def test_large_mult_vec():
# 10M * 500 = 2.5B >= INT_MAX
nrows = 10000000
ncols = 500
dense = 250
nnz = nrows * dense
rowptrs = np.arange(0, nnz + 1, dense, dtype=np.int64)
assert len(rowptrs) == nrows + 1
assert rowptrs[-1] == nnz
try:
_log.info('allocating indexes')
colinds = np.empty(nnz, dtype=np.intc)
_log.info('allocating values')
values = np.zeros(nnz)
except MemoryError:
skip('insufficient memory')
_log.info('randomizing array contents')
fill_rows(values, colinds, nrows, ncols, dense)
csr = CSR(nrows, ncols, nnz, rowptrs, colinds, values)
v = np.random.randn(ncols)
res = csr.mult_vec(v)
assert res.shape == (nrows, )
assert np.all(~np.isnan(res))
def _train_matrix_cd(mat: CSR, this: np.ndarray, other: np.ndarray,
reg: float):
"""
One half of an explicit ALS training round using coordinate descent.
Args:
mat: the :math:`m \\times n` matrix of ratings
this: the :math:`m \\times k` matrix to train
other: the :math:`n \\times k` matrix of sample features
reg: the regularization term
"""
nr = mat.nrows
nf = other.shape[1]
assert mat.ncols == other.shape[0]
assert mat.nrows == this.shape[0]
assert this.shape[1] == nf
frob = 0.0
for i in prange(nr):
cols = mat.row_cs(i)
if len(cols) == 0:
continue
vals = mat.row_vs(i)
w = this[i, :].copy()
_rr_solve(other, cols, vals, w, reg * len(cols), 2)
delta = this[i, :] - w
frob += np.dot(delta, delta)
this[i, :] = w
return np.sqrt(frob)
def test_mult_ab_by_size(kernel, benchmark, size):
A = sps.random(size, size, 0.1, format='csr')
B = sps.random(size, size, 0.1, format='csr')
A = CSR.from_scipy(A)
B = CSR.from_scipy(B)
# make sure it's compiled
A.multiply(B)
def op():
A.multiply(B)
benchmark(op)
def test_mult_abt_by_density(kernel, benchmark, density):
A = sps.random(100, 100, density, format='csr')
B = sps.random(100, 100, density, format='csr')
A = CSR.from_scipy(A)
B = CSR.from_scipy(B)
# make sure it's compiled
A.multiply(B, transpose=True)
def op():
A.multiply(B, transpose=True)
benchmark(op)
def test_mult_ab(kernel, benchmark):
A = sps.random(100, 500, 0.1, format='csr')
B = sps.random(500, 200, 0.2, format='csr')
A = CSR.from_scipy(A)
B = CSR.from_scipy(B)
# make sure it's compiled
A.multiply(B)
def op():
A.multiply(B)
benchmark(op)
def test_csr_from_coo_novals(data, nrows, ncols):
n = nrows * ncols
nnz = data.draw(st.integers(0, int(n * 0.75)))
_log.info('testing %d×%d (%d nnz) with no values', nrows, ncols, nnz)
coords = st.integers(0, max(n - 1, 0))
coords = data.draw(nph.arrays(np.int32, nnz, elements=coords, unique=True))
rows = np.mod(coords, nrows, dtype=np.int32)
cols = np.floor_divide(coords, nrows, dtype=np.int32)
csr = CSR.from_coo(rows, cols, None, (nrows, ncols))
rowinds = csr.rowinds()
assert csr.nrows == nrows
assert csr.ncols == ncols
assert csr.nnz == nnz
for i in range(nrows):
sp = csr.rowptrs[i]
ep = csr.rowptrs[i + 1]
assert ep - sp == np.sum(rows == i)
points, = np.nonzero(rows == i)
assert len(points) == ep - sp
po = np.argsort(cols[points])
points = points[po]
assert all(np.sort(csr.colinds[sp:ep]) == cols[points])
assert all(np.sort(csr.row_cs(i)) == cols[points])
assert all(rowinds[sp:ep] == i)
row = csr.row(i)
assert np.sum(row) == ep - sp
def test_shard(csr):
SHARD_SIZE = 1000
shards = csr._shard_rows(SHARD_SIZE)
# we have the whole matrix
assert sum(s.nnz for s in shards) == csr.nnz
# everything is in spec
assert all(s.nnz <= SHARD_SIZE for s in shards)
# all row counts match
assert np.all(
np.concatenate([s.row_nnzs() for s in shards]) == csr.row_nnzs())
# all column indices match
assert np.all(np.concatenate([s.colinds for s in shards]) == csr.colinds)
# all values match
assert np.all(np.concatenate([s.values for s in shards]) == csr.values)
# we can reassemble the shards
csr2 = CSR._assemble_shards(shards)
assert csr2.nrows == csr.nrows
assert csr2.ncols == csr.ncols
assert csr2.nnz == csr.nnz
assert np.all(csr2.rowptrs == csr.rowptrs)
assert np.all(csr2.colinds == csr.colinds)
assert np.all(csr2.values == csr.values)
def mult_ab(a_h, b_h):
"""
Multiply matrices A and B.
