tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
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Merge pull request #61 from SELab-3-2026/feat/final-plots

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plot: final plotting code used to create plots showed on poster
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RobinMeersman 2026-05-19 10:30:31 +02:00 • committed by GitHub
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8 changed files with 78 additions and 64 deletions
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3
.gitignore vendored
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@ -523,3 +523,6 @@ Network Trash Folder
Temporary Items
.apdisk
*.pdf
# plot directory
poster_plots/

6
configs/centralized-final.yaml
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@ -23,7 +23,7 @@ morphology:
morph_mode: CENTRALIZED
experiment:
exp_name: "final-models/centralized/"
exp_name: "final-models-v2/centralized/"
seed: 42
torch_deterministic: true
cuda: true
@ -34,8 +34,8 @@ logging:
save_checkpoints: true
upload_final_model: true
upload_checkpoints: true
checkpoint_frequency: 20
wandb_project_name: "final-models"
checkpoint_frequency: 10
wandb_project_name: "final-models-v2"
evaluation:
evaluate_checkpoints: true

6
configs/evaluation/poster.yaml
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@ -9,12 +9,14 @@ eval_seed: 0
# Cross-model comparison settings
# We use 10 episodes to get a more robust average for the final poster results.
comparison_base_seed: 0
comparison_num_episodes: 2
comparison_num_episodes: 10
comparison_output_csv: "runs/evaluation/comparison.csv"
# Paths to the .cleanrl_model files to be compared (relative to workspace root).
comparison_models:
- "runs/input-space-2-arms/2026-05-02/08-14-58/final_model.flax"
- "runs/final-v2-centralized/artifacts/12-19-01_checkpoint_v22/checkpoint_step_230.flax"
- "runs/final-v2-fully-conn/artifacts/14-02-00_checkpoint_v17/checkpoint_step_180.flax"
- "runs/final-v2-ring/artifacts/15-27-03_checkpoint_v21/checkpoint_step_220.flax"
# Path to the morphologies to evaluate against.
comparison_morphologies:

6
configs/fully-connected-final.yaml
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@ -26,7 +26,7 @@ morphology:
morph_mode: FULLY_CONNECTED
experiment:
exp_name: "final-models/fully-connected/"
exp_name: "final-models-v2/fully-connected/"
seed: 42
torch_deterministic: true
cuda: true
@ -37,8 +37,8 @@ logging:
save_checkpoints: true
upload_final_model: true
upload_checkpoints: true
checkpoint_frequency: 20
wandb_project_name: "final-models"
checkpoint_frequency: 10
wandb_project_name: "final-models-v2"
evaluation:
evaluate_checkpoints: true

6
configs/ring-final.yaml
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@ -26,7 +26,7 @@ morphology:
morph_mode: RING
experiment:
exp_name: "final-models/ring/"
exp_name: "final-models-v2/ring/"
seed: 42
torch_deterministic: true
cuda: true
@ -37,8 +37,8 @@ logging:
save_checkpoints: true
upload_final_model: true
upload_checkpoints: true
checkpoint_frequency: 20
wandb_project_name: "final-models"
checkpoint_frequency: 10
wandb_project_name: "final-models-v2"
evaluation:
evaluate_checkpoints: true

