import Forums.Classifier.transformer
import pickle, re


class Transformer:
    def __init__(self):
        self.ngram_list = ['res', 'vir', 'att', 'tta', 'tac', 'web', 'cat', 'rse', 'cra', 'rac', 'nso', 'omw', 'mwa', 'tec', 'boo', 'adv', 'abl', 'can', 'mmi', 'cke', 'bot', 'oks', 'ick', 'eak', 'whe', 'val', 'acc', 'mon', 'dvi', 'nto', 'phi', 'deo', 'hao', 'aos', 'pst', 'ddo', 'dos', 'iru', 'kit', 'jac']

        self.ngram_index_dict = self.get_ngram_index()

    def get_ngram_index(self):
        ngram_index = {}
        index = 0
        for ngram in self.ngram_list:
            if ngram not in ngram_index:
                ngram_index[ngram] = index
                index += 1
        return ngram_index

    def binary_vector(self, text):
        vec = [0] * len(self.ngram_index_dict)
        for ngram, index in self.ngram_index_dict.items():
            if ngram in text:
                vec[index] = 1
        return vec

    def frequency_vector(self, text):
        vec = [0] * len(self.ngram_index_dict)
        for ngram, index in self.ngram_index_dict.items():
            vec[index] = text.count(ngram)
        return vec

    def relative_frequency_vector(self, freq_vec):
        total = 0
        for count in freq_vec:
            total += count

        vec = [0] * len(freq_vec)
        if total > 0:
            for i, count in enumerate(freq_vec):
                vec[i] = count / total
        return vec

    def transform(self, text):
        clean_sent = re.sub(r'[^a-zA-Z ]', '', text).lower()

        bin_vec = self.binary_vector(clean_sent)

        return bin_vec


def load(file_path):
    with open(file_path, 'rb') as f:
        return pickle.load(f)


def fix(fname):
    t = load(fname)
    new_t = Forums.Classifier.transformer.Transformer()
    new_t.ngram_list = t.ngram_list
    new_t.ngram_index_dict = t.ngram_index_dict
    Forums.Classifier.transformer.save(new_t, fname + ".new")


fix("topic_title_transformer.pickle")
# fix("title_transformer.pickle")