def compute(self, dataset_pool):
inc = reshape(self.get_dataset().get_attribute_of_dataset(self.hh_income),
(self.get_dataset().get_reduced_n(), 1))
cost = self.get_dataset().get_2d_dataset_attribute(self.housing_cost)
I = inc-cost
affordability = where(I > 0, ln(I+1),I)
return affordability
def test_my_inputs(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='land_covers',
table_data={
'relative_x': array([1, 2, 1, 2]),
'relative_y': array([1, 1, 2, 2]),
"lct": array([10, 10, 4, 3])
})
dataset_pool = DatasetPool(package_order=['biocomplexity'],
storage=storage)
footprint = array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
dataset_pool._add_dataset('constant', {
"FOOTPRINT": footprint,
'AG': 10,
})
gridcell = dataset_pool.get_dataset('land_cover')
gridcell.compute_variables(self.variable_name,
dataset_pool=dataset_pool)
values = gridcell.get_attribute(self.variable_name)
should_be = array([[1, 1], [0, 0]])
should_be = correlate(ma.filled(should_be.astype(int32), 0),
footprint,
mode="reflect")
should_be = less_equal((should_be / 5.0), 400)
should_be = ln(distance_transform_edt(should_be) +
1) / dag.standardization_constant_distance
should_be = ravel(transpose(should_be)) # flatten by id
self.assert_(ma.allclose(values, should_be, rtol=1e-7),
msg="Error in " + self.variable_name)
def test_my_inputs(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(
table_name='land_covers',
table_data={
'relative_x': array([1,2,1,2]),
'relative_y': array([1,1,2,2]),
"lct": array([11,2,4,3])
}
)
dataset_pool = DatasetPool(package_order=['biocomplexity'],
storage=storage)
dataset_pool._add_dataset(
'constant',
{
"CELLSIZE":30,
"ALL_URBAN":['HU', 'MU', 'LU'],
'HU': 1,
'MU': 2,
'LU': 3
}
)
gridcell = dataset_pool.get_dataset('land_cover')
gridcell.compute_variables(self.variable_name,
dataset_pool=dataset_pool)
values = gridcell.get_attribute(self.variable_name)
should_be = array([1, 0, 1, 0], dtype=float32)
should_be = ln(30*distance_transform_edt(should_be)+1) / ddt1.standardization_constant_distance
self.assert_(ma.allclose( values, should_be, rtol=1e-7),
msg = "Error in " + self.variable_name)
def test_my_inputs(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(
table_name='land_covers',
table_data={
'relative_x': array([1,2,1,2]),
'relative_y': array([1,1,2,2]),
"comm_add4": array([1, 2, 5, 15])
}
)
dataset_pool = DatasetPool(package_order=['biocomplexity'],
storage=storage)
dataset_pool._add_dataset(
'constant',
{
"CELLSIZE": 250 # this results in a 3x3 grid, (750/250)x(750/250)
}
)
gridcell = dataset_pool.get_dataset('land_cover')
gridcell.compute_variables(self.variable_name,
dataset_pool=dataset_pool)
values = gridcell.get_attribute(self.variable_name)
should_be = array([1*4+2*2+5*2+15, 1*2+2*4+5+15*2, 1*2+2+5*4+15*2, 1+2*2+5*2+15*4])
should_be = ln(should_be + 1) / 10.0
self.assert_(ma.allclose( values, should_be, rtol=1e-7),
msg = "Error in " + self.variable_name)
def test_my_inputs(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='land_covers',
table_data={
'relative_x': array([1, 2, 1, 2]),
'relative_y': array([1, 1, 2, 2]),
"lct": array([11, 2, 4, 3])
})
dataset_pool = DatasetPool(package_order=['biocomplexity'],
storage=storage)
dataset_pool._add_dataset(
'constant', {
"CELLSIZE": 30,
"ALL_URBAN": ['HU', 'MU', 'LU'],
'HU': 1,
'MU': 2,
'LU': 3
})
gridcell = dataset_pool.get_dataset('land_cover')
gridcell.compute_variables(self.variable_name,
dataset_pool=dataset_pool)
values = gridcell.get_attribute(self.variable_name)
should_be = array([1, 0, 1, 0], dtype=float32)
should_be = ln(30 * distance_transform_edt(should_be) +
1) / ddt1.standardization_constant_distance
self.assert_(ma.allclose(values, should_be, rtol=1e-7),
msg="Error in " + self.variable_name)
def compute(self, dataset_pool):
cellsize = dataset_pool.get_dataset('constants')['CELLSIZE']
fpdimension = int(self.footprint_width / cellsize)
fp = ones((fpdimension, fpdimension), dtype="int32")
summed = correlate( ma.filled( self.get_dataset().get_2d_attribute( self.comm_add ), 0.0 ), \
fp, mode="reflect" )
return ln(self.get_dataset().flatten_by_id(summed)+1)/10.0
def test_my_inputs_for_hmps(self):
variable_name = "biocomplexity.land_cover.hmps"
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='land_covers',
table_data={
'relative_x': array([1, 2, 1, 2]),
'relative_y': array([1, 1, 2, 2]),
"lct": array([1, 2, 1, 4]),
})
dataset_pool = DatasetPool(package_order=['biocomplexity'],
storage=storage)
dataset_pool._add_dataset(
'constant', {
"FOOTPRINT": array([[0, 1, 0], [1, 1, 1], [0, 1, 0]]),
'HU': 1,
})
land_cover = dataset_pool.get_dataset('land_cover')
land_cover.compute_variables(variable_name, dataset_pool=dataset_pool)
values = land_cover.get_attribute(variable_name)
should_be = array([2, 2, 2, 2], dtype=float32)
should_be = ln(should_be + 1) / SSSmps.standardization_constant_MPS
self.assert_(ma.allclose(values, should_be, rtol=1e-7),
msg="Error in " + variable_name)
def _run_stochastic_test_poisson(
self, function, expected_results, number_of_iterations, significance_level=0.01, transformation=None
):
"""
Run the given function for the specified number_of_iterations.
