===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
main() {
Reading examples from ../dataset2/train {
18815 examples, 22 features, 5 labels
} [1.1s, cum. 1.3s]
Iteration 0 {
numMistakes = 10526/18815 = 0.559
}
Iteration 1 {
numMistakes = 10377/18815 = 0.552
}
Iteration 2 {
numMistakes = 10389/18815 = 0.552
}
Iteration 3 {
numMistakes = 10306/18815 = 0.548
}
Iteration 4 {
numMistakes = 10343/18815 = 0.550
}
Iteration 5 {
numMistakes = 10271/18815 = 0.546
}
Iteration 6 {
numMistakes = 10462/18815 = 0.556
}
Iteration 7 {
numMistakes = 10223/18815 = 0.543
}
Iteration 8 {
numMistakes = 10236/18815 = 0.544
}
Iteration 9 {
numMistakes = 10263/18815 = 0.545
}
Writing parameters to params
} [3.5s]
=== END program1: ./run learn ../dataset2/train --- OK [4s]
===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
main() {
Reading parameters from params
Reading examples from ../program0/evalTrain.in {
18815 examples, 22 features, 5 labels
} [1.1s, cum. 1.3s]
Predicting
} [1.4s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [2s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [1s]
===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
main() {
Reading parameters from params
Reading examples from ../program0/evalTest.in {
8063 examples, 22 features, 5 labels
}
Predicting
}
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]
real 0m16.813s
user 0m9.397s
sys 0m1.432s
Run specification
supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) sgd-logistic-stepsize0.5-iter10: Stochastic gradient descent (loss=logistic, stepSize = 1/numUpdates^0.5, take 10 passes over training data)
(dataset:Dataset) HumanActivityRecognition_WearableAccelerometers: This is part of a dataset we'll publish in October, 25th of 2012. You can check our website to get it for free. The data come from triaxial wearable accelerometers mounted at the subjects' waist, left thigh, right ankle, and right upper arm. The 4 accelerometers were used at the same time, to collect data from 4 subjects doing 5 activities. Each activity is a class in the dataset:
class
1=sitting
2=sitting down
3=standing
4=standing up
5=walking
The dataset has the label (class) and 22 columns as follow:
The dataset can't be published before October 25th, but you can use this one to work on the data and wait for the complete dataset with 165,633 samples. If you have any doubt, please ask us: wugulino@inf.puc-rio.br
Have a good work!
When you generate a run, you can set a time limit for the run (no more than 24 hours). After that point, we will terminate the program.
Your program can use 1.5GB of memory. More information here.
Go to the page for the run and look at the log file for signs of the responsible error.
You can also download the run and run it locally on your machine (a README file should
be included in the download which provides more information).
We said that a run was simply a program/dataset pair, but that's not the full story.
A run actually includes other helper programs such as the evaluation program and
various programs for reductions (e.g., one-versus-all, hyperparameter tuning).
More formally, a run is a given by a run specification,
which can be found on the page for any run.
A run specification is a tree where each internal node represents a program
and its children represents the arguments to be passed into its constructor.
For example, the one-versus-all program takes your binary classification program
as a constructor argument and behaves like a multiclass classification program.
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