def test_sample_wrong_X():
"""Test either if an error is raised when X is different at fitting
and sampling"""
# Create the object
cc = ClusterCentroids(random_state=RND_SEED)
cc.fit(X, Y)
assert_raises(RuntimeError, cc.sample, np.random.random((100, 40)),
np.array([0] * 50 + [1] * 50))
def test_sample_wrong_X():
"""Test either if an error is raised when X is different at fitting
and sampling"""
# Create the object
cc = ClusterCentroids(random_state=RND_SEED)
cc.fit(X, Y)
assert_raises(RuntimeError, cc.sample, np.random.random((100, 40)),
np.array([0] * 50 + [1] * 50))
def test_cc_fit():
"""Test the fitting method"""
# Define the parameter for the under-sampling
ratio = 'auto'
# Create the object
cc = ClusterCentroids(ratio=ratio, random_state=RND_SEED)
# Fit the data
cc.fit(X, Y)
# Check if the data information have been computed
assert_equal(cc.min_c_, 0)
assert_equal(cc.maj_c_, 1)
assert_equal(cc.stats_c_[0], 3)
assert_equal(cc.stats_c_[1], 7)
def test_cc_fit():
"""Test the fitting method"""
# Define the parameter for the under-sampling
ratio = 'auto'
# Create the object
cc = ClusterCentroids(ratio=ratio, random_state=RND_SEED)
# Fit the data
cc.fit(X, Y)
# Check if the data information have been computed
assert_equal(cc.min_c_, 0)
assert_equal(cc.maj_c_, 1)
assert_equal(cc.stats_c_[0], 500)
assert_equal(cc.stats_c_[1], 4500)
