%gedZddlmZddlmZmZmZmZmZm Z m Z m Z m Z m Z mZmZddlZddlZ ddlZdZddlmZddlmZmZmZdd lmZd d l m!Z!d d l"m#Z#d d l$m%Z%d dl&m'Z'd dl(m)Z)e de!e%e)Z*Gddee*Z+y#e$rdZYWwxYw)zI Container for the result of running the generate quantities (GQ) method )Counter) AnyDictGenericHashableListMutableMappingNoReturnOptionalTupleTypeVarUnionoverloadNTF)Method)build_xarray_dataflatten_chains get_logger)scan_generic_csv) CmdStanMCMC)InferenceMetadata) CmdStanMLE)RunSet) CmdStanVBFitc eZdZdZdededdfdZdefdZdede jfd Z de fd Z deeeffd Zedefd Zedeefd ZedeedffdZedefdZddddddedededede jf dZ d,deeeedfdededej<fdZe d,deddeeeedfdedede!f dZ"e d,dddeeeedfdededdf dZ" d,deeeedfdededdfd Z"d!ed"ede jfd#Z#d"edeee jffd$Z$d-d%Z%dedeeeffd&Z&dede jfd'Z'deededej<fd(Z(d.d)e)eddfd*Z*edee+e,e-ffd+Z.y)/ CmdStanGQz~ Container for outputs from CmdStan generate_quantities run. Created by :meth:`CmdStanModel.generate_quantities`. runset previous_fitreturnNc|jtjk(s$tdj |j||_||_tjd|_ |j}t||_ y)zInitialize object.zJWrong runset method, expecting generate_quantities runset, found method {}N) methodrGENERATE_QUANTITIES ValueErrorformatrrnparray_draws_validate_csv_filesr _metadata)selfrrconfigs Q/var/www/dash_apps/app1/venv/lib/python3.12/site-packages/cmdstanpy/stanfit/gq.py__init__zCmdStanGQ.__init__4sp }} : ::""(&"7  !-"$((2, ))+*62cxdj|jj|j|jjj j dg}dj|dj|jjdj|jj}|S)NzCmdStanGQ: model={} chains={}{}r)cmdz%{} csv_files: {} output_files: {}z ) r&rmodelchains_args method_argscomposejoin csv_files stdout_files)r,reprs r.__repr__zCmdStanGQ.__repr__Gs077 KK   KK KK   ) ) 1 1! 1 <  =CC  KK -- . KK 00 1   r0attrc|jdrtd| |j|S#t$r}t|jd}~wwxYw)z)Synonymous with ``fit.stan_variable(attr)_zUnknown variable name N) startswithAttributeError stan_variabler%args)r,r=es r. __getattr__zCmdStanGQ.__getattr__TsV ??3  #9$!@A A *%%d+ + * !&&) ) *s2 AA  Ac:|j|jSN)_assemble_generated_quantities__dict__r,s r. __getstate__zCmdStanGQ.__getstate__^s ++-}}r0c xi}t|jD]}|dk(r$t|jj|},t|jj|}|D]L}|dvs||||k7st dj |jj|||||||S)z Checks that Stan CSV output files for all chains are consistent and returns dict containing config and column names. Raises exception when inconsistencies detected. r)path)id fitted_paramsdiagnostic_file metric_file profile_fileinitseedstart_datetimezFCmdStan config mismatch in Stan CSV file {}: arg {} is {}, expected {})ranger4rrr9r%r&)r,dzeroidrestkeys r.r*zCmdStanGQ._validate_csv_filesfst{{# AAv(..q1)..q1!C  "#J%*4(88> $ 5 5a 8 # %c %c 9 B r0c.|jjS)zNumber of chains.)rr4rJs r.r4zCmdStanGQ.chainss{{!!!r0c.|jjS)z Chain ids.)r chain_idsrJs r.r]zCmdStanGQ.chain_idss{{$$$r0.c4|jjdS)z< Names of generated quantities of interest. column_names)r+cmdstan_configrJs r.r_zCmdStanGQ.column_namess ~~,,^< 4,,j9)):: $$GD--y9 $$( &&33F&&F$+6F?