input example set does not have a predicted label attribute

mndtmndt Member Posts: 1 Contributor I
edited November 30 in Help

I'm trying to use "fit trend" and "Neural Net" tor find the trend line fot a time series.

Desipite using  "Set Role" operator to set the attribute as lable, I still receive the error "input example set does not have a predicted label attribute" in latest version of Rapidminer studio:

Here is the project:

 

<?xml version="1.0" encoding="UTF-8"?><process version="7.4.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.4.000" 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="read_csv" compatibility="7.4.000" expanded="true" height="68" name="Read CSV" width="90" x="45" y="34">
        <parameter key="csv_file" value="C:\Users\__\Desktop\Sample.csv"/>
        <parameter key="column_separators" value=","/>
        <parameter key="trim_lines" value="false"/>
        <parameter key="use_quotes" value="true"/>
        <parameter key="quotes_character" value="&quot;"/>
        <parameter key="escape_character" value="\"/>
        <parameter key="skip_comments" value="false"/>
        <parameter key="comment_characters" value="#"/>
        <parameter key="parse_numbers" value="true"/>
        <parameter key="decimal_character" value="."/>
        <parameter key="grouped_digits" value="false"/>
        <parameter key="grouping_character" value=","/>
        <parameter key="date_format" value=""/>
        <parameter key="first_row_as_names" value="false"/>
        <list key="annotations">
          <parameter key="0" value="Name"/>
        </list>
        <parameter key="time_zone" value="SYSTEM"/>
        <parameter key="locale" value="English (United States)"/>
        <parameter key="encoding" value="windows-1252"/>
        <list key="data_set_meta_data_information">
          <parameter key="0" value="&lt;Ticker&gt;.true.polynominal.attribute"/>
          <parameter key="1" value="&lt;Per&gt;.true.polynominal.attribute"/>
          <parameter key="2" value="&lt;DTYYYYMMDD&gt;.true.integer.attribute"/>
          <parameter key="3" value="&lt;TIME&gt;.true.integer.attribute"/>
          <parameter key="4" value="&lt;Open&gt;.true.real.attribute"/>
          <parameter key="5" value="&lt;High&gt;.true.real.attribute"/>
          <parameter key="6" value="&lt;Low&gt;.true.real.attribute"/>
          <parameter key="7" value="&lt;Close&gt;.true.real.label"/>
          <parameter key="8" value="&lt;Vol&gt;.true.integer.attribute"/>
          <parameter key="9" value="&lt;Openint&gt;.true.real.attribute"/>
        </list>
        <parameter key="read_not_matching_values_as_missings" value="true"/>
        <parameter key="datamanagement" value="double_array"/>
        <parameter key="data_management" value="auto"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="7.4.000" expanded="true" height="82" name="Set Role" width="90" x="179" y="34">
        <parameter key="attribute_name" value="&lt;Close&gt;"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="series:fit_trend" compatibility="5.3.000" expanded="true" height="68" name="Fit Trend" width="90" x="313" y="34">
        <parameter key="attribute" value="&lt;Close&gt;"/>
        <parameter key="keep_original_attribute" value="true"/>
        <process expanded="true">
          <operator activated="true" class="neural_net" compatibility="7.4.000" expanded="true" height="82" name="Neural Net" width="90" x="179" y="85">
            <list key="hidden_layers"/>
            <parameter key="training_cycles" value="500"/>
            <parameter key="learning_rate" value="0.5"/>
            <parameter key="momentum" value="0.4"/>
            <parameter key="decay" value="true"/>
            <parameter key="shuffle" value="true"/>
            <parameter key="normalize" value="true"/>
            <parameter key="error_epsilon" value="1.0E-5"/>
            <parameter key="use_local_random_seed" value="false"/>
            <parameter key="local_random_seed" value="1992"/>
          </operator>
          <connect from_port="example set" to_op="Neural Net" to_port="training set"/>
          <connect from_op="Neural Net" from_port="model" to_port="model"/>
          <portSpacing port="source_example set" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Read CSV" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Fit Trend" to_port="example set"/>
      <connect from_op="Fit Trend" from_port="example set with trend" 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>

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Answers

  • Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 869   Unicorn

    "Fit Trend" requires a prediction rather than just a label.  So you already must have built a model and you feed that into "Fit Trend" and it then fits a trendline to the prediction.  See the attached process.

     

    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
    Thomas_Ott
  • jeroen_willemsjeroen_willems Member Posts: 1 Contributor I

    Hi Brian,

    I used your process, but the trend output is the same as the prediction output. Even if I use default model operator as inner learner for the fit trend. Do I something wrong with the date attribute?

     

    In addition why is this operator needing a predicted label attribute? In the tutorial of Thomas Ott (http://www.neuralmarkettrends.com/rapidminer-5-0-video-tutorial-8/) a fit trend operator is directly used on a data set. I tried building this process as well, but get the same error as above above (example set is missing predicted label attribute).

     

    Hope you can help me.

     

    Thanks,

    Jeroen

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