def test_best_split_with_combination_combining_if_too_small():
"""
Test passing in a perfect split data, with a single catagory merges needed
"""
arr = np.array(([1] * 5) + ([2] * 10))
orig_arr = arr.copy()
ndarr = np.array(([1, 2, 3] * 5) + ([2, 2, 3] * 3) + ([3, 2, 3] * 5) +
[1, 2, 3] * 2).reshape(15, 3)
orig_ndarr = ndarr.copy()
tree = CHAID.Tree.from_numpy(ndarr,
arr,
min_child_node_size=5,
alpha_merge=0.055)
split = tree.generate_best_split(tree.vectorised_array, tree.observed)
assert list_ordered_equal(
ndarr, orig_ndarr
), 'Calling chaid should have no side affects for original numpy arrays'
assert list_ordered_equal(
arr, orig_arr
), 'Calling chaid should have no side affects for original numpy arrays'
assert split.column_id == 0, 'Identifies correct column to split on'
assert list_unordered_equal(
split.split_map, [[1], [2, 3]]), 'Correctly identifies categories'
assert list_unordered_equal(split.surrogates,
[]), 'No surrogates should be generated'
assert split.p < 0.055
def test_spliting_identical_values():
"""
Test that passing in identical data cannot be split
"""
arr = np.array(([1] * 5) + ([1] * 5))
orig_arr = arr.copy()
ndarr = np.array(([1, 2, 3] * 5) + ([2, 2, 3] * 5)).reshape(10, 3)
orig_ndarr = ndarr.copy()
tree = CHAID.Tree.from_numpy(ndarr, arr, min_child_node_size=0)
split = tree.generate_best_split(tree.vectorised_array, tree.observed)
assert list_ordered_equal(ndarr, orig_ndarr), \
'Calling chaid should have no side affects for original numpy arrays'
assert list_ordered_equal(arr, orig_arr), \
'Identifies correct column to split on'
assert not split.valid(), \
'Should not be able to split data with no skew'
def test_spliting_identical_values():
"""
Test that passing in identical data cannot be split
"""
arr = np.array(([1] * 5) + ([1] * 5))
orig_arr = arr.copy()
ndarr = np.array(([1, 2, 3] * 5) + ([2, 2, 3] * 5)).reshape(10, 3)
orig_ndarr = ndarr.copy()
tree = CHAID.Tree.from_numpy(ndarr, arr, min_child_node_size=0)
split = tree.generate_best_split(
tree.vectorised_array,
tree.observed
)
assert list_ordered_equal(ndarr, orig_ndarr), \
'Calling chaid should have no side affects for original numpy arrays'
assert list_ordered_equal(arr, orig_arr), \
'Identifies correct column to split on'
assert not split.valid(), \
'Should not be able to split data with no skew'
def test_best_split_unique_values():
"""
Test passing in a perfect split data, with no catagory merges needed
"""
arr = np.array(([1] * 5) + ([2] * 5))
orig_arr = arr.copy()
ndarr = np.array(([1, 2, 3] * 5) + ([2, 2, 3] * 5)).reshape(10, 3)
orig_ndarr = ndarr.copy()
tree = CHAID.Tree.from_numpy(ndarr, arr, min_child_node_size=0)
split = tree.generate_best_split(
tree.vectorised_array,
tree.observed
)
assert list_ordered_equal(ndarr, orig_ndarr), 'Calling chaid should have no side affects for original numpy arrays'
assert list_ordered_equal(arr, orig_arr), 'Calling chaid should have no side affects for original numpy arrays'
assert split.column_id == 0, 'Identifies correct column to split on'
assert list_unordered_equal(split.split_map, [[1], [2]]), 'Correctly identifies catagories'
assert list_unordered_equal(split.surrogates, []), 'No surrogates should be generated'
assert split.p < 0.015
def test_best_split_with_combination_combining_if_too_small():
"""
Test passing in a perfect split data, with a single catagory merges needed
"""
arr = np.array(([1] * 5) + ([2] * 10))
orig_arr = arr.copy()
ndarr = np.array(([1, 2, 3] * 5) + ([2, 2, 3] * 3) + ([3, 2, 3] * 5) + [1, 2, 3] * 2).reshape(15, 3)
orig_ndarr = ndarr.copy()
tree = CHAID.Tree.from_numpy(ndarr, arr, min_child_node_size=5, alpha_merge=0.055)
split = tree.generate_best_split(
tree.vectorised_array,
tree.observed
)
assert list_ordered_equal(ndarr, orig_ndarr), 'Calling chaid should have no side affects for original numpy arrays'
assert list_ordered_equal(arr, orig_arr), 'Calling chaid should have no side affects for original numpy arrays'
assert split.column_id == 0, 'Identifies correct column to split on'
assert list_unordered_equal(split.split_map, [[1], [2, 3]]), 'Correctly identifies categories'
assert list_unordered_equal(split.surrogates, []), 'No surrogates should be generated'
assert split.p < 0.055
def test_best_split_unique_values():
"""
Test passing in a perfect split data, with no catagory merges needed
"""
arr = np.array(([1] * 5) + ([2] * 5))
orig_arr = arr.copy()
ndarr = np.array(([1, 2, 3] * 5) + ([2, 2, 3] * 5)).reshape(10, 3)
orig_ndarr = ndarr.copy()
tree = CHAID.Tree.from_numpy(ndarr, arr, min_child_node_size=0)
split = tree.generate_best_split(
tree.vectorised_array,
tree.observed
)
assert list_ordered_equal(ndarr, orig_ndarr), 'Calling chaid should have no side affects for original numpy arrays'
assert list_ordered_equal(arr, orig_arr), 'Calling chaid should have no side affects for original numpy arrays'
assert split.column_id == 0, 'Identifies correct column to split on'
assert list_unordered_equal(split.split_map, [[1], [2]]), 'Correctly identifies catagories'
assert list_unordered_equal(split.surrogates, []), 'No surrogates should be generated'
assert split.p < 0.015
def test_changing_copy(self):
""" Test that altering the copy doesn't alter the original """
self.copy.arr[0] = 55.0
assert not list_ordered_equal(self.copy, self.orig), 'Altering one vector should not affect the other'
def test_deep_copy_does_copy(self):
""" Ensure a copy actually happens when deep_copy is called """
assert id(self.orig) != id(self.copy), 'The vector objects must be different'
assert list_ordered_equal(self.copy, self.orig), 'Vector contents must be the same'
def test_changing_copy(self):
""" Test that altering the copy doesn't alter the original """
self.copy.arr[0] = 55.0
assert not list_ordered_equal(self.copy, self.orig), 'Altering one vector should not affected the other'
def test_deep_copy_does_copy(self):
""" Ensure a copy actually happens when deep_copy is called """
assert id(self.orig) != id(self.copy), 'The vector objects must be different'
assert list_ordered_equal(self.copy, self.orig), 'Vector contents must be the same'
