Python sklearn.metrics.pairwise.cosine_similarity() Examples

def cos_sim(ind1,ind2=1999):
    view1 = np.load("test_v1.npy")[0:ind1]
    view2 = np.load("test_v2.npy")[0:ind2]
    #val = []
    MAP=0
    for i,j in enumerate(view1):
        val=[]
        AP=0
        for x in view2:            
            val.append(cosine_similarity(j,x)[0].tolist())
        #val=val[0].tolist()
        #print val[0].tolist()
        val=[(q,p)for p,q in enumerate(val)]
        #print val
        val.sort()
        val.reverse()
        t = [w[1]for w in val[0:7]]
        for x,y in enumerate(t):
            if y in range(i,i+5):
                AP+=1/(x+1)
        print(t)
        print(AP)
        MAP+=AP
    print('MAP is : ',MAP/ind1) 

def test_cosine_similarity():
    # Test the cosine_similarity.

    rng = np.random.RandomState(0)
    X = rng.random_sample((5, 4))
    Y = rng.random_sample((3, 4))
    Xcsr = csr_matrix(X)
    Ycsr = csr_matrix(Y)

    for X_, Y_ in ((X, None), (X, Y),
                   (Xcsr, None), (Xcsr, Ycsr)):
        # Test that the cosine is kernel is equal to a linear kernel when data
        # has been previously normalized by L2-norm.
        K1 = pairwise_kernels(X_, Y=Y_, metric="cosine")
        X_ = normalize(X_)
        if Y_ is not None:
            Y_ = normalize(Y_)
        K2 = pairwise_kernels(X_, Y=Y_, metric="linear")
        assert_array_almost_equal(K1, K2) 

def transform(self, X: dt.Frame):
        X.replace([None, math.inf, -math.inf], self._repl_val)
        from flair.embeddings import WordEmbeddings, BertEmbeddings, DocumentPoolEmbeddings, Sentence
        if self.embedding_name in ["glove", "en"]:
            self.embedding = WordEmbeddings(self.embedding_name)
        elif self.embedding_name in ["bert"]:
            self.embedding = BertEmbeddings()
        self.doc_embedding = DocumentPoolEmbeddings([self.embedding])
        output = []
        X = X.to_pandas()
        text1_arr = X.iloc[:, 0].values
        text2_arr = X.iloc[:, 1].values
        for ind, text1 in enumerate(text1_arr):
            try:
                text1 = Sentence(str(text1).lower())
                self.doc_embedding.embed(text1)
                text2 = text2_arr[ind]
                text2 = Sentence(str(text2).lower())
                self.doc_embedding.embed(text2)
                score = cosine_similarity(text1.get_embedding().reshape(1, -1),
                                          text2.get_embedding().reshape(1, -1))[0, 0]
                output.append(score)
            except:
                output.append(-99)
        return np.array(output) 

def compared(request):
    if request.method == 'POST':
        if len(request.FILES) != 2:
            return HttpResponse('{"status":false,"data":"","msg":"图片参数错误！"}')
        starttime = time.time()
        name1 = str(random.randint(10000, 99999)) + str(time.time())  # 随机名字
        name2 = str(random.randint(10000, 99999)) + str(time.time())

        handle_uploaded_file(request.FILES['face1'], str(name1))
        handle_uploaded_file(request.FILES['face2'], str(name2))

        tz1 = get_feature(root + "RestServer/upload/" + str(name1))

        tz2 = get_feature(root + "RestServer/upload/" + str(name2))

        comparedValue = pw.cosine_similarity(tz1, tz2)[0][0]

        os.remove(root + "RestServer/upload/" + str(name1))
        os.remove(root + "RestServer/upload/" + str(name2))
        endtime = time.time()
        Runtime=endtime-starttime
        return HttpResponse('{"status":true,"data":"' + str(comparedValue) + '","msg":"成功","runtime": ' + str(Runtime) + '  }')
    else:
        return HttpResponse('{"status":false,"data":"","msg":"请求不合法"}')
    return HttpResponse('{"status":false,"data":"","msg":"未知错误"}') 

