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