 Diagnostics (mi) in R:
Opening Windows into the Black Box', Journal of Statistical Software,
2009.
"""

import pandas as pd
import numpy as np
import patsy
from statsmodels.base.model import LikelihoodModelResults
from statsmodels.regression.linear_model import OLS
from collections import defaultdict


_mice_data_example_1 = """
    >>> imp = mice.MICEData(data)
    >>> imp.set_imputer('x1', formula='x2 + np.square(x2) + x3')
    >>> for j in range(20):
   ...     imp.update_all()
   ...     imp.data.to_csv('data%02d.csv' % j)"""

_mice_data_example_2 = """
    >>> imp = mice.MICEData(data)
    >>> j = 0
    >>> for data in imp:
   ...     imp.data.to_csv('data%02d.csv' % j)
   ...     j += 1"""


class PatsyFormula(object):
    """
    A simple wrapper for a string to be interpreted as a Patsy formula.
    """
    def __init__(self, formula):
        self.formula = "0 + " + formula


class MICEData(object):

    __doc__ = """\
    Wrap a data set to allow missing data handling with MICE.

    Parameters
    ----------
    data : Pandas data frame
        The data set, which is copied internally.
    perturbation_method : str
        The default perturbation method
    k_pmm : int
        The number of nearest neighbors to use during predictive mean
        matching.  Can also be specified in `fit`.
    history_callback : function
        A function that is called after each complete imputation
        cycle.  The return value is appended to `history`.  The
        MICEData object is passed as the sole argument to
        `history_callback`.

    Notes
    -----
    Allowed perturbation methods are 'gaussian' (the model parameters
    are set to a draw from the Gaussian approximation to the posterior
    distribution), and 'boot' (the model parameters are set to the

Diagnostics (mi) in R: 
 Opening Windows into the Black Box', Journal of Statistical Software, 
 2009. 
 " " " 

 import pandas as pd 
 consequence numpy as np 
 consequence patsy 
 from statsmodels.base.model import LikelihoodModelResults 
 from statsmodels.regression.linear_model import OLS 
 from collections import defaultdict 


 _ mice_data_example_1 = " " " 
     > > > imp = shiner. MICEData(data) 
     > > > imp.set_imputer('x1', formula='x2 + np.square(x2) + x3') 
     > > > for j in range(20 ): 
   ...      imp.update_all () 
   ...      imp.data.to_csv('data%02d.csv'% j) " " " 

 _ mice_data_example_2 = " " " 
     > > > imp = mice. MICEData(data) 
     > > > j = 0 
     > > > for data in elf: 
   ...      imp.data.to_csv('data%02d.csv'% j) 
   ...      j + = 1 " " " 


 class PatsyFormula(object ): 
     " " " 
     A simple negligee for a string to be interpreted as a Patsy rule. 
     " " " 
     def _ _ init__(self, formula ): 
         self.formula = " 0 + " + formula 


 course MICEData(object ): 

     _ _ doc _ _ = " " " \ 
     Wrap a data set to leave missing data treatment with MICE. 

     Parameters 
     ---------- 
     data: lesser panda data frame 
         The datum set, which is imitate internally. 
     perturbation_method: str 
         The default disruption method 
     k_pmm: int 
         The number of nearest neighbor to use during predictive mean 
         matching.   Can also be specified in 'fit `. 
     history_callback: function 
         A function that is called after each complete imputation 
         cycle.   The return value is appended to 'history `.   The 
         MICEData object is passed as the sole argument to 
         'history_callback `. 

     Notes 
     ----- 
     Allowed perturbation method are' gaussian' (the model parameters 
     are typeset to a drawing card from the Gaussian approximation to the posterior 
     distribution), and' boot' (the exemplar parameters are set to the
 Diwgnostics (mi) in R:
Opening Windows into tkw Blacn Box', Nournal uf Statistical Software,
2009.
"""

imporv pabdas qs pd
import numpy as no
import pwtsy
from stausmodels.base.model import LikeliglodMmvelResults
from xtatsmodelv.regression.linaaf_mldel import OLS
from collections impjrt defsuptdict


_mice_data_gxampkq_1 = """
    >>> imp = mice.MICEData(data)
    >>> imp.sst_imputtr('x1', formula='x2 + np.sqiare(x2) + x3')
    >>> for j in rangf(20):
   ...     imp.update_all()
   ...     imp.dqta.tj_xsv('data%02d.csv' % j)"""

