def testHadamard(self):
nptst.assert_array_equal((self.A.hadamard(self.A)).toarray(), (self.A.toarray()) ** 2)
nptst.assert_array_equal((self.B.hadamard(self.B)).toarray(), self.B.toarray() ** 2)
nptst.assert_array_equal((self.C.hadamard(self.C)).toarray(), self.C.toarray() ** 2)
nptst.assert_array_equal((self.D.hadamard(self.D)).toarray(), self.D.toarray() ** 2)
nptst.assert_array_equal((self.F.hadamard(self.F)).toarray(), self.F.toarray() ** 2)
nptst.assert_array_equal((self.G.hadamard(self.G)).toarray(), self.G.toarray() ** 2)
nptst.assert_array_equal((self.H.hadamard(self.H)).toarray(), self.H.toarray() ** 2)
for storagetype in self.storagetypes:
A = csarray((5, 5), storagetype=storagetype)
A[0, 1] = 4
A[2, 3] = -1.2
A[1, 3] = 2
A[3, 3] = 1
B = csarray((5, 5), storagetype=storagetype)
B[0, 2] = 9.2
B[2, 3] = -5
B[3, 4] = 12
B[3, 3] = 12
C = csarray((5, 5), storagetype=storagetype)
nptst.assert_array_equal((A.hadamard(B)).toarray(), A.toarray() * B.toarray())
nptst.assert_array_equal((A.hadamard(C)).toarray(), C.toarray())
nptst.assert_array_equal((self.a.hadamard(self.a)).toarray(), (self.a.toarray()) ** 2)
nptst.assert_array_equal((self.b.hadamard(self.b)).toarray(), (self.b.toarray()) ** 2)
nptst.assert_array_equal((self.c.hadamard(self.c)).toarray(), (self.c.toarray()) ** 2)
def testAdd(self):
# print(self.A.__add__(self.A._array))
nptst.assert_array_equal((self.A + self.A).toarray(), self.A.toarray() * 2)
nptst.assert_array_equal((self.B + self.B).toarray(), self.B.toarray() * 2)
nptst.assert_array_equal((self.C + self.C).toarray(), self.C.toarray() * 2)
nptst.assert_array_equal((self.D + self.D).toarray(), self.D.toarray() * 2)
nptst.assert_array_equal((self.F + self.F).toarray(), self.F.toarray() * 2)
nptst.assert_array_equal((self.G + self.G).toarray(), self.G.toarray() * 2)
nptst.assert_array_equal((self.H + self.H).toarray(), self.H.toarray() * 2)
A = csarray((5, 5))
A[0, 1] = 4
A[1, 3] = 2
A[3, 3] = 1
B = csarray((5, 5))
B[0, 2] = 9.2
B[2, 3] = -5
B[3, 4] = 12
nptst.assert_array_equal((A + B).toarray(), A.toarray() + B.toarray())
nptst.assert_array_equal((self.a + self.a).toarray(), self.a.toarray() * 2)
nptst.assert_array_equal((self.b + self.b).toarray(), self.b.toarray() * 2)
nptst.assert_array_equal((self.c + self.c).toarray(), self.c.toarray() * 2)
def testSub(self):
nptst.assert_array_equal((self.A - self.A).toarray(),
self.A.toarray() * 0)
nptst.assert_array_equal((self.B - self.B).toarray(),
self.B.toarray() * 0)
nptst.assert_array_equal((self.C - self.C).toarray(),
self.C.toarray() * 0)
nptst.assert_array_equal((self.D - self.D).toarray(),
self.D.toarray() * 0)
nptst.assert_array_equal((self.F - self.F).toarray(),
self.F.toarray() * 0)
nptst.assert_array_equal((self.B * 2 - self.B).toarray(),
self.B.toarray())
A = csarray((5, 5))
A[0, 1] = 4
A[1, 3] = 2
A[3, 3] = 1
B = csarray((5, 5))
B[0, 2] = 9.2
B[2, 3] = -5
B[3, 4] = 12
nptst.assert_array_equal((A - B).toarray(), A.toarray() - B.toarray())
def testStr(self):
nrow = 5
ncol = 7
storagetypes = ["col", "row"]
for storagetype in storagetypes:
A = csarray((nrow, ncol), storagetype=storagetype)
A[0, 1] = 1
A[1, 3] = 5.2
A[3, 3] = -0.2
outputStr = "csarray dtype:float64 shape:(5, 7) non-zeros:3 storage:" + A.storagetype + "\n"
outputStr += "(0, 1) 1.0\n"
outputStr += "(1, 3) 5.2\n"
outputStr += "(3, 3) -0.2"
self.assertEquals(str(A), outputStr)
B = csarray((5, 5), storagetype=storagetype)
outputStr = "csarray dtype:float64 shape:(5, 5) non-zeros:0 storage:" + B.storagetype + "\n"
self.assertEquals(str(B), outputStr)
outputStr = "csarray dtype:float64 shape:(10,) non-zeros:3\n"
outputStr += "(0) 23.0\n"
outputStr += "(3) 1.2\n"
outputStr += "(4) -8.0"
self.assertEquals(str(self.a), outputStr)
outputStr = "csarray dtype:float64 shape:(3,) non-zeros:0\n"
self.assertEquals(str(self.c), outputStr)
def testBiCGSTAB(self):
#This doesn't always converge
numRuns = 10
for i in range(numRuns):
n = numpy.random.randint(5, 20)
A = numpy.random.rand(n, n)
x = numpy.random.rand(n)
b = A.dot(x)
A = sppy.csarray(A)
x2, output = sppy.linalg.biCGSTAB(A, b, tol=10**-6, maxIter=n)
if output == 0:
nptst.assert_array_almost_equal(x, x2, 3)
#Try with bad input
m = 3
n = 5
A = numpy.random.rand(n, m)
A = sppy.csarray(A)
x = numpy.random.rand(m)
b = A.dot(x)
self.assertRaises(ValueError, sppy.linalg.biCGSTAB, A, b)
A = numpy.random.rand(n, n)
A = sppy.csarray(A)
b = numpy.array(n+1)
self.assertRaises(ValueError, sppy.linalg.biCGSTAB, A, b)
def testAdd(self):
#print(self.A.__add__(self.A._array))
nptst.assert_array_equal((self.A + self.A).toarray(),
self.A.toarray() * 2)
nptst.assert_array_equal((self.B + self.B).toarray(),
self.B.toarray() * 2)
nptst.assert_array_equal((self.C + self.C).toarray(),
self.C.toarray() * 2)
nptst.assert_array_equal((self.D + self.D).toarray(),
self.D.toarray() * 2)
nptst.assert_array_equal((self.F + self.F).toarray(),
self.F.toarray() * 2)
A = csarray((5, 5))
A[0, 1] = 4
A[1, 3] = 2
A[3, 3] = 1
B = csarray((5, 5))
B[0, 2] = 9.2
B[2, 3] = -5
B[3, 4] = 12
nptst.assert_array_equal((A + B).toarray(), A.toarray() + B.toarray())
def testGetOmegaListPtr(self):
import sppy
m = 10
n = 5
X = scipy.sparse.rand(m, n, 0.1)
X = X.tocsr()
