Study E — Impact of \(p_e\) on Convergence (Large Batch)

Replication of Study A with a larger batch size, to assess whether the convergence behaviour as a function of the sparsity \(p_e\) is stable with larger batch size.

Data generated by: run_study_E.py

Paper section: Appendix G.3 — Additional Sparsity Analysis (Figure 23)

Parameter	Value
Problem Size \(N\)	100
Num. Parallel Nodes \(P\)	1000
Learning Rate \(\alpha\)	0.1
Batch Size \(M\)	1000
Bernouilli Prob. \(p_e\)	[0.001, 0.1]
Max. Steps \(S\)	10000
Oracle Proportion \(p_w\)	0.5

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import sys
import os
import glob

# Add parent directory to path
sys.path.append('..')

from plotting.plot_utility import plot_metric_vs_param, compute_steps_to_thresholds, setup_plot_style

%matplotlib inline
setup_plot_style()

def get_latest_study_folder(study_prefix):
    pattern = f'../studies/results/{study_prefix}_*/'
    folders = glob.glob(pattern)
    if folders:
        return max(folders)  # Latest by name (timestamp-based)
    return None

mode = 'auto'  # Change to 'manual' to specify your own path

if mode == 'auto':
    results_dir = get_latest_study_folder('E_p_e_impact_large_batch')
    if results_dir is None:
        raise FileNotFoundError("No Study E results found. Run run_study_E.py first.")
    print(f"Auto-detected folder: {results_dir}")
else:
    results_dir = '../studies/results/E_p_e_impact_large_batch_XXXXXX_XXXXXX/'
    print(f"Using manual path: {results_dir}")

p_e_values = np.load(results_dir + 'p_e_values.npy')
p_diff_matrix = np.load(results_dir + 'p_diff_matrix.npy')
steps_to_convergence = np.load(results_dir + 'steps_to_convergence.npy')

print(f"\np_e range: {p_e_values[0]:.6f} to {p_e_values[-1]:.6f}")
print(f"Number of p_e values: {len(p_e_values)}")
print(f"p_diff_matrix shape: {p_diff_matrix.shape}")

Auto-detected folder: ../studies/results/E_p_e_impact_large_batch_20260420_212337/

p_e range: 0.001000 to 0.100000
Number of p_e values: 50
p_diff_matrix shape: (50, 10000)

Error \(p_\text{diff}\) vs \(p_e\) — Iso-Step Curves

Same as Study A but with larger batch size (1000 instead of 100). Shows a similar behaviour despite the larger batch size.

fig, ax = plt.subplots()

plot_metric_vs_param(
    p_e_values,
    p_diff_matrix,
    ax,
    draw_best=True,
    best='min',
    add_min_curve=False,
    xscale='log',
    yscale='linear',
    xlim=(1e-3, 1e-1),
    ylim=(0.02, 0.51),
)

ax.axvline(x=0.01, color='blue', linestyle='--', label='$p_e=1/N$')
ax.set_xlabel('$p_e$')
ax.set_ylabel('$\\frac{1}{P \\times N}\sum|w_i-w_i^\mathrm{true}|$')

ax.axvline(x=7e-2, color='red', linestyle='-')
p = patches.Rectangle((7e-2,0), 1, 10000, linewidth=0, fill=True, fc='white', ec='red', hatch='\\\\\\\\',zorder=2, label='Improvement $<10\%$')
ax.add_patch(p)
ax.legend(loc='upper left', bbox_to_anchor=(0.025,0.975))

plt.tight_layout()
plt.show()

Steps to Convergence vs \(p_e\) — Iso-\(p_\text{diff}\) Curves

Each curve corresponds to a fixed convergence threshold. Shows whether the \(p_e\) beahviour remains stable under larger batch training compared to Study A.

See Paper Appendix G.3. Figure 23.

thresholds, steps_data = compute_steps_to_thresholds(p_e_values, p_diff_matrix, n_thresholds=999)

fig, ax = plt.subplots()

plot_metric_vs_param(
    p_e_values,
    steps_data+1,
    ax,
    draw_best=True,
    xscale='log',
    yscale='log',
    xlim=(1e-3, 1e-1),
    ylim=(100, 25_000),
    value_labels=thresholds,
    add_min_curve=False
)

ax.axvline(x=0.01, color='blue', linestyle='--', label='$p_e=1/N$')
ax.set_xlabel('$p_e$')
ax.set_ylabel('Steps to convergence')

ax.axvline(x=7e-2, color='red', linestyle='-')
p = patches.Rectangle((7e-2,0), 1, 25000, linewidth=0, fill=True, fc='white', ec='red', hatch='\\\\\\\\',zorder=2, label='Improvement $<10\%$')
ax.add_patch(p)
ax.legend(loc='upper right', bbox_to_anchor=(0.99,0.99))

plt.tight_layout()
plt.show()