def __init__(self, gfactors=None, parameter_dict=None, start_year=JSON_START_YEAR, num_years=DEFAULT_NUM_YEARS): super(Policy, self).__init__() if gfactors is None: self._gfactors = Growfactors() elif isinstance(gfactors, Growfactors): self._gfactors = gfactors else: raise ValueError('gfactors is not None or a Growfactors instance') if parameter_dict is None: # read default parameters self._vals = self._params_dict_from_json_file() elif isinstance(parameter_dict, dict): self._vals = parameter_dict else: raise ValueError('parameter_dict is not None or a dictionary') if num_years < 1: raise ValueError('num_years cannot be less than one') syr = start_year lyr = start_year + num_years - 1 self._inflation_rates = self._gfactors.price_inflation_rates(syr, lyr) self._apply_clp_cpi_offset(self._vals['_cpi_offset'], num_years) self._wage_growth_rates = self._gfactors.wage_growth_rates(syr, lyr) self.initialize(start_year, num_years) self.reform_warnings = '' self.reform_errors = '' self._ignore_errors = False
def __init__(self, gfactors=Growfactors(), parameter_dict=None, start_year=JSON_START_YEAR, num_years=DEFAULT_NUM_YEARS): """ Policy class constructor. """ # pylint: disable=too-many-arguments # pylint: disable=too-many-branches super(Policy, self).__init__() if not isinstance(gfactors, Growfactors): raise ValueError('gfactors is not a Growfactors instance') self._gfactors = gfactors if parameter_dict is None: # read default parameters self._vals = self._params_dict_from_json_file() elif isinstance(parameter_dict, dict): self._vals = parameter_dict else: raise ValueError('parameter_dict is not None or a dictionary') if num_years < 1: raise ValueError('num_years cannot be less than one') syr = start_year lyr = start_year + num_years - 1 self._inflation_rates = gfactors.price_inflation_rates(syr, lyr) self._wage_growth_rates = gfactors.wage_growth_rates(syr, lyr) self.initialize(start_year, num_years)
def with_suffix(gdict, growdiff_baseline_dict, growdiff_response_dict): """ Return param_base:year dictionary having only suffix parameters. """ if bool(growdiff_baseline_dict) or bool(growdiff_response_dict): gdiff_baseline = Growdiff() gdiff_baseline.update_growdiff(growdiff_baseline_dict) gdiff_response = Growdiff() gdiff_response.update_growdiff(growdiff_response_dict) growfactors = Growfactors() gdiff_baseline.apply_to(growfactors) gdiff_response.apply_to(growfactors) else: growfactors = None pol = Policy(gfactors=growfactors) pol.ignore_reform_errors() odict = dict() for param in gdict.keys(): odict[param] = dict() for year in sorted(gdict[param].keys()): odict[param][year] = dict() for suffix in gdict[param][year].keys(): plist = getattr(pol, param).tolist() dvals = plist[int(year) - Policy.JSON_START_YEAR] odict[param][year] = [dvals] idx = Policy.JSON_REFORM_SUFFIXES[suffix] odict[param][year][0][idx] = gdict[param][year][suffix] udict = {int(year): {param: odict[param][year]}} pol.implement_reform(udict) return odict
def cps_constructor(data=None, no_benefits=False, exact_calculations=False, gfactors=Growfactors()): """ Static method returns a Records object instantiated with CPS input data. This works in a analogous way to Records(), which returns a Records object instantiated with PUF input data. This is a convenience method that eliminates the need to specify all the details of the CPS input data just as the default values of the arguments of the Records class constructor eliminate the need to specify all the details of the PUF input data. """ if data is None: data = os.path.join(Records.CUR_PATH, 'cps.csv.gz') if no_benefits: benefits_filename = None else: benefits_filename = Records.CPS_BENEFITS_FILENAME return Records(data=data, exact_calculations=exact_calculations, gfactors=gfactors, weights=Records.CPS_WEIGHTS_FILENAME, adjust_ratios=Records.CPS_RATIOS_FILENAME, benefits=benefits_filename, start_year=Records.CPSCSV_YEAR)
class Policy(ParametersBase): """ Policy is a subclass of the abstract ParametersBase class, and therefore, inherits its methods (none of which are shown here). Constructor for the federal tax policy class. Parameters ---------- gfactors: Growfactors class instance containing price inflation rates and wage growth rates parameter_dict: dictionary of PARAM:DESCRIPTION pairs dictionary of policy parameters; if None, default policy parameters are read from the current_law_policy.json file. start_year: integer first calendar year for historical policy parameters. num_years: integer number of calendar years for which to specify policy parameter values beginning with start_year. Raises ------ ValueError: if gfactors is not a Growfactors class instance. if parameter_dict is neither None nor a dictionary. if num_years is less than one. Returns ------- class instance: Policy """ DEFAULTS_FILENAME = 'current_law_policy.json' JSON_START_YEAR = 2013 # remains the same unless earlier data added LAST_KNOWN_YEAR = 2017 # last year for which indexed param vals are known LAST_BUDGET_YEAR = 2027 # increases by one every calendar year DEFAULT_NUM_YEARS = LAST_BUDGET_YEAR - JSON_START_YEAR + 1 def __init__(self, gfactors=None, parameter_dict=None, start_year=JSON_START_YEAR, num_years=DEFAULT_NUM_YEARS): super(Policy, self).