'repm': [
'real_estate_price_model',
'sanfrancisco.estimation.repm_specification', None
],
"BLDGLCM-nonres": [
"building_location_choice_model",
'sanfrancisco.estimation.bldglcm_specification', "nonresidential"
],
"BLDGLCM-res": [
"building_location_choice_model",
'sanfrancisco.estimation.bldglcm_specification', "residential"
],
}
if __name__ == '__main__':
# model = 'repm'
# model = 'hlcm'
# model = 'blcm'
# model = "BLDGLCM-nonres"
model = "BLDGLCM-res"
config = Baseline()
config.merge(my_configuration)
config['config_changes_for_estimation'] = ConfigChangesForEstimation()
er = EstimationRunner(models[model][0],
specification_module=models[model][1],
model_group=models[model][2],
configuration=config,
save_estimation_results=True)
er.estimate()
# Make you private changes in sanfrancisco.configs.baseline_estimation.
#!!!!!!!!!!!!
from sanfrancisco.estimation.my_estimation_config import my_configuration
from urbansim.estimation.estimation_runner import EstimationRunner
from sanfrancisco.configs.baseline import Baseline
from sanfrancisco.configs.config_changes_for_estimation import ConfigChangesForEstimation
models = {
'hlcm': ['household_location_choice_model', 'sanfrancisco.estimation.hlcm_specification', None],
'blcm': ['business_location_choice_model', 'sanfrancisco.estimation.blcm_specification', None],
'repm': ['real_estate_price_model', 'sanfrancisco.estimation.repm_specification', None],
"BLDGLCM-nonres": ["building_location_choice_model", 'sanfrancisco.estimation.bldglcm_specification', "nonresidential"],
"BLDGLCM-res": ["building_location_choice_model", 'sanfrancisco.estimation.bldglcm_specification', "residential"],
}
if __name__ == '__main__':
# model = 'repm'
# model = 'hlcm'
# model = 'blcm'
# model = "BLDGLCM-nonres"
model = "BLDGLCM-res"
config = Baseline()
config.merge(my_configuration)
config['config_changes_for_estimation'] = ConfigChangesForEstimation()
er = EstimationRunner(models[model][0], specification_module=models[model][1], model_group=models[model][2],
configuration=config, save_estimation_results=True)
er.estimate()
