Java 类名：com.alibaba.alink.operator.batch.feature.TreeModelEncoderBatchOp
Python 类名：TreeModelEncoderBatchOp
使用树模型，将输入数据编码为特征。
| 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 | ||
| numThreads | 组件多线程线程个数 | 组件多线程线程个数 | Integer | 1 |
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')
gbdtTrainOp = GbdtTrainBatchOp()\
.setFeatureCols(['f0', 'f1', 'f2', 'f3'])\
.setLabelCol('label')\
.linkFrom(batchSource)
encoderBatchOp = TreeModelEncoderBatchOp()\
.setPredictionCol("encoded_features")
encoderStreamOp = TreeModelEncoderStreamOp(gbdtTrainOp)\
.setPredictionCol("encoded_features")
encoderBatchOp.linkFrom(gbdtTrainOp, batchSource).print()
encoderStreamOp.linkFrom(streamSource).print()
StreamOperator.execute()
import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.feature.TreeModelEncoderBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.feature.TreeModelEncoderStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import com.alibaba.alink.operator.batch.classification.GbdtTrainBatchOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class TreeModelEncoderBatchOpTest {
@Test
public void testTreeModelEncoderBatchOp() 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 gbdtTrainOp = new GbdtTrainBatchOp()
.setFeatureCols(new String[] {"f0", "f1", "f2", "f3"})
.setLabelCol("label")
.linkFrom(batchSource);
BatchOperator encoderBatchOp = new TreeModelEncoderBatchOp()
.setPredictionCol("encoded_features");
StreamOperator encoderStreamOp =new TreeModelEncoderStreamOp(gbdtTrainOp)
.setPredictionCol("encoded_features");
encoderBatchOp.linkFrom(gbdtTrainOp, batchSource).print();
encoderStreamOp.linkFrom(streamSource).print();
StreamOperator.execute();
}
}
| f0 | f1 | f2 | f3 | label | encoded_features |
|---|---|---|---|---|---|
| 1.0000 | A | 0 | 0 | 0 | $123$0:1.0 2:1.0 4:1.0 6:1.0 8:1.0 10:1.0 12:1.0 14:1.0 16:1.0 18:1.0 20:1.0 22:1.0 24:1.0 26:1.0 28:1.0 30:1.0 32:1.0 34:1.0 36:1.0 38:1.0 40:1.0 42:1.0 44:1.0 46:1.0 47:1.0 48:1.0 49:1.0 50:1.0 51:1.0 52:1.0 53:1.0 54:1.0 55:1.0 56:1.0 57:1.0 58:1.0 59:1.0 60:1.0 61:1.0 62:1.0 63:1.0 64:1.0 65:1.0 66:1.0 67:1.0 68:1.0 69:1.0 70:1.0 71:1.0 72:1.0 73:1.0 74:1.0 75:1.0 76:1.0 77:1.0 78:1.0 79:1.0 80:1.0 81:1.0 82:1.0 83:1.0 84:1.0 85:1.0 86:1.0 87:1.0 88:1.0 89:1.0 90:1.0 91:1.0 92:1.0 93:1.0 94:1.0 95:1.0 96:1.0 97:1.0 98:1.0 99:1.0 100:1.0 101:1.0 102:1.0 103:1.0 104:1.0 105:1.0 106:1.0 107:1.0 108:1.0 109:1.0 110:1.0 111:1.0 112:1.0 113:1.0 114:1.0 115:1.0 116:1.0 117:1.0 118:1.0 119:1.0 120:1.0 121:1.0 122:1.0 |