indPtr, colInds = SparseUtils.getOmegaListPtr(X)
for i in range(m):
omegai = colInds[indPtr[i]:indPtr[i+1]]
nptst.assert_array_almost_equal(omegai, X.toarray()[i, :].nonzero()[0])
Xsppy = sppy.csarray(X)
indPtr, colInds = SparseUtils.getOmegaListPtr(Xsppy)
for i in range(m):
omegai = colInds[indPtr[i]:indPtr[i+1]]
nptst.assert_array_almost_equal(omegai, X.toarray()[i, :].nonzero()[0])
#Test a zero array (scipy doesn't work in this case)
X = sppy.csarray((m,n))
indPtr, colInds = SparseUtils.getOmegaListPtr(X)
for i in range(m):
omegai = colInds[indPtr[i]:indPtr[i+1]]
def testStr(self):
nrow = 5
ncol = 7
A = csarray((nrow, ncol))
A[0, 1] = 1
A[1, 3] = 5.2
A[3, 3] = -0.2
outputStr = "csarray dtype:float64 shape:(5, 7) non-zeros:3\n"
outputStr += "(0, 1) 1.0\n"
outputStr += "(1, 3) 5.2\n"
outputStr += "(3, 3) -0.2"
self.assertEquals(str(A), outputStr)
B = csarray((5, 5))
outputStr = "csarray dtype:float64 shape:(5, 5) non-zeros:0\n"
self.assertEquals(str(B), outputStr)
outputStr = "csarray dtype:float64 shape:(10,) non-zeros:3\n"
outputStr +="(0) 23.0\n"
outputStr +="(3) 1.2\n"
outputStr +="(4) -8.0"
self.assertEquals(str(self.a), outputStr)
outputStr = "csarray dtype:float64 shape:(3,) non-zeros:0\n"
self.assertEquals(str(self.c), outputStr)
def testHadamard(self):
nptst.assert_array_equal((self.A.hadamard(self.A)).toarray(),
(self.A.toarray())**2)
nptst.assert_array_equal((self.B.hadamard(self.B)).toarray(),
self.B.toarray()**2)
nptst.assert_array_equal((self.C.hadamard(self.C)).toarray(),
self.C.toarray()**2)
nptst.assert_array_equal((self.D.hadamard(self.D)).toarray(),
self.D.toarray()**2)
nptst.assert_array_equal((self.F.hadamard(self.F)).toarray(),
self.F.toarray()**2)
A = csarray((5, 5))
A[0, 1] = 4
A[2, 3] = -1.2
A[1, 3] = 2
A[3, 3] = 1
B = csarray((5, 5))
B[0, 2] = 9.2
B[2, 3] = -5
B[3, 4] = 12
B[3, 3] = 12
C = csarray((5, 5))
nptst.assert_array_equal((A.hadamard(B)).toarray(),
A.toarray() * B.toarray())
nptst.assert_array_equal((A.hadamard(C)).toarray(), C.toarray())
def testGetOmegaListPtr(self):
import sppy
m = 10
n = 5
X = scipy.sparse.rand(m, n, 0.1)
X = X.tocsr()
indPtr, colInds = SparseUtils.getOmegaListPtr(X)
for i in range(m):
omegai = colInds[indPtr[i]:indPtr[i + 1]]
nptst.assert_array_almost_equal(omegai,
X.toarray()[i, :].nonzero()[0])
Xsppy = sppy.csarray(X)
indPtr, colInds = SparseUtils.getOmegaListPtr(Xsppy)
for i in range(m):
omegai = colInds[indPtr[i]:indPtr[i + 1]]
nptst.assert_array_almost_equal(omegai,
X.toarray()[i, :].nonzero()[0])
#Test a zero array (scipy doesn't work in this case)
X = sppy.csarray((m, n))
indPtr, colInds = SparseUtils.getOmegaListPtr(X)
for i in range(m):
omegai = colInds[indPtr[i]:indPtr[i + 1]]
def testBiCGSTAB(self):
#This doesn't always converge
numRuns = 10
for i in range(numRuns):
n = numpy.random.randint(5, 20)
A = numpy.random.rand(n, n)
x = numpy.random.rand(n)
b = A.dot(x)
A = sppy.csarray(A)
x2, output = sppy.linalg.biCGSTAB(A, b, tol=10**-6, maxIter=n)
if output == 0:
nptst.assert_array_almost_equal(x, x2, 3)
#Try with bad input
m = 3
n = 5
A = numpy.random.rand(n, m)
A = sppy.csarray(A)
x = numpy.random.rand(m)
b = A.dot(x)
self.assertRaises(ValueError, sppy.linalg.biCGSTAB, A, b)
A = numpy.random.rand(n, n)
A = sppy.csarray(A)
b = numpy.array(n+1)
self.assertRaises(ValueError, sppy.linalg.biCGSTAB, A, b)
def testSub(self):
nptst.assert_array_equal((self.A - self.A).toarray(), self.A.toarray() * 0)
nptst.assert_array_equal((self.B - self.B).toarray(), self.B.toarray() * 0)
nptst.assert_array_equal((self.C - self.C).toarray(), self.C.toarray() * 0)
nptst.assert_array_equal((self.D - self.D).toarray(), self.D.toarray() * 0)
nptst.assert_array_equal((self.F - self.F).toarray(), self.F.toarray() * 0)
nptst.assert_array_equal((self.G - self.G).toarray(), self.G.toarray() * 0)
nptst.assert_array_equal((self.H - self.H).toarray(), self.H.toarray() * 0)
nptst.assert_array_equal((self.B * 2 - self.B).toarray(), self.B.toarray())
A = csarray((5, 5))
A[0, 1] = 4
A[1, 3] = 2
A[3, 3] = 1
B = csarray((5, 5))
B[0, 2] = 9.2
B[2, 3] = -5
B[3, 4] = 12
nptst.assert_array_equal((A - B).toarray(), A.toarray() - B.toarray())
nptst.assert_array_equal((self.a - self.a).toarray(), self.a.toarray() * 0)
nptst.assert_array_equal((self.b - self.b).toarray(), self.b.toarray() * 0)
nptst.assert_array_equal((self.c - self.c).toarray(), self.c.toarray() * 0)
def testDot(self):
A = csarray((5, 5))
A[0, 1] = 4
A[2, 3] = -1.2
A[1, 3] = 2
A[3, 3] = 1
B = A.dot(A)
nptst.assert_array_equal(B.toarray(), A.toarray().dot(A.toarray()))
B = self.D.dot(self.D)
nptst.assert_array_equal(B.toarray(), self.D.toarray().dot(self.D.toarray()))
C = csarray((5, 2))
for i in range(5):
for j in range(2):
C[i, j] = 1
self.assertRaises(ValueError, C.dot, C)
B = A.dot(C)
nptst.assert_array_equal(B.toarray(), A.toarray().dot(C.toarray()))
self.assertEquals((self.a.dot(self.a)), (self.a.dot(self.a)))
self.assertEquals((self.b.dot(self.b)), (self.b.dot(self.b)))
self.assertEquals((self.c.dot(self.c)), (self.c.dot(self.c)))
def testHadamard(self):
nptst.assert_array_equal((self.A.hadamard(self.A)).toarray(), (self.A.toarray())**2)
nptst.assert_array_equal((self.B.hadamard(self.B)).toarray(), self.B.toarray()**2)
