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BYONBC.py
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"""
Author: Razine Ahmed Bensari, [email protected]
Author: Annes Cherrid, [email protected]
"""
from NaiveBayesClassifier import NaiveBayesClassifier
from decimal import Decimal
class BYONBC:
def __init__(self):
self.model_ca = NaiveBayesClassifier(0, 3, 0.3, "./training_files/ca_training-tweets.txt", "ca",
18318)
self.model_gl = NaiveBayesClassifier(0, 3, 0.3, "./training_files/gl_training-tweets.txt", "gl",
18318)
self.model_en = NaiveBayesClassifier(0, 3, 0.3, "./training_files/en_training-tweets.txt", "en",
18318)
self.model_es = NaiveBayesClassifier(0, 3, 0.3, "./training_files/es_training-tweets.txt", "es",
18318)
self.model_pt = NaiveBayesClassifier(0, 3, 0.3, "./training_files/pt_training-tweets.txt", "pt",
18318)
self.model_eu = NaiveBayesClassifier(0, 3, 0.3, "./training_files/eu_training-tweets.txt", "eu",
18318)
self.arrayModel = [self.model_ca, self.model_gl, self.model_en, self.model_es, self.model_pt, self.model_eu]
self.probability = 0
self.totalTweetCount = 18318
def trainClassifier(self):
print("Training BYONBC. . .")
for model in self.arrayModel:
model.constructModel()
def testClassifier(self):
print("Testing BYONBC. . .")
trace_file_name = "./trace_files/trace_BYONBC.txt"
trace_file = open(trace_file_name, "w")
with open("./OriginalDataSet/test-tweets-given.txt", "r") as file:
for line in file:
tweetArray = line.split("\t")
result = []
for model in self.arrayModel:
result.append((model.calculateProbability(tweetArray[3]), model.language))
highest_prob = self.get_highest_prob(result)
if tweetArray[2] == highest_prob[1]: # Correct
prob = "{:.2E}".format(Decimal(highest_prob[0]))
s = str(tweetArray[0] + " " + highest_prob[1] + " " + str(prob) + " " + tweetArray[
2] + " " + "correct" "\n")
trace_file.write(str(s))
else:
prob = "{:.2E}".format(Decimal(highest_prob[0]))
s = str(tweetArray[0] + " " + highest_prob[1] + " " + str(prob) + " " + tweetArray[
2] + " " + "wrong" "\n")
trace_file.write(str(s))
trace_file.close()
def get_highest_prob(self, result):
highest_tupl = result[0]
for tupl in result:
if tupl[0] > highest_tupl[0]:
highest_tupl = tupl
return highest_tupl