(train_file, test_file) = sys.argv[1:3]
print "Learning model..."
solver = Solver()
train_data = read_data(train_file)
solver.train(train_data)
print "Loading test data..."
test_data = read_data(test_file)
print "Testing classifiers..."
scorer = Score()
Algorithms = ("Simplified", "HMM VE", "HMM MAP")
Algorithm_labels = [ str(i+1) + ". " + Algorithms[i] for i in range(0, len(Algorithms) ) ]
for (s, gt) in test_data:
outputs = {"0. Ground truth" : gt}
# run all algorithms on the sentence
for (algo, label) in zip(Algorithms, Algorithm_labels):
outputs[label] = solver.solve( algo, s)
posteriors = { o: solver.posterior( s, outputs[o] ) for o in outputs }
Score.print_results(s, outputs, posteriors)
scorer.score(outputs)
scorer.print_scores()
print "----"
end_time=time.time()
# print (end_time-start_time)
Algorithm_labels = [
str(i + 1) + ". " + Algorithms[i] for i in range(0, len(Algorithms))
]
for (s, gt) in test_data:
emission = solver.emission_probability(s, train_data, tags_list)
outputs = {"0. Ground truth": gt}
# run all algorithms on the sentence
maxx = []
for (algo, label) in zip(Algorithms, Algorithm_labels):
outputs[label], proba = solver.solve(algo, s, prob_all, tags_list,
start_p, transition, emission)
maxx.append(proba)
# calculate posteriors for each output under each model
count = 0
posteriors = {
o: {
a: solver.posterior(a, maxx[0], maxx[1], maxx[2])
for a in Algorithms
}
for o in outputs
}
Score.print_results(s, outputs, posteriors, Algorithms)
scorer.score(outputs, gt)
scorer.print_scores()
print("----")
(train_file, test_file) = sys.argv[1:3]
print "Learning model..."
solver = Solver()
train_data = read_data(train_file)
solver.train(train_data)
print "Loading test data..."
test_data = read_data(test_file)
print "Testing classifiers..."
scorer = Score()
Algorithms = ("Naive", "Sampler", "Max marginal", "MAP", "Best")
for (s, gt) in test_data:
outputs = {"0. Ground truth" : [[gt,], []]}
# run all algorithms on the sentence
for i in range(0, len(Algorithms)):
outputs[ str(i+1) + ". " + Algorithms[i] ] = solver.solve(Algorithms[i], s)
# compute posteriors for each solution
posteriors = { algo: [ solver.posterior(s, output) for output in outputs[algo][0] ] for algo in outputs }
Score.print_results(s, outputs, posteriors)
scorer.score(outputs)
scorer.print_scores()
print "----"