:[g3 dZddlZddlZddlZddlZddlZddlmZddlmZddl m Z m Z ddl m Z ddlmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZddlZddlm Z m!Z!dd l"m#Z#m$Z$m%Z%m&Z&dd l'm(Z(dd l)m*Z*m+Z+m,Z,m-Z-dd l.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8dd l9m:Z:m;Z;ddlZ>m?Z?m@Z@mAZAmBZBmCZCmDZDmEZEmFZFmGZGmHZHddlImJZJddlKmLZMddlNmOZOerddlPZPddlQZQddlPmRZSddlPmTZUddlPmVZWnKe+deXdZWe+deXdZSe+deXdZUe+deXdZPe+deXdZQedZYed ZZed!e/ed"Z[gd#Z\ejd$Z^d%e_d&eeee`eee`e_ffd'ee`ee_e`fffd(Zad%e_d)ee`d*eee`e_fd'ee`ee_e`fffd+Zbdd,ZcGd-d.e jZdd/ee#d'e#fd0Zed1ed2d'd2fd3ZfGd4d5Zged6Zhd1ed2d7ed8ehfd9ed:ee`d'ehf d;Zieee`efZjdd?e0ZlGd@dAegZmGdBdCemZn ddDee$dEeeeeefdFeedGeodHe_dIeejdJe_dKepdLee1d'e4fdMZqdDee$dEeeeeefdFeedGeodHe_dKepdIeejd'e1fdNZrdOepdPed'e1fdQZsd%e_dReee`efd1d2d'ee`ee`e_fffdSZtd'eee`effdTZudUegdVeeeege`fd'ee`fdWZvd:ee`d1d2d'dfdXZwdYexdZe_d9exd[ee_d\ee`d]e_d'ee1eee1e`fffd^Zyd1d2d_ee`efdReee`efd`ee`efdUegdae_dVeeeege`fdbee3dcee2ddee_deee_epfdfee/dgeee(dhee2d'ee[fdiZze7 dddddddjdddkd1d2d`ee`efdUegdae_dVeeeege`fdbee3dcee2ddee_dfee/deee_epfdgeee(dhee2d'efdlZLdmepdned'epfdoZ{dpedqedree_dmepd'ef dsZ|dtedudvdpeZdweeYdnee_d8fdxee_e`fd'eYfdyZ}due/dze_dmepd{epdPed'eee_d8fee_e`fff d|Z~d1d2d}ee/edvfd'dvfd~Zd1d2d_ee`efd}ee/edvfdpeZdepdGeodepdepdepdepdepde&depd'eYfdZdejdddddjddf d1ed2d}ee[e/dvfdpeegeZfdepdGeodepdepdepdepdepde&depd'efdZd1d2d_ee`efd}ee/edvfdpeZde&de`dGeodepdweeYdepd'eYfdZdd/ejdjddfd1ed2d}ee[e/dvfdpeZde&de`dGeodepdweeYdepd'efdZd1ed2dee`dJee_dPed'eegeeeege`fff dZed}dd1d2d'efdZGdde?ZeHddd}gGddeeBZeHddd}gGddee>ZeHddd}gdGddeeAZeHdd}dgdGddeZeHdd}dgdGddeZy)a Dask extensions for distributed training ---------------------------------------- See :doc:`Distributed XGBoost with Dask ` for simple tutorial. Also :doc:`/python/dask-examples/index` for some examples. There are two sets of APIs in this module, one is the functional API including ``train`` and ``predict`` methods. Another is stateful Scikit-Learner wrapper inherited from single-node Scikit-Learn interface. The implementation is heavily influenced by dask_xgboost: https://github.com/dask/dask-xgboost Optional dask configuration =========================== - **xgboost.scheduler_address**: Specify the scheduler address, see :ref:`tracker-ip`. .. versionadded:: 1.6.0 .. code-block:: python dask.config.set({"xgboost.scheduler_address": "192.0.0.100"}) # We can also specify the port. dask.config.set({"xgboost.scheduler_address": "192.0.0.100:12345"}) N) defaultdict)contextmanager)partialupdate_wrapper)Thread) TYPE_CHECKINGAny AwaitableCallableDict GeneratorIterableListOptionalSequenceSetTuple TypedDictTypeVarUnion) collectiveconfig)_T FeatureNames FeatureTypesIterationRange)TrainingCallback) DataFrame LazyLoaderconcatlazy_isinstance) BoosterDataIterDMatrixMetric ObjectiveQuantileDMatrix XGBoostError_check_distributed_params_deprecate_positional_args_expect) _is_cudf_ser_is_cupy_alike) XGBClassifierXGBClassifierBaseXGBModel XGBRankerXGBRankerMixInXGBRegressorBase _can_use_qdm_check_rf_callback_cls_predict_proba_objective_decorator_wrap_evaluation_matricesxgboost_model_doc) RabitTracker)train) get_n_threads)array)bag) dataframeddzdask.dataframedaz dask.arraydbzdask.bagdask distributedzdask.distributed)da.Array dd.DataFramez dd.Series)rFrG TrainReturnTboosterhistory) CommunicatorContext DaskDMatrixDaskQuantileDMatrixDaskXGBRegressorDaskXGBClassifier