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Recognition rate (%) for a prediction Model

Mario1Mario1 Member Posts: 2 Contributor I
edited December 2018 in Help

Hello everybody

That is my Model

 

First thing I  have to say is, I am a beginner with rapidminer. However, I am trying to build a

Predictive Model with a time serie.

  • First I prepared the data.
    • All attributes are real
  • I used the windowing operator
  • Now I want to train that model and measure the recognition rate in percentage
  • this does not work with any performance operator
  • I have no Idea how to solve that problem

 

<?xml version="1.0" encoding="UTF-8"?><process version="8.1.003">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="8.1.003" 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" breakpoints="after" class="read_csv" compatibility="8.1.003" expanded="true" height="68" name="Read CSV" width="90" x="45" y="85">
<parameter key="csv_file" value="C:\Users\Letti\Documents\ZHAW\BachelorArbeit\Artefakte\Measurements01052018\KopieYviQuartier.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"/>
<parameter key="read_all_values_as_polynominal" value="false"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="MARKE.true.real.attribute"/>
<parameter key="1" value="Rechnen.true.real.attribute"/>
<parameter key="2" value="Motordrehzahl /min.true.real.attribute"/>
<parameter key="3" value="MARKE2.false.real.attribute"/>
<parameter key="4" value="Fahrzeuggeschwindigkeit km/h.true.real.attribute"/>
<parameter key="5" value="MARKE3.false.real.attribute"/>
<parameter key="6" value="Gaspedalstellung %.true.real.attribute"/>
<parameter key="7" value="MARKE4.false.real.attribute"/>
<parameter key="8" value="Motordrehmoment Nm.true.real.attribute"/>
<parameter key="9" value="MARKE5.false.real.attribute"/>
<parameter key="10" value="Druck im Bremskraftverstärker hPa.true.real.attribute"/>
<parameter key="11" value="MARKE6.false.real.attribute"/>
<parameter key="12" value="Ladedruck: Istwert hPa.true.real.attribute"/>
<parameter key="13" value="MARKE7.false.real.attribute"/>
<parameter key="14" value="Beschleunigung m/s².true.real.attribute"/>
<parameter key="15" value="MARKE8.false.real.attribute"/>
<parameter key="16" value="Bremsdruck Mpa.true.real.attribute"/>
<parameter key="17" value="MARKE9.false.real.attribute"/>
<parameter key="18" value="Längsbeschleunigung m/s².true.real.attribute"/>
<parameter key="19" value="MARKE10.false.real.attribute"/>
<parameter key="20" value="Status des Motors.false.polynominal.attribute"/>
<parameter key="21" value="MARKE11.false.real.attribute"/>
<parameter key="22" value="Fahrzeugquerbeschleunigung g.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" breakpoints="after" class="series:windowing" compatibility="7.4.000" expanded="true" height="82" name="Windowing" width="90" x="380" y="85">
<parameter key="series_representation" value="encode_series_by_examples"/>
<parameter key="window_size" value="5"/>
<parameter key="step_size" value="1"/>
<parameter key="create_single_attributes" value="true"/>
<parameter key="create_label" value="true"/>
<parameter key="select_label_by_dimension" value="false"/>
<parameter key="label_attribute" value="Rechnen"/>
<parameter key="horizon" value="1"/>
<parameter key="add_incomplete_windows" value="false"/>
<parameter key="stop_on_too_small_dataset" value="true"/>
</operator>
<operator activated="false" class="set_role" compatibility="8.1.003" expanded="true" height="82" name="Set Role" width="90" x="447" y="238">
<parameter key="attribute_name" value=""/>
<parameter key="target_role" value="id"/>
<list key="set_additional_roles">
<parameter key="MARKE" value="id"/>
</list>
</operator>
<operator activated="true" class="series:sliding_window_validation" compatibility="7.4.000" expanded="true" height="145" name="Validation" width="90" x="715" y="85">
<parameter key="create_complete_model" value="false"/>
<parameter key="training_window_width" value="5"/>
<parameter key="training_window_step_size" value="1"/>
<parameter key="test_window_width" value="5"/>
<parameter key="horizon" value="1"/>
<parameter key="cumulative_training" value="false"/>
<parameter key="average_performances_only" value="true"/>
<process expanded="true">
<operator activated="true" class="support_vector_machine" compatibility="8.1.003" expanded="true" height="124" name="SVM" width="90" x="246" y="85">
<parameter key="kernel_type" value="dot"/>
<parameter key="kernel_gamma" value="1.0"/>
<parameter key="kernel_sigma1" value="1.0"/>
<parameter key="kernel_sigma2" value="0.0"/>
<parameter key="kernel_sigma3" value="2.0"/>
<parameter key="kernel_shift" value="1.0"/>
<parameter key="kernel_degree" value="2.0"/>
<parameter key="kernel_a" value="1.0"/>
<parameter key="kernel_b" value="0.0"/>
<parameter key="kernel_cache" value="200"/>
<parameter key="C" value="0.0"/>
<parameter key="convergence_epsilon" value="0.001"/>
<parameter key="max_iterations" value="100000"/>
<parameter key="scale" value="true"/>
<parameter key="calculate_weights" value="true"/>
<parameter key="return_optimization_performance" value="true"/>
<parameter key="L_pos" value="1.0"/>
<parameter key="L_neg" value="1.0"/>
<parameter key="epsilon" value="0.0"/>
<parameter key="epsilon_plus" value="0.0"/>
<parameter key="epsilon_minus" value="0.0"/>
<parameter key="balance_cost" value="false"/>
<parameter key="quadratic_loss_pos" value="false"/>
<parameter key="quadratic_loss_neg" value="false"/>
<parameter key="estimate_performance" value="false"/>
</operator>
<connect from_port="training" to_op="SVM" to_port="training set"/>
<connect from_op="SVM" from_port="model" to_port="model"/>
<connect from_op="SVM" from_port="exampleSet" to_port="through 1"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
<portSpacing port="sink_through 2" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="8.1.003" expanded="true" height="82" name="Apply Model" width="90" x="112" y="34">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_port="averagable 1"/>
<connect from_op="Apply Model" from_port="model" to_port="averagable 2"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="source_through 2" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
<portSpacing port="sink_averagable 3" spacing="0"/>
<description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="266" y="97">Any Perfomance Operator&lt;br/&gt;doen' work!!!&lt;br/&gt;(Missing here)</description>
</process>
</operator>
<connect from_op="Read CSV" from_port="output" to_op="Windowing" to_port="example set input"/>
<connect from_op="Windowing" from_port="example set output" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="training" to_port="result 1"/>
<connect from_op="Validation" from_port="averagable 1" 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>