59
scripts/plots/analyze_comparisons.py
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@ -6,18 +6,17 @@ Rate, Distance Remaining).
"""
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from plot_config import (
COLORS,
apply_style,
BEST_PERFORMER_MARKER,
BEST_PERFORMER_TEXT,
BEST_PERFORMER_COLOR,
create_common_parser,
BEST_PERFORMER_TEXT,
COLORS,
LEGEND_KWARGS,
apply_style,
create_common_parser,
)
@ -84,7 +83,8 @@ def plot_grouped_bar(
fig, ax = plt.subplots(figsize=figsize)
bar_width = 0.35
x_indices = np.arange(len(morphologies))
group_spacing = 1.3
x_indices = np.arange(len(morphologies)) * group_spacing
all_bars = {}
all_means = []
@ -118,7 +118,7 @@ def plot_grouped_bar(
)
all_bars[arch] = (x_pos, means, stds, bars)
for m_idx, m in enumerate(morphologies):
for m_idx, _ in enumerate(morphologies):
m_means = {arch: all_bars[arch][1][m_idx] for arch in architectures}
best_arch = (
max(m_means, key=m_means.get) if higher_is_better else min(m_means, key=m_means.get)
@ -144,12 +144,13 @@ def plot_grouped_bar(
x_ticks_pos = (
x_indices
+ bar_width # center the label in the 3 bars
+ (bar_width / 2 if len(architectures) % 2 == 0 else 0)
- (bar_width / 2 if len(architectures) == 2 else 0)
)
ax.set_xticks(x_ticks_pos)
ax.set_xticklabels([f"{m} Arms" for m in morphologies])
ax.tick_params(axis="x", pad=25) # More padding for the squares
ax.tick_params(axis="x") # More padding for the squares
# X-axis at zero
ax.axhline(0, color="black", linewidth=1.5)
@ -172,20 +173,7 @@ def plot_grouped_bar(
plt.FuncFormatter(lambda x, _: f"{x:.2f}" if abs(x) < 10 else f"{x:.0f}")
)
_add_square_placeholders(ax, x_ticks_pos, [f"{m} Arms" for m in morphologies])
# Add custom legend entry for best performer
ax.plot(
[],
[],
marker=BEST_PERFORMER_MARKER,
color="w",
markerfacecolor=BEST_PERFORMER_COLOR,
markersize=15,
# label="Best Performance",
ls="",
)
ax.legend(**LEGEND_KWARGS, ncol=len(architectures) + 1)
ax.legend(**LEGEND_KWARGS, ncol=len(architectures))
ax.set_facecolor("white")
fig.patch.set_facecolor("white")
@ -305,22 +293,7 @@ def plot_grouped_bar_alt(
plt.FuncFormatter(lambda x, _: f"{x:.2f}" if abs(x) < 10 else f"{x:.0f}")
)
# In this alt plot, placeholders might be per architecture
_add_square_placeholders(
ax, x_indices, [arch.replace("_", "\n").title() for arch in architectures]
)
ax.plot(
[],
[],
marker=BEST_PERFORMER_MARKER,
color="w",
markerfacecolor=BEST_PERFORMER_COLOR,
markersize=15,
# label="Best Performance",
ls="",
)
ax.legend(**LEGEND_KWARGS, ncol=len(morphologies) + 1)
ax.legend(**LEGEND_KWARGS, ncol=len(morphologies))
ax.set_facecolor("white")
fig.patch.set_facecolor("white")
@ -358,8 +331,8 @@ if __name__ == "__main__":
plot_grouped_bar(
df=df,
metric_col="approx_max_velocity",
ylabel="Max Forward Velocity (cm/s)",
title="Graceful Degradation: Velocity Across Morphologies",
ylabel="",
title="Maximal forward velocity (in cm/s)",
output_filename="poster_plot_velocity.png",
output_dir=OUTPUT_DIR,
higher_is_better=True,

