import numpy as np
from scipy import sparse

rows = np.array([0, 0, 1, 2, 2])
cols = np.array([0, 3, 1, 0, 2])
values = np.array([10.0, 2.5, 3.0, 4.5, 8.0])

features = sparse.coo_array((values, (rows, cols)), shape=(3, 4)).tocsr()

sparse.save_npz("feature_matrix.npz", features)
loaded = sparse.load_npz("feature_matrix.npz")

print(f"saved format: {features.format}")
print(f"loaded type: {type(loaded).__name__}")
print(f"loaded format: {loaded.format}")
print(f"shape: {loaded.shape}")
print(f"stored values: {loaded.nnz}")
print("dense rows:")
for row in loaded.toarray():
    print(row)
print("same sparse class:", type(loaded) is type(features))
print("same dense values:", np.array_equal(loaded.toarray(), features.toarray()))