__init__() if gfactors is None: self._gfactors = Growfactors() elif isinstance(gfactors, Growfactors): self._gfactors = gfactors else: raise ValueError('gfactors is not None or a Growfactors instance') if parameter_dict is None: # read default parameters self._vals = self._params_dict_from_json_file() elif isinstance(parameter_dict, dict): self._vals = parameter_dict else: raise ValueError('parameter_dict is not None or a dictionary') if num_years < 1: raise ValueError('num_years cannot be less than one') syr = start_year lyr = start_year + num_years - 1 self._inflation_rates = self._gfactors.price_inflation_rates(syr, lyr) self._apply_clp_cpi_offset(self._vals['_cpi_offset'], num_years) self._wage_growth_rates = self._gfactors.wage_growth_rates(syr, lyr) self.initialize(start_year, num_years) self.reform_warnings = '' self.reform_errors = '' self._ignore_errors = False def inflation_rates(self): """ Returns list of price inflation rates starting with JSON_START_YEAR. """ return self._inflation_rates def wage_growth_rates(self): """ Returns list of wage growth rates starting with JSON_START_YEAR. """ return self._wage_growth_rates def implement_reform(self, reform): """ Implement multi-year policy reform and leave current_year unchanged. Parameters ---------- reform: dictionary of one or more YEAR:MODS pairs see Notes to Parameters _update method for info on MODS structure Raises ------ ValueError: if reform is not a dictionary. if each YEAR in reform is not an integer. if minimum YEAR in the YEAR:MODS pairs is less than start_year. if minimum YEAR in the YEAR:MODS pairs is less than current_year. if maximum YEAR in the YEAR:MODS pairs is greater than end_year. if Policy._validate_parameter_names generates any error messages. Returns ------- nothing: void Notes ----- Given a reform dictionary, typical usage of the Policy class is as follows:: policy = Policy() policy.implement_reform(reform) In the above statements, the Policy() call instantiates a policy object (policy) containing current-law policy parameters, and the implement_reform(reform) call applies the (possibly multi-year) reform specified in reform and then sets the current_year to the value of current_year when implement_reform was called with parameters set for that pre-call year. An example of a multi-year, multi-parameter reform is as follows:: reform = { 2016: { '_EITC_c': [[900, 5000, 8000, 9000]], '_II_em': [7000], '_SS_Earnings_c': [300000] }, 2017: { '_SS_Earnings_c': [500000], '_SS_Earnings_c_cpi': False }, 2019: { '_EITC_c': [[1200, 7000, 10000, 12000]], '_II_em': [9000], '_SS_Earnings_c': [700000], '_SS_Earnings_c_cpi': True } } Notice that each of the four YEAR:MODS pairs is specified as required by the private _update method, whose documentation provides several MODS dictionary examples. IMPORTANT NOTICE: when specifying a reform dictionary always group all reform provisions for a specified year into one YEAR:MODS pair. If you make the mistake of specifying two or more YEAR:MODS pairs with the same YEAR value, all but the last one will be overwritten, and therefore, not part of the reform. This is because Python expects unique (not multiple) dictionary keys. There is no way to catch this error, so be careful to specify reform dictionaries correctly. """ # check that all reform dictionary keys are integers if not isinstance(reform, dict): raise ValueError('ERROR: YYYY PARAM reform is not a dictionary') if not reform: return # no reform to implement reform_years = sorted(list(reform.keys())) for year in reform_years: if not isinstance(year, int): msg = 'ERROR: {} KEY {}' details = 'KEY in reform is not an integer calendar year' raise ValueError(msg.format(year, details)) # check range of remaining reform_years first_reform_year = min(reform_years) if first_reform_year < self.start_year: msg = 'ERROR: {} YEAR reform provision in YEAR < start_year={}' raise ValueError(msg.format(first_reform_year, self.start_year)) if first_reform_year < self.current_year: msg = 'ERROR: {} YEAR reform provision in YEAR < current_year={}' raise ValueError(msg.format(first_reform_year, self.current_year)) last_reform_year = max(reform_years) if last_reform_year > self.end_year: msg = 'ERROR: {} YEAR reform provision in YEAR > end_year={}' raise ValueError(msg.format(last_reform_year, self.end_year)) # validate reform parameter names and types self._validate_parameter_names_types(reform) if not self._ignore_errors and self.reform_errors: raise ValueError(self.reform_errors) # optionally apply cpi_offset to