def test_dense_tf_idf():
hv = HashingVectorizer(dim=1000, probes=3)
hv.vectorize(JUNK_FOOD_DOCS)
hv.vectorize(NOTJUNK_FOOD_DOCS)
# extract the TF-IDF data
X = hv.get_tfidf()
assert_equal(X.shape, (11, 1000))
# label junk food as -1, the others as +1
y = np.ones(X.shape[0])
y[:6] = -1
# train and test a classifier
clf = DenseLinearSVC(C=10).fit(X[1:-1], y[1:-1])
assert_equal(clf.predict([X[0]]), [-1])
assert_equal(clf.predict([X[-1]]), [1])
def test_dense_sparse_idf_sanity():
hv = HashingVectorizer(dim=100, probes=3)
shv = SparseHashingVectorizer(dim=100, probes=3)
hv.vectorize(JUNK_FOOD_DOCS)
shv.vectorize(JUNK_FOOD_DOCS)
# check that running TF IDF estimates are the same
dense_tf_idf = hv.get_tfidf()
sparse_tfidf = shv.get_tfidf().todense()
assert_array_almost_equal(dense_tf_idf, sparse_tfidf)
# check that incremental behaviour stays the same
hv.vectorize(NOTJUNK_FOOD_DOCS)
shv.vectorize(NOTJUNK_FOOD_DOCS)
dense_tf_idf = hv.get_tfidf()
sparse_tfidf = shv.get_tfidf().todense()
assert_array_almost_equal(dense_tf_idf, sparse_tfidf)
