print "Loading test data..."
test_data = read_data(test_file)
print "Testing classifiers..."
scorer = Score()
Algorithms = ("Simplified", "HMM", "Complex")
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 "----"
(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 "----"
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", "Complex")
for (s, gt) in test_data:
# Changed the probability distribution for ground truth
outputs = {"0. Ground truth": [[gt, ], [[1]*len(s)]]}
# 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
# modified the parameter for posterior function
# Instead of passing the label we are passing the probability list
posteriors = { algo: [ solver.posterior(s, output) for output in outputs[algo][1] ] for algo in outputs }
Score.print_results(s, outputs, posteriors)
scorer.score(outputs)
scorer.print_scores()
print "----"
print "Testing classifiers..."
scorer = Score()
Algorithms = ("Simplified", "HMM", "Complex")
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(algo.split(".")[1].strip(), 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 "----"
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 = {}
for algo in outputs:
posteriors[algo] = [solver.posterior(s, output) for output in outputs[algo][0]]
Score.print_results(s, outputs, posteriors)
scorer.score(outputs)
scorer.print_scores()
print "----"
