^j *dZddlZddlZddlmZddlmZddlmZm Z ddl m Z ddl m Z mZmZmZmZmZmZmZmZddlmZddlZddlmZd d lmZmZm Z m!Z!m"Z"d d l#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)d d l*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4gd Z5e rddl6Z6ddl7Z7edZ8edZ9edZ:eejve(e)ejxfZ=e>e?fZ@GddZAdeeAeBffdZCGddeZDdedddfdZEdddeeFdeeeFdfeeFeBfffdZGdee=de=fdZHdedeeefd ZId!e,d"eeFde,fd#ZJd$eeFefd%ee,d&eeeFe=fd'eFd(eBd)eBd*eKd+eBd,eAd-edee,fd.ZLd/e8d0eKdee=fd1ZMd'eFd2eeFdeeFeBffd3ZNdddddddddddddd4 ddd/e9d5e8d$eeFefd%ee,d6ee:d7ee8d8ee:d9eeee9e8fd:eeeFd;eee9ee9fdeeeeOeFfd?eee8d@eee:dAee0dBeeeeBeeBdCffd-ede,f(dDZPdEe=dFe,dGeKdHeKdIeKdJeKd-ede=fdKZQdLdLdLdLdMdFe,d/e9dddGeKdHeKdIeKdJeKd-ededNedNffdOZRGdPdQZSGdRdSe+eSZTGdTdUe.eSZUGdVdWe-eSZVy)XzDistributed training with LightGBM and dask.distributed. This module enables you to perform distributed training with LightGBM on dask.Array and dask.DataFrame collections. It is based on dask-lightgbm, which was based on dask-xgboost. N) defaultdict)deepcopy)Enumauto)partial) TYPE_CHECKINGAnyDictIterableListOptionalTupleTypeUnion)urlparse) LightGBMError_choose_param_value_ConfigAliases _log_info _log_warning)PANDAS_INSTALLEDSKLEARN_INSTALLEDLGBMNotFittedErrorconcat pd_DataFrame pd_Series) LGBMClassifier LGBMModel LGBMRanker LGBMRegressor#_LGBM_ScikitCustomObjectiveFunction_LGBM_ScikitEvalMetricType_lgbmmodel_doc_custom_eval_note_lgbmmodel_doc_fit_lgbmmodel_doc_predict_validate_eval_set_Xy)DaskLGBMClassifierDaskLGBMRankerDaskLGBMRegressor)dask.array.Arraydask.dataframe.DataFramedask.dataframe.Series)r+r,)r+r-c eZdZdefdZddZy) _RemoteSocketreturncRtjtjtj|_|jjtjtj d|jj d|jjdS)Nr)r)socketAF_INET SOCK_STREAM setsockopt SOL_SOCKET SO_REUSEADDRbind getsocknameselfs X/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/lightgbm/dask.pyacquirez_RemoteSocket.acquireKsjmmFNNF4F4FG  v00&2E2EqI !{{&&(++Nc8|jjyN)r3closer;s r=releasez_RemoteSocket.releaseQs r?)r0N)__name__ __module__ __qualname__intr>rCr?r=r/r/Js,, r?r/r0c>t}|j}||fSrA)r/r>)sports r= _acquire_portrLUsA 99;D d7Nr?cHeZdZdZeZeZeZeZy) _DatasetNameszPlaceholder names used by lightgbm.dask internals to say 'also evaluate the training data'. Avoid duplicating the training data when the validation set refers to elements of training data. N) rDrErF__doc__rTRAINSET SAMPLE_WEIGHT INIT_SCOREGROUPrHr?r=rNrN[s% vHFMJ FEr?rNclientdistributed.Clientc$ddlm}||S|S)zChoose a Dask client to use. Parameters ---------- client : distributed.Client or None Dask client. Returns ------- client : distributed.Client A Dask client. r)default_client)dask.distributedrW)rTrWs r=_get_dask_clientrYgs0 ~ r?workerszdistributed.client.FuturecPi}|D]}|jt|gdd||<!i}i}|jD]U\}}|jtjd|||<|jtjd|||<W|j |}||fS)aAssign an open port to each worker. Returns ------- worker_to_socket_future: dict mapping from worker address to a future pointing to the remote socket. worker_to_port: dict mapping from worker address to an open port in the worker's host. F)rZallow_other_workerspurerr)submitrLitemsoperator itemgettergather)rTrZworker_to_futureworkerworker_to_socket_futureworker_to_port_future socket_futureworker_to_ports r=_assign_open_ports_to_workersri|s #)== H % $1$   !!1!7!7!9] *0--8K8KA8NP]*^'(. h6I6I!6Lm(\f%] ]]#89N "N 22r?seqcht|dtjrtj|dSt|dtt fr t |dSt|dtjrtj|dStdt|djd)Nr)axiscsrformatzXData must be one of: numpy arrays, pandas dataframes, sparse matrices (from scipy). Got .) isinstancenpndarray concatenaterrrssspmatrixvstack TypeErrortyperD)rjs r=_concatrzs#a&"**%~~c** CF\95 6c"" CFBKK (yyU++fgklopqlrgsg|g|f}}~   r?argsc^|Dcgc]}|Dcgc]}|| c}c}}Scc}wcc}}wrArH)r{argzs r=_remove_list_paddingrs(9= ># -1q}Q - >> - >s )$ $ )) lgbm_modelrequired_namesc|D]=}||jvri|j|<||jvs/i|j|<?|S)aAppend missing (key, value) pairs to a LightGBM model's evals_result_ and best_score_ OrderedDict attrs based on a set of required eval_set names. Allows users to rely on expected eval_set names being present when fitting DaskLGBM estimators with ``eval_set``. ) evals_result_ best_score_)rr eval_names r=_pad_eval_namesrsW$3 J44 424J $ $Y / J22 202J " "9 - 3 r?params model_factory list_of_partsmachineslocal_listen_port num_machines return_modeltime_out remote_socketkwargsc  ||||d} |j| t|t} t|D cgc]} | d c} } t|D cgc]} | d c} }d|dvrt|D cgc]} | d c} }nd}d|dvrt|D cgc]} | d c} }nd}d|dvrt|D cgc]} | d c} }nd}t d|D}| j d d}| j d }d}d}d}d}d}|rtd |D}td |D}g}g}|rg}|rg}| rg}g}t|D]Q}g}g} g}!g}"g}#|D]}$|$j d s|r||}%nd|}%|$d |}&|&tjur)|j|$d| j|$dn(|j|&d| j|&d|%|vr|j|%|$j d}'|'r>|'|tjur|!j|$dn|!j|'||$j d}(|(r>|(|tjur|"j|$dn|"j|(||$j d})|)sZ|)|tjur|#j|$d|#j|)|t!|| |!|"|#\}} }!}"}#|r&|jt|t| fn|j||!r|jt|!|"r|jt|"|#s8|jt|#Tt|Dcgc] }||vs| }*}|r|*Dcgc]}|| }}|r|*Dcgc]}|| c}| d <|d}+d},n$t#d|D}+t#d|D},|di|}-||j% | r|-j&| |f||||+|,||||d | n|-j&| |f|||+|,|||d| t)|-ddr|-j*j- |r t/|-}-|r|-SdScc} wcc} wcc} wcc} wcc} wcc}wcc}wcc}w#t)|-ddr|-j*j-wwxYw)N)rrrrdatalabelweightrgroup init_scorec3RK|]}t|jdg!yw)eval_setN)lenget.0xs r= z_train_part..s D#aeeJ+,Ds%' eval_nameseval_class_weightc3BK|]}|jdduyw)eval_sample_weightNrrs r=rz_train_part..s $dQRQUU+?%@%L$dc3BK|]}|jdduyw)eval_init_scoreNrrs r=rz_train_part..s!^1!%%(9":$"F!