def setUp(self):
'Test remove constraint'
model = CyLPModel()
x = model.addVariable('x', 3)
y = model.addVariable('y', 2)
A = csc_matrixPlus(([1, 2, 1, 1], ([0, 0, 1, 1], [0, 1, 0, 2])), shape=(2, 3))
B = csc_matrixPlus(([1, 1], ([0, 1], [0, 2])), shape=(2, 3))
D = np.matrix([[1., 2.],[0, 1]])
a = CyLPArray([3, 2.5])
b = CyLPArray([4.2, 3])
x_u= CyLPArray([2., 3.5])
model.addConstraint(A * x <= a, 'res1')
model.addConstraint(2 <= B * x + D * y <= b, 'res2')
model.addConstraint(y >= 0)
model.addConstraint(1.1 <= x[1:3] <= x_u)
model.addConstraint(x[0] >= 0.1)
c = CyLPArray([1., -2., 3.])
model.objective = c * x + 2 * y[0] + 2 * y[1]
s = CyClpSimplex(model)
self.s = s
self.x = x
self.y = y
def test(self):
model = CyLPModel()
x = model.addVariable('x', 3)
A = np.matrix([[1,2,3], [1,1,1]])
b = CyLPArray([5, 3])
model.addConstraint(A * x == b)
model.addConstraint(x >= 0)
model.objective = 1*x[0] + 1*x[1] + 1.1 * x[2]
# Solve it a first time
s = CyClpSimplex(model)
s.primal()
sol = s.primalVariableSolution['x']
self.assertTrue((abs(sol - np.array([1,2,0]) ) <= 10**-6).all())
# Add a cut
s.addConstraint(x[0] >= 1.1)
s.primal()
sol = s.primalVariableSolution['x']
self.assertTrue((abs(sol - np.array([1.1, 1.8, 0.1]) ) <= 10**-6).all())
# Change the objective function
c = csr_matrixPlus([[1, 10, 1.1]]).T
s.objective = c.T * x
s.primal()
sol = s.primalVariableSolution['x']
self.assertTrue((abs(sol - np.array([2, 0, 1]) ) <= 10**-6).all())
def test(self):
model = CyLPModel()
x = model.addVariable('x', 3)
A = np.matrix([[1, 2, 3], [1, 1, 1]])
b = CyLPArray([5, 3])
model.addConstraint(A * x == b)
model.addConstraint(x >= 0)
model.objective = 1 * x[0] + 1 * x[1] + 1.1 * x[2]
# Solve it a first time
s = CyClpSimplex(model)
s.primal()
sol = s.primalVariableSolution['x']
self.assertTrue((abs(sol - np.array([1, 2, 0])) <= 10**-6).all())
# Add a cut
s.addConstraint(x[0] >= 1.1)
s.primal()
sol = s.primalVariableSolution['x']
self.assertTrue((abs(sol - np.array([1.1, 1.8, 0.1])) <= 10**-6).all())
# Change the objective function
c = csr_matrixPlus([[1, 10, 1.1]]).T
s.objective = c.T * x
s.primal()
sol = s.primalVariableSolution['x']
self.assertTrue((abs(sol - np.array([2, 0, 1])) <= 10**-6).all())
def setUp(self):
'Test remove constraint'
model = CyLPModel()
x = model.addVariable('x', 3)
y = model.addVariable('y', 2)
A = csc_matrixPlus(([1, 2, 1, 1], ([0, 0, 1, 1], [0, 1, 0, 2])),
shape=(2, 3))
B = csc_matrixPlus(([1, 1], ([0, 1], [0, 2])), shape=(2, 3))
D = np.matrix([[1., 2.], [0, 1]])
a = CyLPArray([3, 2.5])
b = CyLPArray([4.2, 3])
x_u = CyLPArray([2., 3.5])
model.addConstraint(A * x <= a, 'res1')
model.addConstraint(2 <= B * x + D * y <= b, 'res2')
model.addConstraint(y >= 0)
model.addConstraint(1.1 <= x[1:3] <= x_u)
model.addConstraint(x[0] >= 0.1)
c = CyLPArray([1., -2., 3.])
model.objective = c * x + 2 * y[0] + 2 * y[1]
s = CyClpSimplex(model)
self.s = s
self.x = x
self.y = y
def solve_cylp(self, verbose: bool = True, **kwargs):
""" Inspired by the cvxpy cbc layer, ty :) """
from cylp.cy import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray
cons = self.combine_cons()
n = int(self.next_var_idx - 1) # Number of variables.
# Maximize c@x s.t. A@x <= b (variable bounds done by constraints)
c = self.objective.rawdense().squeeze()[
1:n + 1] # Objective coefficients, no constants.
A = cons.raw()[:, 1:n + 1] # Constraint coefficients.
b = cons[:, 0].rawdense().squeeze() * -1.0 # Constraint constants.
model = CyLPModel()
x = model.addVariable("x", n) # Variables
model.objective = c
# I hate this so much. Casting A to a matrix causes A.__mul__ to be
# be called, which raises NotImplemented, so then x.__rmul__ is called
# which gets the job done. Using np.matmul or np.dot doesn't
# trigger x.__rmul__
# model.addConstraint(np.matrix(A) * x <= CyLPArray(b)) # Works
model.addConstraint(x.__rmul__(A) <= CyLPArray(b))
model = CyClpSimplex(model) # Convert model
model.logLevel = 0 if not verbose else model.logLevel
is_integer_model = not np.isclose(self.int_vars.sum(), 0)
if is_integer_model:
model.setInteger(x[np.argwhere(self.int_vars)])
cbcModel = model.getCbcModel()
cbcModel.solve()
status = cbcModel.status
solmodel = cbcModel
else:
status = model.initialSolve()
solmodel = model
sol_x = np.hstack( # Pad back to original shape
(
[1.0], # Special constant 1.0 variable.
