"""
This file is part of CLIMADA.
Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS.
CLIMADA is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free
Software Foundation, version 3.
CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with CLIMADA. If not, see <https://www.gnu.org/licenses/>.
---
Define Uncertainty Cost Benefit class
"""
__all__ = ['CalcCostBenefit']
import logging
import time
from functools import partial
from typing import Optional, Union
import pandas as pd
from climada.engine.cost_benefit import CostBenefit
from climada.engine.unsequa import Calc, InputVar, UncCostBenefitOutput
from climada.util import log_level
from climada.hazard import Hazard
from climada.entity import Entity
LOGGER = logging.getLogger(__name__)
# Future planed features:
# - Add 'efc' (frequency curve) to UncCostBenenfit
[docs]class CalcCostBenefit(Calc):
"""
Cost Benefit uncertainty analysis class
This is the base class to perform uncertainty analysis on the outputs of
climada.engine.costbenefit.CostBenefit().
Attributes
----------
value_unit : str
Unit of the exposures value
haz_input_var : climada.engine.uncertainty.input_var.InputVar
Present Hazard uncertainty variable
ent_input_var : climada.engine.uncertainty.input_var.InputVar
Present Entity uncertainty variable
haz_unc_fut_Var: climada.engine.uncertainty.input_var.InputVar
Future Hazard uncertainty variable
ent_fut_input_var : climada.engine.uncertainty.input_var.InputVar
Future Entity uncertainty variable
_input_var_names : tuple(str)
Names of the required uncertainty variables
('haz_input_var', 'ent_input_var', 'haz_fut_input_var', 'ent_fut_input_var')
_metric_names : tuple(str)
Names of the cost benefit output metrics
('tot_climate_risk', 'benefit', 'cost_ben_ratio', 'imp_meas_present', 'imp_meas_future')
"""
_input_var_names = (
'haz_input_var',
'ent_input_var',
'haz_fut_input_var',
'ent_fut_input_var',
)
"""Names of the required uncertainty variables"""
_metric_names = (
'tot_climate_risk',
'benefit',
'cost_ben_ratio',
'imp_meas_present',
'imp_meas_future',
)
"""Names of the cost benefit output metrics"""
[docs] def __init__(
self,
haz_input_var: Union[InputVar, Hazard],
ent_input_var: Union[InputVar, Entity],
haz_fut_input_var: Optional[Union[InputVar, Hazard]] = None,
ent_fut_input_var: Optional[Union[InputVar, Entity]] = None,
):
"""Initialize UncCalcCostBenefit
Sets the uncertainty input variables, the cost benefit metric_names,
and the units.
Parameters
----------
haz_input_var : climada.engine.uncertainty.input_var.InputVar
or climada.hazard.Hazard
Hazard uncertainty variable or Hazard for the present Hazard
in climada.engine.CostBenefit.calc
ent_input_var : climada.engine.uncertainty.input_var.InputVar
or climada.entity.Entity
Entity uncertainty variable or Entity for the present Entity
in climada.engine.CostBenefit.calc
haz_fut_input_var: climada.engine.uncertainty.input_var.InputVar
or climada.hazard.Hazard, optional
Hazard uncertainty variable or Hazard for the future Hazard
The Default is None.
ent_fut_input_var : climada.engine.uncertainty.input_var.InputVar
or climada.entity.Entity, optional
Entity uncertainty variable or Entity for the future Entity
in climada.engine.CostBenefit.calc
"""
Calc.__init__(self)
self.haz_input_var = InputVar.var_to_inputvar(haz_input_var)
self.ent_input_var = InputVar.var_to_inputvar(ent_input_var)
self.haz_fut_input_var = InputVar.var_to_inputvar(haz_fut_input_var)
self.ent_fut_input_var = InputVar.var_to_inputvar(ent_fut_input_var)
self.value_unit = self.ent_input_var.evaluate().exposures.value_unit
self.check_distr()
[docs] def uncertainty(self, unc_data, pool=None, **cost_benefit_kwargs):
"""
Computes the cost benefit for each sample in unc_output.sample_df.
By default, imp_meas_present, imp_meas_future, tot_climate_risk,
benefit, cost_ben_ratio are computed.
This sets the attributes:
unc_output.imp_meas_present_unc_df,
unc_output.imp_meas_future_unc_df
unc_output.tot_climate_risk_unc_df
unc_output.benefit_unc_df
unc_output.cost_ben_ratio_unc_df
unc_output.unit
unc_output.cost_benefit_kwargs
Parameters
----------
unc_data : climada.engine.uncertainty.unc_output.UncOutput
Uncertainty data object with the input parameters samples
pool : pathos.pools.ProcessPool, optional
Pool of CPUs for parralel computations. Default is None.
The default is None.
cost_benefit_kwargs : keyword arguments
Keyword arguments passed on to climada.engine.CostBenefit.calc()
Returns
-------
unc_output : climada.engine.uncertainty.unc_output.UncCostBenefitOutput
Uncertainty data object in with the cost benefit outputs for each
sample and all the sample data copied over from unc_sample.
Raises
------
ValueError:
If no sampling parameters defined, the uncertainty distribution
cannot be computed.
See Also
--------
climada.engine.cost_benefit:
Compute risk and adptation option cost benefits.
