决策树回归 (DecisionTreeRegressor)

Java 类名：com.alibaba.alink.pipeline.regression.DecisionTreeRegressor
Python 类名：DecisionTreeRegressor

功能介绍

• 本函数支持cart回归

• 支持带样本权重的训练

参数说明

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')
DecisionTreeRegressor()\
    .setPredictionCol('pred')\
    .setLabelCol('label')\
    .setFeatureCols(['f0', 'f1', 'f2', 'f3'])\
    .fit(batchSource)\
    .transform(batchSource)\
    .print()
DecisionTreeRegressor()\
    .setPredictionCol('pred')\
    .setLabelCol('label')\
    .setFeatureCols(['f0', 'f1', 'f2', 'f3'])\
    .fit(batchSource)\
    .transform(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.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import com.alibaba.alink.pipeline.regression.DecisionTreeRegressor;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class DecisionTreeRegressorTest {
    @Test
    public void testDecisionTreeRegressor() 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");
        new DecisionTreeRegressor()
            .setPredictionCol("pred")
            .setLabelCol("label")
            .setFeatureCols("f0", "f1", "f2", "f3")
            .fit(batchSource)
            .transform(batchSource)
            .print();
        new DecisionTreeRegressor()
            .setPredictionCol("pred")
            .setLabelCol("label")
            .setFeatureCols("f0", "f1", "f2", "f3")
            .fit(batchSource)
            .transform(streamSource)
            .print();
        StreamOperator.execute();
    }
}

运行结果

批预测结果

| f0 | f1 | f2 | f3 | label | pred | | —- | —- | —- | —- | —- | —- |

| 1.0000 | A | 0 | 0 | 0 | 0.0000 |

| 2.0000 | B | 1 | 1 | 0 | 0.0000 |

| 3.0000 | C | 2 | 2 | 1 | 1.0000 |

| 4.0000 | D | 3 | 3 | 1 | 1.0000 |

流预测结果

| f0 | f1 | f2 | f3 | label | pred | | —- | —- | —- | —- | —- | —- |

| 1.0000 | A | 0 | 0 | 0 | 0.0000 |

| 4.0000 | D | 3 | 3 | 1 | 1.0000 |

| 3.0000 | C | 2 | 2 | 1 | 1.0000 |

| 2.0000 | B | 1 | 1 | 0 | 0.0000 |