DaskXGBRankerDaskXGBRFRegressorDaskXGBRFClassifierr;predictinplace_predictz[xgboost.dask] n_workersaddrsreturnc Ti} t|dtr |dd}|dd}t|||d}n'|d}t|ts|Jt||d}|j t |j }d|_|j |j|j|S#t$rb}t|dkrtjd t|dt|dt|t||dd}Yd}~|Sd}~wwxYw) Nrr<task)rVhost_ipportsortby)rVr[r])targetTzCFailed to bind address '%s', trying to use '%s' instead. Error: %s) isinstancetupler:strstartrwait_fordaemonupdate worker_argsr(lenLOGGERwarning_try_start_tracker) rVrWenvr[r\ rabit_trackeraddrthreades P/var/www/html/bid-api/venv/lib/python3.12/site-packages/xgboost/dask/__init__.pyrkrks+')C 7 eAh &AhqkG8A;D(# M8DdC(DL 88(#T&M }556   =,,./ J  7 u:>  R aM aM F  !E!"I6 J 7sB6B<< D'AD""D'addr_from_daskaddr_from_userc"t|||g}|S)z8Start Rabit tracker, recurse to try different addresses.)rk)rVrrrsrls rq_start_trackerrus Y(H IC Jc ddl}tjdk(rd}|dz }tj |yy#t$r}td|d}~wwxYw)Nrz6Dask needs to be installed in order to use this moduleWindowsz5Windows is not officially supported for dask/xgboost,z contribution are welcomed.)rD ImportErrorplatformsystemrirj)rDrpmsgs rq_assert_dask_supportr}sa I%E ,,s&  D  s; A AAc,eZdZdZdeddffd ZxZS)rLzNA context controlling collective communicator initialization and finalization.argsrXNc >t|di|tj}tj5}|j }|d|j }|d}dddddt|j z|jd<y#1swY3xYw)Nworkersidz[xgboost.dask-z]: DMLC_TASK_ID) super__init__rE get_worker worker_clientscheduler_infoaddressrbr)selfrworkerclientinfowwid __class__s rqrzCommunicatorContext.__init__s  4 '')  & & (F((*DY/AD'C)'5SE$>?R$R .!) (s (BB__name__ __module__ __qualname____doc__r r __classcell__rs@rqrLrLs"X Ss St S SrvrLvaluec t|S#t$r-tjjt |dcYSwxYw)z#Concatenate sequence of partitions.raxis)r TypeErrorrAmultilist)rs rqdconcatrs;4e} 4xxtE{334s 3AArdistributed.Clientc&t|ttjtdfsDt t ttjtdgt||tj}|S|}|S)z#Simple wrapper around testing None.N)r`typerE get_clientrr+)rrets rq_xgb_get_clientrs{ ftK$:$:$<=tDzJ K T+0023T$Z@$v, O  '-n+ "C J;AC Jrvc#`eZdZdZe dddddddddddddd dddedeed eed eed eed e d ee dee deedeedeedeedeede ddf dZ de fdZ ddddedeedeed eedeedeedeedeeddfdZdedeeeffdZdefdZy)rMaDMatrix holding on references to Dask DataFrame or Dask Array. Constructing a `DaskDMatrix` forces all lazy computation to be carried out. Wait for the input data explicitly if you want to see actual computation of constructing `DaskDMatrix`. See doc for :py:obj:`xgboost.DMatrix` constructor for other parameters. DaskDMatrix accepts only dask collection. .. note:: DaskDMatrix does not repartition or move data between workers. It's the caller's responsibility to balance the data. .. versionadded:: 1.0.0 Parameters ---------- client : Specify the dask client used for training. Use default client returned from dask if it's set to None. NF) weight base_marginmissingsilent feature_names feature_typesgroupqidlabel_lower_boundlabel_upper_boundfeature_weightsenable_categoricalrrdatalabelrrrrrrrrrrrrrXc tt|}||_| |_||ntj |_||_| | td| tdt|jdk7rtd|jt|tjtj fs.inconsistentssA&i[j\B"4ykc%j\:?