def infer_prepare_params(basic_or_complex, fileToInfer):
train_parser = MyParser("../train.wtag")
seenWordsToTagsDict = train_parser.getSeenWordsToTagsDict()
fb, filePrefix = None, None
if basic_or_complex == 'basic':
fb = BasicFeatureVectorBuilder(train_parser, 0)
filePrefix = 'finish_basic_opt_v_'
elif basic_or_complex == 'complex':
fb = ComplexFeatureVectorBuilder(train_parser, False)
filePrefix = 'finish_complex_opt_v_'
else:
assert (False)
fn = str(fileToInfer).replace('.', '').replace('/', '')
parser = MyParser(fileToInfer)
splitted = parser.splitted
mle = MLE(train_parser.getUniqueTags(), splitted, fb)
prefixed = [
filename for filename in os.listdir('.')
if filename.startswith(filePrefix)
]
prefixed.sort()
print(prefixed)
results = []
for v_file in prefixed:
v = np.loadtxt(v_file)
vit = Viterbi(mle, mle.allTags, v, seenWordsToTagsDict)
res_file = open(fn + "_results_" + v_file, 'w')
exp_file = open(fn + "_expected_" + v_file, 'w')
accuracy = infer_aux(exp_file, res_file, v_file, splitted, vit)
res_file.close()
exp_file.close()
results = results + [accuracy]
infer_aux_results(prefixed, results, fileToInfer, fn)
def train():
train_parser = MyParser("../train.wtag")
seenSentencesToTagsDict = train_parser.getSeenWordsToTagsDict()
parser = MyParser("../comp748.wtag")
splitted = parser.splitted
fb = BasicFeatureVectorBuilder(parser,0)
mle = MLE(parser.getUniqueTags(), splitted, fb)
v = np.loadtxt("opt_v_3.txt")
sentences = list(map(lambda tuples: [t[0] for t in tuples], splitted))
expected_tags = list(map(lambda tuples: [t[1] for t in tuples], splitted))
seenSentencesToTagsDict = parser.getSeenWordsToTagsDict()
vit = Viterbi(mle, mle.allTags, v, seenSentencesToTagsDict)
total_res = 0
words_count = 0
total_time = 0
for s,expected,idx in zip(sentences,expected_tags,range(0,len(splitted))):
curr_word_len = len(s)
words_count = words_count + curr_word_len
start = time.time()
tags = vit.inference(s)
res_file = open("test_wtag748_results.txt",'a')
for item in tags:
res_file.write("%s " % item)
res_file.write("\n")
res_file.close()
exp_file = open("test_wtag748_expected.txt", 'a')
for item in expected:
exp_file.write("%s " % item)
exp_file.write("\n")
exp_file.close()
stop = time.time()
e = np.array([hash(x) for x in expected])
t = np.array([hash(x) for x in tags])
current_correct = np.sum(e == t)
print("---------------------")
print("Inference for sentence# ", idx, " took: ", stop - start, " seconds")
total_time = total_time + (stop-start)
print("Current sentence accuracy: ", current_correct, " of: ", curr_word_len)
total_res = total_res + current_correct
print("Total sentence accuracy: ", total_res, " of: ", words_count, "=", (100*total_res)/words_count, "%")
print("Total time for ", idx, " sentences: ", (total_time / 60), " minutes")
