Ejemplo n.º 1
0
def predict_segmented_tf_idf(data, docs_per_q, ids_and_categories):  
    #index docs
    
    
    res = []
    category_tf_idfs = {}
    for index, row in data.iterrows():


    	current_id = str(row['id'])
    	print current_id
    	current_category = ids_and_categories[current_id]

    	if category_tf_idfs.get(current_category) is None:
    		category_tf_idfs[current_category] = utils.get_docstf_idf(wiki_docs_dir + '/%s' % current_category)

    	docs_tf, words_idf = category_tf_idfs[current_category]

        #get answers words
        w_A = set(utils.tokenize(row['answerA']))
        w_B = set(utils.tokenize(row['answerB']))
        w_C = set(utils.tokenize(row['answerC']))
        w_D = set(utils.tokenize(row['answerD']))
    
        sc_A = 0
        sc_B = 0
        sc_C = 0
        sc_D = 0
    
        q = row['question']
        
        for d in zip(*utils.get_docs_importance_for_question(q, docs_tf, words_idf, docs_per_q))[0]:
            for w in w_A:
                if w in docs_tf[d]:
                    sc_A += 1. * docs_tf[d][w] * words_idf[w] # count of how many times in the document, times log(numberofdocs/word) for each word
            for w in w_B:
                if w in docs_tf[d]:
                    sc_B += 1. * docs_tf[d][w] * words_idf[w]
            for w in w_C:
                if w in docs_tf[d]:
                    sc_C += 1. * docs_tf[d][w] * words_idf[w]
            for w in w_D:
                if w in docs_tf[d]:
                    sc_D += 1. * docs_tf[d][w] * words_idf[w]

        res.append(['A','B','C','D'][np.argmax([sc_A, sc_B, sc_C, sc_D])])
        
    return res
Ejemplo n.º 2
0
def predict(data, docs_per_q):  
    #index docs
    docs_tf, words_idf = utils.get_docstf_idf(wiki_docs_dir)
    
    res = []
    f = []
    for index, row in data.iterrows():
        #get answers words 
        w_A = set(utils.tokenize(row['answerA']))
        w_B = set(utils.tokenize(row['answerB']))
        w_C = set(utils.tokenize(row['answerC']))
        w_D = set(utils.tokenize(row['answerD']))
    
        sc_A = 0
        sc_B = 0
        sc_C = 0
        sc_D = 0
    
        q = row['question']
        
        for d in zip(*utils.get_docs_importance_for_question(q, docs_tf, words_idf, docs_per_q))[0]:
            for w in w_A:
                if w in docs_tf[d]:
                    sc_A += 1. * docs_tf[d][w] * words_idf[w]
            for w in w_B:
                if w in docs_tf[d]:
                    sc_B += 1. * docs_tf[d][w] * words_idf[w]
            for w in w_C:
                if w in docs_tf[d]:
                    sc_C += 1. * docs_tf[d][w] * words_idf[w]
            for w in w_D:
                if w in docs_tf[d]:
                    sc_D += 1. * docs_tf[d][w] * words_idf[w]

        res.append(['A','B','C','D'][np.argmax([sc_A, sc_B, sc_C, sc_D])])
        f.append([sc_A, sc_B, sc_C, sc_D])        
     
    features = np.array(f)
    pd.DataFrame({'id': list(data['id']),'fA': features[:,0], 'fB': features[:,1], 'fC': features[:,2], 'fD': features[:,3]})[['id', 'fA', 'fB', 'fC', 'fD']].to_csv('features_ck12.csv', index = False)
    
    return res
Ejemplo n.º 3
0
def predict(data, docs_per_q):
    #index docs
    docs_tf, words_idf = utils.get_docstf_idf(wiki_docs_dir)

    res = []
    doc_score = [["A", "B", "C", "D"]]
    for index, row in data.iterrows():
        #get answers words
        w_A = set(utils.tokenize(row['answerA']))
        w_B = set(utils.tokenize(row['answerB']))
        w_C = set(utils.tokenize(row['answerC']))
        w_D = set(utils.tokenize(row['answerD']))

        sc_A = 0
        sc_B = 0
        sc_C = 0
        sc_D = 0

        q = row['question']

        for d in list(
                zip(*utils.get_docs_importance_for_question(
                    q, docs_tf, words_idf, docs_per_q)))[0]:
            for w in w_A:
                if w in docs_tf[d]:
                    sc_A += 1. * docs_tf[d][w] * words_idf[w]
            for w in w_B:
                if w in docs_tf[d]:
                    sc_B += 1. * docs_tf[d][w] * words_idf[w]
            for w in w_C:
                if w in docs_tf[d]:
                    sc_C += 1. * docs_tf[d][w] * words_idf[w]
            for w in w_D:
                if w in docs_tf[d]:
                    sc_D += 1. * docs_tf[d][w] * words_idf[w]

