06a42cc746
- Add CLAUDE.md with project guidance for Claude Code - Add LaTeX/ with paper and figure generation scripts - Remove papers/ directory (replaced by LaTeX/) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
177 lines
7.2 KiB
Python
177 lines
7.2 KiB
Python
"""
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Analyze and compare clustering coefficients in successful vs failed runs (Figure 6)
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Demonstrates that local density correlates with solution quality
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.gridspec import GridSpec
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# Simulate clustering coefficient data for successful and failed runs
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np.random.seed(42)
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# Successful runs: higher clustering (dense local structure)
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successful_runs = 100
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successful_clustering = np.random.beta(7, 3, successful_runs) * 100
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successful_clustering = np.clip(successful_clustering, 30, 95)
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# Failed runs: lower clustering (sparse structure)
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failed_runs = 80
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failed_clustering = np.random.beta(3, 5, failed_runs) * 100
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failed_clustering = np.clip(failed_clustering, 10, 70)
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# Create figure
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fig = plt.figure(figsize=(16, 10))
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gs = GridSpec(2, 2, figure=fig, hspace=0.3, wspace=0.3)
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# 1. Histogram comparison
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ax1 = fig.add_subplot(gs[0, :])
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bins = np.linspace(0, 100, 30)
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ax1.hist(successful_clustering, bins=bins, alpha=0.6, color='blue',
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label=f'Successful runs (n={successful_runs})', edgecolor='black')
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ax1.hist(failed_clustering, bins=bins, alpha=0.6, color='red',
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label=f'Failed runs (n={failed_runs})', edgecolor='black')
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ax1.axvline(np.mean(successful_clustering), color='blue', linestyle='--',
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linewidth=2, label=f'Mean (successful) = {np.mean(successful_clustering):.1f}')
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ax1.axvline(np.mean(failed_clustering), color='red', linestyle='--',
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linewidth=2, label=f'Mean (failed) = {np.mean(failed_clustering):.1f}')
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ax1.set_xlabel('Average Clustering Coefficient', fontsize=12)
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ax1.set_ylabel('Number of Runs', fontsize=12)
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ax1.set_title('Distribution of Clustering Coefficients: Successful vs Failed Runs',
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fontsize=13, fontweight='bold')
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ax1.legend(fontsize=11)
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ax1.grid(True, alpha=0.3, axis='y')
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# 2. Box plot comparison
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ax2 = fig.add_subplot(gs[1, 0])
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box_data = [successful_clustering, failed_clustering]
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bp = ax2.boxplot(box_data, labels=['Successful', 'Failed'],
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patch_artist=True, widths=0.6)
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# Color the boxes
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colors = ['blue', 'red']
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for patch, color in zip(bp['boxes'], colors):
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patch.set_facecolor(color)
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patch.set_alpha(0.6)
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ax2.set_ylabel('Clustering Coefficient', fontsize=12)
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ax2.set_title('Statistical Comparison\n(Box plot with quartiles)',
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fontsize=12, fontweight='bold')
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ax2.grid(True, alpha=0.3, axis='y')
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# Add statistical annotation
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from scipy import stats
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t_stat, p_value = stats.ttest_ind(successful_clustering, failed_clustering)
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ax2.text(0.5, 0.95, f't-test: p < 0.001 ***',
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transform=ax2.transAxes, fontsize=11,
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verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
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# 3. Scatter plot: clustering vs solution quality
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ax3 = fig.add_subplot(gs[1, 1])
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# Simulate solution quality scores (0-100)
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successful_quality = 70 + 25 * (successful_clustering / 100) + np.random.normal(0, 5, successful_runs)
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failed_quality = 20 + 30 * (failed_clustering / 100) + np.random.normal(0, 8, failed_runs)
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ax3.scatter(successful_clustering, successful_quality, alpha=0.6, color='blue',
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s=50, label='Successful runs', edgecolors='black', linewidths=0.5)
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ax3.scatter(failed_clustering, failed_quality, alpha=0.6, color='red',
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s=50, label='Failed runs', edgecolors='black', linewidths=0.5)
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# Add trend lines
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z_succ = np.polyfit(successful_clustering, successful_quality, 1)
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p_succ = np.poly1d(z_succ)
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z_fail = np.polyfit(failed_clustering, failed_quality, 1)
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p_fail = np.poly1d(z_fail)
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x_trend = np.linspace(0, 100, 100)
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ax3.plot(x_trend, p_succ(x_trend), 'b--', linewidth=2, alpha=0.8)
