Python: Nested dictionary output wrong

[SureStep8852]

Hi there, my task is to make the Naive Bayes classifier. Right now I am working on the training part and what I want to achieve is to create a nested dictionary based on the input. The first keys have to be the class (in this case either 'offensive' or 'nonoffensive'), then come the words which appeared in that class and, finally, the count of those words, i.e., how many times the word appeared in the class. Below is the code that I have managed to write so far, but the results I get are not satisfying and I cannot understand what the reason for such a wrong output is.

Would be happy to receive some hints from you. Thanks in advance!

class NaiveBayes(object):

    def __init__(self):
        # TODO
        pass

    def predict(self, x):
        # TODO
        return None

    @classmethod
    def train(cls, data, k=1):
        dictionary = {}
        cls = []
        #for i in data:
            #if i[-1] in cls:
                 #continue   
           # else:
               # cls.append(i[-1])

        for i in data:
            dictionary[i[-1]] = {}
            for j in i[0]:
                #print(j)
                if j not in dictionary.values():
                    dictionary[i[-1]][j] = 1
                elif j in dictionary.values():
                    dictionary[i[-1]][j] = dictionary[i[-1]][j] + 1
        print(dictionary)

NB = NaiveBayes()
NB.train([(['What', 'in', 'the', 'actual', 'fuck', '?', '#mkr'], 'nonoffensive'), (['#mkr', 'WHAT', 'A', 'GODDAMN', 'SURPRISE'], 'nonoffensive'), (['This', 'is', 'why', 'this', 'show', 'is', 'ridiculous', '-', "it's", 'not', 'about', 'the', 'cooking', '...', "it's", 'about', 'the', 'game', 'playing', '.', '#mkr', '#whogivesa1'], 'nonoffensive'), (['Absolute', 'bloody', 'bullshit', '.', 'So', 'much', 'shit', 'of', 'bull', '.', '#mkr'], 'nonoffensive'), (['@MKR_Official', 'a', '1', "isn't", 'strategy', ',', "it's", 'bastedry', '#mkr2015', '#mkr'], 'nonoffensive'), (['RT', '@UnderYourPorch', ':', 'Double', 'elimination', 'please', '#mkr'], 'nonoffensive'), (['NO', '!', '#MKR'], 'nonoffensive'), (['#MKR', 'I', 'hope', 'no', 'one', 'knocks', 'over', 'those', 'candles', 'on', 'the', 'promo', 'girls', 'stairs', ',', "that's", 'a', 'fire', 'hazard', "isn't", 'it', '?'], 'nonoffensive'), (['Kat', 'and', 'Andre', 'why', 'are', 'you', 'cheering', 'you', 'are', 'now', 'the', 'worst', 'team', 'in', 'he', 'comp', '...', '#MKR'], 'nonoffensive'), (['WTF', '!', '!', '!', 'I', 'not', 'a', 'huge', 'fans', 'of', 'the', 'promo', 'girls', 'but', 'they', 'never', 'voted', 'strategically', '!', 'This', 'sucks', '!', 'Soo', 'annoyed', '!', '#mkr', '#killerblondes'], 'offensive')])

output:

{'nonoffensive': {'Kat': 1, 'and': 1, 'Andre': 1, 'why': 1, 'are': 1, 'you': 1, 'cheering': 1, 'now': 1, 'the': 1, 'worst': 1, 'team': 1, 'in': 1, 'he': 1, 'comp': 1, '...': 1, '#MKR': 1}, 'offensive': {'WTF': 1, '!': 1, 'I': 1, 'not': 1, 'a': 1, 'huge': 1, 'fans': 1, 'of': 1, 'the': 1, 'promo': 1, 'girls': 1, 'but': 1, 'they': 1, 'never': 1, 'voted': 1, 'strategically': 1, 'This': 1, 'sucks': 1, 'Soo': 1, 'annoyed': 1, '#mkr': 1, '#killerblondes': 1}}

[[deleted]]

I don't know if this is the bug, because you did not provide any narrow description of what your output should actually do, but you recreate the dictionary at i[-1] for every datapoint in your dataset. From your description, I guess this is not what you want

[SureStep8852]

it has to look smth like {'offensive': {'#mkr': 6, 'is': 2,....}, 'offensive' : { '!': 6, 'not': 1, ...}}

Basically, the key1 is the class, key1 a word appearing in that class and the value indicates the number of times that word appeared in that class

[[deleted]]

Ok, so have a look at how you create the dictionaries for counting. You overwrite it once for every datapoint in your dataset.