learning curve
jpmor82
New Altair Community Member
Hi,
I'm trying to do a learning curve in rapidminer 5, but the log don't output anything, can anyone help me??
xml process:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" expanded="true" name="Root">
<parameter key="logverbosity" value="3"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="1"/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<parameter key="parallelize_main_process" value="false"/>
<process expanded="true" height="235" width="547">
<operator activated="true" class="read_excel" expanded="true" height="60" name="Read Excel" width="90" x="45" y="30">
<parameter key="excel_file" value="C:\Users\Joao\Desktop\Aprendizagem Computacional - Janela Digital\Dados sem missing values (9253)\FINAL_AVEIRO_e_ILHAVO(sem mv).xls"/>
<parameter key="sheet_number" value="1"/>
<parameter key="row_offset" value="0"/>
<parameter key="column_offset" value="0"/>
<parameter key="first_row_as_names" value="true"/>
<list key="annotations"/>
</operator>
<operator activated="true" class="set_role" expanded="true" height="76" name="Set Role" width="90" x="179" y="30">
<parameter key="name" value="Preço"/>
<parameter key="target_role" value="label"/>
</operator>
<operator activated="true" class="nominal_to_numerical" expanded="true" height="94" name="Nominal to Numerical" width="90" x="313" y="30">
<parameter key="return_preprocessing_model" value="false"/>
<parameter key="create_view" value="false"/>
<parameter key="attribute_filter_type" value="0"/>
<parameter key="attribute" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="0"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="4"/>
<parameter key="block_type" value="0"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="0"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
</operator>
<operator activated="true" class="create_learning_curve" expanded="true" height="60" name="LearningCurve" width="90" x="447" y="30">
<parameter key="training_ratio" value="0.7"/>
<parameter key="step_fraction" value="0.05"/>
<parameter key="start_fraction" value="0.05"/>
<parameter key="sampling_type" value="2"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="-1"/>
<parameter key="parallelize_training" value="false"/>
<parameter key="parallelize_test" value="false"/>
<process expanded="true" height="423" width="303">
<operator activated="true" class="support_vector_machine" expanded="true" height="112" name="SVM" width="90" x="45" y="30">
<parameter key="kernel_type" value="0"/>
<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="0.0010"/>
<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 set" to_op="SVM" to_port="training set"/>
<connect from_op="SVM" from_port="model" to_port="through 1"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
<portSpacing port="sink_through 2" spacing="0"/>
</process>
<process expanded="true" height="423" width="303">
<operator activated="true" class="subprocess" expanded="true" height="94" name="OperatorChain" width="90" x="45" y="30">
<parameter key="parallelize_nested_chain" value="false"/>
<process expanded="true" height="423" width="656">
<operator activated="true" class="apply_model" expanded="true" height="76" name="ModelApplier" width="90" x="112" y="30">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="performance" expanded="true" height="76" name="Performance" width="90" x="313" y="30">
<parameter key="use_example_weights" value="true"/>
</operator>
<connect from_port="in 1" to_op="ModelApplier" to_port="unlabelled data"/>
<connect from_port="in 2" to_op="ModelApplier" to_port="model"/>
<connect from_op="ModelApplier" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="out 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="source_in 2" spacing="0"/>
<portSpacing port="source_in 3" spacing="0"/>
<portSpacing port="sink_out 1" spacing="0"/>
<portSpacing port="sink_out 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="log" expanded="true" height="76" name="ProcessLog" width="90" x="179" y="30">
<parameter key="filename" value="C:\Users\Joao\Desktop\Aprendizagem Computacional - Janela Digital\a.log"/>
<list key="log"/>
<parameter key="sorting_type" value="0"/>
<parameter key="sorting_k" value="100"/>
<parameter key="persistent" value="false"/>
</operator>
<operator activated="true" class="log_to_data" expanded="true" height="94" name="Log to Data" width="90" x="170" y="202"/>
<connect from_port="test set" to_op="OperatorChain" to_port="in 1"/>
<connect from_port="through 1" to_op="OperatorChain" to_port="in 2"/>
<connect from_op="OperatorChain" from_port="out 1" to_op="ProcessLog" to_port="through 1"/>
<connect from_op="ProcessLog" from_port="through 1" to_op="Log to Data" to_port="through 1"/>
<connect from_op="Log to Data" from_port="through 1" to_port="performance"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="source_through 2" spacing="0"/>
<portSpacing port="sink_performance" spacing="0"/>
</process>
</operator>
<connect from_op="Read Excel" from_port="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_op="LearningCurve" to_port="exampleSet"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
</process>
</operator>
</process>
I'm trying to do a learning curve in rapidminer 5, but the log don't output anything, can anyone help me??
