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")
#"residential_units",
# "avg_income",
#"commercial_sqft",
#"t_max",
# "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")
compute_capacity_flag=True,
capacity_string=vacant_capacity,
number_of_agents_string=number_of_agents,
number_of_units_string="capacity",
run_config={"lottery_max_iterations":10})
seed(1)
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])
})
