Java 类名:com.alibaba.alink.pipeline.nlp.Tokenizer
Python 类名:Tokenizer
功能介绍
使用方式
文本列通过参数 selectedCol 指定,输出列通过 outputCol 指定。
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 |
---|---|---|---|---|---|---|
selectedCol | 选中的列名 | 计算列对应的列名 | String | ✓ | ||
outputCol | 输出结果列 | 输出结果列列名,可选,默认null | 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 |
代码示例
Python 代码
df = pd.DataFrame([
[0, 'That is an English Book!'],
[1, 'Do you like math?'],
[2, 'Have a good day!']
])
inOp1 = BatchOperator.fromDataframe(df, schemaStr='id long, text string')
op = Tokenizer().setSelectedCol("text")
op.transform(inOp1).print()
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.pipeline.nlp.Tokenizer;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class TokenizerTest {
@Test
public void testTokenizer() throws Exception {
List <Row> df = Arrays.asList(
Row.of(0, "That is an English Book!"),
Row.of(1, "Do you like math?"),
Row.of(2, "Have a good day!")
);
BatchOperator <?> inOp1 = new MemSourceBatchOp(df, "id int, text string");
Tokenizer op = new Tokenizer().setSelectedCol("text");
op.transform(inOp1).print();
}
}
运行结果
id | text |
---|---|
0 | that is an english book! |
1 | do you like math? |
2 | have a good day! |