What am i doing wrong with my model?

UmaimaUmaima Member Posts: 3 Newbie
I have to classify twitter data using neural net. Everytime i run my process, the message in the screenshot pops up. Sometimes the process keeps running forever without producing an output. i even tried reducing my sample size to -200,100, even 50! but it still doesnt work. Can someone kindly tell me how to fix my problem?
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Answers

  • varunm1varunm1 Member Posts: 661   Unicorn
    edited May 23
    Hello @Umaima

    Looks like your memory is not sufficient for the process. Can you check RAM usagw

    Thanks,
    Varun

    mbs
  • rfuentealbarfuentealba Moderator, RapidMiner Certified Analyst, Member, University Professor Posts: 394   Unicorn
    Hello, @Umaima,
    • Open RapidMiner Studio
    • Press Cmd + ,
    • You will see a window named RapidMiner Studio Preferences.
    • There, click on the System pane.
    • In the Maximum amount of memory, make sure you have a value that is not too low. Depending on how old is your computer, I would recommend putting something like 2048 or 4096 before trying again. You may want to see the About This Mac system dialog to decide how much memory you want to put there.
    • Make sure you leave some memory for the operating system when you manipulate that value. It would be nice if you open the Activity Monitor to see what else is consuming memory.
    Hope this helps,