May you can give me an input for a solution?

 

Thx

Mario

 

Best Answer

  • Options
    MaerkliMaerkli Member Posts: 84 Guru
    Solution Accepted

    Hallo Mario,

    Your XML file does not populate the Process window.

    Best regards,

    Maerkli

Answers

  • Options
    lionelderkrikorlionelderkrikor Moderator, RapidMiner Certified Analyst, Member Posts: 1,195 Unicorn

    Hi @Mario1,

     

    Can you share your dataset please ?

     

    Regards,

     

    Lionel

  • Options
    Mario1Mario1 Member Posts: 2 Contributor I

    Hi Lionel

     

    Thanks for your answer. :-)

    Of course in can...... 

    I want to try to predict the attribute "Rechnen". This attribute has only two states. Yes/No; 0,1.

    I don't know which one is better...... 

     

    Regards,

    Mario

  • Options
    lionelderkrikorlionelderkrikor Moderator, RapidMiner Certified Analyst, Member Posts: 1,195 Unicorn

    Hi @Mario1,

     

    I just discovered your dataset.

    Windowing operator is adapted for "time series" problems.

    In your case, it seems that your problem is not a "time series" problem but a classification problem.

    In deed, I suppose you want to predict the value of "Rechnen" according to the value of the other real attributes ?

     

    You can find here a (classic) template of process for a classification task : 

