from data import Data from naivebayes import NaiveBayes filename = "datasets/weatherNominal.td" ## filename = "datasets/titanic.td" ## filename = "datasets/cmc.td" d = Data(filename) d.report() pr = NaiveBayes(d) pr.train() pr.show() for (v, c_true) in d.test_set: c_pred = pr.predict(v)[0] print(v, ":") print(" ", c_pred, "( true class:", c_true, ")") ## print(pr.predict(("Class:1st","Sex:Female","Age:Child"))) ## print(pr.predict(("Class:Crew","Sex:Female","Age:Child")))
#pos_class = "Survived:No" # datafile = "cmcTr.txt" # pos_class = "contraceptive-method:none" d = Data(datafile) prnb = NaiveBayes(d) prnb.train() r = Roc(prnb, pos_class) r.do_curve() print "Predicting", pos_class, "for data file", datafile, print "with", int(r.curve[2]), "positive instances and", int( r.curve[3]), "negative instances" if print_numbers: prnb.show() print "Scores for predicting", pos_class, ":" for e in sorted(r.preds): print e print "===" print "Curve coordinates:" for e in zip(r.curve[0], r.curve[1]): print e r.draw_curve()