solmodel.primalVariableSolution["x"], # Actual solution
np.zeros(self.max_vars - n - 1), # Unused vars
))
solution = {
"status": status,
"primal": sol_x,
"value": solmodel.objectiveValue
}
return solution, solution["primal"]
def test_Sparse(self):
model = CyLPModel()
x = model.addVariable('x', 3)
y = model.addVariable('y', 2)
A = csc_matrixPlus(([1, 2, 1, 1], ([0, 0, 1, 1], [0, 1, 0, 2])), shape=(2, 3))
B = csc_matrixPlus(([1, 1], ([0, 1], [0, 2])), shape=(2, 3))
D = np.matrix([[1., 2.],[0, 1]])
a = CyLPArray([5, 2.5])
b = CyLPArray([4.2, 3])
x_u= CyLPArray([2., 3.5])
model.addConstraint(A*x <= a)
model.addConstraint(2 <= B * x + D * y <= b)
model.addConstraint(y >= 0)
model.addConstraint(1.1 <= x[1:3] <= x_u)
c = CyLPArray([1., -2., 3.])
model.objective = c * x + 2 * y[0] + 2 * y[1]
s = CyClpSimplex(model)
s.primal()
sol = np.concatenate((s.primalVariableSolution['x'],
s.primalVariableSolution['y']))
self.assertTrue((abs(sol -
np.array([0.2, 2, 1.1, 0, 0.9]) ) <= 10**-6).all())
def test_Sparse(self):
model = CyLPModel()
x = model.addVariable('x', 3)
y = model.addVariable('y', 2)
A = csc_matrixPlus(([1, 2, 1, 1], ([0, 0, 1, 1], [0, 1, 0, 2])),
shape=(2, 3))
B = csc_matrixPlus(([1, 1], ([0, 1], [0, 2])), shape=(2, 3))
D = np.matrix([[1., 2.], [0, 1]])
a = CyLPArray([5, 2.5])
b = CyLPArray([4.2, 3])
x_u = CyLPArray([2., 3.5])
model.addConstraint(A * x <= a)
model.addConstraint(2 <= B * x + D * y <= b)
model.addConstraint(y >= 0)
model.addConstraint(1.1 <= x[1:3] <= x_u)
c = CyLPArray([1., -2., 3.])
model.objective = c * x + 2 * y[0] + 2 * y[1]
s = CyClpSimplex(model)
s.primal()
sol = np.concatenate(
(s.primalVariableSolution['x'], s.primalVariableSolution['y']))
self.assertTrue(
(abs(sol - np.array([0.2, 2, 1.1, 0, 0.9])) <= 10**-6).all())
def LP_solver_cylp(A_Matrix, B_vectors, weights, really_verbose=False):
"""
Solve the Linear Programming problem given in Giangrande et al, 2012 using
the CyLP module.
Parameters
----------
A_Matrix : matrix
Row augmented A matrix, see :py:func:`construct_A_matrix`
B_vectors : matrix
Matrix containing B vectors, see :py:func:`construct_B_vectors`
weights : array
Weights.
really_verbose : bool
True to print CLP messaging. False to suppress.
Returns
-------
soln : array
Solution to LP problem.
See Also
--------
LP_solver_cvxopt : Solve LP problem using the CVXOPT module.
LP_solver_pyglpk : Solve LP problem using the PyGLPK module.
"""
from cylp.cy.CyClpSimplex import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray
n_gates = weights.shape[1] // 2
n_rays = B_vectors.shape[0]
soln = np.zeros([n_rays, n_gates])
# Create CyLPModel and initialize it
model = CyLPModel()
G = np.matrix(A_Matrix)
h = CyLPArray(np.empty(B_vectors.shape[1]))
x = model.addVariable('x', G.shape[1])
model.addConstraint(G * x >= h)
#c = CyLPArray(np.empty(weights.shape[1]))
c = CyLPArray(np.squeeze(weights[0]))
model.objective = c * x
# import model in solver
s = CyClpSimplex(model)
# disable logging
if not really_verbose:
s.logLevel = 0
for raynum in range(n_rays):
# set new B_vector values for actual ray
s.setRowLowerArray(np.squeeze(np.asarray(B_vectors[raynum])))
# set new weights (objectives) for actual ray
#s.setObjectiveArray(np.squeeze(np.asarray(weights[raynum])))
# solve with dual method, it is faster
s.dual()
# extract primal solution
soln[raynum, :] = s.primalVariableSolution['x'][n_gates: 2 * n_gates]
# apply smoothing filter on a per scan basis
soln = smooth_and_trim_scan(soln, window_len=5, window='sg_smooth')
return soln
def LP_solver_cylp_mp(A_Matrix, B_vectors, weights, really_verbose=False,
proc=1):
"""
Solve the Linear Programming problem given in Giangrande et al, 2012 using
the CyLP module using multiple processes.
Parameters
----------
A_Matrix : matrix
Row augmented A matrix, see :py:func:`construct_A_matrix`
B_vectors : matrix
Matrix containing B vectors, see :py:func:`construct_B_vectors`
weights : array
Weights.
really_verbose : bool
True to print CLP messaging. False to suppress.
proc : int
Number of worker processes.
Returns
-------
soln : array
Solution to LP problem.
See Also
--------
LP_solver_cvxopt : Solve LP problem using the CVXOPT module.
LP_solver_pyglpk : Solve LP problem using the PyGLPK module.
LP_solver_cylp : Solve LP problem using the CyLP module using single
process.