^rrvalid_rrr eval_groupc3&K|] \}}| ywrArH)rX_s r=rz_train_part..T:41aQ:c3&K|] \}}| ywrArH)rrys r=rz_train_part..Urr) sample_weightrreval_Xeval_yrrrr)rrrrrrrfitted_F)rrrH)update issubclassr rzmaxpopranyrangerNrPappendextendrQrRrSrtuplerCfitgetattrbooster_ free_networkr).rrrrrrrrrrnetwork_params is_rankerrrrrrrn_evalsrrlocal_eval_setlocal_eval_nameslocal_eval_sample_weightlocal_eval_init_scorelocal_eval_grouphas_eval_sample_weighthas_eval_init_scoreevals_result_namesmissing_eval_component_idxix_ey_ew_e init_score_eg_epartevals_result_namer eval_weightrreval_component_idx local_eval_X local_eval_ymodels. r= _train_partrs.$ N  MM.!=*5I }5!AfI5 6D 7AQwZ7 8E=##}=!!H+=>-""];7;<}Q''}E!a oEF  DmDDGL$/J #67N# !$$dVc$d!d!!^P]!^^ !') $ $& ! ! &("w> 6ACCCLC%) 2xx +(21 %*0 % +A.}555JJtF|,JJtG}-JJx{+JJx{+$,>>&--.?@"hh';< "1~)D)DD 4>2 ;q>2"&((+<"="&q)]-E-EE$++D,>?$++OA,>?!XXl3 !!} (;(;; 4=1 :a=1S) 2X0DCcS_ad/e ,Cc<%%ws|WS\&BC*11!4(// =%,,W\-BC '' 5}> 6B*/w_A1D^;^a__ 7IJ! 1 J J I[*\A+:: :>::  #F #E  *  EII %%###; 5++   EII %%###; 5+   5)U + NN ' ' )5AST 5*d*I67> < F@`J*\V 5)U + NN ' ' ) ,sA R4 R91 R> S= S8 S S  S" S:=S*Tr is_matrixc|j}t|tjrW|r|jddk(s%J|j dk(s|jddk(sJ|j j}|S)Nr) to_delayedrqrrrsshapendimflattentolist)rrpartss r=_split_to_partsrsn OO E%$ ;;q>Q& &&::?ekk!n&9 99 &&( Lr?worker_addressesc|jd}tt|t|k7rtd|dt t}|D]3}|jd\}}||j t |5i}|D]>}t|j}|std|d||j||<@|S)aCreate a worker_map from machines list. Given ``machines`` and a list of Dask worker addresses, return a mapping where the keys are ``worker_addresses`` and the values are ports from ``machines``. Parameters ---------- machines : str A comma-delimited list of workers, of the form ``ip1:port,ip2:port``. worker_addresses : list of str An iterable of Dask worker addresses, of the form ``{protocol}{hostname}:{port}``, where ``port`` is the port Dask's scheduler uses to talk to that worker. Returns ------- result : Dict[str, int] Dictionary where keys are work addresses in the form expected by Dask and values are a port for LightGBM to use. ,z Found duplicates in 'machines' (zA). Each entry in 'machines' must be a unique IP-port combination.:z/Could not parse host name from worker address '') splitrset ValueErrorraddrGrhostnamer) rrmachine_addressesmachine_to_portaddresshostrKout worker_hosts r=_machines_to_worker_maprs,!s+ 3 !"c*;&<<.xj8y z  "#&O$-]]3' d!!#d),- C#:w'00 NwiWXYZ Z&{3779G : Jr? rrrrrrrrrrr eval_metriceval_atrrrrrrrrrrrrrr.c  ddlm}ddlm}t tfdtjdD}tfdtjd D}td d hd }d |vrtdd dd d <tjddD]'}|vstd|dj|)t|d}t|d}t||dDcgc] \}}||d }}}t|}|(t|d} t!|D] }!| |!||!d<|(t|d}"t!|D] }!|"|!||!d<|(t|d}#t!|D] }!|#|!||!d<t#|| | }|rst%d|D}$t't(}%| rt't(}&|rt't(}'|rt't(}(t+|D]\}!