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Bastian Wiesner
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presentation figure improvements made
1 parent 5d62209 commit 9418a50

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‎output/figures/margin_vs_best_DeepSeek_V3.png‎

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‎output/figures/margin_vs_best_Kimi_K2.png‎

-42.8 KB
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‎output/figures/margin_vs_best_overview.png‎

-91.6 KB
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‎output/figures/score_heatmaps.png‎

-936 Bytes
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‎presentation_final/assets/margin_vs_best_overview.png‎

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‎presentation_final/assets/score_heatmaps.png‎

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‎presentation_final/sections/18_results.tex‎

Lines changed: 0 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -23,8 +23,6 @@ \subsection{Scoring Analysis}
2323
\begin{itemize}
2424
\item Regions with higher baseline emissions can achieve larger
2525
absolute reductions even at modest relative savings.
26-
\item Both metrics should be reported: relative savings show policy
27-
effectiveness, absolute savings quantify real-world impact.
2826
\end{itemize}
2927

3028
\end{frame}

‎src/visualize_results.py‎

Lines changed: 96 additions & 112 deletions
Original file line numberDiff line numberDiff line change
@@ -751,10 +751,17 @@ def plot_threshold_space_per_scenario(df: pd.DataFrame, output_dir: Path):
751751

752752

753753
def plot_margin_vs_best(df: pd.DataFrame, output_dir: Path):
754-
"""For each hysteresis margin, plot the best score / savings / overhead.
754+
"""For each hysteresis margin, plot score / savings / overhead of the best-scoring run.
755755
756-
2×5 grid (models × regions), with three y-axes per subplot.
756+
2×5 grid (models × regions). Uses _all_ data (optimized over start dates).
757+
For each hysteresis margin, finds the run with the highest score and plots
758+
that run's score, savings, and overhead.
757759
"""
760+
df_all = df[df["file_type"] == "all"]
761+
if df_all.empty:
762+
print(" No _all_ data found, skipping margin vs best overview.")
763+
return
764+
758765
fig, axes = plt.subplots(
759766
len(MODEL_ORDER),
760767
len(REGION_ORDER),
@@ -763,69 +770,52 @@ def plot_margin_vs_best(df: pd.DataFrame, output_dir: Path):
763770
sharey=False,
764771
)
765772

766-
line_styles = {"01-01": "-", "07-01": "--"}
767-
768773
for col_idx, region in enumerate(REGION_ORDER):
769774
for row_idx, model in enumerate(MODEL_ORDER):
770775
ax = axes[row_idx, col_idx]
771776
ax2 = ax.twinx()
772-
subset = df[(df["model"] == model) & (df["region"] == region)]
777+
subset = df_all[(df_all["model"] == model) & (df_all["region"] == region)]
773778

774779
if subset.empty:
775780
ax.set_visible(False)
776781
ax2.set_visible(False)
777782
continue
778783

779-
for start_date, start_group in subset.groupby("start_date"):
780-
if start_group.empty:
781-
continue
782-
ls = line_styles.get(start_date, "-")
783-
label_suffix = START_LABELS.get(start_date, start_date)
784-
785-
agg = (
786-
start_group.groupby("hysteresis_margin")
787-
.agg(
788-
best_score=("score", "max"),
789-
best_savings=("co2_save_pct", "max"),
790-
best_overhead=("overhead_pct", "min"),
791-
)
792-
.reset_index()
793-
.sort_values("hysteresis_margin")
794-
)
784+
# For each hysteresis margin, find the run with highest score
785+
best_per_margin = subset.loc[
786+
subset.groupby("hysteresis_margin")["score"].idxmax()
787+
].sort_values("hysteresis_margin")
795788

796-
if len(agg) < 2:
797-
continue
789+
if len(best_per_margin) < 2:
790+
continue
798791