"""
from cylp.cy.CyClpSimplex import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray
import multiprocessing as mp
n_gates = weights.shape[1] // 2
n_rays = B_vectors.shape[0]
soln = np.zeros([n_rays, n_gates])
# Create CyLPModel and initialize it
model = CyLPModel()
G = np.matrix(A_Matrix)
h = CyLPArray(np.empty(B_vectors.shape[1]))
x = model.addVariable('x', G.shape[1])
model.addConstraint(G * x >= h)
c = CyLPArray(np.empty(weights.shape[1]))
#c = CyLPArray(np.squeeze(weights[0]))
model.objective = c * x
chunksize = int(n_rays/proc)
# check if equal sized chunks can be distributed to worker processes
if n_rays % chunksize != 0:
print("Problem of %d rays cannot be split to %d worker processes!\n\r"
"Fallback to 1 process!" % (n_rays, proc))
chunksize = n_rays # fall back to one process
proc = 1
print("Calculating with %d processes, %d rays per chunk" %
(proc, chunksize))
def worker(model, B_vectors, weights, ray, chunksize, out_q):
"""
The worker function, invoked in a process.
The results are placed in a dictionary that's pushed to a queue.
"""
outdict = {}
iray = int(ray/chunksize)
outdict[iray] = solve_cylp(model, B_vectors, weights, ray, chunksize)
out_q.put(outdict)
# Queue for LP solutions
out_q = mp.Queue()
procs = []
# fire off worker processes
for raynum in range(0, n_rays, chunksize):
p = mp.Process(target=worker, args=(
model, B_vectors, weights, raynum, chunksize, out_q))
procs.append(p)
p.start()
# collecting results
resultdict = {}
for raynum in range(0, n_rays, chunksize):
resultdict.update(out_q.get())
# Wait for all worker processes to finish
for p in procs:
p.join()
# copy results in output array
for raynum in range(0, int(n_rays / chunksize)):
soln[raynum * chunksize:raynum * chunksize + chunksize, :] = (
resultdict[raynum])
# apply smoothing filter to output array
soln = smooth_and_trim_scan(soln, window_len=5, window='sg_smooth')
return soln
def LP_solver_cylp(A_Matrix, B_vectors, weights, really_verbose=False):
"""
Solve the Linear Programming problem given in Giangrande et al, 2012 using
the CyLP module.
Parameters
----------
A_Matrix : matrix
Row augmented A matrix, see :py:func:`construct_A_matrix`
B_vectors : matrix
Matrix containing B vectors, see :py:func:`construct_B_vectors`
weights : array
Weights.
really_verbose : bool
True to print CLP messaging. False to suppress.
Returns
-------
soln : array
Solution to LP problem.
See Also
--------
LP_solver_cvxopt : Solve LP problem using the CVXOPT module.
LP_solver_pyglpk : Solve LP problem using the PyGLPK module.
"""
from cylp.cy.CyClpSimplex import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray
n_gates = weights.shape[1] // 2
n_rays = B_vectors.shape[0]
soln = np.zeros([n_rays, n_gates])
# Create CyLPModel and initialize it
model = CyLPModel()
G = np.matrix(A_Matrix)
h = CyLPArray(np.empty(B_vectors.shape[1]))
x = model.addVariable('x', G.shape[1])
model.addConstraint(G * x >= h)
#c = CyLPArray(np.empty(weights.shape[1]))
c = CyLPArray(np.squeeze(weights[0]))
model.objective = c * x
# import model in solver
s = CyClpSimplex(model)
# disable logging
if not really_verbose:
s.logLevel = 0
for raynum in range(n_rays):
# set new B_vector values for actual ray
s.setRowLowerArray(np.squeeze(np.asarray(B_vectors[raynum])))
# set new weights (objectives) for actual ray
#s.setObjectiveArray(np.squeeze(np.asarray(weights[raynum])))
# solve with dual method, it is faster
s.dual()
# extract primal solution
soln[raynum, :] = s.primalVariableSolution['x'][n_gates:2 * n_gates]
# apply smoothing filter on a per scan basis
soln = smooth_and_trim_scan(soln, window_len=5, window='sg_smooth')
return soln
def LP_solver_cylp_mp(A_Matrix,
B_vectors,
weights,
really_verbose=False,
proc=1):
"""
Solve the Linear Programming problem given in Giangrande et al, 2012 using
the CyLP module using multiple processes.
Parameters
----------
A_Matrix : matrix
Row augmented A matrix, see :py:func:`construct_A_matrix`
B_vectors : matrix
Matrix containing B vectors, see :py:func:`construct_B_vectors`
weights : array
Weights.
really_verbose : bool
True to print CLP messaging. False to suppress.
proc : int
Number of worker processes.
Returns
-------
soln : array
Solution to LP problem.
See Also
--------
LP_solver_cvxopt : Solve LP problem using the CVXOPT module.
LP_solver_pyglpk : Solve LP problem using the PyGLPK module.
LP_solver_cylp : Solve LP problem using the CyLP module using single
process.
"""
from cylp.cy.CyClpSimplex import CyClpSimplex
from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray
import multiprocessing as mp
n_gates = weights.shape[1] // 2
n_rays = B_vectors.shape[0]
soln = np.zeros([n_rays, n_gates])
# Create CyLPModel and initialize it
model = CyLPModel()
G = np.matrix(A_Matrix)
h = CyLPArray(np.empty(B_vectors.shape[1]))
x = model.addVariable('x', G.shape[1])
model.addConstraint(G * x >= h)
c = CyLPArray(np.empty(weights.shape[1]))
#c = CyLPArray(np.squeeze(weights[0]))
model.objective = c * x
chunksize = int(n_rays / proc)
# check if equal sized chunks can be distributed to worker processes
if n_rays % chunksize != 0:
print("Problem of %d rays cannot be split to %d worker processes!\n\r"
"Fallback to 1 process!" % (n_rays, proc))
chunksize = n_rays # fall back to one process
proc = 1
print("Calculating with %d processes, %d rays per chunk" %
(proc, chunksize))
def worker(model, B_vectors, weights, ray, chunksize, out_q):
"""
The worker function, invoked in a process.
The results are placed in a dictionary that's pushed to a queue.