inflation_rates and re-initialize if Policy._cpi_offset_in_reform(reform): known_years = self._apply_reform_cpi_offset(reform) self.set_default_vals(known_years=known_years) # implement the reform year by year precall_current_year = self.current_year reform_parameters = set() for year in reform_years: self.set_year(year) reform_parameters.update(reform[year].keys()) self._update({year: reform[year]}) self.set_year(precall_current_year) # validate reform parameter values self._validate_parameter_values(reform_parameters) def current_law_version(self): """ Return Policy object same as self except with current-law policy. """ startyear = self.start_year numyears = self.num_years clv = Policy(self._gfactors, parameter_dict=None, start_year=startyear, num_years=numyears) clv.set_year(self.current_year) return clv JSON_REFORM_SUFFIXES = { # MARS-indexed suffixes and list index numbers 'single': 0, 'joint': 1, 'separate': 2, 'headhousehold': 3, 'widow': 4, # EIC-indexed suffixes and list index numbers '0kids': 0, '1kid': 1, '2kids': 2, '3+kids': 3, # idedtype-indexed suffixes and list index numbers 'medical': 0, 'statelocal': 1, 'realestate': 2, 'casualty': 3, 'misc': 4, 'interest': 5, 'charity': 6 } @staticmethod def translate_json_reform_suffixes(indict, growdiff_baseline_dict, growdiff_response_dict): """ Replace any array parameters with suffixes in the specified JSON-derived "policy" dictionary, indict, and return a JSON-equivalent dictionary containing constructed array parameters and containing no parameters with suffixes, odict. """ # define no_suffix function used only in this method def no_suffix(idict): """ Return param_base:year dictionary having only no-suffix parameters. """ odict = dict() suffixes = Policy.JSON_REFORM_SUFFIXES.keys() for param in idict.keys(): param_pieces = param.split('_') suffix = param_pieces[-1] if suffix not in suffixes: odict[param] = idict[param] return odict # define group_dict function used only in this method def suffix_group_dict(idict): """ Return param_base:year:suffix dictionary with each idict value. """ gdict = dict() suffixes = Policy.JSON_REFORM_SUFFIXES.keys() for param in idict.keys(): param_pieces = param.split('_') suffix = param_pieces[-1] if suffix in suffixes: del param_pieces[-1] param_base = '_'.join(param_pieces) if param_base not in gdict: gdict[param_base] = dict() for year in sorted(idict[param].keys()): if year not in gdict[param_base]: gdict[param_base][year] = dict() gdict[param_base][year][suffix] = idict[param][year][0] return gdict # define with_suffix function used only in this method def with_suffix(gdict, growdiff_baseline_dict, growdiff_response_dict): """ Return param_base:year dictionary having only suffix parameters. """ if bool(growdiff_baseline_dict) or bool(growdiff_response_dict): gdiff_baseline = Growdiff() gdiff_baseline.update_growdiff(growdiff_baseline_dict) gdiff_response = Growdiff() gdiff_response.update_growdiff(growdiff_response_dict) growfactors = Growfactors() gdiff_baseline.apply_to(growfactors) gdiff_response.apply_to(growfactors) else: growfactors = None pol = Policy(gfactors=growfactors) pol.ignore_reform_errors() odict = dict() for param in gdict.keys(): odict[param] = dict() for year in sorted(gdict[param].keys()): odict[param][year] = dict() for suffix in gdict[param][year].keys(): plist = getattr(pol, param).tolist() dvals = plist[int(year) - Policy.JSON_START_YEAR] odict[param][year] = [dvals] idx = Policy.JSON_REFORM_SUFFIXES[suffix] odict[param][year][0][idx] = gdict[param][year][suffix] udict = {int(year): {param: odict[param][year]}} pol.implement_reform(udict) return odict # high-level logic of translate_json_reform_suffixes method: # - construct odict containing just parameters without a suffix odict = no_suffix(indict) # - group params with suffix into param_base:year:suffix dictionary gdict = suffix_group_dict(indict) # - add to odict the consolidated values for parameters with a suffix if gdict: odict.update( with_suffix(gdict, growdiff_baseline_dict, growdiff_response_dict)) # - return policy dictionary containing constructed parameter arrays return odict def ignore_reform_errors(self): """ Sets self._ignore_errors to True. """ self._ignore_errors = True # ----- begin private methods of Policy class ----- def _apply_clp_cpi_offset(self, cpi_offset_clp_data, num_years): """ Call this method from Policy constructor after self._inflation_rates has been set and before base class initialize method is called. (num_years is number of years for which inflation rates are specified) """ ovalues = cpi_offset_clp_data['value'] if len(ovalues) < num_years: # extrapolate last known value ovalues = ovalues + ovalues[-1:] * (num_years - len(ovalues)) for idx in range(0, num_years): infrate = round(self._inflation_rates[idx] + ovalues[idx], 6) self._inflation_rates[idx] = infrate @staticmethod def _cpi_offset_in_reform(reform): """ Return true if cpi_offset is in reform; otherwise return false. """ for year in reform: for name in reform[year]: if name == '_cpi_offset': return True return False def _apply_reform_cpi_offset(self, reform): """ Call this method ONLY if _cpi_offset_in_reform returns True. Apply CPI offset to inflation rates and revert indexed parameter values in preparation for re-indexing. Also, return known_years which is (first cpi_offset year - start year + 1). """ # extrapolate cpi_offset reform self.set_year(self.start_year) first_cpi_offset_year = 0 for year in sorted(reform.keys()): self.set_year(year) if '_cpi_offset' in reform[year]: if first_cpi_offset_year == 0: first_cpi_offset_year = year oreform = {'_cpi_offset': reform[year]['_cpi_offset']} self._update({year: oreform}) self.set_year(self.start_year) assert first_cpi_offset_year > 0 # adjust inflation rates cpi_offset = getattr(self, '_cpi_offset') for idx in range(0, self.num_years): infrate = round(self._inflation_rates[idx] + cpi_offset[idx], 6) self._inflation_rates[idx] = infrate # revert CPI-indexed parameter values to current_law_policy.json values for name in self._vals.keys(): if self._vals[name]['cpi_inflated']: setattr(self, name, self._vals[name]['value']) # return known_years return first_cpi_offset_year - self.start_year + 1 def _validate_parameter_names_types(self, reform): """ Check validity of parameter names and parameter types used in the specified reform dictionary. """ # pylint: disable=too-many-branches,too-many-nested-blocks data_names = set(self._vals.keys()) for year in sorted(reform.keys()): for name in reform[year]: if name.endswith('_cpi'): if isinstance(reform[year][name], bool): pname = name[:-4] # root parameter name if pname not in data_names: msg = '{} {} unknown parameter name' self.reform_errors += ('ERROR: ' + msg.format(year, name) + '\n') else: # check if root parameter is cpi inflatable if not self._vals[pname]['cpi_inflatable']: msg = '{} {} parameter is not cpi inflatable' self.reform_errors += ( 'ERROR: ' + msg.format(year, pname) + '\n') else: msg = '{} {} parameter is not true or false' self.reform_errors += ('ERROR: ' + msg.format(year, name) + '\n') else: # if name does not end with '_cpi' if name not in data_names: msg = '{} {} unknown parameter name' self.reform_errors += ('ERROR: ' + msg.format(year, name) + '\n') else: # check parameter value type bool_type = self._vals[name]['boolean_value'] int_type = self._vals[name]['integer_value'] assert isinstance(reform[year][name], list) pvalue = reform[year][name][0] if isinstance(pvalue, list): scalar = False # parameter value is a list else: scalar = True # parameter value is a scalar pvalue = [pvalue] # make scalar a single-item list for idx in range(0, len(pvalue)): if scalar: pname = name else: pname = '{}_{}'.format(name, idx) pvalue_boolean = ( isinstance(pvalue[idx], bool) or (isinstance(pvalue[idx], int) and (pvalue[idx] == 0 or pvalue[idx] == 1)) or (isinstance(pvalue[idx], float) and (pvalue[idx] == 0.0 or pvalue[idx] == 1.0))) if bool_type: if not pvalue_boolean: msg = '{} {} value {} is not boolean' self.reform_errors += ( 'ERROR: ' + msg.format(year, pname, pvalue[idx]) + '\n') elif int_type: if not isinstance(pvalue[idx], int): msg = '{} {} value {} is not integer' self.reform_errors += ( 'ERROR: ' + msg.format(year, pname, pvalue[idx]) + '\n') else: # param is neither bool_type nor int_type if not isinstance(pvalue[idx], (float, int)): msg = '{} {} value {} is not a number' self.reform_errors += ( 'ERROR: ' + msg.format(year, pname, pvalue[idx]) + '\n') def _validate_parameter_values(self, parameters_set): """ Check values of parameters in specified parameter_set using range information from the current_law_policy.json file. """ # pylint: disable=too-many-locals # pylint: disable=too-many-branches # pylint: disable=too-many-nested-blocks rounding_error = 100.0 # above handles non-rounding of inflation-indexed parameter values clp = self.current_law_version() parameters = sorted(parameters_set) syr = Policy.JSON_START_YEAR for pname in parameters: if pname.endswith('_cpi'): continue # *_cpi parameter values validated elsewhere pvalue = getattr(self, pname) for vop, vval in self._vals[pname]['range'].items(): if isinstance(vval, six.string_types): if vval == 'default': vvalue = getattr(clp, pname) if vop == 'min': vvalue -= rounding_error # the follow branch can never be reached, so it # is commented out because it can never be tested # (see test_range_infomation in test_policy.py) # --> elif vop == 'max': # --> vvalue += rounding_error else: vvalue = getattr(self, vval) else: vvalue = np.full(pvalue.shape, vval) assert pvalue.shape == vvalue.shape assert len(pvalue.shape) <= 2 if len(pvalue.shape) == 2: scalar = False # parameter value is a list else: scalar = True # parameter value is a scalar for idx in np.ndindex(pvalue.shape): out_of_range = False if vop == 'min' and pvalue[idx] < vvalue[idx]: out_of_range = True msg = '{} {} value {} < min value {}' extra = self._vals[pname]['out_of_range_minmsg'] if extra: msg += ' {}'.format(extra) if vop == 'max' and pvalue[idx] > vvalue[idx]: out_of_range = True msg = '{} {} value {} > max value {}' extra = self._vals[pname]['out_of_range_maxmsg'] if extra: msg += ' {}'.format(extra) if out_of_range: action = self._vals[pname]['out_of_range_action'] if scalar: name = pname else: name = '{}_{}'.format(pname, idx[1]) if extra: msg += '_{}'.format(idx[1]) if action == 'warn': self.reform_warnings += ('WARNING: ' + msg.format( idx[0] + syr, name, pvalue[idx], vvalue[idx]) + '\n') if action == 'stop': self.reform_errors += ('ERROR: ' + msg.format( idx[0] + syr, name, pvalue[idx], vvalue[idx]) + '\n')
def __init__(self, data='puf.csv', exact_calculations=False, gfactors=Growfactors(), weights=WEIGHTS_PATH, adjust_ratios=ADJUST_RATIOS_PATH, start_year=PUFCSV_YEAR): # pylint: disable=too-many-arguments # read specified data self._read_data(data, exact_calculations) # check that three sets of split-earnings variables have valid values msg = 'expression "{0} == {0}p + {0}s" is not true for every record' tol = 0.020001 # handles "%.2f" rounding errors if not np.allclose(self.e00200, (self.e00200p + self.e00200s), rtol=0.0, atol=tol): raise ValueError(msg.format('e00200')) if not np.allclose(self.e00900, (self.e00900p + self.e00900s), rtol=0.0, atol=tol): raise ValueError(msg.format('e00900')) if not np.allclose(self.e02100, (self.e02100p + self.e02100s), rtol=0.0, atol=tol): raise ValueError(msg.format('e02100')) # check that ordinary dividends are no less than qualified dividends other_dividends = np.maximum(0., self.e00600 - self.e00650) if not np.allclose(self.e00600, self.e00650 + other_dividends, rtol=0.0, atol=tol): msg = 'expression "e00600 >= e00650" is not true for every record' raise ValueError(msg) # handle grow factors is_correct_type = isinstance(gfactors, Growfactors) if gfactors is not None and not is_correct_type: msg = 'gfactors is neither None nor a Growfactors instance' raise ValueError(msg) self.gfactors = gfactors # read sample weights self.WT = None self._read_weights(weights) self.ADJ = None self._read_adjust(adjust_ratios) # weights must be same size as tax record data if not self.WT.empty and self.dim != len(self.WT): # scale-up sub-sample weights by year-specific factor sum_full_weights = self.WT.sum() self.WT = self.WT.iloc[self.index] sum_sub_weights = self.WT.sum() factor = sum_full_weights / sum_sub_weights self.WT = self.WT * factor # specify current_year and FLPDYR values if isinstance(start_year, int): self._current_year = start_year self.FLPDYR.fill(start_year) else: msg = 'start_year is not an integer' raise ValueError(msg) # consider applying initial-year grow factors if gfactors is not None and start_year == Records.PUF_YEAR: self._blowup(start_year) # construct sample weights for current_year wt_colname = 'WT{}'.format(self.current_year) if wt_colname in self.WT.columns: self.s006 = self.WT[wt_colname] * 0.01
def init(self, input_data, tax_year, reform, assump, growdiff_response, aging_input_data, exact_calculations): """ TaxCalcIO class post-constructor method that completes initialization. Parameters ---------- First four parameters are same as for TaxCalcIO constructor: input_data, tax_year, reform, assump. growdiff_response: Growdiff object or None growdiff_response Growdiff object is used only by the TaxCalcIO.growmodel_analysis method; must be None in all other cases. aging_input_data: boolean whether or not to extrapolate Records data from data year to tax_year. exact_calculations: boolean specifies whether or not exact tax calculations are done without any smoothing of "stair-step" provisions in the tax law. """ # pylint: disable=too-many-arguments,too-many-locals # pylint: disable=too-many-statements,too-many-branches self.errmsg = '' # get parameter dictionaries from --reform and --assump files paramdict = Calculator.read_json_param_objects(reform, assump) # create Behavior object beh = Behavior() beh.update_behavior(paramdict['behavior']) self.behavior_has_any_response = beh.has_any_response() # create gdiff_baseline object gdiff_baseline = Growdiff() gdiff_baseline.update_growdiff(paramdict['growdiff_baseline']) # create Growfactors clp object that incorporates gdiff_baseline gfactors_clp = Growfactors() gdiff_baseline.apply_to(gfactors_clp) # specify gdiff_response object if growdiff_response is None: gdiff_response = Growdiff() gdiff_response.update_growdiff(paramdict['growdiff_response']) elif isinstance(growdiff_response, Growdiff): gdiff_response = growdiff_response else: gdiff_response = None msg = 'TaxCalcIO.more_init: growdiff_response is neither None ' msg += 'nor a Growdiff object' self.errmsg += 'ERROR: {}\n'.format(msg) if