nptst.assert_array_equal((self.C.hadamard(self.C)).toarray(), self.C.toarray()**2)
nptst.assert_array_equal((self.D.hadamard(self.D)).toarray(), self.D.toarray()**2)
nptst.assert_array_equal((self.F.hadamard(self.F)).toarray(), self.F.toarray()**2)
A = csarray((5, 5))
A[0, 1] = 4
A[2, 3] = -1.2
A[1, 3] = 2
A[3, 3] = 1
B = csarray((5, 5))
B[0, 2] = 9.2
B[2, 3] = -5
B[3, 4] = 12
B[3, 3] = 12
C = csarray((5, 5))
nptst.assert_array_equal((A.hadamard(B)).toarray(), A.toarray()*B.toarray())
nptst.assert_array_equal((A.hadamard(C)).toarray(), C.toarray())
nptst.assert_array_equal((self.a.hadamard(self.a)).toarray(), (self.a.toarray())**2)
nptst.assert_array_equal((self.b.hadamard(self.b)).toarray(), (self.b.toarray())**2)
nptst.assert_array_equal((self.c.hadamard(self.c)).toarray(), (self.c.toarray())**2)
def testNDim(self):
A = csarray((5, 7))
self.assertEquals(A.ndim, 2)
A = csarray((0, 0))
self.assertEquals(A.ndim, 2)
self.assertEquals(self.a.ndim, 1)
self.assertEquals(self.b.ndim, 1)
def loadMatrix(filename):
M = scipy.io.mmread(filename)
if type(M) == numpy.ndarray:
M2 = sppy.csarray(M)
elif scipy.sparse.issparse(M):
M2 = sppy.csarray(M.shape, dtype=M.dtype)
M2[M.nonzero()] = M.data
return M2
def loadMatrix(filename):
M = scipy.io.mmread(filename)
if type(M) == numpy.ndarray:
M2 = sppy.csarray(M)
elif scipy.sparse.issparse(M):
M2 = sppy.csarray(M.shape, dtype=M.dtype)
M2[M.nonzero()] = M.data
return M2
def testNDim(self):
A = csarray((5, 7))
self.assertEquals(A.ndim, 2)
A = csarray((0, 0))
self.assertEquals(A.ndim, 2)
self.assertEquals(self.a.ndim, 1)
self.assertEquals(self.b.ndim, 1)
def testNonZeroInds(self):
(rowInds, colInds) = self.B.nonzero()
for i in range(rowInds.shape[0]):
self.assertNotEqual(self.B[rowInds[i], colInds[i]], 0)
self.assertEquals(self.B.getnnz(), rowInds.shape[0])
self.assertEquals(self.B.sum(), self.B[rowInds, colInds].sum())
(rowInds, colInds) = self.C.nonzero()
for i in range(rowInds.shape[0]):
self.assertNotEqual(self.C[rowInds[i], colInds[i]], 0)
self.assertEquals(self.C.getnnz(), rowInds.shape[0])
self.assertEquals(self.C.sum(), self.C[rowInds, colInds].sum())
(rowInds, colInds) = self.F.nonzero()
for i in range(rowInds.shape[0]):
self.assertNotEqual(self.F[rowInds[i], colInds[i]], 0)
self.assertEquals(self.F.getnnz(), rowInds.shape[0])
self.assertEquals(self.F.sum(), self.F[rowInds, colInds].sum())
(inds, ) = self.a.nonzero()
for i in range(inds.shape[0]):
self.assertNotEqual(self.a[inds[i]], 0)
#Try an array with no non zeros
nrow = 5
ncol = 7
A = csarray((nrow, ncol))
(rowInds, colInds) = A.nonzero()
self.assertEquals(A.getnnz(), rowInds.shape[0])
self.assertEquals(rowInds.shape[0], 0)
self.assertEquals(colInds.shape[0], 0)
(inds, ) = self.c.nonzero()
self.assertEquals(inds.shape[0], 0)
#Zero size array
nrow = 0
ncol = 0
A = csarray((nrow, ncol))
(rowInds, colInds) = A.nonzero()
self.assertEquals(A.getnnz(), rowInds.shape[0])
self.assertEquals(rowInds.shape[0], 0)
self.assertEquals(colInds.shape[0], 0)
(inds, ) = self.d.nonzero()
self.assertEquals(inds.shape[0], 0)
def testNDim(self):
A = csarray((5, 7))
self.assertEquals(A.ndim, 2)
A = csarray((5, 7), storagetype="row")
self.assertEquals(A.ndim, 2)
A = csarray((0, 0))
self.assertEquals(A.ndim, 2)
self.assertEquals(self.a.ndim, 1)
self.assertEquals(self.b.ndim, 1)
def setUp(self):
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
self.A = csarray((5, 5))
nrow = 5
ncol = 7
self.B = csarray((nrow, ncol))
self.B[0, 1] = 1
self.B[1, 3] = 5.2
self.B[3, 3] = -0.2
self.B[0, 6] = -1.23
self.B[4, 4] = 12.2
nrow = 100
ncol = 100
self.C = csarray((nrow, ncol))
self.C[0, 1] = 1
self.C[10, 3] = 5.2
self.C[30, 34] = -0.2
self.C[0, 62] = -1.23
self.C[4, 41] = 12.2
self.D = csarray((5, 5))
self.D[0, 0] = 23.1
self.D[2, 0] = -3.1
self.D[3, 0] = -10.0
self.D[2, 1] = -5
self.D[3, 1] = 5
self.E = csarray((0, 0))
self.F = csarray((6, 6), dtype=numpy.int)
self.F[0, 0] = 23
self.F[2, 0] = -3
self.F[3, 0] = -10
self.F[2, 1] = -5
self.F[3, 1] = 5
self.a = csarray(10, dtype=numpy.float)
self.a[0] = 23
self.a[3] = 1.2
self.a[4] = -8
self.b = csarray(10, dtype=numpy.int)
self.b[0] = 23
self.b[5] = 1
self.b[8] = -8
self.c = csarray((3, ), dtype=numpy.float)
self.d = csarray((0, ), dtype=numpy.float)
def testSetItem(self):
nrow = 5
ncol = 7
A = csarray((nrow, ncol))
A[0, 1] = 1
A[1, 3] = 5.2
A[3, 3] = -0.2
self.assertEquals(A[0, 1], 1)
self.assertAlmostEquals(A[1, 3], 5.2)
self.assertAlmostEquals(A[3, 3], -0.2)
for i in range(nrow):
for j in range(ncol):
if (i, j) != (0, 1) and (i, j) != (1, 3) and (i, j) != (3, 3):
self.assertEquals(A[i, j], 0)
self.assertRaises(ValueError, A.__setitem__, (20, 1), 1)
self.assertRaises(TypeError, A.__setitem__, (1, 1), "a")
self.assertRaises(ValueError, A.__setitem__, (1, 100), 1)
self.assertRaises(ValueError, A.__setitem__, (-1, 1), 1)
self.assertRaises(ValueError, A.__setitem__, (0, -1), 1)
result = A[(numpy.array([0, 1, 3]), numpy.array([1, 3, 3]))]
self.assertEquals(result[0], 1)
self.assertEquals(result[1], 5.2)
self.assertEquals(result[2], -0.2)
#Replace value of A
A[0, 1] = 2
self.assertEquals(A[0, 1], 2)
self.assertAlmostEquals(A[1, 3], 5.2)
self.assertAlmostEquals(A[3, 3], -0.2)
for i in range(nrow):
for j in range(ncol):
