Source code for taxcalc.data
"""
Tax-Calculator abstract base data class.
"""
# CODING-STYLE CHECKS:
# pycodestyle data.py
# pylint --disable=locally-disabled data.py
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 = pfile.read()
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
# Override this empty method in subclass