@  JrvpartsNcP|jdk(s|jdsJdyy)Nr<zData should be partitioned by row. To avoid this specify the number of columns for your dask Array explicitly. e.g. chunks=(partition_size, X.shape[1]))ndimr)rs rq check_columnsz2DaskDMatrix._map_local_data..check_columns}s-::?ekk!n 7 4n?rvdcj|}|j}t|tjr(||j j }|S|}|S)zBreaking data into partitions, a trick borrowed from dask_xgboost. `to_delayed` downgrades high-level objects into numpy or pandas equivalents . )persist to_delayedr`rndarrayflattentolist)r delayed_obj delayed_listrrrs rqrz/DaskDMatrix._map_local_data..to_delayeds` q!A,,.K+u}}5k*.9.A.A.C.J.J.L   +  rvmetac| |}|Syrr)r meta_partsrrs rq flatten_metaz1DaskDMatrix._map_local_data..flatten_metas,6t,< !!rvrm_partsnamecd|-tt|k(sJd||||<yy)NXr)rrX_partsrrs rq append_metaz0DaskDMatrix._map_local_data..append_metasF"7|s7|3\S'463&d #rvrrrrrrfinished)keys) dask.delayedrrr rbrr_DaskCollectionrrangerhitemsappendrmaprDdelayedcomputerEwaitstatuspartition_order enumeratekey schedulerwho_hasrnextiterrrresult)$rrrrrrrrrrry_partsw_parts margin_parts qid_partsll_partslu_partsr packed_partsi part_dictrr delayed_parts fut_partspartkey_to_partitionrrrrrrrrrs$ ` @@@@@@rqrzDaskDMatrix._map_local_datads ) s) (+ 48I KN     4  / d7m  x8 Xd7m=T  T"u%w'#K0  %  12 12+17*; &$w-!8 & & & GW%GX&L-0Iu%H12H12 24 s7|$A,.I#kkm U!&q #,    * %(,C l,K'L /5nn].K y)))D;;*, 9dkk 9, " +GAt-.D  *,8AAytDHHdNyA4:4D4D4L4L'01yt$((y15M5 / ;Fd:K #MMOLC tDM* + 2 23CC3H I,%  "#'D  *0)H)O)O)Q#QD  = *B1/ $RsVF4L=L>LA L L4LL " L-L.BL6L7 L LL worker_addrc |j|j|j|j|j|j j |d|jdS)z\Create a dictionary of objects that can be pickled for function arguments. N)rrrrrrr)rrrrrrgetr)rr s rq_create_fn_argszDaskDMatrix._create_fn_argssV "//!//#33||"&"9"9__((d;++  rvc|jS)zxGet the number of columns (features) in the DMatrix. Returns ------- number of columns )rrs rqnum_colzDaskDMatrix.num_cols||rvr)NNNNNNN)rrrrr*_DataTrrfloatboolrrrr rrrbr r rrrrrvrqrMrM s, ,0 : -115#'0404+/)-7;7;59#(#: $: : ( : ) : o.: %: :  -:  -: (: o &: $O4: $O4: "/2!: "!#: $ %:  : x&9&,0-115)-597;7;@$@@( @ /* @ o. @o &@"/2@$O4@$O4@ @D  3  4S>  rvrM_MapRetTfunc.refsrc&Kt|}g}|D]z}g}|D]D}t|tr!|j|j |4|j|F|j fdg|d|gdd}|j||dt ttdttfd} tj|} | j| | d{}|j|jd{} | S7-7w)z.Map a function onto partitions of each worker.c|i|gSrr)rkwargsrs rqz'map_worker_partitions..sT4%:6%:$;rvF)purerallow_other_workersresultsrXc|D]}||cSyrr)rvs rq first_validz*map_worker_partitions..first_validsA}rvN)rr`rMrrsubmitrrrrC from_delayed reductionrr) rrrrfuturesrnrreffutr r?rs ` rqmap_worker_partitionsr's V $FGC#{+ C//56 C  fmm ;  F %   s!$Xhx&89hx>P //' "C k;7 7C>>#&--/ /F M 8 /s$CDD  &DDDD list_of_partscttsJidtdtddffd }t D]D\}}||d||d||d||d||d ||d ||d FS) NrrrXcj||vr ||}nd}||vrg|<|j|yyr)r)rrr r(rs rqrz!_get_worker_parts..append3sR =# # #D)DD  6!!t 4L   % rvrrrrrrr)r`rrrbr)r(rr_rs` @rq_get_worker_partsr,/s mT ** *#%F&#&S&T&-(1q&q'q(q- q%q%&q%&) MrvceZdZdZ ddeedeeedeeedeeedeeedeeed eeed eed eeeeefd eed dffd Z de d eefdZ d efdZ ddZ ded efdZxZS)DaskPartitionIterz*A data iterator for `DaskQuantileDMatrix`.NrrrrrrrrrrrXc Z||_||_||_||_||_||_||_||_| |_| |_ t|jtjjsJtjjtdf} t|j| sJt|j| sJt|j| sJt|j | sJt|j | sJd|_t |Edy)NrT) release_data)_data_label_weight _base_margin_qid_label_lower_bound_label_upper_bound_feature_names_feature_types_feature_weightsr` collectionsabcrr_iterrr) rrrrrrrrrrrtypesrs rqrzDaskPartitionIter.__init__Ls   ' "3"3++ /$**koo&>&>???))4:6$++u---$,,...