if (i, j) != (0, 1) and (i, j) != (1, 3) and (i, j) != (3, 3):
self.assertEquals(A[i, j], 0)
#Try setting items with arrays
A = csarray((nrow, ncol))
A[numpy.array([0, 1]), numpy.array([2, 3])] = numpy.array([1.2, 2.4])
self.assertEquals(A.getnnz(), 2)
self.assertEquals(A[0, 2], 1.2)
self.assertEquals(A[1, 3], 2.4)
A[numpy.array([2, 4]), numpy.array([2, 3])] = 5
self.assertEquals(A[2, 2], 5)
self.assertEquals(A[4, 3], 5)
def testSetItem(self):
nrow = 5
ncol = 7
A = csarray((nrow, ncol))
A[0, 1] = 1
A[1, 3] = 5.2
A[3, 3] = -0.2
self.assertEquals(A[0, 1], 1)
self.assertAlmostEquals(A[1, 3], 5.2)
self.assertAlmostEquals(A[3, 3], -0.2)
for i in range(nrow):
for j in range(ncol):
if (i, j) != (0, 1) and (i, j) != (1, 3) and (i, j) != (3, 3):
self.assertEquals(A[i, j], 0)
self.assertRaises(ValueError, A.__setitem__, (20, 1), 1)
self.assertRaises(TypeError, A.__setitem__, (1, 1), "a")
self.assertRaises(ValueError, A.__setitem__, (1, 100), 1)
self.assertRaises(ValueError, A.__setitem__, (-1, 1), 1)
self.assertRaises(ValueError, A.__setitem__, (0, -1), 1)
result = A[(numpy.array([0, 1, 3]), numpy.array([1, 3, 3]))]
self.assertEquals(result[0], 1)
self.assertEquals(result[1], 5.2)
self.assertEquals(result[2], -0.2)
#Replace value of A
A[0, 1] = 2
self.assertEquals(A[0, 1], 2)
self.assertAlmostEquals(A[1, 3], 5.2)
self.assertAlmostEquals(A[3, 3], -0.2)
for i in range(nrow):
for j in range(ncol):
if (i, j) != (0, 1) and (i, j) != (1, 3) and (i, j) != (3, 3):
self.assertEquals(A[i, j], 0)
#Try setting items with arrays
A = csarray((nrow, ncol))
A[numpy.array([0, 1]), numpy.array([2, 3])] = numpy.array([1.2, 2.4])
self.assertEquals(A.getnnz(), 2)
self.assertEquals(A[0, 2], 1.2)
self.assertEquals(A[1, 3], 2.4)
A[numpy.array([2, 4]), numpy.array([2, 3])] = 5
self.assertEquals(A[2, 2], 5)
self.assertEquals(A[4, 3], 5)
def time_ns():
density = 10**-3
ns = var_range * 10**4
times = numpy.zeros((5, ns.shape[0]))
for i, n in enumerate(ns):
# Generate random sparse matrix
inds = numpy.random.randint(n, size=(2, n * n * density))
data = numpy.random.rand(n * n * density)
A = scipy.sparse.csc_matrix((data, inds), (n, n))
A_sppy = sppy.csarray(A, storagetype="row")
L = GeneralLinearOperator.asLinearOperator(A_sppy, parallel=True)
print(A.shape, A.nnz)
times[0, i] = time_reps(svds, (A, k), reps)
times[1, i] = time_reps(svdp, (A, k), reps)
# times[2, i] = time_reps(sparsesvd, (A, k), reps)
times[3, i] = time_reps(truncated_svd.fit, (A,), reps)
times[4, i] = time_reps(sppy.linalg.rsvd, (L, k, p, n_iter), reps)
print(n, density, times[:, i])
plt.figure(1)
plt.plot(ns, times[0, :], 'k-', label="ARPACK")
plt.plot(ns, times[1, :], 'r-', label="PROPACK")
# plt.plot(ns, times[2, :], 'b-', label="SparseSVD")
plt.plot(ns, times[3, :], 'k--', label="sklearn RSVD")
plt.plot(ns, times[4, :], 'r--', label="sppy RSVD")
plt.legend(loc="upper left")
plt.xlabel("n")
plt.ylabel("time (s)")
plt.savefig("time_ns.png", format="png")
def testSplitNnz(self):
numRuns = 100
import sppy
for i in range(numRuns):
m = numpy.random.randint(5, 50)
n = numpy.random.randint(5, 50)
X = scipy.sparse.rand(m, n, 0.5)
X = X.tocsc()
split = numpy.random.rand()
X1, X2 = SparseUtils.splitNnz(X, split)
nptst.assert_array_almost_equal((X1 + X2).todense(), X.todense())
for i in range(numRuns):
m = numpy.random.randint(5, 50)
n = numpy.random.randint(5, 50)
X = scipy.sparse.rand(m, n, 0.5)
X = X.tocsc()
X = sppy.csarray(X)
split = numpy.random.rand()
X1, X2 = SparseUtils.splitNnz(X, split)
nptst.assert_array_almost_equal((X1 + X2).toarray(), X.toarray())
def testMean(self):
self.assertEquals(self.A.mean(), 0)
self.assertAlmostEquals(self.B.mean(), 0.4848571428571428)
self.assertAlmostEquals(self.C.mean(), 0.001697)
self.assertAlmostEquals(self.H.mean(), 0.4848571428571428)
D = csarray((0, 0))
self.assertTrue(math.isnan(D.mean()))
self.assertEquals(self.F.mean(), 10 / float(36))
nptst.assert_array_equal(self.A.mean(0), self.A.sum(0) / self.A.shape[0])
nptst.assert_array_equal(self.B.mean(0), self.B.sum(0) / self.B.shape[0])
nptst.assert_array_equal(self.C.mean(0), self.C.sum(0) / self.C.shape[0])
nptst.assert_array_equal(self.D.mean(0), self.D.sum(0) / self.D.shape[0])
nptst.assert_array_equal(self.F.mean(0), self.F.sum(0) / float(self.F.shape[0]))
nptst.assert_array_equal(self.G.mean(0), self.G.sum(0) / self.G.shape[0])
nptst.assert_array_equal(self.H.mean(0), self.H.sum(0) / self.H.shape[0])
nptst.assert_array_equal(self.A.mean(1), self.A.sum(1) / self.A.shape[1])
nptst.assert_array_equal(self.B.mean(1), self.B.sum(1) / self.B.shape[1])
nptst.assert_array_equal(self.C.mean(1), self.C.sum(1) / self.C.shape[1])
nptst.assert_array_equal(self.D.mean(1), self.D.sum(1) / self.D.shape[1])
nptst.assert_array_equal(self.F.mean(1), self.F.sum(1) / float(self.F.shape[1]))
nptst.assert_array_equal(self.G.mean(1), self.G.sum(1) / self.G.shape[1])
nptst.assert_array_equal(self.H.mean(1), self.H.sum(1) / self.H.shape[1])
self.assertEquals(self.a.mean(), 1.6199999999999999)
self.assertEquals(self.b.mean(), 1.6)
self.assertEquals(self.c.mean(), 0.0)
self.assertTrue(math.isnan(self.d.mean()))
def submatrix(X, inds):
"""
Take a sparse matrix in coo format and pick out inds indices relative to
X.data. Returns a csc matrix.
