Inference code

Creating Labeled UWAR Files

uwar_combine takes several .uwar files or a directory containing a single session and creates 1 contiguous UWAR file. It can also take a text label file and insert the labels as TAG items in the combined UWAR stream. Label files are of the form timestamp (in seconds), newline, string label:

0.000000
null
703.125000
null
884.375000
walk2
1000.000000
walk3
1074.218750
walk3
1250.000000
walk4
1601.562500
run1
1777.343750
walk1

Example Command: <bash>uwar_combine -scandir "001 MSB lab 1 - 100907/" -labelFile "001 MSB lab 1 - 100907/sub1-lab1-ms_resampled.txt" -out ./src_data/glen_sub001_lab1_combined.uwar</bash>

Creating ARFF Training Features files

You can run the inference engine without a classifier, specifying just the features (in the .XML configuration file) you would like to compute. When you use the -label command it the inference engine will read the TAG objects from the UWAR stream as labels for ground truth. Note that you must have a label in the UWAR stream every ~2137 seconds otherwise the inference engine will crash. Example Command: <bash>inference -xml generateFeaturesForTraining.xml -uwarin ./src_data/glen_sub001_lab1_combined.uwar -arffout ./trainingFeatures/glen_sub001_lab1_features.arff -label -silent></bash> Example .xml training file: <xml></xml>

Training Process

Generate Source .uwar files with Labels

<bash> ./bin/uwar_combine -scandir "/projects/ubicomp3/glen/subject-tests/MSB-001/001 MSB lab 1 - 100907/" -labelFile "/projects/ubicomp3/glen/subject-tests/MSB-001/001 MSB lab 1 - 100907/sub1-lab1-ms.txt" -out ./src_data/glen_sub001_lab1_combined.uwar ./bin/uwar_combine -scandir "/projects/ubicomp3/glen/subject-tests/MSB-006/006 MSB lab 1/" -labelFile "/projects/ubicomp3/glen/subject-tests/MSB-006/006 MSB lab 1/s ub6-lab1-ms.txt" -out ./src_data/glen_sub006_lab1_combined.uwar ./bin/uwar_combine -scandir "/projects/ubicomp3/glen/subject-tests/MSB-017/017 MSB lab 2 - 102207/" -labelFile "/projects/ubicomp3/glen/subject-tests/MSB-017/017 MSB lab 2 - 102207/sub17-lab2-ms.txt" -out ./src_data/glen_sub017_lab2_combined.uwar </bash>

Generate Features by running inference

Run the inference engine without a classifier model to simply output ARFF files containing computed features. <bash>
1. Generate training features:
2. infernce -xml generateFeaturesForTraining.xml -uwarin <file> -arffout <output> -label -slient
./bin/inference -xml generateFeaturesForTraining.xml -uwarin ./src_data/glen_sub001_lab1_combined.uwar -arffout ./src_data/features/glen_sub001_lab1_features.arff -label -si lent ./bin/inference -xml generateFeaturesForTraining.xml -uwarin ./src_data/glen_sub006_lab1_combined.uwar -arffout ./src_data/features/glen_sub006_lab1_features.arff -label -si lent ./bin/inference -xml generateFeaturesForTraining.xml -uwarin ./src_data/glen_sub017_lab2_combined.uwar -arffout ./src_data/features/glen_sub017_lab2_features.arff -label -si lent </bash>

Create a training set

Combine the .arff files into a single training arff file: <bash>
1. Combine sub001 and sub006 to create our training set:
echo "Combining .arff files to create Training set" rm ./src_data/train/sub001_sub006_features.arff ./training_bin/arffcat.pl ./src_data/features/glen_sub001_lab1_features.arff ./src_data/features/glen_sub006_lab1_features.arff > ./src_data/train/sub001_sub006_features.arf f </bash>

Create the Class/ Not Class ARFF files

To train the boosted decision stumps classifier we need examples of Class and Not Class to feed the stumps classifier. allacts2binacts.pl performs this operation (using actset2binactset.pl). It will generate output files: <user prefix>PositiveClassName.arff <bash> cd ./training_bin perl allacts2binats.pl ../src_data/train/sub001_sub006_features.arff ../src_data/train/trainSet_sub001_006__ cd .. </bash>

Train the Boosted Stumps Classifiers

Train the classifier using our Class/Not Class ARFF files: <bash>
1. perl ./training_bin/boostedstumpall.pl <input file path> <input file base> <output file>
cd ./training_bin perl boostedstumpall.pl ../src_data/train/ trainSet_sub001_006__ ../src_data/trainedClassifier/boostedClassifier cd .. </bash>

Compile and Classify Test Traces

Once we've generated a classifier we have to turn the Weka raw output into a usable form and then classify the data. In this case we use 'glen_sub017_lab2_features.arff' as our test data set that we want output for. <bash> cd ./training_bin rm ../src_data/trainedClassifier/classifier.csv rm ../src_data/trainedClassifier/margins.csv perl compile_classifier.pl ../src_data/trainedClassifier/stump_output.raw > ../src_data/trainedClassifier/classifier.csv perl run_classifier.pl ../src_data/features/glen_sub017_lab2_features.arff ../src_data/trainedClassifier/classifier.csv > ../src_data/trainedClassifier/margins.csv cd ../ </bash>

Analyze Output from Classifier

After classification we want to create a confusion matrix to see how well our classifier is doing. <bash> cd ./training_bin rm ../src_data/trainedClassifier/confusion.csv cat ../src_data/trainedClassifier/margins.csv | perl gen_confusion_matrix.pl geom 0.0 > ../src_data/trainedClassifier/confusion.csv cat ../src_data/trainedClassifier/confusion.csv cd ../ </bash>