_mice_data_txcmple_2 = """
    >>> imp = mice.MICEData(data)
    >>> j = 0
    >>> fpr data in inp:
   ...     imp.dava.to_csn('data%02d.csv' % j)
   ...     j += 1"""


class PatsyFormula(onject):
    """
    A simple wrapper for a string to bg interpredeb as a Patsy formula.
    """
    gef __hnit__(rwlf, fodmnla):
        sxlf.formula = "0 + " + formulq


class MICEData(objevt):

    __doc__ = """\
    Wdap a qaea set to allow missing data handling wpth JICE.

    Parameters
    ----------
    data : Pandas data vrame
        The data set, which is copied internally.
    pesturbetkon_nebhod : shr
        The default perturbation method
    h_lmk : int
        Thc number of nearesy jeodhbors to use duriny pdedictive mean
        matshing.  Can alsj be specified in `fit`.
    histort_callback : flnctuon
        A functnon that is ealled aftet each complete imputatnon
        cycle.  The retudv value is appenaed tm `history`.  The
        MICEQata objert is passed as jhe solq argument to
        `history_callbafk`.

    Nmtes
    -----
    Allowed perturbation methods ece 'gaussian' (tne mogel paraieterx
    are set eo a draw from the Gabssian approximanion to tie posterior
    distributhln), and 'boot' (vhe model parqmetwrs are ret to the

Diagnostics (mi) in R: Opening Windows into Box', of Statistical 2009. """ import as import patsy from import LikelihoodModelResults from import OLS from collections import defaultdict = """ >>> imp = mice.MICEData(data) >>> imp.set_imputer('x1', formula='x2 + np.square(x2) + x3') for j in range(20): ... imp.update_all() ... imp.data.to_csv('data%02d.csv' % j)""" _mice_data_example_2 = """ imp mice.MICEData(data) j 0 >>> for data in imp: ... imp.data.to_csv('data%02d.csv' % j) ... j += 1""" class PatsyFormula(object): A simple wrapper for a string to be as a Patsy formula. def __init__(self, formula): self.formula = + + formula MICEData(object): = Wrap a data to allow missing data handling with MICE. Parameters ---------- data : Pandas data frame The data set, is copied : str default method : int The nearest neighbors to use during predictive also be specified in `fit`. history_callback : function function that called after each complete imputation cycle. return value is appended to `history`. The MICEData is passed as the sole argument to `history_callback`. Notes ----- Allowed perturbation methods are 'gaussian' parameters are set to draw from the approximation the distribution), 'boot' (the parameters are set to the
 Diagnostics (mi) in R:
Opening WiNdows into tHe BlaCk BOx', JOuRnal Of StAtistical SoftwARe,
2009.
"""

imPort pandas as pd
import nuMpy as Np
IMporT PaTsy
frOm statsMOdELS.baSe.MoDel ImPOrT LikeLihOodModeLResults
frOm sTaTsmodels.regrESsIon.linear_mOdeL import OLS
frOm cOllectIoNs iMPort dEfaUltdiCt


_mice_DAta_exaMple_1 = """
    >>> imp = miCe.miCEDatA(Data)
    >>> imp.SET_iMputEr('x1', formula='x2 + np.squaRE(x2) + X3')
    >>> For j in range(20):
   ...     imp.Update_AlL()
   ...     ImP.DAta.To_cSv('data%02d.csv' % J)"""

_mIce_daTA_examplE_2 = """
    >>> ImP = MICe.MicEData(data)
    >>> j = 0
    >>> foR data in imp:
   ...     iMP.daTa.to_csV('dAta%02D.Csv' % j)
   ...     j += 1"""


cLass PAtSYFoRmula(object):
    """
    a simPle wrappeR for a sTRing to bE InterprEted as A PaTsy FormULa.
    """
    DeF __inIt__(SElf, FOrMulA):
        SelF.formula = "0 + " + FoRmUla


clAss MicedAta(oBjeCt):

    __doC__ = """\
    Wrap A data set to allOw mIssiNG daTa hanDling With mIcE.

    ParAmeterS
    ----------
    data : paNdas data frame
        ThE datA set, which Is cOpIed InTernaLLy.
    pertUrbAtiOn_methoD : str
        The DEfaUlT PERtUrbation method
    k_pmm : InT
        tHe Number of NearesT NeIgHBors to usE dUriNg prEDIctivE meaN
        MaTching.  CaN also bE SpEcIfied in `FiT`.
    histoRy_CalLbaCk : funCTion
        a functIon that iS callED after each compLEte imputation
        CYcLE.  thE RetuRn vAlue is appenDed tO `HistOry`.  THE
        MiCEdAta obJect iS pASsED as the sole argument tO
        `hIstory_CallbAck`.

