score.py

from sentence_transformers import util, SentenceTransformer
from transformers import BertModel
from nltk.tokenize import sent_tokenize
from nltk import word_tokenize, pos_tag
import torch
import numpy as np
import tqdm

def compute_sentencewise_scores(model, query_sents, candidate_sents, tokenizer=None):
    if isinstance(model, SentenceTransformer): 
        # if the model is using SentenceTrasformer style
        q_v, c_v = get_embedding(model, query_sents, candidate_sents)
    elif isinstance(model, BertModel): 
        # if the model is BERT-style model using transformers library
        inputs = tokenizer(
            query_sents + candidate_sents, 
            padding=True, 
            truncation=True, 
            return_tensors="pt", 
            max_length=512
        )
        inputs.to(model.device)
        result = model(**inputs)
        embeddings = result.last_hidden_state[:, 0, :].detach().cpu().numpy()
        q_v = embeddings[:len(query_sents)]
        c_v = embeddings[len(query_sents):]
    else:
        raise ValueError('model not supported at the time')
    assert(q_v.shape[1] == c_v.shape[1])
    assert(q_v.shape[0] == len(query_sents))
    assert(c_v.shape[0] == len(candidate_sents))
    return util.cos_sim(q_v, c_v)
    
def get_embedding(model, query_sents, candidate_sents):
    q_v = model.encode(query_sents)
    c_v = model.encode(candidate_sents)
    return q_v, c_v

def get_top_k(score_mat, K=3):
    """
    Pick top K sentences to show
    """
    picked_scores, picked_sent = torch.sort(-score_mat, axis=1) 
    picked_sent = picked_sent[:,:K]
    picked_scores = -picked_scores[:,:K]
    return picked_sent, picked_scores

def get_words(sent):
    """
    Input: list of sentences
    Output: list of list of words per sentence, all words in, index of starting words for each sentence
    """
    words = []
    sent_start_id = [] # keep track of the word index where the new sentence starts
    counter = 0
    for x in sent:
        w = word_tokenize(x)
        nw = len(w)
        counter += nw
        words.append(w)
        sent_start_id.append(counter)
    words = [word_tokenize(x) for x in sent]
    all_words = [item for sublist in words for item in sublist]
    sent_start_id.pop()
    sent_start_id = [0] + sent_start_id
    assert(len(sent_start_id) == len(sent))
    return words, all_words, sent_start_id

def get_match_phrase(w1, w2, method='pos'):
    """
    Input: list of words for query and candidate text
    Output: word list and binary mask of matching phrases between the inputs
    """
    mask1 = np.zeros(len(w1))
    mask2 = np.zeros(len(w2))
    if method == 'pos':
        # POS tags that should be considered for matching phrase
        include = [
            'NN',
            'NNS',
            'NNP',
            'NNPS',
            'LS',
            'SYM',
            'FW'
        ]
        pos1 = pos_tag(w1)
        pos2 = pos_tag(w2)
        for i, (w, p) in enumerate(pos2):
            for j, (w_, p_) in enumerate(pos1):
                if w.lower() == w_.lower() and p in include:
                    mask2[i] = 1
                    mask1[j] = 1
    return mask1, mask2

def remove_spaces(words, attrs):
    # make the output more readable by removing unnecessary spacings from the tokenizer
    # e.g.
    # 1. spacing for parenthesis
    # 2. spacing for single/double quotations
    # 3. spacing for commas and periods
    # 4. spacing for possessive quotations
    assert(len(words) == len(attrs))
    word_out, attr_out = [], []
    idx, single_q, double_q = 0, 0, 0
    while idx < len(words):
        # stick to the word that appears right before
        if words[idx] in [',', '.', '%', ')', ':', '?', ';', "'s", '”', "''"]:
            ww = word_out.pop()
            aa = attr_out.pop()
            word_out.append(ww + words[idx])
            attr_out.append(aa)
            idx += 1
        # stick to the word that appears right after
        elif words[idx] in ["(", '“']:
            word_out.append(words[idx] + words[idx+1])
            attr_out.append(attrs[idx+1])
            idx += 2
        # quotes 
        elif words[idx] == '"':
            double_q += 1
            if double_q == 2:
                # this is closing quote: stick to word before
                ww = word_out.pop()
                aa = attr_out.pop()
                word_out.append(ww + words[idx])
                attr_out.append(aa)
                idx += 1
                double_q = 0
            else:
                # this is opening quote: stick to the word after
                word_out.append(words[idx] + words[idx+1])
                attr_out.append(attrs[idx+1])
                idx += 2
        elif words[idx] == "'":
            single_q += 1
            if single_q == 2:
                # this is closing quote: stick to word before
                ww = word_out.pop()
                aa = attr_out.pop()
                word_out.append(ww + words[idx])
                attr_out.append(aa)
                idx += 1
                single_q = 0
            else:
                if words[idx-1][-1] == 's': #possessive quote
                    # stick to the word before, reset counter
                    ww = word_out.pop()
                    aa = attr_out.pop()
                    word_out.append(ww + words[idx])
                    attr_out.append(aa)
                    idx += 1
                    single_q = 0
                else:
                    # this is opening quote: stick to the word after
                    word_out.append(words[idx] + words[idx+1])
                    attr_out.append(attrs[idx+1])
                    idx += 2
        elif words[idx] == '``':
            # this is opening quote: stick to the word after, but change to real double quote
            word_out.append('"' + words[idx+1])
            attr_out.append(attrs[idx+1])
            idx += 2
        elif words[idx] == "''":
            # this is closing quote: stick to word before, but change to real double quote
            ww = word_out.pop()
            aa = attr_out.pop()
            word_out.append(ww + '"')
            attr_out.append(aa)
            idx += 1
        else:
            word_out.append(words[idx])
            attr_out.append(attrs[idx])
            idx += 1         
            