def get_closest_docs(uri):
    #user_doc = requests.get(uri).text
    r = requests.get(uri)
    if r.status_code == 200:
        user_doc = r.text
        print("URI content length",len(user_doc))
        code, _ = separate_code_and_comments(user_doc,"user doc")
        normalized_code = normalize_text(code, remove_stop_words=False, only_letters=False, return_list=True)
        model.random.seed(0)
        user_vector = model.infer_vector(normalized_code)
        print("finding similar...")
        sys.stdout.flush()
        stored_urls = list()
        stored_vectors = list()
        for url in vectors:
            stored_urls.append(url)
            stored_vectors.append(vectors[url])
        pair_sims = cosine_similarity(user_vector.reshape(1, -1), stored_vectors)
        indices = (-pair_sims[0]).argsort()[:5]
        return [(stored_urls[index],round(float(pair_sims[0][index]),2)) for index in indices]
    else:
        print("URL returned status code", r.status_code)
        raise ValueError('URL error') 

def clustering(self, threshold):
        """分不同词性的聚类

        :return: partition: dict {word_id: cluster_id}
        """
        print("Louvain clustering")
        partition = {}
        part_offset = 0
        for etype, ners in self.type_entity_dict.items():
            sub_id_mapping = [self.word2id[ner0] for ner0 in ners if ner0 in self.word2id]
            if len(sub_id_mapping) == 0:
                continue
            emb_mat_sub = self.emb_mat[sub_id_mapping, :]
            cos_sims = cosine_similarity(emb_mat_sub)
            cos_sims -= np.eye(len(emb_mat_sub))
            adj_mat = (cos_sims > threshold).astype(int)
            G = nx.from_numpy_array(adj_mat)
            partition_sub = community.best_partition(G)
            for sub_id, main_id in enumerate(sub_id_mapping):
                sub_part_id = partition_sub[sub_id]
                partition[main_id] = sub_part_id + part_offset
            part_offset += max(partition_sub.values()) + 1
        return partition 

def cosine_sim(x, y):
    try:
        if type(x) is np.ndarray: x = x.reshape(1, -1) # get rid of the warning
        if type(y) is np.ndarray: y = y.reshape(1, -1)
        d = cosine_similarity(x, y)
        d = d[0][0]
    except:
        print x
        print y
        d = 0.
    return d

 #   Copyright 2017 Cisco Systems, Inc.
 #  
 #   Licensed under the Apache License, Version 2.0 (the "License");
 #   you may not use this file except in compliance with the License.
 #   You may obtain a copy of the License at
 #  
 #     http://www.apache.org/licenses/LICENSE-2.0
 #  
 #   Unless required by applicable law or agreed to in writing, software
 #   distributed under the License is distributed on an "AS IS" BASIS,
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License. 

def _get_similarity_values(self, q1_csc, q2_csc):
        cosine_sim = []
        manhattan_dis = []
        eucledian_dis = []
        jaccard_dis = []
        minkowsk_dis = []
        
        for i,j in zip(q1_csc, q2_csc):
            sim = cs(i, j)
            cosine_sim.append(sim[0][0])
            sim = md(i, j)
            manhattan_dis.append(sim[0][0])
            sim = ed(i, j)
            eucledian_dis.append(sim[0][0])
            i_ = i.toarray()
            j_ = j.toarray()
            try:
                sim = jsc(i_, j_)
                jaccard_dis.append(sim)
            except:
                jaccard_dis.append(0)
                
            sim = minkowski_dis.pairwise(i_, j_)
            minkowsk_dis.append(sim[0][0])
        return cosine_sim, manhattan_dis, eucledian_dis, jaccard_dis, minkowsk_dis 

def _compute_sim(self, R, k):
        # compute the similarity between all the items. This calculates the
        # similarity between each ITEM
        sim = cosine_similarity(R.T)

        # Only keep the similarities of the top K, setting all others to zero
        # (negative since we want descending)
        not_top_k = np.argsort(-sim, axis=1)[:, k:]  # shape=(n_items, k)

        if not_top_k.shape[1]:  # only if there are cols (k < n_items)
            # now we have to set these to zero in the similarity matrix
            row_indices = np.repeat(range(not_top_k.shape[0]),
                                    not_top_k.shape[1])
            sim[row_indices, not_top_k.ravel()] = 0.

        return sim 

def decision_function(self, X):
        """Evaluate the cosine similarity between document-term matrix and X.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_timestamps)
            Test samples.

        Returns
        -------
        X : array-like, shape (n_samples, n_classes)
            osine similarity between the document-term matrix and X.

