredictStreamOp(model)\
  .setPredictionCol('p')

predictor.linkFrom(test_data).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.classification.MultilayerPerceptronTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.classification.MultilayerPerceptronPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

import java.util.Arrays;
import java.util.List;

public class MultilayerPerceptronPredictStreamOpTest {
	@Test
	public void testMultilayerPerceptronPredictStreamOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of(5.0, 2.0, 3.5, 1.0, "Iris-versicolor"),
			Row.of(5.1, 3.7, 1.5, 0.4, "Iris-setosa"),
			Row.of(6.4, 2.8, 5.6, 2.2, "Iris-virginica"),
			Row.of(6.0, 2.9, 4.5, 1.5, "Iris-versicolor"),
			Row.of(4.9, 3.0, 1.4, 0.2, "Iris-setosa"),
			Row.of(5.7, 2.6, 3.5, 1.0, "Iris-versicolor"),
			Row.of(4.6, 3.6, 1.0, 0.2, "Iris-setosa"),
			Row.of(5.9, 3.0, 4.2, 1.5, "Iris-versicolor"),
			Row.of(6.3, 2.8, 5.1, 1.5, "Iris-virginica"),
			Row.of(4.7, 3.2, 1.3, 0.2, "Iris-setosa"),
			Row.of(5.1, 3.3, 1.7, 0.5, "Iris-setosa"),
			Row.of(5.5, 2.4, 3.8, 1.1, "Iris-versicolor")
		);
		BatchOperator <?> train_data = new MemSourceBatchOp(df,
			"sepal_length double, sepal_width double, petal_length double, petal_width double, category string");
		StreamOperator <?> test_data = new MemSourceStreamOp(df,
			"sepal_length double, sepal_width double, petal_length double, petal_width double, category string");
		BatchOperator <?> mlpc = new MultilayerPerceptronTrainBatchOp()
			.setFeatureCols("sepal_length", "sepal_width", "petal_length", "petal_width")
			.setLabelCol("category")
			.setLayers(new int[] {4, 5, 3})
			.setMaxIter(10);
		BatchOperator <?> model = mlpc.linkFrom(train_data);
		StreamOperator <?> predictor = new MultilayerPerceptronPredictStreamOp(model)
			.setPredictionCol("p");
		predictor.linkFrom(test_data).print();
		StreamOperator.execute();
	}
}

运行结果

sepal_length	sepal_width	petal_length	petal_width	category	p
5.0000	2.0000	3.5000	1.0000	Iris-versicolor	Iris-versicolor
5.1000	3.7000	1.5000	0.4000	Iris-setosa	Iris-versicolor
6.4000	2.8000	5.6000	2.2000	Iris-virginica	Iris-versicolor
6.0000	2.9000	4.5000	1.5000	Iris-versicolor	Iris-versicolor
4.9000	3.0000	1.4000	0.2000	Iris-setosa	Iris-versicolor
5.7000	2.6000	3.5000	1.0000	Iris-versicolor	Iris-versicolor
4.6000	3.6000	1.0000	0.2000	Iris-setosa	Iris-setosa
5.9000	3.0000	4.2000	1.5000	Iris-versicolor	Iris-versicolor
6.3000	2.8000	5.1000	1.5000	Iris-virginica	Iris-versicolor
4.7000	3.2000	1.3000	0.2000	Iris-setosa	Iris-versicolor
5.1000	3.3000	1.7000	0.5000	Iris-setosa	Iris-versicolor
5.5000	2.4000	3.8000	1.1000	Iris-versicolor	Iris-versicolor