"""
outdict = {}
iray = int(ray / chunksize)
outdict[iray] = solve_cylp(model, B_vectors, weights, ray, chunksize)
out_q.put(outdict)
# Queue for LP solutions
out_q = mp.Queue()
procs = []
# fire off worker processes
for raynum in range(0, n_rays, chunksize):
p = mp.Process(target=worker,
args=(model, B_vectors, weights, raynum, chunksize,
out_q))
procs.append(p)
p.start()
# collecting results
resultdict = {}
for raynum in range(0, n_rays, chunksize):
resultdict.update(out_q.get())
# Wait for all worker processes to finish
for p in procs:
p.join()
# copy results in output array
for raynum in range(0, int(n_rays / chunksize)):
soln[raynum * chunksize:raynum * chunksize +
chunksize, :] = (resultdict[raynum])
# apply smoothing filter to output array
soln = smooth_and_trim_scan(soln, window_len=5, window='sg_smooth')
return soln
def solve_lp_for_year(puf, Stage_I_factors, Stage_II_targets, year, tol):
puf_length = len(puf.s006)
print("Preparing coefficient matrix for year {} .....".format(year))
s006 = np.where(puf.e02400 > 0,
puf.s006 * Stage_I_factors[year]["APOPSNR"] / 100,
puf.s006 * Stage_I_factors[year]["ARETS"] / 100)
single_return = np.where((puf.mars == 1) & (puf.filer == 1), s006, 0)
joint_return = np.where(
((puf.mars == 2) | (puf.mars == 3)) & (puf.filer == 1), s006, 0)
hh_return = np.where((puf.mars == 4) & (puf.filer == 1), s006, 0)
return_w_SS = np.where((puf.e02400 > 0) & (puf.filer == 1), s006, 0)
dependent_exempt_num = (puf.xocah + puf.xocawh + puf.xoodep +
puf.xopar) * s006
interest = puf.e00300 * s006
dividend = puf.e00600 * s006
biz_income = np.where(puf.e00900 > 0, puf.e00900, 0) * s006
biz_loss = np.where(puf.e00900 < 0, -puf.e00900, 0) * s006
cap_gain = np.where(
(puf.p23250 + puf.p22250) > 0, puf.p23250 + puf.p22250, 0) * s006
annuity_pension = puf.e01700 * s006
sch_e_income = np.where(puf.e02000 > 0, puf.e02000, 0) * s006
sch_e_loss = np.where(puf.e02000 < 0, -puf.e02000, 0) * s006
ss_income = np.where(puf.filer == 1, puf.e02400, 0) * s006
unemployment_comp = puf.e02300 * s006
# Wage distribution
wage_1 = np.where(puf.e00100 <= 0, puf.e00200, 0) * s006
wage_2 = np.where(
(puf.e00100 > 0) & (puf.e00100 <= 10000), puf.e00200, 0) * s006
wage_3 = np.where(
(puf.e00100 > 10000) & (puf.e00100 <= 20000), puf.e00200, 0) * s006
wage_4 = np.where(
(puf.e00100 > 20000) & (puf.e00100 <= 30000), puf.e00200, 0) * s006
wage_5 = np.where(
(puf.e00100 > 30000) & (puf.e00100 <= 40000), puf.e00200, 0) * s006
wage_6 = np.where(
(puf.e00100 > 40000) & (puf.e00100 <= 50000), puf.e00200, 0) * s006
wage_7 = np.where(
(puf.e00100 > 50000) & (puf.e00100 <= 75000), puf.e00200, 0) * s006
wage_8 = np.where(
(puf.e00100 > 75000) & (puf.e00100 <= 100000), puf.e00200, 0) * s006
wage_9 = np.where(
(puf.e00100 > 100000) & (puf.e00100 <= 200000), puf.e00200, 0) * s006
wage_10 = np.where(
(puf.e00100 > 200000) & (puf.e00100 <= 500000), puf.e00200, 0) * s006
wage_11 = np.where(
(puf.e00100 > 500000) & (puf.e00100 <= 1000000), puf.e00200, 0) * s006
wage_12 = np.where(puf.e00100 > 1000000, puf.e00200, 0) * s006
# Set up the matrix
One_half_LHS = np.vstack(
(single_return, joint_return, hh_return, return_w_SS,
dependent_exempt_num, interest, dividend, biz_income, biz_loss,
cap_gain, annuity_pension, sch_e_income, sch_e_loss, ss_income,
unemployment_comp, wage_1, wage_2, wage_3, wage_4, wage_5, wage_6,
wage_7, wage_8, wage_9, wage_10, wage_11, wage_12))
# Coefficients for r and s
A1 = np.matrix(One_half_LHS)
A2 = np.matrix(-One_half_LHS)
print("Preparing targets for year {} .....".format(year))
APOPN = Stage_I_factors[year]["APOPN"]
b = []
b.append(Stage_II_targets[year]["Single Returns"] - single_return.sum())
b.append(Stage_II_targets[year]["Joint Returns"] - joint_return.sum())
target_name = "Head of Household Returns"
b.append(Stage_II_targets[year][target_name] - hh_return.sum())
target_name = "Number of Returns w/ Gross Security Income"
b.append(Stage_II_targets[year][target_name] - return_w_SS.sum())
target_name = "Number of Dependent Exemptions"
b.append(Stage_II_targets[year][target_name] - dependent_exempt_num.sum())
AINTS = Stage_I_factors[year]["AINTS"]
INTEREST = (Stage_II_targets[year]["Taxable Interest Income"] * APOPN /
AINTS * 1000 - interest.sum())
ADIVS = Stage_I_factors[year]["ADIVS"]
DIVIDEND = (
