model - “FakedGensimDict”类型的参数计算度量以评估每个主题的连贯性时出错

Question

我正在尝试计算一个指标来评估这段代码中我的语料库中每个主题的连贯性：

import tmtoolkit
from tmtoolkit.topicmod.evaluate import metric_coherence_gensim
def topic_model_coherence_generator(topic_num_start=2,
                                topic_num_end=6,
                                norm_corpus='',
                                cv_matrix='',
                                cv=''):
norm_corpus_tokens = [doc11.split() for doc11 in norm_corpus]
models = []
coherence_scores = []

for i in range(topic_num_start, topic_num_end):
    print(i)
    cur_lda = LatentDirichletAllocation(n_components=i,
                                        max_iter=10000,
                                        random_state=0)
    cur_lda.fit_transform(cv_matrix)
    cur_coherence_score = metric_coherence_gensim(
        measure='c_v',
        top_n=5,
        topic_word_distrib=cur_lda.components_,
        dtm=cv.fit_transform(norm_corpus),
        vocab=np.array(cv.get_feature_names()),
        texts=norm_corpus_tokens)
    models.append(cur_lda)
    coherence_scores.append(np.mean(cur_coherence_score)
return models, coherence_scores

%%time
ts = 2
te = 10
models, coherence_scores = topic_model_coherence_generator(
    ts, te, norm_corpus=norm_corpus, cv=cv, cv_matrix=cv_matrix)

它显示此错误：TypeError：“FakedGensimDict”类型的参数不可迭代

谁能帮我修复这段代码？？