sklearn...TfidfVectorizer僅當(dāng)分析器返回對象列表時，在訓(xùn)練后立即應(yīng)用它才有效nltk.tree.Tree。這是一個謎，因為模型在應(yīng)用之前總是從文件加載。與在該會話中進行訓(xùn)練時相比，在自己的會話開始時加載和應(yīng)用模型文件時，調(diào)試顯示模型文件沒有任何錯誤或不同。分析儀在這兩種情況下均適用并正常工作。下面是一個幫助重現(xiàn)這種神秘行為的腳本：import joblibimport numpy as npfrom nltk import Treefrom sklearn.feature_extraction.text import TfidfVectorizerdef lexicalized_production_analyzer(sentence_trees):    productions_per_sentence = [tree.productions() for tree in sentence_trees]    return np.concatenate(productions_per_sentence)def train(corpus):    model = TfidfVectorizer(analyzer=lexicalized_production_analyzer)    model.fit(corpus)    joblib.dump(model, "model.joblib")def apply(corpus):    model = joblib.load("model.joblib")    result = model.transform(corpus)    return result# exmaple datatrees = [Tree('ROOT', [Tree('FRAG', [Tree('S', [Tree('VP', [Tree('VBG', ['arkling']), Tree('NP', [Tree('NP', [Tree('NNS', ['dots'])]), Tree('VP', [Tree('VBG', ['nestling']), Tree('PP', [Tree('IN', ['in']), Tree('NP', [Tree('DT', ['the']), Tree('NN', ['grass'])])])])])])]), Tree(',', [',']), Tree('VP', [Tree('VBG', ['winking']), Tree('CC', ['and']), Tree('VP', [Tree('VBG', ['glimmering']), Tree('PP', [Tree('IN', ['like']), Tree('NP', [Tree('NNS', ['jewels'])])])])]), Tree('.', ['.'])])]), Tree('ROOT', [Tree('FRAG', [Tree('NP', [Tree('NP', [Tree('NNP', ['Rose']), Tree('NNS', ['petals'])]), Tree('NP', [Tree('NP', [Tree('ADVP', [Tree('RB', ['perhaps'])]), Tree(',', [',']), Tree('CC', ['or']), Tree('NP', [Tree('DT', ['some'])]), Tree('NML', [Tree('NN', ['kind'])])]), Tree('PP', [Tree('IN', ['of']), Tree('NP', [Tree('NN', ['confetti'])])])])]), Tree('.', ['.'])])])]corpus = [trees, trees, trees]首先訓(xùn)練模型并保存model.joblib文件。train(corpus)result = apply(corpus)print("number of elements in results: " + str(result.getnnz()))print("shape of results: " + str(result.shape))我們打印結(jié)果數(shù).getnnz()以表明該模型正在處理 120 個元素：number of elements in results: 120shape of results: (3, 40)但是該模型兩次都是從文件加載的，并且沒有全局變量（我知道），因此我們無法想到為什么它在一種情況下有效而在另一種情況下不起作用。