thesis-anne/NaiveBayes_Interactive.py

'''
Naive Bayes Classifier
======================

basic implementation of naive bayes.
prints out probabilities for classes needed for interactive labeling.
'''

import csv

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import recall_score, precision_score
from sklearn.model_selection import StratifiedKFold
from sklearn.naive_bayes import GaussianNB

class NaiveBayes_Interactive:

    def make_naive_bayes(dataset):
        '''fits naive bayes model
        '''
        print('# fitting model')
        print('# ...')

        # split data into text and label set
        # join title and text
        X = dataset['Title'] + ' ' + dataset['Text']
        y = dataset['Label']

        cv = CountVectorizer()

        # stratified k-fold cross-validation as split method
        kf = StratifiedKFold(n_splits=10, shuffle=True, random_state=5)

        classifier = GaussianNB()

        # metrics
        recall_scores = []
        precision_scores = []
        f1_scores = []

        # probabilities of each class (of each fold)
        class_prob = []
        # counts number of training samples observed in each class 
        class_counts = []

        # for each fold
        n = 0
        for train, test in kf.split(X,y):

            n += 1
            print('# split no. ' + str(n))

            # using CountVectorizer:
            # fit the training data and then return the matrix
            training_data = cv.fit_transform(X[train], y[train]).toarray()
            # transform testing data and return the matrix
            testing_data = cv.transform(X[test]).toarray()

            #fit classifier
            classifier.fit(training_data, y[train])
            #predict class
            predictions_train = classifier.predict(training_data)
            predictions_test = classifier.predict(testing_data)

            #print and store metrics
            rec = recall_score(y[test], predictions_test)
            print('rec: ' + str(rec))
            recall_scores.append(rec)
            prec = precision_score(y[test], predictions_test)
            print('prec: ' + str(prec))
            print('#')
            precision_scores.append(prec)
            # equation for f1 score
            f1_scores.append(2 * (prec * rec)/(prec + rec))

            class_prob.append(classifier.class_prior_)
            class_counts.append(classifier.class_count_)

        ##########################
        #print metrics of test set
        print('-------------------------')
        print('prediction of testing set:')
        print('Precision score: min = {}, max = {}, average = {}'
                .format(min(precision_scores),
                        max(precision_scores),
                        sum(precision_scores)/float(len(precision_scores))))
        print('Recall score: min = {}, max = {}, average = {}'
                .format(min(recall_scores),
                        max(recall_scores),
                        sum(recall_scores)/float(len(recall_scores))))
        print('F1 score: min = {}, max = {}, average = {}'
                .format(min(f1_scores),
                        max(f1_scores),
                        sum(f1_scores)/float(len(f1_scores))))
        print()
        # print probability of each class
        print('probability of each class:')
        print()
        print(class_prob)
        print()
        print('number of samples of each class:')
        print()
        print(class_counts)
        print()

        ##### nur für overfit testing ###########
        #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()

    ######## nur für resubstitutionsfehler benötigt ########
    def analyze_errors(dataset):
        '''calculates resubstitution error
        shows indices of false classified articles
        uses Gaussian Bayes with train test split
        '''
        X_train_test = dataset['Title'] + ' ' + 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('Errors at index:')
        print()
        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 metrics
        print('F1 score: ', format(f1_score(y_train_test, predictions)))

    if __name__ == '__main__':

        print('# starting naive bayes')
        print('# ...')

        file = 'classification_labelled_corrected.csv'

        # read csv file
        print('# reading dataset')
        print('# ...')

        data = pd.read_csv(file,
                           sep='|',
                           engine='python',
                           decimal='.',
                           quotechar='\'',
                           quoting=csv.QUOTE_NONE)

        make_naive_bayes(data)

        print('#')
        print('# ending naive bayes')