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import collections
import numpy as np
import pytest
from embodied.core import selectors
class TestSampleTree:
@pytest.mark.parametrize('branching', [2, 3, 5, 10])
def test_root_sum(self, branching):
tree = selectors.SampleTree(branching)
entries = range(50)
for index, uprob in enumerate(entries):
assert tree.root.uprob == sum(entries[:index])
tree.insert(index, uprob)
@pytest.mark.parametrize('inserts', [1, 2, 10, 100])
@pytest.mark.parametrize('branching', [2, 3, 5, 10])
def test_depth_inserts(self, inserts, branching):
tree = selectors.SampleTree(branching)
for index in range(inserts):
tree.insert(index, 1)
assert len(tree) == inserts
depths = self._find_leave_depths(tree)
target = max(1, int(np.ceil(np.log(inserts) / np.log(branching))))
assert all(x == target for x in depths)
@pytest.mark.parametrize('inserts', [2, 10, 100])
@pytest.mark.parametrize('remove_every', [2, 3, 4])
@pytest.mark.parametrize('branching', [2, 3, 5, 10])
def test_depth_removals(self, inserts, remove_every, branching):
tree = selectors.SampleTree(branching)
for index in range(0, inserts, 1):
tree.insert(index, 1)
removals = list(range(0, inserts, remove_every))
for index in removals:
tree.remove(index)
assert len(tree) == inserts - len(removals)
depths = self._find_leave_depths(tree)
target = max(1, int(np.ceil(np.log(inserts) / np.log(branching))))
assert all(x == target for x in depths)
@pytest.mark.parametrize('inserts', [2, 10, 100])
@pytest.mark.parametrize('branching', [2, 3, 5, 10])
def test_removal_num_nodes(self, inserts, branching):
tree = selectors.SampleTree(branching)
assert len(self._get_flat_nodes(tree)) == 1
rng = np.random.default_rng(seed=0)
for key in rng.permutation(np.arange(inserts)):
tree.insert(key, 1)
num_nodes = len(self._get_flat_nodes(tree))
for key in rng.permutation(np.arange(inserts)):
tree.remove(key)
assert len(self._get_flat_nodes(tree)) == 1
for key in rng.permutation(np.arange(inserts)):
tree.insert(key, 1)
assert len(self._get_flat_nodes(tree)) == num_nodes
@pytest.mark.parametrize('branching', [2, 3, 5, 10])
def test_sample_single(self, branching):
tree = selectors.SampleTree(branching)
tree.insert(12, 1.0)
tree.insert(123, 1.0)
tree.insert(42, 1.0)
tree.remove(12)
tree.remove(42)
for _ in range(10):
assert tree.sample() == 123
@pytest.mark.parametrize('inserts', [2, 10])
@pytest.mark.parametrize('branching', [2, 3, 5, 10])
@pytest.mark.parametrize('uprob', [1e-5, 1.0, 1e5])
def test_sample_uniform(self, inserts, branching, uprob):
tree = selectors.SampleTree(branching, seed=0)
keys = list(range(inserts))
for key in keys:
tree.insert(key, 1.0)
for key in keys[::3]:
tree.remove(key)
keys.remove(key)
histogram = collections.defaultdict(int)
for _ in range(100 * len(keys)):
key = tree.sample()
histogram[key] += 1
assert len(histogram) > 0
assert len(histogram) == len(keys)
assert all(k in histogram for k in keys)
for key, count in histogram.items():
prob = count / (100 * len(keys))
assert prob > 0.5 * (1 / len(keys))
@pytest.mark.parametrize('scale', [1e-5, 1, 1e5])
@pytest.mark.parametrize('branching', [2, 3, 5, 10])
def test_sample_frequencies(self, scale, branching):
tree = selectors.SampleTree(branching, seed=0)
keys = [0, 1, 2, 3, 4, 5]
uprobs = [0, 3, 1, 1, 2, 2]
entries = dict(zip(keys, uprobs))
for key, uprob in entries.items():
tree.insert(key, scale * uprob)
histogram = collections.defaultdict(int)
for _ in range(100 * len(entries)):
key = tree.sample()
histogram[key] += 1
assert len(histogram) > 0
total = sum(entries.values())
for key, uprob in entries.items():
if uprob == 0:
assert key not in histogram
for key, count in histogram.items():
prob = count / (100 * len(entries))
target = entries[key] / total
assert 0.7 * target < prob < 1.3 * target
@pytest.mark.parametrize('branching', [2, 3, 5, 10])
def test_update_frequencies(self, branching):
tree = selectors.SampleTree(branching, seed=0)
keys = [0, 1, 2, 3, 4, 5]
uprobs = [0, 3, 1, 1, 2, 2]
entries = dict(zip(keys, uprobs))
for key in entries.keys():
tree.insert(key, 100)
for key, uprob in entries.items():
tree.update(key, uprob)
histogram = collections.defaultdict(int)
for _ in range(100 * len(entries)):
key = tree.sample()
histogram[key] += 1
assert len(histogram) > 0
total = sum(entries.values())
for key, uprob in entries.items():
if uprob == 0:
assert key not in histogram
for key, count in histogram.items():
prob = count / (100 * len(entries))
target = entries[key] / total
assert 0.7 * target < prob < 1.3 * target
@pytest.mark.parametrize('branching', [2, 3, 5, 10])
def test_zero_probs_mixed(self, branching):
tree = selectors.SampleTree(branching, seed=0)
impossible = []
for index in range(100):
if index % 3 == 0:
tree.insert(index, 1.0)
else:
tree.insert(index, 0.0)
impossible.append(index)
for _ in range(1000):
assert tree.sample() not in impossible
@pytest.mark.parametrize('branching', [2, 3, 5, 10])
def test_zero_probs_only(self, branching):
tree = selectors.SampleTree(branching, seed=0)
for index in range(100):
tree.insert(index, 0.0)
for _ in range(1000):
assert tree.sample() in range(100)
@pytest.mark.parametrize('branching', [2, 3, 5, 10])
def test_infinity_probs(self, branching):
tree = selectors.SampleTree(branching, seed=0)
possible = []
for index in range(100):
if index % 3 == 0:
tree.insert(index, np.inf)
possible.append(index)
else:
tree.insert(index, 1.0)
for _ in range(1000):
assert tree.sample() in possible
def _find_leave_depths(self, tree):
depths = []
queue = [(tree.root, 0)]
while queue:
node, depth = queue.pop()
if hasattr(node, 'children'):
for child in node.children:
queue.append((child, depth + 1))
else:
depths.append(depth)
assert len(depths) > 0
return depths
def _get_flat_nodes(self, tree):
nodes = []
queue = [tree.root]
while queue:
node = queue.pop()
nodes.append(node)
if hasattr(node, 'children'):
queue += node.children
return nodes
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