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随机森林预测 (RandomForestPredictBatchOp)

Java 类名：com.alibaba.alink.operator.batch.classification.RandomForestPredictBatchOp

Python 类名：RandomForestPredictBatchOp

功能介绍

随机森林一种经典的有监督学习非线性决策树模型，可以解决分类，回归和其他的一些决策树模型可以解决的问题，通常可以拿到比单决策树更好的效果。

算法原理

通过 Bagging 的方法组合多棵决策树，生成最终的模型。

参数说明

名称	中文名称	描述	类型	是否必须？	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
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([
    [1.0, "A", 0, 0, 0],
    [2.0, "B", 1, 1, 0],
    [3.0, "C", 2, 2, 1],
    [4.0, "D", 3, 3, 1]
])
batchSource = BatchOperator.fromDataframe(
    df, schemaStr=' f0 double, f1 string, f2 int, f3 int, label int')
streamSource = StreamOperator.fromDataframe(
    df, schemaStr=' f0 double, f1 string, f2 int, f3 int, label int')

trainOp = RandomForestTrainBatchOp()\
    .setLabelCol('label')\
    .setFeatureCols(['f0', 'f1', 'f2', 'f3'])\
    .linkFrom(batchSource)
predictBatchOp = RandomForestPredictBatchOp()\
    .setPredictionDetailCol('pred_detail')\
    .setPredictionCol('pred')
predictStreamOp = RandomForestPredictStreamOp(trainOp)\
    .setPredictionDetailCol('pred_detail')\
    .setPredictionCol('pred')

predictBatchOp.linkFrom(trainOp, batchSource).print()
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.classification.RandomForestPredictBatchOp;
import com.alibaba.alink.operator.batch.classification.RandomForestTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.classification.RandomForestPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class RandomForestPredictBatchOpTest {
	@Test
	public void testRandomForestPredictBatchOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of(1.0, "A", 0, 0, 0),
			Row.of(2.0, "B", 1, 1, 0),
			Row.of(3.0, "C", 2, 2, 1),
			Row.of(4.0, "D", 3, 3, 1)
		);

		BatchOperator <?> batchSource = new MemSourceBatchOp(
			df, " f0 double, f1 string, f2 int, f3 int, label int");
		StreamOperator <?> streamSource = new MemSourceStreamOp(
			df, " f0 double, f1 string, f2 int, f3 int, label int");
		BatchOperator <?> trainOp = new RandomForestTrainBatchOp()
			.setLabelCol("label")
			.setFeatureCols("f0", "f1", "f2", "f3")
			.linkFrom(batchSource);
		BatchOperator <?> predictBatchOp = new RandomForestPredictBatchOp()
			.setPredictionDetailCol("pred_detail")
			.setPredictionCol("pred");
		StreamOperator <?> predictStreamOp = new RandomForestPredictStreamOp(trainOp)
			.setPredictionDetailCol("pred_detail")
			.setPredictionCol("pred");
		predictBatchOp.linkFrom(trainOp, batchSource).print();
		predictStreamOp.linkFrom(streamSource).print();
		StreamOperator.execute();
	}
}

运行结果

f0	f1	f2	f3	label	pred	pred_detail
1.0000	A	0	0	0	0	{“0”:1.0,“1”:0.0}
2.0000	B	1	1	0	0	{“0”:1.0,“1”:0.0}
3.0000	C	2	2	1	1	{“0”:0.0,“1”:1.0}
4.0000	D	3	3	1	1	{“0”:0.0,“1”:1.0}

算法使用

我们给定 Adult 数据集，在这个场景下介绍随机森林的使用步骤

数据集

Adult

训练集

训练数据集的基本统计结果为

Adult train
Summary:

colName	count	missing	sum	mean	variance	min	max
age	32560	0	1256214	38.5815	186.0665	17	90
workclass	32560	1836	NaN	NaN	NaN	NaN	NaN
fnlwgt	32560	0	6179243539	189780.207	11141029667.4508	12285	1484705
education	32560	0	NaN	NaN	NaN	NaN	NaN
education_num	32560	0	328231	10.0808	6.6186	1	16
marital_status	32560	0	NaN	NaN	NaN	NaN	NaN
occupation	32560	1843	NaN	NaN	NaN	NaN	NaN
relationship	32560	0	NaN	NaN	NaN	NaN	NaN
race	32560	0	NaN	NaN	NaN	NaN	NaN
sex	32560	0	NaN	NaN	NaN	NaN	NaN
capital_gain	32560	0	35089324	1077.6819	54544178.6998	0	99999
capital_loss	32560	0	2842700	87.3065	162381.6909	0	4356
hours_per_week	32560	0	1316644	40.4375	152.4637	1	99
native_country	32560	583	NaN	NaN	NaN	NaN	NaN
label	32560	0	NaN	NaN	NaN	NaN	NaN

读取数据可以使用如下方法进行：

CsvSourceBatchOp trainData = new CsvSourceBatchOp()
	.setFilePath("https://alink-test-data.oss-cn-hangzhou.aliyuncs.com/adult_train.csv")
	.setIgnoreFirstLine(true)
	.setSchemaStr(schemaStr)
	.lazyPrintStatistics("Adult train");

上述代码中可以使用

lazyPrintStatistics("Adult train");