\})}*|)j,}+|)|ur7|*|ur3t!|D]$},|%|,j/t0j2&nt|)d}-t|*d}.t!|$D]m}/|/|z},|/|+kr |-|/}0|.|/}1nd}0d}1|/|kr|%|,j/|0g|1gf:|%|,ddj/|0|%|,ddj/|1o| r| |!|ur3t!|D]$},&|,j/t0j4&ndt| |!d}2t!|$D]F}/|/|+kr|2|/}3nd}3|/|z},|/|kr&|,j/|3g0&|,dj/|3H|r||!|ur3t!|D]$},(|,j/t0j6&ndt||!d}4t!|$D]F}/|/|+kr|4|/}5nd}5|/|z},|/|kr(|,j/|5g0(|,dj/|5H|s*||!|ur4t!|D]$},'|,j/t0j8&et||!d}6t!|$D]F}/|/|+kr|6|/}7nd}7|/|z},|/|kr'|,j/|7g0'|,dj/|7H|%j;D]5\},}8|8||,d <| r &|,||,d!<|r (|,||,d"<|s+'|,||,d#<7t)t=||}|j?|}|||D]}9|9j@d$k(s|9cS|D9cic]}9|9jB|9}:}9|jE|};t't(}<|;j;D].\}=}>|j/|:|=0|r;|.2seEerc3&K|]}|v ywrArHrs r=rz_train..5sYUf_Yrr tree_learnerr)main_param_namer default_value>rvotingfeature data_parallelvoting_parallelfeature_parallelzParameter tree_learner set to z/, which is not allowed. Using "data" as defaultr num_threadsz Parameter z will be ignored.T)rrF)strict)rrrrr)rrrc3:K|]}|djyw)rN) npartitionsrs r=rz_train..ms"F1Q4#3#3"FsrrrrrerrorzWorker z was not allocated eval_set data. Therefore evals_result_ and best_score_ data may be unreliable. Try rebalancing data across workers.rrrrip0z$Using passed-in 'machines' parameter)rrz3Using passed-in 'local_listen_port' for all workersz'local_listen_port' was provided in Dask training parameters, but at least one machine in the cluster has multiple Dask worker processes running on it. Please omit 'local_listen_port' or pass 'machines'.z%Finding random open ports for workers)rTrZrrrx) rrrrrrrrrrZr\r]timeout)4daskrrXr_DaskImportErrorTypesrrrrrrrrrziprrr'rrlist enumerater rrNrPrQrRrSr_mapcomputestatuskeywho_hasnextiterresultncoreskeysrrrrdictfromkeysrijoinr^rrb _other_params)TrTrrrrrrrrrrrrrrrrrrrrerrlisten_port_in_paramsmachines_in_paramsallowed_tree_learners param_alias data_parts label_partsrrrn_parts weight_partsr group_partsinit_score_partsn_largest_eval_parts eval_setseval_sample_weights eval_groupseval_init_scoresX_evaly_evaln_this_eval_parts parts_idx eval_x_parts eval_y_partsjrr eval_w_partsreval_init_score_partsr eval_g_partsre_setrkey_to_part_dictr worker_maprrZrd has_eval_set master_worker worker_ncoresrrrerworker_address_to_porta unique_hostsmsgworker_addressrKrrfutures_classifiersresultsvrparamsT ` r=_trainrNs ^K )f F e^=O=OPc=deeY.:L:LZ:XYY &F n%::,VN-C,DDs t "(~ &)).-H$ & :k]2CD E JJ{ #$ !dd;J!u>K36z;W[3\ ]!Qa! $ ]E ]%jG &MUK w 1A!-aE!HX  1 %5EB w /A +AE!HW  /* eLw 9A%5a%8E!H\ " 9%hvfUH #"FX"FF    juk  bmnrbsK grswgx #,X#6S C A & 2 2 ~&E/!&wHIi(// 0F0FGH /FdK .FeL 34@A !G I,,*1o*1o""7{!),33cUSENC"),R03::3?!),R03::3?