W-PredictiveApriori Error

PD_72

PD_72 New Altair Community Member

October 2019 edited November 5 in Community Q&A

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:

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	-

......
......

Image: https://us.v-cdn.net/6030995/uploads/editor/7b/qf8uwfbfb9s9.jpg

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)

Image: https://us.v-cdn.net/6030995/uploads/editor/gn/dhyweya9rok9.jpg

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

sgenzer
Altair Employee

October 2019

hi @PD_72 the weka extension is quite outdated at this point and no longer supported by RapidMiner. If you're trying to do association mining, I'd recommend using the native core tools for this.

https://academy.rapidminer.com/learn/article/cross-selling-do-you-want-fries-with-that

https://academy.rapidminer.com/learn/video/text-association-rules

Scott

1

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