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import importlib
import operator
import os
import sparse
from utils import benchmark
import numpy as np
import scipy.sparse as sps
LEN = 10000
DENSITY = 0.001
ITERS = 3
rng = np.random.default_rng(0)
if __name__ == "__main__":
print("Elementwise Example:\n")
for func_name in ["multiply", "add", "greater_equal"]:
print(f"{func_name} benchmark:\n")
s1_sps = sps.random(LEN, LEN, format="csr", density=DENSITY, random_state=rng) * 10
s1_sps.sum_duplicates()
s2_sps = sps.random(LEN, LEN, format="csr", density=DENSITY, random_state=rng) * 10
s2_sps.sum_duplicates()
# ======= Finch =======
os.environ[sparse._ENV_VAR_NAME] = "Finch"
importlib.reload(sparse)
s1 = sparse.asarray(s1_sps.asformat("csc"), format="csc")
s2 = sparse.asarray(s2_sps.asformat("csc"), format="csc")
func = getattr(sparse, func_name)
# Compile & Benchmark
result_finch = benchmark(func, args=[s1, s2], info="Finch", iters=ITERS)
# ======= Numba =======
os.environ[sparse._ENV_VAR_NAME] = "Numba"
importlib.reload(sparse)
s1 = sparse.asarray(s1_sps)
s2 = sparse.asarray(s2_sps)
func = getattr(sparse, func_name)
# Compile & Benchmark
result_numba = benchmark(func, args=[s1, s2], info="Numba", iters=ITERS)
# ======= SciPy =======
s1 = s1_sps
s2 = s2_sps
if func_name == "multiply":
func, args = s1.multiply, [s2]
elif func_name == "add":
func, args = operator.add, [s1, s2]
elif func_name == "greater_equal":
func, args = operator.ge, [s1, s2]
# Compile & Benchmark
result_scipy = benchmark(func, args=args, info="SciPy", iters=ITERS)
np.testing.assert_allclose(result_numba.todense(), result_scipy.toarray())
np.testing.assert_allclose(result_finch.todense(), result_numba.todense())
np.testing.assert_allclose(result_finch.todense(), result_scipy.toarray())
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