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228add1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 | """
Model comparison module.
Applies weighted scoring to select the best model.
Weights: 50% F1 + 20% accuracy + 15% latency + 10% size + 5% calibration.
"""
import json
from pathlib import Path
from typing import Optional
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
# Scoring weights
DEFAULT_WEIGHTS = {
'macro_f1': 0.50,
'accuracy': 0.20,
'latency': 0.15,
'model_size': 0.10,
'calibration': 0.05,
}
def normalize_metric(values: list[float], higher_is_better: bool = True) -> list[float]:
"""Min-max normalize metrics to [0, 1] range."""
if not values:
return values
min_val = min(values)
max_val = max(values)
if max_val == min_val:
return [1.0] * len(values)
if higher_is_better:
return [(v - min_val) / (max_val - min_val) for v in values]
else:
return [(max_val - v) / (max_val - min_val) for v in values]
def compute_weighted_scores(
model_reports: list[dict],
weights: Optional[dict] = None,
) -> list[dict]:
"""
Compute weighted composite scores for model comparison.
Args:
model_reports: list of per-model evaluation reports
weights: scoring weights dict
Returns:
list of scored models, sorted by composite score (descending)
"""
if weights is None:
weights = DEFAULT_WEIGHTS
# Extract raw metrics
model_names = []
f1_scores = []
accuracies = []
latencies = []
sizes = []
for report in model_reports:
model_names.append(report['model_name'])
metrics = report.get('metrics', {})
f1_scores.append(metrics.get('macro_f1', 0))
accuracies.append(metrics.get('accuracy', 0))
latencies.append(report.get('latency', {}).get('avg_ms', 100))
sizes.append(report.get('model_size_mb', 100))
# Normalize
norm_f1 = normalize_metric(f1_scores, higher_is_better=True)
norm_acc = normalize_metric(accuracies, higher_is_better=True)
norm_lat = normalize_metric(latencies, higher_is_better=False)
norm_size = normalize_metric(sizes, higher_is_better=False)
# Compute composite score
results = []
for i, name in enumerate(model_names):
composite = (
weights['macro_f1'] * norm_f1[i]
+ weights['accuracy'] * norm_acc[i]
+ weights['latency'] * norm_lat[i]
+ weights['model_size'] * norm_size[i]
# calibration placeholder (set to 0.5 if not provided)
+ weights['calibration'] * 0.5
)
results.append({
'model_name': name,
'composite_score': round(composite, 4),
'raw_metrics': {
'macro_f1': f1_scores[i],
'accuracy': accuracies[i],
'latency_ms': latencies[i],
'model_size_mb': sizes[i],
},
'normalized_metrics': {
'macro_f1': round(norm_f1[i], 4),
'accuracy': round(norm_acc[i], 4),
'latency': round(norm_lat[i], 4),
'model_size': round(norm_size[i], 4),
},
'rank': 0, # filled below
})
# Sort and rank
results.sort(key=lambda x: x['composite_score'], reverse=True)
for i, r in enumerate(results):
r['rank'] = i + 1
return results
def plot_comparison_radar(
scored_models: list[dict],
save_path: str | Path = None,
title: str = 'Model Comparison',
) -> None:
"""Plot radar chart comparing models on normalized metrics."""