Args:
a_h: the handle of matrix A
b_h: the handle of matrix B
Returns:
the handle of the product; it must be released when no longer needed.
"""
assert a_h.ncols == b_h.nrows
c_rp = np.zeros(a_h.nrows + 1, np.intc)
# step 1: symbolic multiplication
c_ci = _sym_mm(a_h, b_h, c_rp)
c_nnz = c_rp[a_h.nrows]
# step 2: numeric multiplication
c_vs = _num_mm(a_h, b_h, c_rp, c_ci)
# build the result
return CSR(a_h.nrows, b_h.ncols, c_nnz, c_rp, c_ci, c_vs)
def test_csr_str():
rows = np.array([0, 0, 1, 3], dtype=np.int32)
cols = np.array([1, 2, 0, 1], dtype=np.int32)
vals = np.arange(4, dtype=np.float_)
csr = CSR.from_coo(rows, cols, vals)
assert '4x3' in str(csr)
def mkh(csr):
vs = csr._required_values().astype(np.float64)
csr2 = CSR(csr.nrows, csr.ncols, csr.nnz, csr.rowptrs, csr.colinds, vs)
if csr.nnz == 0:
return mkl_h(0, csr.nrows, csr.ncols, csr2)
return _make_handle(csr2)
def test_csr_rowinds():
rows = np.array([0, 0, 1, 3], dtype=np.int32)
cols = np.array([1, 2, 0, 1], dtype=np.int32)
vals = np.arange(4, dtype=np.float_)
csr = CSR.from_coo(rows, cols, vals)
ris = csr.rowinds()
assert all(ris == rows)
def test_empty(nrows, ncols):
csr = CSR.empty(nrows, ncols)
assert csr.nrows == nrows
assert csr.ncols == ncols
assert csr.nnz == 0
assert all(csr.rowptrs == 0)
assert len(csr.rowptrs) == nrows + 1
assert len(csr.colinds) == 0
def test_csr_row_extent_fixed():
rows = np.array([0, 0, 1, 3], dtype=np.int32)
cols = np.array([1, 2, 0, 1], dtype=np.int32)
vals = np.arange(4, dtype=np.float_) + 1
csr = CSR.from_coo(rows, cols, vals)
assert csr.row_extent(0) == (0, 2)
assert csr.row_extent(1) == (2, 3)
assert csr.row_extent(2) == (3, 3)
assert csr.row_extent(3) == (3, 4)
def test_csr_row_fixed():
rows = np.array([0, 0, 1, 3], dtype=np.int32)
cols = np.array([1, 2, 0, 1], dtype=np.int32)
vals = np.arange(4, dtype=np.float_) + 1
csr = CSR.from_coo(rows, cols, vals)
assert all(csr.row(0) == np.array([0, 1, 2], dtype=np.float_))
assert all(csr.row(1) == np.array([3, 0, 0], dtype=np.float_))
assert all(csr.row(2) == np.array([0, 0, 0], dtype=np.float_))
assert all(csr.row(3) == np.array([0, 4, 0], dtype=np.float_))
def test_csr_from_coo_fixed():
"Make a CSR from COO data"
rows = np.array([0, 0, 1, 3], dtype=np.int32)
cols = np.array([1, 2, 0, 1], dtype=np.int32)
vals = np.arange(4, dtype=np.float_)
csr = CSR.from_coo(rows, cols, vals)
assert csr.nrows == 4
assert csr.ncols == 3
assert csr.nnz == 4
assert csr.values == approx(vals)
def test_unit_norm(csr: CSR):
# assume(spm.nnz >= 10)
backup = csr.copy()
m2 = csr.normalize_rows('unit')
assert len(m2) == csr.nrows
assert m2.dtype == csr.values.dtype
for i in range(csr.nrows):
vs = csr.row_vs(i)
bvs = backup.row_vs(i)
if len(vs) > 0:
assert m2[i] == approx(np.linalg.norm(bvs))
if m2[i] > 0:
assert np.linalg.norm(vs) == approx(1.0)
assert vs * m2[i] == approx(backup.row_vs(i))
else:
assert all(np.isnan(vs))
else:
assert m2[i] == 0.0
def test_csr_set_values():
rows = np.array([0, 0, 1, 3], dtype=np.int32)
cols = np.array([1, 2, 0, 1], dtype=np.int32)
vals = np.arange(4, dtype=np.float_)
