I am using bag of words to classify text. It's working well but I am wondering how to add a feature which is not a word.
Here is my sample code.
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsRestClassifier
X_train = np.array(["new york is a hell of a town",
"new york was originally dutch",
"new york is also called the big apple",
"nyc is nice",
"the capital of great britain is london. london is a huge metropolis which has a great many number of people living in it. london is also a very old town with a rich and vibrant cultural history.",
"london is in the uk. they speak english there. london is a sprawling big city where it's super easy to get lost and i've got lost many times.",
"london is in england, which is a part of great britain. some cool things to check out in london are the museum and buckingham palace.",
"london is in great britain. it rains a lot in britain and london's fogs are a constant theme in books based in london, such as sherlock holmes. the weather is really bad there.",])
y_train = [[0],[0],[0],[0],[1],[1],[1],[1]]
X_test = np.array(["it's a nice day in nyc",
'i loved the time i spent in london, the weather was great, though there was a nip in the air and i had to wear a jacket.'
])
target_names = ['Class 1', 'Class 2']
classifier = Pipeline([
('vectorizer', CountVectorizer(min_df=1,max_df=2)),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC()))])
classifier.fit(X_train, y_train)
predicted = classifier.predict(X_test)
for item, labels in zip(X_test, predicted):
print '%s => %s' % (item, ', '.join(target_names[x] for x in labels))
Now it is clear that the text about London tends to be much longer than the text about New York. How would I add length of the text as a feature? Do I have to use another way of classification and then combine the two predictions? Is there any way of doing it along with the bag of words? Some sample code would be great -- I'm very new to machine learning and scikit learn.
Count vectorizer creates a matrix with documents and token counts (bag of terms/tokens) therefore it is also known as document term matrix (dtm).
CountVectorizer is a great tool provided by the scikit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text.
In order to address this, scikit-learn provides utilities for the most common ways to extract numerical features from text content, namely: tokenizing strings and giving an integer id for each possible token, for instance by using white-spaces and punctuation as token separators.
As shown in the comments, this is a combination of a FunctionTransformer
, a FeaturePipeline
and a FeatureUnion
.
import numpy as np
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import FunctionTransformer
X_train = np.array(["new york is a hell of a town",
"new york was originally dutch",
"new york is also called the big apple",
"nyc is nice",
"the capital of great britain is london. london is a huge metropolis which has a great many number of people living in it. london is also a very old town with a rich and vibrant cultural history.",
"london is in the uk. they speak english there. london is a sprawling big city where it's super easy to get lost and i've got lost many times.",
"london is in england, which is a part of great britain. some cool things to check out in london are the museum and buckingham palace.",
"london is in great britain. it rains a lot in britain and london's fogs are a constant theme in books based in london, such as sherlock holmes. the weather is really bad there.",])
y_train = np.array([[0],[0],[0],[0],[1],[1],[1],[1]])
X_test = np.array(["it's a nice day in nyc",
'i loved the time i spent in london, the weather was great, though there was a nip in the air and i had to wear a jacket.'
])
target_names = ['Class 1', 'Class 2']
def get_text_length(x):
return np.array([len(t) for t in x]).reshape(-1, 1)
classifier = Pipeline([
('features', FeatureUnion([
('text', Pipeline([
('vectorizer', CountVectorizer(min_df=1,max_df=2)),
('tfidf', TfidfTransformer()),
])),
('length', Pipeline([
('count', FunctionTransformer(get_text_length, validate=False)),
]))
])),
('clf', OneVsRestClassifier(LinearSVC()))])
classifier.fit(X_train, y_train)
predicted = classifier.predict(X_test)
predicted
This will add the length of the text to the features used by the classifier.
I assume that the new feature that you want to add is numeric. Here is my logic. First transform the text into sparse using TfidfTransformer
or something similar. Then convert the sparse representation to a pandas DataFrame
and add your new column which I assume is numeric. At the end, you may want to convert your data frame back to sparse
matrix using scipy
or any other module that you feel comfortable with. I assume that your data is in a pandas DataFrame
called dataset
containing a 'Text Column'
and a 'Numeric Column'
. Here is some code.
dataset = pd.DataFrame({'Text Column':['Sample Text1','Sample Text2'], 'Numeric Column': [2,1]})
dataset.head()
Numeric Column Text Column
0 2 Sample Text1
1 1 Sample Text2
from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer
from scipy import sparse
tv = TfidfVectorizer(min_df = 0.05, max_df = 0.5, stop_words = 'english')
X = tv.fit_transform(dataset['Text column'])
vocab = tv.get_feature_names()
X1 = pd.DataFrame(X.toarray(), columns = vocab)
X1['Numeric Column'] = dataset['Numeric Column']
X_sparse = sparse.csr_matrix(X1.values)
Finally, you may want to;
print(X_sparse.shape)
print(X.shape)
to ensure that the new column was successfully added. I hope this helps.
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