"""
if unc_data.samples_df.empty:
raise ValueError("No sample was found. Please create one first" +
"using UncImpact.make_sample(N)")
samples_df = unc_data.samples_df.copy(deep=True)
unit = self.value_unit
LOGGER.info("The freq_curve is not saved. Please "
"change the risk_func (see climada.engine.cost_benefit) "
"if return period information is needed")
start = time.time()
one_sample = samples_df.iloc[0:1].iterrows()
cb_metrics = map(self._map_costben_calc, one_sample)
[imp_meas_present,
imp_meas_future,
tot_climate_risk,
benefit,
cost_ben_ratio] = list(zip(*cb_metrics))
elapsed_time = (time.time() - start)
self.est_comp_time(unc_data.n_samples, elapsed_time, pool)
#Compute impact distributions
with log_level(level='ERROR', name_prefix='climada'):
if pool:
LOGGER.info('Using %s CPUs.', pool.ncpus)
chunksize = min(unc_data.n_samples // pool.ncpus, 100)
cb_metrics = pool.map(partial(self._map_costben_calc, **cost_benefit_kwargs),
samples_df.iterrows(),
chunsize = chunksize)
else:
cb_metrics = map(partial(self._map_costben_calc, **cost_benefit_kwargs),
samples_df.iterrows())
#Perform the actual computation
with log_level(level='ERROR', name_prefix='climada'):
[imp_meas_present,
imp_meas_future,
tot_climate_risk,
benefit,
cost_ben_ratio] = list(zip(*cb_metrics)) #Transpose list of list
# Assign computed impact distribution data to self
tot_climate_risk_unc_df = \
pd.DataFrame(tot_climate_risk, columns = ['tot_climate_risk'])
benefit_unc_df = pd.DataFrame(benefit)
benefit_unc_df.columns = [
column + ' Benef'
for column in benefit_unc_df.columns]
cost_ben_ratio_unc_df = pd.DataFrame(cost_ben_ratio)
cost_ben_ratio_unc_df.columns = [
column + ' CostBen'
for column in cost_ben_ratio_unc_df.columns]
imp_metric_names = ['risk', 'risk_transf', 'cost_meas',
'cost_ins']
im_periods = dict()
for imp_meas, period in zip([imp_meas_present, imp_meas_future],
['present', 'future']):
df_imp_meas = pd.DataFrame()
name = 'imp_meas_' + period
if imp_meas[0]:
for imp in imp_meas:
met_dic = {}
for meas, imp_dic in imp.items():
metrics = [imp_dic['risk'],
imp_dic['risk_transf'],
*imp_dic['cost']]
dic_tmp = {meas + ' - ' + m_name + ' - ' + period: [m_value]
for m_name, m_value
in zip(imp_metric_names, metrics)
}
met_dic.update(dic_tmp)
df_imp_meas = df_imp_meas.append(
pd.DataFrame(met_dic), ignore_index=True
)
im_periods[name + '_unc_df'] = df_imp_meas
cost_benefit_kwargs = {
key: str(val)
for key, val in cost_benefit_kwargs.items()}
cost_benefit_kwargs = tuple(cost_benefit_kwargs.items())
return UncCostBenefitOutput(samples_df=samples_df,
imp_meas_present_unc_df=im_periods['imp_meas_present_unc_df'],
imp_meas_future_unc_df=im_periods['imp_meas_future_unc_df'],
tot_climate_risk_unc_df=tot_climate_risk_unc_df,
cost_ben_ratio_unc_df=cost_ben_ratio_unc_df,
benefit_unc_df=benefit_unc_df,
unit=unit,
cost_benefit_kwargs=cost_benefit_kwargs)
def _map_costben_calc(self, param_sample, **kwargs):
"""
Map to compute cost benefit for all parameter samples in parallel
Parameters
----------
param_sample : pd.DataFrame.iterrows()
Generator of the parameter samples
kwargs :
Keyword arguments passed on to climada.engine.CostBenefit.calc()
Returns
-------
list
icost benefit metrics list for all samples containing
imp_meas_present, imp_meas_future, tot_climate_risk,
benefit, cost_ben_ratio
"""
# [1] only the rows of the dataframe passed by pd.DataFrame.iterrows()
haz_samples = param_sample[1][self.haz_input_var.labels].to_dict()
ent_samples = param_sample[1][self.ent_input_var.labels].to_dict()
haz_fut_samples = param_sample[1][self.haz_fut_input_var.labels].to_dict()
ent_fut_samples = param_sample[1][self.ent_fut_input_var.labels].to_dict()
haz = self.haz_input_var.evaluate(**haz_samples)
ent = self.ent_input_var.evaluate(**ent_samples)
haz_fut = self.haz_fut_input_var.evaluate(**haz_fut_samples)
ent_fut = self.ent_fut_input_var.evaluate(**ent_fut_samples)
cb = CostBenefit()
ent.exposures.assign_centroids(haz, overwrite=False)
if ent_fut:
ent_fut.exposures.assign_centroids(haz_fut if haz_fut else haz, overwrite=False)
cb.calc(hazard=haz, entity=ent, haz_future=haz_fut, ent_future=ent_fut,
save_imp=False, assign_centroids=False, **kwargs)
# Extract from climada.impact the chosen metrics
return [cb.imp_meas_present,
cb.imp_meas_future,
cb.tot_climate_risk,
cb.benefit,
cb.cost_ben_ratio
]