52
scripts/plots/analyze_convergence.py
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@ -44,6 +44,7 @@ class Columns(str, Enum):
# ... (rest of the file remains same, just need to update plotting functions and obtain_data)
"""Column names expected in every evaluation CSV."""
CHECKPOINT = "checkpoint"
ARCH = "architecture"
TIMESTEPS = "trained_timesteps"
REWARD = "eval_return"
@ -112,6 +113,7 @@ def load_metrics(file_mapping: dict[str, str]) -> pd.DataFrame:
and returns the combined DataFrame with only the required columns.
"""
required = [
Columns.CHECKPOINT,
Columns.TIMESTEPS,
Columns.REWARD,
Columns.INITIAL_XY_DIST,
@ -133,6 +135,9 @@ def load_metrics(file_mapping: dict[str, str]) -> pd.DataFrame:
continue
df = df[required].copy()
df[Columns.VELOCITY] = (df[Columns.INITIAL_XY_DIST] - df[Columns.FINAL_XY_DIST]) / df[
Columns.EVAL_STEPS
]
df[Columns.ARCH] = arch_name
df[Columns.VELOCITY] = (df[Columns.INITIAL_XY_DIST] - df[Columns.FINAL_XY_DIST]) / df[
Columns.EVAL_STEPS
@ -143,11 +148,17 @@ def load_metrics(file_mapping: dict[str, str]) -> pd.DataFrame:
return pd.concat(dfs, ignore_index=True) if dfs else pd.DataFrame()
def _convergence_timestep(series: pd.Series, timesteps: pd.Series) -> float:
def _convergence_timestep(
series: pd.Series, timesteps: pd.Series, checkpoints: pd.Series
) -> tuple[float, int, int]:
"""Returns the first timestep where the smoothed series reaches 95% of its peak."""
smoothed = series.rolling(window=SMOOTHING_WINDOW, min_periods=1).mean()
threshold = smoothed.max() * CONVERGENCE_THRESHOLD
return timesteps[smoothed >= threshold].iloc[0]
mask = smoothed >= threshold
first_idx = mask.idxmax()
return timesteps.loc[first_idx], first_idx, checkpoints.loc[first_idx]
def analyze_convergence(df: pd.DataFrame) -> pd.DataFrame:
@ -157,21 +168,40 @@ def analyze_convergence(df: pd.DataFrame) -> pd.DataFrame:
"""
results = []
centralized_base = 0
for arch in df[Columns.ARCH].unique():
arch_data = df[df[Columns.ARCH] == arch].sort_values(Columns.TIMESTEPS)
reward_timestep, reward_checkpoint_idx, reward_checkpoint = _convergence_timestep(
arch_data[Columns.REWARD],
arch_data[Columns.TIMESTEPS],
arch_data[Columns.CHECKPOINT],
)
velocity_timestep, velocity_checkpoint_idx, velocity_checkpoint = _convergence_timestep(
arch_data[Columns.VELOCITY],
arch_data[Columns.TIMESTEPS],
arch_data[Columns.CHECKPOINT],
)
results.append(
{
"Architecture": arch,
"Reward_Convergence_Timestep": _convergence_timestep(
arch_data[Columns.REWARD], arch_data[Columns.TIMESTEPS]
),
"Velocity_Convergence_Timestep": _convergence_timestep(
arch_data[Columns.VELOCITY], arch_data[Columns.TIMESTEPS]
),
"Reward_Convergence_Timestep": reward_timestep,
"Reward_Convergence_Checkpoint_Idx": reward_checkpoint_idx,
"Reward_Convergence_Checkpoint": reward_checkpoint,
"Velocity_Convergence_Timestep": velocity_timestep,
"Velocity_Convergence_Checkpoint_Idx": velocity_checkpoint_idx,
"Velocity_Convergence_Checkpoint": velocity_checkpoint,
}
)
if arch == "centralized 5 arms":
centralized_base = reward_checkpoint
else:
print(arch, "speedup:", 1 - reward_checkpoint / centralized_base)
return pd.DataFrame(results)
@ -333,6 +363,12 @@ def run_analysis(output_dir: str, **kwargs):
return
results = analyze_convergence(df)
print(
results[
["Architecture", "Reward_Convergence_Checkpoint_Idx", "Reward_Convergence_Checkpoint"]
]
)
plot_results(df, results, output_dir, **kwargs)
logger.info("Analysis complete. Plots saved to disk.")

4
scripts/plots/plot_config.py
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@ -6,7 +6,7 @@ import matplotlib.pyplot as plt
COLORS = {
"CENTRALIZED": "#0D567C", # Blue
"FULLY_CONNECTED": "#8C0E0F", # Reddish
"RING_LEVEL": "#E1BA6D", # Pale Yellow
"RING": "#FCB305", # Pale Yellow
}
@ -42,7 +42,7 @@ BEST_PERFORMER_COLOR = "#D4AF37" # Gold
# Centralized Legend Configuration
LEGEND_KWARGS = {
"loc": "upper center",
"bbox_to_anchor": (0.5, -0.5),
"bbox_to_anchor": (0.5, -0.12),
"frameon": False,
}