Uses Bayesian statistics to determine whether the produced results are
within the specified significance_level of the expected_results.
"""
K = expected_results.size
sum_y = zeros(K, dtype=float32)
x_kr = zeros((number_of_iterations, K), dtype=float32)
for i in range(number_of_iterations):
y_r = function()
x_kr[i, :] = try_transformation(y_r, transformation)
sum_y = sum_y + x_kr[i, :]
lambdak = sum_y / float(number_of_iterations)
lambdanull = try_transformation(expected_results.astype(float32), transformation)
# print lambdak
# print lambdanull
sumxk = sum(x_kr, axis=0)
LRTS = 2.0 * (
(number_of_iterations * (lambdanull - lambdak).sum())
+ (ln(lambdak / ma.masked_where(lambdanull == 0, lambdanull)) * sumxk).sum()
)
prob = chisqprob(LRTS, K)
# print LRTS, prob
logger.log_status("Stochastic Test Poisson: LRTS=" + str(LRTS) + ", df=", str(K), ", p=" + str(prob))
return (prob >= significance_level, "prob=%f < significance level of %f" % (prob, significance_level))
def test_my_inputs(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='land_covers',
table_data={
'relative_x': array([1, 2, 1, 2]),
'relative_y': array([1, 1, 2, 2]),
"comm_add4": array([1, 2, 5, 15])
})
dataset_pool = DatasetPool(package_order=['biocomplexity'],
storage=storage)
dataset_pool._add_dataset(
'constant',
{
"CELLSIZE":
250 # this results in a 3x3 grid, (750/250)x(750/250)
})
gridcell = dataset_pool.get_dataset('land_cover')
gridcell.compute_variables(self.variable_name,
dataset_pool=dataset_pool)
values = gridcell.get_attribute(self.variable_name)
should_be = array([
1 * 4 + 2 * 2 + 5 * 2 + 15, 1 * 2 + 2 * 4 + 5 + 15 * 2,
1 * 2 + 2 + 5 * 4 + 15 * 2, 1 + 2 * 2 + 5 * 2 + 15 * 4
])
should_be = ln(should_be + 1) / 10.0
self.assert_(ma.allclose(values, should_be, rtol=1e-7),
msg="Error in " + self.variable_name)
def compute(self, dataset_pool):
cellsize = dataset_pool.get_dataset('constants')['CELLSIZE']
fpdimension = int(self.footprint_width / cellsize)
fp = ones((fpdimension, fpdimension), dtype="int32")
summed = correlate( ma.filled( self.get_dataset().get_2d_attribute( self.comm_add ), 0.0 ), \
fp, mode="reflect" )
return ln(self.get_dataset().flatten_by_id(summed) + 1) / 10.0
def test_my_inputs_for_hmps(self):
variable_name = "biocomplexity.land_cover.hmps"
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(
table_name='land_covers',
table_data={
'relative_x': array([1,2,1,2]),
'relative_y': array([1,1,2,2]),
"lct": array([1, 2, 1, 4]),
}
)
dataset_pool = DatasetPool(package_order=['biocomplexity'],
storage=storage)
dataset_pool._add_dataset(
'constant',
{
"FOOTPRINT": array([[0,1,0], [1,1,1], [0,1,0]]),
'HU': 1,
}
)
land_cover = dataset_pool.get_dataset('land_cover')
land_cover.compute_variables(variable_name,
dataset_pool=dataset_pool)
values = land_cover.get_attribute(variable_name)
should_be = array([2, 2, 2, 2], dtype=float32)
should_be = ln(should_be + 1) / SSSmps.standardization_constant_MPS
self.assert_(ma.allclose( values, should_be, rtol=1e-7),
msg = "Error in " + variable_name)
def compute(self, dataset_pool):
inc = reshape(
self.get_dataset().get_attribute_of_dataset(self.hh_income),
(self.get_dataset().get_reduced_n(), 1))
cost = self.get_dataset().get_2d_dataset_attribute(self.housing_cost)
I = inc - cost
affordability = where(I > 0, ln(I + 1), I)
return affordability
def test_my_inputs(self):
values = VariableTestToolbox().compute_variable(self.variable_name,
{"building":{
"land_value": array([10, 5, 0])},
},
dataset = "building")
should_be = ln(array([10, 5, 0]))
self.assertEqual(ma.allclose(values, should_be, rtol=1e-2), True, msg = "Error in " + self.variable_name)
def compute(self, dataset_pool):
constants = dataset_pool.get_dataset('constants')
ag = constants["AG"]
lct = ma.filled(self.get_dataset().get_2d_attribute(self.land_cover_type), 0)
footprint = constants['FOOTPRINT']
is_lct_ag = lct==ag
summed = self._compute_patch_size_of_cover_types(is_lct_ag, footprint)
summed = less_equal(summed, 400).astype(bool8)