$++U333$115999$115999  d+rvattrcNt||t|||jSyr)getattrr=)rr?s rq_getzDaskPartitionIter._getps( 4  *4&tzz2 2rvc4|j|jS)z5Utility function for obtaining current batch of data.)r1r=rs rqrzDaskPartitionIter.datauszz$**%%rvcd|_y)zReset the iteratorrN)r=rs rqresetzDaskPartitionIter.resetys  rv input_datac |jt|jk(ry||j|j d|j dd|j d|j d|j d|j d|j |j |j |xjd z c_y ) zYield next batch of datarr2r3Nr5r4r6r7) rrrrrrrrrrrr<)r=rhr1rrBr8r9r:)rrFs rqrzDaskPartitionIter.next}s ::TZZ ())H%99Y' &! .1"ii(<="ii(<=---- 11  a rv) NNNNNNNNNrXN)rrrrrr rrrrrbrBrrEr rrrrs@rqr.r.Is%4 &*&*+/#'1515049=)-",3i",S "",c# ", d3i( ", d3i ",$DI.",$DI.", -", c49n 56","#", ",H# &c&xCrvr.c'eZdZdZe dddddddddddddddddddedeed eed eed eed e d ee dee e e e fdeedeedeedeedeedeedeede ddf$fdZdedeee fffd ZxZS)rNz.A dask version of :py:class:`QuantileDMatrix`.NF)rrrrrrmax_binr%rrrrrrrrrrrrrrrrrJr%rrrrrrrXct||||||| | ||||||| || |_d|_| t | |_yd|_y)N)rrrrrrrrrrrrrrrT)rrrJrr_ref)rrrrrrrrrrrJr%rrrrrrrs rqrzDaskQuantileDMatrix.__init__sj, #//+''1  " .1o2c7 4 rvr czt||}|j|d<|j|j|d<|S)NrJr%)rrrJrL)rr rrs rqrz#DaskQuantileDMatrix._create_fn_argss;w&{3,,Y 99 ))DK rvr)rrrrr*rrrrrrrr rrr$rrbr rrrs@rqrNrNs8 ,0 (H -115#'049=!%!%+/)-7;7;59#('(H$(H(H( (H ) (Ho.(H%(H(H -(H c49n 56(H#(Hg (H((Ho &(H $O4!(H"$O4#(H$"/2%(H&!'(H( )(H (HT34S>rvrNc0eZdZdZdededdffd ZxZS)DaskDeviceQuantileDMatrixz`Use `DaskQuantileDMatrix` instead. .. deprecated:: 1.7.0 .. versionadded:: 1.2.0 rrrXNcXtjdtt||i|y)Nz)Please use `DaskQuantileDMatrix` instead.)warningswarn FutureWarningrr)rrrrs rqrz"DaskDeviceQuantileDMatrix.__init__s$ A=Q $)&)rvrrs@rqrOrOs'*c*S*T**rvrOrrrrnthreadrrJrr%c "tj} |Kd| jd} tj | t t jd|||||} | St|} tdi| |||d} t | |||||}|S)Nworker  has an empty DMatrix.rr)rrrJr%r)rrr)rrTrJr%rr) rErrrirjr'remptyr,r.)rrrrrTrrJrr%rr|r unzipped_dictitdmatrixs rq_create_quantile_dmatrixr]s # # %F }''=>s  KK ''1  %e,M    ##'  B  - G Nrvc tj}|}|Id|jd} tj | t t jd|||} | Std} dtt| dt| fd} t|} i}| jD]\}}| |}|||<t d i|||||||d }|S) zdGet data that local to worker from DaskDMatrix. Returns ------- A DMatrix object. rVrWrX)rrrTrrXc>td|Dryt|S)Nc3$K|]}|du ywrr).0r s rq z:_create_dmatrix..concat_or_none..%s-tt|s)anyr)rs rqconcat_or_nonez'_create_dmatrix..concat_or_none$s -- -t}rv)rrrrTrrr) rErrrirjr$rrYrrrr,r)rrrrrTrrrr(r|rr_rerZ concated_dictrrrr\s rq_create_dmatrixrgs  # # %FM''=>s  KK ''1    AXhqk2x{ &m4M$&M#))+ U 5 ! c, ##-'G Nrvrrc 2|r tdi|Stdi|S)Nr)r]rg)rrs rq_dmatrix_from_list_of_partsri;s!'1&11  $V $$rvdconfigc2Kdg}d}d}|y|D]}||vstd||jdd}|'|jdr|jdr|dd}|# tj j |\}}|||f}nd} tj j|jj}|jd }|jt|||d{} | S#t$rY{wxYw#t$rd}Y>wxYw7&w) zBGet rabit context arguments from data distribution in DaskDMatrix.scheduler_addressNrzUnknown configuration: []r<z/:)rr  startswithendswithrEcommget_address_host_portget_address_hostrrstrip Exceptionrun_on_schedulerru) rVrjr valid_configr[r\k user_addr sched_addrrls rq_get_rabit_argsr|AsK((L!GDA $ #:1#!>??++148  7#5#5c#:w?O?OPS?TamG    + 0 0 F Fw O dO   %%66v7G7G7O7OP %%d+ '' :y C J'     s`DA D#"C5 DADD.D/D5 D>DDD DDDDcDtjjddS)Nxgboost)default)rDrr rrvrq_get_dask_configros ;;??9d? 33rvdtrainevalsc^t|jj}|r~|D]y}t|dk(sJt |dt rt |dt sJ|d|urCt|djj}|j|}{t|S)Nr_rr<) setrrrhr`rMrbunionr)rr X_worker_maprprs rq_get_workers_from_datarzs!!2!2!7!7!9:L Aq6Q; ;adK0Z!c5J JJtv~QqT__1134J'--j9L   rvcK|jjd{}|dj}t||z }|rt d|y76w)NrzMissing required workers: )ridentityrr RuntimeError)rrrcurrent_workersmissing_workerss rq_check_workers_are_alivers`!!**, ,D9o**,O'l_4O77HIJJ -sAA7A train_reftrain_idevals_id evals_name n_threadscLtdi|d|i}g}t|D]\}} |||k(r|j|||f%| jdd'| d|k7r t d| d=tdi| ||d} ntdi| d|i} |j| ||f||fS)NrTr%zSThe training DMatrix should be used as a reference to evaluation `QuantileDMatrix`.)rTr%r)rirrr r) rrrrrrXyrrr%eval_Xys rq_get_dmatricesrs % Dy D) DB')ED/3 A;( " LL"jm, -  775$  +5zX% *E 1SCSPRSG1KCKKG gz!