我正在创建一个简单的推荐系统，根据推文的相似性推荐其他用户。我使用tfidf将所有文本向量化，并且成功将数据拟合到MultinomialNB中，但每次尝试预测时都会遇到错误。

我尝试将数据重塑为数组，但得到的错误是无法将字符串转换为浮点数。我是否可以使用这种算法来处理这些数据？我尝试了不同的列，看看是否能得到结果，但得到的是相同的位置错误。

ValueError                                Traceback (most recent call last)<ipython-input-39-a982bc4e1f49> in <module>     20     nb_mul.fit(train_idf,y_train)     21     user_knn = UserUser(10, min_sim = 0.4, aggregate='weighted-average')---> 22     nb_mul.predict(y_test)     23     #nb_mul.predict(np.array(test['Tweets'], test['Sentiment']))     24     #TODO: find a way to predict with test data~/anaconda2/lib/python3.6/site-packages/sklearn/naive_bayes.py in predict(self, X)     64             Predicted target values for X     65         """---> 66         jll = self._joint_log_likelihood(X)     67         return self.classes_[np.argmax(jll, axis=1)]     68 ~/anaconda2/lib/python3.6/site-packages/sklearn/naive_bayes.py in _joint_log_likelihood(self, X)    728         check_is_fitted(self, "classes_")    729 --> 730         X = check_array(X, accept_sparse='csr')    731         return (safe_sparse_dot(X, self.feature_log_prob_.T) +    732                 self.class_log_prior_)~/anaconda2/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)    525             try:    526                 warnings.simplefilter('error', ComplexWarning)--> 527                 array = np.asarray(array, dtype=dtype, order=order)    528             except ComplexWarning:    529                 raise ValueError("Complex data not supported\n"~/anaconda2/lib/python3.6/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)    536     537     """--> 538     return array(a, dtype, copy=False, order=order)    539     540 ValueError: could not convert string to float: '["b\'RT @Avalanche: Only two cities have two teams in the second round of the playoffs...\\\\n\\\\nDenver and Boston!\\\\n\\\\n#MileHighBasketball #GoAvsGo http\\\\xe2\\\\x80\\\\xa6\'"]'

for train, test in xf.partition_users(final_test[['user','Tweets','Sentiment']],5, xf.SampleFrac(0.2)):    x_train = []    for index, row in train.iterrows():        x_train.append(row['Tweets'])    y_train = np.array(train['Sentiment'])    y_test = np.array([test['user'],test['Tweets']])    #print(y_train)    tfidf = TfidfVectorizer(min_df=5, max_df = 0.8, sublinear_tf=True, use_idf=True,stop_words='english', lowercase=False)    train_idf = tfidf.fit(x_train)    train_idf = train_idf.transform(x_train)    nb_mul = MultinomialNB()    nb_mul.fit(train_idf,y_train)    user_knn = UserUser(10, min_sim = 0.4, aggregate='weighted-average')    nb_mul.predict(y_test)

数据看起来像这样

   user                                             Tweets  \0              2287418996  ["b'RT @HPbasketball: This stuff is 100% how K...   1              2287418996  ["b'@KeuchelDBeard I may need to rewatch Begin...   2              2287418996  ["b'@keithlaw Is that the stated reason for th...   3              2287418996  ['b"@keithlaw @Yanks23242 I definitely don\'t ...   4              2287418996  ["b'@Yanks23242 @keithlaw Sorry, please sub Jo...        Sentiment  Score  0          neu  0.815  1          neu  0.744  2          neu  1.000  3          neu  0.863  4          neu  0.825 

再次强调，我的期望是输入用户及其推文和情感，并基于相似性推荐数据中的另一位用户。

回答：

您不应直接将推文输入到分类器中。您需要使用已拟合的TfidfVectorizer来将文本转换为向量。

进行以下更改

nb_mul.predict(tfidf.transform(test['Tweets']))

请理解，这个模型只能给出测试数据推文的情感。

如果您的意图是推荐，尝试使用其他推荐方法。