distances = distance_transform_edt(summed)
return self.get_dataset().flatten_by_id(ln(distances + 1)
/ self.standardization_constant_distance )
def compute(self, dataset_pool):
constants = dataset_pool.get_dataset('constants')
cellsize = constants["CELLSIZE"]
all_urban_types = constants["ALL_URBAN"]
all_urban_types = map(lambda key: constants[key], all_urban_types)
lct = ma.filled(self.get_dataset().get_2d_attribute(self.land_cover_type), 0)
is_lct_all_urban = zeros(shape=lct.shape, dtype="int32")
for urban_type in all_urban_types:
is_lct_all_urban += equal(lct, urban_type)
temp = logical_not(is_lct_all_urban)
dd = ln(cellsize*(distance_transform_edt(temp))+1) / self.standardization_constant_distance
return self.get_dataset().flatten_by_id(dd).astype(float32)
def compute(self, dataset_pool):
constants = dataset_pool.get_dataset('constants')
ag = constants["AG"]
lct = ma.filled(
self.get_dataset().get_2d_attribute(self.land_cover_type), 0)
footprint = constants['FOOTPRINT']
is_lct_ag = lct == ag
summed = self._compute_patch_size_of_cover_types(is_lct_ag, footprint)
summed = less_equal(summed, 400).astype(bool8)
distances = distance_transform_edt(summed)
return self.get_dataset().flatten_by_id(
ln(distances + 1) / self.standardization_constant_distance)
def compute(self, dataset_pool):
constants = dataset_pool.get_dataset('constants')
covertypes_of_interest = self._cover_type_translation(constants)
lct = ma.filled(self.get_dataset().get_2d_attribute(self.land_cover_type), 0)
footprint = constants['FOOTPRINT']
is_lct_of_interest = reduce(lambda prev_answer, lct_num: logical_or(prev_answer, lct==lct_num),
covertypes_of_interest,
zeros(shape=lct.shape, dtype=int32))
summed = self._compute_patch_size_of_cover_types(is_lct_of_interest, footprint)
return self.get_dataset().flatten_by_id(ln(summed + 1)
/ self.standardization_constant_MPS )
def test_my_inputs(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(
table_name='gridcells',
table_data={
'grid_id': array([1,2,3]),
'housing_cost': array([42, 84, 2]),
}
)
storage.write_table(
table_name='households',
table_data={
'household_id': array([1, 2, 3]),
'income': array([42, 77, 99]),
}
)
dataset_pool = DatasetPool(package_order=['urbansim'],
storage=storage)
household_x_gridcell = dataset_pool.get_dataset('household_x_gridcell')
household_x_gridcell.compute_variables(self.variable_name,
dataset_pool=dataset_pool)
values = household_x_gridcell.get_attribute(self.variable_name)
should_be = array([[ 0, -42, ln(41)],
[ ln(36), -7, ln(76)],
[ ln(58), ln(16), ln(98)]])
self.assert_(ma.allclose(values, should_be, rtol=1e-7),
msg="Error in " + self.variable_name)
def test_my_inputs(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='gridcells',
table_data={
'grid_id': array([1, 2, 3]),
'housing_cost': array([42, 84, 2]),
})
storage.write_table(table_name='households',
table_data={
'household_id': array([1, 2, 3]),
'income': array([42, 77, 99]),
})
dataset_pool = DatasetPool(package_order=['urbansim'], storage=storage)
household_x_gridcell = dataset_pool.get_dataset('household_x_gridcell')
household_x_gridcell.compute_variables(self.variable_name,
dataset_pool=dataset_pool)
values = household_x_gridcell.get_attribute(self.variable_name)
should_be = array([[0, -42, ln(41)], [ln(36), -7, ln(76)],
[ln(58), ln(16), ln(98)]])
self.assert_(ma.allclose(values, should_be, rtol=1e-7),
msg="Error in " + self.variable_name)
def test_my_inputs(self):
avg_value = array([21,22,27,42])
some_gridcell_zone_ids = array([1,2,1,3])
grid_id = array([1,2,3,4])
values = VariableTestToolbox().compute_variable(self.variable_name,
{"zone":{
"zone_id":array([1,2, 3]),
"avg_val_per_unit_commercial": array([10, 5, 0])},
},
dataset = "zone")
should_be = ln(array([10, 5, 0]))
self.assertEqual(ma.allclose(values, should_be, rtol=1e-2), True, msg = "Error in " + self.variable_name)
def test_my_inputs(self):
avg_value = array([21,22,27,42])
some_gridcell_zone_ids = array([1,2,1,3])
