def test_derive_meta_w_transform(self):
from emat.examples import road_test
s, db, m = road_test(yamlfile='road_test2.yaml')
db.get_db_info()
m.design_experiments(n_samples=10, design_name='tiny')
db.read_experiment_all(None, None)
with pytest.raises(emat.PendingExperimentsError):
m.create_metamodel_from_design('tiny', random_state=123)
m.run_experiments(design_name='tiny')
mm = emat.create_metamodel(
m.scope,
db.read_experiment_all(s.name, 'tiny'),
random_state=123,
metamodel_id=db.get_new_metamodel_id(None),
)
assert mm.scope != m.scope # now not equal as road_test2 has transforms that are stripped.
mm.db = db
tiny2 = m.design_experiments(n_samples=10, design_name='tiny2', random_seed=456)
assert tiny2.iloc[0]['debt_type'] == 'GO Bond'
assert dict(tiny2.iloc[0].drop('debt_type')) == approx({
'alpha': 0.10428005571929212,
'amortization_period': 33,
'beta': 4.8792451185772014,
'expand_capacity': 61.4210886403998,
'input_flow': 137,
'interest_rate': 0.03099304322197216,
'interest_rate_lock': 0,
'unit_cost_expansion': 121.85520427974882,
'value_of_time': 0.002953613029133872,
'yield_curve': 0.016255990123028242,
'free_flow_time': 60,
'initial_capacity': 100})
result2 = mm.run_experiments('tiny2')
# print(dict(mm.read_experiment_measures('tiny2').iloc[0]))
#
# print({
# 'no_build_travel_time': 81.6839454971052,
# 'build_travel_time': 61.91038371206646,
# 'time_savings': 44.94189289289446,
# 'value_of_time_savings': 2904.081661408463,
# 'net_benefits': -34.09931528157315,
# 'cost_of_capacity_expansion': 1085.3565091745982,
# 'present_cost_expansion': 19923.66625500023,
# })
#
assert dict(mm.read_experiment_measures('tiny2').iloc[0]) == approx({
'no_build_travel_time': 81.6839454971052,
'build_travel_time': 61.91038371206646,
'log_build_travel_time': 4.120826572003798,
'time_savings': 44.94189289289446,
'value_of_time_savings': 2904.081661408463,
'net_benefits': -34.09931528157315,
'cost_of_capacity_expansion': 1085.3565091745982,
'present_cost_expansion': 19923.66625500023,
})
assert m.run_experiment(tiny2.iloc[0]) == approx({
'no_build_travel_time': 89.07004237532217,
'build_travel_time': 62.81032484779827,
'log_build_travel_time': np.log(62.81032484779827),
'time_savings': 26.259717527523904,
'value_of_time_savings': 10.62586300480175,
'present_cost_expansion': 7484.479303360477,
'cost_of_capacity_expansion': 395.69034710662226,
'net_benefits': -385.0644841018205,
})
with pytest.raises(ValueError):
# no metamodels stored
mm3 = db.read_metamodel(None, None)
db.write_metamodel(mm)
mm2 = db.read_metamodel(None, 1)
mm3 = db.read_metamodel(None, None)
assert mm2 == mm == mm3
assert mm2 is not mm
assert mm2.function(**(tiny2.iloc[0])) == approx({
'no_build_travel_time': 81.6839454971052,
'build_travel_time': 61.91038371206646,
'log_build_travel_time': 4.120826572003798,
'time_savings': 44.94189289289446,
'value_of_time_savings': 2904.081661408463,
'net_benefits': -34.09931528157315,
'cost_of_capacity_expansion': 1085.3565091745982,
'present_cost_expansion': 19923.66625500023,
})
mm3.metamodel_id = db.get_new_metamodel_id(None)
db.write_metamodel(mm3)
with pytest.raises(ValueError):
# now too many to get without giving an ID
mm4 = db.read_metamodel(None, None)
def test_robust_evaluation(self):
# %%
import os
test_dir = os.path.dirname(__file__)
from emat.workbench import ema_logging, MultiprocessingEvaluator, SequentialEvaluator
from emat.examples import road_test
import numpy, pandas, functools
from emat import Measure
s, db, m = road_test()
MAXIMIZE = Measure.MAXIMIZE
MINIMIZE = Measure.MINIMIZE
robustness_functions = [
Measure(
'Expected Net Benefit',
kind=Measure.INFO,
variable_name='net_benefits',
function=numpy.mean,
),
Measure(
'Probability of Net Loss',
kind=MINIMIZE,
variable_name='net_benefits',
function=lambda x: numpy.mean(x < 0),
min=0,
max=1,
),
Measure(
'95%ile Travel Time',
kind=MINIMIZE,
variable_name='build_travel_time',
function=functools.partial(numpy.percentile, q=95),
min=60,
max=150,
),
Measure(