        """
        check_is_fitted(self, ['vocabulary_', 'tfidf_', 'idf_',
                               '_tfidf', 'classes_'])
        X = check_array(X)
        X_bow = self._bow.transform(X)
        vectorizer = CountVectorizer(vocabulary=self._tfidf.vocabulary_)
        X_transformed = vectorizer.transform(X_bow).toarray()
        return cosine_similarity(X_transformed, self.tfidf_) 

def test_init():
    default = Spanning_Forest()
    assert default.metric == skm.manhattan_distances
    assert default.center == np.mean
    assert default.reduction == np.sum
    change = Spanning_Forest(dissimilarity=skm.euclidean_distances,
                             center=np.median, reduction=np.max)
    assert change.metric == skm.euclidean_distances
    assert change.center == np.median
    assert change.reduction == np.max
    
    sym = Spanning_Forest(affinity=skm.cosine_similarity)
    assert isinstance(sym.metric, types.LambdaType)
    test_distance = -np.log(skm.cosine_similarity(data[:2,]))
    comparator = sym.metric(data[:2,])
    np.testing.assert_allclose(test_distance, comparator) 

def save_model(model: Model, tokenizer: Tokenizer):
    """
    Saves the important parts of the model
    :param model: Keras model to save
    :param tokenizer: Keras Tokenizer to save
    """
    for layer in model.layers:
        if '_biases' in layer.name or '_embeddings' in layer.name:
            np.save(file=f'{OUTPUT_FOLDER}{layer.name}', arr=layer.get_weights()[0])

    # save tokenizer
    pickle.dump(obj=tokenizer.index_word, file=open(f'{OUTPUT_FOLDER}{INDEX2WORD}', 'wb'))
    pickle.dump(obj=tokenizer.word_index, file=open(f'{OUTPUT_FOLDER}{WORD2INDEX}', 'wb'))

    # save combined embeddings & correlation matrix
    agg_embeddings = np.load(f'{OUTPUT_FOLDER}{CENTRAL_EMBEDDINGS}.npy') + \
                     np.load(f'{OUTPUT_FOLDER}{CONTEXT_EMBEDDINGS}.npy')

    np.save(file=f'{OUTPUT_FOLDER}{AGGREGATED_EMBEDDINGS}', arr=agg_embeddings)
    np.save(file=f'{OUTPUT_FOLDER}{CORRELATION_MATRIX}', arr=cosine_similarity(cosine_similarity(agg_embeddings))) 

def test_cosine_similarity():
    # Test the cosine_similarity.

    rng = np.random.RandomState(0)
    X = rng.random_sample((5, 4))
    Y = rng.random_sample((3, 4))
    Xcsr = csr_matrix(X)
    Ycsr = csr_matrix(Y)

    for X_, Y_ in ((X, None), (X, Y),
                   (Xcsr, None), (Xcsr, Ycsr)):
        # Test that the cosine is kernel is equal to a linear kernel when data
        # has been previously normalized by L2-norm.
        K1 = pairwise_kernels(X_, Y=Y_, metric="cosine")
        X_ = normalize(X_)
        if Y_ is not None:
            Y_ = normalize(Y_)
        K2 = pairwise_kernels(X_, Y=Y_, metric="linear")
        assert_array_almost_equal(K1, K2) 

def evaluate(self, category_projection):
        assert issubclass(type(category_projection), CategoryProjectionBase)
        topics = category_projection.get_nearest_terms()
        total_similarity = 0
        for topic in topics.values():
            topic_vectors = np.array([self.get_vector(term) for term in topic])
            #simport pdb; pdb.set_trace()
            sim_matrix = cosine_similarity(topic_vectors)
            tril_sim_matrix = np.tril(sim_matrix)
            mean_similarity = tril_sim_matrix.sum()/(tril_sim_matrix.shape[0] ** 2 - tril_sim_matrix.shape[0]) / 2
            total_similarity += mean_similarity
        return total_similarity/len(topics) 

def process(self,data):
        claim_bow = self.bow_vectorizer.transform(self.claims(data))
        claim_tfs = self.tfreq_vectorizer.transform(claim_bow)
        claim_tfidf = self.tfidf_vectorizer.transform(self.claims(data))

        body_texts = self.texts(data)
        body_bow = self.bow_vectorizer.transform(body_texts)
        body_tfs = self.tfreq_vectorizer.transform(body_bow)
        body_tfidf = self.tfidf_vectorizer.transform(body_texts)

        cosines = np.array([cosine_similarity(c, b)[0] for c,b in zip(claim_tfidf,body_tfidf)])

        return hstack([body_tfs,claim_tfs,cosines]) 

def process(self, data):
        claim_bow = self.bow_vectorizer.transform(self.claims(data))
        claim_tfs = self.tfreq_vectorizer.transform(claim_bow)
        claim_tfidf = self.tfidf_vectorizer.transform(self.claims(data))