grid_id = array([1,2,3,4])
values = VariableTestToolbox().compute_variable(self.variable_name,
{"zone":{
"zone_id":array([1,2, 3]),
"avg_val_per_unit_commercial": array([10, 5, 0])},
},
dataset = "zone")
should_be = ln(array([10, 5, 0]))
self.assertEqual(ma.allclose(values, should_be, rtol=1e-2), True, msg = "Error in " + self.variable_name)
def compute(self, dataset_pool):
constants = dataset_pool.get_dataset('constants')
cellsize = constants["CELLSIZE"]
all_urban_types = constants["ALL_URBAN"]
all_urban_types = map(lambda key: constants[key], all_urban_types)
lct = ma.filled(
self.get_dataset().get_2d_attribute(self.land_cover_type), 0)
is_lct_all_urban = zeros(shape=lct.shape, dtype="int32")
for urban_type in all_urban_types:
is_lct_all_urban += equal(lct, urban_type)
temp = logical_not(is_lct_all_urban)
dd = ln(cellsize * (distance_transform_edt(temp)) +
1) / self.standardization_constant_distance
return self.get_dataset().flatten_by_id(dd).astype(float32)
def compute(self, dataset_pool):
constants = dataset_pool.get_dataset('constants')
covertypes_of_interest = self._cover_type_translation(constants)
lct = ma.filled(
self.get_dataset().get_2d_attribute(self.land_cover_type), 0)
footprint = constants['FOOTPRINT']
is_lct_of_interest = reduce(
lambda prev_answer, lct_num: logical_or(prev_answer, lct == lct_num
), covertypes_of_interest,
zeros(shape=lct.shape, dtype=int32))
summed = self._compute_patch_size_of_cover_types(
is_lct_of_interest, footprint)
return self.get_dataset().flatten_by_id(
ln(summed + 1) / self.standardization_constant_MPS)
def _run_stochastic_test_poisson(self,
function,
expected_results,
number_of_iterations,
significance_level=0.01,
transformation=None):
"""
Run the given function for the specified number_of_iterations.
Uses Bayesian statistics to determine whether the produced results are
within the specified significance_level of the expected_results.
"""
K = expected_results.size
sum_y = zeros(K, dtype=float32)
x_kr = zeros((number_of_iterations, K), dtype=float32)
for i in range(number_of_iterations):
y_r = function()
x_kr[i, :] = try_transformation(y_r, transformation)
sum_y = sum_y + x_kr[i, :]
lambdak = sum_y / float(number_of_iterations)
lambdanull = try_transformation(expected_results.astype(float32),
transformation)
# print lambdak
# print lambdanull
sumxk = sum(x_kr, axis=0)
LRTS = 2.0 * (
(number_of_iterations * (lambdanull - lambdak).sum()) +
(ln(lambdak / ma.masked_where(lambdanull == 0, lambdanull)) *
sumxk).sum())
prob = chisqprob(LRTS, K)
#print LRTS, prob
logger.log_status(
"Stochastic Test Poisson: LRTS=" + str(LRTS) + ", df=", str(K),
", p=" + str(prob))
return (prob >= significance_level,
"prob=%f < significance level of %f" %
(prob, significance_level))
def test_my_inputs(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(
table_name='land_covers',
table_data={
'relative_x': array([1,2,1,2]),
'relative_y': array([1,1,2,2]),
"lct": array([10,10,4,3])
}
)
dataset_pool = DatasetPool(package_order=['biocomplexity'],
storage=storage)
footprint = array([[0,1,0], [1,1,1], [0,1,0]])
dataset_pool._add_dataset(
'constant',
{
"FOOTPRINT": footprint,
'AG': 10,
}
)
gridcell = dataset_pool.get_dataset('land_cover')
gridcell.compute_variables(self.variable_name,
dataset_pool=dataset_pool)
values = gridcell.get_attribute(self.variable_name)
should_be = array([[1,1],[0,0]])
should_be = correlate(ma.filled(should_be.astype(int32), 0), footprint, mode="reflect")
should_be = less_equal((should_be/5.0), 400)
should_be = ln(distance_transform_edt(should_be)+1) / dag.standardization_constant_distance
should_be = ravel(transpose(should_be)) # flatten by id
self.assert_(ma.allclose( values, should_be, rtol=1e-7),
msg = "Error in " + self.variable_name)
def run(self, data, upc_sequence, resources=None):
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
N, neqs, V = data.shape
max_iter = resources.get("max_iterations", 100) # default
sc = SessionConfiguration()
