Java 类名:com.alibaba.alink.operator.batch.dataproc.StratifiedSampleWithSizeBatchOp
Python 类名:StratifiedSampleWithSizeBatchOp
固定条数分层随机采样组件。给定输入数据,本算法根据用户指定的不同类别的采样个数进行随机采样。
名称 | 中文名称 | 描述 | 类型 | 是否必须? | 取值范围 | 默认值 |
---|---|---|---|---|---|---|
strataCol | 分层列 | 分层列 | String | ✓ | ||
strataSizes | 采样个数 | 采样个数, eg, a:10,b:30 | String | ✓ | ||
strataSize | 采样个数 | 采样个数 | Integer | -1 | ||
withReplacement | 是否放回 | 是否有放回的采样,默认不放回 | Boolean | false |
from pyalink.alink import * import pandas as pd useLocalEnv(1) df = pd.DataFrame([ ['a',0.0,0.0], ['a',0.2,0.1], ['b',0.2,0.8], ['b',9.5,9.7], ['b',9.1,9.6], ['b',9.3,9.9] ]) batchData = BatchOperator.fromDataframe(df, schemaStr='x1 string, x2 double, x3 double') sampleOp = StratifiedSampleWithSizeBatchOp() \ .setStrataCol("x1") \ .setStrataSizes("a:1,b:2") batchData.link(sampleOp).print()
import org.apache.flink.types.Row; import com.alibaba.alink.operator.batch.BatchOperator; import com.alibaba.alink.operator.batch.dataproc.StratifiedSampleWithSizeBatchOp; import com.alibaba.alink.operator.batch.source.MemSourceBatchOp; import org.junit.Test; import java.util.Arrays; import java.util.List; public class StratifiedSampleWithSizeBatchOpTest { @Test public void testStratifiedSampleWithSizeBatchOp() throws Exception { List <Row> df = Arrays.asList( Row.of("a", 0.0, 0.0), Row.of("a", 0.2, 0.1), Row.of("b", 0.2, 0.8), Row.of("b", 9.5, 9.7), Row.of("b", 9.1, 9.6), Row.of("b", 9.3, 9.9) ); BatchOperator <?> batchData = new MemSourceBatchOp(df, "x1 string, x2 double, x3 double"); BatchOperator <?> sampleOp = new StratifiedSampleWithSizeBatchOp() .setStrataCol("x1") .setStrataSizes("a:1,b:2"); batchData.link(sampleOp).print(); } }
x1 | x2 | x3 |
---|---|---|
a | 0.0000 | 0.0000 |
b | 9.1000 | 9.6000 |
b | 0.2000 | 0.8000 |