'99%ile Present Cost',
kind=Measure.INFO,
variable_name='present_cost_expansion',
function=functools.partial(numpy.percentile, q=99),
),
Measure(
'Expected Present Cost',
kind=Measure.INFO,
variable_name='present_cost_expansion',
function=numpy.mean,
),
]
# %%
numpy.random.seed(42)
os.chdir(test_dir)
with SequentialEvaluator(m) as evaluator:
r1 = m.robust_evaluate(
robustness_functions,
scenarios=20,
policies=5,
evaluator=evaluator,
)
stable_df('./road_test_robust_evaluate.pkl.gz', r1)
numpy.random.seed(7)
from emat.workbench.em_framework.samplers import sample_uncertainties
scenes = sample_uncertainties(m, 20)
scenes0 = pandas.DataFrame(scenes)
stable_df('./test_robust_evaluation_scenarios.pkl.gz', scenes0)
from emat import Constraint
constraint_1 = Constraint(
"Maximum Log Expected Present Cost",
outcome_names="Expected Present Cost",
function=Constraint.must_be_less_than(4000),
)
constraint_2 = Constraint(
"Minimum Capacity Expansion",
parameter_names="expand_capacity",
function=Constraint.must_be_greater_than(10),
)
constraint_3 = Constraint(
"Maximum Paygo",
parameter_names='debt_type',
outcome_names='99%ile Present Cost',
function=lambda i, j: max(0, j - 1500) if i == 'Paygo' else 0,
)
from emat.optimization import HyperVolume, EpsilonProgress, SolutionViewer, ConvergenceMetrics
convergence_metrics = ConvergenceMetrics(
HyperVolume.from_outcomes(robustness_functions),
EpsilonProgress(),
SolutionViewer.from_model_and_outcomes(m, robustness_functions),
)
numpy.random.seed(8)
random.seed(8)
# Test robust optimize
with SequentialEvaluator(m) as evaluator:
robust = m.robust_optimize(
robustness_functions,
scenarios=scenes,
nfe=25,
constraints=[
constraint_1,
constraint_2,
constraint_3,
],
epsilons=[0.05,]*len(robustness_functions),
convergence=convergence_metrics,
evaluator=evaluator,
)
robust_results, convergence = robust.result, robust.convergence
stable_df('test_robust_results.pkl.gz', robust_results)
def test_derive_meta(self):
from emat.examples import road_test
s, db, m = road_test()
db.get_db_info()
m.design_experiments(n_samples=10, design_name='tiny')
db.read_experiment_all(None, None)
with pytest.raises(emat.PendingExperimentsError):
m.create_metamodel_from_design('tiny', random_state=123)
m.run_experiments(design_name='tiny')
mm = emat.create_metamodel(
m.scope,
db.read_experiment_all(s.name, 'tiny'),
random_state=123,
metamodel_id=db.get_new_metamodel_id(None),
)
mm.db = db # add db after creation to prevent writing it into the db
assert mm.scope == m.scope
tiny2 = m.design_experiments(n_samples=10, design_name='tiny2', random_seed=456)
assert tiny2.iloc[0]['debt_type'] == 'GO Bond'
stable_df('./test_tiny2.pkl.gz',tiny2)
result2 = mm.run_experiments('tiny2')
tiny2out = mm.read_experiment_measures('tiny2')
stable_df('./test_tiny2out.pkl.gz', tiny2out)
with pytest.raises(ValueError):
# no metamodels stored
mm3 = db.read_metamodel(None, None)
db.write_metamodel(mm)
mm2 = db.read_metamodel(None, 1)
mm3 = db.read_metamodel(None, None)
assert mm2 == mm == mm3
assert mm2 is not mm
print(mm2.function(**(tiny2.iloc[0])))
assert mm2.function(**(tiny2.iloc[0])) == approx({
'no_build_travel_time': 83.57502327972276,
'build_travel_time': 62.221693766038015,
'time_savings': 57.612063365257995,
'value_of_time_savings': 3749.2913256457214,
'net_benefits': 395.55020765212254,
'cost_of_capacity_expansion': 1252.6916865286616,
'present_cost_expansion': 23000.275573551233,
})
mm3.metamodel_id = db.get_new_metamodel_id(None)
db.write_metamodel(mm3)
with pytest.raises(ValueError):
# now too many to get without giving an ID
mm4 = db.read_metamodel(None, None)
def test_robust_evaluation(self):
# %%
import os
test_dir = os.path.dirname(__file__)
from ema_workbench import ema_logging, MultiprocessingEvaluator, SequentialEvaluator
from emat.examples import road_test
import numpy, pandas, functools
from emat import Measure
s, db, m = road_test()
MAXIMIZE = Measure.MAXIMIZE
MINIMIZE = Measure.MINIMIZE
robustness_functions = [
