Merged PR 130: #1595: load session double run
## Summary - Fixed the bug where session gets run twice when loading a session via CLI - Added a test for the CLI run - xskipped while the bugfix for load session acting odd is tbd - Fixed a minor bug in PrimAITE session where session_path is overwritten ## Test process Added a new test for CLI, but xskipped while a different bug is tbd Ran it locally and no longer runs another session after the loaded session ``` (venv) PS D:\Projects\ARCD\PrimAITE\PrimAITE> primaite session --load [REDACTED for security]\primaite\sessions\2023-07-20\2023-07-20_15-01-11 2023-07-20 15:04:21,320: Using: AgentFramework.SB3, AgentIdentifier.PPO, ActionType.NODE, observation_space=NODE_LINK_TABLE, Training: 5 episodes @ 256 stepsEvaluation: 5 episodes @ 256 steps 2023-07-20 15:04:21,335: Environment configuration loaded Environment configuration loaded 2023-07-20 15:04:21,775: Welcome to the Primary-level AI Training Environment (PrimAITE) (version: 2.0.0rc1) 2023-07-20 15:04:21,775: The output directory for this session is: C:\Users\czar.echavez\primaite\sessions\2023-07-20\2023-07-20_15-04-21 2023-07-20 15:04:21,779: Beginning learning for 10 episodes @ 256 time steps... 2023-07-20 15:04:22,379: Episode: 1, Average Reward: -0.0020839843750000003 2023-07-20 15:04:23,137: Episode: 2, Average Reward: -0.0021933593750000004 2023-07-20 15:04:23,831: Episode: 3, Average Reward: -0.0022617187500000003 2023-07-20 15:04:24,486: Episode: 4, Average Reward: -0.002373046874999999 2023-07-20 15:04:25,125: Episode: 5, Average Reward: -0.0018066406250000014 2023-07-20 15:04:25,791: Episode: 6, Average Reward: -0.0017597656250000013 2023-07-20 15:04:26,415: Episode: 7, Average Reward: -0.0018437500000000014 2023-07-20 15:04:27,053: Episode: 8, Average Reward: -0.0019101562500000015 2023-07-20 15:04:27,715: Episode: 9, Average Reward: -0.0016777343750000013 2023-07-20 15:04:28,359: Episode: 10, Average Reward: -0.0015976562500000012 2023-07-20 15:04:28,550: Finished learning 2023-07-20 15:04:30,851: Beginning deterministic evaluation for 5 episodes @ 256 time steps... 2023-07-20 15:04:31,243: Episode: 1, Average Reward: -0.0018515625000000014 2023-07-20 15:04:31,663: Episode: 2, Average Reward: -0.0018515625000000014 2023-07-20 15:04:32,112: Episode: 3, Average Reward: -0.0018515625000000014 2023-07-20 15:04:32,505: Episode: 4, Average Reward: -0.0018515625000000014 2023-07-20 15:04:32,904: Episode: 5, Average Reward: -0.0018515625000000014 2023-07-20 15:04:32,998: Finished evaluation ``` Also fixed the xskipped tests, since the double running seems to have caused the issue of rewards not matching. Added a test that runs the PrimAITE in CLI ## Checklist - [x] This PR is linked to a **work item** - [x] I have performed **self-review** of the code - [x] I have written **tests** for any new functionality added with this PR - [x] I have updated the **documentation** if this PR changes or adds functionality - [x] I have run **pre-commit** checks for code style #1595: - Fixed the...
This commit is contained in:
Czar Echavez
@@ -173,15 +173,18 @@ def session(tc: Optional[str] = None, ldc: Optional[str] = None, load: Optional[
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from primaite.main import run
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if load is not None:
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# run a loaded session
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run(session_path=load)
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if not tc:
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tc = main_training_config_path()
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else:
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# start a new session using tc and ldc
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if not tc:
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tc = main_training_config_path()
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if not ldc:
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ldc = dos_very_basic_config_path()
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if not ldc:
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ldc = dos_very_basic_config_path()
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run(training_config_path=tc, lay_down_config_path=ldc)
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run(training_config_path=tc, lay_down_config_path=ldc)
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@app.command()
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@@ -72,13 +72,7 @@ class PrimaiteSession:
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if not isinstance(lay_down_config_path, Path):
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lay_down_config_path = Path(lay_down_config_path)
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self._lay_down_config_path: Final[Union[Path, str]] = lay_down_config_path
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self._lay_down_config: Dict = lay_down_config.load(self._lay_down_config_path)
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self._agent_session: AgentSessionABC = None # noqa
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self.session_path: Path = None # noqa
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self.timestamp_str: str = None # noqa
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self.learning_path: Path = None # noqa
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self.evaluation_path: Path = None # noqa
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self._lay_down_config: Dict = lay_down_config.load(self._lay_down_config_path) # noqa
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def setup(self) -> None:
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"""Performs the session setup."""