#;#A#A#CD%19D $&I   %%//99#>FFH  ((4 1--d3 Z!  .)!D  *a" $  !$++ij!Q.>"?@ @ 99IInia@KK j!Q   {{9:q!+,,?s"I4varsc|4t|tr|g}n|}ttj |}|rt|j t r0|j jstjdn}t|j tr0|j jstjdn3t|j trtjd|jgdt|jz}g}g}|!D]}||jj vrL|jj |} |j#|j| j$| j&h|r||j jj vr`|j jj |} |j#|j j| j$| j&|dvr|j)|t+dj-|n|}|}|j/||} |j1|} | j2\} } }t5j6t5j8d| dz| j;d| | j<}t5j>t5j8d| dz| j;d| | j<}t5j8d| | zdzj;d| | j<}t5j@|||| gd } tCjDtG| | }|r%|r#|rtCjH| ||gd |S| S|r|t| jJ}t|jJ}tM||zjODcgc] \}}|dkDr|}}}tCjH| jQ| jSd |gd S|r||S|Scc}}w)a Returns the generated quantities draws as a pandas DataFrame. Flattens all chains into single column. Container variables (array, vector, matrix) will span multiple columns, one column per element. E.g. variable 'matrix[2,2] foo' spans 4 columns: 'foo[1,1], ... foo[2,2]'. :param vars: optional list of variable names. :param inc_warmup: When ``True`` and the warmup draws are present in the output, i.e., the sampler was run with ``save_warmup=True``, then the warmup draws are included. Default value is ``False``. See Also -------- CmdStanGQ.draws CmdStanGQ.draws_xr CmdStanMCMC.draws_pd rhrirj)chain__iter__draw__Unknown variable: {}rcrrk)datartrt)rtT)drop)*rmstrlistdictfromkeysrrrnrrorrprrHr_r+rsrrrend_idxappendr%r&_previous_draws_pdrshaper'repeatarangereshapeTtile concatenatepd DataFramerconcatrtrrqr reset_index)r,rrcrf vars_list all_columnsgq_cols mcmc_varsvarinfoprevious_draws_pdrn_drawsn_chainsr? chains_coliter_coldraw_coldraws_pdryrzr{r|r}s r.rzCmdStanGQ.draws_pd s2  $$!F  T]]956I 4,,k:))66 $$> 4,,j9)):: $$GD--y9 $$( ++-5T=N=N8OO !    I$..222>>33C8DNN))$..4<<H3$*;*;*E*E*O*O#O,,66@@ED$$))66 NNT\\ ;;NN3'$%;%B%B3%GHH% I("GI 33IzJ j 1${{1 IIbii8a<0' : WQ' * Q  GGBIIa1-x 8 WQ' * Q  IIa'H,1 2 WQ' * Q   HhFQO<<&  )yy) )#   )( DL+334F(**+F$+6F?#;#A#A#CD%19D 99%**4*8DD$DO  G$ $sQr,)zCmdStanGQ[CmdStanMLE]zCmdStanGQ[CmdStanVB]cyrGr"r,rrcrfs r.draws_xrzCmdStanGQ.draws_xr r0zCmdStanGQ[CmdStanMCMC]z xr.DatasetcyrGr"rs r.rzCmdStanGQ.draws_xrrr0cts tdt|jts tdg}g}|t|t r|g}n|}|D]{}||j jvs|rE||jj jvr#|j||j|ctdj|n}t|j jj}|rN|jj jjD]}||vs||vs |j||D]}|j||j|jj}|jj j } | dd| dd| d| d|d } |r7| d r2||jj"z }|jj"| d <i} |j$t'j(|d } |D]6}t+| |j j||j-| 8|rO|D]J}t+| |jj j||jj-| Lt/j0| | | j3dddS)a9 Returns the generated quantities draws as a xarray Dataset. This method can only be called when the underlying fit was made through sampling, it cannot be used on MLE or VB outputs. :param vars: optional list of variable names. :param inc_warmup: When ``True`` and the warmup draws are present in the MCMC sample, then the warmup draws are included. Default value is ``False``. See Also -------- CmdStanGQ.draws CmdStanGQ.draws_pd CmdStanMCMC.draws_xr z>Package "xarray" is not installed, cannot produce draws array.zKMethod "draws_xr" is only