dataset_pool = sc.get_dataset_pool()
sample_rate = dataset_pool.get_dataset("sample_rate")
CLOSE = sc["CLOSE"]
info_filename = sc["info_file"]
info_filename = os.path.join('.', info_filename)
info_file = open(info_filename, "a")
constraint_dict = {1:'constrained', 0:'unconstrained'}
swing_cases_fix = 0 #set swing alternatives to constrained (1) or unconstrained (0)
prob_correlation = None
choice_set = resources['_model_'].choice_set
J = choice_set.size()
alt_id = choice_set.get_id_attribute()
movers = choice_set.get_attribute('movers')
resources.check_obligatory_keys(["capacity_string"])
supply = choice_set.get_attribute(resources["capacity_string"])
index = resources.get("index", None)
if index is None: # no sampling case, alternative set is the full choice_set
index = arange(J)
if index.ndim <= 1:
index = repeat(index[newaxis,:], N, axis=0)
if resources.get('aggregate_to_dataset', None):
aggregate_dataset = dataset_pool.get_dataset(resources.get('aggregate_to_dataset'))
choice_set_aggregate_id = choice_set.get_attribute(aggregate_dataset.get_id_name()[0])
index = aggregate_dataset.get_id_index(choice_set_aggregate_id[index].ravel()).reshape(index.shape)
supply = aggregate_dataset.get_attribute(resources["capacity_string"])
J = aggregate_dataset.size()
movers = aggregate_dataset.get_attribute("movers")
demand_history = movers[:, newaxis]
resources.merge({"index":index})
pi = ones(index.shape, dtype=float32) #initialize pi
#average_omega = ones(J,dtype=float32) #initialize average_omega
logger.start_block('Outer Loop')
for i in range(max_iter):
logger.log_status('Outer Loop Iteration %s' % i)
result = self.bhhh_estimation.run(data, modified_upc_sequence, resources)
del self.bhhh_estimation; collect()
self.bhhh_estimation = bhhh_mnl_estimation()
probability = modified_upc_sequence.get_probabilities()
if data.shape[2] == V: #insert a placeholder for ln(pi) in data
data = concatenate((data,ones((N,neqs,1),dtype=float32)), axis=2)
coef_names = resources.get("coefficient_names")
coef_names = concatenate( (coef_names, array(["ln_pi"])) )
resources.merge({"coefficient_names":coef_names})
else:
beta_ln_pi = result['estimators'][where(coef_names == 'ln_pi')][0]
logger.log_status("mu = 1/%s = %s" % (beta_ln_pi, 1/beta_ln_pi))
prob_hat = safe_array_divide(probability, pi ** beta_ln_pi)
#prob_hat = safe_array_divide(probability, pi)
prob_hat_sum = prob_hat.sum(axis=1, dtype=float32)
if not ma.allclose(prob_hat_sum, 1.0):
logger.log_status("probability doesn't sum up to 1, with minimum %s, and maximum %s" %
(prob_hat_sum.min(), prob_hat_sum.max()))
probability = normalize(prob_hat)
demand = self.mnl_probabilities.get_demand(index, probability, J) * 1 / sample_rate
demand_history = concatenate((demand_history,
demand[:, newaxis]),
axis=1)
sdratio = safe_array_divide(supply, demand, return_value_if_denominator_is_zero=2.0)
sdratio_matrix = sdratio[index]
## debug info
from numpy import histogram
from opus_core.misc import unique
cc = histogram(index.ravel(), unique(index.ravel()))[0]
logger.log_status( "=================================================================")
logger.log_status( "Probability min: %s, max: %s" % (probability.min(), probability.max()) )
logger.log_status( "Demand min: %s, max: %s" % (demand.min(), demand.max()) )
logger.log_status( "sdratio min: %s, max: %s" % (sdratio.min(), sdratio.max()) )
logger.log_status( "demand[sdratio==sdratio.min()]=%s" % demand[sdratio==sdratio.min()] )
logger.log_status( "demand[sdratio==sdratio.max()]=%s" % demand[sdratio==sdratio.max()] )
logger.log_status( "Counts of unique submarkets in alternatives min: %s, max: %s" % (cc.min(), cc.max()) )
logger.log_status( "=================================================================")
constrained_locations_matrix, omega, info = self.inner_loop(supply, demand, probability,
index, sdratio_matrix,
J, max_iteration=max_iter)
inner_iterations, constrained_locations_history, swing_index, average_omega_history = info
for idx in swing_index:
