Phil 8.26.16

7:00 – 4:00 ASRC

    • Adding more model feedback
    • Something more to think about WRT Group Polarization models? Collective Memory and Spatial Sorting in Animal Groups
    • Need to be able to associate an @attribute  key/value map with Labeled2Dmatrix rows so that we can compare different nominal values across a shared set of numeric columns. This may wind up being a derived class?
      • Working on adding an array of key/value maps;
      • Forgot to add the name to the @data section – oops!
      • text is added to ARFF out. Should I add it to the xlsx outputs as well?
    • Here’s the initial run against the random test data within the class (L2D.arff).
=== Run information ===

Scheme: weka.classifiers.bayes.NaiveBayes
Relation: testdata
Instances: 8
Attributes: 12
name
sv1
sv2
sv3
p1
p2
p3
p4
s1
s2
s3
s4
Test mode: split 66.0% train, remainder test

=== Classifier model (full training set) ===

Naive Bayes Classifier

Class
Attribute p1 p2 p3 p4 s1 s2 s3 s4
(0.13) (0.13) (0.13) (0.13) (0.13) (0.13) (0.13) (0.13)
=======================================================================
sv1
p4-sv1 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0
s2-sv1 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0
p2-sv1 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0
s1-sv1 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0
[total] 4.0 5.0 4.0 5.0 5.0 5.0 4.0 4.0

sv2
p2-sv2 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0
s4-sv2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0
p1-sv2 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
s1-sv2 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0
[total] 5.0 5.0 4.0 4.0 5.0 4.0 4.0 5.0

sv3
p2-sv3 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0
p1-sv3 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
s4-sv3 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0
p3-sv3 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0
p4-sv3 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0
s2-sv3 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0
s1-sv3 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0
[total] 8.0 8.0 8.0 8.0 8.0 8.0 7.0 8.0

p1
mean 1 0 0 0 1 1 0 0
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

p2
mean 0 1 0 0 1 0 1 0
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

p3
mean 0 0 1 0 1 0 0 1
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

p4
mean 0 0 0 1 1 0 0 1
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

s1
mean 1 1 1 1 1 0 0 0
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

s2
mean 1 0 0 0 0 1 0 0
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

s3
mean 0 1 0 0 0 0 1 0
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

s4
mean 0 0 1 1 0 0 0 1
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1



Time taken to build model: 0 seconds

=== Evaluation on test split ===

Time taken to test model on training split: 0 seconds

=== Summary ===

Correctly Classified Instances 0 0 %
Incorrectly Classified Instances 3 100 %
Kappa statistic 0
Mean absolute error 0.2499
Root mean squared error 0.4675
Relative absolute error 108.2972 %
Root relative squared error 133.419 %
Total Number of Instances 3

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.000 0.333 0.000 0.000 0.000 0.000 ? ? p1
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.333 p2
0.000 0.333 0.000 0.000 0.000 0.000 ? ? p3
0.000 0.000 0.000 0.000 0.000 0.000 ? ? p4
0.000 0.000 0.000 0.000 0.000 0.000 0.500 0.500 s1
0.000 0.000 0.000 0.000 0.000 0.000 1.000 1.000 s2
0.000 0.333 0.000 0.000 0.000 0.000 ? ? s3
0.000 0.000 0.000 0.000 0.000 0.000 ? ? s4
Weighted Avg. 0.000 0.000 0.000 0.000 0.000 0.000 0.500 0.611

=== Confusion Matrix ===

a b c d e f g h <-- classified as
0 0 0 0 0 0 0 0 | a = p1
0 0 0 0 0 0 1 0 | b = p2
0 0 0 0 0 0 0 0 | c = p3
0 0 0 0 0 0 0 0 | d = p4
0 0 1 0 0 0 0 0 | e = s1
1 0 0 0 0 0 0 0 | f = s2
0 0 0 0 0 0 0 0 | g = s3
0 0 0 0 0 0 0 0 | h = s4
  • Need to add text data from xml or from other(wrapper info? structured data? UI selections?) sources