Dimension mismatch when I try to apply tf-idf to test set

Question

I am trying to apply a new pre-processing algorithm to my dataset, following this answer: Encoding text in ML classifier

What I have tried now is the following:

def test_tfidf(data, ngrams = 1):

    df_temp = data.copy(deep = True)
    df_temp = basic_preprocessing(df_temp)
    
    tfidf_vectorizer = TfidfVectorizer(ngram_range=(1, ngrams))
    tfidf_vectorizer.fit(df_temp['Text'])

    list_corpus = df_temp["Text"].tolist()
    list_labels = df_temp["Label"].tolist()

    X = tfidf_vectorizer.transform(list_corpus)
    
    return X, list_labels

(I would suggest to refer to the link I mentioned above for all the code). When I try to apply the latter two function to my dataset:

train_x, train_y, count_vectorizer  = tfidf(undersample_train, ngrams = 1)
testing_set = pd.concat([X_test, y_test], axis=1)
test_x, test_y = test_tfidf(testing_set, ngrams = 1)

full_result = full_result.append(training_naive(train_x, test_x, train_y, test_y), ignore_index = True)

I get this error:

---> 12 full_result = full_result.append(training_naive(train_x, test_x, train_y, test_y, ), ignore_index = True) 
---> 14     y_pred = clf.predict(X_test_naive)

ValueError: dimension mismatch

The function mentioned in the error is:

def training_naive(X_train_naive, X_test_naive, y_train_naive, y_test_naive, preproc):
    
    clf = MultinomialNB() 
    clf.fit(X_train_naive, y_train_naive)
    y_pred = clf.predict(X_test_naive)
        
    return 

Any help in understanding what is wrong in my new definition and/or in applying the tf-idf to my dataset (please refer here for the relevant parts: Encoding text in ML classifier ), it would be appreciated.

Update: I think this question/answer might be useful as well for helping me in figure out the issue: scikit-learn ValueError: dimension mismatch

if I replace test_x, test_y = test_tfidf(testing_set, ngrams = 1) with test_x, test_y = test_tfidf(undersample_train, ngrams = 1) it does not return any error. However, I do not think it is right, as I am getting values very very high (99% on all statistics)

Answer 1

When using transformes ( TfidfVectorizer in this case), you must use the same object ot transform both train and test data. The transformer is typically fitted using the training data only, and then re-used to transform the test data.

The correct way to do this in your case:

def tfidf(data, ngrams = 1):

    df_temp = data.copy(deep = True)
    df_temp = basic_preprocessing(df_temp)
    
    tfidf_vectorizer = TfidfVectorizer(ngram_range=(1, ngrams))
    tfidf_vectorizer.fit(df_temp['Text'])

    list_corpus = df_temp["Text"].tolist()
    list_labels = df_temp["Label"].tolist()

    X = tfidf_vectorizer.transform(list_corpus)
    
    return X, list_labels, tfidf_vectorizer


def test_tfidf(data, vectorizer, ngrams = 1):

    df_temp = data.copy(deep = True)
    df_temp = basic_preprocessing(df_temp)

    # No need to create a new TfidfVectorizer here!

    list_corpus = df_temp["Text"].tolist()
    list_labels = df_temp["Label"].tolist()

    X = vectorizer.transform(list_corpus)
    
    return X, list_labels

# this method is copied from the other SO question
def training_naive(X_train_naive, X_test_naive, y_train_naive, y_test_naive, preproc):
    
    clf = MultinomialNB() # Gaussian Naive Bayes
    clf.fit(X_train_naive, y_train_naive)

    res = pd.DataFrame(columns = ['Preprocessing', 'Model', 'Precision', 'Recall', 'F1-score', 'Accuracy'])
    
    y_pred = clf.predict(X_test_naive)
    
    f1 = f1_score(y_pred, y_test_naive, average = 'weighted')
    pres = precision_score(y_pred, y_test_naive, average = 'weighted')
    rec = recall_score(y_pred, y_test_naive, average = 'weighted')
    acc = accuracy_score(y_pred, y_test_naive)
    
    res = res.append({'Preprocessing': preproc, 'Model': 'Naive Bayes', 'Precision': pres, 
                     'Recall': rec, 'F1-score': f1, 'Accuracy': acc}, ignore_index = True)

    return res 

train_x, train_y, count_vectorizer  = tfidf(undersample_train, ngrams = 1)
testing_set = pd.concat([X_test, y_test], axis=1)
test_x, test_y = test_tfidf(testing_set, count_vectorizer, ngrams = 1)

full_result = full_result.append(training_naive(train_x, test_x, train_y, test_y, count_vectorizer), ignore_index = True)