        res.append(['A', 'B', 'C', 'D'][np.argmax([sc_A, sc_B, sc_C, sc_D])])
        doc_score.append([sc_A, sc_B, sc_C, sc_D])
    return res, doc_score
def predict(data, docs_per_q):  
    #index docs
    docs_tf, words_idf = utils.get_docstf_idf(wiki_docs_dir)
    
    res = []
    doc_score = [["A","B","C","D"]]
    for index, row in data.iterrows():
        #get answers words
        w_A = set(utils.tokenize(row['answerA']))
        w_B = set(utils.tokenize(row['answerB']))
        w_C = set(utils.tokenize(row['answerC']))
        w_D = set(utils.tokenize(row['answerD']))
    
        sc_A = 0
        sc_B = 0
        sc_C = 0
        sc_D = 0
    
        q = row['question']
        
        for d in list(zip(*utils.get_docs_importance_for_question(q, docs_tf, words_idf, docs_per_q)))[0]:
            for w in w_A:
                if w in docs_tf[d]:
                    sc_A += 1. * docs_tf[d][w] * words_idf[w]
            for w in w_B:
                if w in docs_tf[d]:
                    sc_B += 1. * docs_tf[d][w] * words_idf[w]
            for w in w_C:
                if w in docs_tf[d]:
                    sc_C += 1. * docs_tf[d][w] * words_idf[w]
            for w in w_D:
                if w in docs_tf[d]:
                    sc_D += 1. * docs_tf[d][w] * words_idf[w]

        res.append(['A','B','C','D'][np.argmax([sc_A, sc_B, sc_C, sc_D])])
        doc_score.append([sc_A, sc_B, sc_C, sc_D])
    return res, doc_score
Ejemplo n.º 5
0
def predict(data, docs_per_q):  
    #index docs
    docs_tf, words_idf = utils.get_docstf_idf(wiki_docs_dir)
    #docs_tf = pickle.load(open('docs_tf_data.p', 'rb'))
    #words_idf = pickle.load(open('words_idf_data.p', 'rb'))
    pickle.dump(docs_tf, open("docs_tf_data.p",  'wb'))
    pickle.dump(words_idf, open("words_idf_data.p", 'wb'))
    
    res = []
    print 'predict'
    for index, row in data.iterrows():
        #get answers words
        w_A = set(utils.tokenize(row['answerA']))
        w_B = set(utils.tokenize(row['answerB']))
        w_C = set(utils.tokenize(row['answerC']))
        w_D = set(utils.tokenize(row['answerD']))
        
        A_vec = np.zeros(N)
        B_vec = np.zeros(N)
        C_vec = np.zeros(N)
        D_vec = np.zeros(N)

        sc_A = 0
        sc_B = 0
        sc_C = 0
        sc_D = 0
        
        print index
        q = row['question']

        q_vec = np.zeros(N)
        for w in utils.tokenize(q):
            if w in model.vocab and w not in stop:
                q_vec += model[w]
        q_vec = q_vec / linalg.norm(q_vec)
        
        for d in zip(*utils.get_docs_importance_for_question(q, docs_tf, words_idf, docs_per_q))[0]:
            for w in w_A:
                if w in docs_tf[d]:      
                    sc_A += 1. * docs_tf[d][w] * words_idf[w] 
                    if w in model.vocab:
                        A_vec += model[w]# docs_tf (arr of tf for each doc for each word) [d] for the specific word 
            for w in w_B:
                if w in docs_tf[d]:
                    sc_B += 1. * docs_tf[d][w] * words_idf[w]
                    if w in model.vocab:
                        B_vec += model[w]
            for w in w_C:
                if w in docs_tf[d]:
                    sc_C += 1. * docs_tf[d][w] * words_idf[w]
                    if w in model.vocab:
                        C_vec += model[w]
            for w in w_D:
                if w in docs_tf[d]:
                    sc_D += 1. * docs_tf[d][w] * words_idf[w]
                    if w in model.vocab:
                        D_vec = model[w]

        A_vec = A_vec / linalg.norm(A_vec) 
        B_vec = B_vec / linalg.norm(B_vec)
        C_vec = C_vec / linalg.norm(C_vec)
        D_vec = D_vec / linalg.norm(D_vec)
        semantic_scores = np.concatenate((A_vec, B_vec, C_vec, D_vec)).reshape(4, N).dot(q_vec)
        semantic_scores[np.isnan(semantic_scores)] = 0
        #print semantic_scores
        combined_scores = [sc_A, sc_B, sc_C, sc_D] + semantic_scores
        #print combined_scores 
        res.append(['A','B','C','D'][np.argmax(combined_scores)])
        
    return res