Feature/algo eval (#1074)
# Changes ## Dependencies - New extra "eval" ## Api Extension - `Experiment` and `ExperimentConfig` now have a `name`, that can however be overridden when `Experiment.run()` is called - When building an `Experiment` from an `ExperimentConfig`, the user has the option to add info about seeds to the name. - New method in `ExperimentConfig` called `build_default_seeded_experiments` - `SamplingConfig` has an explicit training seed, `test_seed` is inferred. - New `evaluation` package for repeating the same experiment with multiple seeds and aggregating the results (important extension!). Currently in alpha state. - Loggers can now restore the logged data into python by using the new `restore_logged_data` ## Breaking Changes - `AtariEnvFactory` (in examples) now receives explicit train and test seeds - `EnvFactoryRegistered` now requires an explicit `test_seed` - `BaseLogger.prepare_dict_for_logging` is now abstract --------- Co-authored-by: Maximilian Huettenrauch <m.huettenrauch@appliedai.de> Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de> Co-authored-by: Michael Panchenko <35432522+MischaPanch@users.noreply.github.com>
This commit is contained in:
maxhuettenrauch
GitHub
parent
9c0b3e7292
commit
ade85ab32b
2
.github/workflows/lint_and_docs.yml
vendored
2
.github/workflows/lint_and_docs.yml
vendored
@ -29,7 +29,7 @@ jobs:
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key: venv-${{ hashFiles('poetry.lock') }}
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- name: Install the project dependencies
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run: |
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poetry install --with dev
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poetry install --with dev --extras "eval"
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- name: Lint
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run: poetry run poe lint
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- name: Types
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2
.github/workflows/pytest.yml
vendored
2
.github/workflows/pytest.yml
vendored
@ -33,7 +33,7 @@ jobs:
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key: venv-${{ hashFiles('poetry.lock') }}
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- name: Install the project dependencies
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run: |
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poetry install --with dev --extras "envpool"
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poetry install --with dev --extras "envpool eval"
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- name: wandb login
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run: |
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poetry run wandb login e2366d661b89f2bee877c40bee15502d67b7abef
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@ -261,4 +261,8 @@ BA
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BH
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BO
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BD
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configs
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postfix
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backend
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rliable
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hl
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@ -66,7 +66,13 @@ def main(
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replay_buffer_save_only_last_obs=True,
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)
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env_factory = AtariEnvFactory(task, experiment_config.seed, frames_stack, scale=scale_obs)
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env_factory = AtariEnvFactory(
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task,
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sampling_config.train_seed,
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sampling_config.test_seed,
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frames_stack,
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scale=scale_obs,
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)
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builder = (
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DQNExperimentBuilder(env_factory, experiment_config, sampling_config)
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@ -98,7 +104,7 @@ def main(
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)
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experiment = builder.build()
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experiment.run(log_name)
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experiment.run(override_experiment_name=log_name)
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if __name__ == "__main__":
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@ -65,7 +65,13 @@ def main(
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replay_buffer_save_only_last_obs=True,
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)
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env_factory = AtariEnvFactory(task, experiment_config.seed, frames_stack, scale=scale_obs)
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env_factory = AtariEnvFactory(
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task,
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sampling_config.train_seed,
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sampling_config.test_seed,
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frames_stack,
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scale=scale_obs,
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)
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experiment = (
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IQNExperimentBuilder(env_factory, experiment_config, sampling_config)
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@ -90,7 +96,7 @@ def main(
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.with_epoch_stop_callback(AtariEpochStopCallback(task))
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.build()
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)
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experiment.run(log_name)
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experiment.run(override_experiment_name=log_name)
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if __name__ == "__main__":
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@ -70,7 +70,13 @@ def main(
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replay_buffer_save_only_last_obs=True,
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)
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env_factory = AtariEnvFactory(task, experiment_config.seed, frames_stack)
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env_factory = AtariEnvFactory(
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task,
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sampling_config.train_seed,
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sampling_config.test_seed,
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frames_stack,
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scale=scale_obs,
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)
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builder = (
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PPOExperimentBuilder(env_factory, experiment_config, sampling_config)
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@ -109,7 +115,7 @@ def main(
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),
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)
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experiment = builder.build()
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experiment.run(log_name)
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experiment.run(override_experiment_name=log_name)
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if __name__ == "__main__":
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@ -66,7 +66,13 @@ def main(
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replay_buffer_save_only_last_obs=True,
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)
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env_factory = AtariEnvFactory(task, experiment_config.seed, frames_stack, scale=scale_obs)
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env_factory = AtariEnvFactory(
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task,
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sampling_config.train_seed,
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sampling_config.test_seed,
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frames_stack,
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scale=scale_obs,
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)
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builder = (
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DiscreteSACExperimentBuilder(env_factory, experiment_config, sampling_config)
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@ -97,7 +103,7 @@ def main(
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),
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)
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experiment = builder.build()
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experiment.run(log_name)
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experiment.run(override_experiment_name=log_name)
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if __name__ == "__main__":
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@ -383,7 +383,7 @@ def make_atari_env(
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:return: a tuple of (single env, training envs, test envs).
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"""
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env_factory = AtariEnvFactory(task, seed, frame_stack, scale=bool(scale))
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env_factory = AtariEnvFactory(task, seed, seed + training_num, frame_stack, scale=bool(scale))
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envs = env_factory.create_envs(training_num, test_num)
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return envs.env, envs.train_envs, envs.test_envs
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@ -392,7 +392,8 @@ class AtariEnvFactory(EnvFactoryRegistered):
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def __init__(
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self,
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task: str,
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seed: int,
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train_seed: int,
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test_seed: int,
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frame_stack: int,
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scale: bool = False,
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use_envpool_if_available: bool = True,
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@ -409,7 +410,8 @@ class AtariEnvFactory(EnvFactoryRegistered):
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log.info("Not using envpool, because it is not available")
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super().__init__(
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task=task,
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seed=seed,
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train_seed=train_seed,
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test_seed=test_seed,
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venv_type=VectorEnvType.SUBPROC_SHARED_MEM,
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envpool_factory=envpool_factory,
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)
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@ -16,7 +16,13 @@ from tianshou.utils.logging import run_main
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def main() -> None:
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experiment = (
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DQNExperimentBuilder(
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EnvFactoryRegistered(task="CartPole-v1", seed=0, venv_type=VectorEnvType.DUMMY),
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EnvFactoryRegistered(
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task="CartPole-v1",
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seed=0,
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venv_type=VectorEnvType.DUMMY,
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train_seed=0,
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test_seed=10,
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),
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ExperimentConfig(
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persistence_enabled=False,
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watch=True,
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@ -54,7 +54,12 @@ def main(
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repeat_per_collect=repeat_per_collect,
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)
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env_factory = MujocoEnvFactory(task, experiment_config.seed, obs_norm=True)
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env_factory = MujocoEnvFactory(
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task,
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train_seed=sampling_config.train_seed,
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test_seed=sampling_config.test_seed,
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obs_norm=True,
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)
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experiment = (
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A2CExperimentBuilder(env_factory, experiment_config, sampling_config)
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@ -78,7 +83,7 @@ def main(
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.with_critic_factory_default(hidden_sizes, nn.Tanh)
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.build()
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)
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experiment.run(log_name)
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experiment.run(override_experiment_name=log_name)
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if __name__ == "__main__":
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@ -51,7 +51,12 @@ def main(
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start_timesteps_random=True,
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)
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env_factory = MujocoEnvFactory(task, experiment_config.seed, obs_norm=False)
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env_factory = MujocoEnvFactory(
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task,
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train_seed=sampling_config.train_seed,
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test_seed=sampling_config.test_seed,
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obs_norm=False,
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)
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experiment = (
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DDPGExperimentBuilder(env_factory, experiment_config, sampling_config)
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@ -69,7 +74,7 @@ def main(
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.with_critic_factory_default(hidden_sizes)
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.build()
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)
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experiment.run(log_name)
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experiment.run(override_experiment_name=log_name)
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if __name__ == "__main__":
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@ -37,7 +37,7 @@ def make_mujoco_env(
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:return: a tuple of (single env, training envs, test envs).
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"""
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envs = MujocoEnvFactory(task, seed, obs_norm=obs_norm).create_envs(
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envs = MujocoEnvFactory(task, seed, seed + num_train_envs, obs_norm=obs_norm).create_envs(
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num_train_envs,
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num_test_envs,
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)
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@ -73,13 +73,15 @@ class MujocoEnvFactory(EnvFactoryRegistered):
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def __init__(
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self,
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task: str,
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seed: int,
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train_seed: int,
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test_seed: int,
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obs_norm: bool = True,
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venv_type: VectorEnvType = VectorEnvType.SUBPROC_SHARED_MEM,
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) -> None:
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super().__init__(
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task=task,
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seed=seed,
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train_seed=train_seed,
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test_seed=test_seed,
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venv_type=venv_type,
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envpool_factory=EnvPoolFactory() if envpool_is_available else None,
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)
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@ -56,7 +56,12 @@ def main(
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repeat_per_collect=repeat_per_collect,
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)
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env_factory = MujocoEnvFactory(task, experiment_config.seed, obs_norm=True)
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env_factory = MujocoEnvFactory(
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task,
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train_seed=sampling_config.train_seed,
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test_seed=sampling_config.test_seed,
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obs_norm=True,
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)
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experiment = (
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NPGExperimentBuilder(env_factory, experiment_config, sampling_config)
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@ -80,7 +85,7 @@ def main(
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.with_critic_factory_default(hidden_sizes, torch.nn.Tanh)
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.build()
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)
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experiment.run(log_name)
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experiment.run(override_experiment_name=log_name)
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if __name__ == "__main__":
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@ -61,7 +61,12 @@ def main(
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repeat_per_collect=repeat_per_collect,
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)
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env_factory = MujocoEnvFactory(task, experiment_config.seed, obs_norm=True)
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env_factory = MujocoEnvFactory(
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task,
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train_seed=sampling_config.train_seed,
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test_seed=sampling_config.test_seed,
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obs_norm=True,
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)
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experiment = (
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PPOExperimentBuilder(env_factory, experiment_config, sampling_config)
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@ -90,7 +95,7 @@ def main(
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.with_critic_factory_default(hidden_sizes, torch.nn.Tanh)
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.build()
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)
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experiment.run(log_name)
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experiment.run(override_experiment_name=log_name)
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if __name__ == "__main__":
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184
examples/mujoco/mujoco_ppo_hl_multi.py
Normal file
184
examples/mujoco/mujoco_ppo_hl_multi.py
Normal file
@ -0,0 +1,184 @@
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#!/usr/bin/env python3
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"""The high-level multi experiment script demonstrates how to use the high-level API of TianShou to train
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a single configuration of an experiment (here a PPO agent on mujoco) with multiple non-intersecting seeds.
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Thus, the experiment will be repeated `num_experiments` times.
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For each repetition, a policy seed, train env seeds, and test env seeds are set that
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are non-intersecting with the seeds of the other experiments.
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Each experiment's results are stored in a separate subdirectory.
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The final results are aggregated and turned into useful statistics with the rliable API.
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The call to `eval_experiments` will load the results from the log directory and
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create an interp-quantile mean plot for the returns as well as a performance profile plot.
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These plots are saved in the log directory and displayed in the console.
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"""
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import os
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import sys
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from collections.abc import Sequence
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from typing import Literal
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import torch
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from examples.mujoco.mujoco_env import MujocoEnvFactory
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from tianshou.evaluation.launcher import RegisteredExpLauncher
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from tianshou.evaluation.rliable_evaluation_hl import RLiableExperimentResult
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from tianshou.highlevel.config import SamplingConfig
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from tianshou.highlevel.env import VectorEnvType
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from tianshou.highlevel.experiment import (
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ExperimentConfig,
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PPOExperimentBuilder,
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)
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from tianshou.highlevel.logger import LoggerFactoryDefault
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from tianshou.highlevel.params.dist_fn import (
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DistributionFunctionFactoryIndependentGaussians,
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)
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from tianshou.highlevel.params.lr_scheduler import LRSchedulerFactoryLinear
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from tianshou.highlevel.params.policy_params import PPOParams
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from tianshou.utils import logging
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from tianshou.utils.logging import datetime_tag
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log = logging.getLogger(__name__)
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def main(
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experiment_config: ExperimentConfig,
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task: str = "Ant-v4",
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num_experiments: int = 5,
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buffer_size: int = 4096,
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hidden_sizes: Sequence[int] = (64, 64),
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lr: float = 3e-4,
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gamma: float = 0.99,
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epoch: int = 3,
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step_per_epoch: int = 30000,
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step_per_collect: int = 2048,
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repeat_per_collect: int = 10,
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batch_size: int = 64,
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training_num: int = 10,
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test_num: int = 10,
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rew_norm: bool = True,
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vf_coef: float = 0.25,
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ent_coef: float = 0.0,
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gae_lambda: float = 0.95,
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bound_action_method: Literal["clip", "tanh"] | None = "clip",
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lr_decay: bool = True,
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max_grad_norm: float = 0.5,
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eps_clip: float = 0.2,
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dual_clip: float | None = None,
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value_clip: bool = False,
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norm_adv: bool = False,
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recompute_adv: bool = True,
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run_experiments_sequentially: bool = True,
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) -> str:
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"""Use the high-level API of TianShou to evaluate the PPO algorithm on a MuJoCo environment with multiple seeds for
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a given configuration. The results for each run are stored in separate sub-folders. After the agents are trained,
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the results are evaluated using the rliable API.
