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2c4ca2f | 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 | #!/usr/bin/env python3
"""
Generate scatter plots showing R² evolution across epochs
Each point = one expression generated at that epoch
"""
import json
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
from collections import defaultdict
def load_report(json_file):
"""Load report with full history"""
with open(json_file, 'r') as f:
return json.load(f)
def extract_epochs_data(experiment):
"""Extract all expressions across all epochs from an experiment"""
epochs_data = []
# Get history from experiment
history = experiment.get('history', [])
for epoch_data in history:
epoch = epoch_data.get('epoch', 0)
expressions = epoch_data.get('expressions', [])
for expr in expressions:
r2 = expr.get('r2', -1.0)
is_valid = expr.get('is_valid', False)
epochs_data.append({
'epoch': epoch,
'r2': r2,
'is_valid': is_valid,
'expression': expr.get('expression', '')
})
return epochs_data
def plot_experiment_evolution(model, benchmark, algorithm, epochs_data, output_file):
"""Plot scatter of R² scores across epochs"""
# Separate valid and invalid
valid_epochs = [d['epoch'] for d in epochs_data if d['is_valid']]
valid_r2 = [d['r2'] for d in epochs_data if d['is_valid']]
invalid_epochs = [d['epoch'] for d in epochs_data if not d['is_valid']]
invalid_r2 = [d['r2'] for d in epochs_data if not d['is_valid']]
# Create plot
fig, ax = plt.subplots(figsize=(12, 6))
# Plot invalid expressions (red, small)
if invalid_epochs:
ax.scatter(invalid_epochs, invalid_r2,
c='red', alpha=0.3, s=20, label='Invalid')
# Plot valid expressions (blue, larger)
if valid_epochs:
ax.scatter(valid_epochs, valid_r2,
c='blue', alpha=0.6, s=40, label='Valid')
# Find best expression
if valid_r2:
best_idx = np.argmax(valid_r2)
best_epoch = valid_epochs[best_idx]
best_r2 = valid_r2[best_idx]
# Mark best with star
ax.scatter([best_epoch], [best_r2],
c='gold', marker='*', s=500,
edgecolors='black', linewidths=2,
label=f'Best (R²={best_r2:.4f})', zorder=10)
# Style
ax.set_xlabel('Epoch', fontsize=12, fontweight='bold')
ax.set_ylabel('R² Score', fontsize=12, fontweight='bold')
ax.set_title(f'{model} + {benchmark} + {algorithm.upper()}',
fontsize=14, fontweight='bold')
ax.grid(True, alpha=0.3)
ax.legend(loc='lower right', fontsize=10)
# Add horizontal line at R² = 0
ax.axhline(y=0, color='black', linestyle='--', linewidth=1, alpha=0.5)
# Set y-axis limits
all_r2 = valid_r2 + invalid_r2
if all_r2:
ymin = min(all_r2) - 0.1
ymax = max(all_r2) + 0.1
ax.set_ylim(ymin, ymax)
# Add statistics text
stats_text = f'Total: {len(epochs_data)}\n'
stats_text += f'Valid: {len(valid_r2)} ({len(valid_r2)/len(epochs_data)*100:.1f}%)\n'
if valid_r2:
stats_text += f'Best R²: {max(valid_r2):.4f}\n'
stats_text += f'Avg R² (valid): {np.mean(valid_r2):.4f}'
ax.text(0.02, 0.98, stats_text,
transform=ax.transAxes,
verticalalignment='top',
bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5),
fontsize=9)
plt.tight_layout()
plt.savefig(output_file, dpi=150, bbox_inches='tight')
plt.close()
def main():
# Load data
print("Loading raw_results.json...")
raw_file = Path("evaluation_results_aws/raw_results.json")
with open(raw_file, 'r') as f:
experiments = json.load(f)
print(f"Loaded {len(experiments)} experiments")
# Create output directory
output_dir = Path("evaluation_results_aws/plots/evolution_by_epoch")
output_dir.mkdir(parents=True, exist_ok=True)
# Process each experiment
experiment_counter = 0
# We need to extract model, benchmark, algorithm from raw data
# The raw_results.json contains full experiments with history
# Load report.json to get the mapping
report_file = Path("evaluation_results_aws/report.json")
with open(report_file, 'r') as f:
report = json.load(f)
summary_table = report['summary_table']
# Match experiments from raw_results with summary_table
for idx, exp in enumerate(experiments):
if not exp.get('success', False):
continue
# Get metadata from summary_table (same order)
if idx < len(summary_table):
meta = summary_table[idx]
model = meta['model']
benchmark = meta['benchmark']
algorithm = meta['algorithm']
else:
print(f"Warning: No metadata for experiment {idx}")
continue
print(f"[{experiment_counter+1}/{len(experiments)}] Processing: {model} + {benchmark} + {algorithm}")
# Extract epochs data
epochs_data = extract_epochs_data(exp)
if not epochs_data:
print(f" [SKIP] No data found")
continue
# Generate plot
output_file = output_dir / f"{model}_{benchmark}_{algorithm}.png"
plot_experiment_evolution(model, benchmark, algorithm, epochs_data, output_file)
print(f" [OK] Saved: {output_file}")
experiment_counter += 1
print(f"\n[DONE] Generated {experiment_counter} plots in {output_dir}")
print(f"\nPlots saved to: {output_dir.absolute()}")
if __name__ == "__main__":
main()
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