您可以通过给定一个缺失值的配置列表，来实现将输入表的缺失值用指定的值来填充。
背景信息
- 将数值型的空值替换为最大值，最小值，均值或者一个自定义的值。
- 将字符型的空值，空字符串，空值和空字符串，指定值替换为一个自定义的值。
- 待填充的缺失值可以选择空值或空字符，也可以自定义。
缺失值如果选择空字符串，则填充的目标列应是STRING型。
- 数值型替换可以自定义，也可以直接选择替换成数值最大值，最小值或者均值。
组件配置
您可以使用以下任意一种方式，配置缺失值填充组件参数。
方式一：可视化方式
在PAI-Designer（原PAI-Studio）工作流页面配置组件参数。
| 页签 | 参数 | 描述 |
|---|---|---|
| 参数设置 | 填充的字段 | 默认全选，多余列不影响预测结果。 |
| 原值 |
| |
| 替换为 |
| |
| configs | ID列。 说明 勾选高级选项时展示。 | |
| 执行调优 | 计算核心数 | |
| 每个核内存数 |
方式二：PAI命令方式
使用PAI命令方式，配置该组件参数。您可以使用SQL脚本组件进行PAI命令调用，详情请参见SQL脚本。
PAI -name FillMissingValues
-project algo_public
-Dconfigs="poutcome,null-empty,testing" \
-DoutputParaTableName="test_input_model_output"
-DoutputTableName="test_3"
-DinputTablePartitions="pt=20150501"
-DinputTableName="bank_data_partition";| 参数名称 | 是否必选 | 参数描述 | 默认值 |
|---|---|---|---|
| inputTableName | 是 | 输入表的表名。 | 无 |
| inputTablePartitions | 否 | 输入表中，参与训练的分区。支持以下格式：
说明 如果指定多个分区，则使用英文逗号（,）分隔。 | 所有分区 |
| outputTableName | 是 | 输出结果表。 | 无 |
| configs | 是 | 缺失值填充的配置。 例如格式 col1, null, 3.14; col2, empty, hello; col3, empty-null, world，其中null表示空值，empty表示空字符。
| 无 |
| outputParaTableName | 是 | 配置输出表。 | 输出表1为非分区表 |
| inputParaTableName | 否 | 配置输入表。 | 无 |
| lifecycle | 否 | 输出表的生命周期，取值范围为[1,3650]。 | 无 |
| coreNum | 否 | 计算的核心数目，取值为正整数。 | 系统自动分配 |
| memSizePerCore | 否 | 每个核心的内存（单位是兆），取值范围为(1, 65536)。 | 系统自动分配 |
示例
- 使用SQL语句，生成测试数据。
drop table if exists fill_missing_values_test_input; create table fill_missing_values_test_input( col_string string, col_bigint bigint, col_double double, col_boolean boolean, col_datetime datetime); insert overwrite table fill_missing_values_test_input select * from ( select '01' as col_string, 10 as col_bigint, 10.1 as col_double, True as col_boolean, cast('2016-07-01 10:00:00' as datetime) as col_datetime from dual union all select cast(null as string) as col_string, 11 as col_bigint, 10.2 as col_double, False as col_boolean, cast('2016-07-02 10:00:00' as datetime) as col_datetime from dual union all select '02' as col_string, cast(null as bigint) as col_bigint, 10.3 as col_double, True as col_boolean, cast('2016-07-03 10:00:00' as datetime) as col_datetime from dual union all select '03' as col_string, 12 as col_bigint, cast(null as double) as col_double, False as col_boolean, cast('2016-07-04 10:00:00' as datetime) as col_datetime from dual union all select '04' as col_string, 13 as col_bigint, 10.4 as col_double, cast(null as boolean) as col_boolean, cast('2016-07-05 10:00:00' as datetime) as col_datetime from dual union all select '05' as col_string, 14 as col_bigint, 10.5 as col_double, True as col_boolean, cast(null as datetime) as col_datetime from dual ) tmp;输入数据说明。+------------+------------+------------+-------------+--------------+ | col_string | col_bigint | col_double | col_boolean | col_datetime | +------------+------------+------------+-------------+--------------+ | 04 | 13 | 10.4 | NULL | 2016-07-05 10:00:00 | | 02 | NULL | 10.3 | true | 2016-07-03 10:00:00 | | 03 | 12 | NULL | false | 2016-07-04 10:00:00 | | NULL | 11 | 10.2 | false | 2016-07-02 10:00:00 | | 01 | 10 | 10.1 | true | 2016-07-01 10:00:00 | | 05 | 14 | 10.5 | true | NULL | +------------+------------+------------+-------------+--------------+ - 运行命令。