#@&"%a(M9%*7^[ +I6==m>Y>YZ[$38J18MY^#_L##78 K00".q/C"&C$%K w;/ :AA3%H/ :2>EEcJ K"1%3%*7^U (3::=;S;STU-<QRAS_d,e)"#78 Q00+@+CL+/L$%K w;,Y7>> ~N,Y7;BB<P Qa=E)%*7^K #I.55m6I6IJK$3 1 QV#WL"#78 C00".q/C"&C$%K w;' 2993%@' 226==cB CSS Cl!* 1 H Iu+0E) Z (!9LY9Wi !566Fy6Qi !231 $C((%)]]3CEV%W " = >>[Z__./? ; #%; 88WmWsWsWu v?S~t(112!D6 : v -.L6&0%5%5%7#" "FM!   'CfCm]6-BC'4V<%ZZ C0155fdC M1H %  (mm/0G!'QQq'G' AJE !#''(;< 1E    # #E4 0 1 #'' 3 1E    # #E4 0 1 ##NI>- t,- L} !K@AsJKR ^`:~L$ w*(sB dd .d&d+$d0 Ad6d<d< d ddrr raw_score pred_proba pred_leaf pred_contribc |jddk(rtjg}n1|r|j|f|||d|}n|j|f|||d|}t |t rkdt|d}t|dsJ|t|jdk(rt ||j}|St||jd }|S) Nr)rOrQrRzLpredict(X) for lightgbm.dask estimators should always return an array, not 'z', when X is a pandas Dataframe. If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues.r)index predictions)rUname) rrrarray predict_probapredictrqrryhasattrrrUr) rrrOrPrQrRrr error_msgs r= _predict_partr]ms zz!}" $$$  %      %    $ % [[_`f[gZhiJ J vw'22' v||  !!& ;F MvTZZmLF Mr?F)rOrPrQrRr+c vtttfs td ddl}ddl}ddl}ddlm} t||jjr'|jtf|||||d|jSt||j j"r|j$} | dkDr/|r,t|j&t(j*rt-tf|d|dd d|} |j/} |j0j3| dddf}| d t4t6d t8d t6fd }|j| }|j:dz}|j<d}t?| Dcgc]}g}}|j&}tA|j/D]S\}}t?| D]@}|j j3||||||f|}||jC|BUt|t(jDrt-t(jFd}nFt|t(jHrt-t(jFd}nt(jF}g}t?| D]M}|jC|j j3| ||||jJd|f|O|S|jM|dgjO}t-tf|||||d|}||}|j<df}i} tQ|jJdkDr||jJdfz }nd| d<|jR|f||d| StUdtW|jXd#t$r} td| d} ~ wwxYwcc}w)aInner predict routine. Parameters ---------- model : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class Fitted underlying model. data : Dask Array or Dask DataFrame of shape = [n_samples, n_features] Input feature matrix. raw_score : bool, optional (default=False) Whether to predict raw scores. pred_proba : bool, optional (default=False) Should method return results of ``predict_proba`` (``pred_proba=True``) or ``predict`` (``pred_proba=False``). pred_leaf : bool, optional (default=False) Whether to predict leaf index. pred_contrib : bool, optional (default=False) Whether to predict feature contributions. **kwargs Other parameters passed to ``predict`` or ``predict_proba`` method. Returns ------- predicted_result : Dask Array of shape = [n_samples] or shape = [n_samples, n_classes] The predicted values. X_leaves : Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes] If ``pred_leaf=True``, the predicted leaf of every tree for each sample. X_SHAP_values : Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1] If ``pred_contrib=True``, the feature contributions for each sample. 