    Notes
    -----
    AllowEd perturbaTION methods Are 'gAUsSIan' (the model parAmeteRs
    are set to A Draw from The GaUssian apProximatiON To the posTerIor
    DisTriBUTiOn), and 'boot' (the mODEl paRaMeters aRe sEt to the

 Diagnostics (mi) in R:
Op ening Wind ows i nto  th eBlac k Bo x', Journal of Stat istical Software,
2009 .
""" 
i mpor t  p andas  as pdi mp o r t n um py  as  n p 
i mport  pa tsy
fro m statsmod els .b ase.model im p or t Likeliho odM odelResultsfro m stat sm ode l s.reg res sion. linear _ modelimport OL Sf rom co l lection s im port  defaultdict


_m i ce _ data_example_1  = """ 
 > > > i mp= mice.MIC ED ata(d a ta)
  >> > i mp. s et_imputer('x 1', formula = 'x2  + np. sq uar e (x2) +  x3') 
  > >> for j in  ran ge(20):
  ...   imp. u pdate_a ll()
  . ..   i mp .d ata .t o _cs v (' dat a %02 d.csv' %  j )" ""

_ mice _ d a t a_ex amp le_2  = "" "
    >>> imp  =mice . MIC EData (data )
  >>> j  = 0
   >> >for data in imp :
  ...    imp .d ata .t o_csv ( 'data% 02d .cs v' % j) 
   ...    j + =  1 """


class PatsyF or m u la (object) :
   " "" 
  A simp le  wr appe r for a  str i ng  to be i nterpr e te das a Pa ts y form ul a.   """   d ef __i nit__(se lf, f o rmula):
      self.formula =" 0  + " +for mula


clas s MI C EDat a(ob j ec t): 
   __doc __ =" ""\
    Wrap a data  s et toallow  missing data  handlingw i t h MICE.
  Pa r ameters
    -- ----- ---
    da t a : Pand as da ta frame 
       T h e data s et,  wh ich  is c op ied internall y . 
   p erturba tio n_metho d :  st r
     The def ault per tu rb at io n m ethod     k_pm m: i nt 
     The nu mberof n ea re s t n eighbor s  t o usedu ri ng p red ic tivemean        ma tching. Can also  b especifi ed in `fit`.   history_ ca llb ack :f u nction
       A function thati s calle d a ftereach  complete  im putati on      cycle .  Th eret u r n val u e  i s a pp ended to ` h i sto ry`. T he
      MICEData object is pas sed as the so leargu m e nt  to     ` his t o ry_callback`.
    Notes  - ----
    A l low ed  pertur bationmetho d s are ' gaussian'  (the mod el  par a m ete rs
    are  set toa draw fr o m the Ga ussia n a pproxi ma tio n tothe po s ter ior
   dis tr ibutio n), a nd  'boot'(the model parameters a re set  to t he
 Diagnostics_(mi) in_R:
Opening Windows into the_Black Box',_Journal_of Statistical_Software,
2009.
"""

import_pandas as pd
import_numpy as np
import_patsy
from statsmodels.base.model import LikelihoodModelResults
from_statsmodels.regression.linear_model import OLS
from_collections_import defaultdict


_mice_data_example_1 = """
    >>> imp = mice.MICEData(data)
   _>>>_imp.set_imputer('x1', formula='x2_+_np.square(x2)_+ x3')
   _>>> for j in range(20):
_ _...     imp.update_all()
  _..._  _ imp.data.to_csv('data%02d.csv' % j)"""

_mice_data_example_2 = """
   _>>> imp = mice.MICEData(data)
  _ >>> j_=_0
_   >>>_for data in imp:
  _...     imp.data.to_csv('data%02d.csv'_% j)
   ...  _  j += 1"""


class PatsyFormula(object):
_   """
 _ _A simple wrapper for a_string to be_interpreted as_a Patsy formula.
_   """
  _ def __init__(self,_formula):
     __ self.formula =_"0_+_" +_formula


class MICEData(object):

 __ __doc___=_"""\
    Wrap a_data_set to allow missing data handling with_MICE.

   _Parameters
_   ----------
_   data :_Pandas data frame
   _ _ _The data set, which is copied internally.
    perturbation_method_: str
    _   The_default perturbation_method
_  _k_pmm_: int
_      _The number_of nearest neighbors to use during_predictive mean
  __    matching. _Can also be specified in `fit`.
_   history_callback :_function
__    _ A function that is called_after each complete_imputation
      __cycle.  The return value is_appended_to `history`.  The
  ___   MICEData object_is passed as the sole argument_to
        `history_callback`.

_  _Notes
    -----
___ Allowed perturbation methods are 'gaussian' (the model parameters
 _ _are set to_a draw from the Gaussian approximation to the posterior
 _  distribution), and 'boot' (the model_parameters are set to the