    assert(len(word_out) == len(attr_out))
    return word_out, attr_out

def scale_scores(arr, vmin=0.1, vmax=1):
    # rescale positive and negative attributions to be between vmin and vmax.
    # while keeping 0 at 0.
    pos_max, pos_min = np.max(arr[arr > 0]), np.min(arr[arr > 0])
    out = (arr - pos_min) / (pos_max - pos_min) * (vmax - vmin) + vmin
    idx = np.where(arr == 0.0)[0]
    out[idx] = 0.0
    return out

def mark_words(query_sents, words, all_words, sent_start_id, sent_ids, sent_scores):
    """
    Mark the words that are highlighted, both by in terms of sentence and phrase
    """
    num_query_sent = sent_ids.shape[0]
    num_cand_sent = sent_ids.shape[1]
    num_words = len(all_words)
    
    output = dict()
    output['all_words'] = all_words
    output['words_by_sentence'] = words
    
    # for each query sentence, mark the highlight information
    for i in range(num_query_sent):
        output[i] = dict()
        for j in range(1, num_cand_sent+1): # for each number of selected sentences from candidate
            query_words = word_tokenize(query_sents[i])
            is_selected_sent = np.zeros(num_words)
            is_selected_phrase = np.zeros(num_words)
            word_scores = np.zeros(num_words)
            
            # for each selected sentences from the candidate, compile information
            for sid, sscore in zip(sent_ids[i][:j], sent_scores[i][:j]):
                #print(len(sent_start_id), sid, sid+1)
                if sid+1 < len(sent_start_id):
                    sent_range = (sent_start_id[sid], sent_start_id[sid+1])
                    is_selected_sent[sent_range[0]:sent_range[1]] = 1
                    word_scores[sent_range[0]:sent_range[1]] = sscore
                    _, is_selected_phrase[sent_range[0]:sent_range[1]] = \
                        get_match_phrase(query_words, all_words[sent_range[0]:sent_range[1]])
                else:
                    is_selected_sent[sent_start_id[sid]:] = 1
                    word_scores[sent_start_id[sid]:] = sscore
                    _, is_selected_phrase[sent_start_id[sid]:] = \
                        get_match_phrase(query_words, all_words[sent_start_id[sid]:])
            
            # scale the word_scores: maximum value gets the darkest, minimum value gets the lightest color
            if j > 1:
                word_scores = scale_scores(word_scores)
                        
            # update selected phrase scores (-1 meaning a different color in gradio)
            word_scores[is_selected_sent+is_selected_phrase==2] = -0.5
                
            output[i][j] = {
                'is_selected_sent': is_selected_sent,
                'is_selected_phrase': is_selected_phrase,
                'scores': word_scores
            }

    return output

def get_highlight_info(model, tokenizer, text1, text2, K=None, top_pair_num=5):
    """
    Get highlight information from two texts
    """
    sent1 = sent_tokenize(text1) # query
    sent2 = sent_tokenize(text2) # candidate
    score_mat = compute_sentencewise_scores(model, sent1, sent2, tokenizer=tokenizer)
    
    if K is None: # if K is not set, get all information
        K = score_mat.shape[1]
        
    sent_ids, sent_scores = get_top_k(score_mat, K=K)
    words2, all_words2, sent_start_id2 = get_words(sent2)
    info = mark_words(sent1, words2, all_words2, sent_start_id2, sent_ids, sent_scores)
    
