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

from BagOfWords import BagOfWords

import csv

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

class MultinomialNaiveBayes:

	def make_mnb(dataset, sklearn_cv=True, percentile=100):
		'''fits naive bayes model with StratifiedKFold
		'''
		print('# starting multinomial naive bayes')
		print('# ...')

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

		if sklearn_cv:
			cv = CountVectorizer()

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

		classifier = MultinomialNB(alpha=1.0e-10,
								   fit_prior=False,
								   class_prior=None)

		# 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 skf.split(X,y):

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

			if sklearn_cv:
				# use sklearn 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()
			else:
				# use my own BagOfWords python implementation
				stemming = True
				rel_freq = True
				extracted_words = BagOfWords.extract_all_words(X[train])
				vocab = BagOfWords.make_vocab(extracted_words)

				# fit the training data and then return the matrix
				training_data = BagOfWords.make_matrix(extracted_words,
								vocab, rel_freq, stemming)
				# transform testing data and return the matrix
				extracted_words = BagOfWords.extract_all_words(X[test])
				testing_data = BagOfWords.make_matrix(extracted_words,
								vocab, rel_freq, stemming)

			# apply select percentile
			selector = SelectPercentile(percentile=percentile)
			selector.fit(training_data, y[train])

			# new reduced data sets
			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)
			# print('train:')
			# print(y[train])
			# print('test:')
			# print(y[test])
			# print()
			# print('pred')
			# print(predictions_test)

			#print and store metrics
			rec = recall_score(y[test], predictions_test, average='weighted')
			print('rec: ' + str(rec))
			recall_scores.append(rec)
			prec = precision_score(y[test], predictions_test, average='weighted')
			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_)

		##########################
		# probability estimates for the test vector (testing_data)
		class_probs = classifier.predict_proba(testing_data)

		# number of samples encountered for each class during fitting
		# this value is weighted by the sample weight when provided
		class_count = classifier.class_count_

		# classes in order used
		classes = classifier.classes_
		
		print('average: recall, precision, f1 score')
		print(sum(recall_scores)/10, sum(precision_scores)/10, sum(f1_scores)/10)
		
		
		# return classes and vector of class estimates
		return recall_scores, precision_scores, f1_scores, class_probs

	######## nur für resubstitutionsfehler benötigt ########
	def analyze_errors(training, testing):
		'''calculates resubstitution error
		shows indices of false classified articles
		uses Gaussian Bayes with train test split
		'''
		X_train = training['Title'] + ' ' + training['Text']
		y_train = training['Label']
		
		X_test = testing['Title'] + ' ' + testing['Text']
		y_test = testing['Label']

		count_vector = CountVectorizer()

		# fit the training data and then return the matrix
		training_data = count_vector.fit_transform(X_train).toarray()

		# transform testing data and return the matrix
		testing_data = count_vector.transform(X_test).toarray()

		# Naive Bayes
		classifier = MultinomialNB(alpha=1.0e-10,
								   fit_prior=False,
								   class_prior=None)
		# fit classifier
		classifier.fit(training_data, y_train)

		# Predict class
		predictions = classifier.predict(testing_data)

		print(type(y_test))
		print(len(y_test))
		print(type(predictions))
		print(len(predictions))

		print('Errors at index:')
		print()
		n = 0
		for i in range(len(y_test)):
			if y_test[i] != predictions[i]:
				n += 1
				print('error no.{}'.format(n))
				print('prediction at index {} is: {}, but actual is: {}'
				.format(i, predictions[i], y_test[i]))
				print(X_test[i])
				print(y_test[i])
				print()
		#print metrics
		print('F1 score: ', format(f1_score(y_test, predictions)))

if __name__ == '__main__':

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

	# read current data set from csv
	df = pd.read_csv('../data/interactive_labeling_round_11.csv',
			  sep='|',
			  usecols=range(1,13), # drop first column 'unnamed'
			  encoding='utf-8',
			  quoting=csv.QUOTE_NONNUMERIC,
			  quotechar='\'')

	# select only labeled articles
	MultinomialNaiveBayes.make_mnb(df.loc[df['Label'] != -1].reset_index(drop=True), sklearn_cv=True, percentile=100)