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






























false







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

< UnknownStatistics id="20">
0








false







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

< UnknownStatistics id="31">
0








false







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

< UnknownStatistics id="42">
0








false







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

< UnknownStatistics id="53">
0








false







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

< UnknownStatistics id="64">
0








false







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

< UnknownStatistics id="75">
0








false

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

< UnknownStatistics id="86">
0








false







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

< UnknownStatistics id="97">
0








false







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







FEBRERO
2
1
0.0
10

gensym10


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0

< UnknownStatistics id="119">
0








false







<名称>米ARZO
2
1
0.0
11

gensym11


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0

< UnknownStatistics id="130">
0








false







ABRIL
2
1
0.0
12

gensym12


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0

< UnknownStatistics id="141">
0








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






<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

< UnknownStatistics id="152">
0








false

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

< UnknownStatistics id="163">
0








false







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

< UnknownStatistics id="174">
0








false







AGOSTO
2
1
0.0
16

gensym16


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0

< UnknownStatistics id="185">
0








false







SEPTIEMBRE
2
1
0.0
17

gensym17


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0

< UnknownStatistics id="196">
0








false







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

< UnknownStatistics id="207">
0








false







NOVIEMBRE
2
1
0.0
19

gensym19


5.0
5.0
58


5.0
5.0
58.0
58.0


0.0
1.0

< UnknownStatistics id="218">
0








false







DICIEMBRE
2
1
0.0
20

gensym20


4.0
4.0
58


4.0
4.0
58.0
58.0


0.0
1.0

< UnknownStatistics id="229">
0








false

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

< UnknownStatistics id="240">
0








true
label







<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

< UnknownStatistics id="251">
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


-0.1560200102743829
-0.17582029926206988
-0.7740285937759676
-0.9038269028374833
-0.27059583303007956
0.03136487189831409
0.07417921264358038
-0.16499163346747733
-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


-0.029648474020890977
-0.03292043216656503
-0.14263738541617635
-0.1485518522426571
-0.048585319967903356
3.653651278846922
4.564027723165906
-3.291911862527626
-87.21991496798663
-237.63182121660725
144.38161538998014
78.61646099278232
-37.406876234658206
-69.88742816153336
113.03246597524173
-109.52683559029087
3.5938454100387993
69.34521327499581
38.77735273803564
NaN


-2.082873553420323
-2.1599567410147706
-5.778738471172516
-6.307532394530493
-2.4712374697905823
0.42731299350831803
0.7668786010964543
-1.6255044187841143
-5.011525277698878
-9.410564364213005
7.7791783029038
8.350748019310187
-3.1563590676348565
-5.091288540059193
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
<二> 0.9982649526072929双> < /
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!