gdiff_response is not None: some_gdiff_response = gdiff_response.has_any_response() if self.behavior_has_any_response and some_gdiff_response: msg = 'ASSUMP file cannot specify any "behavior" when using ' msg += 'GrowModel or when ASSUMP file has "growdiff_response"' self.errmsg += 'ERROR: {}\n'.format(msg) # create Growfactors ref object that has both gdiff objects applied gfactors_ref = Growfactors() gdiff_baseline.apply_to(gfactors_ref) if gdiff_response is not None: gdiff_response.apply_to(gfactors_ref) # create Policy objects if self.specified_reform: pol = Policy(gfactors=gfactors_ref) try: pol.implement_reform(paramdict['policy']) self.errmsg += pol.reform_errors except ValueError as valerr_msg: self.errmsg += valerr_msg.__str__() else: pol = Policy(gfactors=gfactors_clp) clp = Policy(gfactors=gfactors_clp) # check for valid tax_year value if tax_year < pol.start_year: msg = 'tax_year {} less than policy.start_year {}' msg = msg.format(tax_year, pol.start_year) self.errmsg += 'ERROR: {}\n'.format(msg) if tax_year > pol.end_year: msg = 'tax_year {} greater than policy.end_year {}' msg = msg.format(tax_year, pol.end_year) self.errmsg += 'ERROR: {}\n'.format(msg) # any errors imply cannot proceed with calculations if self.errmsg: return # set policy to tax_year pol.set_year(tax_year) clp.set_year(tax_year) # read input file contents into Records objects if aging_input_data: if self.cps_input_data: recs = Records.cps_constructor( gfactors=gfactors_ref, exact_calculations=exact_calculations) recs_clp = Records.cps_constructor( gfactors=gfactors_clp, exact_calculations=exact_calculations) else: # if not cps_input_data recs = Records(data=input_data, gfactors=gfactors_ref, exact_calculations=exact_calculations) recs_clp = Records(data=input_data, gfactors=gfactors_clp, exact_calculations=exact_calculations) else: # input_data are raw data that are not being aged recs = Records(data=input_data, gfactors=None, exact_calculations=exact_calculations, weights=None, adjust_ratios=None, start_year=tax_year) recs_clp = copy.deepcopy(recs) if tax_year < recs.data_year: msg = 'tax_year {} less than records.data_year {}' msg = msg.format(tax_year, recs.data_year) self.errmsg += 'ERROR: {}\n'.format(msg) # create Calculator objects con = Consumption() con.update_consumption(paramdict['consumption']) self.calc = Calculator(policy=pol, records=recs, verbose=True, consumption=con, behavior=beh, sync_years=aging_input_data) self.calc_clp = Calculator(policy=clp, records=recs_clp, verbose=False, consumption=con, sync_years=aging_input_data) # remember parameter dictionary for reform documentation self.param_dict = paramdict
def __init__(self, data='puf.csv', exact_calculations=False, gfactors=Growfactors(), weights=PUF_WEIGHTS_FILENAME, adjust_ratios=PUF_RATIOS_FILENAME, benefits=None, start_year=PUFCSV_YEAR): # pylint: disable=too-many-arguments,too-many-locals self.__data_year = start_year # read specified data self._read_data(data, exact_calculations) # check that three sets of split-earnings variables have valid values msg = 'expression "{0} == {0}p + {0}s" is not true for every record' tol = 0.020001 # handles "%.2f" rounding errors if not np.allclose( self.e00200, (self.e00200p + self.e00200s), rtol=0.0, atol=tol): raise ValueError(msg.format('e00200')) if not np.allclose( self.e00900, (self.e00900p + self.e00900s), rtol=0.0, atol=tol): raise ValueError(msg.format('e00900')) if not np.allclose( self.e02100, (self.e02100p + self.e02100s), rtol=0.0, atol=tol): raise ValueError(msg.format('e02100')) # check that ordinary dividends are no less than qualified dividends other_dividends = np.maximum(0., self.e00600 - self.e00650) if not np.allclose( self.e00600, self.e00650 + other_dividends, rtol=0.0, atol=tol): msg = 'expression "e00600 >= e00650" is not true for every record' raise ValueError(msg) del other_dividends # check that total pension income is no less than taxable pension inc nontaxable_pensions = np.maximum(0., self.e01500 - self.e01700) if not np.allclose( self.e01500, self.e01700 + nontaxable_pensions, rtol=0.0, atol=tol): msg = 'expression "e01500 >= e01700" is not true for every record' raise ValueError(msg) del nontaxable_pensions # handle grow factors is_correct_type = isinstance(gfactors, Growfactors) if gfactors is not None and not is_correct_type: msg = 'gfactors is neither None nor a Growfactors instance' raise ValueError(msg) self.gfactors = gfactors # read sample weights self.WT = None self._read_weights(weights) self.ADJ = None self._read_ratios(adjust_ratios) # read extrapolated benefit variables self.BEN = None self._read_benefits(benefits) # weights must be same size as tax record data if not self.WT.empty and self.array_length != len(self.WT): # scale-up sub-sample weights by year-specific factor sum_full_weights = self.WT.sum() self.WT = self.WT.iloc[self.__index] sum_sub_weights = self.WT.sum() factor = sum_full_weights / sum_sub_weights self.WT *= factor # specify current_year and FLPDYR values if isinstance(start_year, int): self.__current_year = start_year self.FLPDYR.fill(start_year) else: msg = 'start_year is not an integer' raise ValueError(msg) # construct sample weights for current_year wt_colname = 'WT{}'.format(self.current_year) if wt_colname in self.WT.columns: self.s006 = self.WT[wt_colname] * 0.01 # specify that variable values do not include behavioral responses self.behavioral_responses_are_included = False