"""
if type(inds) != numpy.ndarray:
inds = numpy.random.permutation(X.nnz)[0:inds]
if scipy.sparse.issparse(X):
rowInds, colInds = X.nonzero()
rowInds = rowInds[inds]
colInds = colInds[inds]
vals = numpy.array(X[X.nonzero()]).ravel()[inds]
if scipy.sparse.isspmatrix_csc(X):
return scipy.sparse.csc_matrix((vals, (rowInds, colInds)),
X.shape)
elif scipy.sparse.isspmatrix_csr(X):
return scipy.sparse.csr_matrix((vals, (rowInds, colInds)),
X.shape)
else:
#Assume a sppy array
rowInds, colInds = X.nonzero()
rowInds = rowInds[inds]
colInds = colInds[inds]
vals = X.values()[inds]
import sppy
Y = sppy.csarray(X.shape, storagetype=X.storagetype, dtype=X.dtype)
Y.put(vals, rowInds, colInds, init=True)
return Y
def sparseMatrix(vals,
rowInds,
colInds,
shape,
mattype,
storagetype="col"):
"""
Create a sparse matrix of the given mattype with X[rowInds, colInds] = vals. The
choices for type are "csarray" and "scipy"
"""
import sppy
if mattype == "csarray":
rowInds = numpy.array(rowInds, numpy.int32)
colInds = numpy.array(colInds, numpy.int32)
X = sppy.csarray(shape, dtype=vals.dtype, storagetype=storagetype)
X.put(vals, rowInds, colInds, True)
elif mattype == "scipy":
if storagetype == "row":
X = scipy.sparse.csr_matrix((vals, (rowInds, colInds)),
shape=shape)
elif storagetype == "col":
X = scipy.sparse.csc_matrix((vals, (rowInds, colInds)),
shape=shape)
else:
raise ValueError("Unknown storagetype: " + storagetype)
else:
raise ValueError("Unknown mattype: " + mattype)
return X
def rand(shape, density, dtype=numpy.float, storagetype="col"):
"""
Generate a random sparse matrix with m rows and n cols with given density
and dtype.
:param shape: The shape of the output array (m, n)
:param density: The proportion of non zero elements to create
:param dtype: The data type of the output array (only supports floats at the moment)
:param storagetype: The storage type of the csarray ("row" or "col")
:type storagetype: `str`
"""
result = csarray(shape, dtype, storagetype=storagetype)
size = result.size
numEntries = int(size * density)
inds = numpy.random.randint(0, size, numEntries)
if result.ndim == 2:
rowInds, colInds = numpy.unravel_index(inds, shape)
rowInds = numpy.array(rowInds, numpy.int32)
colInds = numpy.array(colInds, numpy.int32)
result.put(numpy.array(numpy.random.rand(numEntries), dtype),
rowInds,
colInds,
init=True)
elif result.ndim == 1:
result[inds] = numpy.array(numpy.random.rand(numEntries), dtype)
return result
def generateSparseBinaryMatrix(shape, p, w=0.9, sd=0, csarray=False, verbose=False, indsPerRow=50):
"""
Create an underlying matrix Z = UsV.T of rank p and then go through each row
and threshold so that a proportion quantile numbers are kept. The final matrix
is a 0/1 matrix. We order each row of Z in ascending order and then keep those bigger
than u. In other words w=0 keeps all numbers and w=1.0 keeps none.
"""
m, n = shape
U, s, V = SparseUtils.generateLowRank(shape, p)
X = (U*s).dot(V.T)
wv = numpy.random.randn(m)*sd + w
wv = numpy.clip(wv, 0, 1)
r = SparseUtilsCython.computeR2((U*s), V, wv, indsPerRow=indsPerRow)
for i in range(m):
X[i, X[i, :] >= r[i]] = 1
X[i, X[i, :] < r[i]] = 0
if csarray:
import sppy
X = sppy.csarray(X, storagetype="row")
else:
X = scipy.sparse.csr_matrix(X)
if verbose:
return X, U, s, V, wv
else:
return X
def testReserve(self):
A = csarray((5, 5))
A.reserve(5)
A[0, 1] = 4
A[2, 3] = -1.2
A[1, 3] = 2
A[3, 3] = 1
def testCopy(self):
A = csarray((5, 5))
A[0, 0] = 1
A[1, 0] = 2
A[4, 2] = 3
self.assertEquals(A[0, 0], 1)
self.assertEquals(A[1, 0], 2)
self.assertEquals(A[4, 2], 3)
B = A.copy()
A[0, 0] = 2
A[1, 0] = 3
A[4, 2] = 4
A[4, 4] = 5
self.assertEquals(A[0, 0], 2)
self.assertEquals(A[1, 0], 3)
self.assertEquals(A[4, 2], 4)
self.assertEquals(A[4, 4], 5)
self.assertEquals(A.getnnz(), 4)
self.assertEquals(B[0, 0], 1)
self.assertEquals(B[1, 0], 2)
self.assertEquals(B[4, 2], 3)
self.assertEquals(B.getnnz(), 3)
F = self.F.copy()
F[0, 0] = -15
self.assertEquals(F[0, 0], -15)
self.assertEquals(self.F[0, 0], 23)
def testDiag(self):
nptst.assert_array_equal(self.A.diag(), numpy.zeros(5))
nptst.assert_array_equal(self.B.diag(), numpy.array([ 0, 0, 0, -0.2, 12.2]))
nptst.assert_array_equal(self.C.diag(), numpy.zeros(100))
D = csarray((3, 3))
D[0, 0] = -1
D[1, 1] = 3.2
D[2, 2] = 34
nptst.assert_array_equal(D.diag(), numpy.array([-1, 3.2, 34]))
E = csarray((0, 0))
nptst.assert_array_equal(E.diag(), numpy.array([]))
nptst.assert_array_equal(self.F.diag(), numpy.array([23, 0, 0, 0, 0, 0]) )
def testSum(self):
nrow = 5
ncol = 7
A = csarray((nrow, ncol))
A[0, 1] = 1
A[1, 3] = 5.2
A[3, 3] = -0.2
self.assertEquals(A.sum(), 6.0)
A[3, 4] = -1.2
self.assertEquals(A.sum(), 4.8)
A[0, 0] = 1.34
self.assertEquals(A.sum(), 6.14)
A[0, 0] = 0
self.assertEquals(A.sum(), 4.8)
self.assertEquals(self.A.sum(), 0.0)
self.assertEquals(self.B.sum(), 16.97)
self.assertEquals(self.C.sum(), 16.97)
self.assertAlmostEquals(self.D.sum(), 10)
self.assertEquals(self.F.sum(), 10)
#Test sum along axes
nptst.assert_array_equal(self.A.sum(0), numpy.zeros(5))
nptst.assert_array_equal(self.B.sum(0), numpy.array([0, 1, 0, 5, 12.2, 0, -1.23]))
nptst.assert_array_equal(self.D.sum(0), numpy.array([10, 0, 0, 0, 0]))
nptst.assert_array_equal(self.A.sum(1), numpy.zeros(5))
nptst.assert_array_almost_equal(self.B.sum(1), numpy.array([-0.23, 5.2, 0, -0.2, 12.2]))
nptst.assert_array_equal(self.D.sum(1), numpy.array([23.1, 0, -8.1, -5, 0]))
def setDiff(self, graph):
"""
Find the edges in the current graph which are not present in the input
graph. Replaces the edges in the current graph with adjacencies.
:param graph: the input graph.
:type graph: :class:`apgl.graph.CsArrayGraph`
:returns: The graph which is the set difference of the edges of this graph and graph.
"""
Parameter.checkClass(graph, CsArrayGraph)
if graph.getNumVertices() != self.getNumVertices():
raise ValueError(
"Can only add edges from graph with same number of vertices")
if self.undirected != graph.undirected:
raise ValueError(
"Both graphs must be either undirected or directed")
A1 = self.adjacencyMatrix()
A2 = graph.adjacencyMatrix()
A1 = A1 - A2
A1 = (A1 + numpy.abs(A1**2)) / 2
newGraph = CsArrayGraph(self.vList, self.undirected)
newGraph.W = sppy.csarray(A1)
return newGraph
def profileDot2(self):
density = 0.01
m = 10000
n = 10000
a_sppy = sppy.rand((m, n), density, storagetype='row')
a_sppy_T = sppy.csarray(a_sppy.T, storagetype="col")
ProfileUtils.profile('a_sppy.dot(a_sppy_T)', globals(), locals())
def rand(shape, density, dtype=numpy.float, storagetype="col"):
"""
Generate a random sparse matrix with m rows and n cols with given density
and dtype.