}-." u9rv global_configparamsnum_boost_roundobjfevalearly_stopping_rounds verbose_eval xgb_model callbacks custom_metricc6 Kt||}t||d{tt|||d{}t |dt dt t tt tffdtdtt dttdtdtdttf  f d }tj||4d{|A|Dcgc]\}}| }}}|Dcgc]\}}| }}}|Dcgc] }t|}}ng}g}g}t!||||t|||g|g|zd |id{}|cdddd{S7@7%7cc}}wcc}}wcc}w707 #1d{7swYyxYww) N parameters rabit_argsrrrrrrXc tj}|j}t||} |j | | di} t di|5t jdi5t||g|||| d\} } t|| | t| dk7r| nd } dddddd jdk7r | d}|Sd}|S#1swY/xYw#1swY3xYw)N)rTn_jobs)rrrr) rrr evals_resultrrrrrrrrrIr) rErcopyr=rfrLrconfig_contextr worker_trainrhnum_row)rrrrrrrr local_paramr local_historyrrrJrrrrrrrrrrs rqdispatched_trainz&_train_async..dispatched_trains'') oo' !+v6 yIFG35 .: .0E0E0V 0V&"%# IB#" /*"5zQeD+&;)## G1W .6 ::<1 "(+C  C E1W0V . .s$C )=C&C C C  C)r)rrr|rhr)r rbrrrdictrrHrE MultiLockrr')rrrjrrrrrrrrrrrr _rabit_argsrrn evals_datarrrs ` ` ``````` rq _train_asyncrs %VU3G "7F 333'G gvFFKf%22eCHo-.22I 2 s) 2  22 , 22h$$Wf55  (-.1!J.(-.1!J.'12z!1zH2JJH,     vJ    h#     /655q4Fn6..2  6555sFE'FE*BFE-F F( E/4F< E5FE;!.FFF F!F"F*F-F/FFF F FFT)rrrrrrrrctt|}t} |jtft j td| S)a8Train XGBoost model. .. versionadded:: 1.0.0 .. note:: Other parameters are the same as :py:func:`xgboost.train` except for `evals_result`, which is returned as part of function return value instead of argument. Parameters ---------- client : Specify the dask client used for training. Use default client returned from dask if it's set to None. Returns ------- results: dict A dictionary containing trained booster and evaluation history. `history` field is the same as `eval_result` from `xgboost.train`. .. code-block:: python {'booster': xgboost.Booster, 'history': {'train': {'logloss': ['0.48253', '0.35953']}, 'eval': {'logloss': ['0.480385', '0.357756']}}} )rrj)r}rlocalsrrr get_configr) rrrrrrrrrrrrrs rqr;r;sSZ V $F 8D 6;; '') "    rvis_df output_shapec&|xrt|dkSNr_r)rrs rq_can_output_dfrJs  +S&!++rvr predictioncolumnsct||jrt|dd}t|ddrZddl}|j dk(r"|j i|tjS|j ||tj|}|S|j dk(rt i|tj|St ||tj|}|S)z0Return dataframe for prediction when applicable.indexNzcudf.core.dataframerr)rdtype)rrr) rrrAr!cudfsizerrfloat32)rrrrrrs rq_maybe_dataframerNseZ--. gt, 4!6 D !#~~b'~OOG5==(J  !# WEMMQVWW"G5==J rvmapped_predictrJzdistributed.Futurerrc XKt|j}t|dk\r%t|tj r t dtt|tj |r|+t|tjr|j}n|}t j|||d||tjj|}t|dk(r|jdddf}|S|;t|tjtj fr|j!} n|} t|dk(rdg} g} ndg} t|tj r!t#t%t|dz } n't%t|dz D cgc]} | dz } } t|dk(r1t#|j&} t| t"sJ|df| d<nd} tj(|||d|| | | | t*j, }|Scc} ww) NzGUse `da.Array` or `DaskDMatrix` when output has more than 2 dimensions.T)rr<rr_F)chunks drop_axisnew_axisr)rarrhr`rArrrrBrto_dask_dataframemap_partitionsutils make_metailocr to_dask_arrayrrr map_blocksrr)rrJrrrrrbase_margin_df predictionsbase_margin_arrayrrrrs rq_direct_predict_implrksDIIK G <A*T2<<"@  U  jr||4lC  "z+rxx'H--/ )N''      ##D)  |  !%**1a40KX U  "z "))R\\2( 5@4M4M4O  +  |  !01sI.0HI$ -c,&7!&; <=+0\1BQ1F+GH+GaAE+GH |  !