Source code for climada.engine.unsequa.calc_cost_benefit

This file is part of CLIMADA.

Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS.

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terms of the GNU General Public License as published by the Free
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Define Uncertainty Cost Benefit class

__all__ = ['CalcCostBenefit']

import logging
import time
import itertools

from typing import Optional, Union
import pandas as pd
import numpy as np
import pathos.multiprocessing as mp
# use pathos.multiprocess fork of multiprocessing for compatibility
# wiht notebooks and other environments

from climada.engine.cost_benefit import CostBenefit
from climada.engine.unsequa import Calc, InputVar, UncCostBenefitOutput
from climada.engine.unsequa.calc_base import _sample_parallel_iterator, _multiprocess_chunksize, _transpose_chunked_data
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 : InputVar or Hazard Present Hazard uncertainty variable ent_input_var : InputVar or Entity Present Entity uncertainty variable haz_unc_fut_Var: InputVar or Hazard Future Hazard uncertainty variable ent_fut_input_var : InputVar or Entity 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_sample, processes=1, chunksize=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_sample : climada.engine.uncertainty.unc_output.UncOutput Uncertainty data object with the input parameters samples processes : int, optional Number of CPUs to use for parralel computations. The default is 1 (not parallel) cost_benefit_kwargs : keyword arguments Keyword arguments passed on to climada.engine.CostBenefit.calc() chunksize: int, optional Size of the sample chunks for parallel processing. Default is equal to the number of samples divided by the number of processes. 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. Notes ----- Parallelization logic is described in the base class here :py:class:`~climada.engine.unsequa.calc_base.Calc` See Also -------- climada.engine.cost_benefit: compute risk and adptation option cost benefits. """ if unc_sample.samples_df.empty: raise ValueError("No sample was found. Please create one first" + "using UncImpact.make_sample(N)") # copy may not be needed, but is kept to prevent potential # data corruption issues. The computational cost should be # minimal as only a list of floats is copied. samples_df = unc_sample.samples_df.copy(deep=True) if chunksize is None: chunksize = _multiprocess_chunksize(samples_df, processes) unit = self.value_unit"The freq_curve is not saved. Please " "change the risk_func (see climada.engine.cost_benefit) " "if return period information is needed") one_sample = samples_df.iloc[0:1] start = time.time() self._compute_cb_metrics(one_sample, cost_benefit_kwargs, chunksize=1, processes=1) elapsed_time = (time.time() - start) self.est_comp_time(unc_sample.n_samples, elapsed_time, processes) #Compute impact distributions [ imp_meas_present, imp_meas_future, tot_climate_risk, benefit, cost_ben_ratio ] = self._compute_cb_metrics(samples_df, cost_benefit_kwargs, chunksize, processes) # 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 = pd.concat( [df_imp_meas, pd.DataFrame(met_dic)], ignore_index=True, sort=False ) 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 _compute_cb_metrics( self, samples_df, cost_benefit_kwargs, chunksize, processes ): """Compute the uncertainty metrics Parameters ---------- samples_df : pd.DataFrame dataframe of input parameter samples cost_benefit_kwargs: kwargs arguments to be passed to the cost_benefit.calc method chunksize : int size of the samples chunks processes : int number of processes to use Returns ------- list values of impact metrics per sample """ with log_level(level='ERROR', name_prefix='climada'): p_iterator = _sample_parallel_iterator( samples=samples_df, chunksize=chunksize, ent_input_var=self.ent_input_var, haz_input_var=self.haz_input_var, ent_fut_input_var=self.ent_fut_input_var, haz_fut_input_var=self.haz_fut_input_var, cost_benefit_kwargs=cost_benefit_kwargs ) if processes>1: with mp.Pool(processes=processes) as pool:'Using %s CPUs.', processes) cb_metrics = pool.starmap( _map_costben_calc, p_iterator ) else: cb_metrics = itertools.starmap( _map_costben_calc, p_iterator ) #Perform the actual computation with log_level(level='ERROR', name_prefix='climada'): return _transpose_chunked_data(cb_metrics)
def _map_costben_calc( sample_chunks, ent_input_var, haz_input_var, ent_fut_input_var, haz_fut_input_var, cost_benefit_kwargs ): """ Map to compute cost benefit for all parameter samples in parallel Parameters ---------- sample_chunks : pd.DataFrame Dataframe of the parameter samples haz_input_var : InputVar Hazard uncertainty variable or Hazard for the present Hazard in climada.engine.CostBenefit.calc ent_input_var : InputVar Entity uncertainty variable or Entity for the present Entity in climada.engine.CostBenefit.calc haz_fut_input_var: InputVar Hazard uncertainty variable or Hazard for the future Hazard ent_fut_input_var : InputVar Entity uncertainty variable or Entity for the future Entity in climada.engine.CostBenefit.calc cost_benefit_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 """ uncertainty_values = [] for _, sample in sample_chunks.iterrows(): haz_samples = sample[haz_input_var.labels].to_dict() ent_samples = sample[ent_input_var.labels].to_dict() haz_fut_samples = sample[haz_fut_input_var.labels].to_dict() ent_fut_samples = sample[ent_fut_input_var.labels].to_dict() haz = haz_input_var.evaluate(**haz_samples) ent = ent_input_var.evaluate(**ent_samples) haz_fut = haz_fut_input_var.evaluate(**haz_fut_samples) ent_fut = 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, **cost_benefit_kwargs) # Extract from climada.impact the chosen metrics uncertainty_values.append([ cb.imp_meas_present, cb.imp_meas_future, cb.tot_climate_risk, cb.benefit, cb.cost_ben_ratio ]) # Transpose list return list(zip(*uncertainty_values))