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:param experiment_config:
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:param task: a mujoco task name
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:param num_experiments: how many experiments to run with different seeds
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:param buffer_size:
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:param hidden_sizes:
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:param lr:
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:param gamma:
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:param epoch:
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:param step_per_epoch:
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:param step_per_collect:
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:param repeat_per_collect:
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:param batch_size:
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:param training_num:
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:param test_num:
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:param rew_norm:
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||||
:param vf_coef:
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:param ent_coef:
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||||
:param gae_lambda:
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:param bound_action_method:
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||||
:param lr_decay:
|
||||
:param max_grad_norm:
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||||
:param eps_clip:
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||||
:param dual_clip:
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:param value_clip:
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:param norm_adv:
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||||
:param recompute_adv:
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:param run_experiments_sequentially: if True, the experiments are run sequentially, otherwise in parallel.
|
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LIMITATIONS: currently, the parallel execution does not seem to work properly on linux.
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It might generally be undesired to run multiple experiments in parallel on the same machine,
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as a single experiment already uses all available CPU cores by default.
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||||
:return: the directory where the results are stored
|
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"""
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persistence_dir = os.path.abspath(os.path.join("log", task, "ppo", datetime_tag()))
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||||
experiment_config.persistence_base_dir = persistence_dir
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log.info(f"Will save all experiment results to {persistence_dir}.")
|
||||
experiment_config.watch = False
|
||||
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||||
sampling_config = SamplingConfig(
|
||||
num_epochs=epoch,
|
||||
step_per_epoch=step_per_epoch,
|
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batch_size=batch_size,
|
||||
num_train_envs=training_num,
|
||||
num_test_envs=test_num,
|
||||
num_test_episodes=test_num,
|
||||
buffer_size=buffer_size,
|
||||
step_per_collect=step_per_collect,
|
||||
repeat_per_collect=repeat_per_collect,
|
||||
)
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||||
|
||||
env_factory = MujocoEnvFactory(
|
||||
task,
|
||||
train_seed=sampling_config.train_seed,
|
||||
test_seed=sampling_config.test_seed,
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||||
obs_norm=True,
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venv_type=VectorEnvType.SUBPROC_SHARED_MEM_FORK_CONTEXT
|
||||
if sys.platform == "darwin"
|
||||
else VectorEnvType.SUBPROC_SHARED_MEM,
|
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)
|
||||
|
||||
experiments = (
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PPOExperimentBuilder(env_factory, experiment_config, sampling_config)
|
||||
.with_ppo_params(
|
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PPOParams(
|
||||
discount_factor=gamma,
|
||||
gae_lambda=gae_lambda,
|
||||
action_bound_method=bound_action_method,
|
||||
reward_normalization=rew_norm,
|
||||
ent_coef=ent_coef,
|
||||
vf_coef=vf_coef,
|
||||
max_grad_norm=max_grad_norm,
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||||
value_clip=value_clip,
|
||||
advantage_normalization=norm_adv,
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eps_clip=eps_clip,
|
||||
dual_clip=dual_clip,
|
||||
recompute_advantage=recompute_adv,
|
||||
lr=lr,
|
||||
lr_scheduler_factory=LRSchedulerFactoryLinear(sampling_config)
|
||||
if lr_decay
|
||||
else None,
|
||||
dist_fn=DistributionFunctionFactoryIndependentGaussians(),
|
||||
),
|
||||
)
|
||||
.with_actor_factory_default(hidden_sizes, torch.nn.Tanh, continuous_unbounded=True)
|
||||
.with_critic_factory_default(hidden_sizes, torch.nn.Tanh)
|
||||
.with_logger_factory(LoggerFactoryDefault("tensorboard"))
|
||||
.build_default_seeded_experiments(num_experiments)
|
||||
)
|
||||
|
||||
if run_experiments_sequentially:
|
||||
launcher = RegisteredExpLauncher.sequential.create_launcher()
|
||||
else:
|
||||
launcher = RegisteredExpLauncher.joblib.create_launcher()
|
||||
launcher.launch(experiments)
|
||||
|
||||
return persistence_dir
|
||||
|
||||
|
||||
def eval_experiments(log_dir: str) -> RLiableExperimentResult:
|
||||
"""Evaluate the experiments in the given log directory using the rliable API."""
|
||||
rliable_result = RLiableExperimentResult.load_from_disk(log_dir)
|
||||
rliable_result.eval_results(show_plots=True, save_plots=True)
|
||||
return rliable_result
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
log_dir = logging.run_cli(main, level=logging.INFO)
|
||||
assert isinstance(log_dir, str) # for mypy
|
||||
evaluation_result = eval_experiments(log_dir)
|
||||
@ -57,7 +57,12 @@ def main(
|
||||
start_timesteps_random=True,
|
||||
)
|
||||
|
||||
env_factory = MujocoEnvFactory(task, experiment_config.seed, obs_norm=False)
|
||||
env_factory = MujocoEnvFactory(
|
||||
task,
|
||||
train_seed=sampling_config.train_seed,
|
||||
test_seed=sampling_config.test_seed,
|
||||
obs_norm=False,
|
||||
)
|
||||
|
||||
experiment = (
|
||||
REDQExperimentBuilder(env_factory, experiment_config, sampling_config)
|
||||
@ -78,7 +83,7 @@ def main(
|
||||
.with_critic_ensemble_factory_default(hidden_sizes)
|
||||
.build()
|
||||
)
|
||||
experiment.run(log_name)
|
||||
experiment.run(override_experiment_name=log_name)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -49,7 +49,12 @@ def main(
|
||||
repeat_per_collect=repeat_per_collect,
|
||||
)
|
||||
|
||||
env_factory = MujocoEnvFactory(task, experiment_config.seed, obs_norm=True)
|
||||
env_factory = MujocoEnvFactory(
|
||||
task,
|
||||
train_seed=sampling_config.train_seed,
|
||||
test_seed=sampling_config.test_seed,
|
||||
obs_norm=True,
|
||||
)
|
||||
|
||||
experiment = (
|
||||
PGExperimentBuilder(env_factory, experiment_config, sampling_config)
|
||||
@ -67,7 +72,7 @@ def main(
|
||||
.with_actor_factory_default(hidden_sizes, torch.nn.Tanh, continuous_unbounded=True)
|
||||
.build()
|
||||
)
|
||||
experiment.run(log_name)
|
||||
experiment.run(override_experiment_name=log_name)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -52,7 +52,12 @@ def main(
|
||||
start_timesteps_random=True,
|
||||
)
|
||||
|
||||
env_factory = MujocoEnvFactory(task, experiment_config.seed, obs_norm=False)
|
||||
env_factory = MujocoEnvFactory(
|
||||
task,
|
||||
train_seed=sampling_config.train_seed,
|
||||
test_seed=sampling_config.test_seed,
|
||||
obs_norm=False,
|
||||
)
|
||||
|
||||
experiment = (
|
||||
SACExperimentBuilder(env_factory, experiment_config, sampling_config)
|
||||
@ -75,7 +80,7 @@ def main(
|
||||
.with_common_critic_factory_default(hidden_sizes)
|
||||
.build()
|
||||
)
|
||||
experiment.run(log_name)
|
||||
experiment.run(override_experiment_name=log_name)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -58,7 +58,12 @@ def main(
|
||||
start_timesteps_random=True,
|
||||
)
|
||||
|
||||
env_factory = MujocoEnvFactory(task, experiment_config.seed, obs_norm=False)
|
||||
env_factory = MujocoEnvFactory(
|
||||
task,
|
||||
train_seed=sampling_config.train_seed,
|
||||
test_seed=sampling_config.test_seed,
|
||||
obs_norm=False,
|
||||
)
|
||||
|
||||
experiment = (
|
||||
TD3ExperimentBuilder(env_factory, experiment_config, sampling_config)
|
||||
@ -80,7 +85,7 @@ def main(
|
||||
.with_common_critic_factory_default(hidden_sizes, torch.nn.Tanh)
|
||||
.build()
|
||||
)
|
||||
experiment.run(log_name)
|
||||
experiment.run(override_experiment_name=log_name)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -58,7 +58,12 @@ def main(
|
||||
repeat_per_collect=repeat_per_collect,
|
||||
)
|
||||
|
||||
env_factory = MujocoEnvFactory(task, experiment_config.seed, obs_norm=True)
|
||||
env_factory = MujocoEnvFactory(
|
||||
task,
|
||||
train_seed=sampling_config.train_seed,
|
||||
test_seed=sampling_config.test_seed,
|
||||
obs_norm=True,
|
||||
)
|
||||
|
||||
experiment = (
|
||||
TRPOExperimentBuilder(env_factory, experiment_config, sampling_config)
|
||||
@ -84,7 +89,7 @@ def main(
|
||||
.with_critic_factory_default(hidden_sizes, torch.nn.Tanh)
|
||||
.build()
|
||||
)
|
||||
experiment.run(log_name)
|
||||
experiment.run(override_experiment_name=log_name)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
690
poetry.lock
generated
690
poetry.lock
generated
@ -152,6 +152,45 @@ files = [
|
||||
{file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "arch"
|
||||
version = "5.3.1"
|
||||
description = "ARCH for Python"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "arch-5.3.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:75fa6f9386ecc2df81bcbf5d055a290a697482ca51e0b3459dab183d288993cb"},
|
||||
{file = "arch-5.3.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f9c9220d331618322517e0f2b3b3529f9c51f5e5a891441da4a107fd2d6d7fce"},
|
||||
{file = "arch-5.3.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3c503acacf88786a78c0ea6606e292c7bfa66e42603c72b7d9fe8dca021a9ddf"},
|
||||
{file = "arch-5.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:92dbbae9bc19aa38492a1b5968d855e7f69f18e626bfba3dd42e43182ea7907d"},
|
||||
{file = "arch-5.3.1-cp310-cp310-win_amd64.whl", hash = "sha256:522e19656759a9b8408cda652ddadaf8e65e23aff433c4b22a11ea79bd3c2b67"},
|
||||
{file = "arch-5.3.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:4c23b5138198127bc1a7ec432139fbe855d399e51f6391125b5dc3ab2f4a7860"},
|
||||
{file = "arch-5.3.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aadb88a0199b51c6134634618fd074ffbb430a5d3c43126da0b6d259447e1f36"},
|
||||
{file = "arch-5.3.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:96eb779fd90f16787376bc3ada24f3e162bc74f746d1fc3fb809ec36f954007e"},
|
||||
{file = "arch-5.3.1-cp37-cp37m-win32.whl", hash = "sha256:7694ea6085bf817e09ddc8fcb4a871a0f255d3b6b486696cfa16121df591fdb9"},
|
||||
{file = "arch-5.3.1-cp37-cp37m-win_amd64.whl", hash = "sha256:32df883248a7d6f7ee204bf9ccb4a141ece43ab3b06ee22627cb84c8b4b7d24b"},
|
||||
{file = "arch-5.3.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:ef94fd5738fc0bccc4ee8a27871d5d7052b3962d784b397acf7f7bcc3afc34f4"},
|
||||
{file = "arch-5.3.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:74e629d33ff41ab2a0917f475703826fd3c0976a3dc236873b19b41f719afe5b"},
|
||||
{file = "arch-5.3.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:84c3944a47d28923bad70a7a6a11081d55482b80ef6abb8581a7f98e05ec9584"},
|
||||
{file = "arch-5.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b862462dd22297073b772e08144f31b7be05080b4063de5ce794c969d0348a94"},
|
||||
{file = "arch-5.3.1-cp38-cp38-win32.whl", hash = "sha256:ae2e8026085ca841e6c31144913462e79706c8604e46deda4558ec252a4c5833"},
|
||||
{file = "arch-5.3.1-cp38-cp38-win_amd64.whl", hash = "sha256:0cb9b0c5751a3a0ecefe47842b40a04dae393d7754489128ec22df0649d49b52"},
|
||||
{file = "arch-5.3.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:03a5cb976ffb230f59d827242e072cf605f70a993be0e7069d30378e13cb60f5"},
|
||||