available when original fit is done via Sampling.rstan_version_major.stan_version_minorstan_version_patchr3) stan_versionr3num_draws_sampling save_warmupnum_draws_warmup)chaindrawr)coordsattrsrr.)XARRAY_INSTALLED RuntimeErrorrmrrrr+rsrr%r&rkeysremoverHrr`rr]r'rrrxrDataset transpose) r,rrcrfmcmc_vars_listdup_varsrr num_draws sample_configrr coordinatess r.rzCmdStanGQ.draws_xrs0 P $++[95   $$!F    Mdnn666!t00::DDD&--c2 ,()?)F)Fs)KLL MT^^55::<=I,,66@@EEG3C)+>0I&--c23 "C   S ! " ++-%%88 ))33BB ,-ABC112311235#7+"+ 0  - 6 **;; ;I(,(9(9(J(JE$ %.0^^IIi(6  C ((- j 1   % !%%//99#>%%++z+B zz${%@JJ VS  r0rkwargsc ||jjjj}|jjj}||vs(||vs$t d|ddj ||zz||vr0t j|jj|fi|S|j|j|jddxs|jdd\}}t|j|d}|jj|j|}|S) ai Return a numpy.ndarray which contains the set of draws for the named Stan program variable. Flattens the chains, leaving the draws in chain order. The first array dimension, corresponds to number of draws in the sample. The remaining dimensions correspond to the shape of the Stan program variable. Underlyingly draws are in chain order, i.e., for a sample with N chains of M draws each, the first M array elements are from chain 1, the next M are from chain 2, and the last M elements are from chain N. * If the variable is a scalar variable, the return array has shape ( draws * chains, 1). * If the variable is a vector, the return array has shape ( draws * chains, len(vector)) * If the variable is a matrix, the return array has shape ( draws * chains, size(dim 1), size(dim 2) ) * If the variable is an array with N dimensions, the return array has shape ( draws * chains, size(dim 1), ..., size(dim N)) For example, if the Stan program variable ``theta`` is a 3x3 matrix, and the sample consists of 4 chains with 1000 post-warmup draws, this function will return a numpy.ndarray with shape (4000,3,3). This functionaltiy is also available via a shortcut using ``.`` - writing ``fit.a`` is a synonym for ``fit.stan_variable("a")`` :param var: variable name :param kwargs: Additional keyword arguments are passed to the underlying fit's ``stan_variable`` method if the variable is not a generated quantity. See Also -------- CmdStanGQ.stan_variables CmdStanMCMC.stan_variable CmdStanMLE.stan_variable CmdStanPathfinder.stan_variable CmdStanVB.stan_variable CmdStanLaplace.stan_variable zUnknown variable name: z Available variables are z, rcFrdrN)rr+rsrr%r8r' atleast_1drBrHrugetrr)extract_reshape) r,rrmodel_var_names gq_var_namesdraw1r?routs r.rBzCmdStanGQ.stan_variables:X++55??DDF~~//446 &#*=)#/++))Ol:;<  l "==/!!