191 lines
7.6 KiB
Python
191 lines
7.6 KiB
Python
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'''
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Naive Bayes Classifier
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======================
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Naive Bayes is a probabilistic classifier that is able to predict,
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given an observation of an input, a probability distribution over a set of classes,
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rather than only outputting the most likely class that the observation should belong to.
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'Naive' means, that it assumes that the value of a particular feature
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(word in an article) is independent of the value of any other feature,
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given the class variable (label). It considers each of these features
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to contribute independently to the probability that it belongs to its category,
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regardless of any possible correlations between these features.
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'''
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from BagOfWords import BagOfWords
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from CsvHandler import CsvHandler
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.feature_selection import SelectPercentile
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from sklearn.metrics import recall_score, precision_score
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from sklearn.model_selection import StratifiedKFold
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from sklearn.model_selection import train_test_split
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from sklearn.naive_bayes import GaussianNB
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# toDo: für Julian erst mal ohne SelectPercentile machen
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class NaiveBayes():
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def make_naive_bayes(dataset):
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'''fits naive bayes model with StratifiedKFold, uses my BOW
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'''
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print('# starting naive bayes')
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print()
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# alternative: use only articles' header => may give better results
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X = dataset['Title'] + ' ' + dataset['Text']
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y = dataset['Label']
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# use stratified k-fold cross-validation as split method
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skf = StratifiedKFold(n_splits = 10, shuffle=True)
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classifier = GaussianNB()
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# lists for metrics
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recall_scores = []
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precision_scores = []
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f1_scores = []
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# for each fold
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n = 0
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for train, test in skf.split(X,y):
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# BOW
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vocab = BagOfWords.make_vocab(X[train])
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# fit the training data and then return the matrix
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training_data = BagOfWords.make_matrix(X[train], vocab)
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# transform testing data and return the matrix
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testing_data = BagOfWords.make_matrix(X[test], vocab)
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# apply select percentile
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selector = SelectPercentile(percentile=25)
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selector.fit(training_data, y[train])
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training_data_r = selector.transform(training_data)
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testing_data_r = selector.transform(testing_data)
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#fit classifier
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classifier.fit(training_data_r, y[train])
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#predict class
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predictions_train = classifier.predict(training_data_r)
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predictions_test = classifier.predict(testing_data_r)
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#store metrics
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rec = recall_score(y[test], predictions_test)
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recall_scores.append(rec)
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prec = precision_score(y[train], predictions_train)
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precision_scores.append(prec)
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# equation for f1 score
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f1_scores.append(2 * (prec * rec)/(prec + rec))
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#print metrics of test set
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print('prediction of testing set:')
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print('F1 score: min = {0:.2f}, max = {0:.2f}, average = {0:.2f}'
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.format(min(f1_scores), max(f1_scores), sum(f1_scores)/float(len(f1_scores))))
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print()
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#print('overfit testing: prediction of training set')
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#print('F1 score: min = {0:.2f}, max = {0:.2f}, average = {0:.2f}'.
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#format(min(f1_scores_train), max(f1_scores_train),sum(f1_scores_train)/float(len(f1_scores_train))))
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#print()
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print('# ending naive bayes')
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print()
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def make_naive_bayes_CV(dataset):
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'''alternative: uses CountVectorizer (faster)
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'''
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# alternative: use only articles' header => may give better results
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X = dataset['Title'] + '.' + dataset['Text'] + '.'
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y = dataset['Label']
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# use stratified k-fold cross-validation as split method
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skf = StratifiedKFold(n_splits = 10, shuffle=True)
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count_vector = CountVectorizer()
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classifier = GaussianNB()
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# lists for metrics predicted on test/train set
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f1_scores, f1_scores_train = []
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# for each fold (10 times)
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# fold number
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n = 0
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for train, test in skf.split(X,y):
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# fit the training data and then return the matrix
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training_data = count_vector.fit_transform(X[train], y[train]).toarray()
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# transform testing data and return the matrix
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testing_data = count_vector.transform(X[test]).toarray()
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# apply select percentile
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selector = SelectPercentile(percentile=25)
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selector.fit(training_data, y[train])
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training_data_r = selector.transform(training_data)
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testing_data_r = selector.transform(testing_data)
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#fit classifier
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classifier.fit(training_data_r, y[train])
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#predict class
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predictions_train = classifier.predict(training_data_r)
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predictions_test = classifier.predict(testing_data_r)
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#store metrics predicted on test set
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f1_scores.append(f1_score(y[test], predictions_test))
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#store metrics predicted on train set
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f1_scores_train.append(f1_score(y[train], predictions_train))
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#print metrics of test set
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print('--------------------')
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print('prediction of testing set:')
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print('F1 score: min = {}, max = {}, average = {}'.format(min(f1_scores), max(f1_scores),sum(f1_scores)/float(len(f1_scores))))
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print()
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print('prediction of training set:')
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print('F1 score: min = {}, max = {}, average = {}'.format(min(f1_scores_train), max(f1_scores_train),sum(f1_scores_train)/float(len(f1_scores_train))))
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print()
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# def analyze_errors_cv(dataset):
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# '''calculates resubstitution error
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# shows indices of false classified articles
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# uses Gaussian Bayes with train test split
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# '''
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# X_train_test = dataset['Text']
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# y_train_test = dataset['Label']
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# count_vector = CountVectorizer()
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# # fit the training data and then return the matrix
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# training_data = count_vector.fit_transform(X_train_test).toarray()
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# # transform testing data and return the matrix
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# testing_data = count_vector.transform(X_train_test).toarray()
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# # Naive Bayes
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# classifier = GaussianNB()
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# # fit classifier
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# classifier.fit(training_data, y_train_test)
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# # Predict class
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# predictions = classifier.predict(testing_data)
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# print()
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# print('errors at index:')
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# n = 0
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# for i in range(len(y_train_test)):
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# if y_train_test[i] != predictions[i]:
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# n += 1
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# print('error no.{}'.format(n))
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# print('prediction at index {} is: {}, but actual is: {}'.format(i, predictions[i], y_train_test[i]))
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# print(X_train_test[i])
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# print(y_train_test[i])
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# print()
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# print()
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# #print metrics
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# print('F1 score: ', format(f1_score(y_train_test, predictions)))
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