{file = "arch-5.3.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:38857f8b2ca2fc46c7e1ac7889354eb4f16e7360283586a3730004097648b539"},
|
||||
{file = "arch-5.3.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fd37af7633ae1d5d5719b5eaa7ed97b9a3450f2ed699e188c2c67f7e88ca7b44"},
|
||||
{file = "arch-5.3.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:236a8dc7414d557a59cae5dd39efff4fb49ab3fb792b68212f6c03a0c088d947"},
|
||||
{file = "arch-5.3.1-cp39-cp39-win32.whl", hash = "sha256:aabfc7b96416d6b3054164292ee364d1e86d2906a152faf1489562ba1669b2df"},
|
||||
{file = "arch-5.3.1-cp39-cp39-win_amd64.whl", hash = "sha256:bed3352ab7d4ae79a206acb618f786a3f4bc4080e1b90f8c0b19c5a070a365a0"},
|
||||
{file = "arch-5.3.1.tar.gz", hash = "sha256:106f15c8770a34f71239b6c88f8517814e6b7fea3b8f2e009b3a8a23fd7e77c2"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.17"
|
||||
pandas = ">=1.0"
|
||||
property-cached = ">=1.6.4"
|
||||
scipy = ">=1.3"
|
||||
statsmodels = ">=0.11"
|
||||
|
||||
[[package]]
|
||||
name = "argon2-cffi"
|
||||
version = "23.1.0"
|
||||
@ -719,6 +758,69 @@ traitlets = ">=4"
|
||||
[package.extras]
|
||||
test = ["pytest"]
|
||||
|
||||
[[package]]
|
||||
name = "contourpy"
|
||||
version = "1.2.1"
|
||||
description = "Python library for calculating contours of 2D quadrilateral grids"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "contourpy-1.2.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bd7c23df857d488f418439686d3b10ae2fbf9bc256cd045b37a8c16575ea1040"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5b9eb0ca724a241683c9685a484da9d35c872fd42756574a7cfbf58af26677fd"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4c75507d0a55378240f781599c30e7776674dbaf883a46d1c90f37e563453480"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:11959f0ce4a6f7b76ec578576a0b61a28bdc0696194b6347ba3f1c53827178b9"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:eb3315a8a236ee19b6df481fc5f997436e8ade24a9f03dfdc6bd490fea20c6da"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39f3ecaf76cd98e802f094e0d4fbc6dc9c45a8d0c4d185f0f6c2234e14e5f75b"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:94b34f32646ca0414237168d68a9157cb3889f06b096612afdd296003fdd32fd"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:457499c79fa84593f22454bbd27670227874cd2ff5d6c84e60575c8b50a69619"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-win32.whl", hash = "sha256:ac58bdee53cbeba2ecad824fa8159493f0bf3b8ea4e93feb06c9a465d6c87da8"},
|
||||
{file = "contourpy-1.2.1-cp310-cp310-win_amd64.whl", hash = "sha256:9cffe0f850e89d7c0012a1fb8730f75edd4320a0a731ed0c183904fe6ecfc3a9"},
|
||||
{file = "contourpy-1.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6022cecf8f44e36af10bd9118ca71f371078b4c168b6e0fab43d4a889985dbb5"},
|
||||
{file = "contourpy-1.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ef5adb9a3b1d0c645ff694f9bca7702ec2c70f4d734f9922ea34de02294fdf72"},
|
||||
{file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6150ffa5c767bc6332df27157d95442c379b7dce3a38dff89c0f39b63275696f"},
|
||||
{file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4c863140fafc615c14a4bf4efd0f4425c02230eb8ef02784c9a156461e62c965"},
|
||||
{file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:00e5388f71c1a0610e6fe56b5c44ab7ba14165cdd6d695429c5cd94021e390b2"},
|
||||
{file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d4492d82b3bc7fbb7e3610747b159869468079fe149ec5c4d771fa1f614a14df"},
|
||||
{file = "contourpy-1.2.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:49e70d111fee47284d9dd867c9bb9a7058a3c617274900780c43e38d90fe1205"},
|
||||
{file = "contourpy-1.2.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:b59c0ffceff8d4d3996a45f2bb6f4c207f94684a96bf3d9728dbb77428dd8cb8"},
|
||||
{file = "contourpy-1.2.1-cp311-cp311-win32.whl", hash = "sha256:7b4182299f251060996af5249c286bae9361fa8c6a9cda5efc29fe8bfd6062ec"},
|
||||
{file = "contourpy-1.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:2855c8b0b55958265e8b5888d6a615ba02883b225f2227461aa9127c578a4922"},
|
||||
{file = "contourpy-1.2.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:62828cada4a2b850dbef89c81f5a33741898b305db244904de418cc957ff05dc"},
|
||||
{file = "contourpy-1.2.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:309be79c0a354afff9ff7da4aaed7c3257e77edf6c1b448a779329431ee79d7e"},
|
||||
{file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e785e0f2ef0d567099b9ff92cbfb958d71c2d5b9259981cd9bee81bd194c9a4"},
|
||||
{file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1cac0a8f71a041aa587410424ad46dfa6a11f6149ceb219ce7dd48f6b02b87a7"},
|
||||
{file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:af3f4485884750dddd9c25cb7e3915d83c2db92488b38ccb77dd594eac84c4a0"},
|
||||
{file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ce6889abac9a42afd07a562c2d6d4b2b7134f83f18571d859b25624a331c90b"},
|
||||
{file = "contourpy-1.2.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:a1eea9aecf761c661d096d39ed9026574de8adb2ae1c5bd7b33558af884fb2ce"},
|
||||
{file = "contourpy-1.2.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:187fa1d4c6acc06adb0fae5544c59898ad781409e61a926ac7e84b8f276dcef4"},
|
||||
{file = "contourpy-1.2.1-cp312-cp312-win32.whl", hash = "sha256:c2528d60e398c7c4c799d56f907664673a807635b857df18f7ae64d3e6ce2d9f"},
|
||||
{file = "contourpy-1.2.1-cp312-cp312-win_amd64.whl", hash = "sha256:1a07fc092a4088ee952ddae19a2b2a85757b923217b7eed584fdf25f53a6e7ce"},
|
||||
{file = "contourpy-1.2.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bb6834cbd983b19f06908b45bfc2dad6ac9479ae04abe923a275b5f48f1a186b"},
|
||||
{file = "contourpy-1.2.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:1d59e739ab0e3520e62a26c60707cc3ab0365d2f8fecea74bfe4de72dc56388f"},
|
||||
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||||
[package.dependencies]
|
||||
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|
||||
|
||||
[package.extras]
|
||||
bokeh = ["bokeh", "selenium"]
|
||||
docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
|
||||
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|
||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
@ -798,6 +900,21 @@ files = [
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||||
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||||
|
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[[package]]
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|
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|
||||
|
||||
[[package]]
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||||
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|
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@ -1174,6 +1291,71 @@ docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1
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|
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[[package]]
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name = "fonttools"
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||||
|
||||
[package.dependencies]
|
||||
numpy = {version = ">=1.23.2", markers = "python_version == \"3.11\""}
|
||||
python-dateutil = ">=2.8.2"
|
||||
pytz = ">=2020.1"
|
||||
tzdata = ">=2022.7"
|
||||
|
||||
[package.extras]
|
||||
all = ["PyQt5 (>=5.15.9)", "SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)", "beautifulsoup4 (>=4.11.2)", "bottleneck (>=1.3.6)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=2022.12.0)", "fsspec (>=2022.11.0)", "gcsfs (>=2022.11.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.9.2)", "matplotlib (>=3.6.3)", "numba (>=0.56.4)", "numexpr (>=2.8.4)", "odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "pandas-gbq (>=0.19.0)", "psycopg2 (>=2.9.6)", "pyarrow (>=10.0.1)", "pymysql (>=1.0.2)", "pyreadstat (>=1.2.0)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "qtpy (>=2.3.0)", "s3fs (>=2022.11.0)", "scipy (>=1.10.0)", "tables (>=3.8.0)", "tabulate (>=0.9.0)", "xarray (>=2022.12.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)", "zstandard (>=0.19.0)"]
|
||||
aws = ["s3fs (>=2022.11.0)"]
|
||||
clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.3.0)"]
|
||||
compression = ["zstandard (>=0.19.0)"]
|
||||
computation = ["scipy (>=1.10.0)", "xarray (>=2022.12.0)"]
|
||||
consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
|
||||
excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)"]
|
||||
feather = ["pyarrow (>=10.0.1)"]
|
||||
fss = ["fsspec (>=2022.11.0)"]
|
||||
gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"]
|
||||
hdf5 = ["tables (>=3.8.0)"]
|
||||
html = ["beautifulsoup4 (>=4.11.2)", "html5lib (>=1.1)", "lxml (>=4.9.2)"]
|
||||
mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"]
|
||||
output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"]
|
||||
parquet = ["pyarrow (>=10.0.1)"]
|
||||
performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"]
|
||||
plot = ["matplotlib (>=3.6.3)"]
|
||||
postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"]
|
||||
pyarrow = ["pyarrow (>=10.0.1)"]
|
||||
spss = ["pyreadstat (>=1.2.0)"]
|
||||
sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"]
|
||||
test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
|
||||
xml = ["lxml (>=4.9.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "pandocfilters"
|
||||
version = "1.5.0"
|
||||
@ -3460,6 +3942,24 @@ files = [
|
||||
{file = "pathtools-0.1.2.tar.gz", hash = "sha256:7c35c5421a39bb82e58018febd90e3b6e5db34c5443aaaf742b3f33d4655f1c0"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "patsy"
|
||||
version = "0.5.6"
|
||||
description = "A Python package for describing statistical models and for building design matrices."
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "patsy-0.5.6-py2.py3-none-any.whl", hash = "sha256:19056886fd8fa71863fa32f0eb090267f21fb74be00f19f5c70b2e9d76c883c6"},
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{file = "patsy-0.5.6.tar.gz", hash = "sha256:95c6d47a7222535f84bff7f63d7303f2e297747a598db89cf5c67f0c0c7d2cdb"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.4"
|
||||
six = "*"
|
||||
|
||||
[package.extras]
|
||||
test = ["pytest", "pytest-cov", "scipy"]
|
||||
|
||||
[[package]]
|
||||
name = "pettingzoo"
|
||||
version = "1.24.2"
|
||||
@ -3503,7 +4003,7 @@ ptyprocess = ">=0.5"
|
||||
name = "pillow"
|
||||
version = "10.2.0"
|
||||
description = "Python Imaging Library (Fork)"
|
||||
optional = true
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pillow-10.2.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:7823bdd049099efa16e4246bdf15e5a13dbb18a51b68fa06d6c1d4d8b99a796e"},
|
||||
@ -3709,6 +4209,17 @@ files = [
|
||||
[package.dependencies]
|
||||
wcwidth = "*"
|
||||
|
||||
[[package]]
|
||||
name = "property-cached"
|
||||
version = "1.6.4"
|
||||
description = "A decorator for caching properties in classes (forked from cached-property)."
|
||||
optional = true
|
||||
python-versions = ">= 3.5"
|
||||
files = [
|
||||
{file = "property-cached-1.6.4.zip", hash = "sha256:3e9c4ef1ed3653909147510481d7df62a3cfb483461a6986a6f1dcd09b2ebb73"},
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "protobuf"
|
||||
version = "3.20.3"
|
||||
@ -4094,6 +4605,20 @@ files = [
|
||||
{file = "PyOpenGL-3.1.7.tar.gz", hash = "sha256:eef31a3888e6984fd4d8e6c9961b184c9813ca82604d37fe3da80eb000a76c86"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pyparsing"
|
||||
version = "3.1.2"
|
||||
description = "pyparsing module - Classes and methods to define and execute parsing grammars"
|
||||
optional = false
|
||||
python-versions = ">=3.6.8"
|
||||
files = [
|
||||
{file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"},
|
||||
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|
||||
]
|
||||
|
||||
[package.extras]
|
||||
diagrams = ["jinja2", "railroad-diagrams"]
|
||||
|
||||
[[package]]
|
||||
name = "pytest"
|
||||
version = "7.4.3"
|
||||
@ -4157,6 +4682,17 @@ files = [
|
||||
{file = "python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pytz"
|
||||
version = "2024.1"
|
||||
description = "World timezone definitions, modern and historical"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
|
||||
{file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pywin32"
|
||||
version = "306"
|
||||
@ -4537,6 +5073,28 @@ files = [
|
||||
{file = "rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "rliable"
|
||||
version = "1.0.8"
|
||||
description = "rliable: Reliable evaluation on reinforcement learning and machine learning benchmarks."
