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W-PredictiveApriori Error
Learner I
Hi, my dataset = data from bibliotek,
568 rows (user) x 8000 Columns (book), i need prediction next book for users.
Where is problem?
Thx for help.
Sample dataset after "Multiply" :
250 attributes:
......
......
Log:
XML:
568 rows (user) x 8000 Columns (book), i need prediction next book for users.
Where is problem?
Thx for help.
Sample dataset after "Multiply" :
250 attributes:
| Role | Name | Type | Range | Missings | Comment |
|---|---|---|---|---|---|
| - | human | nominal | ⊇ {1022, 1031, 1033, 1036, 1044, 1049, 1052, 1053, 1063, ...} | no missing values | - |
| - | average(book)_19247.0 | binominal | = {false, true} | no missing values | - |
| - | average(book)_28680.0 | binominal | = {false, true} | no missing values | - |
......
Log:
Oct 18, 2019 8:10:13 PM INFO: No filename given for result file, using stdout for logging results!
Oct 18, 2019 8:10:13 PM INFO: Process //Local Repository/processes/pokus2 starts
Oct 18, 2019 8:10:21 PM SEVERE: Process failed: W-PredictiveApriori caused an error: java.lang.Exception: Dataset has to many attributes for prior estimation!
Oct 18, 2019 8:10:21 PM SEVERE: Here:
Oct 18, 2019 8:10:21 PM SEVERE: Process[1] (Process)
Oct 18, 2019 8:10:21 PM SEVERE: subprocess 'Main Process'
Oct 18, 2019 8:10:21 PM SEVERE: +- Retrieve data_bez_1 pivot[1] (Retrieve)
Oct 18, 2019 8:10:21 PM SEVERE: +- Numerical to Binominal[1] (Numerical to Binominal)
Oct 18, 2019 8:10:21 PM SEVERE: +- Replace Missing Values (Series)[1] (Replace Missing Values (Series))
Oct 18, 2019 8:10:21 PM SEVERE: +- Multiply (3)[1] (Multiply)
Oct 18, 2019 8:10:21 PM SEVERE: ==> +- W-PredictiveApriori[1] (W-PredictiveApriori)
XML:
<?xml version="1.0" encoding="UTF-8"?><process version="9.4.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.4.000" expanded="true" name="Process" origin="GENERATED_TEMPLATE">
<parameter key="logverbosity" value="status"/>
<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" class="retrieve" compatibility="9.4.001" expanded="true" height="68" name="Retrieve data_bez_1 pivot" width="90" x="45" y="187">
<parameter key="repository_entry" value="../data/data_bez_1 pivot"/>
</operator>
<operator activated="true" class="numerical_to_binominal" compatibility="9.4.001" expanded="true" height="82" name="Numerical to Binominal" width="90" x="179" y="187">
<parameter key="attribute_filter_type" value="value_type"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="numeric"/>
<parameter key="use_value_type_exception" value="true"/>
<parameter key="except_value_type" value="integer"/>
<parameter key="block_type" value="value_series"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="value_series_end"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
<parameter key="min" value="0.0"/>
<parameter key="max" value="0.0"/>
</operator>
<operator activated="true" class="time_series:replace_missing_values" compatibility="9.4.001" expanded="true" height="68" name="Replace Missing Values (Series)" width="90" x="313" y="187">
<parameter key="attribute_filter_type" value="no_missing_values"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="nominal"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="time"/>
<parameter key="block_type" value="single_value"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="value_matrix_row_start"/>
<parameter key="invert_selection" value="true"/>
<parameter key="include_special_attributes" value="false"/>
<parameter key="has_indices" value="false"/>
<parameter key="indices_attribute" value=""/>
<parameter key="overwrite_attributes" value="true"/>
<parameter key="new_attributes_postfix" value="_cleaned"/>
<parameter key="replace_type_numerical" value="value"/>
<parameter key="replace_type_nominal" value="value"/>
<parameter key="replace_type_date_time" value="previous value"/>
<parameter key="replace_value_numerical" value="0.0"/>
<parameter key="replace_value_nominal" value="false"/>
<parameter key="replace_value_date_time" value="false"/>
<parameter key="skip_other_missings" value="false"/>
<parameter key="replace_infinity" value="true"/>
<parameter key="replace_empty_strings" value="true"/>
<parameter key="ensure_finite_values" value="false"/>
</operator>
<operator activated="true" class="multiply" compatibility="9.4.001" expanded="true" height="82" name="Multiply (3)" width="90" x="447" y="187"/>
<operator activated="true" class="weka:W-PredictiveApriori" compatibility="7.3.000" expanded="true" height="68" name="W-PredictiveApriori" width="90" x="581" y="289">
<parameter key="N" value="2.147483642E9"/>
<parameter key="A" value="false"/>
<parameter key="c" value="-1.0"/>
</operator>
<connect from_op="Retrieve data_bez_1 pivot" from_port="output" to_op="Numerical to Binominal" to_port="example set input"/>
<connect from_op="Numerical to Binominal" from_port="example set output" to_op="Replace Missing Values (Series)" to_port="example set"/>
<connect from_op="Replace Missing Values (Series)" from_port="example set" to_op="Multiply (3)" to_port="input"/>
<connect from_op="Multiply (3)" from_port="output 1" to_op="W-PredictiveApriori" to_port="example set"/>
<connect from_op="W-PredictiveApriori" from_port="associator" 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"/>
<description align="left" color="yellow" colored="false" height="35" resized="true" width="849" x="20" y="655">Outputs: association rules, frequent item set<br></description>
</process>
</operator>
</process>
Tagged:
0
Answers
https://academy.rapidminer.com/learn/article/cross-selling-do-you-want-fries-with-that
https://academy.rapidminer.com/learn/video/text-association-rules
Scott