def reform_documentation(params): """ Generate reform documentation. Parameters ---------- params: dict compound dictionary structured as dict returned from the static Calculator method read_json_param_objects() Returns ------- doc: String the documentation for the policy reform specified in params """ # pylint: disable=too-many-statements,too-many-branches # nested function used only in reform_documentation def param_doc(years, change, base): """ Parameters ---------- years: list of change years change: dictionary of parameter changes base: Policy or Growdiff object with baseline values syear: parameter start calendar year Returns ------- doc: String """ # nested function used only in param_doc def lines(text, num_indent_spaces, max_line_length=77): """ Return list of text lines, each one of which is no longer than max_line_length, with the second and subsequent lines being indented by the number of specified num_indent_spaces; each line in the list ends with the '\n' character """ if len(text) < max_line_length: # all text fits on one line line = text + '\n' return [line] # all text does not fix on one line first_line = True line_list = list() words = text.split() while words: if first_line: line = '' first_line = False else: line = ' ' * num_indent_spaces while (words and (len(words[0]) + len(line)) < max_line_length): line += words.pop(0) + ' ' line = line[:-1] + '\n' line_list.append(line) return line_list # begin main logic of param_doc # pylint: disable=too-many-nested-blocks assert len(years) == len(change.keys()) basevals = getattr(base, '_vals', None) assert isinstance(basevals, dict) doc = '' for year in years: # write year base.set_year(year) doc += '{}:\n'.format(year) # write info for each param in year for param in sorted(change[year].keys()): # ... write param:value line pval = change[year][param] if isinstance(pval, list): pval = pval[0] if basevals[param]['boolean_value']: if isinstance(pval, list): pval = [ True if item else False for item in pval ] else: pval = bool(pval) doc += ' {} : {}\n'.format(param, pval) # ... write optional param-index line if isinstance(pval, list): pval = basevals[param]['col_label'] pval = [str(item) for item in pval] doc += ' ' * (4 + len(param)) + '{}\n'.format(pval) # ... write name line if param.endswith('_cpi'): rootparam = param[:-4] name = '{} inflation indexing status'.format(rootparam) else: name = basevals[param]['long_name'] for line in lines('name: ' + name, 6): doc += ' ' + line # ... write optional desc line if not param.endswith('_cpi'): desc = basevals[param]['description'] for line in lines('desc: ' + desc, 6): doc += ' ' + line # ... write baseline_value line if isinstance(base, Policy): if param.endswith('_cpi'): rootparam = param[:-4] bval = basevals[rootparam].get( 'cpi_inflated', False) else: bval = getattr(base, param[1:], None) if isinstance(bval, np.ndarray): # pylint: disable=no-member bval = bval.tolist() if basevals[param]['boolean_value']: bval = [ True if item else False for item in bval ] elif basevals[param]['boolean_value']: bval = bool(bval) doc += ' baseline_value: {}\n'.format(bval) else: # if base is Growdiff object # all Growdiff parameters have zero as default value doc += ' baseline_value: 0.0\n' return doc # begin main logic of reform_documentation # create Policy object with pre-reform (i.e., baseline) values # ... create gdiff_baseline object gdb = Growdiff() gdb.update_growdiff(params['growdiff_baseline']) # ... create Growfactors clp object that incorporates gdiff_baseline gfactors_clp = Growfactors() gdb.apply_to(gfactors_clp) # ... create Policy object containing pre-reform parameter values clp = Policy(gfactors=gfactors_clp) # generate documentation text doc = 'REFORM DOCUMENTATION\n' doc += 'Baseline Growth-Difference Assumption Values by Year:\n' years = sorted(params['growdiff_baseline'].keys()) if years: doc += param_doc(years, params['growdiff_baseline'], gdb) else: doc += 'none: using default baseline growth assumptions\n' doc += 'Policy Reform Parameter Values by Year:\n' years = sorted(params['policy'].keys()) if years: doc += param_doc(years, params['policy'], clp) else: doc += 'none: using current-law policy parameters\n' return doc