logger.log_status("swinging alt with id %s set to %s" % (alt_id[idx], constraint_dict[swing_cases_fix]))
constrained_locations_matrix[index==idx] = swing_cases_fix
if swing_index.size > 0:
info_file.write("swing of constraints found with id %s \n" % alt_id[swing_index])
info_file.write("outer_iteration, %i, " % i + ", ".join([str(i)]*(len(inner_iterations))) + "\n")
info_file.write("inner_iteration, , " + ", ".join(inner_iterations) + "\n")
info_file.write("id, sdratio, " + ", ".join(["avg_omega"]*len(inner_iterations)) + "\n")
for idx in swing_index:
line = str(alt_id[idx]) + ','
line += str(sdratio[idx]) + ','
line += ",".join([str(x) for x in average_omega_history[idx,]])
line += "\n"
info_file.write(line)
info_file.write("\n")
info_file.flush()
outer_iterations = [str(i)] * len(inner_iterations)
prob_min = [str(probability.min())] * len(inner_iterations)
prob_max = [str(probability.max())] * len(inner_iterations)
pi_new = self.mnl_probabilities.get_pi(sdratio_matrix, omega, constrained_locations_matrix)
data[:,:,-1] = ln(pi_new)
#diagnostic output
if not ma.allclose(pi, pi_new, atol=CLOSE):
if i > 0: #don't print this for the first iteration
logger.log_status("min of abs(pi(l+1) - pi(l)): %s" % absolute(pi_new - pi).min())
logger.log_status("max of abs(pi(l+1) - pi(l)): %s" % absolute(pi_new - pi).max())
logger.log_status("mean of pi(l+1) - pi(l): %s" % (pi_new - pi).mean())
logger.log_status('Standard Deviation pi(l+1) - pi(l): %s' % standard_deviation(pi_new - pi))
logger.log_status('correlation of pi(l+1) and pi(l): %s' % corr(pi_new.ravel(), pi.ravel())[0,1])
pi = pi_new
probability_old = probability # keep probability of the previous loop, for statistics computation only
else: #convergence criterion achieved, quiting outer loop
logger.log_status("pi(l) == pi(l+1): Convergence criterion achieved")
info_file.write("\nConstrained Locations History:\n")
info_file.write("outer_iteration," + ",".join(outer_iterations) + "\n")
info_file.write("inner_iteration," + ",".join(inner_iterations) + "\n")
info_file.write("minimum_probability," + ",".join(prob_min) + "\n")
info_file.write("maximum_probability," + ",".join(prob_max) + "\n")
for row in range(J):
line = [str(x) for x in constrained_locations_history[row,]]
info_file.write(str(alt_id[row]) + "," + ",".join(line) + "\n")
info_file.flush()
info_file.write("\nDemand History:\n")
i_str = [str(x) for x in range(i)]
info_file.write("outer_iteration, (movers)," + ",".join(i_str) + "\n")
#info_file.write(", ,\n")
for row in range(J):
line = [str(x) for x in demand_history[row,]]
info_file.write(str(alt_id[row]) + "," + ",".join(line) + "\n")
demand_history_info_criteria = [500, 100, 50, 20]
for criterion in demand_history_info_criteria:
com_rows_index = where(movers <= criterion)[0]
info_file.write("\nDemand History for alternatives with less than or equal to %s movers in 1998:\n" % criterion)
i_str = [str(x) for x in range(i)]
info_file.write("outer_iteration, (movers)," + ",".join(i_str) + "\n")
#info_file.write(", movers,\n")
for row in com_rows_index:
line = [str(x) for x in demand_history[row,]]
info_file.write(str(alt_id[row]) + "," + ",".join(line) + "\n")
#import pdb; pdb.set_trace()
#export prob correlation history
correlation_indices, prob_correlation = self.compute_prob_correlation(probability_old, probability, prob_hat, index, resources)
info_file.write("\nCorrelation of Probabilities:\n")
c_name = ['corr(p_ij p~_ij)', 'corr(p_ij p^_ij)', 'corr(p_ij dummy)', 'corr(p~_ij p^_ij)', 'corr(p~_ij dummy)', 'corr(p^_ij dummy)']
info_file.write("com_id, " + ",".join(c_name) + "\n")
#info_file.write(", ,\n")
for row in range(correlation_indices.size):
line = [str(x) for x in prob_correlation[row,]]
info_file.write(str(alt_id[correlation_indices[row]]) + "," + ",".join(line) + "\n")
info_file.close()
result['pi'] = pi
return result
logger.end_block()
try:info_file.close()
except:pass
raise RuntimeError, "max iteration reached without convergence."
