def test_renn_sample_wrong_X():
"""Test either if an error is raised when X is different at fitting
and sampling"""
# Create the object
renn = RepeatedEditedNearestNeighbours(random_state=RND_SEED)
renn.fit(X, Y)
assert_raises(RuntimeError, renn.sample,
np.random.random((100, 40)), np.array([0] * 50 + [1] * 50))
def test_renn_sample_wrong_X():
"""Test either if an error is raised when X is different at fitting
and sampling"""
# Create the object
renn = RepeatedEditedNearestNeighbours(random_state=RND_SEED)
renn.fit(X, Y)
assert_raises(RuntimeError, renn.sample, np.random.random((100, 40)),
np.array([0] * 50 + [1] * 50))
def test_renn_fit():
"""Test the fitting method"""
# Create the object
renn = RepeatedEditedNearestNeighbours(random_state=RND_SEED)
# Fit the data
renn.fit(X, Y)
# Check if the data information have been computed
assert_equal(renn.min_c_, 0)
assert_equal(renn.maj_c_, 1)
assert_equal(renn.stats_c_[0], 500)
assert_equal(renn.stats_c_[1], 4500)
def test_renn_fit():
"""Test the fitting method"""
# Create the object
renn = RepeatedEditedNearestNeighbours(random_state=RND_SEED)
# Fit the data
renn.fit(X, Y)
# Check if the data information have been computed
assert_equal(renn.min_c_, 0)
assert_equal(renn.maj_c_, 1)
assert_equal(renn.stats_c_[0], 500)
assert_equal(renn.stats_c_[1], 4500)
