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? 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
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

  1. RandomForest
  2. weka