tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
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feat(downloader): added downloading of artifacts + moved script to tool directory

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This commit is contained in:
Robin Meersman 2026-05-14 21:42:50 +02:00
parent becd6798e9
commit 03b1b33974
2 changed files with 84 additions and 15 deletions
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30
scripts/plots/analyze_convergence.py
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@ -45,19 +45,22 @@ class Columns(str, Enum):
"""Column names expected in every evaluation CSV."""
ARCH = "architecture"
TIMESTEPS = "total_trained_timesteps"
REWARD = "accumulated_reward"
TIMESTEPS = "trained_timesteps"
REWARD = "eval_return"
VELOCITY = "velocity"
EVAL_STEPS = "eval_steps"
FINAL_XY_DIST = "final_xy_dist"
INITIAL_XY_DIST = "initial_xy_dist"
REACHED_TARGET = "reached_target"
# Maps architecture display names to the path of their evaluation CSV.
# Update these paths once real evaluation data is available.
FILE_MAPPING: dict[str, str] = {
"centralized 2 arms": "runs/dummy/dummy_centralized_2_arms.csv",
"centralized 5 arms": "runs/dummy/dummy_centralized_5_arms.csv",
"decentralized fully connected": "runs/dummy/dummy_decentralized_fully_connected.csv",
"decentralized ring-level": "runs/dummy/dummy_decentralized_ring-level.csv",
"decentralized segment-level": "runs/dummy/dummy_decentralized_segment-level.csv",
# "centralized 2 arms": "runs/dummy/dummy_centralized_2_arms.csv",
"centralized 5 arms": "runs/final-v2-centralized/checkpoint_evaluation.csv",
"decentralized fully connected": "runs/final-v2-fully-conn/checkpoint_evaluation.csv",
"decentralized ring-level": "runs/final-v2-ring/checkpoint_evaluation.csv",
}
# Architecture profiles for dummy data generation: (max_reward, max_velocity, sigmoid_speed)
@ -108,7 +111,13 @@ def load_metrics(file_mapping: dict[str, str]) -> pd.DataFrame:
Loads one CSV per architecture, injects the architecture name as a column,
and returns the combined DataFrame with only the required columns.
"""
required = [Columns.TIMESTEPS, Columns.REWARD, Columns.VELOCITY]
required = [
Columns.TIMESTEPS,
Columns.REWARD,
Columns.INITIAL_XY_DIST,
Columns.FINAL_XY_DIST,
Columns.EVAL_STEPS,
]
dfs = []
for arch_name, filepath in file_mapping.items():
@ -125,6 +134,10 @@ def load_metrics(file_mapping: dict[str, str]) -> pd.DataFrame:
df = df[required].copy()
df[Columns.ARCH] = arch_name
df[Columns.VELOCITY] = (df[Columns.INITIAL_XY_DIST] - df[Columns.FINAL_XY_DIST]) / df[
Columns.EVAL_STEPS
]
dfs.append(df)
return pd.concat(dfs, ignore_index=True) if dfs else pd.DataFrame()
@ -303,6 +316,7 @@ def plot_results(df: pd.DataFrame, results: pd.DataFrame, output_dir: str, **kwa
def obtain_data() -> pd.DataFrame:
"""Resolves the file mapping, falling back to generated dummy CSVs if needed."""
global USING_DUMMY_DATA
if not any(os.path.exists(p) for p in FILE_MAPPING.values()):
logger.info("No real evaluation files found. Generating dummy CSVs at expected locations.")
generate_dummy_csvs(FILE_MAPPING)