def skip_test_bma(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(table_name='dataset',
table_data={
"id":
arange(100) + 1,
"attr1":
concatenate((random.randint(0, 10, 50),
random.randint(20, 40, 50))),
"attr2":
random.ranf(100),
"outcome":
array(50 * [0] + 50 * [1])
})
ds = Dataset(in_storage=storage, in_table_name='dataset', id_name="id")
specification = EquationSpecification(
variables=array(["constant", "attr2", "attr1"]),
coefficients=array(["constant", "ba2", "ba1"]))
filename = 'bma_output.pdf'
model = RegressionModel(
estimate_config={'bma_imageplot_filename': filename})
model.estimate(specification,
ds,
"outcome",
procedure="opus_core.bma_for_linear_regression_r")
def estimate(self,
specification,
dataset,
outcome_attribute="housing_price",
index=None,
procedure="opus_core.estimate_linear_regression",
data_objects=None,
estimate_config=None,
debuglevel=0):
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
if self.filter_attribute <> None:
res = Resources({"debug": debuglevel})
index = dataset.get_filtered_index(self.filter_attribute,
threshold=0,
index=index,
dataset_pool=self.dataset_pool,
resources=res)
return RegressionModel.estimate(self,
specification,
dataset,
outcome_attribute,
index,
procedure,
estimate_config=estimate_config,
debuglevel=debuglevel)
def estimate(self, specification, dataset, outcome_attribute, index=None, **kwargs):
if index is None:
index = arange(dataset.size())
data_objects = kwargs.get("data_objects",{})
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
# filter out agents for this group
new_index = self.group_member.get_index_of_my_agents(dataset, index, dataset_pool=self.dataset_pool)
return RegressionModel.estimate(self, specification, dataset, outcome_attribute,
index=index[new_index], **kwargs)
def estimate(self, specification, dataset, outcome_attribute="urbansim.gridcell.ln_total_land_value", index = None,
procedure="opus_core.estimate_linear_regression", data_objects=None,
estimate_config=None, debuglevel=0):
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
if self.filter <> None:
res = Resources({"debug":debuglevel})
index = dataset.get_filtered_index(self.filter, threshold=0, index=index, dataset_pool=self.dataset_pool,
resources=res)
return RegressionModel.estimate(self, specification, dataset, outcome_attribute, index, procedure,
estimate_config=estimate_config, debuglevel=debuglevel)
def skip_test_bma(self):
storage = StorageFactory().get_storage('dict_storage')
storage.write_table(
table_name='dataset',
table_data={
"id":arange(100)+1,
"attr1":concatenate((random.randint(0,10, 50), random.randint(20,40, 50))),
"attr2":random.ranf(100),
"outcome": array(50*[0]+50*[1])
}
)
ds = Dataset(in_storage=storage, in_table_name='dataset', id_name="id")
specification = EquationSpecification(
variables=array(["constant", "attr2", "attr1"]),
coefficients=array(["constant", "ba2", "ba1"]))
filename = 'bma_output.pdf'
model = RegressionModel(estimate_config={'bma_imageplot_filename': filename})
model.estimate(specification, ds, "outcome", procedure="opus_core.bma_for_linear_regression_r")
def estimate(
self,
specification,
dataset,
outcome_attribute="urbansim.gridcell.logistic_fraction_residential_land",
index=None,
procedure="opus_core.estimate_linear_regression",
data_objects=None,
estimate_config=None,
debuglevel=0):
return RegressionModel.estimate(self,
specification,
dataset,
outcome_attribute,
index,
procedure,
data_objects=data_objects,
estimate_config=estimate_config,
debuglevel=debuglevel)
def estimate(self,
specification,
dataset,
outcome_attribute,
index=None,
**kwargs):
if index is None:
index = arange(dataset.size())
data_objects = kwargs.get("data_objects", {})
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
# filter out agents for this group
new_index = self.group_member.get_index_of_my_agents(
dataset, index, dataset_pool=self.dataset_pool)
return RegressionModel.estimate(self,
specification,
dataset,
outcome_attribute,
index=index[new_index],
**kwargs)
result = hlcm2.run(specification, coefficients, households)
#coef, results = hlcm2.estimate(specification, agent_set=households, debuglevel=1)
#hlcm2.plot_choice_histograms(capacity=locations.get_attribute("capacity"))
households.get_attribute("location")
# Regression model
locations.add_primary_attribute(name="distance_to_cbd", data=[5,10,5,1,20,0,7,7,3])