-1,=Ffd+ ++%a*F1IFmm      --   3IsF%H*' H%3A7H*featuresinplacec t|tsJtjj d}|j d|}|r)|j }|jddk(rd|d<t|d}|j|fdd i|}t|jdkDr|jdnd} i} t||jrt| D]} d | | < |j| fS) z@Create a dummy test sample to infer output shape for prediction.ir< predict_typemarginT output_margin)rvalidate_featuresFf4)r`rrrandom RandomStaterandnrpopr$rTrhrrr) rJrrrrrng test_samplem test_predt n_columnsrrs rq_infer_predict_outputrs h $$ $ ,, " "4 (C))Ax(K ::n % 1&*F? # 5A FeFvFJ'*:+;+;'CCr pred_leaf pred_contribsapprox_contribspred_interactionsriteration_range strict_shapec  ()Kt||d{} t|ttjt j fsAttttjt j gt|dtdtdtdttdtdtf    f d }t|tjt j frw|j|j!t"| |j$dt|t j d  d{\}}t'|| |d||d{S|j|j!t"| |j)d d  d{\}}|j*}|j,(|j.)|j0dtd t2t4tfdt6j8f()   f d }g}g}g}g}t;|j<j?}|D]l}|j<|}|jA||jAtC||gz|jA|Dcgc]}||jDc}ntG||D],\}}|j!d||g}|jI|.t;tG||||}tK|d}|D !cgc] \}} }!}| }}!} }}|D !cgc] \}} }!}|  }}!} }}|D !cgc] \}} }!}| }}!} }}g}"tG||D],\}}|j!|| ||g}#|"jI|#.g}$|jM|d{}tO|D]D\}%}&|$jItjP|"|%|&f|ddzt6jRFtjT|$d}'|'S7777cc}wcc}}!} }wcc}}!} }wcc}}!} }w7w)NrJ partitionrrr+rXc  tjdi5t| d}|j|     }t ||||}|cdddS#1swYyxYw)NT)rrr) rrrrrrrrrr)rrr$rTr)rJrrrr+rpredtrrrrrrrrrrs rqrz&_predict_async..mapped_predictsz " " 3] 3#'A OO+#+ /"3"3 /)$ E%YwFE%4 3 3s 8AA"r<F) rrrrrrrrr) rJrrrrrrrrrr c  |d}|jdd}tjdi 5t| |d}|j |    }|cdddS#1swYyxYw)NrrT)rrrrr)rrrrrrrrr)r rrr$rT)rJr rrrrrrrrrrrrrrrrs rqdispatched_predictz*_predict_async..dispatched_predictAsF|hh}d3  " " 3] 3'++#' AOO+#+ /"3"3 /)$ E)4 3 3s -A%%A.c&|djdS)Nrr)r)r s rqrz _predict_async..estF|'9'9!'.isadrv)r)rrrr)+rr`rMrBrrArrr+rr"r rrrrr!rrrrrrrrr rbrrrrrextendrhrziprsortedgatherrr"r concatenate)*rrrrrrrrrrrrr_boosterr _output_shaperrr+rr all_parts all_orders all_shapes all_workersworkers_addressr r(r rsparts_with_orderrorderr$farraysrrowsrrrs* ` ````````` @@rq_predict_asyncrs'vu55H d["((BLLA Bbhh EtDzRSS%(15@DS NQ 0$2<<01$*NN MM%A r||4+#+ /"3)  %   t* HdD-   #NN !\\^''+/%  OL! **O&&M&&MllGG4S>emm2IJJK4??//12O&  4 '3}- =>O?4884OP ' {I.4 MM 1 Q=Q QQ %AQ=Q>A0Q0Q3Q6Q9QFrXc tt|}|jtfdt j it S)aRun prediction with a trained booster. .. note:: Using ``inplace_predict`` might be faster when some features are not needed. See :py:meth:`xgboost.Booster.predict` for details on various parameters. When output has more than 2 dimensions (shap value, leaf with strict_shape), input should be ``da.Array`` or ``DaskDMatrix``. .. versionadded:: 1.0.0 Parameters ---------- client: Specify the dask client used for training. Use default client returned from dask if it's set to None. model: The trained model. It can be a distributed.Future so user can pre-scatter it onto all workers. data: Input data used for prediction. When input is a dataframe object, prediction output is a series. missing: Used when input data is not DaskDMatrix. Specify the value considered as missing. Returns ------- prediction: dask.array.Array/dask.dataframe.Series When input data is ``dask.array.Array`` or ``DaskDMatrix``, the return value is an array, when input data is ``dask.dataframe.DataFrame``, return value can be ``dask.dataframe.Series``, ``dask.dataframe.DataFrame``, depending on the output shape. r)r}rrrrrr) rrrrrrrrrrrrs rqrTrTs;b V $F 6;;~ UV5F5F5H UFH UUrvrc  Kt|}t||d{} t|tjt j fs.mapped_predictsf " " 3] 3 00 /)'"3)1J4&iWeL 4 3s AAr<T)rrrrrr)rrr`rBrrArrr+rrr"r rrrrr!rrr)rrrrrrrrrrrJrrrs ` ```` ` rq_inplace_predict_asyncrs{V $F%fe44G dRXXr||4 52<< 8$t*EFFz rxxryy1(2<< CT+EVWXXc    * ! ZZ]T2<<0%+%    KE4&{E4 S5:  s4GF>E:GGG9G:GGGc tt|}|jtfdt j it S)aInplace prediction. See doc in :py:meth:`xgboost.Booster.inplace_predict` for details. .. versionadded:: 1.1.0 Parameters ---------- client: Specify