def test_reset(
self, env: CounterpointEnv, actions: List[int],
expected: np.ndarray
) -> None:
"""Test `reset` method."""
for action in actions:
env.step(action)
observation = env.reset()
np.testing.assert_equal(observation, expected)
assert env.piece.current_time_in_eighths == 8
def test_estimate_number_of_trials(env: CounterpointEnv, actions: List[int],
n_trials_estimation_depth: int,
n_trials_estimation_width: int,
n_trials_factor: float) -> None:
"""Test `estimate_number_of_trials` function."""
for action in actions:
env.step(action)
result = estimate_number_of_trials(env, n_trials_estimation_depth,
n_trials_estimation_width,
n_trials_factor)
assert result > 0
def main() -> None:
"""Parse CLI arguments, train agent, and test it."""
cli_args = parse_cli_args()
default_config_path = 'configs/default_config.yml'
default_config_path = resource_filename(__name__, default_config_path)
config_path = cli_args.config_path or default_config_path
with open(config_path) as config_file:
settings = yaml.safe_load(config_file)
results_dir = settings['piece']['rendering_params']['dir']
if not os.path.isdir(results_dir):
os.mkdir(results_dir)
piece = Piece(**settings['piece'])
env = CounterpointEnv(piece, **settings['environment'])
best_action_sequences = optimize_with_monte_carlo_beam_search(
env, **settings['agent'])
env.verbose = True
for i_episode, action_sequence in enumerate(best_action_sequences):
print(f"\nPiece #{i_episode}:")
env.reset()
for action in action_sequence:
observation, reward, done, info = env.step(action)
env.render()
print(f"Reward is {reward}.")
def roll_in(env: CounterpointEnv, actions: List[int]) -> EnvWithActions:
"""
Do roll-in actions.
:param env:
environment
:param actions:
sequence of roll-in actions
:return:
environment after roll-in actions
"""
env.reset()
for action in actions:
env.step(action)
env_with_actions = EnvWithActions(env, actions)
return env_with_actions
def test_reward(
self, env: CounterpointEnv, actions: List[int], expected: float
) -> None:
"""Test that `step` method returns proper reward."""
for action in actions:
observation, reward, done, info = env.step(action)
assert done
assert round(reward, 4) == expected
def test_observation(
self, env: CounterpointEnv, actions: List[int],
expected: np.ndarray
) -> None:
"""Test that `step` method returns proper observation."""
for action in actions:
observation, reward, done, info = env.step(action)
assert not done
np.testing.assert_equal(observation, expected)
def test_info(
self, env: CounterpointEnv, actions: List[int],
expected: np.ndarray
) -> None:
"""Test that `step` method returns proper info about next actions."""
for action in actions:
observation, reward, done, info = env.step(action)
result = info['next_actions']
assert result == expected
class TestCounterpointEnv:
"""Tests for `CounterpointEnv` class."""
@pytest.mark.parametrize(
"env, actions, expected",
[
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': ['rearticulation_stability'],
'params': {}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {1: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[14, 6, 8],
# `expected`
np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
),
]
)
def test_observation(
self, env: CounterpointEnv, actions: List[int],
expected: np.ndarray
) -> None:
"""Test that `step` method returns proper observation."""
for action in actions:
observation, reward, done, info = env.step(action)
assert not done
np.testing.assert_equal(observation, expected)
@pytest.mark.parametrize(
"env, actions, expected",
[
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': [
'rhythmic_pattern_validity',
'rearticulation_stability',
'consonance_on_strong_beat',
'resolution_of_suspended_dissonance',
],
'params': {}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {1: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[13, 15],
# `expected`
[6, 11]
),
]
)
def test_info(
self, env: CounterpointEnv, actions: List[int],
expected: np.ndarray
) -> None:
"""Test that `step` method returns proper info about next actions."""
for action in actions:
observation, reward, done, info = env.step(action)
result = info['next_actions']
assert result == expected
@pytest.mark.parametrize(
"env, actions, expected",
[
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': ['rearticulation_stability'],
'params': {}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {1: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[14, 6, 8, 11, 5, 15, 9],
# `expected`
0
),
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': ['rearticulation_stability'],
'params': {}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {4: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[14, 6, 8, 11, 5, 15, 9],
# `expected`
1
),
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'C4', 'C3', 'C4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': ['absence_of_large_intervals'],
'params': {
'absence_of_large_intervals': {
'max_n_semitones': 7
}
}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {1: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[14, 12],
# `expected`
-100
),
]
)
def test_reward(
self, env: CounterpointEnv, actions: List[int], expected: float
) -> None:
"""Test that `step` method returns proper reward."""
for action in actions:
observation, reward, done, info = env.step(action)
assert done
assert round(reward, 4) == expected
@pytest.mark.parametrize(
"env, actions, expected",
[
(
# `env`
CounterpointEnv(
piece=Piece(
tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': ['rearticulation_stability'],
'params': {}
},
rendering_params={}
),
scoring_coefs={'number_of_skips': 1},
scoring_fn_params={
'number_of_skips': {'rewards': {1: 1}}
},
reward_for_dead_end=-100,
),
# `actions`
[14, 6, 8, 11, 5, 15],
# `expected`
np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
),
]
)
def test_reset(
self, env: CounterpointEnv, actions: List[int],
expected: np.ndarray
) -> None:
"""Test `reset` method."""
for action in actions:
env.step(action)
observation = env.reset()
np.testing.assert_equal(observation, expected)
assert env.piece.current_time_in_eighths == 8
"env, actions, n_trials_estimation_depth, n_trials_estimation_width, "
"n_trials_factor",
[
(
# `env`
CounterpointEnv(
piece=Piece(tonic='C',
scale_type='major',
cantus_firmus=['C4', 'D4', 'E4', 'D4', 'C4'],
counterpoint_specifications={
'start_note': 'E4',
'end_note': 'E4',
'lowest_note': 'G3',
'highest_note': 'G4',
'start_pause_in_eighths': 4,
'max_skip_in_degrees': 2,
},
rules={
'names': ['rearticulation_stability'],
'params': {}
},
rendering_params={}),
reward_for_dead_end=-100,
scoring_coefs={'entropy': 1},
scoring_fn_params={},
),
# `actions`
[1],
# `n_trials_estimation_depth`
2,
# `n_trials_estimation_width`