    Rodrigo.
    varunm1sgenzerdbabrauskaite
  • UmaimaUmaima Member Posts: 3 Newbie
    I tried that but it still doesnt work....It just keeps processing for a long time.
  • sgenzersgenzer 12Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,383  Community Manager
    @Umaima can you please post your XML so we can see exactly what you're doing? https://community.rapidminer.com/discussion/37047
    dbabrauskaitembs
  • UmaimaUmaima Member Posts: 3 Newbie
    <?xml version="1.0" encoding="UTF-8"?><process version="9.2.000">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="9.2.000" 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="9.2.000" expanded="true" height="68" name="Retrieve TweetDataForAssignment2" width="90" x="45" y="85">
            <parameter key="repository_entry" value="//Local Repository/data/TweetDataForAssignment2"/>
          </operator>
          <operator activated="true" class="sample" compatibility="9.2.000" expanded="true" height="82" name="Sample" width="90" x="179" y="85">
            <parameter key="sample" value="absolute"/>
            <parameter key="balance_data" value="false"/>
            <parameter key="sample_size" value="2000"/>
            <parameter key="sample_ratio" value="0.1"/>
            <parameter key="sample_probability" value="0.1"/>
            <list key="sample_size_per_class"/>
            <list key="sample_ratio_per_class"/>
            <list key="sample_probability_per_class"/>
            <parameter key="use_local_random_seed" value="false"/>
            <parameter key="local_random_seed" value="1992"/>
          </operator>
          <operator activated="true" class="filter_examples" compatibility="9.2.000" expanded="true" height="103" name="Filter Examples" width="90" x="112" y="238">
            <parameter key="parameter_expression" value=""/>
            <parameter key="condition_class" value="custom_filters"/>
            <parameter key="invert_filter" value="false"/>
            <list key="filters_list">
              <parameter key="filters_entry_key" value="Location.is_not_missing."/>
            </list>
            <parameter key="filters_logic_and" value="true"/>
            <parameter key="filters_check_metadata" value="true"/>
          </operator>
          <operator activated="true" class="nominal_to_numerical" compatibility="9.2.000" expanded="true" height="103" name="Nominal to Numerical (2)" width="90" x="313" y="289">
            <parameter key="return_preprocessing_model" value="false"/>
            <parameter key="create_view" value="false"/>
            <parameter key="attribute_filter_type" value="subset"/>
            <parameter key="attribute" value=""/>
            <parameter key="attributes" value="|Content|Location|Time|UserHomeTown"/>
            <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="file_path"/>
            <parameter key="block_type" value="single_value"/>
            <parameter key="use_block_type_exception" value="false"/>
            <parameter key="except_block_type" value="single_value"/>
            <parameter key="invert_selection" value="false"/>
            <parameter key="include_special_attributes" value="false"/>
            <parameter key="coding_type" value="dummy coding"/>
            <parameter key="use_comparison_groups" value="false"/>
            <list key="comparison_groups"/>
            <parameter key="unexpected_value_handling" value="all 0 and warning"/>
            <parameter key="use_underscore_in_name" value="false"/>
          </operator>
          <operator activated="true" class="subprocess" compatibility="9.2.000" expanded="true" height="82" name="Subprocess" width="90" x="447" y="85">
            <process expanded="true">
              <operator activated="true" class="replace_missing_values" compatibility="9.2.000" expanded="true" height="103" name="Replace Missing Values" width="90" x="45" y="34">
                <parameter key="return_preprocessing_model" value="false"/>
                <parameter key="create_view" value="false"/>
                <parameter key="attribute_filter_type" value="all"/>
                <parameter key="attribute" value=""/>
                <parameter key="attributes" value=""/>
                <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="default" value="average"/>
                <list key="columns">
                  <parameter key="UserHomeTown" value="average"/>
                </list>
              </operator>
              <operator activated="true" class="set_role" compatibility="9.2.000" expanded="true" height="82" name="Set Role" width="90" x="179" y="34">
                <parameter key="attribute_name" value="Location"/>
                <parameter key="target_role" value="label"/>
                <list key="set_additional_roles"/>
              </operator>
              <operator activated="true" class="nominal_to_numerical" compatibility="9.2.000" expanded="true" height="103" name="Nominal to Numerical" width="90" x="313" y="34">
                <parameter key="return_preprocessing_model" value="false"/>
                <parameter key="create_view" value="false"/>
                <parameter key="attribute_filter_type" value="subset"/>
                <parameter key="attribute" value=""/>
                <parameter key="attributes" value="|Content|Location|Time|UserHomeTown"/>
                <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="file_path"/>
                <parameter key="block_type" value="single_value"/>
                <parameter key="use_block_type_exception" value="false"/>
                <parameter key="except_block_type" value="single_value"/>
                <parameter key="invert_selection" value="false"/>
                <parameter key="include_special_attributes" value="false"/>
                <parameter key="coding_type" value="unique integers"/>
                <parameter key="use_comparison_groups" value="false"/>
                <list key="comparison_groups"/>
                <parameter key="unexpected_value_handling" value="all 0 and warning"/>
                <parameter key="use_underscore_in_name" value="false"/>
              </operator>
              <connect from_port="in 1" to_op="Replace Missing Values" to_port="example set input"/>
              <connect from_op="Replace Missing Values" 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="Nominal to Numerical" to_port="example set input"/>
              <connect from_op="Nominal to Numerical" from_port="example set output" to_port="out 1"/>
              <portSpacing port="source_in 1" spacing="0"/>
              <portSpacing port="source_in 2" spacing="0"/>
              <portSpacing port="sink_out 1" spacing="0"/>
              <portSpacing port="sink_out 2" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="neural_net" compatibility="9.2.000" expanded="true" height="82" name="Neural Net" width="90" x="581" y="85">
            <list key="hidden_layers"/>
            <parameter key="training_cycles" value="500"/>
            <parameter key="learning_rate" value="0.3"/>
            <parameter key="momentum" value="0.2"/>
            <parameter key="decay" value="false"/>
            <parameter key="shuffle" value="true"/>
            <parameter key="normalize" value="true"/>
            <parameter key="error_epsilon" value="1.0E-4"/>
            <parameter key="use_local_random_seed" value="false"/>
            <parameter key="local_random_seed" value="1992"/>
          </operator>
          <operator activated="true" class="apply_model" compatibility="9.2.000" expanded="true" height="82" name="Apply Model" width="90" x="581" y="238">
            <list key="application_parameters"/>
            <parameter key="create_view" value="false"/>
          </operator>
          <connect from_op="Retrieve TweetDataForAssignment2" from_port="output" to_op="Sample" to_port="example set input"/>
          <connect from_op="Sample" from_port="example set output" to_op="Filter Examples" to_port="example set input"/>
          <connect from_op="Filter Examples" from_port="example set output" to_op="Subprocess" to_port="in 1"/>
          <connect from_op="Filter Examples" from_port="unmatched example set" to_op="Nominal to Numerical (2)" to_port="example set input"/>
          <connect from_op="Nominal to Numerical (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Subprocess" from_port="out 1" to_op="Neural Net" to_port="training set"/>
          <connect from_op="Neural Net" from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_op="Apply Model" from_port="labelled data" 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>

  • sgenzersgenzer 12Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,383  Community Manager
    can you also please post your data set? I cannot replicate your work unless I have your "TweetDataForAssignment2"
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