    # get top sentence pairs from the query and candidate (score, index_pair) to show upfront
    top_pairs = []
    ii = np.unravel_index(np.argsort(np.array(sent_scores).ravel())[-top_pair_num:], sent_scores.shape)
    for i, j in zip(ii[0][::-1], ii[1][::-1]):
        score = sent_scores[i,j].item()
        index_pair = (i, sent_ids[i,j].item())
        top_pairs.append((score, index_pair)) # list of (score, (sent_id_query, sent_id_candidate))
        
    # convert top_pairs to corresponding highlights format for GRadio Interpretation component
    top_pairs_info = dict()
    count = 0
    for s, (sidq, sidc) in top_pairs:
        q_sent = sent1[sidq]
        c_sent = sent2[sidc]
        q_words = word_tokenize(q_sent)
        c_words = word_tokenize(c_sent)
        mask1, mask2 = get_match_phrase(q_words, c_words)
        sc = 0.5
        mask1 *= -sc # mark matching phrases as blue (-1: darkest)
        mask2 *= -sc # mark matching phrases as blue
        assert(len(mask1) == len(q_words) and len(mask2) == len(c_words))
        
        # spacing
        q_words, mask1 = remove_spaces(q_words, mask1)
        c_words, mask2 = remove_spaces(c_words, mask2)
        
        top_pairs_info[count] = {
            'query': {
                'original': q_sent,
                'interpretation': list(zip(q_words, mask1))
            },
            'candidate': {
                'original': c_sent,
                'interpretation': list(zip(c_words, mask2))
            },
            'score': s,
            'sent_idx': (sidq, sidc) 
        }
        count += 1
    
    return sent_ids, sent_scores, info, top_pairs_info

### Document-level operations
def predict_docscore(doc_model, tokenizer, query, titles, abstracts, batch=20):
    # compute document scores for each papers
    
    # concatenate title and abstract
    title_abs = []
    for t, a in zip(titles, abstracts):
        if t is not None and a is not None:
            title_abs.append(t + ' [SEP] ' + a) # title + abstract
            
    num_docs = len(title_abs) 
    no_iter = int(np.ceil(num_docs / batch))
    scores = []
    with torch.no_grad():
        # batch
        for i in tqdm.tqdm(range(no_iter)):
            # preprocess the input
            inputs = tokenizer(
                [query] + title_abs[i*batch:(i+1)*batch], 
                padding=True, 
                truncation=True, 
                return_tensors="pt", 
                max_length=512
            )
            inputs.to(doc_model.device)
            result = doc_model(**inputs)
        
            # take the first token in the batch as the embedding
            embeddings = result.last_hidden_state[:, 0, :].detach().cpu().numpy()
        
            # compute cosine similarity
            q_emb = embeddings[0,:]
            p_emb = embeddings[1:,:]
            nn = np.linalg.norm(q_emb) * np.linalg.norm(p_emb, axis=1)
            scores += list(np.dot(p_emb, q_emb) / nn)

    assert(len(scores) == num_docs)
    
    return scores

def compute_document_score(doc_model, tokenizer, query_title, query_abs, papers, batch=5):
    scores = []
    titles = []
    abstracts = []
    urls = []
    years = []
    citations = []
    for p in papers:
        if p['title'] is not None and p['abstract'] is not None:
            titles.append(p['title'])
            abstracts.append(p['abstract'])
            urls.append(p['url'])
            years.append(p['year'])
            citations.append(p['citationCount'])
    if query_title == '':
        query = query_abs
    else:
        query = query_title + ' [SEP] ' + query_abs # feed in submission title and abstract
    scores = predict_docscore(doc_model, tokenizer, query, titles, abstracts, batch=batch)
    assert(len(scores) == len(abstracts))
    idx_sorted = np.argsort(scores)[::-1]
    
    titles_sorted = [titles[x] for x in idx_sorted]
    abstracts_sorted = [abstracts[x] for x in idx_sorted]
    scores_sorted = [scores[x] for x in idx_sorted]
    urls_sorted = [urls[x] for x in idx_sorted]
    years_sorted = [years[x] for x in idx_sorted]
    citations_sorted = [citations[x] for x in idx_sorted]
    
    return titles_sorted, abstracts_sorted, urls_sorted, scores_sorted, years_sorted, citations_sorted