def test_read_write_boxes(self):
scope = Scope(package_file('model', 'tests', 'road_test.yaml'))
db = SQLiteDB()
scope.store_scope(db)
s1 = Box(name="Speedy", scope=scope)
s1.set_upper_bound('build_travel_time', 70)
s2 = Box(name="Notable", scope=scope, parent="Speedy")
s2.set_lower_bound('expand_capacity', 20)
u = Boxes(s1, s2, scope=scope)
db.write_boxes(u)
scope2 = Scope(package_file('model', 'tests', 'road_test.yaml'))
u2 = db.read_boxes(scope=scope2)
assert u == u2
assert u["Notable"].parent_box_name == u2["Notable"].parent_box_name
s1_ = db.read_box(scope.name, "Speedy")
s2_ = db.read_box(scope.name, "Notable")
assert s1 == s1_
assert s2 == s2_
assert s1.relevant_features == s1_.relevant_features
assert s2.relevant_features == s2_.relevant_features
def test_correlated_latin_hypercube_conflict(self):
scope_file = emat.package_file("model", "tests", "road_test_corr_conflict.yaml")
scp = Scope(scope_file)
with pytest.raises(emat.exceptions.AsymmetricCorrelationError):
scp.design_experiments(
n_samples_per_factor=10,
random_seed=1234,
sampler='lhs',
)
def test_correlated_latin_hypercube_bad(self):
scope_file = emat.package_file("model", "tests", "road_test_corr_bad.yaml")
scp = Scope(scope_file)
with pytest.raises(np.linalg.LinAlgError):
scp.design_experiments(
n_samples_per_factor=10,
random_seed=1234,
sampler='lhs',
)
def test_dump_scope(self):
scp = Scope(self.scope_file)
dumped = scp.dump()
# print("="*40)
# print(dumped)
# print("="*40)
loaded = Scope(scope_def=dumped, scope_file="fake/filename.yaml")
assert loaded == scp # filename is intentionally different but let it go
# but everything else is the same
assert loaded.name == scp.name
assert loaded.get_measures() == scp.get_measures()
assert loaded.get_parameters() == scp.get_parameters()
assert loaded.scope_file != scp.scope_file
assert loaded.scope_file == "fake/filename.yaml"
# fix name, still get equality
loaded.scope_file = scp.scope_file
assert loaded == scp
def test_nonuniform_latin_hypercube(self):
scope_file = emat.package_file("model", "tests", "road_test_nonuniform.yaml")
scp = Scope(scope_file)
exp_def = scp.design_experiments(
n_samples_per_factor=1000,
random_seed=1234,
sampler='lhs',
)
assert len(exp_def) == scp.n_sample_factors() * 1000
assert (exp_def['free_flow_time'] == 60).all()
assert (exp_def['initial_capacity'] == 100).all()
assert np.corrcoef([exp_def.alpha, exp_def.beta])[0, 1] == approx(0.75, rel=0.05)
assert np.corrcoef([exp_def.alpha, exp_def.expand_capacity])[0, 1] == approx(0.0, abs=0.02)
assert np.corrcoef([exp_def.input_flow, exp_def.value_of_time])[0, 1] == approx(-0.5, rel=0.05)
assert np.corrcoef([exp_def.unit_cost_expansion, exp_def.value_of_time])[0, 1] == approx(0.9, rel=0.05)
assert exp_def.interest_rate_lock.sum() == approx(len(exp_def) * 0.2)
assert np.percentile(exp_def.alpha,np.linspace(0,100,50)) == approx(
[0.10037393, 0.10722119, 0.10994485, 0.11204394, 0.11383709,
0.11544182, 0.11691345, 0.11829399, 0.11959909, 0.12084863,
0.12205279, 0.12321800, 0.12435285, 0.12546474, 0.12655958,
0.12763503, 0.12869873, 0.12975137, 0.13079620, 0.13183375,
0.13287082, 0.13390854, 0.13494651, 0.13598528, 0.13703149,
0.13808180, 0.13914411, 0.14021784, 0.14130323, 0.14240609,
0.14352608, 0.14466756, 0.14583411, 0.14702908, 0.14825720,
0.14951875, 0.15082844, 0.15218376, 0.15359963, 0.15508120,
0.15664534, 0.15831425, 0.16010073, 0.16203921, 0.16418886,
0.16662357, 0.16946999, 0.17301416, 0.17804383, 0.19662857])
assert np.percentile(exp_def.beta,np.linspace(0,100,50)) == approx(
[3.51654751, 3.72503059, 3.82437701, 3.90088124, 3.96222432,
4.01360346, 4.06112277, 4.10640347, 4.14456476, 4.18084719,
4.21812584, 4.24926944, 4.28049053, 4.31181127, 4.34390502,
4.37561590, 4.40541815, 4.43276143, 4.45517485, 4.48062290,
4.50726296, 4.53334164, 4.55737738, 4.57893875, 4.60371011,
4.62590595, 4.64885523, 4.67335218, 4.69475909, 4.71546469,
