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Member "rapidminer-studio-9.5.0/src/main/resources/com/rapidminer/resources/samples/processes/06_Meta/03_LearningCurve.rmp" (7 Nov 2019, 10294 Bytes) of package /linux/misc/rapidminer-studio-9.5.0-src.tar.gz:


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    1 <?xml version="1.0" encoding="UTF-8"?><process version="7.3.000-SNAPSHOT">
    2   <context>
    3     <input/>
    4     <output/>
    5     <macros/>
    6   </context>
    7   <operator activated="true" class="process" compatibility="7.3.000-SNAPSHOT" expanded="true" name="Root">
    8     <parameter key="logverbosity" value="warning"/>
    9     <parameter key="random_seed" value="2004"/>
   10     <parameter key="send_mail" value="never"/>
   11     <parameter key="notification_email" value=""/>
   12     <parameter key="process_duration_for_mail" value="30"/>
   13     <parameter key="encoding" value="SYSTEM"/>
   14     <process expanded="true">
   15       <operator activated="true" class="generate_data" compatibility="7.1.001" expanded="true" height="68" name="ExampleSetGenerator" width="90" x="45" y="34">
   16         <parameter key="target_function" value="sum classification"/>
   17         <parameter key="number_examples" value="300"/>
   18         <parameter key="number_of_attributes" value="5"/>
   19         <parameter key="attributes_lower_bound" value="-1.0"/>
   20         <parameter key="attributes_upper_bound" value="1.0"/>
   21         <parameter key="gaussian_standard_deviation" value="10.0"/>
   22         <parameter key="largest_radius" value="10.0"/>
   23         <parameter key="use_local_random_seed" value="false"/>
   24         <parameter key="local_random_seed" value="1992"/>
   25         <parameter key="datamanagement" value="double_array"/>
   26       </operator>
   27       <operator activated="true" class="add_noise" compatibility="7.1.001" expanded="true" height="103" name="NoiseGenerator" width="90" x="179" y="34">
   28         <parameter key="return_preprocessing_model" value="false"/>
   29         <parameter key="create_view" value="false"/>
   30         <parameter key="attribute_filter_type" value="all"/>
   31         <parameter key="attribute" value=""/>
   32         <parameter key="attributes" value=""/>
   33         <parameter key="use_except_expression" value="false"/>
   34         <parameter key="value_type" value="attribute_value"/>
   35         <parameter key="use_value_type_exception" value="false"/>
   36         <parameter key="except_value_type" value="time"/>
   37         <parameter key="block_type" value="attribute_block"/>
   38         <parameter key="use_block_type_exception" value="false"/>
   39         <parameter key="except_block_type" value="value_matrix_row_start"/>
   40         <parameter key="invert_selection" value="false"/>
   41         <parameter key="include_special_attributes" value="false"/>
   42         <parameter key="random_attributes" value="5"/>
   43         <parameter key="label_noise" value="0.2"/>
   44         <parameter key="default_attribute_noise" value="0.0"/>
   45         <list key="noise"/>
   46         <parameter key="offset" value="0.0"/>
   47         <parameter key="linear_factor" value="1.0"/>
   48         <parameter key="use_local_random_seed" value="false"/>
   49         <parameter key="local_random_seed" value="1992"/>
   50       </operator>
   51       <operator activated="true" class="normalize" compatibility="7.1.001" expanded="true" height="103" name="Normalization" width="90" x="313" y="34">
   52         <parameter key="return_preprocessing_model" value="false"/>
   53         <parameter key="create_view" value="false"/>
   54         <parameter key="attribute_filter_type" value="all"/>
   55         <parameter key="attribute" value=""/>
   56         <parameter key="attributes" value=""/>
   57         <parameter key="use_except_expression" value="false"/>
   58         <parameter key="value_type" value="numeric"/>
   59         <parameter key="use_value_type_exception" value="false"/>
   60         <parameter key="except_value_type" value="real"/>
   61         <parameter key="block_type" value="value_series"/>
   62         <parameter key="use_block_type_exception" value="false"/>
   63         <parameter key="except_block_type" value="value_series_end"/>
   64         <parameter key="invert_selection" value="false"/>
   65         <parameter key="include_special_attributes" value="false"/>
   66         <parameter key="method" value="Z-transformation"/>
   67         <parameter key="min" value="0.0"/>
   68         <parameter key="max" value="1.0"/>
   69       </operator>
   70       <operator activated="true" class="create_learning_curve" compatibility="7.3.000-SNAPSHOT" expanded="true" height="68" name="LearningCurve" width="90" x="447" y="34">
   71         <parameter key="training_ratio" value="0.2"/>
   72         <parameter key="step_fraction" value="0.05"/>
   73         <parameter key="start_fraction" value="0.05"/>
   74         <parameter key="sampling_type" value="stratified sampling"/>
   75         <parameter key="use_local_random_seed" value="false"/>
   76         <parameter key="local_random_seed" value="1992"/>
   77         <process expanded="true">
   78           <operator activated="true" class="support_vector_machine" compatibility="7.3.000-SNAPSHOT" expanded="true" height="124" name="JMySVMLearner" width="90" x="45" y="34">
   79             <parameter key="kernel_type" value="dot"/>
   80             <parameter key="kernel_gamma" value="1.0"/>
   81             <parameter key="kernel_sigma1" value="1.0"/>
   82             <parameter key="kernel_sigma2" value="0.0"/>
   83             <parameter key="kernel_sigma3" value="2.0"/>