Stage_II_targets[year]["Ordinary Dividends"] * APOPN / ADIVS * 1000 -
dividend.sum())
ASCHCI = Stage_I_factors[year]["ASCHCI"]
BIZ_INCOME = (Stage_II_targets[year]["Business Income (Schedule C)"] *
APOPN / ASCHCI * 1000 - biz_income.sum())
ASCHCL = Stage_I_factors[year]["ASCHCL"]
BIZ_LOSS = (Stage_II_targets[year]["Business Loss (Schedule C)"] * APOPN /
ASCHCL * 1000 - biz_loss.sum())
ACGNS = Stage_I_factors[year]["ACGNS"]
CAP_GAIN = (Stage_II_targets[year]["Net Capital Gains in AGI"] * APOPN /
ACGNS * 1000 - cap_gain.sum())
ATXPY = Stage_I_factors[year]["ATXPY"]
target_name = "Taxable Pensions and Annuities"
ANNUITY_PENSION = (
Stage_II_targets[year][target_name] * APOPN / ATXPY * 1000 -
annuity_pension.sum())
ASCHEI = Stage_I_factors[year]["ASCHEI"]
target_name = "Supplemental Income (Schedule E)"
SCH_E_INCOME = (
Stage_II_targets[year][target_name] * APOPN / ASCHEI * 1000 -
sch_e_income.sum())
ASCHEL = Stage_I_factors[year]["ASCHEL"]
SCH_E_LOSS = (Stage_II_targets[year]["Supplemental Loss (Schedule E)"] *
APOPN / ASCHEL * 1000 - sch_e_loss.sum())
ASOCSEC = Stage_I_factors[year]["ASOCSEC"]
APOPSNR = Stage_I_factors[year]["APOPSNR"]
SS_INCOME = (Stage_II_targets[year]["Gross Social Security Income"] *
APOPSNR / ASOCSEC * 1000 - ss_income.sum())
AUCOMP = Stage_I_factors[year]["AUCOMP"]
UNEMPLOYMENT_COMP = (Stage_II_targets[year]["Unemployment Compensation"] *
APOPN / AUCOMP * 1000 - unemployment_comp.sum())
AWAGE = Stage_I_factors[year]["AWAGE"]
target_name = "Wages and Salaries: Zero or Less"
WAGE_1 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_1.sum())
target_name = "Wages and Salaries: $1 Less Than $10,000"
WAGE_2 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_2.sum())
target_name = "Wages and Salaries: $10,000 Less Than $20,000"
WAGE_3 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_3.sum())
target_name = "Wages and Salaries: $20,000 Less Than $30,000"
WAGE_4 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_4.sum())
target_name = "Wages and Salaries: $30,000 Less Than $40,000"
WAGE_5 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_5.sum())
target_name = "Wages and Salaries: $40,000 Less Than $50,000"
WAGE_6 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_6.sum())
target_name = "Wages and Salaries: $50,000 Less Than $75,000"
WAGE_7 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_7.sum())
target_name = "Wages and Salaries: $75,000 Less Than $100,000"
WAGE_8 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_8.sum())
target_name = "Wages and Salaries: $100,000 Less Than $200,000"
WAGE_9 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_9.sum())
target_name = "Wages and Salaries: $200,000 Less Than $500,000"
WAGE_10 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_10.sum())
target_name = "Wages and Salaries: $500,000 Less Than $1 Million"
WAGE_11 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_11.sum())
target_name = "Wages and Salaries: $1 Million and Over"
WAGE_12 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
wage_12.sum())
temp = [
INTEREST, DIVIDEND, BIZ_INCOME, BIZ_LOSS, CAP_GAIN, ANNUITY_PENSION,
SCH_E_INCOME, SCH_E_LOSS, SS_INCOME, UNEMPLOYMENT_COMP, WAGE_1, WAGE_2,
WAGE_3, WAGE_4, WAGE_5, WAGE_6, WAGE_7, WAGE_8, WAGE_9, WAGE_10,
WAGE_11, WAGE_12
]
for m in temp:
b.append(m)
targets = CyLPArray(b)
print("Targets for year {} are:".format(year))
print(targets)
LP = CyLPModel()
r = LP.addVariable("r", puf_length)
s = LP.addVariable("s", puf_length)
print("Adding constraints for year {} .....".format(year))
LP.addConstraint(r >= 0, "positive r")
LP.addConstraint(s >= 0, "positive s")
LP.addConstraint(r + s <= tol, "abs upperbound")
c = CyLPArray((np.ones(puf_length)))
LP.objective = c * r + c * s
LP.addConstraint(A1 * r + A2 * s == targets, "Aggregates")
print("Setting up the LP model for year {} .....".format(year))
model = CyClpSimplex(LP)
print("Solving LP for year {} .....".format(year))
model.initialSolve()
print("DONE solving LP for year {}".format(year))
z = np.empty([puf_length])
z = (1.0 + model.primalVariableSolution["r"] -
model.primalVariableSolution["s"]) * s006 * 100
return z
def Single_Year_Stage_II(puf, Stage_I_factors, Stage_II_targets, year, tol):
length = len(puf.s006)
print("Preparing coefficient matrix...")