4.73676622, 4.75796550, 4.77690613, 4.79738177, 4.81947505,
4.84481408, 4.86954326, 4.89379651, 4.91771359, 4.94100213,
4.97169370, 5.00298714, 5.03525103, 5.07100437, 5.11028866,
5.15061419, 5.19925284, 5.24775527, 5.32086547, 5.49345120])
assert np.percentile(exp_def.input_flow,np.linspace(0,100,50)) == approx(
[ 80.06332381, 83.71770678, 85.93721426, 87.82355332,
89.52967083, 91.11148891, 92.60789787, 94.03509345,
95.41186604, 96.74598771, 98.04479355, 99.31122788,
100.55462827, 101.77808514, 102.98041937, 104.16517008,
105.33959643, 106.50165383, 107.65366600, 108.79827639,
109.93328795, 111.06707367, 112.19316408, 113.31493454,
114.43982739, 115.56182157, 116.68454749, 117.80620837,
118.93524815, 120.06752762, 121.20470208, 122.34781469,
123.50083542, 124.66086016, 125.83390291, 127.02146142,
128.22485477, 129.44681602, 130.68613841, 131.95601658,
133.25403807, 134.58951122, 135.96442305, 137.39295642,
138.89092217, 140.47204147, 142.17835057, 144.06540067,
146.28064479, 149.94588322])
def test_box_universe(self):
scope = Scope(package_file('model', 'tests', 'road_test.yaml'))
s = Box(name="Speedy", scope=scope)
s.set_upper_bound('build_travel_time', 70)
s2 = Box(name="Notable", scope=scope, parent="Speedy")
s2.set_lower_bound('expand_capacity', 20)
u = Boxes(s, s2, scope=scope)
assert u.fancy_names() == [
'Scope: EMAT Road Test', '▶ Speedy', '▷ ▶ Notable'
]
assert u.plain_names() == [None, 'Speedy', 'Notable']
def test_correlated_monte_carlo(self):
scope_file = emat.package_file("model", "tests", "road_test_corr.yaml")
scp = Scope(scope_file)
exp_def = scp.design_experiments(
n_samples_per_factor=100,
random_seed=1234,
sampler='mc',
)
assert len(exp_def) == scp.n_sample_factors() * 100
assert (exp_def['free_flow_time'] == 60).all()
assert (exp_def['initial_capacity'] == 100).all()
assert np.corrcoef([exp_def.alpha, exp_def.beta])[0, 1] == approx(0.75, rel=0.05)
assert np.corrcoef([exp_def.alpha, exp_def.expand_capacity])[0, 1] == approx(0.0, abs=0.05)
assert np.corrcoef([exp_def.input_flow, exp_def.value_of_time])[0, 1] == approx(-0.5, rel=0.05)
assert np.corrcoef([exp_def.unit_cost_expansion, exp_def.value_of_time])[0, 1] == approx(0.9, rel=0.05)
def test_box(self):
scope = Scope(package_file('model', 'tests', 'road_test.yaml'))
with pytest.raises(TypeError):
s = Box(scope=scope)
s = Box(name="Speedy", scope=scope)
s.set_upper_bound('build_travel_time', 70)
with pytest.raises(ScopeError):
s.set_upper_bound('not_a_thing', 70)
assert len(s) == 1
assert 'build_travel_time' in s
assert s.parent_box_name is None
s2 = Box(name="Notable", scope=scope, parent="Speedy")
s2.set_lower_bound('expand_capacity', 20)
assert len(s2) == 1
assert 'build_travel_time' not in s2
assert s2.parent_box_name == 'Speedy'
def test_read_write_box(self):
scope = Scope(package_file('model', 'tests', 'road_test.yaml'))
db = SQLiteDB()
scope.store_scope(db)
s1 = Box(name="Speedy", scope=scope)
s1.set_upper_bound('build_travel_time', 70)
s1.relevant_features.add('debt_type')
s2 = Box(name="Notable", scope=scope, parent="Speedy")
s2.set_lower_bound('expand_capacity', 20)
db.write_box(s1)
db.write_box(s2)
s1_ = db.read_box(scope.name, "Speedy")
s2_ = db.read_box(scope.name, "Notable")
assert s1 == s1_
assert s2 == s2_
assert s1.thresholds == s1_.thresholds
assert s2.thresholds == s2_.thresholds
assert s1.relevant_features == s1_.relevant_features
assert s2.relevant_features == s2_.relevant_features
class TestCoreModelMethods(unittest.TestCase):
'''
tests model and meta-model methods
'''
corem_scope_file = emat.package_file("model", "tests",
"core_model_test.yaml")
scp = Scope(corem_scope_file)
corem = PythonCoreModel(
function=Dummy(),
configuration={
'archive_path':
config.get_subdir('test_directory', 'core_dummy_archive')
},
scope=scp,
)
# =============================================================================
#
# Core model tests
#
# =============================================================================
def test_create_scenario(self):
