def test_min_count_filter(self):
X = np.array([[1.0, 0.0, 0.0],
[2.0, 1.0, 0.0],
[3.0, 0.0, 0.0],
[2.0, 1.0, 0.0],
[4.0, 1.0, 0.0],
[6.0, 9.0, 0.0]])
filt = MinCountFilter(min_count=5)
Xt = filt.fit_transform(X)
# expected X
Xe = X[:,0][np.newaxis].T
assert_array_equal(Xt, Xe)
def test_min_count_filter_sparse(self):
X = sp.csr_matrix([[1.0, 0.0, 0.0],
[2.0, 1.0, 0.0],
[3.0, 0.0, 0.0],
[2.0, 1.0, 0.0],
[4.0, 1.0, 0.0],
[6.0, 9.0, 0.0]])
filt = MinCountFilter(min_count=5)
Xt = filt.fit_transform(X)
# expected X
Xe = X[:,0]
assert Xt.shape == (6,1)
# __eq__ is not properly implemented for sparse matrices
# so apply this trick
assert (Xt - Xe).nnz == 0
