Source code for

Tax-Calculator abstract base data class.
# pycodestyle
# pylint --disable=locally-disabled

import os
import abc
import numpy as np
import pandas as pd
from taxcalc.growfactors import GrowFactors
from taxcalc.utils import read_egg_csv, read_egg_json, json_to_dict

[docs] class Data(): """ Inherit from this class for Records and other collections of cross-sectional data that need to have growth factors and sample weights to age the data to years after the start_year. Parameters ---------- data: string or Pandas DataFrame string describes CSV file in which data reside; DataFrame already contains cross-sectional data for start_year. NOTE: data=None is allowed but the returned instance contains only the data variable information in the specified VARINFO file. NOTE: when using custom data, set this argument to a DataFrame. start_year: integer specifies calendar year of the input data. gfactors: None or GrowFactors class instance None implies empty growth factors DataFrame; instance contains data growth factors. weights: None or string or Pandas DataFrame None creates empty sample weights DataFrame. string describes CSV file in which sample weights reside; DataFrame already contains sample weights. NOTE: when using custom weights, set this argument to a DataFrame. NOTE: assumes weights are integers that are 100 times the real weights. Raises ------ ValueError: if data is not a string or a DataFrame instance. if start_year is not an integer. if gfactors is not None or a GrowFactors class instance if weights is not None or a string or a DataFrame instance. if gfactors and weights are not consistent. if files cannot be found. Returns ------- class instance: Data """ # suppress pylint warnings about uppercase variable names: # pylint: disable=invalid-name # suppress pylint warnings about too many class instance attributes: # pylint: disable=too-many-instance-attributes __metaclass__ = abc.ABCMeta VARINFO_FILE_NAME = None VARINFO_FILE_PATH = None def __init__(self, data, start_year, gfactors=None, weights=None): # initialize data variable info sets and read variable information self.INTEGER_READ_VARS = set() self.MUST_READ_VARS = set() self.USABLE_READ_VARS = set() self.CALCULATED_VARS = set() self.CHANGING_CALCULATED_VARS = set() self.INTEGER_VARS = set() self._read_var_info() if data is not None: # check consistency of specified gfactors and weights if gfactors is None and weights is None: self.__aging_data = False elif gfactors is not None and weights is not None: self.__aging_data = True else: raise ValueError('gfactors and weights are inconsistent') # check start_year type and remember specified start_year if not isinstance(start_year, int): raise ValueError('start_year is not an integer') self.__data_year = start_year self.__current_year = start_year # read specified data self._read_data(data) # handle growth factors if self.__aging_data: if not isinstance(gfactors, GrowFactors): raise ValueError('gfactors is not a GrowFactors instance') self.gfactors = gfactors # read sample weights self.WT = None if self.__aging_data: self._read_weights(weights) # ... weights must be same size as data if self.array_length > len(self.WT.index): raise ValueError("Data has more records than weights.") elif self.array_length < len(self.WT.index): # 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 # ... 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 @property def data_year(self): """ Data class original data year property. """ return self.__data_year @property def current_year(self): """ Data class current calendar year property. """ return self.__current_year @property def array_length(self): """ Length of arrays in Data class's DataFrame. """ return self.__dim
[docs] def increment_year(self): """ Add one to current year; and also does extrapolation & reweighting for new current year if aged_data is True. """ # move to next year self.__current_year += 1 if self.__aging_data: # ... apply variable extrapolation growth factors self._extrapolate(self.__current_year) # ... specify current-year sample weights wt_colname = 'WT{}'.format(self.__current_year) self.s006 = self.WT[wt_colname] * 0.01