799-
ax.plot(
800-
agg["hysteresis_margin"],
801-
agg["best_score"],
802-
color="#2563eb",
803-
linestyle=ls,
804-
linewidth=2,
805-
marker="o",
806-
markersize=4,
807-
label=f"Score ({label_suffix})",
808-
)
809-
ax.plot(
810-
agg["hysteresis_margin"],
811-
agg["best_savings"] / 100,
812-
color="#059669",
813-
linestyle=ls,
814-
linewidth=2,
815-
marker="s",
816-
markersize=4,
817-
label=f"Savings/100 ({label_suffix})",
818-
)
819-
ax2.plot(
820-
agg["hysteresis_margin"],
821-
agg["best_overhead"],
822-
color="#dc2626",
823-
linestyle=ls,
824-
linewidth=2,
825-
marker="^",
826-
markersize=4,
827-
label=f"Overhead ({label_suffix})",
828-
)
792+
ax.plot(
793+
best_per_margin["hysteresis_margin"],
794+
best_per_margin["score"],
795+
color="#2563eb",
796+
linewidth=2,
797+
marker="o",
798+
markersize=4,
799+
label="Score",
800+
)
801+
ax.plot(
802+
best_per_margin["hysteresis_margin"],
803+
best_per_margin["co2_save_pct"] / 100,
804+
color="#059669",
805+
linewidth=2,
806+
marker="s",
807+
markersize=4,
808+
label="Savings/100",
809+
)
810+
ax2.plot(
811+
best_per_margin["hysteresis_margin"],
812+
best_per_margin["overhead_pct"],
813+
color="#dc2626",
814+
linewidth=2,
815+
marker="^",
816+
markersize=4,
817+
label="Overhead",
818+
)
829819

830820
if row_idx == 0:
831821
ax.set_title(region, fontsize=14, fontweight="bold")
@@ -842,15 +832,16 @@ def plot_margin_vs_best(df: pd.DataFrame, output_dir: Path):
842832

843833
if col_idx == len(REGION_ORDER) - 1:
844834
lines1, labels1 = ax.get_legend_handles_labels()
835+
lines2, labels2 = ax2.get_legend_handles_labels()
845836
ax.legend(
846-
lines1, labels1,
847-
fontsize=6,
837+
lines1 + lines2, labels1 + labels2,
838+
fontsize=7,
848839
loc="upper left",
849840
ncol=1,
850841
)
851842

852843
fig.suptitle(
853-
"Hysteresis Margin vs Best Score / Savings / Overhead",
844+
"Hysteresis Margin vs Best Score / Savings / Overhead (All Start Dates)",
854845
fontsize=16,
855846
fontweight="bold",
856847
y=1.0,
@@ -863,13 +854,19 @@ def plot_margin_vs_best(df: pd.DataFrame, output_dir: Path):
863854

864855

865856
def plot_margin_vs_best_per_model(df: pd.DataFrame, output_dir: Path):
866-
"""Per (model, region): margin on x, best score/savings/overhead on y.
857+
"""Per (model, region): margin on x, score/savings/overhead of best-scoring run on y.
867858
868-
Two start dates overlaid with different line styles.
859+
Uses _all_ data (optimized over start dates). For each hysteresis margin,
860+
finds the run with the highest score and plots that run's metrics.
869861
"""
862+
df_all = df[df["file_type"] == "all"]
863+
if df_all.empty:
864+
print(" No _all_ data found, skipping margin vs best per model.")
865+
return
866+
870867
for model in MODEL_ORDER:
871868
fig, axes = plt.subplots(1, len(REGION_ORDER), figsize=(24, 5), sharey=False)
872-
model_df = df[df["model"] == model]
869+
model_df = df_all[df_all["model"] == model]
873870

874871
for col_idx, region in enumerate(REGION_ORDER):
875872
ax = axes[col_idx]
@@ -880,55 +877,41 @@ def plot_margin_vs_best_per_model(df: pd.DataFrame, output_dir: Path):
880877
ax.set_visible(False)
881878
continue
882879

883-
for start_date, start_group in region_df.groupby("start_date"):
884-
if start_group.empty:
885-
continue
886-
label_suffix = START_LABELS.get(start_date, start_date)
887-
ls = "-" if start_date == "01-01" else "--"
888-
889-
agg = (
890-
start_group.groupby("hysteresis_margin")
891-
.agg(
892-
best_score=("score", "max"),
893-
best_savings=("co2_save_pct", "max"),
894-
best_overhead=("overhead_pct", "min"),
895-
)
896-
.reset_index()
897-
.sort_values("hysteresis_margin")
898-
)
899-
if len(agg) < 2:
900-
continue
880+
# For each hysteresis margin, find the run with highest score
881+
best_per_margin = region_df.loc[
882+
region_df.groupby("hysteresis_margin")["score"].idxmax()
883+
].sort_values("hysteresis_margin")
901884