        body_texts = self.texts(data)
        body_bow = self.bow_vectorizer.transform(body_texts)
        body_tfs = self.tfreq_vectorizer.transform(body_bow)
        body_tfidf = self.tfidf_vectorizer.transform(body_texts)

        cosines = np.array([cosine_similarity(c, b)[0] for c, b in zip(claim_tfidf, body_tfidf)])

        return cosines 

def process(self, data):
        claim_bow = self.bow_vectorizer.transform(self.claims(data))
        claim_tfs = self.tfreq_vectorizer.transform(claim_bow)
        claim_tfidf = self.tfidf_vectorizer.transform(self.claims(data))

        body_texts = self.texts(data)
        body_bow = self.bow_vectorizer.transform(body_texts)
        body_tfs = self.tfreq_vectorizer.transform(body_bow)
        body_tfidf = self.tfidf_vectorizer.transform(body_texts)

        cosines = np.array([cosine_similarity(c, b)[0] for c, b in zip(claim_tfidf, body_tfidf)])

        return cosines 

def closest_distances(query_vector,all_feat_vecs,norm='euclidian',num=3):
    if norm=='euclidian':
        dist = np.linalg.norm(query_vector-all_feat_vecs,axis=1)
    if norm =='cosine':
        dist = 1 - cosine_similarity(query_vector.reshape(1, -1),all_feat_vecs)[0]
    idxs = np.arange(0,dist.shape[0])
    return idxs[dist.argsort()][:num] 

def read(self, P, Q):
        A = self.get_answer_canditates(P)
        A_embed = []
        for a in A:
            A_embed.append(self.embedder.embed(a))
        Q_embed = self.embedder.embed(Q)
        similarities_raw = cosine_similarity(A_embed, Q_embed.reshape(1, -1))
        similarities = [s[0] for s in similarities_raw]
        indices_sorted = np.argsort(similarities)[::-1]  # reverse order
        return A[indices_sorted[0]] 

def read(self, P , Q):
        Q = self.stem_string(Q)
        query_tfidf = self.vectorizer.transform([Q])
        similarities_raw = cosine_similarity(self.tfidf_matrix, query_tfidf)
        similarities = []
        for s in similarities_raw:
            similarities.append(s[0])
        max_index = np.argmax(similarities)
        return self.docs[max_index] 

def get_topk_docs(self, query):
        """
        :param query: a string
        :return: top documents according to cosine similarity of embeddings
        """
        emb_query = self.embed_string(query)
        similarities_raw = cosine_similarity(self.emb_matrix, emb_query.reshape(1,-1))
        similarities = [s[0] for s in similarities_raw]
        indices_sorted = np.argsort(similarities)[::-1]  # reverse order
        topk_docs = []
        for i in range(0, self.k):
            topk_docs.append(self.docs[indices_sorted[i]])
        return topk_docs 

def _cosine_sim(vec1, vec2):
    try:
        s = cosine_similarity(vec1.reshape(1, -1), vec2.reshape(1, -1))[0][0]
    except:
        try:
            s = cosine_similarity(vec1, vec2)[0][0]
        except:
            s = config.MISSING_VALUE_NUMERIC
    return s 

def vec_cos_sim(token_input, operation_input):
    operation_string = None
    ref_vector_string = None
    cond_value_string = None
    for opr_sign in ['==', '>=', '<=', '!=', '<>', '<', '>', '=']:
        if opr_sign in operation_input:
            ref_vector_string = operation_input.split(opr_sign)[0]
            operation_string = opr_sign
            cond_value_string = operation_input.split(opr_sign)[1]
            break

    if ref_vector_string and cond_value_string and operation_string:
        try:
            cond_value = float(cond_value_string)
            ref_vector = change_string_to_vector(ref_vector_string)
            token_vector = change_string_to_vector(token_input)
            if len(ref_vector) != len(token_vector):
                print ('len of vectors does not match')
                return False
            if operation_string == "=" or operation_string == "==":
                return cosine_similarity(token_vector, ref_vector) == cond_value
            elif operation_string == "<":
                return cosine_similarity(token_vector, ref_vector) < cond_value
            elif operation_string == ">":
                return cosine_similarity(token_vector, ref_vector) > cond_value
            elif operation_string == ">=":
                return cosine_similarity(token_vector, ref_vector) >= cond_value
            elif operation_string == "<=":
                return cosine_similarity(token_vector, ref_vector) <= cond_value
            elif operation_string == "!=" or operation_string == "<>":
                return cosine_similarity(token_vector, ref_vector) != cond_value
            else:
                return False
        except ValueError:
            # TODO raise tokenregex error
            return False

