Problems with Linear Regression

majotecita

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!

MariusHelf

Hi,

I don't know BizAgi, so I can't help you on that front. Anyways, can you please post your process setup? Seeherehow to do that.

By the way, you can easily export RapidMiner processes via our RapidAnalytics server. You can download it from our website atwww.rapid-i.com.

Best,
Marius

majotecita

Hi,

Thanks for your reply.

Here it's the my process setup:

Thanks for the help!!!!

majotecita

I forgot to put the xml code of the Model

Linear Regression





<客yValueMap id="3"/>









<客yValueMap id="6"/>














false




<客yValueMap id="14"/>


N-1
3
1
0.0
1

gensym1


4538.0
367660.0
58


4538.0
367660.0
58.0
58.0


33.0
106.0


0








false




<客yValueMap id="25"/>


N-2
3
1
0.0
2

gensym2


4511.0
362935.0
58


4511.0
362935.0
58.0
58.0


33.0
106.0


0








false




<客yValueMap id="36"/>


N-12
3
1
0.0
3

gensym3


4542.0
367556.0
58


4542.0
367556.0
58.0
58.0


33.0
106.0


0








false




<客yValueMap id="47"/>


N-24
3
1
0.0
4

gensym4


4596.0
373862.0
58


4596.0
373862.0
58.0
58.0


48.0
106.0


0








false




<客yValueMap id="58"/>


N-36
3
1
0.0
5

gensym5


4744.0
400320.0
58


4744.0
400320.0
58.0
58.0


48.0
116.0


0








false




<客yValueMap id="69"/>


N-12 - N-13
3
1
0.0
6

gensym6


11.0
27823.0
58


11.0
27823.0
58.0
58.0


-49.0
60.0


0








false




<客yValueMap id="80"/>

majotecita

N-24 - N-25
3
1
0.0
7

gensym7


18.0
20788.0
58


18.0
20788.0
58.0
58.0


-39.0
45.0


0








false




<客yValueMap id="91"/>


N-36 - N-37
3
1
0.0
8

gensym8


57.0
21971.0
58


57.0
21971.0
58.0
58.0


-35.0
45.0


0








false




<客yValueMap id="102"/>


ENERO
2
1
0.0
9

gensym9


4.0
4.0
58


4.0
4.0
58.0
58.0


0.0
1.0

< UnknownStatistics id = " 108 " >
0








false




<客yValueMap id="113"/>


FEBRERO
2
1
0.0
10

gensym10


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0


0








false




<客yValueMap id="124"/>


<名称>米ARZO
2
1
0.0
<指数> 11 > < /索引

gensym11


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0


0








false




<客yValueMap id="135"/>


ABRIL
2
1
0.0
12

gensym12


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0


0








false
<属性类= " NumericalAttribute " id = " 144 "爵士ialization="custom">



<客yValueMap id="146"/>


<名称>米AYO
2
1
0.0
13

gensym13


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0


0








false




<客yValueMap id="157"/>

majotecita

JUNIO
2
1
0.0
14

gensym14


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0


0








false




<客yValueMap id="168"/>


JULIO
2
1
0.0
15

gensym15
<统计类=“链表”id = " 170 " >

5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0


0








false




<客yValueMap id="179"/>


AGOSTO
2
1
0.0
16

gensym16


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0


0








false




<客yValueMap id="190"/>


SEPTIEMBRE
2
1
0.0
17

gensym17


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0


0








false




<客yValueMap id="201"/>


OCTUBRE
2
1
0.0
18

gensym18


5.0
5.0
58

< WeightedNumericalStatistics id = " 205 " >
5.0
5.0
58.0
58.0


0.0
1.0


0








false




<客yValueMap id="212"/>


NOVIEMBRE
2
1
0.0
19

gensym19


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0


0








false




<客yValueMap id="223"/>


DICIEMBRE
2
1
0.0
20

gensym20


4.0
4.0
58


4.0
4.0
58.0
58.0


0.0
1.0


0








false




<客yValueMap id="234"/>

majotecita

ANO
3
1
0.0
21

gensym21


178.0
658.0
58


178.0
658.0
58.0
58.0


1.0
5.0


0








true
label




<客yValueMap id="245"/>


<名称>米es a predecir
3
1
0.0
0

gensym


4559.0
370163.0
58


4559.0
370163.0
58.0
58.0


33.0
106.0


0
















true

N-1
N-2
N-12
N-24
N-36
N-12 - N-13
N-24 - N-25
N-36 - N-37
ENERO
FEBRERO
MARZO
ABRIL
MAYO
JUNIO
JULIO
AGOSTO
SEPTIEMBRE
OCTUBRE
NOVIEMBRE
DICIEMBRE
ANO


N-1
N-2
N-12
N-24
N-36
N-12 - N-13
N-24 - N-25
N-36 - N-37
ENERO
FEBRERO
MARZO
ABRIL
MAYO
JUNIO
JULIO
AGOSTO
SEPTIEMBRE
OCTUBRE
NOVIEMBRE
DICIEMBRE
ANO


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0.03136487189831409
0.07417921264358038
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-23.53272424185182
-72.35609076674943
43.96250138016959
23.937786438834202
-11.389953240598805
-21.279898751022948
34.41705460919363
-33.34963144579091
1.0942835995580047
21.114800704201883
10.462481523263921
261.081519289433


0.05087796639475228
0.04255224037822547
1.1620326620942478E-6
2.1794326521273888E-7
0.02011251571285999
0.6755521409765789
0.45459788921624245
0.13889716022182363
1.3100889736383792E-5
1.793720727505388E-11
2.233546902985495E-9
3.976002860284211E-10
0.0033410697757418895
1.0197918736687406E-5
1.2602130450289906E-10
3.4509248236780365E-8
0.7539947727520888
3.1540859725120995E-6
0.003286296075043049
2.1094237467877974E-15


true
true
true
true
true
true
true
true
true
true
true
<布尔>足总lse
true
true
true
true
true
true
true
true
<布尔>足总lse


0.07490613629338166
0.08139991691660807
0.13394421596292033
0.14329326372088502
0.10949811029411527
0.07340022974916523
0.09672875542168176
0.10150180556930871
4.6957209507795294
7.688815247033325
5.651303989749064
2.8665439770761494
3.6085733582692794
4.179668581654486
3.93865185961039
4.837778960039824
3.423176424623841
3.864817728019953
3.308436990144783
20.327756912254312


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-0.14263738541617635
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4.564027723165906
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144.38161538998014
78.61646099278232
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-69.88742816153336
113.03246597524173
-109.52683559029087
3.5938454100387993
69.34521327499581
38.77735273803564
NaN


-2.082873553420323
-2.1599567410147706
-5.778738471172516
-6.307532394530493
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0.42731299350831803
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7.7791783029038
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-3.1563590676348565
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8.738283005443936
-6.8935831341737
0.31966906282905094
5.463336744478127
3.162363845655729
12.843597078438279


0.986567239383258
0.9611854596305677
0.4921026765694041
0.5616411737244934
0.6430673065002668
0.7762687565576827
0.8897249171463226
0.7262628084378114
0.9997237472897619
0.8783561824658114
0.9717415849780795
0.9982649526072929
0.9984553401618238
0.9980507717160555
0.9957964258366678
0.9961696698152795
0.9959108983078104
0.9999959541777077
0.9995788175164931
NaN

majotecita

Please Help me!!!!!

I don't know what else to do!!!!

haddock

HI there,

Youreallyneed to work through help->RapidMIner Tutorial , then the error messages will mean something to you.

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

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!

majotecita

ok... i'll do it.

但是你能告诉我,为什么这个日志出现s 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!