exp_def = {'Auto IVTT Sensitivity': 0.9122442817924445}
self.corem.setup(exp_def)
@pytest.mark.skip(reason="TODO")
def test_set_invalid_exp_variable(self):
exp_def = {'unsupported': 1}
with self.assertRaises(KeyError):
self.corem.setup(exp_def)
@pytest.mark.skip(reason="TODO")
def test_post_process(self):
exp_def = {'Land Use - CBD Focus': 1}
pm = ['Region-wide VMT']
self.corem.post_process(exp_def, pm)
@pytest.mark.skip(reason="TODO")
def test_archive_model(self):
exp_id = 1
archive_path = self.corem.get_exp_archive_path(self.scp.scp_name,
exp_id)
self.corem.archive(archive_path)
@pytest.mark.skip(reason="TODO")
def atest_hook_presence(self):
''' confirm that hooks are present for all performance measures, exp vars'''
# TODO
# set experiment variables
# post process
# import performance measure
@pytest.mark.skip(reason="TODO")
def test_pm_import(self):
pm = [
'Peak Walk-to-transit Boarding', 'Total LRT Boardings',
"PM Trip Time (minutes)", "Daily Transit Share",
"Households within 30 min of CBD",
"Number of Home-based work tours taking <= 45 minutes via transit",
"Downtown to Airport Travel Time", 'OD Volume District 1 to 1',
'190 Daily VHT'
]
pm_vals = self.corem.import_perf_meas(pm)
expected_pm = {
'Peak Walk-to-transit Boarding': 56247.88692999999,
'Total LRT Boardings': 24784.475588,
"PM Trip Time (minutes)": 15.652833,
"Daily Transit Share": 0.019905000000000003,
"Households within 30 min of CBD": 379894,
"Number of Home-based work tours taking <= 45 minutes via transit":
322069.75,
"Downtown to Airport Travel Time": 14.734342999999999,
'OD Volume District 1 to 1': 55642.74609400001,
'190 Daily VHT': 272612.499025
}
self.assertEqual(expected_pm, pm_vals)
class TestMetaModelMethods(unittest.TestCase):
'''
tests model and meta-model methods
'''
metam_scope_file = emat.package_file("model","tests","metam_test.yaml")
metam_scp = Scope(metam_scope_file)
# =============================================================================
#
# Meta model tests
#
# =============================================================================
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_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_exogenously_stratified_k_fold(self):
from emat.learn.splits import ExogenouslyStratifiedKFold
X = np.arange(20)
Y = np.asarray([1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1])
S = np.asarray([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0])
correct = [np.array([0, 1, 2, 14, 15]),
np.array([3, 4, 5, 16]),
np.array([6, 7, 8, 17]),
np.array([9, 10, 11, 18]),
np.array([12, 13, 19])]
for j, (_, k) in zip(correct, ExogenouslyStratifiedKFold(n_splits=5, exo_data=S).split(X, Y)):
assert np.array_equal(j, k)
class TestMetaModelMethods(unittest.TestCase):
'''
tests model and meta-model methods
'''
metam_scope_file = emat.package_file("model", "tests", "metam_test.yaml")
metam_scp = Scope(metam_scope_file)
# =============================================================================
#
# Meta model tests
#
# =============================================================================
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 = m.create_metamodel_from_design('tiny', random_state=123)
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'
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')
assert dict(mm.read_experiment_measures('tiny2').iloc[0]) == approx({
'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 m.function(**(tiny2.iloc[0])) == approx({
'no_build_travel_time':
89.07004237532217,
'build_travel_time':
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,
'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_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 = m.create_metamodel_from_design('tiny', random_state=123)
assert mm.scope != m.scope # now not equal as road_test2 has transforms that are stripped.