    else:
        # TODO raise tokenregex error
        print ('Problem with the operation input') 

def cosineSimilarity(self):
        vec = TfidfVectorizer()
        matrix = vec.fit_transform(self.statements)
        for j in range(1, 5):
            i = j - 1
            print("\tsimilarity of document {} with others".format(i))
            similarity = cosine_similarity(matrix[i:j], matrix)
            print(similarity) 

def cosine(x1,x2):
    #find common ratings
    new_x1, new_x2 = common(x1,x2)
    #compute the cosine similarity between two vectors
    sum = new_x1.dot(new_x2)
    denom = sqrt(new_x1.dot(new_x1)*new_x2.dot(new_x2))
    try:
        return float(sum)/denom
    except ZeroDivisionError:
        return 0

    #return cosine_similarity(x1,x2)[0][0] 

def s_norm(self, test, enroll):
        """ Run speaker normalization (S-Norm) on cached embeddings.

        Args:
            test (np.array): test embedding
            enroll (np.array): enroll embedding

        Returns:
            float: hypothesis
        """
        if self.plda:
            a = self.plda.score(test, self.embeddings).T
            b = self.plda.score(enroll, self.embeddings).T
            c = self.plda.score(enroll, test).T
        else:
            a = cosine_similarity(test, self.embeddings).T
            b = cosine_similarity(enroll, self.embeddings).T
            c = cosine_similarity(enroll, test).T
        scores = []
        for ii in range(test.shape[0]):
            test_scores = []
            for jj in range(enroll.shape[0]):
                test_mean, test_std = np.mean(a.T[ii]), np.std(a.T[ii])
                enroll_mean, enroll_std = np.mean(b.T[jj]), np.std(b.T[jj])
                s = c[ii][jj]
                test_scores.append((((s - test_mean) / test_std + (s - enroll_mean) / enroll_std) / 2))
            scores.append(test_scores)
        return np.array(scores) 

def predict(self, users_ids, sessions_items, topk=5, valid_items=None):         
        acr_embeddings = self.eval_benchmark_params['content_article_embeddings_matrix']

        recent_items_buffer = self.clicked_items_state.get_recent_clicks_buffer()
        if valid_items is None:
            recent_unique_item_ids = np.unique([recent_items_buffer[np.nonzero(recent_items_buffer)]])            
        else:
            recent_unique_item_ids = np.unique(valid_items)
            
        acr_embeddings_recent_items = acr_embeddings[recent_unique_item_ids]


        session_predictions = np.zeros(dtype=np.int64,
                                       shape=[sessions_items.shape[0],
                                              sessions_items.shape[1],
                                              topk])

        for row_idx, session_items in enumerate(sessions_items):    

            for col_idx, item in enumerate(session_items):
                if item != 0:

                    #Computing cosine similarity between this item and all recent items (from buffer)
                    #P.s. Do not need to ignore the current item (whose similarity is always, because this item will not be among the valid items (next click + negative samples not present in the session))
                    similarities = cosine_similarity(acr_embeddings[item].reshape(1, -1), 
                                                     acr_embeddings_recent_items)[0]
                    similar_items_sorted_idx = np.argsort(similarities, axis=0)[::-1]
                    similar_items_ids = recent_unique_item_ids[similar_items_sorted_idx]

                    session_predictions[row_idx, col_idx] = list(self._get_top_n_valid_items(similar_items_ids, topk, valid_items[row_idx, col_idx]))
                    