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = []
|
||||
develop = false
|
||||
|
||||
[package.dependencies]
|
||||
absl-py = ">=0.9.0"
|
||||
arch = "5.3.1"
|
||||
numpy = ">=1.16.4"
|
||||
scipy = ">=1.7.0"
|
||||
seaborn = ">=0.11.2"
|
||||
|
||||
[package.source]
|
||||
type = "git"
|
||||
url = "https://github.com/aai-institute/rliable.git"
|
||||
reference = "HEAD"
|
||||
resolved_reference = "c756ac408d15507481166edb252f5b61cf5628ff"
|
||||
|
||||
[[package]]
|
||||
name = "rpds-py"
|
||||
version = "0.13.0"
|
||||
@ -4804,6 +5362,27 @@ dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyl
|
||||
doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
|
||||
test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
|
||||
|
||||
[[package]]
|
||||
name = "seaborn"
|
||||
version = "0.13.2"
|
||||
description = "Statistical data visualization"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987"},
|
||||
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
matplotlib = ">=3.4,<3.6.1 || >3.6.1"
|
||||
numpy = ">=1.20,<1.24.0 || >1.24.0"
|
||||
pandas = ">=1.2"
|
||||
|
||||
[package.extras]
|
||||
dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"]
|
||||
docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
|
||||
stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
|
||||
|
||||
[[package]]
|
||||
name = "send2trash"
|
||||
version = "1.8.2"
|
||||
@ -5631,6 +6210,101 @@ pure-eval = "*"
|
||||
[package.extras]
|
||||
tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
|
||||
|
||||
[[package]]
|
||||
name = "statsmodels"
|
||||
version = "0.14.0"
|
||||
description = "Statistical computations and models for Python"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
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|
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|
||||
{file = "statsmodels-0.14.0-cp311-cp311-win_amd64.whl", hash = "sha256:a6ad7b8aadccd4e4dd7f315a07bef1bca41d194eeaf4ec600d20dea02d242fce"},
|
||||
{file = "statsmodels-0.14.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:0eea4a0b761aebf0c355b726ac5616b9a8b618bd6e81a96b9f998a61f4fd7484"},
|
||||
{file = "statsmodels-0.14.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4c815ce7a699047727c65a7c179bff4031cff9ae90c78ca730cfd5200eb025dd"},
|
||||
{file = "statsmodels-0.14.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:575f61337c8e406ae5fa074d34bc6eb77b5a57c544b2d4ee9bc3da6a0a084cf1"},
|
||||
{file = "statsmodels-0.14.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8be53cdeb82f49c4cb0fda6d7eeeb2d67dbd50179b3e1033510e061863720d93"},
|
||||
{file = "statsmodels-0.14.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:6f7d762df4e04d1dde8127d07e91aff230eae643aa7078543e60e83e7d5b40db"},
|
||||
{file = "statsmodels-0.14.0-cp312-cp312-win_amd64.whl", hash = "sha256:fc2c7931008a911e3060c77ea8933f63f7367c0f3af04f82db3a04808ad2cd2c"},
|
||||
{file = "statsmodels-0.14.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:3757542c95247e4ab025291a740efa5da91dc11a05990c033d40fce31c450dc9"},
|
||||
{file = "statsmodels-0.14.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:de489e3ed315bdba55c9d1554a2e89faa65d212e365ab81bc323fa52681fc60e"},
|
||||
{file = "statsmodels-0.14.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76e290f4718177bffa8823a780f3b882d56dd64ad1c18cfb4bc8b5558f3f5757"},
|
||||
{file = "statsmodels-0.14.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71054f9dbcead56def14e3c9db6f66f943110fdfb19713caf0eb0f08c1ec03fd"},
|
||||
{file = "statsmodels-0.14.0-cp38-cp38-win_amd64.whl", hash = "sha256:d7fda067837df94e0a614d93d3a38fb6868958d37f7f50afe2a534524f2660cb"},
|
||||
{file = "statsmodels-0.14.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:1c7724ad573af26139a98393ae64bc318d1b19762b13442d96c7a3e793f495c3"},
|
||||
{file = "statsmodels-0.14.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3b0a135f3bfdeec987e36e3b3b4c53e0bb87a8d91464d2fcc4d169d176f46fdb"},
|
||||
{file = "statsmodels-0.14.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce28eb1c397dba437ec39b9ab18f2101806f388c7a0cf9cdfd8f09294ad1c799"},
|
||||
{file = "statsmodels-0.14.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:68b1c768dd94cc5ba8398121a632b673c625491aa7ed627b82cb4c880a25563f"},
|
||||
{file = "statsmodels-0.14.0-cp39-cp39-win_amd64.whl", hash = "sha256:8d1e3e10dfbfcd58119ba5a4d3c7d519182b970a2aebaf0b6f539f55ae16058d"},
|
||||
{file = "statsmodels-0.14.0.tar.gz", hash = "sha256:6875c7d689e966d948f15eb816ab5616f4928706b180cf470fd5907ab6f647a4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.18"
|
||||
packaging = ">=21.3"
|
||||
pandas = ">=1.0"
|
||||
patsy = ">=0.5.2"
|
||||
scipy = ">=1.4,<1.9.2 || >1.9.2"
|
||||
|
||||
[package.extras]
|
||||
build = ["cython (>=0.29.26)"]
|
||||
develop = ["colorama", "cython (>=0.29.26)", "cython (>=0.29.28,<3.0.0)", "flake8", "isort", "joblib", "matplotlib (>=3)", "oldest-supported-numpy (>=2022.4.18)", "pytest (>=7.0.1,<7.1.0)", "pytest-randomly", "pytest-xdist", "pywinpty", "setuptools-scm[toml] (>=7.0.0,<7.1.0)"]
|
||||
docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
|
||||
|
||||
[[package]]
|
||||
name = "statsmodels"
|
||||
version = "0.14.2"
|
||||
description = "Statistical computations and models for Python"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "statsmodels-0.14.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df5d6f95c46f0341da6c79ee7617e025bf2b371e190a8e60af1ae9cabbdb7a97"},
|
||||
{file = "statsmodels-0.14.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a87ef21fadb445b650f327340dde703f13aec1540f3d497afb66324499dea97a"},
|
||||
{file = "statsmodels-0.14.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5827a12e3ede2b98a784476d61d6bec43011fedb64aa815f2098e0573bece257"},
|
||||
{file = "statsmodels-0.14.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:10f2b7611a61adb7d596a6d239abdf1a4d5492b931b00d5ed23d32844d40e48e"},
|
||||
{file = "statsmodels-0.14.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:c254c66142f1167b4c7d031cf8db55294cc62ff3280e090fc45bd10a7f5fd029"},
|
||||
{file = "statsmodels-0.14.2-cp310-cp310-win_amd64.whl", hash = "sha256:0e46e9d59293c1af4cc1f4e5248f17e7e7bc596bfce44d327c789ac27f09111b"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:50fcb633987779e795142f51ba49fb27648d46e8a1382b32ebe8e503aaabaa9e"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:876794068abfaeed41df71b7887000031ecf44fbfa6b50d53ccb12ebb4ab747a"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7a91f6c4943de13e3ce2e20ee3b5d26d02bd42300616a421becd53756f5deb37"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4864a1c4615c5ea5f2e3b078a75bdedc90dd9da210a37e0738e064b419eccee2"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:afbd92410e0df06f3d8c4e7c0e2e71f63f4969531f280fb66059e2ecdb6e0415"},
|
||||
{file = "statsmodels-0.14.2-cp311-cp311-win_amd64.whl", hash = "sha256:8e004cfad0e46ce73fe3f3812010c746f0d4cfd48e307b45c14e9e360f3d2510"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:eb0ba1ad3627705f5ae20af6b2982f500546d43892543b36c7bca3e2f87105e7"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:90fd2f0110b73fc3fa5a2f21c3ca99b0e22285cccf38e56b5b8fd8ce28791b0f"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ac780ad9ff552773798829a0b9c46820b0faa10e6454891f5e49a845123758ab"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:55d1742778400ae67acb04b50a2c7f5804182f8a874bd09ca397d69ed159a751"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:f870d14a587ea58a3b596aa994c2ed889cc051f9e450e887d2c83656fc6a64bf"},
|
||||
{file = "statsmodels-0.14.2-cp312-cp312-win_amd64.whl", hash = "sha256:f450fcbae214aae66bd9d2b9af48e0f8ba1cb0e8596c6ebb34e6e3f0fec6542c"},
|
||||
{file = "statsmodels-0.14.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:201c3d00929c4a67cda1fe05b098c8dcf1b1eeefa88e80a8f963a844801ed59f"},
|
||||
{file = "statsmodels-0.14.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9edefa4ce08e40bc1d67d2f79bc686ee5e238e801312b5a029ee7786448c389a"},
|
||||
{file = "statsmodels-0.14.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:29c78a7601fdae1aa32104c5ebff2e0b72c26f33e870e2f94ab1bcfd927ece9b"},
|
||||
{file = "statsmodels-0.14.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f36494df7c03d63168fccee5038a62f469469ed6a4dd6eaeb9338abedcd0d5f5"},
|
||||
{file = "statsmodels-0.14.2-cp39-cp39-win_amd64.whl", hash = "sha256:8875823bdd41806dc853333cc4e1b7ef9481bad2380a999e66ea42382cf2178d"},
|
||||
{file = "statsmodels-0.14.2.tar.gz", hash = "sha256:890550147ad3a81cda24f0ba1a5c4021adc1601080bd00e191ae7cd6feecd6ad"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.22.3"
|
||||
packaging = ">=21.3"
|
||||
pandas = ">=1.4,<2.1.0 || >2.1.0"
|
||||
patsy = ">=0.5.6"
|
||||
scipy = ">=1.8,<1.9.2 || >1.9.2"
|
||||
|
||||
[package.extras]
|
||||
build = ["cython (>=0.29.33)"]
|
||||
develop = ["colorama", "cython (>=0.29.33)", "cython (>=3.0.10,<4)", "flake8", "isort", "joblib", "matplotlib (>=3)", "pytest (>=7.3.0,<8)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pywinpty", "setuptools-scm[toml] (>=8.0,<9.0)"]
|
||||
docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
|
||||
|
||||
[[package]]
|
||||
name = "swig"
|
||||
version = "4.2.0"
|
||||
@ -5972,6 +6646,17 @@ files = [
|
||||
{file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2024.1"
|
||||
description = "Provider of IANA time zone data"
|
||||
optional = false
|
||||
python-versions = ">=2"
|
||||
files = [
|
||||
{file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
|
||||
{file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "uc-micro-py"
|
||||
version = "1.0.2"
|
||||
@ -6245,6 +6930,7 @@ atari = ["ale-py", "autorom", "opencv_python", "shimmy"]
|
||||
box2d = ["box2d_py", "pygame", "swig"]
|
||||
classic-control = ["pygame"]
|
||||
envpool = ["envpool"]
|
||||
eval = ["docstring-parser", "joblib", "jsonargparse", "rliable", "scipy"]
|
||||
mujoco = ["imageio", "mujoco"]
|
||||
mujoco-py = ["cython", "mujoco-py"]
|
||||
pybullet = ["pybullet"]
|
||||
@ -6254,4 +6940,4 @@ vizdoom = ["vizdoom"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.11"
|
||||
content-hash = "a7aa80de549e7af1147d14f9bdd48659b7018732af34022cc734565af1f742e9"
|
||||
content-hash = "719c5e7c9a09f731789aa753aeb7c37443c48a65e9bb058344c4763e17b7fa58"
|
||||
|
||||
@ -28,10 +28,12 @@ python = "^3.11"
|
||||
deepdiff = "^7.0.1"
|
||||
gymnasium = "^0.28.0"
|
||||
h5py = "^3.9.0"
|
||||
matplotlib = ">=3.0.0"
|
||||
numba = "^0.57.1"
|
||||
numpy = "^1"
|
||||
overrides = "^7.4.0"
|
||||
packaging = "*"
|
||||
pandas = ">=2.0.0"
|
||||
pettingzoo = "^1.22"
|
||||
tensorboard = "^2.5.0"
|
||||
# Torch 2.0.1 causes problems, see https://github.com/pytorch/pytorch/issues/100974
|
||||
@ -57,6 +59,7 @@ docstring-parser = { version = "^0.15", optional = true }
|
||||
envpool = { version = "^0.8.2", optional = true, markers = "sys_platform != 'darwin'"}
|
||||
gymnasium-robotics = { version = "*", optional = true }
|
||||
imageio = { version = ">=2.14.1", optional = true }
|
||||
joblib = { version = "*", optional = true }
|
||||
jsonargparse = {version = "^4.24.1", optional = true}
|
||||
# we need <3 b/c of https://github.com/Farama-Foundation/Gymnasium/issues/749
|
||||
mujoco = { version = ">=2.1.5, <3", optional = true }
|
||||
@ -64,6 +67,8 @@ mujoco-py = { version = ">=2.1,<2.2", optional = true }
|
||||
opencv_python = { version = "*", optional = true }
|
||||
pybullet = { version = "*", optional = true }
|
||||
pygame = { version = ">=2.1.3", optional = true }
|
||||
rliable = {optional = true, git = "https://github.com/aai-institute/rliable.git"}
|
||||
scipy = { version = "*", optional = true }
|
||||
shimmy = { version = ">=0.1.0,<1.0", optional = true }
|
||||
swig = { version = "4.*", optional = true }
|
||||
vizdoom = { version = "*", optional = true }
|
||||
@ -78,6 +83,7 @@ pybullet = ["pybullet"]
|
||||
envpool = ["envpool"]
|
||||
robotics = ["gymnasium-robotics"]
|
||||
vizdoom = ["vizdoom"]
|
||||
eval = ["rliable", "joblib", "scipy", "jsonargparse", "docstring-parser"]
