X-validation process failed
Hi,
I'm executing a x-validation process but it fails and this is the log:
May 14, 2010 4:57:05 PM INFO: Loading initial data.
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'sex', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'lenght', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'diameter', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'height', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'whole weight', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'shucked weight', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'viscera weight', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'shell weight', training: numeric, application: nominal
May 14, 2010 4:57:19 PM SEVERE: Process failed: operator cannot be executed (Cannot clone com.rapidminer.example.set.RemappedExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.RuntimeException: Cannot clone com.rapidminer.example.set.NonSpecialAttributesExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.RuntimeException: Cannot clone com.rapidminer.example.set.SplittedExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.RuntimeException: Cannot clone com.rapidminer.example.set.NonSpecialAttributesExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.RuntimeException: Cannot clone com.rapidminer.example.set.RemappedExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.UnsupportedOperationException: The method getNominalMapping() is not supported by numerical attributes! You probably tried to execute an operator on a numerical data which is only able to handle nominal values. You could use one of the discretization operators before this application.. Cause: java.lang.UnsupportedOperationException: The method getNominalMapping() is not supported by numerical attributes! You probably tried to execute an operator on a numerical data which is only able to handle nominal values. You could use one of the discretization operators before this application... Cause: java.lang.RuntimeException: Cannot clone com.rapidminer.example.set.RemappedExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.UnsupportedOperationException: The method getNominalMapping() is not supported by numerical attributes! You probably tried to execute an operator on a numerical data which is only able to handle nominal values. You could use one of the discretization operators before this application.. (this is repeated a lot of times)
May 14, 2010 4:57:19 PM SEVERE: Here: Process[1] (Process)
subprocess 'Main Process'
+- Read Excel[1] (Read Excel)
+- Nominal to Numerical[1] (Nominal to Numerical)
+- Replace Missing Values[1] (Replace Missing Values)
+- Numerical to Polynominal[1] (Numerical to Polynominal)
+- Set Role[1] (Set Role)
+- Validation[1] (X-Validation)
subprocess 'Training'
| +- Polynomial by Binomial Classification[1] (Polynomial by Binomial Classification)
subprocess 'Learning Process'
| +- SVM[3] (Support Vector Machine (LibSVM))
subprocess 'Testing'
+- Numerical to Polynominal (2)[1] (Numerical to Polynominal)
==> +- Apply Model[1] (Apply Model)
+- Performance (2)[0] (Performance (Classification))
The xml is:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
<context>
<input>
<location/>
</input>
<output>
<location/>
<location/>
</output>
<macros/>
</context>
<operator activated="true" class="process" expanded="true" name="Process">
<process expanded="true" height="469" width="820">
<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:\Documents and Settings\sgmeire\Mis documentos\Trabajo\investigación\Floro\Ejercicios-Yale\Abalone\abalone.xls"/>
</operator>
<operator activated="true" class="nominal_to_numerical" expanded="true" height="94" name="Nominal to Numerical" width="90" x="45" y="165">
<parameter key="attribute" value="rings"/>
</operator>
<operator activated="true" class="replace_missing_values" expanded="true" height="94" name="Replace Missing Values" width="90" x="179" y="120">
<parameter key="default" value="zero"/>
<list key="columns"/>
</operator>
<operator activated="true" class="numerical_to_polynominal" expanded="true" height="76" name="Numerical to Polynominal" width="90" x="179" y="345">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="rings"/>
</operator>
<operator activated="true" class="set_role" expanded="true" height="76" name="Set Role" width="90" x="313" y="255">
<parameter key="name" value="rings"/>
<parameter key="target_role" value="label"/>
</operator>
<operator activated="true" class="x_validation" expanded="true" height="112" name="Validation" width="90" x="380" y="75">
<description>A cross-validation evaluating a linear regression model.</description>
<process expanded="true" height="550" width="165">
