Source code for atomica.reconciliation

Implements the automatic reconciliation algorithm

In any given model run, the parameters calculated by the :class:`ProgramSet`
should ideally match up with the values in the :class:`ParameterSet` so that
there are no discontinuities in parameter value. This may not be the case depending
on the data gathered and the calibration. Reconciliation aims to adjust the
internal parameters of the :class:`ProgramSet` to best match a :class:`ParameterSet`
in a particular year.


import numpy as np
import sciris as sc
from .system import logger
from .system import FrameworkSettings as FS
import pandas as pd
import logging

__all__ = ["reconcile"]

def _extract_targets(result, progset, ti, eval_pars=None):
    # Store the target parset values in the same form as the progset computed parameter values
    # This will also mean that target_vals contains the par UIDs only for the impact parameters
    # requested for reconcilation (if None, then all will be used). For simplicity, just do it
    # for all programs and parameters, only the target ones will actually get looked at.
    # INPUTS
    # - result : A result object for the baseline sim (it won't have a progset because it was run without programs)
    # - progset : The progset to use to get the target pars/pops and coverage denominator
    # - ti : Time indices to extract results from (could be several)
    # - eval_pars: Optional list of (par,pop) names for evaluation - otherwise, uses all impact pars

    # Get the parameter outcomes - assumes simulation end year was the reconciliation year (hence -1 index)
    target_vals = dict()
    for covout in progset.covouts.values():
        par_name, pop_name = covout.par, covout.pop
        if (eval_pars is None and result.framework.get_par(par_name)["targetable"] == "y") or (par_name, pop_name) in eval_pars:
            par = result.get_variable(par_name, pop_name)[0]
            if par.units == FS.QUANTITY_TYPE_NUMBER:
                # If a transition parameter in number units is being targeted, then the program outcome is in units of per person reached
                # at each timestep, while the parameter units are people/year. Thus, we need to convert the model parameter into the program
                # output units prior to reconciling
                target_vals[(par_name, pop_name)] = par.vals[ti] * result.dt / np.array([par.source_popsize(x) for x in ti])
                target_vals[(par_name, pop_name)] = par.vals[ti]

    # Get the coverage denominator in the reconciliation year (it's always the same, so can do it once here)
    num_eligible = {x: y[ti] for x, y in result.get_coverage("eligible").items()}

    return target_vals, num_eligible

def _update_progset(asd_vals, mapping, progset):
    # Updates progset in place, inserting values
    # INPUTS:
    # - asd_vals - asd array
    # - mapping - list of tuples specifying where to insert elements from the ASD array
    #             e.g.  [('unit_cost','BCG'),('outcome','v_rate','0-4','BCG')]
    #             The mapping is
    #             unit_cost - program
    #             capacity_constraint - program
    #             baseline - par,pop
    #             outcome - par,pop,program
    # - progset : ProgramSet to modify, should have only one time
    for x, target in zip(asd_vals, mapping):
        if target[0] == "unit_cost":
            assert len(progset.programs[target[1]].unit_cost.vals) == 1
            progset.programs[target[1]].unit_cost.vals[0] = x
        elif target[0] == "capacity_constraint":
            assert len(progset.programs[target[1]].capacity_constraint.vals) == 1
            progset.programs[target[1]].capacity_constraint.vals[0] = x
        elif target[0] == "baseline":
            progset.covouts[(target[1], target[2])].baseline = x
        elif target[0] == "outcome":
            progset.covouts[(target[1], target[2])].progs[target[3]] = x

def _prepare_bounds(progset, unit_cost_bounds, baseline_bounds, capacity_bounds, outcome_bounds):
    # This is a separate function to _prepare_asd_inputs() because there may be complex logic related to
    # defaults for constructing the bounds. At the end, the bounds dict contains upper and lower limits
    # for every parameter being considered
    # bounds dict : {quantity:{key:(lower,upper)} where quantity is 'unit_cost','capacity_constraint','baseline','outcome', and key is:
    #     for unit_cost - program
    #     for capacity_constraint - program
    #     for baseline - par,pop
    #     for outcome - par,pop,program

    bounds = dict()
    bounds["unit_cost"] = dict()
    bounds["capacity_constraint"] = dict()
    bounds["baseline"] = dict()
    bounds["outcome"] = dict()

    for prog in progset.programs.values():
        if unit_cost_bounds:
            bounds["unit_cost"][] = prog.unit_cost.vals * np.array([1 - unit_cost_bounds, 1 + unit_cost_bounds])
        if capacity_bounds and prog.capacity_constraint.has_data:
            bounds["capacity_constraint"][] = prog.capacity_constraint.vals * np.array([1 - capacity_bounds, 1 + capacity_bounds])