the dask client used for training. Use default client returned from dask if it's set to None. model: See :py:func:`xgboost.dask.predict` for details. data : dask collection. iteration_range: See :py:meth:`xgboost.Booster.predict` for details. predict_type: See :py:meth:`xgboost.Booster.inplace_predict` for details. missing: Value in the input data which needs to be present as a missing value. If None, defaults to np.nan. base_margin: See :py:obj:`xgboost.DMatrix` for details. .. versionadded:: 1.4.0 strict_shape: See :py:meth:`xgboost.Booster.predict` for details. .. versionadded:: 1.4.0 Returns ------- prediction : When input data is ``dask.array.Array``, the return value is an array, when input data is ``dask.dataframe.DataFrame``, return value can be ``dask.dataframe.Series``, ``dask.dataframe.DataFrame``, depending on the output shape. r)r}rrrrrr) rrrrrrrrrs rqrUrUsHf V $F 6;; .4.?.?.A EKX rv tree_methodc(Kdttdtdtffd }td d|i|\}}|d{}|||fSg}|D]<}|d|ur|j ||j |dd{|df>||fS7Q7w) z(A switch function for async environment.r%rrXcRtrtd|d|Stddi|S)N)rr%rJrr)r4rNrM)r%rrrJrs rq _dispatchz2_async_wrap_evaluation_matrices.._dispatch7sA  $&3;A 3&3F33rvcreate_dmatrixNrr<r)rrMr r8r) rrrJrr train_dmatrixrawaitedrps ``` rq_async_wrap_evaluation_matricesr/s4x ,44 45XIXQWXM5''M }e##G  Q4= NN1  ad AaD)*  ' !!(#s!7BB=B:B ;BBDaskScikitLearnBasec#JK ||_|d|_y#d|_wxYww)z-Temporarily set the client for sklearn model.N)r)rrs rq_set_worker_clientr Ks%    t s# # #c eZdZdZdZdedededeedee de f d Z dd edededeedee de f d Z dd edee de fd Z dd edee de fd Zdee fdZdefdZeddZej*ddZdede de fdZy)rz4D<D>Drclt|jj|j|||||S)N)rrrr)r}rrr)rrrrrrs rqrTzDaskScikitLearnBase.predicts> {{    '/#+    rvcK|j|}t|j||j|jd{}t |j|j |d|d{}|S767w)N)rrrT)rrrr)r#rMrrrrTr%)rrrr'r&s rq _apply_asyncz DaskScikitLearnBase._apply_asyncs 33OD( KKLL,,    KK""$+      s$AA?A;/A?4A=5A?=A?cft|jj|j||S)N)r)r}rrr*)rrrs rqapplyzDaskScikitLearnBase.applys, {{ 1 11oVVrvchdttffd }j|jS)NrXcKSwrrrsrqr+z(DaskScikitLearnBase.__await__.._s Ks)r r _client_syncr)rr+s` rqrzDaskScikitLearnBase.__await__s/ 3   #--//rvcH|jj}d|vr|d=|S)N_client)__dict__r)rthiss rq __getstate__z DaskScikitLearnBase.__getstate__s(}}!!#  Y rvc0t|j}|S)zThe dask client used in this model. The `Client` object can not be serialized for transmission, so if task is launched from a worker instead of directly from the client process, this attribute needs to be set at that worker. )rr1)rrs rqrzDaskScikitLearnBase.clients!. rvc<| |jnd|_||_y)NF) asynchronous _asynchronousr1)rclts rqrzDaskScikitLearnBase.clients25S--e rvrrc |j}t|dd} tjd}|rWtj 5}t ||5}|jj|fi|d|i}|cdddcdddS|jj|fi|d|jjiS#t$rd}YwxYw#1swYnxYwcdddS#1swYfxYw)zGet the correct client, when method is invoked inside a worker we should use `worker_client' instead of default client. Nr8FTr7) r1rArErrrr rrr7)rrrr7 in_workerrr3rs rqr/z DaskScikitLearnBase._client_syncs << "4%@L "&&(  ..0F+D&9T.dkk.. $*9E # :910 t{{VVT[[=U=UVV "!  ":9  100s5B? C'#C7 C'? C  C C C''C0)FTNNr)rXr)r9rrXN)rrrrr1rrrrrr rrTr*r,r rr r4propertyrsetterr r/rrvrqrrWskFG&&& & o. & ".1 & &V$"&1548      o.  ".1    *59 ".1 059W W".1W W09S>0d  ]] WWSWSWrvz3Implementation of the Scikit-Learn API for XGBoost. estimatorscJeZdZdZdededeedeedeeeeefdeeedeeed e e e fd ee e e fd eed efd Zeddddddddddededeedeedeeeeefd e e e fd ee e e fdeeedeeed eed dfdZy)rOzAdummy doc string to workaround pylint, replaced by the decorator.ry sample_weightreval_setsample_weight_eval_setbase_margin_eval_setverboserrrXc K|j} tdid|jd|jd|jd|d|ddddd|d |d | d |d |d |ddddd|j d|j d|jd{\} } t|jrt|j}nd}|j| | \}}} |jjtd|jtjt!