def test_kdd_tree_mixed():
stream = RandomTreeGenerator(tree_random_state=1,
sample_random_state=1,
n_num_features=0)
stream.prepare_for_use()
X, _ = stream.next_sample(1000)
X_test, _ = stream.next_sample(10)
# Build tree
cat_features = [i for i in range(25)]
kdtree = KDTree(X,
metric='mixed',
return_distance=True,
categorical_list=cat_features)
# Query tree
dist, idx = kdtree.query(X_test, 4)
expected_idx = [[123, 234, 707, 654], [688, 429, 216, 627],
[463, 970, 566, 399], [18, 895, 640, 996],
[396, 612, 897, 232], [328, 54, 138, 569],
[253, 501, 82, 273], [38, 146, 752, 923],
[946, 808, 271, 363], [951, 111, 708, 5]]
expected_dist = [[2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 0],
[2, 2, 2, 0], [2, 2, 2, 0], [2, 2, 2, 2], [2, 2, 0, 0],
[2, 2, 2, 0], [2, 2, 2, 2]]
assert np.alltrue(idx == expected_idx)
assert np.allclose(dist, expected_dist)
expected_info = 'KDTree: - leaf_size: 40 - metric: mixed - return_distance: True'
assert kdtree.get_info() == expected_info
assert kdtree.get_class_type() == 'data_structure'
def test_kdd_tree_mixed():
stream = RandomTreeGenerator(tree_random_state=1, sample_random_state=1, n_num_features=0)
X, _ = stream.next_sample(1000)
X_test, _ = stream.next_sample(10)
# Build tree
cat_features = [i for i in range(25)]
kdtree = KDTree(X, metric='mixed', return_distance=True, categorical_list=cat_features)
# Query tree
dist, idx = kdtree.query(X_test, 4)
expected_idx = [[123, 234, 707, 654],
[688, 429, 216, 627],
[463, 970, 566, 399],
[18, 895, 640, 996],
[396, 612, 897, 232],
[328, 54, 138, 569],
[253, 501, 82, 273],
[38, 146, 752, 923],
[946, 808, 271, 363],
[951, 111, 708, 5]]
expected_dist = [[2, 2, 2, 2],
[2, 2, 2, 2],
[2, 2, 2, 2],
[2, 2, 2, 0],
[2, 2, 2, 0],
[2, 2, 2, 0],
[2, 2, 2, 2],
[2, 2, 0, 0],
[2, 2, 2, 0],
[2, 2, 2, 2]]
assert np.alltrue(idx == expected_idx)
assert np.allclose(dist, expected_dist)
expected_info = 'KDTree(categorical_list=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ' \
'11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], ' \
'leaf_size=40, metric=mixed, return_distance=True)'
assert kdtree.get_info() == expected_info
assert kdtree._estimator_type == 'data_structure'
def test_kdd_tree_euclidean():
stream = RandomTreeGenerator(tree_random_state=1, sample_random_state=1)
X, _ = stream.next_sample(1000)
X_test, _ = stream.next_sample(10)
# Build tree
kdtree = KDTree(X, metric='euclidean', return_distance=True)
# Query tree
dist, idx = kdtree.query(X_test, 4)
expected_idx = [[855, 466, 348, 996],
[829, 654, 92, 333],
[227, 364, 183, 325],
[439, 482, 817, 501],
[886, 173, 279, 470],
[98, 30, 34, 580],
[959, 773, 374, 819],
[819, 685, 59, 992],
[624, 665, 209, 239],
[524, 807, 506, 191]]
expected_dist = [[1.6366216258724973, 1.631437068636607, 1.5408182139320563, 1.4836054196064452],
[1.7839579422032452, 1.7694587302438618, 1.5339920309706585, 1.5228981881653287],
[1.6512443805072872, 1.637456923425164, 1.61736766513639, 1.5776532815820448],
[1.5843121606184263, 1.571918014408251, 1.5038147281265382, 0.7058569455034059],
[2.052148026638031, 2.0157953468214007, 1.8012794130725434, 1.6572756455115591],
[1.5844032729792423, 1.5688736638121885, 1.55893121879858, 1.4609657517960262],
[1.6819916227667229, 1.6186557774269037, 1.5815309744477162, 1.5720184136312232],
[1.7302164693989817, 1.5964713159009083, 1.4897849225874815, 1.1629448414734906],
[1.6511813695220574, 1.6454651930288255, 1.5926685577827064, 1.4973008307362947],
[1.5982346741983797, 1.5875900895982191, 1.4702209684850878, 1.4676217546305874]]
assert np.alltrue(idx == expected_idx)
assert np.allclose(dist, expected_dist)
expected_info = 'KDTree(categorical_list=None, leaf_size=40, metric=euclidean, return_distance=True)'
assert kdtree.get_info() == expected_info
assert kdtree._estimator_type == 'data_structure'
def demo():
""" _test_kdtree_compare
This demo compares creation and query speed for different kd tree
implementations. They are fed with instances from the covtype dataset.
Three kd tree implementations are compared: SciPy's KDTree, NumPy's
KDTree and scikit-multiflow's KDTree. For each of them the demo will
time the construction of the tree on 1000 instances, and then measure
the time to query 100 instances. The results are displayed in the
terminal.
"""
warnings.filterwarnings("ignore", ".*Passing 1d.*")
stream = FileStream('../data/datasets/covtype.csv', -1, 1)
filter = OneHotToCategorical([[10, 11, 12, 13],
[14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]])
X, y = stream.next_sample(1000)
X = filter.transform(X)
# print(X)
X_find, y = stream.next_sample(100)
X_find = filter.transform(X_find)
print(X_find[4])
# Normal kdtree
start = timer()
scipy = spatial.KDTree(X, leafsize=40)
end = timer()
print("\nScipy KDTree construction time: " + str(end-start))
start = timer()
for i in range(10):
ind = scipy.query(X_find[i], 8)
# print(ind)
end = timer()
print("Scipy KDTree query time: " + str(end - start))
del scipy
# Fast kdtree
start = timer()
opt = KDTree(X, metric='euclidean', return_distance=True)
end = timer()
print("\nOptimal KDTree construction time: " + str(end-start))
start = timer()
for i in range(100):
ind, dist = opt.query(X_find[i], 8)
# print(ind)
# print(dist)
end = timer()
print("Optimal KDTree query time: " + str(end - start))
del opt
# Sklearn kdtree
start = timer()
sk = ng.KDTree(X, metric='euclidean')
end = timer()
print("\nSklearn KDTree construction time: " + str(end-start))
start = timer()
for i in range(100):
ind, dist = sk.query(np.asarray(X_find[i]).reshape(1, -1), 8, return_distance=True)
# print(ind)
# print(dist)
end = timer()
print("Sklearn KDTree query time: " + str(end - start) + "\n")
del sk