|
||||
|
||||
|
||||
[tool.poetry.group.dev]
|
||||
|
||||
@ -2,11 +2,12 @@ from typing import Literal
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
|
||||
from tianshou.utils import BaseLogger
|
||||
from tianshou.utils import TensorboardLogger
|
||||
|
||||
|
||||
class TestBaseLogger:
|
||||
class TestTensorBoardLogger:
|
||||
@staticmethod
|
||||
@pytest.mark.parametrize(
|
||||
"input_dict, expected_output",
|
||||
@ -20,7 +21,8 @@ class TestBaseLogger:
|
||||
| dict[str, dict[str, dict[str, int]]],
|
||||
expected_output: dict[str, int],
|
||||
) -> None:
|
||||
result = BaseLogger.prepare_dict_for_logging(input_dict)
|
||||
logger = TensorboardLogger(SummaryWriter("log/logger"))
|
||||
result = logger.prepare_dict_for_logging(input_dict)
|
||||
assert result == expected_output
|
||||
|
||||
@staticmethod
|
||||
@ -36,7 +38,8 @@ class TestBaseLogger:
|
||||
delimiter: Literal["|", "."],
|
||||
expected_output: dict[str, int],
|
||||
) -> None:
|
||||
result = BaseLogger.prepare_dict_for_logging(input_dict, delimiter=delimiter)
|
||||
logger = TensorboardLogger(SummaryWriter("log/logger"))
|
||||
result = logger.prepare_dict_for_logging(input_dict, delimiter=delimiter)
|
||||
assert result == expected_output
|
||||
|
||||
@staticmethod
|
||||
@ -56,7 +59,8 @@ class TestBaseLogger:
|
||||
exclude_arrays: bool,
|
||||
expected_output: dict[str, np.ndarray],
|
||||
) -> None:
|
||||
result = BaseLogger.prepare_dict_for_logging(input_dict, exclude_arrays=exclude_arrays)
|
||||
logger = TensorboardLogger(SummaryWriter("log/logger"))
|
||||
result = logger.prepare_dict_for_logging(input_dict, exclude_arrays=exclude_arrays)
|
||||
assert result.keys() == expected_output.keys()
|
||||
for val1, val2 in zip(result.values(), expected_output.values(), strict=True):
|
||||
assert np.all(val1 == val2)
|
||||
@ -72,5 +76,6 @@ class TestBaseLogger:
|
||||
input_dict: dict[str, tuple[Literal[1]] | dict[str, str | dict[str, int]]],
|
||||
expected_output: dict[str, int],
|
||||
) -> None:
|
||||
result = BaseLogger.prepare_dict_for_logging(input_dict)
|
||||
logger = TensorboardLogger(SummaryWriter("log/logger"))
|
||||
result = logger.prepare_dict_for_logging(input_dict)
|
||||
assert result == expected_output
|
||||
|
||||
@ -6,9 +6,19 @@ from tianshou.highlevel.env import (
|
||||
|
||||
class DiscreteTestEnvFactory(EnvFactoryRegistered):
|
||||
def __init__(self) -> None:
|
||||
super().__init__(task="CartPole-v0", seed=42, venv_type=VectorEnvType.DUMMY)
|
||||
super().__init__(
|
||||
task="CartPole-v0",
|
||||
train_seed=42,
|
||||
test_seed=1337,
|
||||
venv_type=VectorEnvType.DUMMY,
|
||||
)
|
||||
|
||||
|
||||
class ContinuousTestEnvFactory(EnvFactoryRegistered):
|
||||
def __init__(self) -> None:
|
||||
super().__init__(task="Pendulum-v1", seed=42, venv_type=VectorEnvType.DUMMY)
|
||||
super().__init__(
|
||||
task="Pendulum-v1",
|
||||
train_seed=42,
|
||||
test_seed=1337,
|
||||
venv_type=VectorEnvType.DUMMY,
|
||||
)
|
||||
|
||||
@ -49,7 +49,7 @@ def test_experiment_builder_continuous_default_params(builder_cls: type[Experime
|
||||
sampling_config=sampling_config,
|
||||
)
|
||||
experiment = builder.build()
|
||||
experiment.run("test")
|
||||
experiment.run(override_experiment_name="test")
|
||||
print(experiment)
|
||||
|
||||
|
||||
@ -77,5 +77,32 @@ def test_experiment_builder_discrete_default_params(builder_cls: type[Experiment
|
||||
sampling_config=sampling_config,
|
||||
)
|
||||
experiment = builder.build()
|
||||
experiment.run("test")
|
||||
experiment.run(override_experiment_name="test")
|
||||
print(experiment)
|
||||
|
||||
|
||||
def test_temp_builder_modification() -> None:
|
||||
env_factory = DiscreteTestEnvFactory()
|
||||
sampling_config = SamplingConfig(
|
||||
num_epochs=1,
|
||||
step_per_epoch=100,
|
||||
num_train_envs=2,
|
||||
num_test_envs=2,
|
||||
)
|
||||
builder = PPOExperimentBuilder(
|
||||
experiment_config=ExperimentConfig(persistence_enabled=False),
|
||||
env_factory=env_factory,
|
||||
sampling_config=sampling_config,
|
||||
)
|
||||
original_seed = builder.experiment_config.seed
|
||||
original_train_seed = builder.sampling_config.train_seed
|
||||
|
||||
with builder.temp_config_mutation():
|
||||
builder.experiment_config.seed += 12345
|
||||
builder.sampling_config.train_seed += 456
|
||||
exp = builder.build()
|
||||
|
||||
assert builder.experiment_config.seed == original_seed
|
||||
assert builder.sampling_config.train_seed == original_train_seed
|
||||
assert exp.config.seed == original_seed + 12345
|
||||
assert exp.sampling_config.train_seed == original_train_seed + 456
|
||||
|
||||
@ -6,6 +6,12 @@ import torch
|
||||
from tianshou.data import Batch
|
||||
from tianshou.data.batch import BatchProtocol, arr_type
|
||||
|
||||
TNestedDictValue = np.ndarray | dict[str, "TNestedDictValue"]
|
||||
|
||||
|
||||
d: dict[str, TNestedDictValue] = {"a": {"b": np.array([1, 2, 3])}}
|
||||
d["c"] = np.array([1, 2, 3])
|
||||
|
||||
|
||||
class ObsBatchProtocol(BatchProtocol, Protocol):
|
||||
"""Observations of an environment that a policy can turn into actions.
|
||||
|
||||
0
tianshou/evaluation/__init__.py
Normal file
0
tianshou/evaluation/__init__.py
Normal file
75
tianshou/evaluation/launcher.py
Normal file
75
tianshou/evaluation/launcher.py
Normal file
@ -0,0 +1,75 @@
|
||||
"""Provides a basic interface for launching experiments. The API is experimental and subject to change!."""
|
||||
|
||||
import logging
|
||||
from abc import ABC, abstractmethod
|
||||
from collections.abc import Sequence
|
||||
from copy import copy
|
||||
from dataclasses import asdict, dataclass
|
||||
from enum import Enum
|
||||
from typing import Literal
|
||||
|
||||
from joblib import Parallel, delayed
|
||||
|
||||
from tianshou.highlevel.experiment import Experiment
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class JoblibConfig:
|
||||
n_jobs: int = -1
|
||||
"""The maximum number of concurrently running jobs. If -1, all CPUs are used."""
|
||||
backend: Literal["loky", "multiprocessing", "threading"] | None = "loky"
|
||||
"""Allows to hard-code backend, otherwise inferred based on prefer and require."""
|
||||
verbose: int = 10
|
||||
"""If greater than zero, prints progress messages."""
|
||||
|
||||
|
||||
class ExpLauncher(ABC):
|
||||
@abstractmethod
|
||||
def launch(self, experiments: Sequence[Experiment]) -> None:
|
||||
pass
|
||||
|
||||
|
||||
class SequentialExpLauncher(ExpLauncher):
|
||||
def launch(self, experiments: Sequence[Experiment]) -> None:
|
||||
for exp in experiments:
|
||||
exp.run()
|
||||
|
||||
|
||||
class JoblibExpLauncher(ExpLauncher):
|
||||
def __init__(self, joblib_cfg: JoblibConfig | None = None) -> None:
|
||||
self.joblib_cfg = copy(joblib_cfg) if joblib_cfg is not None else JoblibConfig()
|
||||
# Joblib's backend is hard-coded to loky since the threading backend produces different results
|
||||
if self.joblib_cfg.backend != "loky":
|
||||
log.warning(
|
||||
f"Ignoring the user provided joblib backend {self.joblib_cfg.backend} and using loky instead. "
|
||||
f"The current implementation requires loky to work and will be relaxed soon",
|
||||
)
|
||||
self.joblib_cfg.backend = "loky"
|
||||
|
||||
def launch(self, experiments: Sequence[Experiment]) -> None:
|
||||
Parallel(**asdict(self.joblib_cfg))(delayed(self._safe_execute)(exp) for exp in experiments)
|
||||
|
||||
@staticmethod
|
||||
def _safe_execute(exp: Experiment) -> None:
|
||||
try:
|
||||
exp.run()
|
||||
except BaseException as e:
|
||||
log.error(e)
|
||||
|
||||
|
||||
class RegisteredExpLauncher(Enum):
|
||||
joblib = "joblib"
|
||||
sequential = "sequential"
|
||||
|
||||
def create_launcher(self) -> ExpLauncher:
|
||||
match self:
|
||||
case RegisteredExpLauncher.joblib:
|
||||
return JoblibExpLauncher()
|
||||
case RegisteredExpLauncher.sequential:
|
||||
return SequentialExpLauncher()
|
||||
case _:
|
||||
raise NotImplementedError(
|
||||
f"Launcher {self} is not yet implemented.",
|
||||
)
|
||||
218
tianshou/evaluation/rliable_evaluation_hl.py
Normal file
218
tianshou/evaluation/rliable_evaluation_hl.py
Normal file
@ -0,0 +1,218 @@
|
||||
"""The rliable-evaluation module provides a high-level interface to evaluate the results of an experiment with multiple runs
|
||||
on different seeds using the rliable library. The API is experimental and subject to change!.
|
||||
"""
|
||||
|
||||
import os
|
||||
from dataclasses import asdict, dataclass, fields
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import scipy.stats as sst
|
||||
from rliable import library as rly
|
||||
from rliable import plot_utils
|
||||
|
||||
from tianshou.highlevel.experiment import Experiment
|
||||
from tianshou.utils import logging
|
||||
from tianshou.utils.logger.base import DataScope
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class LoggedSummaryData:
|
||||
mean: np.ndarray
|
||||
std: np.ndarray
|
||||
max: np.ndarray
|
||||
min: np.ndarray
|
||||
|
||||
|
||||
@dataclass
|
||||
class LoggedCollectStats:
|
||||
env_step: np.ndarray | None = None
|
||||
n_collected_episodes: np.ndarray | None = None
|
||||
n_collected_steps: np.ndarray | None = None
|
||||
collect_time: np.ndarray | None = None
|
||||
collect_speed: np.ndarray | None = None
|
||||
returns_stat: LoggedSummaryData | None = None
|
||||
lens_stat: LoggedSummaryData | None = None
|
||||
|
||||
@classmethod
|
||||
def from_data_dict(cls, data: dict) -> "LoggedCollectStats":
|
||||
"""Create a LoggedCollectStats object from a dictionary.
|
||||
|
||||
Converts SequenceSummaryStats from dict format to dataclass format and ignores fields that are not present.
|
||||
"""
|
||||
field_names = [f.name for f in fields(cls)]
|
||||
for k, v in data.items():
|
||||
if k not in field_names:
|
||||
data.pop(k)
|
||||
if isinstance(v, dict):
|
||||
data[k] = LoggedSummaryData(**v)
|
||||
return cls(**data)
|
||||
|
||||
|
||||
@dataclass
|
||||
class RLiableExperimentResult:
|
||||
"""The result of an experiment that can be used with the rliable library."""
|
||||
|
||||
exp_dir: str
|
||||
"""The base directory where each sub-directory contains the results of one experiment run."""
|
||||
|
||||
test_episode_returns_RE: np.ndarray
|
||||
"""The test episodes for each run of the experiment where each row corresponds to one run."""
|
||||
|
||||
env_steps_E: np.ndarray
|
||||
"""The number of environment steps at which the test episodes were evaluated."""
|
||||
|
||||
@classmethod
|
||||
def load_from_disk(cls, exp_dir: str) -> "RLiableExperimentResult":
|
||||
"""Load the experiment result from disk.
|
||||
|
||||
:param exp_dir: The directory from where the experiment results are restored.
|
||||
"""
|
||||
test_episode_returns = []
|
||||
env_step_at_test = None
|
||||
|
||||
# TODO: env_step_at_test should not be defined in a loop and overwritten at each iteration
|
||||
# just for retrieving them. We might need a cleaner directory structure.
|
||||
for entry in os.scandir(exp_dir):
|
||||
if entry.name.startswith(".") or not entry.is_dir():
|
||||
continue
|
||||
|
||||
exp = Experiment.from_directory(entry.path)
|
||||
logger = exp.logger_factory.create_logger(
|
||||
entry.path,
|
||||
entry.name,
|
||||
None,
|
||||
asdict(exp.config),
|
||||
)
|
||||
data = logger.restore_logged_data(entry.path)
|
||||
|
||||
if DataScope.TEST.value not in data or not data[DataScope.TEST.value]:
|
||||
continue
|
||||
restored_test_data = data[DataScope.TEST.value]
|
||||
if not isinstance(restored_test_data, dict):
|
||||
raise RuntimeError(
|
||||
f"Expected entry with key {DataScope.TEST.value} data to be a dictionary, "
|
||||
f"but got {restored_test_data=}.",
|
||||
)
|
||||
test_data = LoggedCollectStats.from_data_dict(restored_test_data)
|
||||
|
||||
if test_data.returns_stat is None:
|
||||
continue
|
||||
test_episode_returns.append(test_data.returns_stat.mean)
|
||||
env_step_at_test = test_data.env_step
|
||||
|
||||
if not test_episode_returns or env_step_at_test is None:
|
||||
raise ValueError(f"No experiment data found in {exp_dir}.")
