43 lines
1.7 KiB
Python
43 lines
1.7 KiB
Python
import pytest
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from primaite import getLogger
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from primaite.config.lay_down_config import dos_very_basic_config_path
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from tests import TEST_CONFIG_ROOT
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_LOGGER = getLogger(__name__)
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@pytest.mark.parametrize(
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"temp_primaite_session",
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[[TEST_CONFIG_ROOT / "train_episode_step.yaml", dos_very_basic_config_path()]],
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indirect=True,
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)
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def test_eval_steps_differ_from_training(temp_primaite_session):
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"""Uses PrimaiteSession class to compare number of episodes used for training and evaluation.
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Train_episode_step.yaml main config:
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num_train_steps = 25
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num_train_episodes = 3
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num_eval_steps = 17
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num_eval_episodes = 1
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"""
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expected_learning_metadata = {"total_episodes": 3, "total_time_steps": 75}
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expected_evaluation_metadata = {"total_episodes": 1, "total_time_steps": 17}
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with temp_primaite_session as session:
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# Run learning and check episode and step counts
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session.learn()
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assert session.env.actual_episode_count == expected_learning_metadata["total_episodes"]
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assert session.env.total_step_count == expected_learning_metadata["total_time_steps"]
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# Run evaluation and check episode and step counts
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session.evaluate()
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assert session.env.actual_episode_count == expected_evaluation_metadata["total_episodes"]
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assert session.env.total_step_count == expected_evaluation_metadata["total_time_steps"]
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# Load the session_metadata.json file and check that the both the
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# learning and evaluation match what is expected above
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metadata = session.metadata_file_as_dict()
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assert metadata["learning"] == expected_learning_metadata
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assert metadata["evaluation"] == expected_evaluation_metadata
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