如何用用户，文本数据调用MultinomialNB.predict（）？

Question

我正在创建一个简单的推荐器，根据推文的相似性推荐其他用户。我使用tfidf来矢量化所有文本，我能够将数据拟合到MultinomialNB但我一直在尝试预测错误

我试图将数据重新整形为数组，但是我得到一个错误，无法将字符串转换为浮点数。我甚至可以将此算法用于此数据吗？我尝试了不同的列来查看是否得到了结果，但是相同的位置错误。

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 

同样，我希望用他们的推文和情绪插入用户，并根据相似性推荐数据中的另一个用户。

Answer 1

您不应该将推文直接提供给分类器。您需要使用拟合的TfidfVectorizer将文本转换为向量。

进行以下更改

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

了解这个模型只会给出测试数据推文的情绪。

如果您的意图是推荐，请尝试使用其他推荐方法。