Status: Done!
Total Time
1s
Max Memory Usage
31M
Domain
MulticlassClassification
Learn time
10s
Train error
0.040
Predict train time
3s
Test error
0.057
Predict test time
1s
Log file
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset6/train
=== START program4: ./run split ../dataset6/train ../program1/cv.train ../program1/cv.test
=== END program4: ./run split ../dataset6/train ../program1/cv.train ../program1/cv.test --- OK [0s]
===== Cross-validator: trying hyperparameter 0.01 =====
=== START _tune-hyperparameter0: ./run setHyperparameter 0.01
Saving hyperparameter 0.01
=== END _tune-hyperparameter0: ./run setHyperparameter 0.01 --- OK [0s]
=== START _tune-hyperparameter0: ./run learn ../cv.train
Processing training examples...
Smoothing and normalizing (reverse = false)...
Saving model...
=== END _tune-hyperparameter0: ./run learn ../cv.train --- OK [1s]
=== START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0
Loading model...
Predicting test examples...
=== END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0 --- OK [1s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0 --- OK [0s]
CV error rate 0.0447761194029851 with hyperparameter 0.01
===== Cross-validator: trying hyperparameter 0.1 =====
=== START _tune-hyperparameter1: ./run setHyperparameter 0.1
Saving hyperparameter 0.1
=== END _tune-hyperparameter1: ./run setHyperparameter 0.1 --- OK [0s]
=== START _tune-hyperparameter1: ./run learn ../cv.train
Processing training examples...
Smoothing and normalizing (reverse = false)...
Saving model...
=== END _tune-hyperparameter1: ./run learn ../cv.train --- OK [1s]
=== START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1
Loading model...
Predicting test examples...
=== END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1 --- OK [1s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1 --- OK [0s]
CV error rate 0.0477611940298507 with hyperparameter 0.1
===== Cross-validator: trying hyperparameter 1.0 =====
=== START _tune-hyperparameter2: ./run setHyperparameter 1.0
Saving hyperparameter 1.0
=== END _tune-hyperparameter2: ./run setHyperparameter 1.0 --- OK [0s]
=== START _tune-hyperparameter2: ./run learn ../cv.train
Processing training examples...
Smoothing and normalizing (reverse = false)...
Saving model...
=== END _tune-hyperparameter2: ./run learn ../cv.train --- OK [1s]
=== START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2
Loading model...
Predicting test examples...
=== END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2 --- OK [1s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2 --- OK [0s]
CV error rate 0.0462686567164179 with hyperparameter 1.0
===== Cross-validator: trying hyperparameter 10.0 =====
=== START _tune-hyperparameter3: ./run setHyperparameter 10.0
Saving hyperparameter 10.0
=== END _tune-hyperparameter3: ./run setHyperparameter 10.0 --- OK [0s]
=== START _tune-hyperparameter3: ./run learn ../cv.train
Processing training examples...
Smoothing and normalizing (reverse = false)...
Saving model...
=== END _tune-hyperparameter3: ./run learn ../cv.train --- OK [1s]
=== START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3
Loading model...
Predicting test examples...
=== END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3 --- OK [1s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3 --- OK [0s]
CV error rate 0.0865671641791045 with hyperparameter 10.0
===== Cross-validator: trying hyperparameter 100.0 =====
=== START _tune-hyperparameter4: ./run setHyperparameter 100.0
Saving hyperparameter 100.0
=== END _tune-hyperparameter4: ./run setHyperparameter 100.0 --- OK [0s]
=== START _tune-hyperparameter4: ./run learn ../cv.train
Processing training examples...
Smoothing and normalizing (reverse = false)...
Saving model...
=== END _tune-hyperparameter4: ./run learn ../cv.train --- OK [1s]
=== START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4
Loading model...
Predicting test examples...
=== END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4 --- OK [1s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4 --- OK [0s]
CV error rate 0.485074626865672 with hyperparameter 100.0
Best hyperparameter value is 0.01; got CV error rate 0.0447761194029851
=== END program1: ./run learn ../dataset6/train --- OK [10s]
===== MAIN: predict/evaluate on train data =====
=== START program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in
=== END program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out
Loading model...
Predicting test examples...
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out --- OK [2s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [3s]
=== START program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out
=== END program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out --- OK [0s]
===== MAIN: predict/evaluate on test data =====
=== START program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in
=== END program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out
Loading model...
Predicting test examples...
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out --- OK [1s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s]
=== START program8: ./run evaluate ../dataset6/test ../program0/evalTest.out
=== END program8: ./run evaluate ../dataset6/test ../program0/evalTest.out --- OK [1s]
real 0m15.220s
user 0m12.561s
sys 0m2.064s
supervised-learning : Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) tune-hyperparameter : Sets the hyperparameter
(numProbes:int) 5
(learner:Program) simple-naive-bayes : A Simple Naive Bayes implementation in Ruby.
(splitter:Program) multiclass-utils : Validates and inspects a dataset in MulticlassClassification format.
(evaluator:Program[Evaluate]) classification-evaluator : Evaluates predictions of classification datasets (discrete outputs).
(dataset:Dataset) molecular-biology-splice : 3190 examples, 244 features
(stripper:Program[Strip]) multiclass-utils : Validates and inspects a dataset in MulticlassClassification format.
(evaluator:Program[Evaluate]) classification-evaluator : Evaluates predictions of classification datasets (discrete outputs).
doTest:
evaluate:
errorRate: 0.0574712643678161
numErrors: 55
numExamples: 957
success: true
time: 1
predict:
predict:
success: true
time: 1
strip:
doTrain:
evaluate:
errorRate: 0.0398566950291088
numErrors: 89
numExamples: 2233
success: true
time: 0
predict:
predict:
success: true
time: 3
strip:
exitCode: 0
learn:
bestCVErrorRate: 0.0447761194029851
bestHyperparameter: 0.01
evaluate0:
errorRate: 0.0447761194029851
numErrors: 30
numExamples: 670
success: true
time: 0
evaluate1:
errorRate: 0.0477611940298507
numErrors: 32
numExamples: 670
success: true
time: 0
evaluate2:
errorRate: 0.0462686567164179
numErrors: 31
numExamples: 670
success: true
time: 0
evaluate3:
errorRate: 0.0865671641791045
numErrors: 58
numExamples: 670
success: true
time: 0
evaluate4:
errorRate: 0.485074626865672
numErrors: 325
numExamples: 670
success: true
time: 0
learn0:
learn1:
learn2:
learn3:
learn4:
predict0:
predict1:
predict2:
predict3:
predict4:
setHyperparameter0:
setHyperparameter1:
setHyperparameter2:
setHyperparameter3:
setHyperparameter4:
split:
success: true
time: 10
success: true
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