xml process:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" expanded="true" name="Root">
<parameter key="logverbosity" value="3"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="1"/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<parameter key="parallelize_main_process" value="false"/>
<process expanded="true" height="235" width="547">
<operator activated="true" class="read_excel" expanded="true" height="60" name="Read Excel" width="90" x="45" y="30">
<parameter key="excel_file" value="C:\Users\Joao\Desktop\Aprendizagem Computacional - Janela Digital\Dados sem missing values (9253)\FINAL_AVEIRO_e_ILHAVO(sem mv).xls"/>
<parameter key="sheet_number" value="1"/>
<parameter key="row_offset" value="0"/>
<parameter key="column_offset" value="0"/>
<parameter key="first_row_as_names" value="true"/>
<list key="annotations"/>
</operator>
<operator activated="true" class="set_role" expanded="true" height="76" name="Set Role" width="90" x="179" y="30">
<parameter key="name" value="Preço"/>
<parameter key="target_role" value="label"/>
</operator>
<operator activated="true" class="nominal_to_numerical" expanded="true" height="94" name="Nominal to Numerical" width="90" x="313" y="30">
<parameter key="return_preprocessing_model" value="false"/>
<parameter key="create_view" value="false"/>
<parameter key="attribute_filter_type" value="0"/>
<parameter key="attribute" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="0"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="4"/>
<parameter key="block_type" value="0"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="0"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
</operator>
<operator activated="true" class="create_learning_curve" expanded="true" height="60" name="LearningCurve" width="90" x="447" y="30">
<parameter key="training_ratio" value="0.7"/>
<parameter key="step_fraction" value="0.05"/>
<parameter key="start_fraction" value="0.05"/>
<parameter key="sampling_type" value="2"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="-1"/>
<parameter key="parallelize_training" value="false"/>
<parameter key="parallelize_test" value="false"/>
<process expanded="true" height="423" width="303">
<operator activated="true" class="support_vector_machine" expanded="true" height="112" name="SVM" width="90" x="45" y="30">
<parameter key="kernel_type" value="0"/>
<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="0.0010"/>
<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 set" to_op="SVM" to_port="training set"/>
<connect from_op="SVM" from_port="model" to_port="through 1"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
<portSpacing port="sink_through 2" spacing="0"/>
</process>
<process expanded="true" height="423" width="303">
<operator activated="true" class="subprocess" expanded="true" height="94" name="OperatorChain" width="90" x="45" y="30">
<parameter key="parallelize_nested_chain" value="false"/>
<process expanded="true" height="423" width="656">
<operator activated="true" class="apply_model" expanded="true" height="76" name="ModelApplier" width="90" x="112" y="30">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="performance" expanded="true" height="76" name="Performance" width="90" x="313" y="30">
<parameter key="use_example_weights" value="true"/>
</operator>
<connect from_port="in 1" to_op="ModelApplier" to_port="unlabelled data"/>
<connect from_port="in 2" to_op="ModelApplier" to_port="model"/>
<connect from_op="ModelApplier" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="out 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="source_in 2" spacing="0"/>
<portSpacing port="source_in 3" spacing="0"/>
<portSpacing port="sink_out 1" spacing="0"/>
<portSpacing port="sink_out 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="log" expanded="true" height="76" name="ProcessLog" width="90" x="179" y="30">
<parameter key="filename" value="C:\Users\Joao\Desktop\Aprendizagem Computacional - Janela Digital\a.log"/>
<list key="log"/>
<parameter key="sorting_type" value="0"/>
<parameter key="sorting_k" value="100"/>
<parameter key="persistent" value="false"/>
</operator>
<operator activated="true" class="log_to_data" expanded="true" height="94" name="Log to Data" width="90" x="170" y="202"/>
<connect from_port="test set" to_op="OperatorChain" to_port="in 1"/>
<connect from_port="through 1" to_op="OperatorChain" to_port="in 2"/>
<connect from_op="OperatorChain" from_port="out 1" to_op="ProcessLog" to_port="through 1"/>
<connect from_op="ProcessLog" from_port="through 1" to_op="Log to Data" to_port="through 1"/>
<connect from_op="Log to Data" from_port="through 1" to_port="performance"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="source_through 2" spacing="0"/>
<portSpacing port="sink_performance" spacing="0"/>
</process>
</operator>
<connect from_op="Read Excel" from_port="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_op="LearningCurve" to_port="exampleSet"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
</process>
</operator>
</process>
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Answers
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The problem is solved, I find that in log operator i have to input what values I want0