| | |j#| |d|||j$|j&|d{}|d|_|j+|d|S77&w)NrrrJrr@rrrArrrBrCrD eval_groupeval_qidrrrTr7rrrjrrrrrrrrrrrrJrKr)get_xgb_paramsrrrrJrrrcallable objectiver7_configure_fitrrrrrget_num_boosting_roundsrr_Booster_set_evaluation_resultrrr@rArrBrCrDrErrrrrrrmetricrs rq _fit_asynczDaskXGBRegressor._fit_asyncs$$&= ;; (( LL        ( $ ,  $: "6   LL! 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Switch to `multi:softproba` insteadF)rrrrrT)allow_unknown_chunksizesrcr) rLrrrrrrBvstackr6rA)rrrrrr&rgrs rq_predict_proba_asyncz&DaskXGBClassifier._predict_proba_asyncs >>_ ,6 w-/#+ .   BII =ryy "'$ a"@&&QQ s2BBABcVt|j|j||||S)N)rrrr)r}r/rh)rrrrrs rq predict_probazDaskXGBClassifier.predict_probas7    % %/#+ !  rvrrcrKt ||||||d{}|r|St|jdk(r|dkDj t }|St|jdk(sJt |tjsJdtdtfd}tj||d}|S7w)Nr<g?r_xrXc&|jdS)Nr<r)argmax)rls rq_argmaxz1DaskXGBClassifier._predict_async.._argmaxsxxQx''rv)r) rrrhrastyperr`rBrr r) rrrrrr pred_probspredsrors rqrz DaskXGBClassifier._predict_asyncs!71 -!2K     z A %#%--c2E z''(A- --j"((3 33 (3 (3 (MM':CE % sB7B5BB7)TNN)rrrrrrrrrrrr"r0rSrZrrhr rjr.rrrrs@rqrPrPDs P P P 0 P o. P 8E/?*J$KLM P!)/)B CP'x'@APsDy!PE'8"345P"/2P Pp4815PT$(8 >$$&= KK (( LL        ( $ ,  $: "6   LL! " $66# $,,%  (!% 3 3Iv Fvv (( ;; ++-$& 88:  "&"<"<nn!)  $ * $Y/ S * s%B+E"-E.BE"E E" E"NF) rrrArrBrGrHrErrCrDrc ttjDcic] \}}|dvs ||}}}|j|jfi|Scc}}wrVrX)rrr@rrrArrBrGrHrErrCrDrryrrs rqrZzDaskXGBRanker.fitNs[& !'!1T!1AQ>S5S1!1T t  9D99Ur[)rrrr*rbr rrrrrrrrrr0r"rSrZr1rrrs@rqrQrQs +:8S8c8 8 @ @ @( @ o & @  0 @o.@8E/?*J$KLM@!)/)B C@'x'@A@Xo67@8O45@sDy!@E(G"345@"/2@ !@F ,0)-3715PT:>8<$)8 learning_rate subsamplecolsample_bynode reg_lambdar~rrrrrXNc .t|d||||d|yNr}rrrrr~rrrrrs rqrzDaskXGBRFRegressor.__init__w-  '-!    rvcBt|}|j|d<|SNnum_parallel_treerrJ n_estimatorsrrrs rqrJz!DaskXGBRFRegressor.get_xgb_params&')&*&7&7"# rvcyNr<rrs rqrNz*DaskXGBRFRegressor.get_num_boosting_roundsrvTrTrr@rArrBrErrCrDrcttjD cic] \} } | dvs | | } } } t|j|j t |di| |Scc} } wNrWrr}rrr5rrrrZrrr@rArrBrErrCrDrryrrrs rqrZzDaskXGBRFRegressor.fith !'!1T!1AQ>S5S1!1T455t~~F  d U A-A-rrrr*rrr rr rbrJrrNrrrrrrr"r0rZrrs@rqrRrRjsp *+%(,/&*    E?  #5/  UO       "S#X 4815PT$(8rBr?rCr@rAglobalsrrrH__all__ getLoggerrirrbrkrur}rLrrrMrr' _DataPartsr,r.rNrOrrr]rgrir|rrrrrrrrrrrrrrTrrUrr rrOrPrQrRrSrrvrqrs  8 #%-$ &JJ-II   6    )2  $ D')%5 6B D')\ 2B D')Z 0B fgi 0D]GI7IJK?@ ) *  F   + ,'' hsmXeCHo%>>? @' #uS#X 'TSMU38_- #uS#X   S*88S"48B<4B4H%9:?S"ppf : ' ) *' 3= !' 'c] '  'T$sCx. ! ZDd3i4H4HHV3+3l * 3 *."*L)*E#tCy.12*c]* *  * J  *** ' **Z3L)3E#tCy.123c]3 3  3  3 J 3 3l%T%S%W% ++%d38n5+?S+ #uS#X +\4(4S>24    (% S8H2I)J K  #Y K #YK 4K K sm      7Dw|,- -.8` `S>`d38n %` cN `  `  ` HU;#345 6` ) ` F `$C=`T "` `!123`F#`l`F  4 :>#"+/#'%)6:&*4 4 cN4 4 4 HU;#345 6 4 ) 4 F 4$C=4 4T "4!1234F#4 44n,$,e,, )-c;?:QQ !Q Q/* Q S/ Q sCx. QQh" " #",0";?"KN" 5c?DcN *+", ).w>R/R)S0Q QS>Q $ 44 5Q  Q  Q  QQQQQQ$QQQp YY!#"&,3V ) *3V w(<< =3V  V# $3V 3V  3V  3V3V3V3V3V$3V3V 3Vl8 8S>8 $ 44 58  8 $ 8  888/*888~'-YY"-1: ) *: w(<< =: :$ :  :  ::/*:: :z" ) *"#"c]" "  ;eK,<&=!>? ?@ "8 *>QW(QWh= g?VS:*,<S:S:lH7l+->l l^ 7 c('c( c(L  k .). .b  k .+. .rv