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@@ -6,10 +6,11 @@ from pathlib import Path
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from typing import Union
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from uuid import uuid4
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import pytest
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from typer.testing import CliRunner
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from primaite import getLogger
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from primaite.agents.sb3 import SB3Agent
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from primaite.cli import app
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from primaite.common.enums import AgentFramework, AgentIdentifier
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from primaite.main import run
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from primaite.primaite_session import PrimaiteSession
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@@ -18,6 +19,24 @@ from tests import TEST_ASSETS_ROOT
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_LOGGER = getLogger(__name__)
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runner = CliRunner()
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sb3_expected_avg_reward_per_episode = {
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10: 0.0,
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11: -0.0011074218750000008,
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12: -0.0010000000000000007,
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13: -0.0016601562500000013,
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14: -0.001400390625000001,
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15: -0.0009863281250000007,
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16: -0.0011855468750000008,
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17: -0.0009511718750000007,
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18: -0.0008789062500000007,
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19: -0.0012226562500000009,
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20: -0.0010292968750000007,
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}
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sb3_expected_eval_rewards = -0.0018515625000000014
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def copy_session_asset(asset_path: Union[str, Path]) -> str:
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"""Copies the asset into a temporary test folder."""
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@@ -43,25 +62,8 @@ def copy_session_asset(asset_path: Union[str, Path]) -> str:
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return copy_path
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@pytest.mark.xfail(
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reason="Loading works fine but the exact values change with code changes, a bug report has been created."
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)
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def test_load_sb3_session():
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"""Test that loading an SB3 agent works."""
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expected_learn_mean_reward_per_episode = {
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10: 0,
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11: -0.008037109374999995,
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12: -0.007978515624999988,
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13: -0.008191406249999991,
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14: -0.00817578124999999,
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15: -0.008085937499999998,
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16: -0.007837890624999982,
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17: -0.007798828124999992,
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18: -0.007777343749999998,
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19: -0.007958984374999988,
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20: -0.0077499999999999835,
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}
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test_path = copy_session_asset(TEST_ASSETS_ROOT / "example_sb3_agent_session")
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loaded_agent = SB3Agent(session_path=test_path)
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@@ -82,7 +84,7 @@ def test_load_sb3_session():
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)
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# run is seeded so should have the expected learn value
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assert learn_mean_rewards == expected_learn_mean_reward_per_episode
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assert learn_mean_rewards == sb3_expected_avg_reward_per_episode
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# run an evaluation
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loaded_agent.evaluate()
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@@ -96,29 +98,14 @@ def test_load_sb3_session():
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assert len(set(eval_mean_reward.values())) == 1
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# the evaluation should be the same as a previous run
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assert next(iter(set(eval_mean_reward.values()))) == -0.009896484374999988
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assert next(iter(set(eval_mean_reward.values()))) == sb3_expected_eval_rewards
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# delete the test directory
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shutil.rmtree(test_path)
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@pytest.mark.xfail(reason="Temporarily don't worry about this not working")
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def test_load_primaite_session():
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"""Test that loading a Primaite session works."""
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expected_learn_mean_reward_per_episode = {
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10: 0,
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11: -0.008037109374999995,
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12: -0.007978515624999988,
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13: -0.008191406249999991,
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14: -0.00817578124999999,
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15: -0.008085937499999998,
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16: -0.007837890624999982,
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17: -0.007798828124999992,
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18: -0.007777343749999998,
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19: -0.007958984374999988,
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20: -0.0077499999999999835,
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}
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test_path = copy_session_asset(TEST_ASSETS_ROOT / "example_sb3_agent_session")
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# create loaded session
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@@ -143,7 +130,7 @@ def test_load_primaite_session():
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)
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# run is seeded so should have the expected learn value
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assert learn_mean_rewards == expected_learn_mean_reward_per_episode
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assert learn_mean_rewards == sb3_expected_avg_reward_per_episode
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# run an evaluation
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session.evaluate()
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@@ -157,29 +144,14 @@ def test_load_primaite_session():
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assert len(set(eval_mean_reward.values())) == 1
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# the evaluation should be the same as a previous run
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assert next(iter(set(eval_mean_reward.values()))) == -0.009896484374999988
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assert next(iter(set(eval_mean_reward.values()))) == sb3_expected_eval_rewards
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# delete the test directory
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shutil.rmtree(test_path)
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@pytest.mark.xfail(reason="Temporarily don't worry about this not working")
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def test_run_loading():
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"""Test loading session via main.run."""
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expected_learn_mean_reward_per_episode = {
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10: 0,
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11: -0.008037109374999995,
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12: -0.007978515624999988,
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13: -0.008191406249999991,
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14: -0.00817578124999999,
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15: -0.008085937499999998,
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16: -0.007837890624999982,
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17: -0.007798828124999992,
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18: -0.007777343749999998,
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19: -0.007958984374999988,
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20: -0.0077499999999999835,
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}
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test_path = copy_session_asset(TEST_ASSETS_ROOT / "example_sb3_agent_session")
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# create loaded session
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@@ -190,7 +162,26 @@ def test_run_loading():
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)
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# run is seeded so should have the expected learn value
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assert learn_mean_rewards == expected_learn_mean_reward_per_episode
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assert learn_mean_rewards == sb3_expected_avg_reward_per_episode
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# delete the test directory
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shutil.rmtree(test_path)
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def test_cli():
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"""Test loading session via CLI."""
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test_path = copy_session_asset(TEST_ASSETS_ROOT / "example_sb3_agent_session")
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result = runner.invoke(app, ["session", "--load", test_path])
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# cli should work
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assert result.exit_code == 0
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learn_mean_rewards = av_rewards_dict(
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next(Path(test_path).rglob("**/learning/average_reward_per_episode_*.csv"), None)
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)
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# run is seeded so should have the expected learn value
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assert learn_mean_rewards == sb3_expected_avg_reward_per_episode
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# delete the test directory
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shutil.rmtree(test_path)
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