def run(V, n, d):
#n
size = n
#V
vocab = V
#d
smoothing = d
in_file1 = "./OriginalDataSet/training-tweets.txt"
n_gram = N_Gram(in_file1, size, vocab, smoothing)
[uni, bi, tri] = n_gram.count()
language_count = n_gram.language_count
tweets = []
eu = Language('eu', uni['eu'], bi['eu'], tri['eu'])
es = Language('es', uni['es'], bi['es'], tri['es'])
pt = Language('pt', uni['pt'], bi['pt'], tri['pt'])
en = Language('en', uni['en'], bi['en'], tri['en'])
ca = Language('ca', uni['ca'], bi['ca'], tri['ca'])
gl = Language('gl', uni['gl'], bi['gl'], tri['gl'])
eu.cal_conditional_probabilities(size, smoothing)
es.cal_conditional_probabilities(size, smoothing)
pt.cal_conditional_probabilities(size, smoothing)
en.cal_conditional_probabilities(size, smoothing)
ca.cal_conditional_probabilities(size, smoothing)
gl.cal_conditional_probabilities(size, smoothing)
eu.export_model(vocab, size, smoothing)
es.export_model(vocab, size, smoothing)
pt.export_model(vocab, size, smoothing)
en.export_model(vocab, size, smoothing)
ca.export_model(vocab, size, smoothing)
gl.export_model(vocab, size, smoothing)
in_file2 = "./OriginalDataSet/test-tweets-given.txt"
input_file = open(in_file2, "r", encoding="utf8")
for line in input_file:
try:
[tweet_id, user_name, language, text] = line.split('\t')
tweets.append(
Tweet(tweet_id, user_name, language, text.strip('\n')))
except:
print('ERROR reading file')
input_file.close()
overall_result = Counter()
language_result = {
'eu': Counter(),
'ca': Counter(),
'gl': Counter(),
'es': Counter(),
'en': Counter(),
'pt': Counter()
}
language_predictions = Counter()
debug = 0
if os.path.exists(f'ModifiedDataSet/trace_{vocab}_{size}_{smoothing}.txt'):
os.remove(f'ModifiedDataSet/trace_{vocab}_{size}_{smoothing}.txt')
for tweet in tweets:
try:
if vocab == 0:
tweet.lower_case()
elif vocab == 1:
tweet.case_sensitive()
elif vocab == 2:
tweet.is_alpha()
tweet.counter()
count = {}
if size == 1:
count = tweet.uni
elif size == 2:
count = tweet.bi
elif size == 3:
count = tweet.tri
score = {}
score['eu'] = eu.score(count, language_count)
score['es'] = es.score(count, language_count)
score['ca'] = ca.score(count, language_count)
score['gl'] = gl.score(count, language_count)
score['pt'] = pt.score(count, language_count)
score['en'] = en.score(count, language_count)
estimate_l = max(score, key=score.get)
estimate_s = score[estimate_l]
if debug < 0:
print(tweet.tweet_id)
print(score)
print(estimate_l, tweet.language)
language_predictions[estimate_l] += 1
if estimate_l == tweet.language:
overall_result['right'] += 1
language_result[tweet.language]['right'] += 1
else:
overall_result['wrong'] += 1
language_result[tweet.language]['wrong'] += 1
# Trace Output File
with open(f'ModifiedDataSet/trace_{vocab}_{size}_{smoothing}.txt',
'a',
encoding='utf8') as trace_file:
correct_wrong = 'correct' if estimate_l == tweet.language else 'wrong'
trace_file.write(
f'{tweet.tweet_id} {estimate_l} {estimate_s:.2E} {tweet.language} {correct_wrong}\n'
)
debug += 1
except Exception as error_msg:
print(f'ERROR calculating score: {error_msg}')
# Eval Output File
with open(f'ModifiedDataSet/eval_{vocab}_{size}_{smoothing}.txt',
'w',
encoding='utf8') as eval_file:
accuracy = round(
overall_result['right'] / sum(overall_result.values()), 4)
per_class_precision = []
per_class_recall = []
for language in language_result:
per_class_precision.append(
round(
language_result[language]['right'] /
language_predictions[language],
4)) if language_predictions[
language] > 0 else per_class_precision.append(0)
per_class_recall.append(
round(
language_result[language]['right'] /
sum(language_result[language].values()), 4))
per_class_f1 = [
round((x * y) / (x + y), 2) if x > 0 or y > 0 else 0.0
for x, y in zip(per_class_precision, per_class_recall)
]
macro_f1 = round(sum(per_class_f1) / len(per_class_f1), 4)
weighted_f1 = 0
for index, language in enumerate(language_result):
weighted_f1 += sum(
language_result[language].values()) * per_class_f1[index]
weighted_f1 = round(weighted_f1 / sum(overall_result.values()), 4)
eval_file.write(f'{accuracy}\n')
eval_file.writelines(f'{c} ' for c in per_class_precision)
eval_file.write('\n')
eval_file.writelines(f'{c} ' for c in per_class_recall)
eval_file.write('\n')
eval_file.writelines(f'{c} ' for c in per_class_f1)
eval_file.write('\n')
eval_file.write(f'{macro_f1} {weighted_f1}')
print('right: ',
(overall_result['right'] / sum(overall_result.values())) * 100, '%')
print('wrong: ',
(overall_result['wrong'] / sum(overall_result.values())) * 100, '%')