s006 = np.where(puf.e02400>0,
puf.s006*Stage_I_factors[year]["APOPSNR"]/100,
puf.s006*Stage_I_factors[year]["ARETS"]/100)
single_return = np.where(puf.mars==1, s006, 0)
joint_return = np.where((puf.mars==2)|(puf.mars==3), s006, 0)
hh_return = np.where(puf.mars==4,s006,0)
return_w_SS = np.where(puf.e02400>0,s006,0)
dependent_exempt_num = (puf.xocah+puf.xocawh+puf.xoodep+puf.xopar)*s006
interest = puf.e00300*s006
dividend = puf.e00600*s006
biz_income = np.where(puf.e00900>0, puf.e00900, 0)*s006
biz_loss = np.where(puf.e00900<0, -puf.e00900, 0)*s006
cap_gain = np.where(puf.e01000>0, puf.e01000, 0)*s006
annuity_pension = puf.e01700*s006
sch_e_income = np.where(puf.e02000>0, puf.e02000, 0)*s006
sch_e_loss = np.where(puf.e02000<0, -puf.e02000, 0)*s006
ss_income = puf.e02400*s006
unemployment_comp = puf.e02300*s006
# Wage distribution
wage_1 = np.where(puf.e00100<=0, puf.e00200,0)*s006
wage_2 = np.where((puf.e00100>0)&(puf.e00100<=10000), puf.e00200,0)*s006
wage_3 = np.where((puf.e00100>10000)&(puf.e00100<=20000), puf.e00200,0)*s006
wage_4 = np.where((puf.e00100>20000)&(puf.e00100<=30000), puf.e00200,0)*s006
wage_5 = np.where((puf.e00100>30000)&(puf.e00100<=40000), puf.e00200,0)*s006
wage_6 = np.where((puf.e00100>40000)&(puf.e00100<=50000), puf.e00200,0)*s006
wage_7 = np.where((puf.e00100>50000)&(puf.e00100<=75000), puf.e00200,0)*s006
wage_8 = np.where((puf.e00100>75000)&(puf.e00100<=100000), puf.e00200,0)*s006
wage_9 = np.where((puf.e00100>100000)&(puf.e00100<=200000), puf.e00200,0)*s006
wage_10 = np.where((puf.e00100>200000)&(puf.e00100<=500000), puf.e00200,0)*s006
wage_11 = np.where((puf.e00100>500000)&(puf.e00100<=1000000), puf.e00200,0)*s006
wage_12 = np.where((puf.e00100>1000000), puf.e00200,0)*s006
# Set up the matrix
One_half_LHS = np.vstack((single_return, joint_return, hh_return, return_w_SS,
dependent_exempt_num, interest, dividend,
biz_income,biz_loss, cap_gain, annuity_pension,
sch_e_income, sch_e_loss, ss_income, unemployment_comp,
wage_1, wage_2, wage_3, wage_4, wage_5, wage_6,
wage_7, wage_8, wage_9, wage_10, wage_11, wage_12))
# Coefficients for r and s
A1 = np.matrix(One_half_LHS)
A2 = np.matrix(-One_half_LHS)
print("Preparing targets for ", year)
APOPN = Stage_I_factors[year]["APOPN"]
b = []
b.append(Stage_II_targets[year]['Single']-single_return.sum())
b.append(Stage_II_targets[year]['Joint']-joint_return.sum())
b.append(Stage_II_targets[year]['HH']-hh_return.sum())
b.append(Stage_II_targets[year]['SS_return']-return_w_SS.sum())
b.append(Stage_II_targets[year]['Dep_return'] - dependent_exempt_num.sum())
AINTS = Stage_I_factors[year]["AINTS"]
INTEREST = Stage_II_targets[year]['INTS']*APOPN/AINTS*1000-interest.sum()
ADIVS = Stage_I_factors[year]["ADIVS"]
DIVIDEND = Stage_II_targets[year]['DIVS']*APOPN/ADIVS*1000 - dividend.sum()
ASCHCI = Stage_I_factors[year]["ASCHCI"]
BIZ_INCOME = Stage_II_targets[year]['SCHCI']*APOPN/ASCHCI*1000 - biz_income.sum()
ASCHCL = Stage_I_factors[year]["ASCHCL"]
BIZ_LOSS = Stage_II_targets[year]['SCHCL']*APOPN/ASCHCL*1000 - biz_loss.sum()
ACGNS = Stage_I_factors[year]["ACGNS"]
CAP_GAIN = Stage_II_targets[year]['CGNS']*APOPN/ACGNS*1000 - cap_gain.sum()
ATXPY = Stage_I_factors[year]["ATXPY"]
ANNUITY_PENSION = Stage_II_targets[year]['Pension']*APOPN/ATXPY*1000 - annuity_pension.sum()
ASCHEI = Stage_I_factors[year]["ASCHEI"]
SCH_E_INCOME = Stage_II_targets[year]["SCHEI"]*APOPN/ASCHEI*1000 - sch_e_income.sum()
ASCHEL = Stage_I_factors[year]["ASCHEL"]
SCH_E_LOSS = Stage_II_targets[year]["SCHEL"]*APOPN/ASCHEL*1000 - sch_e_loss.sum()
ASOCSEC = Stage_I_factors[year]["ASOCSEC"]
APOPSNR = Stage_I_factors[year]["APOPSNR"]
SS_INCOME = Stage_II_targets[year]["SS"]*APOPSNR/ASOCSEC*1000 - ss_income.sum()
AUCOMP = Stage_I_factors[year]["AUCOMP"]
UNEMPLOYMENT_COMP = Stage_II_targets[year]["UCOMP"]*APOPN/AUCOMP*1000 - unemployment_comp.sum()
AWAGE = Stage_I_factors[year]["AWAGE"]
WAGE_1 = Stage_II_targets[year]["WAGE_1"]*APOPN/AWAGE*1000 - wage_1.sum()
WAGE_2 = Stage_II_targets[year]["WAGE_2"]*APOPN/AWAGE*1000 - wage_2.sum()
WAGE_3 = Stage_II_targets[year]["WAGE_3"]*APOPN/AWAGE*1000 - wage_3.sum()