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
def compute(self, dataset_pool):
return ln(self.get_dataset().get_attribute(self.income))
def run(self, data, upc_sequence, resources=None):
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
N, neqs, V = data.shape
max_iter = resources.get("max_iterations", 100) # default
sc = SessionConfiguration()
dataset_pool = sc.get_dataset_pool()
sample_rate = dataset_pool.get_dataset("sample_rate")
CLOSE = sc["CLOSE"]
info_filename = sc["info_file"]
info_filename = os.path.join('.', info_filename)
info_file = open(info_filename, "a")
constraint_dict = {1: 'constrained', 0: 'unconstrained'}
swing_cases_fix = 0 #set swing alternatives to constrained (1) or unconstrained (0)
prob_correlation = None
choice_set = resources['_model_'].choice_set
J = choice_set.size()
alt_id = choice_set.get_id_attribute()
movers = choice_set.get_attribute('movers')
resources.check_obligatory_keys(["capacity_string"])
supply = choice_set.get_attribute(resources["capacity_string"])
index = resources.get("index", None)
if index is None: # no sampling case, alternative set is the full choice_set
index = arange(J)
if index.ndim <= 1:
index = repeat(index[newaxis, :], N, axis=0)
if resources.get('aggregate_to_dataset', None):
aggregate_dataset = dataset_pool.get_dataset(
resources.get('aggregate_to_dataset'))
choice_set_aggregate_id = choice_set.get_attribute(
aggregate_dataset.get_id_name()[0])
index = aggregate_dataset.get_id_index(
choice_set_aggregate_id[index].ravel()).reshape(index.shape)
supply = aggregate_dataset.get_attribute(
resources["capacity_string"])
J = aggregate_dataset.size()
movers = aggregate_dataset.get_attribute("movers")
demand_history = movers[:, newaxis]
resources.merge({"index": index})
pi = ones(index.shape, dtype=float32) #initialize pi
#average_omega = ones(J,dtype=float32) #initialize average_omega
logger.start_block('Outer Loop')
for i in range(max_iter):
logger.log_status('Outer Loop Iteration %s' % i)
result = self.bhhh_estimation.run(data, modified_upc_sequence,
resources)
del self.bhhh_estimation
collect()
self.bhhh_estimation = bhhh_mnl_estimation()
probability = modified_upc_sequence.get_probabilities()
if data.shape[2] == V: #insert a placeholder for ln(pi) in data
data = concatenate((data, ones((N, neqs, 1), dtype=float32)),
axis=2)
coef_names = resources.get("coefficient_names")
coef_names = concatenate((coef_names, array(["ln_pi"])))
resources.merge({"coefficient_names": coef_names})
else:
beta_ln_pi = result['estimators'][where(
coef_names == 'ln_pi')][0]
logger.log_status("mu = 1/%s = %s" %
(beta_ln_pi, 1 / beta_ln_pi))
prob_hat = safe_array_divide(probability, pi**beta_ln_pi)
#prob_hat = safe_array_divide(probability, pi)
prob_hat_sum = prob_hat.sum(axis=1, dtype=float32)
if not ma.allclose(prob_hat_sum, 1.0):
logger.log_status(
"probability doesn't sum up to 1, with minimum %s, and maximum %s"
% (prob_hat_sum.min(), prob_hat_sum.max()))
probability = normalize(prob_hat)
demand = self.mnl_probabilities.get_demand(index, probability,
J) * 1 / sample_rate
demand_history = concatenate((demand_history, demand[:, newaxis]),
axis=1)
sdratio = safe_array_divide(
supply, demand, return_value_if_denominator_is_zero=2.0)
sdratio_matrix = sdratio[index]
## debug info
from numpy import histogram
from opus_core.misc import unique
cc = histogram(index.ravel(), unique(index.ravel()))[0]
logger.log_status(
"================================================================="
)
logger.log_status("Probability min: %s, max: %s" %
(probability.min(), probability.max()))
logger.log_status("Demand min: %s, max: %s" %
(demand.min(), demand.max()))
logger.log_status("sdratio min: %s, max: %s" %
(sdratio.min(), sdratio.max()))
logger.log_status("demand[sdratio==sdratio.min()]=%s" %
demand[sdratio == sdratio.min()])
logger.log_status("demand[sdratio==sdratio.max()]=%s" %
demand[sdratio == sdratio.max()])
logger.log_status(
"Counts of unique submarkets in alternatives min: %s, max: %s"
% (cc.min(), cc.max()))
logger.log_status(
"================================================================="
)
constrained_locations_matrix, omega, info = self.inner_loop(
supply,
demand,
probability,
index,
sdratio_matrix,
J,
max_iteration=max_iter)
inner_iterations, constrained_locations_history, swing_index, average_omega_history = info
for idx in swing_index:
logger.log_status(
"swinging alt with id %s set to %s" %