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)
class TestExperimentMethods(unittest.TestCase):
'''
tests generating experiments
'''
#
# one time test setup
#
scope_file = emat.package_file("scope", "tests", "scope_test.yaml")
scp = Scope(scope_file)
db_test = SQLiteDB(":memory:", initialize=True)
scp.store_scope(db_test)
def test_latin_hypercube(self):
exp_def = self.scp.design_experiments(
n_samples_per_factor=10,
random_seed=1234,
sampler='lhs',
db=self.db_test,
)
assert len(exp_def) == self.scp.n_sample_factors() * 10
assert (exp_def['TestRiskVar'] == 1.0).all()
assert (exp_def['Land Use - CBD Focus']).mean() == approx(1.0326,
abs=1e-3)
assert (exp_def['Freeway Capacity']).mean() == approx(1.5, abs=1e-3)
exp_def2 = self.db_test.read_experiment_parameters(
self.scp.name, 'lhs')
assert (exp_def[exp_def2.columns] == exp_def2).all().all()
def test_latin_hypercube_not_joint(self):
exp_def = self.scp.design_experiments(
n_samples_per_factor=5,
random_seed=1234,
sampler='lhs',
db=self.db_test,
jointly=False,
design_name='lhs_not_joint',
)
# assert len(exp_def) == len(self.scp.get_uncertainties())*5 * len(self.scp.get_levers())*5
assert len(
exp_def
) == 80 # there are only 4 unique policies, times 2 scenarios
assert (exp_def['TestRiskVar'] == 1.0).all()
assert (exp_def['Land Use - CBD Focus']).mean() == approx(1.033,
abs=1e-2)
assert (exp_def['Freeway Capacity']).mean() == approx(1.5, abs=1e-2)
exp_def2 = self.db_test.read_experiment_parameters(
self.scp.name, 'lhs_not_joint')
assert (exp_def[exp_def2.columns] == exp_def2).all().all()
def test_monte_carlo(self):
exp_def = self.scp.design_experiments(
n_samples_per_factor=10,
random_seed=1234,
sampler='mc',
db=self.db_test,
)
assert len(exp_def) == self.scp.n_sample_factors() * 10
assert (exp_def['TestRiskVar'] == 1.0).all()
assert (exp_def['Land Use - CBD Focus']).mean() == approx(1.0326,
abs=0.01)
assert (exp_def['Freeway Capacity']).mean() == approx(1.5, abs=0.01)
exp_def2 = self.db_test.read_experiment_parameters(self.scp.name, 'mc')
assert (exp_def[exp_def2.columns] == exp_def2).all().all()
def test_sensitivity_tests(self):
exp_def = self.scp.design_experiments(
sampler='uni',
db=self.db_test,
)
cols = [
'TestRiskVar', 'Land Use - CBD Focus', 'Freeway Capacity',
'Auto IVTT Sensitivity', 'Shared Mobility',
'Kensington Decommissioning', 'LRT Extension'
]
correct = '{"TestRiskVar":{"0":1.0,"1":1.0,"2":1.0,"3":1.0,"4":1.0,"5":1.0,"6":1.0,"7":1.0},' \
'"Land Use - CBD Focus":{"0":1.0,"1":0.82,"2":1.37,"3":1.0,"4":1.0,"5":1.0,"6":1.0,"7":1.0},' \
'"Freeway Capacity":{"0":1.0,"1":1.0,"2":1.0,"3":2.0,"4":1.0,"5":1.0,"6":1.0,"7":1.0},' \
'"Auto IVTT Sensitivity":{"0":1.0,"1":1.0,"2":1.0,"3":1.0,"4":0.75,"5":1.0,"6":1.0,"7":1.0},' \
'"Shared Mobility":{"0":0.0,"1":0.0,"2":0.0,"3":0.0,"4":0.0,"5":1.0,"6":0.0,"7":0.0},' \
'"Kensington Decommissioning":{"0":false,"1":false,"2":false,"3":false,"4":false,' \
'"5":false,"6":true,"7":false},"LRT Extension":{"0":false,"1":false,"2":false,"3":false,' \
'"4":false,"5":false,"6":false,"7":true}}'
correct = pd.read_json(correct)
for k in cols:
assert (exp_def[k].values == approx(correct[k].values))
exp_def2 = self.db_test.read_experiment_parameters(
self.scp.name, 'uni')
for k in cols:
