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import sparse
import numpy as np
class TensordotSuiteDenseSparse:
"""
Performance comparison for returntype=COO vs returntype=np.ndarray.
tensordot(np.ndarray, COO)
"""
def setup(self):
rng = np.random.default_rng(0)
self.n = rng.random((100, 100))
self.s = sparse.random((100, 100, 100, 100), density=0.01, random_state=rng)
def time_dense(self):
sparse.tensordot(self.n, self.s, axes=([0, 1], [0, 2]))
def time_sparse(self):
sparse.tensordot(self.n, self.s, axes=([0, 1], [0, 2]), return_type=sparse.COO)
class TensordotSuiteSparseSparse:
"""
Performance comparison for returntype=COO vs returntype=np.ndarray.
tensordot(COO, COO)
"""
def setup(self):
rng = np.random.default_rng(0)
self.s1 = sparse.random((100, 100), density=0.01, random_state=rng)
self.s2 = sparse.random((100, 100, 100, 100), density=0.01, random_state=rng)
def time_dense(self):
sparse.tensordot(self.s1, self.s2, axes=([0, 1], [0, 2]), return_type=np.ndarray)
def time_sparse(self):
sparse.tensordot(self.s1, self.s2, axes=([0, 1], [0, 2]))
class TensordotSuiteSparseDense:
"""
Performance comparison for returntype=COO vs returntype=np.ndarray.
tensordot(COO, np.ndarray)
"""
def setup(self):
rng = np.random.default_rng(0)
self.s = sparse.random((100, 100, 100, 100), density=0.01, random_state=rng)
self.n = rng.random((100, 100))
def time_dense(self):
sparse.tensordot(self.s, self.n, axes=([0, 1], [0, 1]))
def time_sparse(self):
sparse.tensordot(self.s, self.n, axes=([0, 1], [0, 1]), return_type=sparse.COO)
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