def test_error_band_cost_range_equiv(default_cost_err):
covering = datamodel.ErrorBandCost(
bands=[
datamodel.CostBand(
error_range=(-2, 2),
cost_function='constant',
cost_function_parameters=datamodel.ConstantCost(
cost=2.0, aggregation='sum', net=True
)
),
datamodel.CostBand(
error_range=(-np.inf, np.inf),
cost_function='constant',
cost_function_parameters=datamodel.ConstantCost(
cost=4.0, aggregation='sum', net=False
)
)
]
)
split = datamodel.ErrorBandCost(
bands=[
datamodel.CostBand(
error_range=(-2, 2),
cost_function='constant',
cost_function_parameters=datamodel.ConstantCost(
cost=2.0, aggregation='sum', net=True
)
),
datamodel.CostBand(
error_range=(-np.inf, -2),
cost_function='constant',
cost_function_parameters=datamodel.ConstantCost(
cost=4.0, aggregation='sum', net=False
)
),
datamodel.CostBand(
error_range=(2, np.inf),
cost_function='constant',
cost_function_parameters=datamodel.ConstantCost(
cost=4.0, aggregation='sum', net=False
)
)
]
)
fx = default_cost_err
obs = pd.Series(0, index=fx.index)
err_cov = deterministic.error_band_cost(
obs, fx, covering, deterministic.error)
err_split = deterministic.error_band_cost(
obs, fx, split, deterministic.error)
assert err_cov == err_split
def test_invalid_costband(constant_cost, datetime_cost, timeofday_cost,
errorband_cost, a, b):
dd = {'constant': constant_cost, 'timeofday': timeofday_cost,
'datetime': datetime_cost, 'errorband': errorband_cost}
with pytest.raises(TypeError):
datamodel.CostBand(
error_range=(0, 2),
cost_function=a,
cost_function_parameters=dd[b]
)
def test_error_band_cost_out_of_range(default_cost_err):
params = datamodel.ErrorBandCost(
bands=[datamodel.CostBand(
error_range=(10, 100),
cost_function='constant',
cost_function_parameters=datamodel.ConstantCost(
cost=2.0, aggregation='sum', net=True
)
)]
)
fx = default_cost_err
obs = pd.Series(0, index=fx.index)
err = deterministic.error_band_cost(
obs, fx, params, deterministic.error)
assert err == 0
def test_error_band_cost_out_of_times(default_cost_err):
params = datamodel.ErrorBandCost(
bands=[datamodel.CostBand(
error_range=(-10, 1),
cost_function='datetime',
cost_function_parameters=datamodel.DatetimeCost(
datetimes=[pd.Timestamp('2020-05-01T02:00-07:00')],
cost=[1.0],
aggregation='sum',
fill='forward',
net=True
)
)]
)
fx = default_cost_err
obs = pd.Series(0, index=fx.index)
err = deterministic.error_band_cost(
obs, fx, params, deterministic.error)
assert err == 0