:param shape: The shape of the output array (m, n)
:param density: The proportion of non zero elements to create
:param dtype: The data type of the output array (only supports floats at the moment)
:param storagetype: The storage type of the csarray ("row" or "col")
:type storagetype: `str`
"""
result = csarray(shape, dtype, storagetype=storagetype)
size = result.size
numEntries = int(size*density)
inds = numpy.random.randint(0, size, numEntries)
if result.ndim == 2:
rowInds, colInds = numpy.unravel_index(inds, shape)
rowInds = numpy.array(rowInds, numpy.int32)
colInds = numpy.array(colInds, numpy.int32)
result.put(numpy.array(numpy.random.rand(numEntries), dtype), rowInds, colInds, init=True)
elif result.ndim == 1:
result[inds] = numpy.array(numpy.random.rand(numEntries), dtype)
return result
def submatrix(X, inds):
"""
Take a sparse matrix in coo format and pick out inds indices relative to
X.data. Returns a csc matrix.
"""
if type(inds) != numpy.ndarray:
inds = numpy.random.permutation(X.nnz)[0:inds]
if scipy.sparse.issparse(X):
rowInds, colInds = X.nonzero()
rowInds = rowInds[inds]
colInds = colInds[inds]
vals = numpy.array(X[X.nonzero()]).ravel()[inds]
if scipy.sparse.isspmatrix_csc(X):
return scipy.sparse.csc_matrix((vals, (rowInds, colInds)), X.shape)
elif scipy.sparse.isspmatrix_csr(X):
return scipy.sparse.csr_matrix((vals, (rowInds, colInds)), X.shape)
else:
#Assume a sppy array
rowInds, colInds = X.nonzero()
rowInds = rowInds[inds]
colInds = colInds[inds]
vals = X.values()[inds]
import sppy
Y = sppy.csarray(X.shape, storagetype=X.storagetype, dtype=X.dtype)
Y.put(vals, rowInds, colInds, init=True)
return Y
def profileDot(self):
#Create random sparse matrix and numpy array
#Test speed of array creation
numpy.random.seed(21)
m = 1000000
n = 1000000
numInds = 10000000
inds = numpy.random.randint(0, m*n, numInds)
inds = numpy.unique(inds)
vals = numpy.random.randn(inds.shape[0])
rowInds, colInds = numpy.unravel_index(inds, (m, n), order="FORTRAN")
rowInds = numpy.array(rowInds, numpy.int32)
colInds = numpy.array(colInds, numpy.int32)
A = csarray((m, n), storageType="rowMajor")
A.put(vals, rowInds, colInds, True)
A.compress()
p = 500
W = numpy.random.rand(n, p)
ProfileUtils.profile('A.dot(W)', globals(), locals())
#Compare versus scipy
#B = scipy.sparse.csc_matrix((vals, (rowInds, colInds)), (m, n))
#ProfileUtils.profile('B.dot(W)', globals(), locals())
#Compare versus pdot
ProfileUtils.profile('A.pdot(W)', globals(), locals())
def testCompress(self):
A = csarray((5, 5))
A[0, 1] = 4
A[2, 3] = -1.2
A[1, 3] = 2
A[3, 3] = 1
A.compress()
def testReserve(self):
A = csarray((5, 5))
A.reserve(5)
A[0, 1] = 4
A[2, 3] = -1.2
A[1, 3] = 2
A[3, 3] = 1
def testCopy(self):
A = csarray((5, 5))
A[0, 0] = 1
A[1, 0] = 2
A[4, 2] = 3
self.assertEquals(A[0, 0], 1)
self.assertEquals(A[1, 0], 2)
self.assertEquals(A[4, 2], 3)
B = A.copy()
A[0, 0] = 2
A[1, 0] = 3
A[4, 2] = 4
A[4, 4] = 5
self.assertEquals(A[0, 0], 2)
self.assertEquals(A[1, 0], 3)
self.assertEquals(A[4, 2], 4)
self.assertEquals(A[4, 4], 5)
self.assertEquals(A.getnnz(), 4)
self.assertEquals(B[0, 0], 1)
self.assertEquals(B[1, 0], 2)
self.assertEquals(B[4, 2], 3)
self.assertEquals(B.getnnz(), 3)
F = self.F.copy()
F[0, 0] = -15
self.assertEquals(F[0, 0], -15)
self.assertEquals(self.F[0, 0], 23)
def epinions(minNnzRows=10, minNnzCols=3, quantile=90):
matrixFileName = PathDefaults.getDataDir() + "epinions/rating.mat"
A = scipy.io.loadmat(matrixFileName)["rating"]
userIndexer = IdIndexer("i")
itemIndexer = IdIndexer("i")
for i in range(A.shape[0]):
userIndexer.append(A[i, 0])
itemIndexer.append(A[i, 1])
rowInds = userIndexer.getArray()
colInds = itemIndexer.getArray()
ratings = A[:, 3]
X = sppy.csarray((len(userIndexer.getIdDict()), len(itemIndexer.getIdDict())), storagetype="row", dtype=numpy.int)
X.put(numpy.array(ratings>3, numpy.int), numpy.array(rowInds, numpy.int32), numpy.array(colInds, numpy.int32), init=True)
X.prune()
X = SparseUtils.pruneMatrixRowAndCols(X, minNnzRows, minNnzCols)
logging.debug("Read file: " + matrixFileName)
logging.debug("Non zero elements: " + str(X.nnz) + " shape: " + str(X.shape))
return X
def testMean(self):
self.assertEquals(self.A.mean(), 0)
self.assertAlmostEquals(self.B.mean(), 0.4848571428571428)
self.assertAlmostEquals(self.C.mean(), 0.001697)
D = csarray((0, 0))
self.assertTrue(math.isnan(D.mean()))
self.assertEquals(self.F.mean(), 10 / float(36))
nptst.assert_array_equal(self.A.mean(0),
self.A.sum(0) / self.A.shape[0])
nptst.assert_array_equal(self.B.mean(0),
self.B.sum(0) / self.B.shape[0])
nptst.assert_array_equal(self.C.mean(0),
self.C.sum(0) / self.C.shape[0])
nptst.assert_array_equal(self.D.mean(0),
self.D.sum(0) / self.D.shape[0])
nptst.assert_array_equal(self.F.mean(0),
self.F.sum(0) / float(self.F.shape[0]))
nptst.assert_array_equal(self.A.mean(1),
self.A.sum(1) / self.A.shape[1])
nptst.assert_array_equal(self.B.mean(1),
self.B.sum(1) / self.B.shape[1])
nptst.assert_array_equal(self.C.mean(1),
self.C.sum(1) / self.C.shape[1])
nptst.assert_array_equal(self.D.mean(1),
self.D.sum(1) / self.D.shape[1])
nptst.assert_array_equal(self.F.mean(1),
self.F.sum(1) / float(self.F.shape[1]))
def flixster(minNnzRows=10, minNnzCols=2, quantile=90):
matrixFileName = PathDefaults.getDataDir() + "flixster/Ratings.timed.txt"
matrixFile = open(matrixFileName)
matrixFile.readline()
userIndexer = IdIndexer("i")
movieIndexer = IdIndexer("i")
ratings = array.array("f")