6pandas and scikit-learn are required for lightgbm.daskrNrr)rrOrPrQrRrTFTr_rr0c ||SrArH)r_rs r=_extractz_predict.._extracts Qxr?r)valuermetarmrncsc drop_axis)chunksrcz6Data must be either Dask Array or Dask DataFrame. Got rp)-allrrr dask.arraydask.bagdask.dataframerrrrq dataframe DataFramemap_partitionsr]valuesrXArray _n_classes_metarurvrrbag from_delayedr r rG n_features_rfrrr csr_matrixrw csc_matrixrrrr map_blocksrxryrD)!rrrTrOrPrQrRrrrr% num_classespredict_functiondelayed_chunksrrrapredsnum_colsnrows_per_chunkrr pred_metar; partitionrr concat_fn out_arraysdata_row predict_fnpred_rowrfmap_blocks_kwargss! r=_predictrsN  "34 5TUUK $001"t""  !%    &  D$**** +&& ?| 4::r{{0S& %!    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This property can be passed in the constructor or updated with ``model.set_params(client=client)``. rFz4Cannot access property client_ before calling fit().)rT)rrrYrTr;s r=client_z_DaskLGBMModel.client_9s+tY.$%[\ \t{{33r?c|jjdd}|jjddt|j}|j ddi||_|S)z4Remove un-picklable attributes before serialization.rTN)__dict__rr$rrrT)r<rTrs r=_lgb_dask_getstatez!_DaskLGBMModel._lgb_dask_getstateEsY""8T2 x.t}}% Hd#$  r?Nrrrrrrrrrrrrrrrrr.rc tttfs td|j d}|j ddt didt|jd|d|d|d|d|d |d |d |d |d | d| d| d| d| d|d|d||}|jdi|j |j|||S)Nr_TrTrrrrrrrrrrrrrrrrrsourcedestrH) rgrrr get_paramsrrNrYrT set_params_lgb_dask_copy_extra_params)r<rrrrrrrrrrrrrrrrrrrs r= _lgb_dask_fitz_DaskLGBMModel._lgb_dask_fitNs5*$&789 XY Y& 8T" #DKK0     (  (  "   "    2 0 , "! "$# $' , -%**,- ((D(A r?c|j}|jdd|di|}|j|||jjdd|S)NrTrrH)rrrr$)r<rrrs r=_lgb_dask_to_localz!_DaskLGBMModel._lgb_dask_to_localsW" 8T"'' ((5(A $/ r?rrc|j}|j}t|jj |j}|D]}t ||||yrA)rrrr  differencesetattr)rrr attributesextra_param_namesrWs r=rz*_DaskLGBMModel._lgb_dask_copy_extra_paramss^ ""$__   12==fkkmL% 2D D$ 4 0 1 2r?)r0rU)rDrErFpropertyrr r rrr_DaskMatrixLike_DaskCollectionr _DaskVectorLiker rstrrr!r#rGrr staticmethodrrHr?r=rr8s  4 4DcN4804+/LP*.KOKO>B>B;?6:<@?C%4I4  4  4  0 4_-4(44o&F GHI4T#Y'4o0FFGH4o0FFGH4%T/%:;4$DtSy)9$:;4"$"784 T/23!4"89#4$%S 5c? :;<%4&'4( )4lYI 2& 12 2$i/0 2  2 2r?rc+eZdZdZddddddddd d d d d d d d ddddddededededededeeee fdeee efdedededededededed eeee jjd!fd"eed#ed$ed%d&ef*fd'ZejjZej%d(\ZZZed)zd*zd+zd,zd-zeze_d.eeeffd/Z d_ddd0d1ed2ed3eed4eed5eeeeefd6eeed7eeed8eeee efd9eeed:eed;eeeeefdd?d@dAdBdCdDdCEZedejCdFeejCdGdzZedejCdHeejCdIdzZedejCdJdKe"dLe_ d`d1edMe#dNedOeedPe#dQe#dRe#d&ed.dSfdTZ$e%j@dUd>dVdWdXdYZe$_ d`d1edMe#dNedOeedPe#dQe#dRe#d&ed.dSfd[Z&e%j@d\d>d]dWdXdYZe&_d.efd^Z'xZ(S)ar(z/Distributed version of lightgbm.LGBMClassifier.gbdtr皙?d@ NMbP??rr boosting_type num_leaves max_depth learning_rate n_estimatorssubsample_for_bin objective class_weightmin_split_gainmin_child_weightmin_child_samples subsamplesubsample_freqcolsample_bytree reg_alpha reg_lambda random_staten_jobsimportance_typerTrrrrrrrrrrrrrrrrrnp.random.GeneratorrrrTrUrc ||_t|did|d|d|d|d|d|d|d|d | d | d | d | d | d|d|d|d|d|d||y)zADocstring is inherited from the lightgbm.LGBMClassifier.