| 2.0000 | B | 1 | 1 | 0 | $123$0:1.0 2:1.0 4:1.0 6:1.0 8:1.0 10:1.0 12:1.0 14:1.0 16:1.0 18:1.0 20:1.0 22:1.0 24:1.0 26:1.0 28:1.0 30:1.0 32:1.0 34:1.0 36:1.0 38:1.0 40:1.0 42:1.0 44:1.0 46:1.0 47:1.0 48:1.0 49:1.0 50:1.0 51:1.0 52:1.0 53:1.0 54:1.0 55:1.0 56:1.0 57:1.0 58:1.0 59:1.0 60:1.0 61:1.0 62:1.0 63:1.0 64:1.0 65:1.0 66:1.0 67:1.0 68:1.0 69:1.0 70:1.0 71:1.0 72:1.0 73:1.0 74:1.0 75:1.0 76:1.0 77:1.0 78:1.0 79:1.0 80:1.0 81:1.0 82:1.0 83:1.0 84:1.0 85:1.0 86:1.0 87:1.0 88:1.0 89:1.0 90:1.0 91:1.0 92:1.0 93:1.0 94:1.0 95:1.0 96:1.0 97:1.0 98:1.0 99:1.0 100:1.0 101:1.0 102:1.0 103:1.0 104:1.0 105:1.0 106:1.0 107:1.0 108:1.0 109:1.0 110:1.0 111:1.0 112:1.0 113:1.0 114:1.0 115:1.0 116:1.0 117:1.0 118:1.0 119:1.0 120:1.0 121:1.0 122:1.0 |
| 3.0000 | C | 2 | 2 | 1 | $123$1:1.0 3:1.0 5:1.0 7:1.0 9:1.0 11:1.0 13:1.0 15:1.0 17:1.0 19:1.0 21:1.0 23:1.0 25:1.0 27:1.0 29:1.0 31:1.0 33:1.0 35:1.0 37:1.0 39:1.0 41:1.0 43:1.0 45:1.0 46:1.0 47:1.0 48:1.0 49:1.0 50:1.0 51:1.0 52:1.0 53:1.0 54:1.0 55:1.0 56:1.0 57:1.0 58:1.0 59:1.0 60:1.0 61:1.0 62:1.0 63:1.0 64:1.0 65:1.0 66:1.0 67:1.0 68:1.0 69:1.0 70:1.0 71:1.0 72:1.0 73:1.0 74:1.0 75:1.0 76:1.0 77:1.0 78:1.0 79:1.0 80:1.0 81:1.0 82:1.0 83:1.0 84:1.0 85:1.0 86:1.0 87:1.0 88:1.0 89:1.0 90:1.0 91:1.0 92:1.0 93:1.0 94:1.0 95:1.0 96:1.0 97:1.0 98:1.0 99:1.0 100:1.0 101:1.0 102:1.0 103:1.0 104:1.0 105:1.0 106:1.0 107:1.0 108:1.0 109:1.0 110:1.0 111:1.0 112:1.0 113:1.0 114:1.0 115:1.0 116:1.0 117:1.0 118:1.0 119:1.0 120:1.0 121:1.0 122:1.0 |
| 4.0000 | D | 3 | 3 | 1 | $123$1:1.0 3:1.0 5:1.0 7:1.0 9:1.0 11:1.0 13:1.0 15:1.0 17:1.0 19:1.0 21:1.0 23:1.0 25:1.0 27:1.0 29:1.0 31:1.0 33:1.0 35:1.0 37:1.0 39:1.0 41:1.0 43:1.0 45:1.0 46:1.0 47:1.0 48:1.0 49:1.0 50:1.0 51:1.0 52:1.0 53:1.0 54:1.0 55:1.0 56:1.0 57:1.0 58:1.0 59:1.0 60:1.0 61:1.0 62:1.0 63:1.0 64:1.0 65:1.0 66:1.0 67:1.0 68:1.0 69:1.0 70:1.0 71:1.0 72:1.0 73:1.0 74:1.0 75:1.0 76:1.0 77:1.0 78:1.0 79:1.0 80:1.0 81:1.0 82:1.0 83:1.0 84:1.0 85:1.0 86:1.0 87:1.0 88:1.0 89:1.0 90:1.0 91:1.0 92:1.0 93:1.0 94:1.0 95:1.0 96:1.0 97:1.0 98:1.0 99:1.0 100:1.0 101:1.0 102:1.0 103:1.0 104:1.0 105:1.0 106:1.0 107:1.0 108:1.0 109:1.0 110:1.0 111:1.0 112:1.0 113:1.0 114:1.0 115:1.0 116:1.0 117:1.0 118:1.0 119:1.0 120:1.0 121:1.0 122:1.0 |