def test_nm1_sample_wrong_X():
"""Test either if an error is raised when X is different at fitting
and sampling"""
# Create the object
nm1 = NearMiss(random_state=RND_SEED)
nm1.fit(X, Y)
assert_raises(RuntimeError, nm1.sample, np.random.random((100, 40)),
np.array([0] * 50 + [1] * 50))
def test_nm2_sample_wrong_X():
"""Test either if an error is raised when X is different at fitting
and sampling"""
# Create the object
nm2 = NearMiss(random_state=RND_SEED, version=VERSION_NEARMISS)
nm2.fit(X, Y)
assert_raises(RuntimeError, nm2.sample, np.random.random((100, 40)),
np.array([0] * 50 + [1] * 50))
def test_nm2_fit():
"""Test the fitting method"""
# Define the parameter for the under-sampling
ratio = 'auto'
# Create the object
nm2 = NearMiss(ratio=ratio, random_state=RND_SEED,
version=VERSION_NEARMISS)
# Fit the data
nm2.fit(X, Y)
# Check if the data information have been computed
assert_equal(nm2.min_c_, 0)
assert_equal(nm2.maj_c_, 1)
assert_equal(nm2.stats_c_[0], 500)
assert_equal(nm2.stats_c_[1], 4500)
def test_nm2_fit():
"""Test the fitting method"""
# Define the parameter for the under-sampling
ratio = 'auto'
# Create the object
nm2 = NearMiss(ratio=ratio, random_state=RND_SEED,
version=VERSION_NEARMISS)
# Fit the data
nm2.fit(X, Y)
# Check if the data information have been computed
assert_equal(nm2.min_c_, 0)
assert_equal(nm2.maj_c_, 1)
assert_equal(nm2.stats_c_[0], 500)
assert_equal(nm2.stats_c_[1], 4500)