        return session_predictions 

def visualize_between_sentences(sentences, vec_list, palette="Viridis256",
                                filename="/notebooks/embedding/between-sentences.png",
                                use_notebook=False):
    df_list, score_list = [], []
    for sent1_idx, sentence1 in enumerate(sentences):
        for sent2_idx, sentence2 in enumerate(sentences):
            vec1, vec2 = vec_list[sent1_idx], vec_list[sent2_idx]
            if np.any(vec1) and np.any(vec2):
                score = cosine_similarity(X=[vec1], Y=[vec2])
                df_list.append({'x': sentence1, 'y': sentence2, 'similarity': score[0][0]})
                score_list.append(score[0][0])
    df = pd.DataFrame(df_list)
    color_mapper = LinearColorMapper(palette=palette, low=np.max(score_list), high=np.min(score_list))
    TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
    p = figure(x_range=sentences, y_range=list(reversed(sentences)),
                x_axis_location="above", plot_width=900, plot_height=900,
                toolbar_location='below', tools=TOOLS,
                tooltips=[('sentences', '@x @y'), ('similarity', '@similarity')])
    p.grid.grid_line_color = None
    p.axis.axis_line_color = None
    p.axis.major_tick_line_color = None
    p.axis.major_label_standoff = 0
    p.xaxis.major_label_orientation = 3.14 / 3
    p.rect(x="x", y="y", width=1, height=1,
            source=df,
            fill_color={'field': 'similarity', 'transform': color_mapper},
            line_color=None)
    color_bar = ColorBar(ticker=BasicTicker(desired_num_ticks=5),
                        color_mapper=color_mapper, major_label_text_font_size="7pt",
                        label_standoff=6, border_line_color=None, location=(0, 0))
    p.add_layout(color_bar, 'right')
    if use_notebook:
        output_notebook()
        show(p)
    else:
        export_png(p, filename)
        print("save @ " + filename) 

def visualize_between_words(words, vecs, palette="Viridis256", filename="/notebooks/embedding/between-words.png",
                            use_notebook=False):
    df_list = []
    for word1_idx, word1 in enumerate(words):
        for word2_idx, word2 in enumerate(words):
            vec1 = vecs[word1_idx]
            vec2 = vecs[word2_idx]
            if np.any(vec1) and np.any(vec2):
                score = cosine_similarity(X=[vec1], Y=[vec2])
                df_list.append({'x': word1, 'y': word2, 'similarity': score[0][0]})
    df = pd.DataFrame(df_list)
    color_mapper = LinearColorMapper(palette=palette, low=1, high=0)
    TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
    p = figure(x_range=list(words), y_range=list(reversed(list(words))),
               x_axis_location="above", plot_width=900, plot_height=900,
               toolbar_location='below', tools=TOOLS,
               tooltips=[('words', '@x @y'), ('similarity', '@similarity')])
    p.grid.grid_line_color = None
    p.axis.axis_line_color = None
    p.axis.major_tick_line_color = None
    p.axis.major_label_standoff = 0
    p.xaxis.major_label_orientation = 3.14 / 3
    p.rect(x="x", y="y", width=1, height=1,
           source=df,
           fill_color={'field': 'similarity', 'transform': color_mapper},
           line_color=None)
    color_bar = ColorBar(ticker=BasicTicker(desired_num_ticks=5),
                         color_mapper=color_mapper, major_label_text_font_size="7pt",
                         label_standoff=6, border_line_color=None, location=(0, 0))
    p.add_layout(color_bar, 'right')
    if use_notebook:
        output_notebook()
        show(p)
    else:
        export_png(p, filename)
        print("save @ " + filename) 

def __call__(self, text):
        if self.tfrecords_mode == 'point':
            assert text.find('||') != -1,"input should cotain two sentences seperated by ||"
            text_a = text.split('||')[0]
            text_b = text.split('||')[-1]
            pred,score = self._get_label([text_a], [text_b], need_preprocess = True)
            return pred[0][0], score[0][0]

        #加载自定义问句(自定义优先)
        if self.sim_mode == 'cross':
            text_list = self.text_list
            label_list = self.label_list
            if self.zdy != {}:
                text_list = self.zdy['text_list'] + text_list
                label_list = self.zdy['label_list'] + label_list
            pred,score = self._get_label([text], self.text_list, need_preprocess = True)
            selected_id = np.argmax(score)
            out_score = score[selected_id]
        elif self.sim_mode == 'represent':
            text_list = self.text_list
            vec_list = self.vec_list
            label_list = self.label_list
            if self.zdy != {}:
                text_list = self.zdy['text_list'] + text_list
                vec_list = np.concatenate([self.zdy['vec_list'], self.vec_list], axis = 0)
                label_list = self.zdy['label_list'] + label_list
            vec = self._get_vecs([text], need_preprocess = True)
            if self.is_distance:
                scores = euclidean_distances(vec, vec_list)[0]
                selected_id = np.argmin(scores)
                out_score = 1 - scores[selected_id]
            else:
                scores = cosine_similarity(vec, vec_list)[0]
                selected_id = np.argmax(scores)
                out_score = scores[selected_id]
        else:
            raise ValueError('unknown sim mode, represent or cross?')
        ret = (label_list[selected_id], out_score, selected_id, \
               self.text_list[selected_id])
        return ret