adding additional features to a bag of words classifier from https://stackoverflow.com/questions/30653642/combining-bag-of-words-and-other-features-in-one-model-using-sklearn-and-pandas

add_additional_features_bag_of_words.py

from sklearn.feature_extraction.text import CountVectorizer

import numpy as np
import pandas as pd
import scipy as sp
posts = pd.read_csv('posts.csv')

# Create vectorizer for function to use
vectorizer = CountVectorizer(binary=False)
y = posts["score"].values.astype(np.float32)

X = sp.sparse.hstack((vectorizer.fit_transform(posts.message),
                      posts[['feature_1', 'feature_2']].values),
                      format='csr')
X_columns = vectorizer.get_feature_names() +
            posts[['feature_1', 'feature_2']].columns.tolist()


print(posts)

print(X_columns)

print(X.toarray())

posts.csv

Do I need to apply this function for test set as well?
or just pass the X_test to predict function?