WAGE_4 = Stage_II_targets[year]["WAGE_4"]*APOPN/AWAGE*1000 - wage_4.sum()
WAGE_5 = Stage_II_targets[year]["WAGE_5"]*APOPN/AWAGE*1000 - wage_5.sum()
WAGE_6 = Stage_II_targets[year]["WAGE_6"]*APOPN/AWAGE*1000 - wage_6.sum()
WAGE_7 = Stage_II_targets[year]["WAGE_7"]*APOPN/AWAGE*1000 - wage_7.sum()
WAGE_8 = Stage_II_targets[year]["WAGE_8"]*APOPN/AWAGE*1000 - wage_8.sum()
WAGE_9 = Stage_II_targets[year]["WAGE_9"]*APOPN/AWAGE*1000 - wage_9.sum()
WAGE_10 = Stage_II_targets[year]["WAGE_10"]*APOPN/AWAGE*1000 - wage_10.sum()
WAGE_11 = Stage_II_targets[year]["WAGE_11"]*APOPN/AWAGE*1000 - wage_11.sum()
WAGE_12 = Stage_II_targets[year]["WAGE_12"]*APOPN/AWAGE*1000 - wage_12.sum()
temp = [INTEREST,DIVIDEND, BIZ_INCOME, BIZ_LOSS, CAP_GAIN, ANNUITY_PENSION, SCH_E_INCOME, SCH_E_LOSS, SS_INCOME, UNEMPLOYMENT_COMP,
WAGE_1,WAGE_2, WAGE_3,WAGE_4, WAGE_5, WAGE_6, WAGE_7,WAGE_8,WAGE_9, WAGE_10, WAGE_11, WAGE_12]
for m in temp:
b.append(m)
targets = CyLPArray(b)
print("Targets for year ", year, " is ", targets)
LP = CyLPModel()
r = LP.addVariable('r', length)
s = LP.addVariable('s', length)
print("Adding constraints")
LP.addConstraint(r >=0, "positive r")
LP.addConstraint(s >=0, "positive s")
LP.addConstraint(r + s <= tol, "abs upperbound")
c = CyLPArray((np.ones(length)))
LP.objective = c * r + c * s
LP.addConstraint(A1 * r + A2 * s == targets, "Aggregates")
print("Setting up the LP model")
model = CyClpSimplex(LP)
print("Solving LP......")
model.initialSolve()
print("DONE!!")
z = np.empty([length])
z = (1+model.primalVariableSolution['r'] - model.primalVariableSolution['s'])*s006
return z
def Single_Year_Stage_II(puf, Stage_I_factors, Stage_II_targets, year, tol):
length = len(puf.s006)
print("Preparing coefficient matrix...")
s006 = np.where(puf.e02400>0,
puf.s006*Stage_I_factors[year]["APOPSNR"]/100,
puf.s006*Stage_I_factors[year]["ARETS"]/100)
single_return = np.where((puf.mars==1) & (puf.filer==1), s006, 0)
joint_return = np.where(((puf.mars==2)|(puf.mars==3)) & (puf.filer==1), s006, 0)
hh_return = np.where((puf.mars==4) & (puf.filer==1),s006,0)
return_w_SS = np.where((puf.e02400>0) & (puf.filer==1),s006,0)
dependent_exempt_num = (puf.xocah+puf.xocawh+puf.xoodep+puf.xopar)*s006
interest = puf.e00300*s006
dividend = puf.e00600*s006
biz_income = np.where(puf.e00900>0, puf.e00900, 0)*s006
biz_loss = np.where(puf.e00900<0, -puf.e00900, 0)*s006
cap_gain = np.where((puf.p23250+puf.p22250)>0, (puf.p23250+puf.p22250), 0)*s006
annuity_pension = puf.e01700*s006
sch_e_income = np.where(puf.e02000>0, puf.e02000, 0)*s006
sch_e_loss = np.where(puf.e02000<0, -puf.e02000, 0)*s006
ss_income = puf.e02400*s006
unemployment_comp = puf.e02300*s006
# Wage distribution
wage_1 = np.where(puf.e00100<=0, puf.e00200,0)*s006
wage_2 = np.where((puf.e00100>0)&(puf.e00100<=10000), puf.e00200,0)*s006
wage_3 = np.where((puf.e00100>10000)&(puf.e00100<=20000), puf.e00200,0)*s006
wage_4 = np.where((puf.e00100>20000)&(puf.e00100<=30000), puf.e00200,0)*s006
wage_5 = np.where((puf.e00100>30000)&(puf.e00100<=40000), puf.e00200,0)*s006
wage_6 = np.where((puf.e00100>40000)&(puf.e00100<=50000), puf.e00200,0)*s006
wage_7 = np.where((puf.e00100>50000)&(puf.e00100<=75000), puf.e00200,0)*s006
wage_8 = np.where((puf.e00100>75000)&(puf.e00100<=100000), puf.e00200,0)*s006
wage_9 = np.where((puf.e00100>100000)&(puf.e00100<=200000), puf.e00200,0)*s006
wage_10 = np.where((puf.e00100>200000)&(puf.e00100<=500000), puf.e00200,0)*s006
wage_11 = np.where((puf.e00100>500000)&(puf.e00100<=1000000), puf.e00200,0)*s006
wage_12 = np.where((puf.e00100>1000000), puf.e00200,0)*s006
# Set up the matrix
One_half_LHS = np.vstack((single_return, joint_return, hh_return, return_w_SS,
dependent_exempt_num, interest, dividend,
biz_income,biz_loss, cap_gain, annuity_pension,
sch_e_income, sch_e_loss, ss_income, unemployment_comp,
wage_1, wage_2, wage_3, wage_4, wage_5, wage_6,
wage_7, wage_8, wage_9, wage_10, wage_11, wage_12))
# Coefficients for r and s
A1 = np.matrix(One_half_LHS)
A2 = np.matrix(-One_half_LHS)
print("Preparing targets for ", year)
APOPN = Stage_I_factors[year]["APOPN"]
b = []
b.append(Stage_II_targets[year]['Single Returns']-single_return.sum())
