def run(self, data, upc_sequence, resources=None):
CLOSE = 0.01
self.mnl_probabilities=upc_sequence.probability_class
self.bhhh_estimation = bhhh_mnl_estimation()
modified_upc_sequence = UPCFactory().get_model(
utilities=None, probabilities="opus_core.mnl_probabilities", choices=None)
modified_upc_sequence.utility_class = upc_sequence.utility_class
result = self.bhhh_estimation.run(data, modified_upc_sequence, resources)
probability = modified_upc_sequence.get_probabilities()
probability_0 = probability
resources.check_obligatory_keys(["capacity"])
supply = resources["capacity"]
if not isinstance(supply, ndarray):
supply = array(supply)
nsupply = supply.size
max_iter = resources.get("max_iterations", None)
if max_iter == None:
max_iter = 100 # default
index = resources.get("index", None)
if index == None:
index = arange(nsupply)
neqs = probability.shape[1]
nobs = probability.shape[0]
if index.ndim <= 1:
index = repeat(reshape(index, (1,index.shape[0])), nobs)
resources.merge({"index":index})
# WARNING: THE SCALING OF DEMAND IS HARD CODED AND NEEDS TO BE MADE AN ARGUMENT
# scale demand to represent 100% from a 0.2% sample
demand = self.mnl_probabilities.get_demand(index, probability, nsupply)*50
#initial calculations
sdratio = ma.filled(supply/ma.masked_where(demand==0, demand),2.0)
sdratio = _round(sdratio, 1.0, atol=CLOSE)
constrained_locations = logical_and(sdratio<1.0,demand-supply>CLOSE).astype("int8")
unconstrained_locations = 1-constrained_locations
excess_demand = (demand-supply)*constrained_locations
global_excess_demand = excess_demand.sum()
sdratio_matrix = sdratio[index]
constrained_locations_matrix = constrained_locations[index]
constrained_ex_ante = constrained_locations_matrix
# Would like to include following print statements in debug printing
# logger.log_status('Total demand:',demand.sum())
# logger.log_status('Total supply:',supply.sum())
logger.log_status('Global excess demand:',global_excess_demand)
# logger.log_status('Constrained locations:',constrained_locations.sum())
unconstrained_locations_matrix = unconstrained_locations[index]
# omega = ones(nobs,type=float32)
# pi = self.constrain_probabilities.get_pi(sdratio_matrix, omega, constrained_locations_matrix, unconstrained_locations_matrix, nobs)
omega = self.mnl_probabilities.get_omega(probability, constrained_locations_matrix, unconstrained_locations_matrix, sdratio_matrix)
omega = _round(omega, 1.0, CLOSE)
print 'Num of constrainted locations: ', constrained_locations.sum()
print 'Num of unconstrainted locations: ', unconstrained_locations.sum()
print 'Min Ex Ante Constraints:',min(constrained_ex_ante.sum(axis=1))
print 'Max Ex Ante Constraints:',max(constrained_ex_ante.sum(axis=1))
#print 'Omega shape',omega.shape
#print 'Omega histogram',histogram(omega,0,4,40)
print 'Minimum Omega',min(omega)
print 'Maximum Omega',max(omega)
print 'Mean Omega:',mean(omega)
print 'Median Omega:',median(omega)
print 'Sum Omega:',omega.sum()
print 'Standard Deviation Omega:',standard_deviation(omega)
print 'Count of Negative Omega',(where(omega<0,1,0).sum())
print 'Count of Omega < 1',(where(omega<1,1,0).sum())
print 'Count of Omega > 2',(where(omega>2,1,0).sum())
print 'Count of Omega > 4',(where(omega>4,1,0).sum())
average_omega = self.mnl_probabilities.get_average_omega(omega, probability, index, nsupply, nobs, demand)
average_omega=_round(average_omega, 1.0, CLOSE)
coef_names = resources.get("coefficient_names", None)
if coef_names is not None:
coef_names = array(coef_names.tolist()+["ln_pi"])
resources.merge({"coefficient_names":coef_names})
data=concatenate((data,ones((nobs,neqs,1),dtype=float32)), axis=2)
prev_omega = omega
prev_constrained_locations_matrix = constrained_locations_matrix
for i in range(max_iter):
print
print 'Iteration',i
pi = self.mnl_probabilities.get_pi(sdratio_matrix, omega, constrained_locations_matrix, unconstrained_locations_matrix, nobs)