csr = CSR.from_coo(rows, cols, vals)
v2 = 10 - vals
csr.values = v2
assert all(csr.values == v2)
def to_handle(csr: CSR) -> mkl_h:
if csr.nnz > _hpkg.max_nnz:
raise ValueError('CSR size {} exceeds max nnz {}'.format(
csr.nnz, _hpkg.max_nnz))
if csr.nnz == 0:
# empty matrices don't really work
return mkl_h(0, csr.nrows, csr.ncols, None)
norm = csr._normalize(np.float64, np.intc)
return _make_handle(norm)
def mult_vec(h: CSR, v):
res = np.zeros(h.nrows)
row = 0
for i in range(h.nnz):
# advance the row if necessary
while i == h.rowptrs[row + 1]:
row += 1
col = h.colinds[i]
res[row] += v[col] * h._e_value(i)
return res
def test_csr_set_values_none():
rows = np.array([0, 0, 1, 3], dtype=np.int32)
cols = np.array([1, 2, 0, 1], dtype=np.int32)
vals = np.arange(4, dtype=np.float_)
csr = CSR.from_coo(rows, cols, vals)
csr.values = None
assert csr.values is None
assert all(csr.row(0) == [0, 1, 1])
assert all(csr.row(1) == [1, 0, 0])
assert all(csr.row(3) == [0, 1, 0])
def test_csr_set_values_oversize():
rows = np.array([0, 0, 1, 3], dtype=np.int32)
cols = np.array([1, 2, 0, 1], dtype=np.int32)
vals = np.arange(4, dtype=np.float_)
csr = CSR.from_coo(rows, cols, vals)
v2 = np.arange(6, dtype=np.float_) + 10
csr.values = v2
assert csr.values is not None
assert all(csr.values == v2[:4])
def test_mult_vec(kernel, benchmark):
A = sps.random(100, 100, 0.1, format='csr')
A = CSR.from_scipy(A)
x = np.random.randn(100)
# make sure it's compiled
y = A.mult_vec(x)
assert len(y) == A.nrows
def op():
A.mult_vec(x)
benchmark(op)
def test_sps_to_csr(data, format):
mat = data.draw(sparse_matrices(format=format))
nr, nc = mat.shape
sp_csr: sps.csr_matrix = mat.tocsr()
csr = CSR.from_scipy(mat)
assert csr.ncols == nc
assert csr.nrows == nr
assert csr.nnz == mat.nnz
assert np.all(csr.rowptrs == sp_csr.indptr)
assert np.all(csr.colinds == sp_csr.indices)
assert np.all(csr.values == sp_csr.data)
def test_csr_set_values_undersize():
rows = np.array([0, 0, 1, 3], dtype=np.int32)
cols = np.array([1, 2, 0, 1], dtype=np.int32)
vals = np.arange(4, dtype=np.float_)
csr = CSR.from_coo(rows, cols, vals)
v2 = np.arange(3, dtype=np.float_) + 5
with raises(ValueError):
csr.values = v2
assert all(csr.values == vals)
def test_csr_from_sps_csr(smat, copy):
"Test creating a CSR from a SciPy CSR matrix"
csr = CSR.from_scipy(smat, copy=copy)
assert csr.nnz == smat.nnz
assert csr.nrows == smat.shape[0]
assert csr.ncols == smat.shape[1]
assert all(csr.rowptrs == smat.indptr)
assert all(csr.colinds == smat.indices)
assert all(csr.values == smat.data)
assert isinstance(csr.rowptrs, np.ndarray)
assert isinstance(csr.colinds, np.ndarray)
if csr.nnz > 0:
assert isinstance(csr.values, np.ndarray)
def _normalize(self, rmat):
rmat = rmat.to_scipy()
# compute column norms
norms = spla.norm(rmat, 2, axis=0)
# and multiply by a diagonal to normalize columns
recip_norms = norms.copy()
is_nz = recip_norms > 0
recip_norms[is_nz] = np.reciprocal(recip_norms[is_nz])
norm_mat = rmat @ sps.diags(recip_norms)
assert norm_mat.shape[1] == rmat.shape[1]
# and reset NaN
norm_mat.data[np.isnan(norm_mat.data)] = 0