categories = ['F1 Score', 'Accuracy', 'Speed', 'Compactness']
n_categories = len(categories)
fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))
angles = [n / float(n_categories) * 2 * np.pi for n in range(n_categories)]
angles += angles[:1] # Complete the circle
colors = ['#2196F3', '#FF5722', '#4CAF50', '#9C27B0', '#FF9800']
for idx, model in enumerate(scored_models):
norm = model['normalized_metrics']
values = [
norm['macro_f1'],
norm['accuracy'],
norm['latency'],
norm['model_size'],
]
values += values[:1]
color = colors[idx % len(colors)]
ax.plot(angles, values, 'o-', linewidth=2, color=color,
label=f"{model['model_name']} ({model['composite_score']:.3f})")
ax.fill(angles, values, color=color, alpha=0.1)
ax.set_xticks(angles[:-1])
ax.set_xticklabels(categories, fontsize=11)
ax.set_ylim(0, 1.1)
ax.set_title(title, fontsize=14, fontweight='bold', pad=20)
ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.0), fontsize=10)
plt.tight_layout()
if save_path:
Path(save_path).parent.mkdir(parents=True, exist_ok=True)
plt.savefig(save_path, dpi=150, bbox_inches='tight')
print(f" Saved radar chart to {save_path}")
plt.close()
def plot_comparison_bar(
scored_models: list[dict],
save_path: str | Path = None,
title: str = 'Model Comparison - Key Metrics',
) -> None:
"""Plot grouped bar chart comparing raw metrics across models."""
model_names = [m['model_name'] for m in scored_models]
metrics_to_plot = {
'Macro F1': [m['raw_metrics']['macro_f1'] for m in scored_models],
'Accuracy': [m['raw_metrics']['accuracy'] for m in scored_models],
}
fig, axes = plt.subplots(1, 3, figsize=(16, 5))
# F1 and Accuracy
x = np.arange(len(model_names))
width = 0.35
axes[0].bar(x - width/2, metrics_to_plot['Macro F1'], width, label='Macro F1', color='#2196F3')
axes[0].bar(x + width/2, metrics_to_plot['Accuracy'], width, label='Accuracy', color='#4CAF50')
axes[0].set_xticks(x)
axes[0].set_xticklabels(model_names, rotation=30, ha='right', fontsize=9)
axes[0].set_ylabel('Score')
axes[0].set_title('Classification Performance')
axes[0].legend()
axes[0].set_ylim(0, 1.05)
# Latency
latencies = [m['raw_metrics']['latency_ms'] for m in scored_models]
axes[1].bar(model_names, latencies, color='#FF5722')
axes[1].set_ylabel('Latency (ms)')
axes[1].set_title('Inference Latency')
plt.setp(axes[1].xaxis.get_majorticklabels(), rotation=30, ha='right', fontsize=9)
# Model Size
sizes = [m['raw_metrics']['model_size_mb'] for m in scored_models]
axes[2].bar(model_names, sizes, color='#9C27B0')
axes[2].set_ylabel('Size (MB)')
axes[2].set_title('Model Size')
plt.setp(axes[2].xaxis.get_majorticklabels(), rotation=30, ha='right', fontsize=9)
fig.suptitle(title, fontsize=14, fontweight='bold', y=1.02)
plt.tight_layout()
if save_path:
Path(save_path).parent.mkdir(parents=True, exist_ok=True)
plt.savefig(save_path, dpi=150, bbox_inches='tight')
print(f" Saved comparison bar chart to {save_path}")
plt.close()
def generate_comparison_report(
model_reports: list[dict],
output_dir: str | Path = None,
weights: Optional[dict] = None,
) -> dict:
"""
Full comparison pipeline:
1. Compute weighted scores
2. Generate radar chart
3. Generate bar chart
4. Save JSON report
5. Return best model recommendation
"""
scored = compute_weighted_scores(model_reports, weights)
if output_dir:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# Plots
plot_comparison_radar(scored, output_dir / 'comparison_radar.png')
plot_comparison_bar(scored, output_dir / 'comparison_bar.png')
# Report JSON
report = {
'weights_used': weights or DEFAULT_WEIGHTS,
'rankings': scored,
'best_model': scored[0]['model_name'],
'recommendation': (
f"Recommended model: {scored[0]['model_name']} "
f"(composite score: {scored[0]['composite_score']:.4f})"
),
}
with open(output_dir / 'comparison_report.json', 'w') as f:
json.dump(report, f, indent=2)
print(f"\n Best model: {scored[0]['model_name']} "
f"(score: {scored[0]['composite_score']:.4f})")
return report
return {'rankings': scored, 'best_model': scored[0]['model_name']}
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