<operator activated="true" class="polynomial_by_binomial_classification" expanded="true" height="76" name="Polynomial by Binomial Classification" width="90" x="45" y="30">
<process expanded="true" height="469" width="796">
<operator activated="true" class="support_vector_machine_libsvm" expanded="true" height="76" name="SVM" width="90" x="362" y="30">
<parameter key="kernel_type" value="poly"/>
<parameter key="cache_size" value="100"/>
<list key="class_weights"/>
</operator>
<connect from_port="training set" to_op="SVM" to_port="training set"/>
<connect from_op="SVM" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
</process>
</operator>
<connect from_port="training" to_op="Polynomial by Binomial Classification" to_port="training set"/>
<connect from_op="Polynomial by Binomial Classification" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true" height="550" width="435">
<operator activated="true" class="numerical_to_polynominal" expanded="true" height="76" name="Numerical to Polynominal (2)" width="90" x="45" y="120"/>
<operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model" width="90" x="180" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" expanded="true" height="76" name="Performance (2)" width="90" x="315" y="30">
<parameter key="main_criterion" value="accuracy"/>
<parameter key="accuracy" value="true"/>
<list key="class_weights"/>
</operator>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Numerical to Polynominal (2)" to_port="example set input"/>
<connect from_op="Numerical to Polynominal (2)" from_port="example set output" 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="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<connect from_op="Read Excel" from_port="output" to_op="Nominal to Numerical" to_port="example set input"/>
<connect from_op="Nominal to Numerical" from_port="example set output" to_op="Replace Missing Values" to_port="example set input"/>
<connect from_op="Replace Missing Values" from_port="example set output" to_op="Numerical to Polynominal" to_port="example set input"/>
<connect from_op="Numerical to Polynominal" 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="Validation" to_port="training"/>
<connect from_op="Validation" from_port="model" 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>
Could anybody help me to solve this problem?
Thank you so much.
Silvana.
I'm executing a x-validation process but it fails and this is the log:
May 14, 2010 4:57:05 PM INFO: Loading initial data.
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'sex', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'lenght', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'diameter', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'height', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'whole weight', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'shucked weight', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'viscera weight', training: numeric, application: nominal
May 14, 2010 4:57:19 PM WARNING: Binary2MultiClass: The value types between training and application differ for attribute 'shell weight', training: numeric, application: nominal
May 14, 2010 4:57:19 PM SEVERE: Process failed: operator cannot be executed (Cannot clone com.rapidminer.example.set.RemappedExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.RuntimeException: Cannot clone com.rapidminer.example.set.NonSpecialAttributesExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.RuntimeException: Cannot clone com.rapidminer.example.set.SplittedExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.RuntimeException: Cannot clone com.rapidminer.example.set.NonSpecialAttributesExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.RuntimeException: Cannot clone com.rapidminer.example.set.RemappedExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.UnsupportedOperationException: The method getNominalMapping() is not supported by numerical attributes! You probably tried to execute an operator on a numerical data which is only able to handle nominal values. You could use one of the discretization operators before this application.. Cause: java.lang.UnsupportedOperationException: The method getNominalMapping() is not supported by numerical attributes! You probably tried to execute an operator on a numerical data which is only able to handle nominal values. You could use one of the discretization operators before this application... Cause: java.lang.RuntimeException: Cannot clone com.rapidminer.example.set.RemappedExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.UnsupportedOperationException: The method getNominalMapping() is not supported by numerical attributes! You probably tried to execute an operator on a numerical data which is only able to handle nominal values. You could use one of the discretization operators before this application.. (this is repeated a lot of times)
