ValueError: Dimension mismatch

Question

I use SciPy and scikit-learn to train and apply a Multinomial Naive Bayes Classifier for binary text classification. Precisely, I use the module sklearn.feature_extraction.text.CountVectorizer for creating sparse matrices that hold word feature counts from text and the module sklearn.naive_bayes.MultinomialNB as the classifier implementation for training the classifier on training data and applying it on test data.

The input to the CountVectorizer is a list of text documents represented as unicode strings. The training data is much larger than the test data. My code looks like this (simplified):

vectorizer = CountVectorizer(**kwargs)  # sparse matrix with training data X_train = vectorizer.fit_transform(list_of_documents_for_training)  # vector holding target values (=classes, either -1 or 1) for training documents # this vector has the same number of elements as the list of documents y_train = numpy.array([1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, ...])  # sparse matrix with test data X_test = vectorizer.fit_transform(list_of_documents_for_testing)  # Training stage of NB classifier classifier = MultinomialNB() classifier.fit(X=X_train, y=y_train)  # Prediction of log probabilities on test data X_log_proba = classifier.predict_log_proba(X_test) 

Problem: As soon as MultinomialNB.predict_log_proba() is called, I get ValueError: dimension mismatch. According to the IPython stacktrace below, the error occurs in SciPy:

/path/to/my/code.pyc --> 177         X_log_proba = classifier.predict_log_proba(X_test)  /.../sklearn/naive_bayes.pyc in predict_log_proba(self, X)     76             in the model, where classes are ordered arithmetically.     77         """ --> 78         jll = self._joint_log_likelihood(X)     79         # normalize by P(x) = P(f_1, ..., f_n)     80         log_prob_x = logsumexp(jll, axis=1)  /.../sklearn/naive_bayes.pyc in _joint_log_likelihood(self, X)     345         """Calculate the posterior log probability of the samples X"""     346         X = atleast2d_or_csr(X) --> 347         return (safe_sparse_dot(X, self.feature_log_prob_.T)     348                + self.class_log_prior_)     349   /.../sklearn/utils/extmath.pyc in safe_sparse_dot(a, b, dense_output)     71     from scipy import sparse     72     if sparse.issparse(a) or sparse.issparse(b): --> 73         ret = a * b     74         if dense_output and hasattr(ret, "toarray"):     75             ret = ret.toarray()  /.../scipy/sparse/base.pyc in __mul__(self, other)     276      277             if other.shape[0] != self.shape[1]: --> 278                 raise ValueError('dimension mismatch')     279      280             result = self._mul_multivector(np.asarray(other)) 

I have no idea why this error occurs. Can anybody please explain it to me and provide a solution for this problem? Thanks a lot in advance!

Answer 1

Sounds to me, like you just need to use vectorizer.transform for the test dataset, since the training dataset fixes the vocabulary (you cannot know the full vocabulary including the training set afterall). Just to be clear, thats vectorizer.transform instead of vectorizer.fit_transform.

Answer 2

Another solution will be using vector.vocabulary

# after trainning the data vector = CountVectorizer() vector.fit(self.x_data) training_data = vector.transform(self.x_data) bayes = MultinomialNB() bayes.fit(training_data, y_data)  # use vector.vocabulary for predict vector = CountVectorizer(vocabulary=vector.vocabulary_) #vocabulary is a parameter, it should be vocabulary_ as it is an attribute. text_vector = vector.transform(text) trained_model.predict_prob(text_vector)