Logistic Regression for Multiclass Classification - Platform For AI

The common logistic regression algorithm is used for binary classification. Machine Learning Platform for AI (PAI) allows you to use the logistic regression algorithm for multiclass classification. The Logistic Regression for Multiclass Classification component supports both the sparse and dense data formats.

Configure the component

You can use one of the following methods to configure the Logistic Regression for Multiclass Classification component.

Method 1: Configure the component on the pipeline page

You can configure the parameters of the Logistic Regression for Multiclass Classification component on the pipeline page of Machine Learning Designer of Machine Learning Platform for AI (PAI). Machine Learning Designer is formerly known as Machine Learning Studio. The following table describes the parameters.


Tab	Parameter	Description
Fields Setting	Training Feature Columns	The feature columns that are selected from the data source for training. The columns of the DOUBLE and BIGINT types are supported. Note A maximum of 20 million features are supported.
	Target Columns	The objective columns in the input table.
	Sparse Format	Specifies whether the input data is in the sparse format.
Parameters Setting	Regularization Type	Valid values: L1, L2, and None.
	Maximum Number of Iterations	The maximum number of iterations. Default value: 100.
	Regularization Coefficient	If the Regularization Type parameter is set to None, this parameter is invalid.
	Minimum Convergence Deviance	The minimum convergence deviance. Default value: 0.000001.

Method 2: Use PAI commands

Configure the component parameters by using PAI commands. You can use the SQL Script component to call PAI commands. For more information, see SQL Script.

PAI -name logisticregression_multi
    -project algo_public
    -DmodelName="xlab_m_logistic_regression_6096"
    -DregularizedLevel="1"
    -DmaxIter="100"
    -DregularizedType="l1"
    -Depsilon="0.000001"
    -DlabelColName="y"
    -DfeatureColNames="pdays,emp_var_rate"
    -DgoodValue="1"
    -DinputTableName="bank_data"


Parameter	Required	Description	Default value
inputTableName	Yes	The name of the input table.	N/A
featureColNames	No	The feature columns that are selected from the input table for training. Note A maximum of 20 million features are supported.	All columns of numeric data types
labelColName	Yes	The name of the label column that is selected from the input table.	N/A
inputTablePartitions	No	The partitions that are selected from the input table for training. Specify this parameter in one of the following formats: partition_name=value name1=value1/name2=value2: multi-level partitions Note If you specify multiple partitions, separate them with commas (,).	Full table
modelName	Yes	The name of the output model.	N/A
regularizedType	No	The regularization type. Valid values: l1, l2, and None.	l1
regularizedLevel	No	The regularization coefficient. If the regularizedType parameter is set to None, this parameter is invalid.	1.0
maxIter	No	The maximum number of iterations of the limited-memory BFGS (L-BFGS) algorithm.	100
epsilon	No	The convergence error. This parameter specifies the conditions to terminate the L-BFGS algorithm. If the difference of log-likelihood between two iterations is less than the value specified by this parameter, the iteration of the L-BFGS algorithm is terminated.	1.0e-06
enableSparse	No	Specifies whether the input data is in the sparse format. Valid values: true and false.	false
itemDelimiter	No	The delimiter that is used to separate key-value pairs when data in the input data is in the sparse format.	,
kvDelimiter	No	The delimiter that is used to separate keys and values when data in the input table is in the sparse format.	:
coreNum	No	The number of cores.	Determined by the system
memSizePerCore	No	The memory size of each core. Unit: MB.	Determined by the system

Example

Execute the following SQL statements to generate training data:

drop table if exists multi_lr_test_input;
create table multi_lr_test_input
as
select
    *
from
(
    select
        cast(1 as double) as f0,
        cast(0 as double) as f1,
        cast(0 as double) as f2,
        cast(0 as double) as f3,
        cast(0 as bigint) as label
    from dual
    union all
        select
            cast(0 as double) as f0,
            cast(1 as double) as f1,
            cast(0 as double) as f2,
            cast(0 as double) as f3,
            cast(0 as bigint) as label
    from dual
    union all
        select
            cast(0 as double) as f0,
            cast(0 as double) as f1,
            cast(1 as double) as f2,
            cast(0 as double) as f3,
            cast(2 as bigint) as label
    from dual
    union all
        select
            cast(0 as double) as f0,
            cast(0 as double) as f1,
            cast(0 as double) as f2,
            cast(1 as double) as f3,
            cast(1 as bigint) as label
    from dual
) a;

The following table provides the generated training data in the multi_lr_test_input table.


f0	f1	f2	f3	label
1.0	0.0	0.0	0.0	0
0.0	0.0	1.0	0.0	2
0.0	0.0	0.0	1.0	1
0.0	1.0	0.0	0.0	0

Run the following PAI command to submit the parameters of the Logistic Regression for Multiclass Classification component:

drop offlinemodel if exists multi_lr_test_model;
PAI -name logisticregression_multi
    -project algo_public
    -DmodelName="multi_lr_test_model"
    -DitemDelimiter=","
    -DregularizedLevel="1"
    -DmaxIter="100"
    -DregularizedType="None"
    -Depsilon="0.000001"
    -DkvDelimiter=":"
    -DlabelColName="label"
    -DfeatureColNames="f0,f1,f2,f3"
    -DenableSparse="false"
    -DinputTableName="multi_lr_test_input";

Run the following PAI command to submit the parameters of the Prediction component:

drop table if exists multi_lr_test_prediction_result;
PAI -name prediction
    -project algo_public
    -DdetailColName="prediction_detail"
    -DmodelName="multi_lr_test_model"
    -DitemDelimiter=","
    -DresultColName="prediction_result"
    -Dlifecycle="28"
    -DoutputTableName="multi_lr_test_prediction_result"
    -DscoreColName="prediction_score"
    -DkvDelimiter=":"
    -DinputTableName="multi_lr_test_input"
    -DenableSparse="false"
    -DappendColNames="label";

View the multi_lr_test_prediction_result table.


label	prediction_result	prediction_score	prediction_detail
0	0	0.9999997274902165	{"0": 0.9999997274902165, "1": 2.324679066261573e-07, "2": 2.324679066261569e-07}
0	0	0.9999997274902165	{"0": 0.9999997274902165, "1": 2.324679066261573e-07, "2": 2.324679066261569e-07}
2	2	0.9999999155958832	{"0": 2.018833979850994e-07, "1": 2.324679066261573e-07, "2": 0.9999999155958832}
1	1	0.9999999155958832	{"0": 2.018833979850994e-07, "1": 0.9999999155958832, "2": 2.324679066261569e-07}