Java 类名：com.alibaba.alink.operator.batch.statistics.VectorChiSquareTestBatchOp
Python 类名：VectorChiSquareTestBatchOp

功能介绍

针对vector数据，进行卡方检验

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 | | —- | —- | —- | —- | —- | —- | —- |

| labelCol | 标签列名 | 输入表中的标签列名 | String | ✓ | | |

| selectedCol | 选中的列名 | 计算列对应的列名 | String | ✓ | 所选列类型为 [DENSE_VECTOR, SPARSE_VECTOR, STRING, VECTOR] | |

代码示例

以下代码仅用于示意，可能需要修改部分代码或者配置环境后才能正常运行！

Python 代码

无python接口

Java 代码

  1. import org.apache.flink.types.Row;
  3. import com.alibaba.alink.operator.batch.BatchOperator;
  4. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  5. import com.alibaba.alink.operator.batch.statistics.VectorChiSquareTestBatchOp;
  6. import org.junit.Test;
  8. import java.util.Arrays;
  10. public class VectorChiSquareTestBatchOpTest {
  12.     @Test
  13.     public void testVectorChiSquareTestBatchOp() throws Exception {
  14.         Row[] testArray = new Row[] {
  15.             Row.of(7, "0.0  0.0  18.0  1.0", 1.0),
  16.             Row.of(8, "0.0  1.0  12.0  0.0", 0.0),
  17.             Row.of(9, "1.0  0.0  15.0  0.1", 0.0),
  18.         };
  20.         String[] colNames = new String[] {"id", "features", "clicked"};
  22.         MemSourceBatchOp source = new MemSourceBatchOp(Arrays.asList(testArray), colNames);
  24.         VectorChiSquareTestBatchOp test = new VectorChiSquareTestBatchOp()
  25.             .setSelectedCol("features")
  26.             .setLabelCol("clicked");
  28.         test.linkFrom(source);
  30.         test.lazyPrintChiSquareTest();
  32.         BatchOperator.execute();
  33.     }
  35. }

运行结果

ChiSquareTest:

| col | p | value | df | | —- | —- | —- | —- |

| 0 | 0.3865 | 0.75 | 1 |

| 1 | 0.3865 | 0.75 | 1 |

| 2 | 0.2231 | 3 | 2 |

| 3 | 0.2231 | 3 | 2 |