def test_zero_subbed_weighted_ndarry():
"""
Test how the split works when 0 independent categorical variable chooses a dependent categorical variable for the weighted case.
In this instance, a very small float is assigned to the 0 value
"""
gender = np.array(
[0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1])
income = np.array(
[0, 0, 1, 1, 2, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
weighting = np.array(([0.9] * int(len(gender) / 2.0)) +
([1.9] * int(len(gender) / 2.0)))
ndarr = np.transpose(np.vstack([gender]))
tree = CHAID.Tree(ndarr, income, alpha_merge=0.9, weights=weighting)
split = tree.generate_best_split(tree.vectorised_array, tree.observed,
weighting)
assert round(split.chi, 4) == 14.5103
assert round(split.p, 4) == 0.0007
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(ndarr, arr)
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 setUp(self):
invalid_split = CHAID.Split(None, None, 0, 1, 0)
self.tree = CHAID.Tree.from_numpy(np.array([[1]]), np.array([1]))
self.tree._tree_store = [
CHAID.Node(
node_id=0,
split=CHAID.Split('a', [[1], [2]], 1, 0.2, 2)
),
CHAID.Node(node_id=1, split=invalid_split, choices=[1], parent=0),
CHAID.Node(
node_id=2,
split=CHAID.Split('c', [[3]], 1, 0.2, 2),
choices=[2],
parent=0
)
]
self.last_node = CHAID.Node(
node_id=3,
split=invalid_split,
choices=[3],
parent=2
)
self.tree._tree_store.append(self.last_node)
def setUp(self):
""" Setup for grouping tests """
arr = np.array([1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 5.0, 10.0])
self.col = CHAID.OrdinalColumn(arr)
def setUp(self):
""" Setup for copy tests"""
arr = np.array([1, 2, 3, 3, 3, 3])
self.orig = CHAID.OrdinalColumn(arr)
self.copy = self.orig.deep_copy()
def setUp(self):
""" Setup for copy tests"""
# Use string so numpy array dtype is object and may store references
arr = np.array(['5.0', '10.0'])
self.orig = CHAID.NominalColumn(arr)
self.copy = self.orig.deep_copy()
def setUp(self):
""" Set up for tree generation tests """
arr = np.array(([1] * 5) + ([2] * 5))
ndarr = np.array(([1, 2, 3] * 5) + ([2, 2, 3] * 5)).reshape(10, 3)
self.tree = CHAID.Tree(ndarr, arr)
def test_invalid_split():
""" Test properties when split invalid """
split = CHAID.Split(None, None, None, 1, 0)
assert split.invalid_reason == None
assert split.column == None
def setUp(self):
""" Setup test data for continuous data """
self.random_arr = np.array(
[0.23198952, 0.26550251, 0.96461057, 0.13733767, 0.76674088,
0.60637166, 0.18822053, 0.78785506, 0.47786053, 0.44448984,
0.88632344, 0.94060264, 0.52900520, 0.68301794, 0.00485769,
0.09299505, 0.41767638, 0.22345506, 0.61899892, 0.53763263,
0.41424529, 0.87527060, 0.10843391, 0.22902548, 0.52043049,
0.82396842, 0.64215622, 0.42827082, 0.76920710, 0.27736853,
0.95756523, 0.45140920, 0.12405161, 0.53774033, 0.72198885,
0.37880053, 0.93554955, 0.44434796, 0.62834896, 0.02788777,
0.30288893, 0.07198041, 0.59731867, 0.63485262, 0.79936557,
0.41154027, 0.82900816, 0.49216809, 0.56649288, 0.26539558,
0.12304309, 0.03233878, 0.64612524, 0.69844021, 0.30560065,
0.05408900, 0.31020185, 0.93087523, 0.27952452, 0.57186781,
0.36214135, 0.34114557, 0.82028983, 0.29795183, 0.21028335,
0.41612748, 0.24781879, 0.19125266, 0.17214954, 0.44039645,
0.84397111, 0.91060384, 0.70898285, 0.27049457, 0.15502956,
0.47580771, 0.21507488, 0.68243381, 0.56233427, 0.22376202,
0.76630117, 0.00162193, 0.15057895, 0.10145753, 0.69406461,
0.81280760, 0.79726816, 0.42523241, 0.56025856, 0.10287649,
0.53337746, 0.82185783, 0.38270064, 0.77411309, 0.01754383,
0.84690273, 0.20057135, 0.37194360, 0.24657089, 0.91520048,
0.65575302, 0.03220805, 0.71449568, 0.97194268, 0.94031990,
0.61484448, 0.46961425, 0.38495625, 0.41865701, 0.81394666,
0.57147433, 0.33414233, 0.13847757, 0.31316325, 0.04371212,
0.36556674, 0.56316862, 0.66761528, 0.02491041, 0.12124478]
)
self.normal_arr = np.array([
215.74655491, 237.0905247 , 193.72021408, 152.89363815,
175.36670032, 232.59086085, 204.20219942, 248.99321897,
267.95686148, 165.7204985 , 177.38110221, 220.40618705,
262.71893125, 240.00774431, 210.85572027, 255.06583994,
232.85274614, 274.71932373, 186.83175676, 241.47832856,
294.98781486, 190.82037054, 143.7991682 , 170.32090888,
207.20320791, 208.10226642, 187.09923858, 178.9242382 ,
155.17266333, 140.69923988, 210.80029533, 193.85525698,
232.69854217, 230.4408611 , 149.34523942, 303.6243051 ,
171.1562868 , 185.24131426, 195.80616026, 224.38213062,
261.77203837, 170.81218927, 216.37943211, 265.25650174,
203.3098626 , 229.84982086, 212.14777791, 265.25335911,
296.11334434, 242.40424522, 270.30264815, 77.97401496,
176.80382943, 156.35135782, 155.29031942, 262.11885208,
161.33251252, 256.05120377, 158.32542953, 189.07183278,
155.72524265, 244.68956731, 286.68689241, 94.08648606,
253.80300049, 161.17371005, 116.94584491, 182.88557535,
182.85752412, 253.42111371, 131.25146323, 264.86407965,
197.3742505 , 296.95506279, 221.01600673, 234.04694958,
154.42957223, 176.94139196, 200.59554949, 170.4040058 ,
229.39358115, 127.43357367, 249.09735255, 227.90731765,
238.9667355 , 163.83410357, 194.88998826, 134.49013182,
154.54356067, 254.19699384, 143.93816979, 256.11031829,
186.56096688, 178.40462838, 159.79032932, 187.7542398 ,
267.18537402, 190.99969385, 130.30080584, 216.12902248,
247.8707783 , 246.49016072, 275.3636918 , 165.69987612,
181.16709806, 193.87951446, 156.03720504, 221.44032879,
182.21405831, 119.22571297, 219.14946203, 140.358539 ,
210.5826685 , 256.57132523, 244.82587339, 153.26377344,
198.44006972, 172.6057332 , 140.26518016, 171.32162943]
)
self.wt = np.array(([1.0] * 60) + ([1.2] * 60))
ndarr = np.array(([2, 3] * 20) + ([2, 5] * 20) + ([3, 4] * 19) + [2, 3] + [1, 2, 5] * 80 + [1, 2, 3] * 40).reshape(120, 4)
self.ndarr = [CHAID.NominalColumn(ndarr[:, i]) for i in range(ndarr.shape[1])]
self.stats_random_data = CHAID.Stats(0.5, 10, .95, self.random_arr)
self.stats_normal_data = CHAID.Stats(0.5, 10, .95, self.normal_arr)