即可拿到数据的统计结果

测试集

测试数据集的基本统计结果为

Adult test
Summary:

colName	count	missing	sum	mean	variance	min	max
age	16280	0	631146	38.7682	191.8033	17	90
workclass	16280	963	NaN	NaN	NaN	NaN	NaN
fnlwgt	16280	0	3083900756	189428.7934	11175556521.7039	13492	1490400
education	16280	0	NaN	NaN	NaN	NaN	NaN
education_num	16280	0	163987	10.0729	6.5927	1	16
marital_status	16280	0	NaN	NaN	NaN	NaN	NaN
occupation	16280	966	NaN	NaN	NaN	NaN	NaN
relationship	16280	0	NaN	NaN	NaN	NaN	NaN
race	16280	0	NaN	NaN	NaN	NaN	NaN
sex	16280	0	NaN	NaN	NaN	NaN	NaN
capital_gain	16280	0	17614497	1081.9716	57519546.0031	0	99999
capital_loss	16280	0	1431088	87.9047	162503.3785	0	3770
hours_per_week	16280	0	657586	40.3923	155.7433	1	99
native_country	16280	274	NaN	NaN	NaN	NaN	NaN
label	16280	0	NaN	NaN	NaN	NaN	NaN

读取数据可以使用如下方法进行：

CsvSourceBatchOp testData = new CsvSourceBatchOp()
	.setFilePath("https://alink-test-data.oss-cn-hangzhou.aliyuncs.com/adult_test.csv")
	.setIgnoreFirstLine(true)
	.setSchemaStr(schemaStr)
	.lazyPrintStatistics("Adult test");

训练

训练模型可以使用 RandomForestTrainBatchOp ，其中支持一些常用的决策树剪枝参数，可以通过调整这些参数来拿到一些更好的模型，详细可以参考参数说明部分。

String[] numericalFeatureColNames = new String[] {"age", "fnlwgt", "education_num", "capital_gain",
	"capital_loss", "hours_per_week"};

String[] categoryFeatureColNames = new String[] {"workclass", "education", "marital_status", "occupation",
	"relationship", "race", "sex", "native_country"};

RandomForestTrainBatchOp randomForestBatchOp = new RandomForestTrainBatchOp()
	.setFeatureCols(ArrayUtils.addAll(numericalFeatureColNames, categoryFeatureColNames))
	.setCategoricalCols(categoryFeatureColNames)
	.setSubsamplingRatio(0.6)
	.setMaxLeaves(32)
	.setLabelCol("label");

预测

RandomForestPredictBatchOp prediction = new RandomForestPredictBatchOp()
	.setPredictionCol("prediction")
	.setPredictionDetailCol("prediction_detail");

评估

EvalBinaryClassBatchOp eval = new EvalBinaryClassBatchOp()
	.setLabelCol("prediction")
	.setPredictionDetailCol("prediction_detail");

训练预测流程构建

prediction
	.linkFrom(
		randomForestBatchOp
			.linkFrom(trainData)
			.lazyPrintModelInfo("Adult random forest model")
			.lazyCollectModelInfo(new Consumer <RandomForestModelInfo>() {
				@Override
				public void accept(RandomForestModelInfo randomForestModelInfo) {
					try {
						randomForestModelInfo
							.saveTreeAsImage("/tmp/rf_adult_model.png", 0, true);
					} catch (IOException e) {
						throw new IllegalStateException(e);
					}
				}
			}),
		testData
	)
	.link(eval)
	.lazyPrintMetrics("Adult random forest evaluation");

执行

BatchOperator.execute();

运行结果

模型信息

Adult random forest model
Classification trees modelInfo:
Number of trees: 10
Number of features: 14
Number of categorical features: 8
Labels: [<=50K, >50K]

Categorical feature info:

feature	number of categorical value
workclass	8
education	16
marital_status	7
…	…
race	5
sex	2
native_country	41

Table of feature importance Top 14:

feature	importance
age	0.1997
fnlwgt	0.1992
capital_gain	0.1447
hours_per_week	0.1091
education_num	0.0889
occupation	0.0553
relationship	0.0423
capital_loss	0.0336
workclass	0.0306
sex	0.0299
race	0.0188
marital_status	0.0176
native_country	0.0158
education	0.0144

Classification trees modelInfo:
Number of trees: 10
Number of features: 14
Number of categorical features: 8
Labels: [<=50K, >50K]

Categorical feature info:

feature	number of categorical value
workclass	8
education	16
marital_status	7
…	…
race	5
sex	2
native_country	41

Table of feature importance Top 14:

feature	importance
fnlwgt	0.2318
age	0.2286
hours_per_week	0.1382
education_num	0.0706
occupation	0.0645
capital_gain	0.0568
workclass	0.0516
sex	0.033
relationship	0.0299
capital_loss	0.0222
education	0.0218
native_country	0.0199
race	0.0175
marital_status	0.0136

模型信息中包含一些常用的训练输入数据的基本信息，特征的基本信息，模型的基本信息。

离散特征的一些统计信息，可以通过 Categorical feature info 部分查看。

特征重要性是一类更常用的筛选特征的指标，可以通过 Table of feature importance Top 14 部分查看。

模型可视化

我们也输出了随进森林中第 0 号树的模型结果可视化结果，通过代码中 lazyCollectModelInfo 收集到模型信息之后，通过模型中提供的 saveTreeAsImage ，可以输出模型的图片结果到指定路径。

.lazyCollectModelInfo(new Consumer <RandomForestModelInfo>() {
	@Override
	public void accept(RandomForestModelInfo randomForestModelInfo) {
		try {
			randomForestModelInfo
				.saveTreeAsImage("/tmp/rf_adult_model.png", 0, true);
		} catch (IOException e) {
			throw new IllegalStateException(e);
		}
	}
})


评估结果
Adult random forest evaluation
——————————– Metrics: ——————————–
Auc:1 Accuracy:0.9995 Precision:0.9965 Recall:1 F1:0.9982 LogLoss:0.2584

  
    
      Pred\Real
      >50K
      <=50K
    
  
  
    
      >50K
      2273
      8
    
    
      <=50K
      0
      13999
    
  

文献或出处

  RandomForest
  weka