缺失值填充

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。

PAI-Studio支持通过可视化或PAI命令的方式，配置该组件参数：

可视化方式


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	Null（数值和string）空字符串（string） Null和空字符串（string）自定义（string）
	替换为	Min（数值型） Max（数值型） Mean（数值型）自定义（数值型和string）
	configs	ID列。说明勾选高级选项时展示。
	执行调优	计算核心数
	执行调优	每个核内存数

PAI命令方式

PAI -name FillMissingValues
    -project algo_public
    -Dconfigs="poutcome,null-empty,testing" \
    -DoutputTableName="test_3"
    -DinputPartitions="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： Partition_name=value name1=value1/name2=value2：多级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。如果选择空字符，则填充的目标列应是STRING型。如果采用最大值、最小值、均值，可以采用变量，其命名规范形如：min, max, mean。如果用户自定义替换值，则使用user-defined，格式例如`col4,user-defined,str,str123`。	无
outputParaTableName	否	配置输出表。	输出表1为非分区表
inputParaTableName	是	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

缺失值填充示例

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}} |
+------------+------------+