    for covout in progset.covouts.values():
        if baseline_bounds:
            bounds["baseline"][(covout.par, covout.pop)] = covout.baseline * np.array([1 - baseline_bounds, 1 + baseline_bounds])
        if outcome_bounds:
            for prog, outcome in covout.progs.items():
                bounds["outcome"][(covout.par, covout.pop, prog)] = outcome * np.array([1 - outcome_bounds, 1 + outcome_bounds])

    return bounds

def _prepare_asd_inputs(progset, bounds):
    # Return initial values, upper-lower bounds, and mapping to insert the arrays into a progset
    # Only quantities that appear in the bounds dict will be reconciled
    x0 = list()
    xmin = list()
    xmax = list()
    mapping = list()

    for prog in progset.programs.values():
        if in bounds["unit_cost"]:  # Might need to check program_specific bounds here
        if in bounds["capacity_constraint"]:  # Might need to check program_specific bounds here

    for covout in progset.covouts.values():
        if (covout.par, covout.pop) in bounds["baseline"]:
            xmin.append(bounds["baseline"][(covout.par, covout.pop)][0])
            xmax.append(bounds["baseline"][(covout.par, covout.pop)][1])
            mapping.append(("baseline", covout.par, covout.pop))

            for prog, outcome in covout.progs.items():
                if (covout.par, covout.pop, prog) in bounds["outcome"]:
                    xmin.append(bounds["outcome"][(covout.par, covout.pop, prog)][0])
                    xmax.append(bounds["outcome"][(covout.par, covout.pop, prog)][1])
                    mapping.append(("outcome", covout.par, covout.pop, prog))

    return x0, xmin, xmax, mapping

def _objective(x, mapping, progset, eval_years, target_vals, num_eligible, dt):
    _update_progset(x, mapping, progset)  # Apply the changes to the progset
    capacities = progset.get_capacities(tvec=eval_years, dt=dt)  # Get number coverage using latest unit costs but default spending
    prop_coverage = progset.get_prop_coverage(tvec=eval_years, dt=dt, capacities=capacities, num_eligible=num_eligible)

    obj = 0.0
    for i in range(0, len(eval_years)):
        outcomes = progset.get_outcomes(prop_coverage=prop_coverage)
        for key in target_vals:  # Key is a (par,pop) tuple
            obj += (target_vals[key][i] - outcomes[key]) ** 2  # Add squared difference in parameter value
    return obj

def _convert_to_single_year(progset, reconciliation_year):
    # Take in a progset
    # Return a progset with values only in the reconciliation year
    # This is then what actually gets reconciled
    # And we can be sure that when we go to make changes in the reconciliation year, we do not
    # have any extra time points

    # Basically, we only need to modify program attributes
    reconciliation_year = sc.promotetoarray(reconciliation_year)
    new_progset = sc.dcp(progset)
    new_progset.tvec = reconciliation_year.copy()

    for prog in new_progset.programs.values():
        # NOTE - because of the TDVE format, we need to do the overwrite for ALL time series provided
        # (because they must all have the same time axis) even if they are not being modified by the reconciliation
        # This results in a clear mapping from the original progbook into the single-year format. Users can always
        # go back and modify the reconciled progbook
        if prog.spend_data.has_data:
            prog.spend_data.vals = prog.spend_data.interpolate(reconciliation_year)
            prog.spend_data.t = reconciliation_year.copy()
            prog.spend_data.assumption = None

        if prog.unit_cost.has_data:
            prog.unit_cost.vals = prog.unit_cost.interpolate(reconciliation_year)
            prog.unit_cost.t = reconciliation_year.copy()
            prog.unit_cost.assumption = None

        if prog.capacity_constraint.has_data:
            prog.capacity_constraint.vals = prog.capacity_constraint.interpolate(reconciliation_year)
            prog.capacity_constraint.t = reconciliation_year.copy()
            prog.capacity_constraint.assumption = None

        # This is tricky - maybe we do want to retain other values? Depends on what ends up happening with coverage
        if prog.coverage.has_data:
            prog.coverage.vals = prog.coverage.interpolate(reconciliation_year)
            prog.coverage.t = reconciliation_year.copy()
            prog.coverage.assumption = None

