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Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
When using SelectKBest or SelectPercentile in sklearn.feature_selection, it's known that we can use following code to get selected features
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Python Code:
import numpy as np
import pandas as pd
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
corpus, y = load_data()
assert type(corpus) == list
assert type(y) == list
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
svc = LinearSVC(penalty='l1', dual=False)
svc.fit(X, y)
selected_feature_names = np.asarray(vectorizer.get_feature_names_out())[np.flatnonzero(svc.coef_)]
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