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Problems with Linear Regression
majotecita
Member Posts: 10 
Contributor II
Hi,
I'm integrating RapidMiner with BizAgi through a Web Service.
In RapidMiner the process finished successfully; but when I use the Web Service I have this problem:
Feb 02, 2012 4:52:35 PM com.rapidminer.tools.WrapperLoggingHandler logWarning
WARNING: LinearRegression: The number of regular attributes of the given example set does not fit the number of attributes of the training example set, training: 0, application: 21
Can you please help me???
I don't know why it's working in RapidMiner but not in WS.
Thanks!
I'm integrating RapidMiner with BizAgi through a Web Service.
In RapidMiner the process finished successfully; but when I use the Web Service I have this problem:
Feb 02, 2012 4:52:35 PM com.rapidminer.tools.WrapperLoggingHandler logWarning
WARNING: LinearRegression: The number of regular attributes of the given example set does not fit the number of attributes of the training example set, training: 0, application: 21
Can you please help me???
I don't know why it's working in RapidMiner but not in WS.
Thanks!
Tagged:
0
Answers
I don't know BizAgi, so I can't help you on that front. Anyways, can you please post your process setup? See here how to do that.
By the way, you can easily export RapidMiner processes via our RapidAnalytics server. You can download it from our website at www.rapid-i.com .
Best,
Marius
Thanks for your reply.
Here it's the my process setup:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.017">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.1.017" 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" height="415" width="748">
<operator activated="true" breakpoints="after" class="read_model" compatibility="5.1.017" expanded="true" height="60" name="Read Model" width="90" x="45" y="75">
<parameter key="model_file" value="C:\Users\Kote\Documents\Universidad\MBE\Proyecto de Tesis\Prediccion Le\RapidMiner\Modelos\RL CG MENSUAL.mod"/>
</operator>
<operator activated="true" breakpoints="after" class="read_csv" compatibility="5.1.017" expanded="true" height="60" name="Read CSV" width="90" x="45" y="255">
<parameter key="csv_file" value="C:\Users\Kote\Documents\Universidad\base de datos validacion CG Mensual.csv"/>
<parameter key="column_separators" value=";"/>
<parameter key="trim_lines" value="false"/>
<parameter key="use_quotes" value="true"/>
<parameter key="quotes_character" value="""/>
<parameter key="escape_character_for_quotes" 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="SYSTEM"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="Mes a Predecir.true.integer.label"/>
<parameter key="1" value="N-1.true.integer.attribute"/>
<parameter key="2" value="N-2.true.integer.attribute"/>
<parameter key="3" value="N-12.true.integer.attribute"/>
<parameter key="4" value="N-24.true.integer.attribute"/>
<parameter key="5" value="N-36.true.integer.attribute"/>
<parameter key="6" value="N-12 - N-13.true.integer.attribute"/>
<parameter key="7" value="N-24 - N-25.true.integer.attribute"/>
<parameter key="8" value="N-36 - N-37.true.integer.attribute"/>
<parameter key="9" value="ENERO.true.numeric.attribute"/>
<parameter key="10" value="FEBRERO.true.numeric.attribute"/>
<parameter key="11" value="MARZO.true.numeric.attribute"/>
<parameter key="12" value="ABRIL.true.numeric.attribute"/>
<parameter key="13" value="MAYO.true.numeric.attribute"/>
<parameter key="14" value="JUNIO.true.numeric.attribute"/>
<parameter key="15" value="JULIO.true.numeric.attribute"/>
<parameter key="16" value="AGOSTO.true.numeric.attribute"/>
<parameter key="17" value="SEPTIEMBRE.true.numeric.attribute"/>
<parameter key="18" value="OCTUBRE.true.numeric.attribute"/>
<parameter key="19" value="NOVIEMBRE.true.numeric.attribute"/>
<parameter key="20" value="DICIEMBRE.true.numeric.attribute"/>
<parameter key="21" value="ANO.true.integer.attribute"/>
</list>
<parameter key="read_not_matching_values_as_missings" value="true"/>
<parameter key="datamanagement" value="double_array"/>
</operator>
<operator activated="true" breakpoints="after" class="apply_model" compatibility="5.1.017" expanded="true" height="76" name="Apply Model" width="90" x="179" y="120">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" breakpoints="after" class="generate_attributes" compatibility="5.1.017" expanded="true" height="76" name="Generate Attributes" width="90" x="313" y="120">
<list key="function_descriptions">
<parameter key="id especialidad" value="1"/>
<parameter key="id mes" value="if(ENERO==1,1,if(FEBRERO==1,2,if(MARZO==1,3,if(ABRIL==1,4,if(MAYO==1,5,if(JUNIO==1,6,if(JULIO==1,7,if(AGOSTO==1,8,if(SEPTIEMBRE==1,9,if(OCTUBRE==1,10,if(NOVIEMBRE==1,11,12)))))))))))"/>
</list>
<parameter key="use_standard_constants" value="true"/>
<parameter key="keep_all" value="true"/>
</operator>
<operator activated="true" breakpoints="after" class="select_attributes" compatibility="5.1.017" expanded="true" height="76" name="Select Attributes" width="90" x="447" y="120">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value="id especialidad|id mes"/>
<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="false"/>
<parameter key="include_special_attributes" value="false"/>
</operator>
<operator activated="true" breakpoints="after" class="write_database" compatibility="5.1.017" expanded="true" height="60" name="Write Database" width="90" x="581" y="120">
<parameter key="define_connection" value="predefined"/>
<parameter key="connection" value="movedb"/>
<parameter key="database_system" value="MySQL"/>
<parameter key="use_default_schema" value="true"/>
<parameter key="table_name" value="pronostico mensual"/>
<parameter key="overwrite_mode" value="append"/>
<parameter key="set_default_varchar_length" value="false"/>
<parameter key="default_varchar_length" value="128"/>
<parameter key="add_generated_primary_keys" value="false"/>
<parameter key="db_key_attribute_name" value="id"/>
<parameter key="batch_size" value="1"/>
</operator>
<connect from_op="Read Model" from_port="output" to_op="Apply Model" to_port="model"/>
<connect from_op="Read CSV" from_port="output" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Generate Attributes" to_port="example set input"/>
<connect from_op="Generate Attributes" from_port="example set output" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Write Database" to_port="input"/>
<connect from_op="Write Database" from_port="through" 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>
Thanks for the help!!!!
I don't know what else to do!!!!
You really need to work through help->RapidMIner Tutorial , then the error messages will mean something to you. It is saying that your model is built out of nothing! So you should look at the process that produced C:\Users\Kote\Documents\Universidad\MBE\Proyecto de Tesis\Prediccion Le\RapidMiner\Modelos\RL CG MENSUAL.mod .
But really, tutorial time will not be wasted!
But can you please tell me, why is this log appears only when i'm triying to execute RapidMiner trough a Java Application??, because when I execute the process directly in RapidMiner there is no problem log... =S
Thanks!