Which operator in the 9.3 version is analogous to the write model in the 5.3 version?

Ruchika_agarwal
Ruchika_agarwal New Altair Community Member
edited November 2024 in Community Q&A
I used the store operator but i am not getting results so i am not completely sure.  Can someone please explain the working methodology? And also suggest another operator,if not the store operater that will help store my model as a mod file?
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Answers

  • varunm1
    varunm1 New Altair Community Member
    Hello @Ruchika_agarwal

    Earlier write model is used to store model in the form of .mod. I am not sure if its part of an extension as I don't see it in Rapidminer. You can use the store operator to store your model in rapidminer repository and then use retrieve operator to retrieve the stored model to apply on data.

    Sample store model XML below.

    <?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="45" y="85">
    <parameter key="repository_entry" value="//Samples/data/Titanic Training"/>
    </operator>
    <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.3.001" expanded="true" height="103" name="Decision Tree" width="90" x="246" y="85">
    <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>
    <operator activated="true" class="store" compatibility="9.3.001" expanded="true" height="68" name="Store" width="90" x="514" y="85">
    <parameter key="repository_entry" value="//Local Repository/processes/test_process_store"/>
    </operator>
    <connect from_op="Retrieve Titanic Training" from_port="output" to_op="Decision Tree" to_port="training set"/>
    <connect from_op="Decision Tree" from_port="model" to_op="Store" to_port="input"/>
    <connect from_op="Store" from_port="through" 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>



    Hope this helps

    Varun
  • sgenzer
    sgenzer
    Altair Employee
    confirmed - the Store operator can be used for all* IOObjects in RapidMiner.

    *can't think of an exception to this rule off the top of my head... :smile:

    Scott
  • rfuentealba
    rfuentealba New Altair Community Member
    Hello,

    Yes, you just connect the mod output from your model to the Store operator and save your model as you would with your data. It has been reworked in RapidMiner 7, IIRC, so you don't need anything else. It just works.

    All the best,

    Rodrigo.

  • jczogalla
    jczogalla New Altair Community Member
    I think the Read/Write model operators were already a bit old in 5.3, but they were kept in the legacy extension (which is bundled) and just recently were marked as deprecated (since RM 9.2).
    You can still use these operators, although it is not recommended, by using the operator classes "legacy:read_model" and "legacy:write_model" in your xml (or load in an old process from 5.3).

    Cheers
    Jan