Doc: Show stats comparing to numpy

What type of report is this?

Improvement

Please describe the issue.

It would be good if a GitHub action ran a test that generated plots comparing performance to numpy, these could then be pushed to a GitHub page and viewable.

If you have a suggestion on how it should be, add it below.

An example is that:

Which shows at which density sparse is more efficient for different numbers of dimensions.

Which I generated with:

import sparse
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm


def test_boolean():
    # Generate density values (11 points between 0.00-0.01)
    densities = np.linspace(0.00, 1.00, num=100)
    dims = range(1, 5)
    size = 2000

    sparse_mem: list[list[int]] = []
    numpy_mem: list[list[int]] = []
    for dim in dims:  # Dimensions 1-3 (0D removed)
        print(f"dim: {dim}")

        dim_size = int(float(size) ** (1 / float(dim)))
        sparse_mem_dim: list[int] = []
        numpy_mem_dim: list[int] = []
        for density in tqdm(densities):

            # Sparse array memory
            sparse_arr = sparse.random([dim_size for _ in range(dim)], density=density)
            sparse_mem_dim.append(sparse_arr.nbytes)

            # Dense array memory
            dense_arr = np.empty([dim_size for _ in range(dim)])
            numpy_mem_dim.append(dense_arr.nbytes)
        sparse_mem.append(sparse_mem_dim)
        numpy_mem.append(numpy_mem_dim)

    # Plotting
    plt.figure(figsize=(10, 6))
    for i, d in enumerate(dims):
        plt.plot(densities, sparse_mem[i], "o", alpha=0.5, label=f"Sparse {d}D ")
        plt.plot(densities, numpy_mem[i], "o", alpha=0.5, label=f"Numpy {d}D")
    plt.xlabel("Density")
    plt.ylabel("Memory Usage (bytes)")
    plt.title(f"Memory Usage vs Density for nD Arrays")
    plt.legend()
    plt.grid(True)
    plt.savefig(f"memory_usage.png")
    plt.close()

Start by reviewing the provided Python example and the repository's existing GitHub Actions and documentation publishing setup. Determine where benchmark plots should be generated and published, then verify that the workflow produces comparisons with NumPy and makes the resulting plots viewable through GitHub Pages.