from opus_core.regression_model import RegressionModel
rm = RegressionModel(regression_procedure="opus_core.linear_regression")
specification = EquationSpecification(
variables=array(["constant", "gridcell.distance_to_cbd"]),
coefficients=array(["constant", "dcbd_coef"]))
coef, other_results = rm.estimate(specification, dataset=locations,
outcome_attribute="gridcell.cost",
procedure="opus_core.estimate_linear_regression")
coef.summary()
dstorage = StorageFactory().get_storage('dict_storage')
dstorage.write_table(
table_name = 'gridcells',
table_data = {'id':array([1,2,3,4]),
'distance_to_cbd':array([2,4,6,8])
})
ds = Dataset(in_storage=dstorage, in_table_name='gridcells',
id_name='id', dataset_name='gridcell')
cost = rm.run(specification, coefficients=coef, dataset=ds)
# "demand_lag1"
# "sum_demand_times_2",
"waterdemand.consumption_re.something_like_sum_demand",
)
)
print "Create a model object"
years = range(2001, 2003)
# single
model = RegressionModel()
print "Estimate coefficients - single"
coefficients, other_est_results = model.estimate(specification, consumption,
outcome_attribute="waterdemand.%s.sum_demand" % consumption_type, # if outcome_attribute is opus_core.func.ln(), the simulation results need to take exp()
index=index_est,
procedure="opus_core.estimate_linear_regression",
data_objects=dataset_pool.datasets_in_pool())
"""Simulate over the set of years."""
for year in years:
print "\nSimulate water demand %s" % year
SimulationState().set_current_time(year)
dataset_pool = SessionConfiguration().get_dataset_pool()
dataset_pool.remove_all_datasets()
gridcells = dataset_pool.get_dataset("gridcell")
#create a ConsumptionDataset instance out of gridcells - simulate water demand for every gridcell
resources = Resources({'data':{
"grid_id":gridcells.get_id_attribute(),
def estimate(self, specification, dataset, outcome_attribute="urbansim.gridcell.logistic_fraction_residential_land", index = None,
procedure="opus_core.estimate_linear_regression", data_objects=None,
estimate_config=None, debuglevel=0):
return RegressionModel.estimate(self, specification, dataset, outcome_attribute, index, procedure, data_objects=data_objects,
estimate_config=estimate_config, debuglevel=debuglevel)
# "demand_lag1"
# "sum_demand_times_2",
"waterdemand.consumption_re.something_like_sum_demand",
)
)
print "Create a model object"
years = range(2001, 2003)
# single
model = RegressionModel()
print "Estimate coefficients - single"
coefficients, other_est_results = model.estimate(specification, consumption,
outcome_attribute="waterdemand.%s.sum_demand" % consumption_type, # if outcome_attribute is opus_core.func.ln(), the simulation results need to take exp()
index=index_est,
procedure="opus_core.estimate_linear_regression",
data_objects=dataset_pool.datasets_in_pool())
"""Simulate over the set of years."""
for year in years:
print "\nSimulate water demand %s" % year
SimulationState().set_current_time(year)
dataset_pool = SessionConfiguration().get_dataset_pool()
dataset_pool.remove_all_datasets()
gridcells = dataset_pool.get_dataset("gridcell")
#create a ConsumptionDataset instance out of gridcells - simulate water demand for every gridcell
resources = Resources({'data':{
"grid_id":gridcells.get_id_attribute(),
result = hlcm2.run(specification, coefficients, households)
#coef, results = hlcm2.estimate(specification, agent_set=households, debuglevel=1)
#hlcm2.plot_choice_histograms(capacity=locations.get_attribute("capacity"))
households.get_attribute("location")
# Regression model
locations.add_primary_attribute(name="distance_to_cbd", data=[5,10,5,1,20,0,7,7,3])
from opus_core.regression_model import RegressionModel
rm = RegressionModel(regression_procedure="opus_core.linear_regression")
specification = EquationSpecification(
variables=array(["constant", "gridcell.distance_to_cbd"]),
coefficients=array(["constant", "dcbd_coef"]))
coef, other_results = rm.estimate(specification, dataset=locations,
outcome_attribute="gridcell.cost",
procedure="opus_core.estimate_linear_regression")
coef.summary()
dstorage = StorageFactory().get_storage('dict_storage')
dstorage.write_table(
table_name = 'gridcells',
table_data = {'id':array([1,2,3,4]),
'distance_to_cbd':array([2,4,6,8])
})
ds = Dataset(in_storage=dstorage, in_table_name='gridcells',
id_name='id', dataset_name='gridcell')
cost = rm.run(specification, coefficients=coef, dataset=ds)