class TrainingAndTesting(object):
def __init__(self):
self.cleaner = Cleaner()
self.feature_tfidf = FeatureVectorTfIdf()
self.classifier = NaiveBayes()
def train_me(self, traning_data, traning_data_type):
data_clean = self.cleaner.lower_and_replace(traning_data)
t = self.feature_tfidf.extract_features(data_clean)
self.classifier.training(t, traning_data_type)
def test_me(self, test_data):
data_clean = self.cleaner.lower_and_replace(test_data)
t = self.feature_tfidf.word_vectors_tfidf(data_clean)
result = self.classifier.testing(t)
return result
@classmethod
def model_evaluation(cls, test_label, original_label):
if len(test_label) != len(original_label):
print('Error!! label length not equal')
return ''
tp = 0
tn = 0
fp = 0
fn = 0
for i in range(len(test_label)):
if test_label[i] == 'negative':
if test_label[i] == original_label[i]:
tn = tn + 1
else:
fn = fn + 1
else:
if test_label[i] == original_label[i]:
tp = tp + 1
else:
fp = fp + 1
p = tp + fn
n = tn + fp
recall = tp / p
precision = tp / (tp + fp)
return {
'Total Test data:': len(test_label),
'Correcly predicted': (tp + tn),
'Wrong Predicted': len(test_label) - (tp + tn),
'Accuracy': ((tp + tn) / (p + n)) * 100,
'Error rate': ((fp + fn) / (p + n)) * 100,
'Recall': recall,
'precision': precision,
'f1 score': (2 * precision * recall) / (precision + recall)
}
def get_amazon_data(self, query='Mobile', total_reviews=5):
from amazon import Amazon
obj = Amazon()
review, rating = obj.search(query=query, total_reviews=total_reviews)
return [review, rating]
def __init__(self):
self.cleaner = Cleaner()
self.feature_tfidf = FeatureVectorTfIdf()
self.classifier = NaiveBayes()
#!/usr/bin/env python
from __future__ import division
from analysis.preprocess import Cleaner
from analysis.learning import FeatureVectorTfIdf, NaiveBayes
__author__ = 'Md Mehrab Alam'
cleaner = Cleaner()
feature_tfidf = FeatureVectorTfIdf()
classifier = NaiveBayes()
class TrainingAndTesting(object):
def __init__(self):
self.cleaner = Cleaner()
self.feature_tfidf = FeatureVectorTfIdf()
self.classifier = NaiveBayes()
def train_me(self, traning_data, traning_data_type):
data_clean = self.cleaner.lower_and_replace(traning_data)
t = self.feature_tfidf.extract_features(data_clean)
self.classifier.training(t, traning_data_type)
def test_me(self, test_data):
data_clean = self.cleaner.lower_and_replace(test_data)
t = self.feature_tfidf.word_vectors_tfidf(data_clean)
result = self.classifier.testing(t)
return result
@classmethod
