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ab849c9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | """Plotly visualization helpers."""
from __future__ import annotations
from typing import Any
import plotly.graph_objects as go
def build_solver_comparison_chart(rows: list[dict[str, Any]], metric: str = "objective_value") -> go.Figure:
if not rows:
return go.Figure()
methods = [r.get("method_label", r.get("method_id", "")) for r in rows]
values = [r.get(metric, 0) for r in rows]
colors = ["#4f46e5" if r.get("winner") else "#94a3b8" for r in rows]
fig = go.Figure(go.Bar(x=methods, y=values, marker_color=colors))
fig.update_layout(
title=f"Method Comparison — {metric.replace('_', ' ').title()}",
xaxis_title="Method",
yaxis_title=metric.replace("_", " ").title(),
template="plotly_white",
height=400,
)
return fig
def build_gap_timeline(rows: list[dict[str, Any]]) -> go.Figure:
if not rows:
return go.Figure()
fig = go.Figure()
for r in rows:
fig.add_trace(go.Scatter(
x=[r.get("elapsed_time_sec", 0)],
y=[r.get("optimality_gap", 0)],
mode="markers+text",
name=r.get("method_label", ""),
text=[r.get("method_label", "")],
textposition="top center",
))
fig.update_layout(
title="Optimality Gap vs Solve Time",
xaxis_title="Elapsed Time (s)",
yaxis_title="Gap (%)",
template="plotly_white",
height=400,
)
return fig
def build_scalability_chart(rows: list[dict[str, Any]]) -> go.Figure:
if not rows:
return go.Figure()
sizes = sorted(set(r.get("size", "") for r in rows))
methods = sorted(set(r.get("method_id", "") for r in rows))
fig = go.Figure()
for mid in methods:
subset = [r for r in rows if r.get("method_id") == mid]
by_size = {r["size"]: r.get("elapsed_time_sec", 0) for r in subset}
fig.add_trace(go.Scatter(
x=sizes,
y=[by_size.get(s, 0) for s in sizes],
mode="lines+markers",
name=mid,
))
fig.update_layout(
title="Scalability — Solve Time by Instance Size",
xaxis_title="Size",
yaxis_title="Time (s)",
template="plotly_white",
height=400,
)
return fig
def build_category_radar(rows: list[dict[str, Any]]) -> go.Figure:
if not rows:
return go.Figure()
cats = sorted(set(r.get("method_category", "") for r in rows))
metrics = ["objective_value", "optimality_gap", "elapsed_time_sec"]
fig = go.Figure()
for cat in cats:
subset = [r for r in rows if r.get("method_category") == cat]
if not subset:
continue
vals = []
for m in metrics:
avg = sum(r.get(m, 0) for r in subset) / len(subset)
vals.append(avg)
fig.add_trace(go.Scatterpolar(
r=vals,
theta=[m.replace("_", " ").title() for m in metrics],
name=cat,
fill="toself",
))
fig.update_layout(
polar=dict(radialaxis=dict(visible=True)),
title="Method Category Profile",
template="plotly_white",
height=450,
)
return fig
def build_heatmap(comparisons: dict[str, Any], metric: str = "objective_value") -> go.Figure:
if not comparisons:
return go.Figure()
problems = list(comparisons.keys())
methods_set: set[str] = set()
for pt_data in comparisons.values():
for inst_data in pt_data.values():
methods_set.update(inst_data.get("results", {}).keys())
methods = sorted(methods_set)
z = []
for pt in problems:
row = []
for mid in methods:
vals = []
for inst_data in comparisons[pt].values():
mdata = inst_data.get("results", {}).get(mid, {})
if mdata:
vals.append(mdata.get(metric, 0))
row.append(sum(vals) / len(vals) if vals else 0)
z.append(row)
fig = go.Figure(go.Heatmap(z=z, x=methods, y=problems, colorscale="Viridis"))
fig.update_layout(
title=f"Benchmark Heatmap — {metric.replace('_', ' ').title()}",
template="plotly_white",
height=500,
)
return fig
def build_progress_chart(result_metrics: dict[str, Any]) -> go.Figure:
fig = go.Figure()
labels = ["Objective", "Best Bound", "Gap %", "Time (s)", "Iterations"]
values = [
result_metrics.get("objective_value", 0),
result_metrics.get("best_bound", 0),
result_metrics.get("optimality_gap", 0),
result_metrics.get("elapsed_time_sec", 0),
result_metrics.get("iterations", 0),
]
fig.add_trace(go.Bar(x=labels, y=values, marker_color=["#4f46e5", "#7c3aed", "#f59e0b", "#10b981", "#6366f1"]))
fig.update_layout(title="Solve Progress Snapshot", template="plotly_white", height=350)
return fig
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