|
||||
|
||||
return cls(
|
||||
test_episode_returns_RE=np.array(test_episode_returns),
|
||||
env_steps_E=np.array(env_step_at_test),
|
||||
exp_dir=exp_dir,
|
||||
)
|
||||
|
||||
def _get_rliable_data(
|
||||
self,
|
||||
algo_name: str | None = None,
|
||||
score_thresholds: np.ndarray | None = None,
|
||||
) -> tuple[dict, np.ndarray, np.ndarray]:
|
||||
"""Return the data in the format expected by the rliable library.
|
||||
|
||||
:param algo_name: The name of the algorithm to be shown in the figure legend. If None, the name of the algorithm
|
||||
is set to the experiment dir.
|
||||
:param score_thresholds: The score thresholds for the performance profile. If None, the thresholds are inferred
|
||||
from the minimum and maximum test episode returns.
|
||||
|
||||
:return: A tuple score_dict, env_steps, and score_thresholds.
|
||||
"""
|
||||
if score_thresholds is None:
|
||||
score_thresholds = np.linspace(
|
||||
np.min(self.test_episode_returns_RE),
|
||||
np.max(self.test_episode_returns_RE),
|
||||
101,
|
||||
)
|
||||
|
||||
if algo_name is None:
|
||||
algo_name = os.path.basename(self.exp_dir)
|
||||
|
||||
score_dict = {algo_name: self.test_episode_returns_RE}
|
||||
|
||||
return score_dict, self.env_steps_E, score_thresholds
|
||||
|
||||
def eval_results(
|
||||
self,
|
||||
algo_name: str | None = None,
|
||||
score_thresholds: np.ndarray | None = None,
|
||||
save_plots: bool = False,
|
||||
show_plots: bool = True,
|
||||
) -> tuple[plt.Figure, plt.Axes, plt.Figure, plt.Axes]:
|
||||
"""Evaluate the results of an experiment and create a sample efficiency curve and a performance profile.
|
||||
|
||||
:param algo_name: The name of the algorithm to be shown in the figure legend. If None, the name of the algorithm
|
||||
is set to the experiment dir.
|
||||
:param score_thresholds: The score thresholds for the performance profile. If None, the thresholds are inferred
|
||||
from the minimum and maximum test episode returns.
|
||||
:param save_plots: If True, the figures are saved to the experiment directory.
|
||||
:param show_plots: If True, the figures are shown.
|
||||
|
||||
:return: The created figures and axes.
|
||||
"""
|
||||
score_dict, env_steps, score_thresholds = self._get_rliable_data(
|
||||
algo_name,
|
||||
score_thresholds,
|
||||
)
|
||||
|
||||
iqm = lambda scores: sst.trim_mean(scores, proportiontocut=0.25, axis=0)
|
||||
iqm_scores, iqm_cis = rly.get_interval_estimates(score_dict, iqm)
|
||||
|
||||
# Plot IQM sample efficiency curve
|
||||
fig_iqm, ax_iqm = plt.subplots(ncols=1, figsize=(7, 5), constrained_layout=True)
|
||||
plot_utils.plot_sample_efficiency_curve(
|
||||
env_steps,
|
||||
iqm_scores,
|
||||
iqm_cis,
|
||||
algorithms=None,
|
||||
xlabel="env step",
|
||||
ylabel="IQM episode return",
|
||||
ax=ax_iqm,
|
||||
)
|
||||
if show_plots:
|
||||
plt.show(block=False)
|
||||
|
||||
if save_plots:
|
||||
iqm_sample_efficiency_curve_path = os.path.abspath(
|
||||
os.path.join(
|
||||
self.exp_dir,
|
||||
"iqm_sample_efficiency_curve.png",
|
||||
),
|
||||
)
|
||||
log.info(f"Saving iqm sample efficiency curve to {iqm_sample_efficiency_curve_path}.")
|
||||
fig_iqm.savefig(iqm_sample_efficiency_curve_path)
|
||||
|
||||
final_score_dict = {algo: returns[:, [-1]] for algo, returns in score_dict.items()}
|
||||
score_distributions, score_distributions_cis = rly.create_performance_profile(
|
||||
final_score_dict,
|
||||
score_thresholds,
|
||||
)
|
||||
|
||||
# Plot score distributions
|
||||
fig_profile, ax_profile = plt.subplots(ncols=1, figsize=(7, 5), constrained_layout=True)
|
||||
plot_utils.plot_performance_profiles(
|
||||
score_distributions,
|
||||
score_thresholds,
|
||||
performance_profile_cis=score_distributions_cis,
|
||||
xlabel=r"Episode return $(\tau)$",
|
||||
ax=ax_profile,
|
||||
)
|
||||
|
||||
if save_plots:
|
||||
profile_curve_path = os.path.abspath(
|
||||
os.path.join(self.exp_dir, "performance_profile.png"),
|
||||
)
|
||||
log.info(f"Saving performance profile curve to {profile_curve_path}.")
|
||||
fig_profile.savefig(profile_curve_path)
|
||||
if show_plots:
|
||||
plt.show(block=False)
|
||||
|
||||
return fig_iqm, ax_iqm, fig_profile, ax_profile
|
||||
@ -53,6 +53,9 @@ class SamplingConfig(ToStringMixin):
|
||||
num_train_envs: int = -1
|
||||
"""the number of training environments to use. If set to -1, use number of CPUs/threads."""
|
||||
|
||||
train_seed: int = 42
|
||||
"""the seed to use for the training environments."""
|
||||
|
||||
num_test_envs: int = 1
|
||||
"""the number of test environments to use"""
|
||||
|
||||
@ -127,6 +130,10 @@ class SamplingConfig(ToStringMixin):
|
||||
temporal aspects (e.g. velocities of moving objects for which only positions are observed).
|
||||
"""
|
||||
|
||||
@property
|
||||
def test_seed(self) -> int:
|
||||
return self.train_seed + self.num_train_envs
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
if self.num_train_envs == -1:
|
||||
self.num_train_envs = multiprocessing.cpu_count()
|
||||
|
||||
@ -412,7 +412,8 @@ class EnvFactoryRegistered(EnvFactory):
|
||||
self,
|
||||
*,
|
||||
task: str,
|
||||
seed: int,
|
||||
train_seed: int,
|
||||
test_seed: int,
|
||||
venv_type: VectorEnvType,
|
||||
envpool_factory: EnvPoolFactory | None = None,
|
||||
render_mode_train: str | None = None,
|
||||
@ -434,7 +435,8 @@ class EnvFactoryRegistered(EnvFactory):
|
||||
super().__init__(venv_type)
|
||||
self.task = task
|
||||
self.envpool_factory = envpool_factory
|
||||
self.seed = seed
|
||||
self.train_seed = train_seed
|
||||
self.test_seed = test_seed
|
||||
self.render_modes = {
|
||||
EnvMode.TRAIN: render_mode_train,
|
||||
EnvMode.TEST: render_mode_test,
|
||||
@ -462,15 +464,16 @@ class EnvFactoryRegistered(EnvFactory):
|
||||
return gymnasium.make(self.task, **kwargs)
|
||||
|
||||
def create_venv(self, num_envs: int, mode: EnvMode) -> BaseVectorEnv:
|
||||
seed = self.train_seed if mode == EnvMode.TRAIN else self.test_seed
|
||||
if self.envpool_factory is not None:
|
||||
return self.envpool_factory.create_venv(
|
||||
self.task,
|
||||
num_envs,
|
||||
mode,
|
||||
self.seed,
|
||||
seed,
|
||||
self._create_kwargs(mode),
|
||||
)
|
||||
else:
|
||||
venv = super().create_venv(num_envs, mode)
|
||||
venv.seed(self.seed)
|
||||
venv.seed(seed)
|
||||
return venv
|
||||
|
||||
@ -1,10 +1,12 @@
|
||||
import os
|
||||
import pickle
|
||||
from abc import abstractmethod
|
||||
from collections.abc import Sequence
|
||||
from collections.abc import Iterator, Sequence
|
||||
from contextlib import contextmanager
|
||||
from copy import copy
|
||||
from dataclasses import dataclass
|
||||
from pprint import pformat
|
||||
from typing import Self
|
||||
from typing import Literal, Self
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
@ -144,6 +146,7 @@ class Experiment(ToStringMixin):
|
||||
agent_factory: AgentFactory,
|
||||
sampling_config: SamplingConfig,
|
||||
logger_factory: LoggerFactory | None = None,
|
||||
name: str | Literal["DATETIME_TAG"] = "DATETIME_TAG",
|
||||
):
|
||||
if logger_factory is None:
|
||||
logger_factory = LoggerFactoryDefault()
|
||||
@ -152,6 +155,22 @@ class Experiment(ToStringMixin):
|
||||
self.env_factory = env_factory
|
||||
self.agent_factory = agent_factory
|
||||
self.logger_factory = logger_factory
|
||||
if name == "DATETIME_TAG":
|
||||
name = datetime_tag()
|
||||
self.name = name
|
||||
|
||||
def get_seeding_info_as_str(self) -> str:
|
||||
"""Useful for creating unique experiment names based on seeds.
|
||||
|
||||
A typical example is to do `experiment.name = f"{experiment.name}_{experiment.get_seeding_info_as_str()}"`.
|
||||
"""
|
||||
return "_".join(
|
||||
[
|
||||
f"exp_seed={self.config.seed}",
|
||||
f"train_seed={self.sampling_config.train_seed}",
|
||||
f"test_seed={self.sampling_config.test_seed}",
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_directory(cls, directory: str, restore_policy: bool = True) -> "Experiment":
|
||||
@ -186,35 +205,42 @@ class Experiment(ToStringMixin):
|
||||
|
||||
def run(
|
||||
self,
|
||||
experiment_name: str | None = None,
|
||||
override_experiment_name: str | Literal["DATETIME_TAG"] | None = None,
|
||||
logger_run_id: str | None = None,
|
||||
raise_error_on_dirname_collision: bool = True,
|
||||
) -> ExperimentResult:
|
||||
"""Run the experiment and return the results.
|
||||
|
||||
:param experiment_name: the experiment name, which corresponds to the directory (within the logging
|
||||
:param override_experiment_name: if not None, will adjust the current instance's `name` name attribute.
|
||||
The name corresponds to the directory (within the logging
|
||||
directory) where all results associated with the experiment will be saved.
|
||||
The name may contain path separators (i.e. `os.path.sep`, as used by `os.path.join`), in which case
|
||||
a nested directory structure will be created.
|
||||
If None, use a name containing the current date and time.
|
||||
If "DATETIME_TAG" is passed, use a name containing the current date and time. This option
|
||||
is useful for preventing file-name collisions if a single experiment is executed repeatedly.
|
||||
:param logger_run_id: Run identifier to use for logger initialization/resumption (applies when
|
||||
using wandb, in particular).
|
||||
:param raise_error_on_dirname_collision: set to `False` e.g., when continuing a previously executed
|
||||
experiment with the same name.
|
||||
:return:
|
||||
"""
|
||||
if experiment_name is None:
|
||||
experiment_name = datetime_tag()
|
||||
if override_experiment_name is not None:
|
||||
if override_experiment_name == "DATETIME_TAG":
|
||||
override_experiment_name = datetime_tag()
|
||||
self.name = override_experiment_name
|
||||
|
||||
# initialize persistence directory
|
||||
use_persistence = self.config.persistence_enabled
|
||||
persistence_dir = os.path.join(self.config.persistence_base_dir, experiment_name)
|
||||
persistence_dir = os.path.join(self.config.persistence_base_dir, self.name)
|
||||
if use_persistence:
|
||||
os.makedirs(persistence_dir, exist_ok=True)
|
||||
os.makedirs(persistence_dir, exist_ok=not raise_error_on_dirname_collision)
|
||||
|
||||
with logging.FileLoggerContext(
|
||||
os.path.join(persistence_dir, self.LOG_FILENAME),
|
||||
enabled=use_persistence and self.config.log_file_enabled,
|
||||
):
|
||||
# log initial information
|
||||
log.info(f"Running experiment (name='{experiment_name}'):\n{self.pprints()}")
|
||||
log.info(f"Running experiment (name='{self.name}'):\n{self.pprints()}")
|
||||
log.info(f"Working directory: {os.getcwd()}")
|
||||
|
||||
self._set_seed()
|
||||
@ -245,7 +271,7 @@ class Experiment(ToStringMixin):
|
||||
if use_persistence:
|
||||
logger = self.logger_factory.create_logger(
|
||||
log_dir=persistence_dir,
|
||||
experiment_name=experiment_name,
|
||||
experiment_name=self.name,
|
||||
run_id=logger_run_id,
|
||||
config_dict=full_config,
|
||||
)
|
||||
@ -338,6 +364,32 @@ class ExperimentBuilder:
|
||||
self._optim_factory: OptimizerFactory | None = None
|
||||
self._policy_wrapper_factory: PolicyWrapperFactory | None = None
|
||||
self._trainer_callbacks: TrainerCallbacks = TrainerCallbacks()
|
||||
self._experiment_name: str = ""
|
||||
|
||||
@contextmanager
|
||||
def temp_config_mutation(self) -> Iterator[Self]:
|
||||
"""Returns the builder instance where the configs can be modified without affecting the current instance."""