__init__.rrrrrrrrrrrrrrrrrrrNrHrTsuper__init__r<rrrrrrrrrrrrrrrrrrrrTr __class__s r=rzDaskLGBMClassifier.__init__4   ' !   (  &  0    & * . 0   * .   "! "&# $% &,) r?**kwargs=client : distributed.Client or None, optional (default=None)  Dask client. K If ``None``, ``distributed.default_client()`` will be used at runtime. Y The Dask client used by this class will not be saved if the model object is pickled. **kwargs r0c"|jSrArr;s r= __getstate__zDaskLGBMClassifier.__getstate__&&((r?rrrrrrrrrrrrrrc  N|jdt||||||| | ||| | d | |S)zB>B;?<@"LPKO" " " 0 " _- " 4o&F GHI "T#Y'"%T/%:;"$DtSy)9$:;""$"78"89"o0FFGH"o0FFGH"" !"H*"))QRoBP!fK^ I5INN9569>>R^C_Ca9bbI:INN>:;i WfHgHi>jjI!!@9>>-#@ABC%%&   CK  '+""'      }          24,33AQ&do| GO  '+""'      }          4:299VQ+do| M7.7r?r(c+beZdZdZddddddddd d d d d d d d ddddddededededededeeee fdeee efdedededededededed eeee jjd!fd"eed#ed$ed%d&ef*fd'ZejjZej%d(\ZZZed)zd*zd+zd,zd-zeze_d.eeeffd/Z d[ddd0d1ed2ed3eed4eed5eeeeefd6eeed7eeed8eeed9eed:eeeeefd;eeeeefd&ed.dfd<Zej@d=d>d?d?d@dAdAdABZedejCdCeejCdDdzZedejCdEeejCdFdzZedejCdGeejCdHdzZedejCdIdJe"dKe_ d\d1edLe#dMedNeedOe#dPe#dQe#d&ed.dRfdSZ$e%j@dTd=dUdVdWdXYe$_d.efdZZ&xZ'S)]r*z.Distributed version of lightgbm.LGBMRegressor.rrrrrrNrrrrrrrrrrrrrrrrrrrrrrrrrrrrTrUrc ||_t|did|d|d|d|d|d|d|d|d | d | d | d | d | d|d|d|d|d|d||y)z@Docstring is inherited from the lightgbm.LGBMRegressor.__init__.rrrrrrrrrrrrrrrrrrrNrHrrs r=rzDaskLGBMRegressor.__init__prr?rrrrrrr0c"|jSrArr;s r=rzDaskLGBMRegressor.__getstate__rr?rrrrrrrrrrrrc  L|jdt||||||| | ||| d | |S)z;Docstring is inherited from the lightgbm.LGBMRegressor.fit.) rrrrrrrrrrrrrH)rr!) r<rrrrrrrrrrrrs r=rzDaskLGBMRegressor.fitsN"  ''!!1+#   r?rrrrrrrreval_class_weight :eval_init_score :rrrz**kwargs Other parameters passed through to ``LGBMRegressor.fit()``. Returns ------- self : lightgbm.DaskLGBMRegressor Returns self. rrOrrrQrRrr+c ntd|j|t|j||||||d |S)z?Docstring is inherited from the lightgbm.LGBMRegressor.predict.rrHrrs r=rZzDaskLGBMRegressor.predictrr?rr!Dask Array of shape = [n_samples]*Dask Array of shape = [n_samples, n_trees]1Dask Array of shape = [n_samples, n_features + 1]rc,|jtS)zCreate regular version of lightgbm.LGBMRegressor from the distributed version. Returns ------- model : lightgbm.LGBMRegressor Local underlying model. )rr!r;s r=rzDaskLGBMRegressor.to_locals&&}55r?)NNNNNNNr)(rDrErFrOrrGrr rr"r!rrrrr rr!rrr_kwargsrr rrrrr rr#rr%rorr$rrZr&rrrs@r=r*r*ms8 $"!'OS37 #"&!