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Select By Weights Criteria

FlixportFlixport Member Posts: 33 Contributor II
edited January 2020 in Help 
Hey,

I am currently building a process for TextMining. I used the TF-IDF as a solution. Briefly and concisely, it's about extracting important information from news. I filter the messages by topic and date so that I can assign the information to the message.
A friend recommended the operator Select by Weights to me. However, I always get an error message with the code:

<?xml version="1.0" encoding="UTF-8"?><process version="9.2.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.2.001" 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="UTF-8"/>
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="9.2.001" expanded="true" height="68" name="Retrieve reut2-000" width="90" x="45" y="85">
        <parameter key="repository_entry" value="reut2-000"/>
      </operator>
      <operator activated="true" class="select_attributes" compatibility="9.2.001" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="85">
        <parameter key="attribute_filter_type" value="subset"/>
        <parameter key="attribute" value=""/>
        <parameter key="attributes" value="|exchanges|orgs|people|text_orig|title|topics|zahlen"/>
        <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="true"/>
        <parameter key="include_special_attributes" value="true"/>
      </operator>
      <operator activated="true" class="generate_id" compatibility="9.2.001" expanded="true" height="82" name="Generate ID" width="90" x="313" y="85">
        <parameter key="create_nominal_ids" value="false"/>
        <parameter key="offset" value="0"/>
      </operator>
      <operator activated="true" breakpoints="after" class="filter_examples" compatibility="9.2.001" expanded="true" height="103" name="Filter Examples" width="90" x="45" y="187">
        <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="places.does_not_equal.?"/>
        </list>
        <parameter key="filters_logic_and" value="true"/>
        <parameter key="filters_check_metadata" value="true"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="9.2.001" expanded="true" height="82" name="Set Role" width="90" x="179" y="187">
        <parameter key="attribute_name" value="places"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" breakpoints="after" class="remove_correlated_attributes" compatibility="9.2.001" expanded="true" height="82" name="Remove Correlated Attributes" width="90" x="380" y="187">
        <parameter key="correlation" value="0.8"/>
        <parameter key="filter_relation" value="greater"/>
        <parameter key="attribute_order" value="random"/>
        <parameter key="use_absolute_correlation" value="true"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
      </operator>
      <operator activated="true" class="subprocess" compatibility="9.2.001" expanded="true" height="124" name="Feature Engineering" width="90" x="581" y="85">
        <process expanded="true">
          <operator activated="true" class="multiply" compatibility="9.2.001" expanded="true" height="124" name="Multiply (2)" width="90" x="112" y="187"/>
          <operator activated="true" class="weight_by_chi_squared_statistic" compatibility="9.2.001" expanded="true" height="82" name="Weight by Chi Squared Statistic" width="90" x="313" y="34">
            <parameter key="normalize_weights" value="false"/>
            <parameter key="sort_weights" value="true"/>
            <parameter key="sort_direction" value="descending"/>
            <parameter key="number_of_bins" value="10"/>
          </operator>
          <operator activated="true" breakpoints="after" class="select_by_weights" compatibility="9.2.001" expanded="true" height="103" name="Select by Weights (ChiSq)" width="90" x="514" y="34">
            <parameter key="weight_relation" value="top k"/>
            <parameter key="weight" value="10.0"/>
            <parameter key="k" value="50"/>
            <parameter key="p" value="0.1"/>
            <parameter key="deselect_unknown" value="true"/>
            <parameter key="use_absolute_weights" value="false"/>
          </operator>
          <operator activated="true" class="store" compatibility="9.2.001" expanded="true" height="68" name="Store" width="90" x="715" y="34">
            <parameter key="repository_entry" value="reut2-000"/>
          </operator>
          <operator activated="true" class="principal_component_analysis" compatibility="9.2.001" expanded="true" height="103" name="PCA" width="90" x="313" y="187">
            <parameter key="dimensionality_reduction" value="keep variance"/>
            <parameter key="variance_threshold" value="0.8"/>
            <parameter key="number_of_components" value="1"/>
          </operator>
          <operator activated="true" class="weight_by_pca" compatibility="9.2.001" expanded="true" height="82" name="Weight by PCA" width="90" x="313" y="340">
            <parameter key="normalize_weights" value="false"/>
            <parameter key="sort_weights" value="true"/>
            <parameter key="sort_direction" value="ascending"/>
            <parameter key="component_number" value="1"/>
          </operator>
          <operator activated="true" breakpoints="after" class="select_by_weights" compatibility="9.2.001" expanded="true" height="103" name="Select by Weights (PCA)" width="90" x="514" y="340">