(alt_id[idx], constraint_dict[swing_cases_fix]))
constrained_locations_matrix[index == idx] = swing_cases_fix
if swing_index.size > 0:
info_file.write("swing of constraints found with id %s \n" %
alt_id[swing_index])
info_file.write("outer_iteration, %i, " % i +
", ".join([str(i)] *
(len(inner_iterations))) + "\n")
info_file.write("inner_iteration, , " +
", ".join(inner_iterations) + "\n")
info_file.write("id, sdratio, " +
", ".join(["avg_omega"] *
len(inner_iterations)) + "\n")
for idx in swing_index:
line = str(alt_id[idx]) + ','
line += str(sdratio[idx]) + ','
line += ",".join(
[str(x) for x in average_omega_history[idx, ]])
line += "\n"
info_file.write(line)
info_file.write("\n")
info_file.flush()
outer_iterations = [str(i)] * len(inner_iterations)
prob_min = [str(probability.min())] * len(inner_iterations)
prob_max = [str(probability.max())] * len(inner_iterations)
pi_new = self.mnl_probabilities.get_pi(
sdratio_matrix, omega, constrained_locations_matrix)
data[:, :, -1] = ln(pi_new)
#diagnostic output
if not ma.allclose(pi, pi_new, atol=CLOSE):
if i > 0: #don't print this for the first iteration
logger.log_status("min of abs(pi(l+1) - pi(l)): %s" %
absolute(pi_new - pi).min())
logger.log_status("max of abs(pi(l+1) - pi(l)): %s" %
absolute(pi_new - pi).max())
logger.log_status("mean of pi(l+1) - pi(l): %s" %
(pi_new - pi).mean())
logger.log_status(
'Standard Deviation pi(l+1) - pi(l): %s' %
standard_deviation(pi_new - pi))
logger.log_status('correlation of pi(l+1) and pi(l): %s' %
corr(pi_new.ravel(), pi.ravel())[0, 1])
pi = pi_new
probability_old = probability # keep probability of the previous loop, for statistics computation only
else: #convergence criterion achieved, quiting outer loop
logger.log_status(
"pi(l) == pi(l+1): Convergence criterion achieved")
info_file.write("\nConstrained Locations History:\n")
info_file.write("outer_iteration," +
",".join(outer_iterations) + "\n")
info_file.write("inner_iteration," +
",".join(inner_iterations) + "\n")
info_file.write("minimum_probability," + ",".join(prob_min) +
"\n")
info_file.write("maximum_probability," + ",".join(prob_max) +
"\n")
for row in range(J):
line = [
str(x) for x in constrained_locations_history[row, ]
]
info_file.write(
str(alt_id[row]) + "," + ",".join(line) + "\n")
info_file.flush()
info_file.write("\nDemand History:\n")
i_str = [str(x) for x in range(i)]
info_file.write("outer_iteration, (movers)," +
",".join(i_str) + "\n")
#info_file.write(", ,\n")
for row in range(J):
line = [str(x) for x in demand_history[row, ]]
info_file.write(
str(alt_id[row]) + "," + ",".join(line) + "\n")
demand_history_info_criteria = [500, 100, 50, 20]
for criterion in demand_history_info_criteria:
com_rows_index = where(movers <= criterion)[0]
info_file.write(
"\nDemand History for alternatives with less than or equal to %s movers in 1998:\n"
% criterion)
i_str = [str(x) for x in range(i)]
info_file.write("outer_iteration, (movers)," +
",".join(i_str) + "\n")
#info_file.write(", movers,\n")
for row in com_rows_index:
line = [str(x) for x in demand_history[row, ]]
info_file.write(
str(alt_id[row]) + "," + ",".join(line) + "\n")
#import pdb; pdb.set_trace()
#export prob correlation history
correlation_indices, prob_correlation = self.compute_prob_correlation(
probability_old, probability, prob_hat, index, resources)
info_file.write("\nCorrelation of Probabilities:\n")
c_name = [
'corr(p_ij p~_ij)', 'corr(p_ij p^_ij)', 'corr(p_ij dummy)',
'corr(p~_ij p^_ij)', 'corr(p~_ij dummy)',
'corr(p^_ij dummy)'
]
info_file.write("com_id, " + ",".join(c_name) + "\n")
#info_file.write(", ,\n")
for row in range(correlation_indices.size):
line = [str(x) for x in prob_correlation[row, ]]
info_file.write(
str(alt_id[correlation_indices[row]]) + "," +
",".join(line) + "\n")
info_file.close()
result['pi'] = pi
return result
logger.end_block()
try:
info_file.close()
except:
pass
raise RuntimeError, "max iteration reached without convergence."
def compute(self, dataset_pool):
return ln(self.get_dataset().get_attribute(self.comm_den) + 1) / 10
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
def compute(self, dataset_pool):
return ln(self.get_dataset().get_attribute(self.dep_variable))
def compute(self, dataset_pool):
return ln(self.get_dataset().get_attribute(self.house_den) + 1) / 10
def compute(self, dataset_pool):
return ln(self.get_dataset().get_attribute(self.income))