|
||||
original_sampling_config = copy(self.sampling_config)
|
||||
original_experiment_config = copy(self.experiment_config)
|
||||
yield self
|
||||
self.sampling_config = original_sampling_config
|
||||
self.experiment_config = original_experiment_config
|
||||
|
||||
@property
|
||||
def experiment_config(self) -> ExperimentConfig:
|
||||
return self._config
|
||||
|
||||
@experiment_config.setter
|
||||
def experiment_config(self, experiment_config: ExperimentConfig) -> None:
|
||||
self._config = experiment_config
|
||||
|
||||
@property
|
||||
def sampling_config(self) -> SamplingConfig:
|
||||
return self._sampling_config
|
||||
|
||||
@sampling_config.setter
|
||||
def sampling_config(self, sampling_config: SamplingConfig) -> None:
|
||||
self._sampling_config = sampling_config
|
||||
|
||||
def with_logger_factory(self, logger_factory: LoggerFactory) -> Self:
|
||||
"""Allows to customize the logger factory to use.
|
||||
@ -415,6 +467,20 @@ class ExperimentBuilder:
|
||||
self._trainer_callbacks.epoch_stop_callback = callback
|
||||
return self
|
||||
|
||||
def with_experiment_name(
|
||||
self,
|
||||
experiment_name: str | Literal["DATETIME_TAG"] = "DATETIME_TAG",
|
||||
) -> Self:
|
||||
"""Sets the name of the experiment.
|
||||
|
||||
:param experiment_name: the name. If "DATETIME_TAG" (default) is given, the current date and time will be used.
|
||||
:return: the builder
|
||||
"""
|
||||
if experiment_name == "DATETIME_TAG":
|
||||
experiment_name = datetime_tag()
|
||||
self._experiment_name = experiment_name
|
||||
return self
|
||||
|
||||
@abstractmethod
|
||||
def _create_agent_factory(self) -> AgentFactory:
|
||||
pass
|
||||
@ -425,9 +491,12 @@ class ExperimentBuilder:
|
||||
else:
|
||||
return self._optim_factory
|
||||
|
||||
def build(self) -> Experiment:
|
||||
def build(self, add_seeding_info_to_name: bool = False) -> Experiment:
|
||||
"""Creates the experiment based on the options specified via this builder.
|
||||
|
||||
:param add_seeding_info_to_name: whether to add a postfix to the experiment name that contains
|
||||
info about the training seeds. Useful for creating multiple experiments that only differ
|
||||
by seeds.
|
||||
:return: the experiment
|
||||
"""
|
||||
agent_factory = self._create_agent_factory()
|
||||
@ -440,9 +509,30 @@ class ExperimentBuilder:
|
||||
agent_factory,
|
||||
self._sampling_config,
|
||||
self._logger_factory,
|
||||
name=self._experiment_name,
|
||||
)
|
||||
if add_seeding_info_to_name:
|
||||
if not experiment.name:
|
||||
experiment.name = experiment.get_seeding_info_as_str()
|
||||
else:
|
||||
experiment.name = f"{experiment.name}_{experiment.get_seeding_info_as_str()}"
|
||||
return experiment
|
||||
|
||||
def build_default_seeded_experiments(self, num_experiments: int) -> list[Experiment]:
|
||||
"""Creates a list of experiments with non-overlapping seeds, starting from the configured seed.
|
||||
|
||||
Each experiment will have a unique name that is created from the original experiment name and the seeds used.
|
||||
"""
|
||||
num_train_envs = self.sampling_config.num_train_envs
|
||||
|
||||
seeded_experiments = []
|
||||
for i in range(num_experiments):
|
||||
with self.temp_config_mutation():
|
||||
self.experiment_config.seed += i
|
||||
self.sampling_config.train_seed += i * num_train_envs
|
||||
seeded_experiments.append(self.build(add_seeding_info_to_name=True))
|
||||
return seeded_experiments
|
||||
|
||||
|
||||
class _BuilderMixinActorFactory(ActorFutureProviderProtocol):
|
||||
def __init__(self, continuous_actor_type: ContinuousActorType):
|
||||
|
||||
@ -32,7 +32,7 @@ class LoggerFactory(ToStringMixin, ABC):
|
||||
class LoggerFactoryDefault(LoggerFactory):
|
||||
def __init__(
|
||||
self,
|
||||
logger_type: Literal["tensorboard", "wandb"] = "tensorboard",
|
||||
logger_type: Literal["tensorboard", "wandb", "pandas"] = "tensorboard",
|
||||
wandb_project: str | None = None,
|
||||
):
|
||||
if logger_type == "wandb" and wandb_project is None:
|
||||
@ -47,17 +47,18 @@ class LoggerFactoryDefault(LoggerFactory):
|
||||
run_id: str | None,
|
||||
config_dict: dict,
|
||||
) -> TLogger:
|
||||
writer = SummaryWriter(log_dir)
|
||||
writer.add_text(
|
||||
"args",
|
||||
str(
|
||||
dict(
|
||||
log_dir=log_dir,
|
||||
logger_type=self.logger_type,
|
||||
wandb_project=self.wandb_project,
|
||||
if self.logger_type in ["wandb", "tensorboard"]:
|
||||
writer = SummaryWriter(log_dir)
|
||||
writer.add_text(
|
||||
"args",
|
||||
str(
|
||||
dict(
|
||||
log_dir=log_dir,
|
||||
logger_type=self.logger_type,
|
||||
wandb_project=self.wandb_project,
|
||||
),
|
||||
),
|
||||
),
|
||||
)
|
||||
)
|
||||
match self.logger_type:
|
||||
case "wandb":
|
||||
wandb_logger = WandbLogger(
|
||||
|
||||
@ -24,7 +24,7 @@ class World:
|
||||
trainer: Optional["BaseTrainer"] = None
|
||||
|
||||
def persist_path(self, filename: str) -> str:
|
||||
return os.path.join(self.persist_directory, filename)
|
||||
return os.path.abspath(os.path.join(self.persist_directory, filename))
|
||||
|
||||
def restore_path(self, filename: str) -> str:
|
||||
if self.restore_directory is None:
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
"""Utils package."""
|
||||
|
||||
from tianshou.utils.logger.base import BaseLogger, LazyLogger
|
||||
from tianshou.utils.logger.tensorboard import BasicLogger, TensorboardLogger
|
||||
from tianshou.utils.logger.tensorboard import TensorboardLogger
|
||||
from tianshou.utils.logger.wandb import WandbLogger
|
||||
from tianshou.utils.lr_scheduler import MultipleLRSchedulers
|
||||
from tianshou.utils.progress_bar import DummyTqdm, tqdm_config
|
||||
@ -15,7 +15,6 @@ __all__ = [
|
||||
"DummyTqdm",
|
||||
"BaseLogger",
|
||||
"TensorboardLogger",
|
||||
"BasicLogger",
|
||||
"LazyLogger",
|
||||
"WandbLogger",
|
||||
"deprecation",
|
||||
|
||||
@ -3,14 +3,14 @@ from abc import ABC, abstractmethod
|
||||
from collections.abc import Callable
|
||||
from enum import Enum
|
||||
from numbers import Number
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
|
||||
VALID_LOG_VALS_TYPE = int | Number | np.number | np.ndarray | float
|
||||
VALID_LOG_VALS = typing.get_args(
|
||||
VALID_LOG_VALS_TYPE,
|
||||
) # I know it's stupid, but we can't use Union type in isinstance
|
||||
# It's unfortunate, but we can't use Union type in isinstance, hence we resort to this
|
||||
VALID_LOG_VALS = typing.get_args(VALID_LOG_VALS_TYPE)
|
||||
|
||||
TRestoredData = dict[str, np.ndarray | dict[str, "TRestoredData"]]
|
||||
|
||||
|
||||
class DataScope(Enum):
|
||||
@ -21,15 +21,7 @@ class DataScope(Enum):
|
||||
|
||||
|
||||
class BaseLogger(ABC):
|
||||
"""The base class for any logger which is compatible with trainer.
|
||||
|
||||
Try to overwrite write() method to use your own writer.
|
||||
|
||||
:param train_interval: the log interval in log_train_data(). Default to 1000.
|
||||
:param test_interval: the log interval in log_test_data(). Default to 1.
|
||||
:param update_interval: the log interval in log_update_data(). Default to 1000.
|
||||
:param info_interval: the log interval in log_info_data(). Default to 1.
|
||||
"""
|
||||
"""The base class for any logger which is compatible with trainer."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
@ -37,12 +29,20 @@ class BaseLogger(ABC):
|
||||
test_interval: int = 1,
|
||||
update_interval: int = 1000,
|
||||
info_interval: int = 1,
|
||||
exclude_arrays: bool = True,
|
||||
) -> None:
|
||||
""":param train_interval: the log interval in log_train_data(). Default to 1000.
|
||||
:param test_interval: the log interval in log_test_data(). Default to 1.
|
||||
:param update_interval: the log interval in log_update_data(). Default to 1000.
|
||||
:param info_interval: the log interval in log_info_data(). Default to 1.
|
||||
:param exclude_arrays: whether to exclude numpy arrays from the logger's output
|
||||
"""
|
||||
super().__init__()
|
||||
self.train_interval = train_interval
|
||||
self.test_interval = test_interval
|
||||
self.update_interval = update_interval
|
||||
self.info_interval = info_interval
|
||||
self.exclude_arrays = exclude_arrays
|
||||
self.last_log_train_step = -1
|
||||
self.last_log_test_step = -1
|
||||
self.last_log_update_step = -1
|
||||
@ -57,46 +57,15 @@ class BaseLogger(ABC):
|
||||
:param data: the data to write with format ``{key: value}``.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def prepare_dict_for_logging(
|
||||
input_dict: dict[str, Any],
|
||||
parent_key: str = "",
|
||||
delimiter: str = "/",
|
||||
exclude_arrays: bool = True,
|
||||
) -> dict[str, VALID_LOG_VALS_TYPE]:
|
||||
"""Flattens and filters a nested dictionary by recursively traversing all levels and compressing the keys.
|
||||
@abstractmethod
|
||||
def prepare_dict_for_logging(self, log_data: dict) -> dict[str, VALID_LOG_VALS_TYPE]:
|
||||
"""Prepare the dict for logging by filtering out invalid data types.
|
||||
|
||||
Filtering is performed with respect to valid logging data types.
|
||||
If necessary, reformulate the dict to be compatible with the writer.
|
||||
|
||||
:param input_dict: The nested dictionary to be flattened and filtered.
|
||||
:param parent_key: The parent key used as a prefix before the input_dict keys.
|
||||
:param delimiter: The delimiter used to separate the keys.
|
||||
:param exclude_arrays: Whether to exclude numpy arrays from the output.
|
||||
:return: A flattened dictionary where the keys are compressed and values are filtered.
|
||||
:param log_data: the dict to be prepared for logging.
|
||||
:return: the prepared dict.
|
||||
"""
|
||||
result = {}
|
||||
|
||||
def add_to_result(
|
||||
cur_dict: dict,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
for key, value in cur_dict.items():
|
||||
if exclude_arrays and isinstance(value, np.ndarray):
|
||||
continue
|
||||
|
||||
new_key = prefix + delimiter + key
|
||||
new_key = new_key.lstrip(delimiter)
|
||||
|
||||
if isinstance(value, dict):
|
||||
add_to_result(
|
||||
value,
|
||||
new_key,
|
||||
)
|
||||
elif isinstance(value, VALID_LOG_VALS):
|
||||
result[new_key] = value
|
||||
|
||||
add_to_result(input_dict, prefix=parent_key)
|
||||
return result
|
||||
|
||||
def log_train_data(self, log_data: dict, step: int) -> None:
|
||||
"""Use writer to log statistics generated during training.