_logger.info('[%s] normalized rating matrix columns', self._timer)
return CSR.from_scipy(norm_mat, False)
def test_csr_sparse_row():
rows = np.array([0, 0, 1, 3], dtype=np.int32)
cols = np.array([1, 2, 0, 1], dtype=np.int32)
vals = np.arange(4, dtype=np.float_)
csr = CSR.from_coo(rows, cols, vals)
assert all(csr.row_cs(0) == np.array([1, 2], dtype=np.int32))
assert all(csr.row_cs(1) == np.array([0], dtype=np.int32))
assert all(csr.row_cs(2) == np.array([], dtype=np.int32))
assert all(csr.row_cs(3) == np.array([1], dtype=np.int32))
assert all(csr.row_vs(0) == np.array([0, 1], dtype=np.float_))
assert all(csr.row_vs(1) == np.array([2], dtype=np.float_))
assert all(csr.row_vs(2) == np.array([], dtype=np.float_))
assert all(csr.row_vs(3) == np.array([3], dtype=np.float_))
def test_csr_row_nnzs(mat):
nrows, ncols = mat.shape
# sparsify the matrix
mat[mat <= 0] = 0
smat = sps.csr_matrix(mat)
# make sure it's sparse
assume(smat.nnz == np.sum(mat > 0))
csr = CSR.from_scipy(smat)
nnzs = csr.row_nnzs()
assert nnzs.sum() == csr.nnz
for i in range(nrows):
row = mat[i, :]
assert nnzs[i] == np.sum(row > 0)
def _compute_similarities(self, rmat):
trmat = rmat.transpose()
nitems = trmat.nrows
m_nbrs = self.save_nbrs
if m_nbrs is None or m_nbrs < 0:
m_nbrs = 0
bounds = _make_blocks(nitems, 1000)
_logger.info('[%s] splitting %d items (%d ratings) into %d blocks',
self._timer, nitems, trmat.nnz, len(bounds))
blocks = [trmat.subset_rows(sp, ep) for (sp, ep) in bounds]
_logger.info('[%s] computing similarities', self._timer)
ptrs = List(bounds)
nbs = List(blocks)
if not nbs:
# oops, this is the bad place
# in non-JIT node, List doesn't actually make the list
nbs = blocks
ptrs = bounds
s_blocks = _sim_blocks(trmat, nbs, ptrs, self.min_sim, m_nbrs)
nnz = sum(b.nnz for b in s_blocks)
tot_rows = sum(b.nrows for b in s_blocks)
_logger.info('[%s] computed %d similarities for %d items in %d blocks',
self._timer, nnz, tot_rows, len(s_blocks))
row_nnzs = np.concatenate([b.row_nnzs() for b in s_blocks])
assert len(row_nnzs) == nitems, \
'only have {} rows for {} items'.format(len(row_nnzs), nitems)
smat = CSR.empty(nitems, nitems, row_nnzs)
start = 0
for bi, b in enumerate(s_blocks):
bnr = b.nrows
end = start + bnr
v_sp = smat.rowptrs[start]
v_ep = smat.rowptrs[end]
_logger.debug('block %d (%d:%d) has %d entries, storing in %d:%d',
bi, start, end, b.nnz, v_sp, v_ep)
smat.colinds[v_sp:v_ep] = b.colinds
smat.values[v_sp:v_ep] = b.values
start = end
_logger.info('[%s] sorting similarity matrix with %d entries',
self._timer, smat.nnz)
_sort_nbrs(smat)
return smat
def test_subset_rows(data):
nrows = data.draw(st.integers(5, 100))
ncols = data.draw(st.integers(1, 100))
dens = data.draw(st.floats(0, 1))
beg = data.draw(st.integers(0, nrows - 1))
end = data.draw(st.integers(beg, nrows - 1))
spm = sps.random(nrows, ncols, dens, format='csr')
csr = CSR.from_scipy(spm)
m2 = csr.subset_rows(beg, end)
assert m2.nrows == end - beg
for i in range(m2.nrows):
assert all(m2.row_cs(i) == csr.row_cs(beg + i))
assert all(m2.row_vs(i) == csr.row_vs(beg + i))