#print 'pi shape',pi.shape
#print 'data shape', data.shape
#print 'min_pi',min(pi,axis=1)
#print 'max_pi',max(pi,axis=1)
#print 'min_data',min(data,axis=1)
#print 'max_data',max(data,axis=1)
data[:,:,-1] = ln(pi)
#data = concatenate((data,(pi[:,:,newaxis])),axis=-1)
#print 'data shape after contatenating pi', data.shape
result = self.bhhh_estimation.run(data, modified_upc_sequence, resources)
#print
#print 'result',result
probability = modified_upc_sequence.get_probabilities()
prob_hat = ma.filled(probability / pi, 0.0)
# HARD CODED
# scale new_demand from 0.2% to 100%
demand_new = self.mnl_probabilities.get_demand(index, prob_hat, nsupply)*50
##update supply-demand ratio
sdratio = ma.filled(supply/ma.masked_where(demand_new==0, demand_new),2.0)
sdratio = _round(sdratio, 1.0, CLOSE)
sdratio_matrix = sdratio[index]
constrained_locations = where(((average_omega*demand_new - supply) > CLOSE),1,0)
unconstrained_locations = 1-constrained_locations
constrained_locations_matrix = constrained_locations[index]
unconstrained_locations_matrix = unconstrained_locations[index]
constrained_ex_post = constrained_locations_matrix
constrained_ex_post_not_ex_ante = where((constrained_ex_post - constrained_ex_ante)==1,1,0)
constrained_ex_ante_not_ex_post = where((constrained_ex_post - constrained_ex_ante)==-1,1,0)
#Assumption 5: if j belongs to constrained ex post and unconstrained ex ante, then p^i_j <= D_j / S_j
print 'Number of individual violating Assumption 5: ', where((probability > 1 / sdratio_matrix)*constrained_ex_post_not_ex_ante)[0].size
#Assumption 6: pi of constrained locations should be less than 1
print 'Number of individual violating Assumption 6: ', where((probability * constrained_ex_post).sum(axis=1) >
(prob_hat * constrained_ex_post).sum(axis=1))[0].size
##OR ?
#print 'Assumption 6: ', where(pi[where(constrained_locations_matrix)] > 1)[0].size
print 'number of constrainted locations: ', constrained_locations.sum()
print 'number of unconstrainted locations: ', unconstrained_locations.sum()
print 'Min Ex Post Constraints:',min(constrained_ex_post.sum(axis=1))
print 'Max Ex Post Constraints:',max(constrained_ex_post.sum(axis=1))
print 'At Least 1 Constrained Ex Ante Not Ex Post*:',where(constrained_ex_ante_not_ex_post.sum(axis=1))[0].size
print 'At Least 1 Constrained Ex Post Not Ex Ante:',where(constrained_ex_post_not_ex_ante.sum(axis=1))[0].size
omega = self.mnl_probabilities.get_omega(prob_hat, constrained_locations_matrix, unconstrained_locations_matrix, sdratio_matrix)
omega = _round(omega, 1.0, CLOSE)
#print 'Omega histogram',histogram(omega,0,4,40)
print 'Minimum Omega',min(omega)
print 'Maximum Omega',max(omega)
print 'Mean Omega:',mean(omega)
print 'Median Omega:',median(omega)
print 'Sum Omega:',omega.sum()
print 'Standard Deviation Omega:',standard_deviation(omega)
print 'Count of Negative Omega',(where(omega<0,1,0).sum())
print 'Count of Omega < 1: ',(where(omega<1,1,0).sum())
print 'Count of Omega > 2: ',(where(omega>2,1,0).sum())
print 'Count of Omega > 4: ',(where(omega>4,1,0).sum())
average_omega = self.mnl_probabilities.get_average_omega(omega, prob_hat, index, nsupply, nobs, demand_new)
average_omega = _round(average_omega, 1.0, CLOSE)
excess_demand = (demand_new-supply)*constrained_locations
global_excess_demand = excess_demand.sum()
#print 'Omega [i], [i-1]',prev_omega, omega,
#print 'Constrained locations [i], [i-1]',constrained_locations_matrix, prev_constrained_locations_matrix
print 'Global Excess Demand',global_excess_demand
if ma.allclose(omega, prev_omega, atol=1e-3) or not any(constrained_locations_matrix - prev_constrained_ex_ante):
print 'omega or constrained ex post unchanged: Convergence criterion achieved'
break
#if global_excess_demand < 1:
#print 'Global excess demand < 1: Convergence criterion achieved'
#break
return result