|
||||
@ -106,8 +75,8 @@ class BaseLogger(ABC):
|
||||
"""
|
||||
# TODO: move interval check to calling method
|
||||
if step - self.last_log_train_step >= self.train_interval:
|
||||
log_data = self.prepare_dict_for_logging(log_data, parent_key=DataScope.TRAIN.value)
|
||||
self.write("train/env_step", step, log_data)
|
||||
log_data = self.prepare_dict_for_logging(log_data)
|
||||
self.write(f"{DataScope.TRAIN.value}/env_step", step, log_data)
|
||||
self.last_log_train_step = step
|
||||
|
||||
def log_test_data(self, log_data: dict, step: int) -> None:
|
||||
@ -118,8 +87,8 @@ class BaseLogger(ABC):
|
||||
"""
|
||||
# TODO: move interval check to calling method (stupid because log_test_data is only called from function in utils.py, not from BaseTrainer)
|
||||
if step - self.last_log_test_step >= self.test_interval:
|
||||
log_data = self.prepare_dict_for_logging(log_data, parent_key=DataScope.TEST.value)
|
||||
self.write(DataScope.TEST.value + "/env_step", step, log_data)
|
||||
log_data = self.prepare_dict_for_logging(log_data)
|
||||
self.write(f"{DataScope.TEST.value}/env_step", step, log_data)
|
||||
self.last_log_test_step = step
|
||||
|
||||
def log_update_data(self, log_data: dict, step: int) -> None:
|
||||
@ -130,8 +99,8 @@ class BaseLogger(ABC):
|
||||
"""
|
||||
# TODO: move interval check to calling method
|
||||
if step - self.last_log_update_step >= self.update_interval:
|
||||
log_data = self.prepare_dict_for_logging(log_data, parent_key=DataScope.UPDATE.value)
|
||||
self.write(DataScope.UPDATE.value + "/gradient_step", step, log_data)
|
||||
log_data = self.prepare_dict_for_logging(log_data)
|
||||
self.write(f"{DataScope.UPDATE.value}/gradient_step", step, log_data)
|
||||
self.last_log_update_step = step
|
||||
|
||||
def log_info_data(self, log_data: dict, step: int) -> None:
|
||||
@ -143,8 +112,8 @@ class BaseLogger(ABC):
|
||||
if (
|
||||
step - self.last_log_info_step >= self.info_interval
|
||||
): # TODO: move interval check to calling method
|
||||
log_data = self.prepare_dict_for_logging(log_data, parent_key=DataScope.INFO.value)
|
||||
self.write(DataScope.INFO.value + "/epoch", step, log_data)
|
||||
log_data = self.prepare_dict_for_logging(log_data)
|
||||
self.write(f"{DataScope.INFO.value}/epoch", step, log_data)
|
||||
self.last_log_info_step = step
|
||||
|
||||
@abstractmethod
|
||||
@ -166,7 +135,7 @@ class BaseLogger(ABC):
|
||||
|
||||
@abstractmethod
|
||||
def restore_data(self) -> tuple[int, int, int]:
|
||||
"""Return the metadata from existing log.
|
||||
"""Restore internal data if present and return the metadata from existing log for continuation of training.
|
||||
|
||||
If it finds nothing or an error occurs during the recover process, it will
|
||||
return the default parameters.
|
||||
@ -174,6 +143,16 @@ class BaseLogger(ABC):
|
||||
:return: epoch, env_step, gradient_step.
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def restore_logged_data(
|
||||
self,
|
||||
log_path: str,
|
||||
) -> TRestoredData:
|
||||
"""Load the logged data from disk for post-processing.
|
||||
|
||||
:return: a dict containing the logged data.
|
||||
"""
|
||||
|
||||
|
||||
class LazyLogger(BaseLogger):
|
||||
"""A logger that does nothing. Used as the placeholder in trainer."""
|
||||
@ -181,6 +160,12 @@ class LazyLogger(BaseLogger):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
def prepare_dict_for_logging(
|
||||
self,
|
||||
data: dict[str, VALID_LOG_VALS_TYPE],
|
||||
) -> dict[str, VALID_LOG_VALS_TYPE]:
|
||||
return data
|
||||
|
||||
def write(self, step_type: str, step: int, data: dict[str, VALID_LOG_VALS_TYPE]) -> None:
|
||||
"""The LazyLogger writes nothing."""
|
||||
|
||||
@ -195,3 +180,6 @@ class LazyLogger(BaseLogger):
|
||||
|
||||
def restore_data(self) -> tuple[int, int, int]:
|
||||
return 0, 0, 0
|
||||
|
||||
def restore_logged_data(self, log_path: str) -> dict:
|
||||
return {}
|
||||
|
||||
@ -1,11 +1,17 @@
|
||||
from collections.abc import Callable
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
from matplotlib.figure import Figure
|
||||
from tensorboard.backend.event_processing import event_accumulator
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
|
||||
from tianshou.utils.logger.base import VALID_LOG_VALS_TYPE, BaseLogger
|
||||
from tianshou.utils.warning import deprecation
|
||||
from tianshou.utils.logger.base import (
|
||||
VALID_LOG_VALS,
|
||||
VALID_LOG_VALS_TYPE,
|
||||
BaseLogger,
|
||||
TRestoredData,
|
||||
)
|
||||
|
||||
|
||||
class TensorboardLogger(BaseLogger):
|
||||
@ -38,9 +44,58 @@ class TensorboardLogger(BaseLogger):
|
||||
self.last_save_step = -1
|
||||
self.writer = writer
|
||||
|
||||
def write(self, step_type: str, step: int, data: dict[str, VALID_LOG_VALS_TYPE]) -> None:
|
||||
def prepare_dict_for_logging(
|
||||
self,
|
||||
input_dict: dict[str, Any],
|
||||
parent_key: str = "",
|
||||
delimiter: str = "/",
|
||||
exclude_arrays: bool = True,
|
||||
) -> dict[str, VALID_LOG_VALS_TYPE]:
|
||||
"""Flattens and filters a nested dictionary by recursively traversing all levels and compressing the keys.
|
||||
|
||||
Filtering is performed with respect to valid logging data types.
|
||||
|
||||
:param input_dict: The nested dictionary to be flattened and filtered.
|
||||
:param parent_key: The parent key used as a prefix before the input_dict keys.
|
||||
:param delimiter: The delimiter used to separate the keys.
|
||||
:param exclude_arrays: Whether to exclude numpy arrays from the output.
|
||||
:return: A flattened dictionary where the keys are compressed and values are filtered.
|
||||
"""
|
||||
result = {}
|
||||
|
||||
def add_to_result(
|
||||
cur_dict: dict,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
for key, value in cur_dict.items():
|
||||
if exclude_arrays and isinstance(value, np.ndarray):
|
||||
continue
|
||||
|
||||
new_key = prefix + delimiter + key
|
||||
new_key = new_key.lstrip(delimiter)
|
||||
|
||||
if isinstance(value, dict):
|
||||
add_to_result(
|
||||
value,
|
||||
new_key,
|
||||
)
|
||||
elif isinstance(value, VALID_LOG_VALS):
|
||||
result[new_key] = value
|
||||
|
||||
add_to_result(input_dict, prefix=parent_key)
|
||||
return result
|
||||
|
||||
def write(self, step_type: str, step: int, data: dict[str, Any]) -> None:
|
||||
scope, step_name = step_type.split("/")
|
||||
self.writer.add_scalar(step_type, step, global_step=step)
|
||||
for k, v in data.items():
|
||||
self.writer.add_scalar(k, v, global_step=step)
|
||||
scope_key = f"{scope}/{k}"
|
||||
if isinstance(v, np.ndarray):
|
||||
self.writer.add_histogram(scope_key, v, global_step=step, bins="auto")
|
||||
elif isinstance(v, Figure):
|
||||
self.writer.add_figure(scope_key, v, global_step=step)
|
||||
else:
|
||||
self.writer.add_scalar(scope_key, v, global_step=step)
|
||||
if self.write_flush: # issue 580
|
||||
self.writer.flush() # issue #482
|
||||
|
||||
@ -81,16 +136,56 @@ class TensorboardLogger(BaseLogger):
|
||||
|
||||
return epoch, env_step, gradient_step
|
||||
|
||||
def restore_logged_data(
|
||||
self,
|
||||
log_path: str,
|
||||
) -> TRestoredData:
|
||||
"""Restores the logged data from the tensorboard log directory.
|
||||
|
||||
class BasicLogger(TensorboardLogger):
|
||||
"""BasicLogger has changed its name to TensorboardLogger in #427.
|
||||
The result is a nested dictionary where the keys are the tensorboard keys
|
||||
and the values are the corresponding numpy arrays. The keys in each level
|
||||
form a nested structure, where the hierarchy is represented by the slashes
|
||||
in the tensorboard key-strings.
|
||||
"""
|
||||
ea = event_accumulator.EventAccumulator(log_path)
|
||||
ea.Reload()
|
||||
|
||||
This class is for compatibility.
|
||||
"""
|
||||
def add_value_to_innermost_nested_dict(
|
||||
data_dict: dict[str, Any],
|
||||
key_string: str,
|
||||
value: Any,
|
||||
) -> None:
|
||||
"""A particular logic, walking through the keys in the
|
||||
`key_string` and adding the value to the `data_dict` in a nested manner,
|
||||
creating nested dictionaries on the fly if necessary, or updating existing ones.
|
||||
The value is added only to the innermost-nested dictionary.
|
||||
|
||||
def __init__(self, *args: Any, **kwargs: Any) -> None:
|
||||
deprecation(
|
||||
"Class BasicLogger is marked as deprecated and will be removed soon. "
|
||||
"Please use TensorboardLogger instead.",
|
||||
)
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
Example:
|
||||
-------
|
||||
>>> data_dict = {}
|
||||
>>> add_value_to_innermost_nested_dict(data_dict, "a/b/c", 1)
|
||||
>>> data_dict
|
||||
{"a": {"b": {"c": 1}}}
|
||||
"""
|
||||
keys = key_string.split("/")
|
||||
|
||||
cur_nested_dict = data_dict
|
||||
# walk through the intermediate keys to reach the innermost-nested dict,
|
||||
# creating nested dictionaries on the fly if necessary
|
||||
for k in keys[:-1]:
|
||||
cur_nested_dict = cur_nested_dict.setdefault(k, {})
|
||||
# After the loop above,
|
||||
# this is the innermost-nested dict, where the value is finally set
|
||||
# for the last key in the key_string
|
||||
cur_nested_dict[keys[-1]] = value
|
||||
|
||||
restored_data: dict[str, np.ndarray | dict] = {}
|
||||
for key_string in ea.scalars.Keys():
|
||||
add_value_to_innermost_nested_dict(
|
||||
restored_data,
|
||||
key_string,
|
||||
np.array([s.value for s in ea.scalars.Items(key_string)]),
|
||||
)
|
||||
|
||||
return restored_data
|
||||
|
||||
@ -6,7 +6,7 @@ from collections.abc import Callable
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
|
||||
from tianshou.utils import BaseLogger, TensorboardLogger
|
||||
from tianshou.utils.logger.base import VALID_LOG_VALS_TYPE
|
||||
from tianshou.utils.logger.base import VALID_LOG_VALS_TYPE, TRestoredData
|
||||
|
||||
with contextlib.suppress(ImportError):
|
||||
import wandb
|
||||
@ -79,8 +79,18 @@ class WandbLogger(BaseLogger):
|
||||
if not wandb.run
|
||||
else wandb.run
|
||||
)
|
||||
# TODO: don't access private attribute!
|
||||
self.wandb_run._label(repo="tianshou") # type: ignore
|
||||
self.tensorboard_logger: TensorboardLogger | None = None
|
||||
self.writer: SummaryWriter | None = None
|
||||
|
||||
def prepare_dict_for_logging(self, log_data: dict) -> dict[str, VALID_LOG_VALS_TYPE]:
|
||||
if self.tensorboard_logger is None:
|
||||
raise Exception(
|
||||
"`logger` needs to load the Tensorboard Writer before "
|
||||
"preparing data for logging. Try `logger.load(SummaryWriter(log_path))`",
|
||||
)
|
||||
return self.tensorboard_logger.prepare_dict_for_logging(log_data)
|
||||
|
||||
def load(self, writer: SummaryWriter) -> None:
|
||||
self.writer = writer
|
||||
@ -95,7 +105,7 @@ class WandbLogger(BaseLogger):
|
||||
|
||||
def write(self, step_type: str, step: int, data: dict[str, VALID_LOG_VALS_TYPE]) -> None:
|
||||
if self.tensorboard_logger is None:
|
||||
raise Exception(
|
||||
raise RuntimeError(
|
||||
"`logger` needs to load the Tensorboard Writer before "
|
||||
"writing data. Try `logger.load(SummaryWriter(log_path))`",
|
||||
)
|
||||
@ -156,3 +166,12 @@ class WandbLogger(BaseLogger):
|
||||
except KeyError:
|
||||
env_step = 0
|
||||
return epoch, env_step, gradient_step
|
||||
|
||||
def restore_logged_data(self, log_path: str) -> TRestoredData:
|
||||
if self.tensorboard_logger is None:
|
||||
raise NotImplementedError(
|
||||
"Restoring logged data directly from W&B is not yet implemented."
|
||||
"Try instantiating the internal TensorboardLogger by calling something"
|
||||
"like `logger.load(SummaryWriter(log_path))`",
|
||||
)
|
||||
return self.tensorboard_logger.restore_logged_data(log_path)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user