Doc: Show stats comparing to numpy #879
Description
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()