Classification Testing

Classify truth-tellers versus liars based on their cluster distributions using several different classification methods from scikit-learn. Designed for use with the ROC-HCI deception dataset, to contrast the temporal modeling of Hidden Markov Models.

Author: Matt Levin

Usage

• python3 clf.py [-m Method] [-i InputFolder] [-k NumberFolds] [-d NumberClusters] [-s RandomSeed]
  • Run with specified method and parameters.
• python3 clf.py --all_methods [-i InputFolder] [-k NumberFolds] [-d NumberClusters] [-s RandomSeed]
  • Run with each available method and specified parameters.

Valid Method Options

• SVM - uses sklearn.svm.SVC as classifier (Support Vector Classifier)
• MLP - uses sklearn.neural_network.MLPClassifier (Multilayer Perceptron Classifier)
• GNB - uses sklearn.naive_bayes.GaussianNB (Gaussian Naive Bayes Classifier)
• BNB - uses sklearn.naive_bayes.BernoulliNB (Bernoulli Naive Bayes Classifier)
• DT - uses sklearn.tree.DecisionTreeClassifier (Decision Tree Classifier)
• KNN - uses sklearn.neighbors.KNeighborsClassifier (K-Nearest Neighbors Classifier)

Example Usage

• python3 clf.py -m MLP -i test -k 10 -s 9999
  • Uses MLPClassifier on test data folder with 10 folds, random state 9999, and default number of clusters.
• python3 clf.py --all_methods -i test -k 5
  • Uses each classifier on test data folder with 5 folds and default random state and number of clusters.
• python3 clf.py -h
  • Shows usage/help message.