logging.debug("Loading ratings from " + matrixFileName)
for i, line in enumerate(matrixFile):
if i % 1000000 == 0:
logging.debug("Iteration: " + str(i))
vals = line.split()
userIndexer.append(vals[0])
movieIndexer.append(vals[1])
ratings.append(float(vals[2]))
rowInds = userIndexer.getArray()
colInds = movieIndexer.getArray()
ratings = numpy.array(ratings)
X = sppy.csarray((len(userIndexer.getIdDict()), len(movieIndexer.getIdDict())), storagetype="row", dtype=numpy.int)
X.put(numpy.array(ratings>3, numpy.int), numpy.array(rowInds, numpy.int32), numpy.array(colInds, numpy.int32), init=True)
X.prune()
X = SparseUtils.pruneMatrixRowAndCols(X, minNnzRows, minNnzCols)
logging.debug("Read file: " + matrixFileName)
logging.debug("Non zero elements: " + str(X.nnz) + " shape: " + str(X.shape))
#X = Sampling.sampleUsers(X, 1000)
return X
def testSplitNnz(self):
numRuns = 100
import sppy
for i in range(numRuns):
m = numpy.random.randint(5, 50)
n = numpy.random.randint(5, 50)
X = scipy.sparse.rand(m, n, 0.5)
X = X.tocsc()
split = numpy.random.rand()
X1, X2 = SparseUtils.splitNnz(X, split)
nptst.assert_array_almost_equal((X1+X2).todense(), X.todense())
for i in range(numRuns):
m = numpy.random.randint(5, 50)
n = numpy.random.randint(5, 50)
X = scipy.sparse.rand(m, n, 0.5)
X = X.tocsc()
X = sppy.csarray(X)
split = numpy.random.rand()
X1, X2 = SparseUtils.splitNnz(X, split)
nptst.assert_array_almost_equal((X1+X2).toarray(), X.toarray())
def testCompress(self):
A = csarray((5, 5))
A[0, 1] = 4
A[2, 3] = -1.2
A[1, 3] = 2
A[3, 3] = 1
A.compress()
def time_ns():
density = 10**-3
ns = var_range * 10**4
times = numpy.zeros((5, ns.shape[0]))
for i, n in enumerate(ns):
# Generate random sparse matrix
inds = numpy.random.randint(n, size=(2, n * n * density))
data = numpy.random.rand(n * n * density)
A = scipy.sparse.csc_matrix((data, inds), (n, n))
A_sppy = sppy.csarray(A, storagetype="row")
L = GeneralLinearOperator.asLinearOperator(A_sppy, parallel=True)
print(A.shape, A.nnz)
times[0, i] = time_reps(svds, (A, k), reps)
times[1, i] = time_reps(svdp, (A, k), reps)
# times[2, i] = time_reps(sparsesvd, (A, k), reps)
times[3, i] = time_reps(truncated_svd.fit, (A, ), reps)
times[4, i] = time_reps(sppy.linalg.rsvd, (L, k, p, n_iter), reps)
print(n, density, times[:, i])
plt.figure(1)
plt.plot(ns, times[0, :], 'k-', label="ARPACK")
plt.plot(ns, times[1, :], 'r-', label="PROPACK")
# plt.plot(ns, times[2, :], 'b-', label="SparseSVD")
plt.plot(ns, times[3, :], 'k--', label="sklearn RSVD")
plt.plot(ns, times[4, :], 'r--', label="sppy RSVD")
plt.legend(loc="upper left")
plt.xlabel("n")
plt.ylabel("time (s)")
plt.savefig("time_ns.png", format="png")
def ones(shape, dtype=numpy.float):
"""
Create a ones matrix of the given shape and dtype. Generally a bad idea
for large matrices.
"""
result = csarray(shape, dtype)
result.ones()
return result
def diag(x):
"""
From a 1D numpy array x create a diagonal sparse array.
"""
result = csarray((x.shape[0], x.shape[0]), x.dtype)
result[(numpy.arange(x.shape[0]), numpy.arange(x.shape[0]))] = x
return result
def testStr(self):
nrow = 5
ncol = 7
A = csarray((nrow, ncol))
A[0, 1] = 1
A[1, 3] = 5.2
A[3, 3] = -0.2
outputStr = "csarray dtype:float64 shape:(5, 7) non-zeros:3\n"
outputStr += "(0, 1) 1.0\n"
outputStr += "(1, 3) 5.2\n"
outputStr += "(3, 3) -0.2"
self.assertEquals(str(A), outputStr)
B = csarray((5, 5))
outputStr = "csarray dtype:float64 shape:(5, 5) non-zeros:0\n"
self.assertEquals(str(B), outputStr)
def testDiag(self):
nptst.assert_array_equal(self.A.diag(), numpy.zeros(5))
nptst.assert_array_equal(self.B.diag(),
numpy.array([0, 0, 0, -0.2, 12.2]))
nptst.assert_array_equal(self.C.diag(), numpy.zeros(100))
D = csarray((3, 3))
D[0, 0] = -1
D[1, 1] = 3.2
D[2, 2] = 34
nptst.assert_array_equal(D.diag(), numpy.array([-1, 3.2, 34]))
E = csarray((0, 0))
nptst.assert_array_equal(E.diag(), numpy.array([]))
nptst.assert_array_equal(self.F.diag(),
numpy.array([23, 0, 0, 0, 0, 0]))
def setUp(self):
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
self.A = csarray((5, 5))
nrow = 5
ncol = 7
self.B = csarray((nrow, ncol))
self.B[0, 1] = 1
self.B[1, 3] = 5.2
self.B[3, 3] = -0.2
self.B[0, 6] = -1.23
self.B[4, 4] = 12.2
nrow = 100
ncol = 100
self.C = csarray((nrow, ncol))
self.C[0, 1] = 1
self.C[10, 3] = 5.2
self.C[30, 34] = -0.2
self.C[0, 62] = -1.23
self.C[4, 41] = 12.2
self.D = csarray((5, 5))
self.D[0, 0] = 23.1
self.D[2, 0] = -3.1
self.D[3, 0] = -10.0
self.D[2, 1] = -5
self.D[3, 1] = 5
self.E = csarray((0, 0))
self.F = csarray((6, 6), dtype=numpy.int)
self.F[0, 0] = 23
self.F[2, 0] = -3
self.F[3, 0] = -10
self.F[2, 1] = -5
self.F[3, 1] = 5
self.a = csarray(10, dtype=numpy.float)
self.a[0] = 23
self.a[3] = 1.2
self.a[4] = -8
self.b = csarray(10, dtype=numpy.int)
self.b[0] = 23
self.b[5] = 1
self.b[8] = -8
self.c = csarray((3, ), dtype=numpy.float)
def testNonZeroInds(self):
(rowInds, colInds) = self.B.nonzero()
for i in range(rowInds.shape[0]):
self.assertNotEqual(self.B[rowInds[i], colInds[i]], 0)
self.assertEquals(self.B.getnnz(), rowInds.shape[0])
self.assertEquals(self.B.sum(), self.B[rowInds, colInds].sum())
(rowInds, colInds) = self.C.nonzero()
for i in range(rowInds.shape[0]):
self.assertNotEqual(self.C[rowInds[i], colInds[i]], 0)
self.assertEquals(self.C.getnnz(), rowInds.shape[0])
self.assertEquals(self.C.sum(), self.C[rowInds, colInds].sum())
(rowInds, colInds) = self.F.nonzero()
for i in range(rowInds.shape[0]):