    <?xml version="1.0" encoding="UTF-8"?><process version="8.2.000">
    <context>
    <input/>
    <output/>
    <macros/>
    </context>
    <operator activated="true" class="process" compatibility="8.2.000" expanded="true" name="Process">
    <process expanded="true">
    <operator activated="true" breakpoints="after" class="read_csv" compatibility="8.2.000" expanded="true" height="68" name="Read CSV" width="90" x="112" y="34">
    <parameter key="csv_file" value="C:\Users\Lionel\Documents\Formations_DataScience\Rapidminer\Tests_Rapidminer\Prediction_Quartier\KopieYviQuartier.csv"/>
    <parameter key="column_separators" value=","/>
    <parameter key="first_row_as_names" value="false"/>
    <list key="annotations">
    <parameter key="0" value="Name"/>
    </list>
    <parameter key="encoding" value="windows-1252"/>
    <list key="data_set_meta_data_information">
    <parameter key="0" value="MARKE.true.real.attribute"/>
    <parameter key="1" value="Rechnen.true.integer.attribute"/>
    <parameter key="2" value="Motordrehzahl /min.true.integer.attribute"/>
    <parameter key="3" value="MARKE2.true.real.attribute"/>
    <parameter key="4" value="Fahrzeuggeschwindigkeit km/h.true.integer.attribute"/>
    <parameter key="5" value="MARKE3.true.real.attribute"/>
    <parameter key="6" value="Gaspedalstellung %.true.integer.attribute"/>
    <parameter key="7" value="MARKE4.true.real.attribute"/>
    <parameter key="8" value="Motordrehmoment Nm.true.real.attribute"/>
    <parameter key="9" value="MARKE5.true.real.attribute"/>
    <parameter key="10" value="Druck im Bremskraftverstärker hPa.true.real.attribute"/>
    <parameter key="11" value="MARKE6.true.real.attribute"/>
    <parameter key="12" value="Ladedruck: Istwert hPa.true.real.attribute"/>
    <parameter key="13" value="MARKE7.true.real.attribute"/>
    <parameter key="14" value="Beschleunigung m/s².true.real.attribute"/>
    <parameter key="15" value="MARKE8.true.real.attribute"/>
    <parameter key="16" value="Bremsdruck Mpa.true.real.attribute"/>
    <parameter key="17" value="MARKE9.true.real.attribute"/>
    <parameter key="18" value="Längsbeschleunigung m/s².true.real.attribute"/>
    <parameter key="19" value="MARKE10.true.real.attribute"/>
    <parameter key="20" value="Status des Motors.true.polynominal.attribute"/>
    <parameter key="21" value="MARKE11.true.real.attribute"/>
    <parameter key="22" value="Fahrzeugquerbeschleunigung g.true.real.attribute"/>
    </list>
    </operator>
    <operator activated="true" class="set_role" compatibility="8.2.000" expanded="true" height="82" name="Set Role" width="90" x="246" y="34">
    <parameter key="attribute_name" value="Rechnen"/>
    <parameter key="target_role" value="label"/>
    <list key="set_additional_roles"/>
    </operator>
    <operator activated="true" class="numerical_to_binominal" compatibility="8.2.000" expanded="true" height="82" name="Numerical to Binominal" width="90" x="380" y="34">
    <parameter key="attribute_filter_type" value="single"/>
    <parameter key="attribute" value="Rechnen"/>
    <parameter key="include_special_attributes" value="true"/>
    <parameter key="max" value="0.9"/>
    </operator>
    <operator activated="true" class="concurrency:cross_validation" compatibility="8.2.000" expanded="true" height="145" name="Cross Validation" width="90" x="581" y="34">
    <process expanded="true">
    <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="8.2.000" expanded="true" height="103" name="Decision Tree" width="90" x="179" y="34"/>
    <connect from_port="training set" to_op="Decision Tree" to_port="training set"/>
    <connect from_op="Decision Tree" from_port="model" to_port="model"/>
    <portSpacing port="source_training set" spacing="0"/>
    <portSpacing port="sink_model" spacing="0"/>
    <portSpacing port="sink_through 1" spacing="0"/>
    </process>
    <process expanded="true">
    <operator activated="true" class="apply_model" compatibility="8.2.000" expanded="true" height="82" name="Apply Model" width="90" x="112" y="34">
    <list key="application_parameters"/>
    </operator>
    <operator activated="true" class="performance_binominal_classification" compatibility="8.2.000" expanded="true" height="82" name="Performance (2)" width="90" x="246" y="34"/>
    <connect from_port="model" to_op="Apply Model" to_port="model"/>
    <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
    <connect from_op="Apply Model" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
    <connect from_op="Performance (2)" from_port="performance" to_port="performance 1"/>
    <portSpacing port="source_model" spacing="0"/>
    <portSpacing port="source_test set" spacing="0"/>
    <portSpacing port="source_through 1" spacing="0"/>
    <portSpacing port="sink_test set results" spacing="0"/>
    <portSpacing port="sink_performance 1" spacing="0"/>
    <portSpacing port="sink_performance 2" 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="Numerical to Binominal" to_port="example set input"/>
    <connect from_op="Numerical to Binominal" from_port="example set output" to_op="Cross Validation" to_port="example set"/>
    <connect from_op="Cross Validation" from_port="example set" to_port="result 1"/>
    <connect from_op="Cross Validation" from_port="performance 1" 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>

    I choose arbitrarily a Decision Tree as model, but you can try the different classification models proposed by RapidMiner 

    to determine which one is the best.

    About that, if you are beginner in RapidMiner, I encourage to see the videos about the basics of RapidMiner (how to train a model, 

    how to validate a model, how to apply a model, how to optimize a model etc.) by following this link.

     

    I hope it helps,

     

    Regards,

     

    Lionel

     

     

     

     

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