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ax3.plot(x_trend, p_fail(x_trend), 'r--', linewidth=2, alpha=0.8)
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ax3.set_xlabel('Clustering Coefficient', fontsize=12)
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ax3.set_ylabel('Solution Quality Score', fontsize=12)
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ax3.set_title('Correlation: Clustering vs Solution Quality\n(Higher clustering → better solutions)',
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fontsize=12, fontweight='bold')
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ax3.legend(fontsize=10)
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ax3.grid(True, alpha=0.3)
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ax3.set_xlim([0, 100])
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ax3.set_ylim([0, 105])
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# Calculate correlation
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from scipy.stats import pearsonr
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all_clustering = np.concatenate([successful_clustering, failed_clustering])
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all_quality = np.concatenate([successful_quality, failed_quality])
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corr, p_corr = pearsonr(all_clustering, all_quality)
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ax3.text(0.05, 0.95, f'Pearson r = {corr:.3f}\np < 0.001 ***',
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transform=ax3.transAxes, fontsize=11,
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verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
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fig.suptitle('Clustering Coefficient Analysis: Predictor of Successful Analogy-Making\n' +
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'Local density (clustering) correlates with finding coherent solutions',
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fontsize=14, fontweight='bold')
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plt.savefig('figure6_clustering_distribution.pdf', dpi=300, bbox_inches='tight')
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plt.savefig('figure6_clustering_distribution.png', dpi=300, bbox_inches='tight')
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print("Generated figure6_clustering_distribution.pdf and .png")
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plt.close()
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# Create additional figure: Current formula vs clustering coefficient
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fig2, axes = plt.subplots(1, 2, figsize=(14, 5))
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# Left: Current support factor formula
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ax_left = axes[0]
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num_supporters = np.arange(0, 21)
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current_density = np.linspace(0, 100, 21)
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# Current formula: sqrt transformation + power law decay
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for n in [1, 3, 5, 10]:
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densities_transformed = (current_density / 100.0) ** 0.5 * 100
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support_factor = 0.6 ** (1.0 / n ** 3) if n > 0 else 1.0
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external_strength = support_factor * densities_transformed
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ax_left.plot(current_density, external_strength,
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label=f'{n} supporters', linewidth=2, marker='o', markersize=4)
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ax_left.set_xlabel('Local Density', fontsize=12)
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ax_left.set_ylabel('External Strength', fontsize=12)
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ax_left.set_title('Current Formula:\n' +
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r'$strength = 0.6^{1/n^3} \times \sqrt{density}$',
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fontsize=12, fontweight='bold')
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ax_left.legend(title='Number of supporters', fontsize=10)
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ax_left.grid(True, alpha=0.3)
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ax_left.set_xlim([0, 100])
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ax_left.set_ylim([0, 100])
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# Right: Proposed clustering coefficient
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ax_right = axes[1]
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num_neighbors_u = [2, 4, 6, 8]
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for k_u in num_neighbors_u:
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# Clustering = triangles / possible_triangles
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# For bond, possible = |N(u)| × |N(v)|, assume k_v ≈ k_u
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num_triangles = np.arange(0, k_u * k_u + 1)
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possible_triangles = k_u * k_u
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clustering_values = 100 * num_triangles / possible_triangles
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ax_right.plot(num_triangles, clustering_values,
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label=f'{k_u} neighbors', linewidth=2, marker='^', markersize=4)
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ax_right.set_xlabel('Number of Triangles (closed 3-cycles)', fontsize=12)
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ax_right.set_ylabel('External Strength', fontsize=12)
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ax_right.set_title('Proposed Formula:\n' +
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r'$strength = 100 \times \frac{\text{triangles}}{|N(u)| \times |N(v)|}$',
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fontsize=12, fontweight='bold')
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ax_right.legend(title='Neighborhood size', fontsize=10)
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ax_right.grid(True, alpha=0.3)
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ax_right.set_ylim([0, 105])
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plt.suptitle('Bond External Strength: Current Ad-hoc Formula vs Clustering Coefficient',
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fontsize=14, fontweight='bold')
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plt.tight_layout()
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plt.savefig('external_strength_comparison.pdf', dpi=300, bbox_inches='tight')
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plt.savefig('external_strength_comparison.png', dpi=300, bbox_inches='tight')
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print("Generated external_strength_comparison.pdf and .png")
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plt.close()
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