Set two featurs as label at the same time

User11050

I do wonder whether it would be possible to set two features as label at the SAME TIME. So the models would consider both as label features.
Regards.

varunm1

Hello @User11050

You can only set one label at a time as per my understanding, you need to loop your process using loop attributes operator and then set the role to a different attribute every time. Please see example below with titanic dataset. @IngoRM any suggestion on this? (looks like a multi-label problem)

<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.3.001" expanded="true" name="Process">
<parameter key="logverbosity" value="init"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="never"/>
<parameter key="notification_email" value=""/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="9.3.001" expanded="true" height="68" name="Retrieve Titanic Training" width="90" x="179" y="34">
<parameter key="repository_entry" value="//Samples/data/Titanic Training"/>
</operator>
<operator activated="true" class="set_role" compatibility="9.3.001" expanded="true" height="82" name="Set Role (3)" width="90" x="313" y="34">
<parameter key="attribute_name" value="Survived"/>
<parameter key="target_role" value="regular"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="concurrency:loop_attributes" compatibility="8.2.000" expanded="true" height="124" name="Loop Attributes" width="90" x="514" y="85">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value="Sex|Survived"/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="attribute_value"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="time"/>
<parameter key="block_type" value="attribute_block"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="value_matrix_row_start"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
<parameter key="attribute_name_macro" value="loop_attribute"/>
<parameter key="reuse_results" value="false"/>
<parameter key="enable_parallel_execution" value="true"/>
<process expanded="true">
<operator activated="true" class="set_role" compatibility="5.3.013" expanded="true" height="82" name="Set Role" width="90" x="112" y="34">
<parameter key="attribute_name" value="%{loop_attribute}"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="concurrency:cross_validation" compatibility="8.2.000" expanded="true" height="145" name="Cross Validation" width="90" x="313" y="34">
<parameter key="split_on_batch_attribute" value="false"/>
<parameter key="leave_one_out" value="false"/>
<parameter key="number_of_folds" value="10"/>
<parameter key="sampling_type" value="automatic"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<parameter key="enable_parallel_execution" value="true"/>
<process expanded="true">
<operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.3.001" expanded="true" height="103" name="Decision Tree" width="90" x="179" y="34">
<parameter key="criterion" value="gain_ratio"/>
<parameter key="maximal_depth" value="10"/>
<parameter key="apply_pruning" value="true"/>
<parameter key="confidence" value="0.1"/>
<parameter key="apply_prepruning" value="true"/>
<parameter key="minimal_gain" value="0.01"/>
<parameter key="minimal_leaf_size" value="2"/>
<parameter key="minimal_size_for_split" value="4"/>
<parameter key="number_of_prepruning_alternatives" value="3"/>
</operator>
<connect from_port="training set" to_op="Decision Tree" to_port="training set"/>
<connect from_op="Decision Tree" from_port="model" to_port="model"/>
<connect from_op="Decision Tree" from_port="exampleSet" to_port="through 1"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
<portSpacing port="sink_through 2" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="9.3.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="9.3.001" expanded="true" height="82" name="Performance (3)" width="90" x="246" y="136">
<parameter key="main_criterion" value="first"/>
<parameter key="accuracy" value="true"/>
<parameter key="classification_error" value="false"/>
<parameter key="kappa" value="true"/>
<parameter key="weighted_mean_recall" value="false"/>
<parameter key="weighted_mean_precision" value="false"/>
<parameter key="spearman_rho" value="false"/>
<parameter key="kendall_tau" value="false"/>
<parameter key="absolute_error" value="false"/>
<parameter key="relative_error" value="false"/>
<parameter key="relative_error_lenient" value="false"/>
<parameter key="relative_error_strict" value="false"/>
<parameter key="normalized_absolute_error" value="false"/>
<parameter key="root_mean_squared_error" value="false"/>
<parameter key="root_relative_squared_error" value="false"/>
<parameter key="squared_error" value="false"/>
<parameter key="correlation" value="false"/>
<parameter key="squared_correlation" value="false"/>
<parameter key="cross-entropy" value="false"/>
<parameter key="margin" value="false"/>
<parameter key="soft_margin_loss" value="false"/>
<parameter key="logistic_loss" value="false"/>
<parameter key="skip_undefined_labels" value="true"/>
<parameter key="use_example_weights" value="true"/>
<list key="class_weights"/>
</operator>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance (3)" to_port="labelled data"/>
<connect from_op="Performance (3)" from_port="performance" to_port="performance 1"/>
<connect from_op="Performance (3)" from_port="example set" to_port="test set results"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="source_through 2" spacing="0"/>
<portSpacing port="sink_test set results" spacing="0"/>
<portSpacing port="sink_performance 1" spacing="0"/>
<portSpacing port="sink_performance 2" spacing="0"/>
</process>
</operator>
<connect from_port="input 1" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Cross Validation" to_port="example set"/>
<connect from_op="Cross Validation" from_port="model" to_port="output 2"/>
<connect from_op="Cross Validation" from_port="test result set" to_port="output 3"/>
<connect from_op="Cross Validation" from_port="performance 1" to_port="output 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="source_input 2" spacing="0"/>
<portSpacing port="sink_output 1" spacing="0"/>
<portSpacing port="sink_output 2" spacing="0"/>
<portSpacing port="sink_output 3" spacing="0"/>
<portSpacing port="sink_output 4" spacing="0"/>
</process>
</operator>
<connect from_op="Retrieve Titanic Training" from_port="output" to_op="Set Role (3)" to_port="example set input"/>
<connect from_op="Set Role (3)" from_port="example set output" to_op="Loop Attributes" to_port="input 1"/>
<connect from_op="Loop Attributes" from_port="output 1" to_port="result 1"/>
<connect from_op="Loop Attributes" from_port="output 3" to_port="result 2"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
</process>
</operator>
</process>

Hope this helps.

IngoRM

That is exactly right.  Predictive models need one label at a time but you can of course create multiple models for different columns like @varunm1 has suggested.  There is also one operator in RapidMiner called Vector Linear Regression which can predict multiple labels at the same time.  However, I personally find the multi-model approach discussed before to be more effective.

Hope that helps,
Ingo

tftemme

In version 9.4.0 there will be an operator 'Multi Label Model' which exactly do this training of multiple models for a selection of attributes. You select the label attributes, and defines in the internal subprocess of the operator the training process for one label. The operator then performs this inner subprocess for every selected label attribute. 
We develop this for the application in time series forecasting domain, where you often want to predict multiple horizons (next day, in two days, in three days, ...) with one model. But it can be used for any situation with multiple label attributes.

Best regards,
Fabian

User11050

Thanks tftemme
I've already running RapidMiner version 9.3 but couldn't find this operator there.
Regards
Mansour

tftemme

Hi,

As I said with the next version 9.4 there will be the operator (this was meant as a teaser for the next release ;-). Its still in the development phase. For now (until 9.4) you would need to set the roles manually.

Best regards,
Fabian

tftemme

To update on this. As stated, since 9.4.1 RM contains now the 'Multi Label Modeling' operator to train a model for multiple label attributes. There are also the operators 'Multi Label Performance' (for evaluating the performance of such a model), and the 'Multi Horizon Forecast' and 'Multi Horizon Performance' operator which handles the time series related multi label operations.

Best regards,
Fabian