How to use hierarchy_depth method in Lemoncheesecake

Best Python code snippet using lemoncheesecake

replay_buffers_test.py

Source:replay_buffers_test.py

1"""Tests for alpacka.training_data.replay_buffers."""2import collections3import numpy as np4import pytest5from alpacka.trainers import replay_buffers6_TestTransition = collections.namedtuple('_TestTransition', ['test_field'])7# Keep _TestTransitions with a single number in the buffer.8_test_datapoint_spec = _TestTransition(test_field=())9def test_uniform_samples_added_transition():10    buf = replay_buffers.UniformReplayBuffer(_test_datapoint_spec, capacity=10)11    stacked_transitions = _TestTransition(np.array([123]))12    buf.add(stacked_transitions)13    assert buf.sample(batch_size=1) == stacked_transitions14def test_uniform_raises_when_sampling_from_an_empty_buffer():15    buf = replay_buffers.UniformReplayBuffer(_test_datapoint_spec, capacity=10)16    with pytest.raises(ValueError):17        buf.sample(batch_size=1)18def test_uniform_samples_all_transitions_eventually_one_add():19    buf = replay_buffers.UniformReplayBuffer(_test_datapoint_spec, capacity=10)20    buf.add(_TestTransition(np.array([0, 1])))21    sampled_transitions = set()22    for _ in range(100):23        sampled_transitions.add(buf.sample(batch_size=1).test_field.item())24    assert sampled_transitions == {0, 1}25def test_uniform_samples_all_transitions_eventually_two_adds():26    buf = replay_buffers.UniformReplayBuffer(_test_datapoint_spec, capacity=10)27    buf.add(_TestTransition(np.array([0, 1])))28    buf.add(_TestTransition(np.array([2, 3])))29    sampled_transitions = set()30    for _ in range(100):31        sampled_transitions.add(buf.sample(batch_size=1).test_field.item())32    assert sampled_transitions == {0, 1, 2, 3}33def test_uniform_samples_different_transitions():34    buf = replay_buffers.UniformReplayBuffer(_test_datapoint_spec, capacity=100)35    buf.add(_TestTransition(np.arange(100)))36    assert len(set(buf.sample(batch_size=3).test_field)) > 137def test_uniform_oversamples_transitions():38    buf = replay_buffers.UniformReplayBuffer(_test_datapoint_spec, capacity=10)39    stacked_transitions = _TestTransition(np.array([0, 1]))40    buf.add(stacked_transitions)41    assert set(buf.sample(batch_size=100).test_field) == {0, 1}42def test_uniform_overwrites_old_transitions():43    buf = replay_buffers.UniformReplayBuffer(_test_datapoint_spec, capacity=4)44    buf.add(_TestTransition(np.arange(3)))45    buf.add(_TestTransition(np.arange(3, 6)))46    # 0, 1 should get overriden.47    assert set(buf.sample(batch_size=100).test_field) == {2, 3, 4, 5}48@pytest.mark.parametrize('hierarchy_depth', [0, 1, 2])49def test_hierarchical_samples_added_transitions(hierarchy_depth):50    buf = replay_buffers.HierarchicalReplayBuffer(51        _test_datapoint_spec, capacity=10, hierarchy_depth=hierarchy_depth52    )53    stacked_transitions = _TestTransition(np.array([123]))54    buf.add(stacked_transitions, [0] * hierarchy_depth)55    assert buf.sample(batch_size=1) == stacked_transitions56def test_hierarchical_samples_buckets_uniformly():57    buf = replay_buffers.HierarchicalReplayBuffer(58        _test_datapoint_spec, capacity=10, hierarchy_depth=159    )60    # Add zeros and ones at a 10:1 ratio.61    buf.add(_TestTransition(np.zeros(10)), [0])62    buf.add(_TestTransition(np.ones(1)), [1])63    # Assert that sampled transitions have a mean value of 0.5.64    mean_value = np.mean(buf.sample(batch_size=1000).test_field)...

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