Measure(
'Expected Net Benefit',
kind=Measure.INFO,
variable_name='net_benefits',
function=numpy.mean,
),
Measure(
'Probability of Net Loss',
kind=MINIMIZE,
variable_name='net_benefits',
function=lambda x: numpy.mean(x < 0),
min=0,
max=1,
),
Measure(
'95%ile Travel Time',
kind=MINIMIZE,
variable_name='build_travel_time',
function=functools.partial(numpy.percentile, q=95),
min=60,
max=150,
),
Measure(
'99%ile Present Cost',
kind=Measure.INFO,
variable_name='present_cost_expansion',
function=functools.partial(numpy.percentile, q=99),
),
Measure(
'Expected Present Cost',
kind=Measure.INFO,
variable_name='present_cost_expansion',
function=numpy.mean,
),
]
# %%
numpy.random.seed(42)
with MultiprocessingEvaluator(m) as evaluator:
r1 = m.robust_evaluate(
robustness_functions,
scenarios=20,
policies=5,
evaluator=evaluator,
)
import pandas
correct = pandas.read_json(
'{"amortization_period":{"0":19,"1":23,"2":50,"3":43,"4":35},"debt_type":{"0":"Rev Bond","1":"Paygo"'
',"2":"GO Bond","3":"Paygo","4":"Rev Bond"},"expand_capacity":{"0":26.3384401031,"1":63.3898549337,"2'
'":51.1360252492,"3":18.7230954832,"4":93.9205959335},"interest_rate_lock":{"0":false,"1":true,"2":fal'
'se,"3":true,"4":false},"Expected Net Benefit":{"0":-157.486494925,"1":-244.2423401934,"2":-189.633908'
'4553,"3":-4.2656265778,"4":-481.1208898635},"Probability of Net Loss":{"0":0.95,"1":1.0,"2":0.95,"3":'
'0.7,"4":1.0},"95%ile Travel Time":{"0":74.6904209781,"1":65.8492894317,"2":67.6932507947,"3":79.09851'
'23853,"4":63.203313888},"99%ile Present Cost":{"0":3789.8036648358,"1":9121.0832380586,"2":7357.89572'
'71441,"3":2694.0416972887,"4":13514.111590462},"Expected Present Cost":{"0":3158.4461451444,"1":7601.'
'5679809722,"2":6132.1164500957,"3":2245.2312484183,"4":11262.7453643551}}')
correct['debt_type'] = correct['debt_type'].astype(
pandas.CategoricalDtype(categories=['GO Bond', 'Rev Bond', 'Paygo'], ordered=True))
pandas.testing.assert_frame_equal(r1, correct)
numpy.random.seed(7)
from ema_workbench.em_framework.samplers import sample_uncertainties
scenes = sample_uncertainties(m, 20)
scenes0 = pandas.DataFrame(scenes)
cachefile = os.path.join(test_dir,'test_robust_results.csv')
if not os.path.exists(cachefile):
scenes0.to_csv(os.path.join(test_dir,'test_robust_evaluation_scenarios.csv'), index=None)
scenes1 = pandas.read_csv(os.path.join(test_dir,'test_robust_evaluation_scenarios.csv'))
pandas.testing.assert_frame_equal(scenes0, scenes1)
from emat import Constraint
constraint_1 = Constraint(
"Maximum Log Expected Present Cost",
outcome_names="Expected Present Cost",
function=Constraint.must_be_less_than(4000),
)
constraint_2 = Constraint(
"Minimum Capacity Expansion",
parameter_names="expand_capacity",
function=Constraint.must_be_greater_than(10),
)
constraint_3 = Constraint(
"Maximum Paygo",
parameter_names='debt_type',
outcome_names='99%ile Present Cost',
function=lambda i, j: max(0, j - 1500) if i == 'Paygo' else 0,
)
from emat.optimization import HyperVolume, EpsilonProgress, SolutionViewer, ConvergenceMetrics
convergence_metrics = ConvergenceMetrics(
HyperVolume.from_outcomes(robustness_functions),
EpsilonProgress(),
SolutionViewer.from_model_and_outcomes(m, robustness_functions),
)
numpy.random.seed(8)
random.seed(8)
# Test robust optimize
with SequentialEvaluator(m) as evaluator:
robust_results, convergence = m.robust_optimize(
robustness_functions,
scenarios=scenes,
nfe=25,
constraints=[
constraint_1,
constraint_2,
constraint_3,
],
epsilons=[0.05,]*len(robustness_functions),
convergence=convergence_metrics,
evaluator=evaluator,
)
cachefile = os.path.join(test_dir,'test_robust_results.csv')
if not os.path.exists(cachefile):
robust_results.to_csv(cachefile, index=None)
correct2 = pandas.read_csv(cachefile)
correct2['debt_type'] = correct2['debt_type'].astype(
pandas.CategoricalDtype(categories=['GO Bond', 'Rev Bond', 'Paygo'], ordered=True))
pandas.testing.assert_frame_equal(robust_results, correct2, check_less_precise=True)