# ----- begin private methods of Data class -----
[docs] def _read_var_info(self): """ Read Data variables metadata from JSON file and specifies static variable name sets listed above. """ assert self.VARINFO_FILE_NAME is not None assert self.VARINFO_FILE_PATH is not None file_path = os.path.join(self.VARINFO_FILE_PATH, self.VARINFO_FILE_NAME) if os.path.isfile(file_path): with open(file_path) as pfile: json_text = vardict = json_to_dict(json_text) else: # find file in conda package vardict = read_egg_json( self.VARINFO_FILE_NAME) # pragma: no cover self.INTEGER_READ_VARS = set(k for k, v in vardict['read'].items() if v['type'] == 'int') FLOAT_READ_VARS = set(k for k, v in vardict['read'].items() if v['type'] == 'float') self.MUST_READ_VARS = set(k for k, v in vardict['read'].items() if v.get('required')) self.USABLE_READ_VARS = self.INTEGER_READ_VARS | FLOAT_READ_VARS INT_CALCULATED_VARS = set(k for k, v in vardict['calc'].items() if v['type'] == 'int') FLOAT_CALCULATED_VARS = set(k for k, v in vardict['calc'].items() if v['type'] == 'float') FIXED_CALCULATED_VARS = set(k for k, v in vardict['calc'].items() if v['type'] == 'unchanging_float') self.CALCULATED_VARS = (INT_CALCULATED_VARS | FLOAT_CALCULATED_VARS | FIXED_CALCULATED_VARS) self.CHANGING_CALCULATED_VARS = FLOAT_CALCULATED_VARS self.INTEGER_VARS = self.INTEGER_READ_VARS | INT_CALCULATED_VARS
[docs] def _read_data(self, data): """ Read data from file or use specified DataFrame as data. """ # pylint: disable=too-many-branches if data is None: return # because there are no data to read # read specified data if isinstance(data, pd.DataFrame): taxdf = data elif isinstance(data, str): if os.path.isfile(data): taxdf = pd.read_csv(data) else: # find file in conda package taxdf = read_egg_csv(data) # pragma: no cover else: msg = 'data is neither a string nor a Pandas DataFrame' raise ValueError(msg) self.__dim = len(taxdf.index) self.__index = taxdf.index # create class variables using taxdf column names READ_VARS = set() self.IGNORED_VARS = set() for varname in list(taxdf.columns.values): if varname in self.USABLE_READ_VARS: READ_VARS.add(varname) if varname in self.INTEGER_READ_VARS: setattr(self, varname, taxdf[varname].astype(np.int32).values) else: setattr(self, varname, taxdf[varname].astype(np.float64).values) else: self.IGNORED_VARS.add(varname) # check that MUST_READ_VARS are all present in taxdf if not self.MUST_READ_VARS.issubset(READ_VARS): msg = 'data missing one or more MUST_READ_VARS' raise ValueError(msg) # delete intermediate taxdf object del taxdf # create other class variables that are set to all zeros UNREAD_VARS = self.USABLE_READ_VARS - READ_VARS ZEROED_VARS = self.CALCULATED_VARS | UNREAD_VARS for varname in ZEROED_VARS: if varname in self.INTEGER_VARS: setattr(self, varname, np.zeros(self.array_length, dtype=np.int32)) else: setattr(self, varname, np.zeros(self.array_length, dtype=np.float64)) # delete intermediate variables del READ_VARS del UNREAD_VARS del ZEROED_VARS
[docs] def zero_out_changing_calculated_vars(self): """ Set to zero all variables in the self.CHANGING_CALCULATED_VARS set. """ for varname in self.CHANGING_CALCULATED_VARS: var = getattr(self, varname) var.fill(0.) del var
[docs] def _read_weights(self, weights): """ Read sample weights from file or use specified DataFrame as weights or create empty DataFrame if None. NOTE: assumes weights are integers equal to 100 times the real weight. """ if weights is None: return if isinstance(weights, pd.DataFrame): WT = weights elif isinstance(weights, str): if os.path.isfile(weights): WT = pd.read_csv(weights) else: # find file in conda package WT = read_egg_csv( os.path.basename(weights)) # pragma: no cover else: msg = 'weights is not None or a string or a Pandas DataFrame' raise ValueError(msg) assert isinstance(WT, pd.DataFrame) setattr(self, 'WT', WT.astype(np.int32)) del WT
[docs] def _extrapolate(self, year): """ Apply to data variables the growth factor values for specified year. """
# Override this empty method in subclass