assert (exp_def2[k].values == approx(correct[k].values))
def test_nonuniform_latin_hypercube(self):
scope_file = emat.package_file("model", "tests",
"road_test_nonuniform.yaml")
scp = Scope(scope_file)
exp_def = scp.design_experiments(
n_samples_per_factor=1000,
random_seed=1234,
sampler='lhs',
)
assert len(exp_def) == scp.n_sample_factors() * 1000
assert (exp_def['free_flow_time'] == 60).all()
assert (exp_def['initial_capacity'] == 100).all()
assert np.corrcoef([exp_def.alpha,
exp_def.beta])[0, 1] == approx(0.75, rel=0.05)
assert np.corrcoef([exp_def.alpha,
exp_def.expand_capacity])[0, 1] == approx(0.0,
abs=0.02)
assert np.corrcoef([exp_def.input_flow,
exp_def.value_of_time])[0, 1] == approx(-0.5,
rel=0.05)
assert np.corrcoef(
[exp_def.unit_cost_expansion,
exp_def.value_of_time])[0, 1] == approx(0.9, rel=0.05)
assert exp_def.interest_rate_lock.sum() == approx(len(exp_def) * 0.2)
assert np.percentile(exp_def.alpha, np.linspace(0, 100, 50)) == approx(
[
0.10037393, 0.10722119, 0.10994485, 0.11204394, 0.11383709,
0.11544182, 0.11691345, 0.11829399, 0.11959909, 0.12084863,
0.12205279, 0.12321800, 0.12435285, 0.12546474, 0.12655958,
0.12763503, 0.12869873, 0.12975137, 0.13079620, 0.13183375,
0.13287082, 0.13390854, 0.13494651, 0.13598528, 0.13703149,
0.13808180, 0.13914411, 0.14021784, 0.14130323, 0.14240609,
0.14352608, 0.14466756, 0.14583411, 0.14702908, 0.14825720,
0.14951875, 0.15082844, 0.15218376, 0.15359963, 0.15508120,
0.15664534, 0.15831425, 0.16010073, 0.16203921, 0.16418886,
0.16662357, 0.16946999, 0.17301416, 0.17804383, 0.19662857
])
assert np.percentile(exp_def.beta, np.linspace(0, 100, 50)) == approx([
3.51654751, 3.72503059, 3.82437701, 3.90088124, 3.96222432,
4.01360346, 4.06112277, 4.10640347, 4.14456476, 4.18084719,
4.21812584, 4.24926944, 4.28049053, 4.31181127, 4.34390502,
4.37561590, 4.40541815, 4.43276143, 4.45517485, 4.48062290,
4.50726296, 4.53334164, 4.55737738, 4.57893875, 4.60371011,
4.62590595, 4.64885523, 4.67335218, 4.69475909, 4.71546469,
4.73676622, 4.75796550, 4.77690613, 4.79738177, 4.81947505,
4.84481408, 4.86954326, 4.89379651, 4.91771359, 4.94100213,
4.97169370, 5.00298714, 5.03525103, 5.07100437, 5.11028866,
5.15061419, 5.19925284, 5.24775527, 5.32086547, 5.49345120
])
assert np.percentile(exp_def.input_flow, np.linspace(
0, 100, 50)) == approx([
80.06332381, 83.71770678, 85.93721426, 87.82355332,
89.52967083, 91.11148891, 92.60789787, 94.03509345,
95.41186604, 96.74598771, 98.04479355, 99.31122788,
100.55462827, 101.77808514, 102.98041937, 104.16517008,
105.33959643, 106.50165383, 107.65366600, 108.79827639,
109.93328795, 111.06707367, 112.19316408, 113.31493454,
114.43982739, 115.56182157, 116.68454749, 117.80620837,
118.93524815, 120.06752762, 121.20470208, 122.34781469,
123.50083542, 124.66086016, 125.83390291, 127.02146142,
128.22485477, 129.44681602, 130.68613841, 131.95601658,
133.25403807, 134.58951122, 135.96442305, 137.39295642,
138.89092217, 140.47204147, 142.17835057, 144.06540067,
146.28064479, 149.94588322
])
def test_null_scope(self):
scp = Scope(None)
assert repr(scp) == "<emat.Scope with no content>"
assert len(scp.get_measures()) == 0
assert len(scp.get_parameters()) == 0
def test_save_scope(self):
scp = Scope(self.scope_file)
scp.store_scope(self.db_test)