The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
"{solved} Problem in visualization of weighting"
Contributor II
Hi .. i know we can get a Graph for weighting but my problem is i have so many attributes and i just need attributes which weights are more than .5
so for doing this i used select by weight then used data to weight operators ....but THE big problem is when i use it ....data to weight,weights all remaining attributes 1 ! which is not correct ..what should i do?
Second question: which operators are good or can be used for visualization...i already know that decision trees provide an excellent visual model....my job needs good graphs and model which are not so hard to explain yet they are attractive too
Danke schon
and here is xml code for my first Q
so for doing this i used select by weight then used data to weight operators ....but THE big problem is when i use it ....data to weight,weights all remaining attributes 1 ! which is not correct ..what should i do?
Second question: which operators are good or can be used for visualization...i already know that decision trees provide an excellent visual model....my job needs good graphs and model which are not so hard to explain yet they are attractive too
Danke schon
and here is xml code for my first Q
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.2.006">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.2.006" expanded="true" name="Process">
<process expanded="true" height="541" width="1017">
<operator activated="true" class="retrieve" compatibility="5.2.006" expanded="true" height="60" name="Retrieve" width="90" x="112" y="300">
<parameter key="repository_entry" value="//HUSSAINI/Data/F C Data"/>
</operator>
<operator activated="true" class="select_attributes" compatibility="5.2.006" expanded="true" height="76" name="Select Attributes" width="90" x="246" y="300">
<parameter key="attribute_filter_type" value="value_type"/>
<parameter key="value_type" value="numeric"/>
</operator>
<operator activated="true" class="multiply" compatibility="5.2.006" expanded="true" height="94" name="Multiply" width="90" x="380" y="255"/>
<operator activated="true" class="featselext:maximum_relevance_weighting" compatibility="1.1.004" expanded="true" height="76" name="MR-Weighting" width="90" x="514" y="390"/>
<operator activated="true" class="featselext:sam_weighting" compatibility="1.1.004" expanded="true" height="76" name="Weight by SAM" width="90" x="514" y="210"/>
<operator activated="true" class="select_by_weights" compatibility="5.2.006" expanded="true" height="94" name="Select by Weights" width="90" x="648" y="210">
<parameter key="weight" value="0.5"/>
</operator>
<operator activated="true" class="data_to_weights" compatibility="5.2.006" expanded="true" height="76" name="Data to Weights" width="90" x="782" y="210"/>
<operator activated="true" class="select_by_weights" compatibility="5.2.006" expanded="true" height="94" name="Select by Weights (2)" width="90" x="648" y="390">
<parameter key="weight" value="0.5"/>
</operator>
<operator activated="true" class="data_to_weights" compatibility="5.2.006" expanded="true" height="76" name="Data to Weights (2)" width="90" x="783" y="300"/>
<connect from_op="Retrieve" from_port="output" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="Weight by SAM" to_port="example set"/>
<connect from_op="Multiply" from_port="output 2" to_op="MR-Weighting" to_port="example set"/>
<connect from_op="MR-Weighting" from_port="weights" to_op="Select by Weights (2)" to_port="weights"/>
<connect from_op="MR-Weighting" from_port="example set" to_op="Select by Weights (2)" to_port="example set input"/>
<connect from_op="Weight by SAM" from_port="weights" to_op="Select by Weights" to_port="weights"/>
<connect from_op="Weight by SAM" from_port="example set" to_op="Select by Weights" to_port="example set input"/>
<connect from_op="Select by Weights" from_port="example set output" to_op="Data to Weights" to_port="example set"/>
<connect from_op="Data to Weights" from_port="weights" to_port="result 1"/>
<connect from_op="Select by Weights (2)" from_port="example set output" to_op="Data to Weights (2)" to_port="example set"/>
<connect from_op="Data to Weights (2)" from_port="weights" 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>
Tagged:
0
Answers
What you need here is Weights to Data, OR a second weighting operator after removing the unimportant attributes. Please see the attached process for an example of the first alternative. It outputs the filtered example set, and the weights of the attributes in the original data set.
Second question: please open one post per question next time, unless the questions are *closely* related. Otherwise the threads will become cluttered, and the forum search won't deliver valuable results for the next user.
However: with "operators" here you probably mean learning algorithms: I have no idea, what you are doing with your data, but since you are mentioning decision trees, I guess you are dealing with classification tasks. In this case, e.g. the linear SVM delivers well understandable models with weights for each feature, similar to the Linear Regression, which can also be used for binary classification tasks. Naive Bayes of course also creates fast and easy models which can be nicely visualized.
Best, Marius
Best, Marius