   84             <parameter key="kernel_shift" value="1.0"/>
   85             <parameter key="kernel_degree" value="2.0"/>
   86             <parameter key="kernel_a" value="1.0"/>
   87             <parameter key="kernel_b" value="0.0"/>
   88             <parameter key="kernel_cache" value="200"/>
   89             <parameter key="C" value="0.0"/>
   90             <parameter key="convergence_epsilon" value="0.0010"/>
   91             <parameter key="max_iterations" value="100000"/>
   92             <parameter key="scale" value="true"/>
   93             <parameter key="calculate_weights" value="true"/>
   94             <parameter key="return_optimization_performance" value="true"/>
   95             <parameter key="L_pos" value="1.0"/>
   96             <parameter key="L_neg" value="1.0"/>
   97             <parameter key="epsilon" value="0.0"/>
   98             <parameter key="epsilon_plus" value="0.0"/>
   99             <parameter key="epsilon_minus" value="0.0"/>
  100             <parameter key="balance_cost" value="false"/>
  101             <parameter key="quadratic_loss_pos" value="false"/>
  102             <parameter key="quadratic_loss_neg" value="false"/>
  103             <parameter key="estimate_performance" value="false"/>
  104           </operator>
  105           <connect from_port="training set" to_op="JMySVMLearner" to_port="training set"/>
  106           <connect from_op="JMySVMLearner" from_port="model" to_port="through 1"/>
  107           <connect from_op="JMySVMLearner" from_port="weights" to_port="through 2"/>
  108           <portSpacing port="source_training set" spacing="0"/>
  109           <portSpacing port="sink_through 1" spacing="0"/>
  110           <portSpacing port="sink_through 2" spacing="0"/>
  111           <portSpacing port="sink_through 3" spacing="0"/>
  112         </process>
  113         <process expanded="true">
  114           <operator activated="true" class="subprocess" compatibility="7.3.000-SNAPSHOT" expanded="true" height="103" name="ApplierChain" width="90" x="45" y="34">
  115             <process expanded="true">
  116               <operator activated="true" class="apply_model" compatibility="7.1.001" expanded="true" height="82" name="ModelApplier" width="90" x="45" y="34">
  117                 <list key="application_parameters"/>
  118                 <parameter key="create_view" value="false"/>
  119               </operator>
  120               <operator activated="true" class="performance" compatibility="7.3.000-SNAPSHOT" expanded="true" height="82" name="Performance" width="90" x="514" y="34">
  121                 <parameter key="use_example_weights" value="true"/>
  122               </operator>
  123               <connect from_port="in 1" to_op="ModelApplier" to_port="unlabelled data"/>
  124               <connect from_port="in 2" to_op="ModelApplier" to_port="model"/>
  125               <connect from_op="ModelApplier" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
  126               <connect from_op="Performance" from_port="performance" to_port="out 1"/>
  127               <portSpacing port="source_in 1" spacing="0"/>
  128               <portSpacing port="source_in 2" spacing="0"/>
  129               <portSpacing port="source_in 3" spacing="0"/>
  130               <portSpacing port="sink_out 1" spacing="0"/>
  131               <portSpacing port="sink_out 2" spacing="0"/>
  132             </process>
  133           </operator>
  134           <operator activated="true" class="log" compatibility="7.3.000-SNAPSHOT" expanded="true" height="103" name="ProcessLog" width="90" x="246" y="34">
  135             <list key="log">
  136               <parameter key="fraction" value="operator.LearningCurve.value.fraction"/>
  137               <parameter key="performance" value="operator.LearningCurve.value.performance"/>
  138             </list>
  139             <parameter key="sorting_type" value="none"/>
  140             <parameter key="sorting_k" value="100"/>
  141             <parameter key="persistent" value="false"/>
  142           </operator>
  143           <connect from_port="test set" to_op="ApplierChain" to_port="in 1"/>
  144           <connect from_port="through 1" to_op="ApplierChain" to_port="in 2"/>
  145           <connect from_port="through 2" to_op="ProcessLog" to_port="through 2"/>
  146           <connect from_op="ApplierChain" from_port="out 1" to_op="ProcessLog" to_port="through 1"/>
  147           <connect from_op="ProcessLog" from_port="through 1" to_port="performance"/>
  148           <portSpacing port="source_test set" spacing="0"/>
  149           <portSpacing port="source_through 1" spacing="0"/>
  150           <portSpacing port="source_through 2" spacing="0"/>
  151           <portSpacing port="source_through 3" spacing="0"/>
  152           <portSpacing port="sink_performance" spacing="0"/>
  153         </process>
  154       </operator>
  155       <connect from_op="ExampleSetGenerator" from_port="output" to_op="NoiseGenerator" to_port="example set input"/>
  156       <connect from_op="NoiseGenerator" from_port="example set output" to_op="Normalization" to_port="example set input"/>
  157       <connect from_op="Normalization" from_port="example set output" to_op="LearningCurve" to_port="exampleSet"/>
  158       <portSpacing port="source_input 1" spacing="0"/>
  159       <portSpacing port="sink_result 1" spacing="0"/>
  160       <description align="left" color="yellow" colored="false" height="48" resized="false" width="400" x="40" y="160">This process plots the learning curve, i.e. the performance with respect to the number of examples which is used for learning.</description>
  161     </process>
  162   </operator>
  163 </process>