902-
ax.plot(
903-
agg["hysteresis_margin"],
904-
agg["best_score"],
905-
color="#2563eb",
906-
linestyle=ls,
907-
linewidth=2,
908-
marker="o",
909-
markersize=4,
910-
label=f"Score ({label_suffix})",
911-
)
912-
ax.plot(
913-
agg["hysteresis_margin"],
914-
agg["best_savings"] / 100,
915-
color="#059669",
916-
linestyle=ls,
917-
linewidth=2,
918-
marker="s",
919-
markersize=4,
920-
label=f"Savings/100 ({label_suffix})",
921-
)
922-
ax2.plot(
923-
agg["hysteresis_margin"],
924-
agg["best_overhead"],
925-
color="#dc2626",
926-
linestyle=ls,
927-
linewidth=2,
928-
marker="^",
929-
markersize=4,
930-
label=f"Overhead ({label_suffix})",
931-
)
885+
if len(best_per_margin) < 2:
886+
continue
887+
888+
ax.plot(
889+
best_per_margin["hysteresis_margin"],
890+
best_per_margin["score"],
891+
color="#2563eb",
892+
linewidth=2,
893+
marker="o",
894+
markersize=4,
895+
label="Score",
896+
)
897+
ax.plot(
898+
best_per_margin["hysteresis_margin"],
899+
best_per_margin["co2_save_pct"] / 100,
900+
color="#059669",
901+
linewidth=2,
902+
marker="s",
903+
markersize=4,
904+
label="Savings/100",
905+
)
906+
ax2.plot(
907+
best_per_margin["hysteresis_margin"],
908+
best_per_margin["overhead_pct"],
909+
color="#dc2626",
910+
linewidth=2,
911+
marker="^",
912+
markersize=4,
913+
label="Overhead",
914+
)
932915

933916
ax.set_title(region, fontsize=12, fontweight="bold")
934917
if col_idx == 0:
@@ -942,10 +925,11 @@ def plot_margin_vs_best_per_model(df: pd.DataFrame, output_dir: Path):
942925
ax.grid(True, alpha=0.3)
943926
if col_idx == len(REGION_ORDER) - 1:
944927
lines1, labels1 = ax.get_legend_handles_labels()
945-
ax.legend(lines1, labels1, fontsize=7, loc="upper left")
928+
lines2, labels2 = ax2.get_legend_handles_labels()
929+
ax.legend(lines1 + lines2, labels1 + labels2, fontsize=7, loc="upper left")
946930

947931
fig.suptitle(
948-
f"Margin vs Best Metrics: {model}",
932+
f"Margin vs Best Metrics: {model} (All Start Dates)",
949933
fontsize=14,
950934
fontweight="bold",
951935
y=1.02,
@@ -2182,7 +2166,7 @@ def plot_score_heatmaps(output_dir: Path):
21822166
scores = _compute_score(savings_grid, overhead_grid, alpha)
21832167

21842168
im = ax.imshow(
2185-
scores,
2169+
scores.T,
21862170
extent=[0, 200, 0, 50],
21872171
origin="lower",
21882172
aspect="auto",
@@ -2518,8 +2502,8 @@ def main():
25182502
print("\nGenerating hysteresis comparison plots (#67)...")
25192503
plot_threshold_space_overview(df_fixed, FIGURES_DIR)
25202504
plot_threshold_space_per_scenario(df_fixed, FIGURES_DIR)
2521-
plot_margin_vs_best(df_fixed, FIGURES_DIR)
2522-
plot_margin_vs_best_per_model(df_fixed, FIGURES_DIR)
2505+
plot_margin_vs_best(df, FIGURES_DIR)
2506+
plot_margin_vs_best_per_model(df, FIGURES_DIR)
25232507

25242508
print_summary_table(df_fixed)
25252509

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