def __init__( self, input_data, tax_year, reform, assump, growdiff_response, # =None in static analysis aging_input_data, exact_calculations): """ TaxCalcIO class constructor. """ # pylint: disable=too-many-arguments # pylint: disable=too-many-locals # pylint: disable=too-many-branches # pylint: disable=too-many-statements # check for existence of INPUT file if isinstance(input_data, six.string_types): # remove any leading directory path from INPUT filename fname = os.path.basename(input_data) # check if fname ends with ".csv" if fname.endswith('.csv'): inp = '{}-{}'.format(fname[:-4], str(tax_year)[2:]) else: msg = 'INPUT file named {} does not end in .csv' raise ValueError(msg.format(fname)) # check existence of INPUT file if not os.path.isfile(input_data): msg = 'INPUT file named {} could not be found' raise ValueError(msg.format(input_data)) elif isinstance(input_data, pd.DataFrame): inp = 'df-{}'.format(str(tax_year)[2:]) else: msg = 'INPUT is neither string nor Pandas DataFrame' raise ValueError(msg) # construct output_filename and delete old output file if it exists if reform is None: self._reform = False ref = '' elif isinstance(reform, six.string_types): self._reform = True # remove any leading directory path from REFORM filename fname = os.path.basename(reform) # check if fname ends with ".json" if fname.endswith('.json'): ref = '-{}'.format(fname[:-5]) else: msg = 'REFORM file named {} does not end in .json' raise ValueError(msg.format(fname)) else: msg = 'TaxCalcIO.ctor reform is neither None nor str' raise ValueError(msg) if assump is None: asm = '' elif isinstance(assump, six.string_types): # remove any leading directory path from ASSUMP filename fname = os.path.basename(assump) # check if fname ends with ".json" if fname.endswith('.json'): asm = '-{}'.format(fname[:-5]) else: msg = 'ASSUMP file named {} does not end in .json' raise ValueError(msg.format(fname)) else: msg = 'TaxCalcIO.ctor assump is neither None nor str' raise ValueError(msg) self._output_filename = '{}{}{}.csv'.format(inp, ref, asm) delete_file(self._output_filename) # get parameter dictionaries from --reform and --assump files param_dict = Calculator.read_json_param_files(reform, assump) # make sure no behavioral response is specified in --assump beh = Behavior() beh.update_behavior(param_dict['behavior']) if beh.has_any_response(): msg = '--assump ASSUMP cannot assume any "behavior"' raise ValueError(msg) # make sure no growdiff_response is specified in --assump gdiff_response = Growdiff() gdiff_response.update_growdiff(param_dict['growdiff_response']) if gdiff_response.has_any_response(): msg = '--assump ASSUMP cannot assume any "growdiff_response"' raise ValueError(msg) # create gdiff_baseline object gdiff_baseline = Growdiff() gdiff_baseline.update_growdiff(param_dict['growdiff_baseline']) # create Growfactors clp object that incorporates gdiff_baseline gfactors_clp = Growfactors() gdiff_baseline.apply_to(gfactors_clp) # specify gdiff_response object if growdiff_response is None: gdiff_response = Growdiff() elif isinstance(growdiff_response, Growdiff): gdiff_response = growdiff_response else: msg = 'TaxCalcIO.ctor growdiff_response is neither None nor {}' raise ValueError(msg.format('a Growdiff object')) # create Growfactors ref object that has both gdiff objects applied gfactors_ref = Growfactors() gdiff_baseline.apply_to(gfactors_ref) gdiff_response.apply_to(gfactors_ref) # create Policy object and implement reform if specified if self._reform: pol = Policy(gfactors=gfactors_ref) pol.implement_reform(param_dict['policy']) clp = Policy(gfactors=gfactors_clp) else: pol = Policy(gfactors=gfactors_clp) # check for valid tax_year value if tax_year < pol.start_year: msg = 'tax_year {} less than policy.start_year {}' raise ValueError(msg.format(tax_year, pol.start_year)) if tax_year > pol.end_year: msg = 'tax_year {} greater than policy.end_year {}' raise ValueError(msg.format(tax_year, pol.end_year)) # set policy to tax_year pol.set_year(tax_year) if self._reform: clp.set_year(tax_year) # read input file contents into Records object(s) if aging_input_data: if self._reform: recs = Records(data=input_data, gfactors=gfactors_ref, exact_calculations=exact_calculations) recs_clp = Records(data=input_data, gfactors=gfactors_clp, exact_calculations=exact_calculations) else: recs = Records(data=input_data, gfactors=gfactors_clp, exact_calculations=exact_calculations) else: # input_data are raw data that are not being aged recs = Records(data=input_data, exact_calculations=exact_calculations, gfactors=None, adjust_ratios=None, weights=None, start_year=tax_year) if self._reform: recs_clp = copy.deepcopy(recs) # create Calculator object(s) con = Consumption() con.update_consumption(param_dict['consumption']) self._calc = Calculator(policy=pol, records=recs, verbose=True, consumption=con, sync_years=aging_input_data) if self._reform: self._calc_clp = Calculator(policy=clp, records=recs_clp, verbose=False, consumption=con, sync_years=aging_input_data)