            <parameter key="weight_relation" value="top k"/>
            <parameter key="weight" value="10.0"/>
            <parameter key="k" value="50"/>
            <parameter key="p" value="0.1"/>
            <parameter key="deselect_unknown" value="true"/>
            <parameter key="use_absolute_weights" value="true"/>
          </operator>
          <operator activated="true" class="store" compatibility="9.2.001" expanded="true" height="68" name="Store (3)" width="90" x="715" y="340">
            <parameter key="repository_entry" value="reut2-000"/>
          </operator>
          <operator activated="true" class="store" compatibility="9.2.001" expanded="true" height="68" name="Store (2)" width="90" x="715" y="187">
            <parameter key="repository_entry" value="reut2-000"/>
          </operator>
          <connect from_port="in 1" to_op="Multiply (2)" to_port="input"/>
          <connect from_op="Multiply (2)" from_port="output 1" to_op="Weight by Chi Squared Statistic" to_port="example set"/>
          <connect from_op="Multiply (2)" from_port="output 2" to_op="PCA" to_port="example set input"/>
          <connect from_op="Multiply (2)" from_port="output 3" to_op="Weight by PCA" to_port="example set"/>
          <connect from_op="Weight by Chi Squared Statistic" from_port="weights" to_op="Select by Weights (ChiSq)" to_port="weights"/>
          <connect from_op="Weight by Chi Squared Statistic" from_port="example set" to_op="Select by Weights (ChiSq)" to_port="example set input"/>
          <connect from_op="Select by Weights (ChiSq)" from_port="example set output" to_op="Store" to_port="input"/>
          <connect from_op="Store" from_port="through" to_port="out 1"/>
          <connect from_op="PCA" from_port="example set output" to_op="Store (2)" to_port="input"/>
          <connect from_op="Weight by PCA" from_port="weights" to_op="Select by Weights (PCA)" to_port="weights"/>
          <connect from_op="Weight by PCA" from_port="example set" to_op="Select by Weights (PCA)" to_port="example set input"/>
          <connect from_op="Select by Weights (PCA)" from_port="example set output" to_op="Store (3)" to_port="input"/>
          <connect from_op="Store (3)" from_port="through" to_port="out 3"/>
          <connect from_op="Store (2)" from_port="through" to_port="out 2"/>
          <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"/>
          <portSpacing port="sink_out 3" spacing="0"/>
          <portSpacing port="sink_out 4" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve reut2-000" from_port="output" to_op="Select Attributes" to_port="example set input"/>
      <connect from_op="Select Attributes" from_port="example set output" to_op="Generate ID" to_port="example set input"/>
      <connect from_op="Generate ID" 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="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Remove Correlated Attributes" to_port="example set input"/>
      <connect from_op="Remove Correlated Attributes" from_port="example set output" to_op="Feature Engineering" to_port="in 1"/>
      <connect from_op="Feature Engineering" from_port="out 1" to_port="result 1"/>
      <connect from_op="Feature Engineering" from_port="out 2" to_port="result 2"/>
      <connect from_op="Feature Engineering" from_port="out 3" to_port="result 3"/>
      <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"/>
      <portSpacing port="sink_result 4" spacing="0"/>
      <description align="left" color="yellow" colored="false" height="278" resized="true" width="815" x="39" y="325">REDUKTION DER DIMENSIONALIT&amp;#196;T&lt;br/&gt;&lt;br/&gt;Hier geht hier darum, die Reduktion der Dimensionalit&amp;#228;t anzustreben. Zwei m&amp;#246;gliche Arten:&lt;br&gt;-- auf Basis PCA (braucht kein Zielvariable)&lt;br&gt;-- auf Basis ChiSquared (Zielvariable vorausus&lt;br&gt;Gibt es eine Zielvariable, so ist es m&amp;#246;glich nur diejenigen Felder zu behalten, die hohes Potenzial f&amp;#252;r ein Model haben.&lt;br&gt;&lt;br&gt;Schritte:&lt;br&gt;a. Input Daten TF-IDF&lt;br&gt;b. Non-TFIDF Felder rausfiltern: exchanges, org, people, usw.&lt;br&gt;c. Filter nur Datens&amp;#228;tze mit vollst&amp;#228;ndigen Werte &amp;#252;r Zielvariable&lt;br&gt;d. Entferne korrelierte TFIDF Felder&lt;br&gt;e. Verwende beiden Methoden zur Reduktion der Dimensionalit&amp;#228;t. Daten speichern.&lt;br&gt;&lt;br&gt;</description>
      <description align="left" color="yellow" colored="false" height="58" resized="true" width="301" x="177" y="22">F&amp;#252;r die Reduktion der Dimensionalit&amp;#228;t bleibt eine Zielvariable und die TF-IDF Felder.</description>
    </process>
  </operator>
</process>
 


The Input is a CSV Data which i download from the Newsholding Reuters.

Thanks


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Best Answer

Answers

  • MartinLiebigMartinLiebig Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,531 RM Data Scientist
    can you maybe link the CSV? the process looks okay on first place and i would need data to check the issue.
    BR,
    Martin
    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany
  • FlixportFlixport Member Posts: 33 Contributor II

    for sure.

    Thanks

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