self.assertNotEqual(self.F[rowInds[i], colInds[i]], 0)
self.assertEquals(self.F.getnnz(), rowInds.shape[0])
self.assertEquals(self.F.sum(), self.F[rowInds, colInds].sum())
#Try an array with no non zeros
nrow = 5
ncol = 7
A = csarray((nrow, ncol))
(rowInds, colInds) = A.nonzero()
self.assertEquals(A.getnnz(), rowInds.shape[0])
self.assertEquals(rowInds.shape[0], 0)
self.assertEquals(colInds.shape[0], 0)
#Zero size array
nrow = 0
ncol = 0
A = csarray((nrow, ncol))
(rowInds, colInds) = A.nonzero()
self.assertEquals(A.getnnz(), rowInds.shape[0])
self.assertEquals(rowInds.shape[0], 0)
self.assertEquals(colInds.shape[0], 0)
def testRecommendAtk(self):
m = 20
n = 50
r = 3
X, U, s, V, wv = SparseUtils.generateSparseBinaryMatrix((m, n),
r,
0.5,
verbose=True)
import sppy
X = sppy.csarray(X)
k = 10
X = numpy.zeros(X.shape)
omegaList = []
for i in range(m):
omegaList.append(numpy.random.permutation(n)[0:5])
X[i, omegaList[i]] = 1
X = sppy.csarray(X)
orderedItems = MCEvaluatorCython.recommendAtk(U, V, k, X)
orderedItems2 = MCEvaluator.recommendAtk(U, V, k, omegaList=omegaList)
nptst.assert_array_equal(orderedItems[orderedItems2 != -1],
orderedItems2[orderedItems2 != -1])
for i in range(m):
items = numpy.intersect1d(omegaList[i], orderedItems[i, :])
self.assertEquals(items.shape[0], 0)
#items = numpy.union1d(omegaList[i], orderedItems[i, :])
#items = numpy.intersect1d(items, orderedItems2[i, :])
#nptst.assert_array_equal(items, numpy.sort(orderedItems2[i, :]))
#Now let's have an all zeros X
X = sppy.csarray(X.shape)
orderedItems = MCEvaluatorCython.recommendAtk(U, V, k, X)
orderedItems2 = MCEvaluator.recommendAtk(U, V, k)
nptst.assert_array_equal(orderedItems, orderedItems2)
def profileGetOmegaList(self):
shape = (20000, 15000)
r = 50
k = 1000000
X = SparseUtils.generateSparseLowRank(shape, r, k)
import sppy
X = sppy.csarray(X)
ProfileUtils.profile('SparseUtils.getOmegaList(X)', globals(),
locals())
def addVertices(self, n):
"""
Adds n vertices to the current graph. This is not an efficient operation
as we create a new weight matrix and copy the old one. The old vertices
are the first m at the start of the new graph.
"""
W2 = sppy.csarray((self.W.shape[0] + n, self.W.shape[0] + n),
self.W.dtype)
W2[self.W.nonzero()] = self.W.values()
self.W = W2
self.vList.addVertices(n)
def testPrecisionAtK(self):
m = 10
n = 5
r = 3
X, U, s, V, wv = SparseUtils.generateSparseBinaryMatrix((m, n),
r,
0.5,
verbose=True)
import sppy
X = sppy.csarray(X)
#print(MCEvaluator.precisionAtK(X, U*s, V, 2))
orderedItems = MCEvaluator.recommendAtk(U, V, n)
self.assertAlmostEquals(MCEvaluator.precisionAtK(X, orderedItems, n),
X.nnz / float(m * n))
k = 2
orderedItems = MCEvaluator.recommendAtk(U * s, V, k)
precision, scoreInds = MCEvaluator.precisionAtK(X,
orderedItems,
k,
verbose=True)
precisions = numpy.zeros(m)
for i in range(m):
nonzeroRow = X.toarray()[i, :].nonzero()[0]
precisions[i] = numpy.intersect1d(scoreInds[i, :],
nonzeroRow).shape[0] / float(k)
self.assertEquals(precision.mean(), precisions.mean())
#Now try random U and V
U = numpy.random.rand(m, 3)
V = numpy.random.rand(m, 3)
orderedItems = MCEvaluator.recommendAtk(U * s, V, k)
precision, scoreInds = MCEvaluator.precisionAtK(X,
orderedItems,
k,
verbose=True)
precisions = numpy.zeros(m)
for i in range(m):
nonzeroRow = X.toarray()[i, :].nonzero()[0]
precisions[i] = numpy.intersect1d(scoreInds[i, :],
nonzeroRow).shape[0] / float(k)
self.assertEquals(precision.mean(), precisions.mean())
def testGetnnz(self):
A = csarray((5, 7))
self.assertEquals(A.getnnz(), 0)
A[0, 0] = 1.0
self.assertEquals(A.getnnz(), 1)
A[2, 1] = 1.0
self.assertEquals(A.getnnz(), 2)
A[2, 5] = 1.0
A[3, 5] = 1.0
self.assertEquals(A.getnnz(), 4)
#If we insert a zero it is not registered as zero
A[4, 4] = 0.0
self.assertEquals(A.getnnz(), 4)
#But erasing an item keeps it (can call prune)
A[3, 5] = 0.0
self.assertEquals(A.getnnz(), 4)
B = csarray((5, 7))
B[(numpy.array([1, 2, 3]), numpy.array([4, 5, 6]))] = 1
self.assertEquals(B.getnnz(), 3)
for i in range(5):
for j in range(7):
B[i, j] = 1
self.assertEquals(B.getnnz(), 35)
self.assertEquals(self.A.getnnz(), 0)
self.assertEquals(self.B.getnnz(), 5)
self.assertEquals(self.C.getnnz(), 5)
self.assertEquals(self.F.getnnz(), 5)
self.assertEquals(self.a.getnnz(), 3)
self.assertEquals(self.b.getnnz(), 3)
self.assertEquals(self.c.getnnz(), 0)
def testRecallAtK(self):
m = 10
n = 5
r = 3
X, U, s, V, wv = SparseUtils.generateSparseBinaryMatrix((m, n),
r,
0.5,
verbose=True)
import sppy
X = sppy.csarray(X)
orderedItems = MCEvaluator.recommendAtk(U, V, n)
self.assertAlmostEquals(MCEvaluator.recallAtK(X, orderedItems, n), 1.0)
k = 2
orderedItems = MCEvaluator.recommendAtk(U * s, V, k)
recall, scoreInds = MCEvaluator.recallAtK(X,
orderedItems,
k,
verbose=True)
recalls = numpy.zeros(m)
for i in range(m):
nonzeroRow = X.toarray()[i, :].nonzero()[0]
recalls[i] = numpy.intersect1d(scoreInds[i, :],
nonzeroRow).shape[0] / float(
nonzeroRow.shape[0])
self.assertEquals(recall.mean(), recalls.mean())
#Now try random U and V
U = numpy.random.rand(m, 3)
V = numpy.random.rand(m, 3)
orderedItems = MCEvaluator.recommendAtk(U, V, k)
recall, scoreInds = MCEvaluator.recallAtK(X,
orderedItems,
k,
verbose=True)
recalls = numpy.zeros(m)
for i in range(m):
nonzeroRow = X.toarray()[i, :].nonzero()[0]
recalls[i] = numpy.intersect1d(scoreInds[i, :],
nonzeroRow).shape[0] / float(
nonzeroRow.shape[0])
self.assertEquals(recall.mean(), recalls.mean())