#"%[_ $&15-0 0  0  0  0 0 0 E#'J"JKL0 uT3Y/00 0  0 0 0 0  !0 "#0 $%0 &uS"))*?*?AV%VWX'0 ( )0 *+0 ,-.-0 ./0 d&&..I-6-@-@-L*NG] J K   Y Y g  g       )d38n)4804LP*.>B;?<@ LPKO      0  _-  4o&F GHI T#Y' %T/%:; "$"78 89 o0FFGH o0FFGH    D*"))QRolP!fc^ I5INN9569>>R^C_Ca9bbIAINN+@ABYy~~^qOrOtEuuI:INN>:;i WfHgHi>jjI!!@9>>-#@ABC%%&   CK  '+""'      }          24,33AQ&BCO GO6-6r?r*c+leZdZdZddddddddd d d d d d d d ddddddededededededeeee fdeee efdedededededededed eeee jjd!fd"eed#ed$ed%d&ef*fd'ZejjZej%d(\ZZZed)zd*zd+zd,zd-zeze_d.eeeffd/Z daddd0d1ed2ed3eed4eed5eed6eeeeefd7eeed8eeed9eeed:eeed;eedeeeeefd?eeeeefd&ed.df d@Zej@dAdBdCdCdDdEdEdEFZedejCdGeejCdHdzZedejCdIdJzdKdLdMzdKdNeejCdIdzZedejCdOdPe"dQe_ dbd1edRe#dSedTeedUe#dVe#dWe#d&ed.dXfdYZ$e%j@dZdAd[d\d]d^_e$_d.efd`Z&xZ'S)cr)z+Distributed version of lightgbm.LGBMRanker.rrrrrrNrrrrrrrrrrrrrrrrrrrrrrrrrrrrTrUrc ||_t|did|d|d|d|d|d|d|d|d | d | d | d | d | d|d|d|d|d|d||y)z=Docstring is inherited from the lightgbm.LGBMRanker.__init__.rrrrrrrrrrrrrrrrrrrNrHrrs r=rzDaskLGBMRanker.__init__!rr?rrrrrrr0c"|jSrArr;s r=rzDaskLGBMRanker.__getstate___rr?rrrrrrrrrrrrr.rrc  R|jdt|||||||| ||| | | | d||S)z8Docstring is inherited from the lightgbm.LGBMRanker.fit.)rrrrrrrrrrrrrrrrH)rr )r<rrrrrrrrrrrrrrrs r=rzDaskLGBMRanker.fitbsW(  $'!!1+!# ! $ r?rrrrrrr r zfeature_name :zCeval_at : list or tuple of int, optional (default=(1, 2, 3, 4, 5))  8z2The evaluation positions of the specified metric. 4rz**kwargs Other parameters passed through to ``LGBMRanker.fit()``. Returns ------- self : lightgbm.DaskLGBMRanker Returns self. rrOrrrQrRrr+c ntd|j|t|j||||||d |S)zB;?6:<@5D&LPKO!& & & 0 & _- & ( &4o&F GHI&T#Y'&%T/%:;&"$"78&T/23&89&tCy%S/12&o0FFGH& o0FFGH!&"#&$ %&P*"))QRolP!fc^ IAINN+@ABYy~~^qOrOtEuuI 4INN#345 P Q GF G HGIinn-=>@AB C D!!@9>>-#@ABC%%&   CK  '+""'      }          24,33AQ&BCO GO3*3r?r))WrOr`r3 collectionsrcopyrenumrr functoolsrtypingrr r r r r rrr urllib.parsernumpyrr scipy.sparsesparserubasicrrrrrcompatrrrrrrsklearnrrr r!r"r#r$r%r&r'__all__r distributedrrrrsrv _DaskPart ImportErrorrrr/rGrLrNrYrrirzrrrrrrr!rNr]rrr(r*r)rHr?r=r0sS #YYY!^^     _`FGCD "**lIr{{B C %j1u]C/0 D X&:;@T*"3 "3#Y"3 400 14S> AB "3J  i  Y  ??T#Y?I $|+ cN|+ ?|+S)^,- |+  |+  |+|+|+|+!|+|+i|+~_$y/))sm) #s(^ )f04,0'+HL&*GKGK:>:>7;268<;?'p p p  p cN p  ? pO,p)p O $ptE/?"BCDEpc#p U?E/,BBC Dp U?E/,BBC Dp!o!67p U49%5 67p d?34!p"o./#p$45%p&eDIuS#X67 8'p()p*+pf . . . .  .  ....lWi Wi Wi ! Wi  Wi  WiWiWiWi t$67 78Wit^2^2BQ7Q7hn6 ~n6bw3Zw3r?