b.append(Stage_II_targets[year]['Joint Returns']-joint_return.sum())
b.append(Stage_II_targets[year]['Head of Household Returns']-hh_return.sum())
b.append(Stage_II_targets[year]['Number of Returns w/ Gross Security Income']-return_w_SS.sum())
b.append(Stage_II_targets[year]['Number of Dependent Exemptions'] - dependent_exempt_num.sum())
AINTS = Stage_I_factors[year]["AINTS"]
INTEREST = Stage_II_targets[year]['Taxable Interest Income']*APOPN/AINTS*1000-interest.sum()
ADIVS = Stage_I_factors[year]["ADIVS"]
DIVIDEND = Stage_II_targets[year]['Ordinary Dividends']*APOPN/ADIVS*1000 - dividend.sum()
ASCHCI = Stage_I_factors[year]["ASCHCI"]
BIZ_INCOME = Stage_II_targets[year]['Business Income (Schedule C)']*APOPN/ASCHCI*1000 - biz_income.sum()
ASCHCL = Stage_I_factors[year]["ASCHCL"]
BIZ_LOSS = Stage_II_targets[year]['Business Loss (Schedule C)']*APOPN/ASCHCL*1000 - biz_loss.sum()
ACGNS = Stage_I_factors[year]["ACGNS"]
CAP_GAIN = Stage_II_targets[year]['Net Capital Gains in AGI']*APOPN/ACGNS*1000 - cap_gain.sum()
ATXPY = Stage_I_factors[year]["ATXPY"]
ANNUITY_PENSION = Stage_II_targets[year]['Taxable Pensions and Annuities']*APOPN/ATXPY*1000 - annuity_pension.sum()
ASCHEI = Stage_I_factors[year]["ASCHEI"]
SCH_E_INCOME = Stage_II_targets[year]["Supplemental Income (Schedule E)"]*APOPN/ASCHEI*1000 - sch_e_income.sum()
ASCHEL = Stage_I_factors[year]["ASCHEL"]
SCH_E_LOSS = Stage_II_targets[year]["Supplemental Loss (Schedule E)"]*APOPN/ASCHEL*1000 - sch_e_loss.sum()
ASOCSEC = Stage_I_factors[year]["ASOCSEC"]
APOPSNR = Stage_I_factors[year]["APOPSNR"]
SS_INCOME = Stage_II_targets[year]["Gross Social Security Income"]*APOPSNR/ASOCSEC*1000 - ss_income.sum()
AUCOMP = Stage_I_factors[year]["AUCOMP"]
UNEMPLOYMENT_COMP = Stage_II_targets[year]["Unemployment Compensation"]*APOPN/AUCOMP*1000 - unemployment_comp.sum()
AWAGE = Stage_I_factors[year]["AWAGE"]
WAGE_1 = Stage_II_targets[year]["Wages and Salaries: Zero or Less"]*APOPN/AWAGE*1000 - wage_1.sum()
WAGE_2 = Stage_II_targets[year]["Wages and Salaries: $1 Less Than $10,000"]*APOPN/AWAGE*1000 - wage_2.sum()
WAGE_3 = Stage_II_targets[year]["Wages and Salaries: $10,000 Less Than $20,000"]*APOPN/AWAGE*1000 - wage_3.sum()
WAGE_4 = Stage_II_targets[year]["Wages and Salaries: $20,000 Less Than $30,000"]*APOPN/AWAGE*1000 - wage_4.sum()
WAGE_5 = Stage_II_targets[year]["Wages and Salaries: $30,000 Less Than $40,000"]*APOPN/AWAGE*1000 - wage_5.sum()
WAGE_6 = Stage_II_targets[year]["Wages and Salaries: $40,000 Less Than $50,000"]*APOPN/AWAGE*1000 - wage_6.sum()
WAGE_7 = Stage_II_targets[year]["Wages and Salaries: $50,000 Less Than $75,000"]*APOPN/AWAGE*1000 - wage_7.sum()
WAGE_8 = Stage_II_targets[year]["Wages and Salaries: $75,000 Less Than $100,000"]*APOPN/AWAGE*1000 - wage_8.sum()
WAGE_9 = Stage_II_targets[year]["Wages and Salaries: $100,000 Less Than $200,000"]*APOPN/AWAGE*1000 - wage_9.sum()
WAGE_10 = Stage_II_targets[year]["Wages and Salaries: $200,000 Less Than $500,000"]*APOPN/AWAGE*1000 - wage_10.sum()
WAGE_11 = Stage_II_targets[year]["Wages and Salaries: $500,000 Less Than $1 Million"]*APOPN/AWAGE*1000 - wage_11.sum()
WAGE_12 = Stage_II_targets[year]["Wages and Salaries: $1 Million and Over"]*APOPN/AWAGE*1000 - wage_12.sum()
temp = [INTEREST,DIVIDEND, BIZ_INCOME, BIZ_LOSS, CAP_GAIN, ANNUITY_PENSION, SCH_E_INCOME, SCH_E_LOSS, SS_INCOME, UNEMPLOYMENT_COMP,
WAGE_1,WAGE_2, WAGE_3,WAGE_4, WAGE_5, WAGE_6, WAGE_7,WAGE_8,WAGE_9, WAGE_10, WAGE_11, WAGE_12]
for m in temp:
b.append(m)
targets = CyLPArray(b)
print("Targets for year ", year, " is ", targets)
LP = CyLPModel()
r = LP.addVariable('r', length)
s = LP.addVariable('s', length)
print("Adding constraints")
LP.addConstraint(r >=0, "positive r")
LP.addConstraint(s >=0, "positive s")
LP.addConstraint(r + s <= tol, "abs upperbound")
c = CyLPArray((np.ones(length)))
LP.objective = c * r + c * s
LP.addConstraint(A1 * r + A2 * s == targets, "Aggregates")
print("Setting up the LP model")
model = CyClpSimplex(LP)
print("Solving LP......")
model.initialSolve()
print("DONE!!")
z = np.empty([length])
z = (1+model.primalVariableSolution['r'] - model.primalVariableSolution['s'])*s006 * 100
return z