May 14, 2010 4:57:19 PM SEVERE: Here: Process[1] (Process)
subprocess 'Main Process'
+- Read Excel[1] (Read Excel)
+- Nominal to Numerical[1] (Nominal to Numerical)
+- Replace Missing Values[1] (Replace Missing Values)
+- Numerical to Polynominal[1] (Numerical to Polynominal)
+- Set Role[1] (Set Role)
+- Validation[1] (X-Validation)
subprocess 'Training'
| +- Polynomial by Binomial Classification[1] (Polynomial by Binomial Classification)
subprocess 'Learning Process'
| +- SVM[3] (Support Vector Machine (LibSVM))
subprocess 'Testing'
+- Numerical to Polynominal (2)[1] (Numerical to Polynominal)
==> +- Apply Model[1] (Apply Model)
+- Performance (2)[0] (Performance (Classification))
The xml is:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
<context>
<input>
<location/>
</input>
<output>
<location/>
<location/>
</output>
<macros/>
</context>
<operator activated="true" class="process" expanded="true" name="Process">
<process expanded="true" height="469" width="820">
<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:\Documents and Settings\sgmeire\Mis documentos\Trabajo\investigación\Floro\Ejercicios-Yale\Abalone\abalone.xls"/>
</operator>
<operator activated="true" class="nominal_to_numerical" expanded="true" height="94" name="Nominal to Numerical" width="90" x="45" y="165">
<parameter key="attribute" value="rings"/>
</operator>
<operator activated="true" class="replace_missing_values" expanded="true" height="94" name="Replace Missing Values" width="90" x="179" y="120">
<parameter key="default" value="zero"/>
<list key="columns"/>
</operator>
<operator activated="true" class="numerical_to_polynominal" expanded="true" height="76" name="Numerical to Polynominal" width="90" x="179" y="345">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="rings"/>
</operator>
<operator activated="true" class="set_role" expanded="true" height="76" name="Set Role" width="90" x="313" y="255">
<parameter key="name" value="rings"/>
<parameter key="target_role" value="label"/>
</operator>
<operator activated="true" class="x_validation" expanded="true" height="112" name="Validation" width="90" x="380" y="75">
<description>A cross-validation evaluating a linear regression model.</description>
<process expanded="true" height="550" width="165">
<operator activated="true" class="polynomial_by_binomial_classification" expanded="true" height="76" name="Polynomial by Binomial Classification" width="90" x="45" y="30">
<process expanded="true" height="469" width="796">
<operator activated="true" class="support_vector_machine_libsvm" expanded="true" height="76" name="SVM" width="90" x="362" y="30">
<parameter key="kernel_type" value="poly"/>
<parameter key="cache_size" value="100"/>
<list key="class_weights"/>
</operator>
<connect from_port="training set" to_op="SVM" to_port="training set"/>
<connect from_op="SVM" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
</process>
</operator>
<connect from_port="training" to_op="Polynomial by Binomial Classification" to_port="training set"/>
<connect from_op="Polynomial by Binomial Classification" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true" height="550" width="435">
<operator activated="true" class="numerical_to_polynominal" expanded="true" height="76" name="Numerical to Polynominal (2)" width="90" x="45" y="120"/>
<operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model" width="90" x="180" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" expanded="true" height="76" name="Performance (2)" width="90" x="315" y="30">
<parameter key="main_criterion" value="accuracy"/>
<parameter key="accuracy" value="true"/>
<list key="class_weights"/>
</operator>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Numerical to Polynominal (2)" to_port="example set input"/>
<connect from_op="Numerical to Polynominal (2)" from_port="example set output" 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="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<connect from_op="Read Excel" from_port="output" to_op="Nominal to Numerical" to_port="example set input"/>
<connect from_op="Nominal to Numerical" from_port="example set output" to_op="Replace Missing Values" to_port="example set input"/>
<connect from_op="Replace Missing Values" from_port="example set output" to_op="Numerical to Polynominal" to_port="example set input"/>
<connect from_op="Numerical to Polynominal" 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="Validation" to_port="training"/>
<connect from_op="Validation" from_port="model" 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>
Could anybody help me to solve this problem?
Thank you so much.
Silvana.