drop table if exists fill_missing_values_test_input_output; drop table if exists fill_missing_values_test_input_model_output; PAI -name FillMissingValues -project algo_public -Dconfigs="col_double,null,mean;col_string,null-empty,str_type_empty;col_bigint,null,max;col_boolean,null,true;col_datetime,null,2016-07-06 10:00:00" -DoutputParaTableName="fill_missing_values_test_input_model_output" -Dlifecycle="28" -DoutputTableName="fill_missing_values_test_input_output" -DinputTableName="fill_missing_values_test_input"; drop table if exists fill_missing_values_test_input_output_using_model; drop table if exists fill_missing_values_test_input_output_using_model_model_output; PAI -name FillMissingValues -project algo_public -DoutputParaTableName="fill_missing_values_test_input_output_using_model_model_output" -DinputParaTableName="fill_missing_values_test_input_model_output" -Dlifecycle="28" -DoutputTableName="fill_missing_values_test_input_output_using_model" -DinputTableName="fill_missing_values_test_input"; - 运行结果。
- fill_missing_values_test_input_output
+------------+------------+------------+-------------+--------------+ | col_string | col_bigint | col_double | col_boolean | col_datetime | +------------+------------+------------+-------------+--------------+ | 04 | 13 | 10.4 | true | 2016-07-05 10:00:00 | | 02 | 14 | 10.3 | true | 2016-07-03 10:00:00 | | 03 | 12 | 10.3 | false | 2016-07-04 10:00:00 | | str_type_empty | 11 | 10.2 | false | 2016-07-02 10:00:00 | | 01 | 10 | 10.1 | true | 2016-07-01 10:00:00 | | 05 | 14 | 10.5 | true | 2016-07-06 10:00:00 | +------------+------------+------------+-------------+--------------+ - fill_missing_values_test_input_model_output
+------------+------------+ | feature | json | +------------+------------+ | col_string | {"name": "fillMissingValues", "type": "string", "paras":{"missing_value_type": "null-empty", "replaced_value": "str_type_empty"}} | | col_bigint | {"name": "fillMissingValues", "type": "bigint", "paras":{"missing_value_type": "null", "replaced_value": 14}} | | col_double | {"name": "fillMissingValues", "type": "double", "paras":{"missing_value_type": "null", "replaced_value": 10.3}} | | col_boolean | {"name": "fillMissingValues", "type": "boolean", "paras":{"missing_value_type": "null", "replaced_value": 1}} | | col_datetime | {"name": "fillMissingValues", "type": "datetime", "paras":{"missing_value_type": "null", "replaced_value": 1467770400000}} | +------------+------------+ - fill_missing_values_test_input_output_using_model
+------------+------------+------------+-------------+--------------+ | col_string | col_bigint | col_double | col_boolean | col_datetime | +------------+------------+------------+-------------+--------------+ | 04 | 13 | 10.4 | true | 2016-07-05 10:00:00 | | 02 | 14 | 10.3 | true | 2016-07-03 10:00:00 | | 03 | 12 | 10.3 | false | 2016-07-04 10:00:00 | | str_type_empty | 11 | 10.2 | false | 2016-07-02 10:00:00 | | 01 | 10 | 10.1 | true | 2016-07-01 10:00:00 | | 05 | 14 | 10.5 | true | 2016-07-06 10:00:00 | +------------+------------+------------+-------------+--------------+ - fill_missing_values_test_input_output_using_model_model_output
+------------+------------+ | feature | json | +------------+------------+ | col_string | {"name": "fillMissingValues", "type": "string", "paras":{"missing_value_type": "null-empty", "replaced_value": "str_type_empty"}} | | col_bigint | {"name": "fillMissingValues", "type": "bigint", "paras":{"missing_value_type": "null", "replaced_value": 14}} | | col_double | {"name": "fillMissingValues", "type": "double", "paras":{"missing_value_type": "null", "replaced_value": 10.3}} | | col_boolean | {"name": "fillMissingValues", "type": "boolean", "paras":{"missing_value_type": "null", "replaced_value": 1}} | | col_datetime | {"name": "fillMissingValues", "type": "datetime", "paras":{"missing_value_type": "null", "replaced_value": 1467770400000}} | +------------+------------+
- fill_missing_values_test_input_output