derekgreene/topic-model-tutorial

Tutorial on topic models in Python with scikit-learn

topic-model-tutorial's Issues

(NMF) calculating coherence in NMF generates different outputs each time when method is called

I am trying to calculate the coherence value on each topic but each time when I run my code it generates different values.

It will be a great help if anyone can answer this.

Thank you in advance

def build_w2c(self, raw_documents):
    docgen = TokenGenerator(raw_documents, self.stop_words)
    new_list = []
    for each in docgen.documents:
        new_list.append(each.split(" "))
    # print(new_list)
    # Build the word2vec model
    self.w2v_model = gensim.models.Word2Vec(size=500, min_count=0.0005, sg=1)
    self.w2v_model.build_vocab(sentences=new_list)
    return self.w2v_model

def get_descriptor(self, all_terms, H, topic_index, top):
    # reverse sort the values to sort the indices
    top_indices = np.argsort(H[topic_index, :])[::-1]
    # now get the terms corresponding to the top-ranked indices
    top_terms = []
    for term_index in top_indices[0:top]:
        top_terms.append(all_terms[term_index])
    return top_terms
def get_coherence(self, k, terms, H):
    k_values = []
    term_rankings = []
    coherences = []
    dict = {}
    for topic_index in range(1, k):
        print(topic_index)
        descriptor = self.get_descriptor(terms , H, topic_index, 10)
        term_rankings.append(descriptor)
    # Now calculate the coherence based on our Word2vec model
    #coherence = self.calculate_coherence(term_rankings)
        coherences.append(self.calculate_coherence(term_rankings))
        print("K=%02d: Coherence=%.4f" % (topic_index, coherences[-1]))
        k_values.append(topic_index)
        dict[topic_index] = coherences[-1]
    max_key = max(dict, key=dict.get)
    return k_values, coherences, max_key

def calculate_coherence(self, term_rankings):
    overall_coherence = 0.0
    for topic_index in range(len(term_rankings)):
        # check each pair of terms
        pair_scores = []
        for pair in combinations(term_rankings[topic_index], 2):
            pair_scores.append(self.w2v_model.similarity(pair[0], pair[1]))
        # get the mean for all pairs in this topic
        topic_score = sum(pair_scores) / len(pair_scores)
        overall_coherence += topic_score
    # get the mean score across all topics
    return overall_coherence / len(term_rankings)

here is my code that I have used in my project.

Output Required:
Each time when I run my code coherence should be the same.

if you can help me to resolve this approach it would be a great help
thank you so much.

Data reference

Can you please give the reference of how the text data has been collected?