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"Learners not working inside missing values"
Learner III
Hi,
I have been trying to use the "impute missing values" operator with different learners, and I haven't been successful. In particular, tree type learners always give me the following error: Does not have sufficient capabilities to deal with example sets with only one label.
This doesn't make much sense to me, as I know you can learn one attribute only with decision trees. Moreover, I tried using Decision tree on its own with the same input data as it gets when used as inner operator of "impute missing values", and it works in that case!
Do you have any idea where that problem come from, or any suggestions as to which learners would be better to use with this operator? here are the processes I'm working on :
With impute missing values operator:
Simon
I have been trying to use the "impute missing values" operator with different learners, and I haven't been successful. In particular, tree type learners always give me the following error: Does not have sufficient capabilities to deal with example sets with only one label.
This doesn't make much sense to me, as I know you can learn one attribute only with decision trees. Moreover, I tried using Decision tree on its own with the same input data as it gets when used as inner operator of "impute missing values", and it works in that case!
Do you have any idea where that problem come from, or any suggestions as to which learners would be better to use with this operator? here are the processes I'm working on :
With impute missing values operator:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>With simply Decision Tree operator:
<process version="5.0">
<context>
<input>
<location/>
</input>
<output>
<location/>
<location/>
</output>
<macros/>
</context>
<operator activated="true" class="process" expanded="true" name="Process">
<parameter key="logverbosity" value="3"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="1"/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true" height="428" width="639">
<operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve" width="90" x="45" y="75">
<parameter key="repository_entry" value="//NewLocalRepository/Preprocessing_I"/>
</operator>
<operator activated="true" class="sample" expanded="true" height="76" name="Sample" width="90" x="179" y="75">
<parameter key="sample" value="0"/>
<parameter key="sample_size" value="5000"/>
<parameter key="sample_ratio" value="0.1"/>
<parameter key="sample_probability" value="0.1"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
</operator>
<operator activated="true" class="work_on_subset" expanded="true" height="76" name="Work on Subset" width="90" x="380" y="75">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value="RFA_2R|RFA_2F|RFA_2A|MSA"/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="numeric"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="11"/>
<parameter key="block_type" value="0"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="8"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
<parameter key="keep_subset_only" value="false"/>
<parameter key="deliver_inner_results" value="false"/>
<process expanded="true" height="428" width="639">
<operator activated="true" class="impute_missing_values" expanded="true" height="60" name="Impute Missing Values" width="90" x="246" y="30">
<parameter key="attribute_filter_type" value="value_type"/>
<parameter key="attribute" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="nominal"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="11"/>
<parameter key="block_type" value="0"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="8"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
<parameter key="iterate" value="true"/>
<parameter key="learn_on_complete_cases" value="true"/>
<parameter key="order" value="0"/>
<parameter key="sort" value="0"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<process expanded="true">
<operator activated="true" class="decision_tree" expanded="true" height="76" name="Decision Tree" width="90" x="274" y="30">
<parameter key="criterion" value="gain_ratio"/>
<parameter key="minimal_size_for_split" value="4"/>
<parameter key="minimal_leaf_size" value="2"/>
<parameter key="minimal_gain" value="0.1"/>
<parameter key="maximal_depth" value="20"/>
<parameter key="confidence" value="0.25"/>
<parameter key="number_of_prepruning_alternatives" value="3"/>
<parameter key="no_pre_pruning" value="false"/>
<parameter key="no_pruning" value="false"/>
</operator>
<connect from_port="example set source" to_op="Decision Tree" to_port="training set"/>
<connect from_op="Decision Tree" from_port="model" to_port="model sink"/>
<portSpacing port="source_example set source" spacing="0"/>
<portSpacing port="sink_model sink" spacing="0"/>
</process>
</operator>
<connect from_port="exampleSet" to_op="Impute Missing Values" to_port="example set in"/>
<connect from_op="Impute Missing Values" from_port="example set out" to_port="example set"/>
<portSpacing port="source_exampleSet" spacing="0"/>
<portSpacing port="sink_example set" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
</operator>
<connect from_op="Retrieve" from_port="output" to_op="Sample" to_port="example set input"/>
<connect from_op="Sample" from_port="example set output" to_op="Work on Subset" to_port="example set"/>
<connect from_op="Work on Subset" from_port="example set" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
<?xml version="1.0" encoding="UTF-8" standalone="no"?>Thanks in advance
<process version="5.0">
<context>
<input>
<location/>
</input>
<output>
<location/>
<location/>
<location/>
</output>
<macros/>
</context>
<operator activated="true" class="process" expanded="true" name="Process">
<parameter key="logverbosity" value="3"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="1"/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true" height="428" width="639">
<operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
<parameter key="repository_entry" value="//NewLocalRepository/Preprocessing_I"/>
</operator>
<operator activated="true" class="sample" expanded="true" height="76" name="Sample" width="90" x="45" y="165">
<parameter key="sample" value="0"/>
<parameter key="sample_size" value="5000"/>
<parameter key="sample_ratio" value="0.1"/>
<parameter key="sample_probability" value="0.1"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
</operator>
<operator activated="true" class="set_role" expanded="true" height="76" name="Set Role" width="90" x="179" y="165">
<parameter key="name" value="MSA"/>
<parameter key="target_role" value="label"/>
</operator>
<operator activated="true" class="select_attributes" expanded="true" height="76" name="Select Attributes" width="90" x="313" y="165">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value="RFA_2R|RFA_2F|RFA_2A|MSA"/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="nominal"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="11"/>
<parameter key="block_type" value="0"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="8"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
</operator>
<operator activated="true" class="decision_tree" expanded="true" height="76" name="Decision Tree" width="90" x="447" y="75">
<parameter key="criterion" value="gain_ratio"/>
<parameter key="minimal_size_for_split" value="4"/>
<parameter key="minimal_leaf_size" value="1"/>
<parameter key="minimal_gain" value="0.1"/>
<parameter key="maximal_depth" value="20"/>
<parameter key="confidence" value="0.25"/>
<parameter key="number_of_prepruning_alternatives" value="3"/>
<parameter key="no_pre_pruning" value="false"/>
<parameter key="no_pruning" value="false"/>
</operator>
<connect from_op="Retrieve" from_port="output" to_op="Sample" to_port="example set input"/>
<connect from_op="Sample" from_port="example set output" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Decision Tree" to_port="training set"/>
<connect from_op="Decision Tree" from_port="model" to_port="result 1"/>
<connect from_op="Decision Tree" from_port="exampleSet" 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>
Simon
Tagged:
0
Answers
I guess there's at least one nominal column in your data that has only one value beside the unknowns. If that's correct, the problem is, that this attribute will be used as label and the tree doesn't know what to do since it cannot . The wisest solution would be to simply always predict this value, but the algorithm does not test on this and simply cancels execution.
I think the easiest workaround would be to filter these attributes out manually using a select attribute operator.
Greetings,
Sebastian