//>v>  ++-$$zz,63zz*E2% qt{{5623..2237GGN r0c ,i}|jjjj}|jjj}|D]}|j|fi|||<|D]}||vs|j|fi|||<|S)aC Return a dictionary mapping Stan program variables names to the corresponding numpy.ndarray containing the inferred values. :param kwargs: Additional keyword arguments are passed to the underlying fit's ``stan_variable`` method if the variable is not a generated quantity. See Also -------- CmdStanGQ.stan_variable CmdStanMCMC.stan_variables CmdStanMLE.stan_variables CmdStanPathfinder.stan_variables CmdStanVB.stan_variables CmdStanLaplace.stan_variables )rr+rsrrB)r,rresultsample_var_namesrnames r.stan_variableszCmdStanGQ.stan_variablesTs$,,66@@EEG~~//446   >D-4--d=f=F4L >$ BD<'1t11$A&At  B r0c |jjdk7ry|jd\}}tj||j t |jftd}t|j D]i}t|jj|d5}d|D}tj|tjdd d |dd|ddf<dddk||_y#1swY}xYw) N)rTrF)dtypeorderrc3DK|]}|jdr|yw)#N)r@).0lines r. z;CmdStanGQ._assemble_generated_quantities..}sI$DOOC4HIs  rr,)rndminskiprows delimiter)r)rrur'emptyr4lenr_floatrVopenrr9loadtxtndarray)r,r?r gq_samplerfdliness r.rHz(CmdStanGQ._assemble_generated_quantitiesps ;;   $ ((D(9 9 "  S):):%; <! 4;;' Edkk++E2C8 BI"I)+1qC* !UA+&        s ' cd}|j}t|tr=|j}|jr!|r||j z }||fS|j }||fSt|t r4d}|jr"t|j}|r|}||fS|dz }||fSd}|jjd}|r|dz }||fS)Nrr) rrmrrrnrrrproptimized_iterations_npvariational_sampler)r,rcrp_fitr opt_iterss r.ruzCmdStanGQ._draws_starts!! e[ )00I!!!7!77I$i!"22E iz *I%% = =>  )Ii&MEi E0066q9IQ ir0c|j}t|tr|j|St|trG|r|j r|j dddfStj|jdddfS|r2tj|j|jgdddfS|jdddfS)zX Extract the draws from self.previous_fit. Return is always 3-d rN) rrmrrrrprr' atleast_2doptimized_params_npvstackvariational_params_npr)r,rcrs r.rvzCmdStanGQ._previous_drawss !! e[ );;*;5 5 z *e4444QW====))g yy00%2J2JKT'++AtG4 4r0c |r|}n tdd}|j}t|tr|j |xsd|St|t r,|r|j r|j|S|j|S|j|S)Nr) slicerrmrrrrpoptimized_iterations_pdoptimized_params_pdvariational_sample_pd)r,rrcselrs r.rzCmdStanGQ._previous_draws_pds +/Cd#C!! e[ )>>$,$:>F F z *e4444S990055..s3 3r0dirc:|jj|y)a Move output CSV files to specified directory. If files were written to the temporary session directory, clean filename. E.g., save 'bernoulli-201912081451-1-5nm6as7u.csv' as 'bernoulli-201912081451-1.csv'. :param dir: directory path See Also -------- stanfit.RunSet.save_csvfiles cmdstanpy.from_csv N)r save_csvfiles)r,rs r.rzCmdStanGQ.save_csvfiless !!#&r0cLtjd|jS)Nz@Property `mcmc_sample` is deprecated, use `previous_fit` instead)rrorrJs r. mcmc_samplezCmdStanGQ.mcmc_samples#  N    r0)NFF)r NrG)/__name__ __module__ __qualname____doc__rrr/rr<r'rrErrKrrr*propertyintr4rr]r r_rrbboolrrrrrrr rrBrrHrurvrr rrrrrr"r0r.rr.sn 333  3& # ** *d)T#s(^)V"""%49%%=eCHo== +!$# _-_- _-  _-  _- _-F-1  NDIsD()NN N  N`-1   CD CcD()       -1   & CcD()       -1  _ CcD()_ _  _  _ BCCCCJtS"**_0E8 & t c3h 65$52::5,4I4+/4 4('#'$'"!U; I#EF!!r0r),r collectionsrtypingrrrrrr r r r r rrnumpyr'pandasrxarrayrr ImportErrorcmdstanpy.cmdstan_argsrcmdstanpy.utilsrrrcmdstanpy.utils.stancsvrmcmcrrbrmlerrrvbrrrr"r0r.rs       *II4' e[*i8p ! p !#sBB  B