    return new_progset

# ASD takes in a list of values. So we need to map all of the things we are optimizing onto

[docs] def reconcile(project, parset, progset, reconciliation_year: float, max_time=10, unit_cost_bounds=0.0, baseline_bounds=0.0, capacity_bounds=0.0, outcome_bounds=0.0, eval_pars=None, eval_range=None): """ Modify a progset to optimally match a parset in a specified year Reconciliation is a mapping from one progset to another. The output progset generates optimally matched output parameter values compared to the specified parset, in the reconciliation year for the progset's default spending. The output progset has internal attributes (such as unit costs) with values that are defined only in the reconciliation year. So while the input progset may have time varying unit costs etc. after reconciliation, they will be constant. The upper and lower bounds for unit costs, baseline, capacity_constraint, and outcome are specified as fractions of the initial value. For example, entering ``unit_cost_bounds=0.2`` would mean that unit costs would be allowed to range from 0.8 to 1.2 times the value in the input progset. :param project: A :class:`Project` instance :param parset: A :class:`ParameterSet` instance (or name of a parset contained in the project) :param progset: A :class:`ProgramSet` instance (or name of a progset contained in the project) :param reconciliation_year: Year to perform reconciliation in :param max_time: Optionally override the maximum execution time in ASD :param unit_cost_bounds: Optionally specify bounds for unit costs. Default is 0.0 (unit costs will not be changed) :param baseline_bounds: Optionally specify bounds for baseline spending. Default is 0.0 (no changes) :param capacity_bounds: Optionally specify bounds for capacity_constraint constraints. Default is 0.0 (no changes) :param outcome_bounds: Optionally specify bounds for outcome values. Default is 0.0 (no changes) :param eval_pars: Optionally select a subset of parameters for comparison. By default, all parameters overwritten by the progset will be used. :param eval_range: Optionally specify a range of years over which to evaluate the progset-parset match. By default, it will only use the reconciliation year :return: tuple containing - A reconciled :class:`ProgramSet` instance - A DataFrame comparing the unreconciled and reconciled progsets - A DataFrame comparing the parset parameters and progset """ # Sanitize inputs parset = project.parset(parset) progset = project.progset(progset) logger.warning("Reconcilation when parameter is in number units not fully tested") reconciliation_year = sc.promotetoarray(reconciliation_year) assert len(reconciliation_year) == 1, "Reconciliation year must be a scalar" if eval_range is None: eval_range = [reconciliation_year[0], reconciliation_year[0] + project.settings.sim_dt] # Do a prerun to get the baseline values and coverage denominator parset_results = project.run_sim(parset=parset, progset=progset, store_results=False) ti = np.where((parset_results.model.t >= eval_range[0]) & (parset_results.model.t < eval_range[1]))[0] eval_years = parset_results.t[ti] target_vals, num_eligible = _extract_targets(parset_results, progset, ti, eval_pars) # Prepare ASD inputs new_progset = _convert_to_single_year(progset, reconciliation_year[0]) bounds = _prepare_bounds(new_progset, unit_cost_bounds, baseline_bounds, capacity_bounds, outcome_bounds) # Convert reconcile() inputs into full detailed bounds x0, xmin, xmax, mapping = _prepare_asd_inputs(new_progset, bounds) # Assemble ASD variables"Reconciling in %.2f, evaluating from %.2f up to %.2f", reconciliation_year, eval_range[0], eval_range[1]) args = { "mapping": mapping, "progset": new_progset, "eval_years": eval_years, "target_vals": target_vals, "num_eligible": num_eligible, "dt": project.settings.sim_dt, } optim_args = { # TODO - tune these variables specifically for reconciliation "xmin": xmin, "xmax": xmax, "verbose": 2, # default verbosity "maxtime": max_time, } log_level = logger.getEffectiveLevel() if log_level < logging.WARNING: optim_args["verbose"] = 2 else: optim_args["verbose"] = 0 opt_result = sc.asd(_objective, x0, args, **optim_args) x_opt = opt_result["x"] _update_progset(x_opt, mapping, new_progset) # Apply the changes to the progset # Before/after for quantities records = [(item[0], item[1:], orig_val, opt_val) for item, orig_val, opt_val in zip(mapping, x0, x_opt)] progset_comparison = pd.DataFrame.from_records(records, columns=["Quantity", "Identifier", "Before reconciliation", "After reconciliation"]) # Before/after for parameters records = [] old_capacities = progset.get_capacities(tvec=eval_years, dt=project.settings.sim_dt) new_capacities = new_progset.get_capacities(tvec=eval_years, dt=project.settings.sim_dt) old_prop_coverage = progset.get_prop_coverage(tvec=eval_years, dt=project.settings.sim_dt, capacities=old_capacities, num_eligible=num_eligible) new_prop_coverage = new_progset.get_prop_coverage(tvec=eval_years, dt=project.settings.sim_dt, capacities=new_capacities, num_eligible=num_eligible) for i, year in enumerate(eval_years): old_outcomes = progset.get_outcomes(prop_coverage={prog: cov[[i]] for prog, cov in old_prop_coverage.items()}) # Program outcomes for this year new_outcomes = new_progset.get_outcomes(prop_coverage={prog: cov[[i]] for prog, cov in new_prop_coverage.items()}) # Program outcomes for this year for (par, pop), target in target_vals.items(): records.append((par, pop, year, target[0], old_outcomes[(par, pop)], new_outcomes[(par, pop)])) parameter_comparison = pd.DataFrame.from_records(records, columns=["Parameter", "Population", "Year", "Target", "Before reconciliation", "After reconciliation"]) parameter_comparison["Difference"] = parameter_comparison["Before reconciliation"] - parameter_comparison["After reconciliation"] return new_progset, progset_comparison, parameter_comparison