def test_knn_graph(self):
"""
Test construction of the k-nearest neighbor graph.
"""
k = 3
## Correct knn similarity graph
ans_radii = np.array([2., 1., 1., 1., 2.])
ans_graph = np.array([[0, 1, 2],
[1, 0, 2],
[2, 1, 3],
[3, 2, 4],
[4, 3, 2]])
## DeBaCl knn similarity graph
for method in ['brute_force', 'kd_tree', 'ball_tree']:
knn, radii = utl.knn_graph(self.X, k=k, method=method)
## Test
assert_array_equal(radii, ans_radii)
for neighbors, ans_neighbors in zip(knn, ans_graph):
self.assertItemsEqual(neighbors, ans_neighbors)
distances = scipy.spatial.distance.pdist(self.X)
distance_matrix = scipy.spatial.distance.squareform(distances)
knn, radii = utl.knn_graph(distance_matrix, k, method='precomputed')
## Test precomputed method
assert_array_equal(radii, ans_radii)
for neighbors, ans_neighbors in zip(knn, ans_graph):
self.assertItemsEqual(neighbors, ans_neighbors)
def construct_tree(X, k, prune_threshold=None, num_levels=None, verbose=False):
"""
Construct a level set tree from tabular data.
Parameters
----------
X : 2-dimensional numpy array
Numeric dataset, where each row represents one observation.
k : int
Number of observations to consider as neighbors to a given point.
prune_threshold : int, optional
Leaf nodes with fewer than this number of members are recursively
merged into larger nodes. If 'None' (the default), then no pruning
is performed.
num_levels : int, optional
Number of density levels in the constructed tree. If None (default),
`num_levels` is internally set to be the number of rows in `X`.
verbose : bool, optional
If True, a progress indicator is printed at every 100th level of tree
construction.
Returns
-------
T : LevelSetTree
A pruned level set tree.
See Also
--------
construct_tree_from_graph, LevelSetTree
Examples
--------
>>> X = numpy.random.rand(100, 2)
>>> tree = debacl.construct_tree(X, k=8, prune_threshold=5)
>>> print tree
+----+-------------+-----------+------------+----------+------+--------+----------+
| id | start_level | end_level | start_mass | end_mass | size | parent | children |
+----+-------------+-----------+------------+----------+------+--------+----------+
| 0 | 0.000 | 0.870 | 0.000 | 0.450 | 100 | None | [3, 4] |
| 3 | 0.870 | 3.364 | 0.450 | 0.990 | 17 | 0 | [] |
| 4 | 0.870 | 1.027 | 0.450 | 0.520 | 35 | 0 | [7, 8] |
| 7 | 1.027 | 1.755 | 0.520 | 0.870 | 8 | 4 | [] |
| 8 | 1.027 | 3.392 | 0.520 | 1.000 | 23 | 4 | [] |
+----+-------------+-----------+------------+----------+------+--------+----------+
"""
sim_graph, radii = _utl.knn_graph(X, k, method='brute_force')
n, p = X.shape
density = _utl.knn_density(radii, n, p, k)
tree = construct_tree_from_graph(adjacency_list=sim_graph, density=density,
prune_threshold=prune_threshold,
num_levels=num_levels, verbose=verbose)
return tree
def test_knn_graph(self):
"""
Test construction of the k-nearest neighbor graph.
"""
k = 3
## Correct knn similarity graph
ans_radii = np.array([2., 1., 1., 1., 2.])
ans_graph = np.array([[0, 1, 2], [1, 0, 2], [2, 1, 3], [3, 2, 4],
[4, 3, 2]])
## DeBaCl knn similarity graph
for method in ['brute_force', 'kd_tree', 'ball_tree']:
knn, radii = utl.knn_graph(self.X, k=k, method=method)
## Test
assert_array_equal(radii, ans_radii)
for neighbors, ans_neighbors in zip(knn, ans_graph):
self.assertItemsEqual(neighbors, ans_neighbors)
def test_knn_graph(self):
"""
Test construction of the k-nearest neighbor graph.
"""
k = 3
## Correct knn similarity graph
ans_radii = np.array([2., 1., 1., 1., 2.])
ans_graph = np.array([[0, 1, 2],
[1, 0, 2],
[2, 1, 3],
[3, 2, 4],
[4, 3, 2]])
## DeBaCl knn similarity graph
for method in ['brute_force', 'kd_tree', 'ball_tree']:
knn, radii = utl.knn_graph(self.X, k=k, method=method)
## Test
assert_array_equal(radii, ans_radii)
for neighbors, ans_neighbors in zip(knn, ans_graph):
self.assertItemsEqual(neighbors, ans_neighbors)