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Returns ------- params : dict A ``params`` dict with exactly one value for ``main_param_name``, and all aliases ``main_param_name`` removed. If both ``main_param_name`` and one or more aliases for it are found, the value of ``main_param_name`` will be preferred. N)rr^rxkeyspop)rrLrrvaparams rC_choose_param_valuers&f F''8G!:'Q/%9q'G:++-'E JJud #  KKM !&,mF #  ++-'E JJud # ,? 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IMM88B ? 8}}--.CDLLbRTRXRX\Zf$2)-5I5 RZZ >>9-L 9  3:p&\Q6s*II4I!I%&I%5+I*I/ file_namec[R"U[S9nSUS3nUb([US5nUR U5 SSS5 U$U$!,(df  U$=f)Ndefaultz pandas_categorical: r)rkdumpsr1openwrite)rrcategorical_json pandas_strrNs rC_dump_pandas_categoricalrksdzz"4>VW()9(:"=J )S !Q GGJ " :" ! s A  A model_strcSn[U5*nUb[U5*n[US5n[X45nUR U[ 5 UR 5n[U5S:aOUS-nMHSSS5 WSRS5R5nURU5(d"USRS5R5nO(Ub%URSSU5nXSR5nWRU5(a"[R"U[U5S5$g!,(df  N=f) Nzpandas_categorical:rbrrrrr) rr rmaxseekr readlinesrr startswithrfindrkrl) rr pandas_keyoffset max_offsetrNlines last_lineidxs rC_load_pandas_categoricalrws+'J*o Fi(( )T "aV0vx( u:?!  #"I$$W-335 ##J//b ((1779I  oodAv.dO))+ J''zz)C O$5677## "s A D88 Ec\rSrSrSrSr\RS\\ \ \ \ 4S\ R4Sj5r\RS\ 4Sj5rSrg ) r1ia Generic data access interface. Object should support the following operations: .. code-block:: # Get total row number. >>> len(seq) # Random access by row index. Used for data sampling. >>> seq[10] # Range data access. Used to read data in batch when constructing Dataset. >>> seq[0:100] # Optionally specify batch_size to control range data read size. >>> seq.batch_size - With random access, **data sampling does not need to go through all data**. - With range data access, there's **no need to read all data into memory thus reduce memory usage**. .. versionadded:: 3.3.0 Attributes ---------- batch_size : int Default size of a batch. irrEc[S5e)aReturn data for given row index. A basic implementation should look like this: .. code-block:: python if isinstance(idx, numbers.Integral): return self._get_one_line(idx) elif isinstance(idx, slice): return np.stack([self._get_one_line(i) for i in range(idx.start, idx.stop)]) elif isinstance(idx, list): # Only required if using ``Dataset.subset()``. return np.array([self._get_one_line(i) for i in idx]) else: raise TypeError(f"Sequence index must be integer, slice or list, got {type(idx).__name__}") Parameters ---------- idx : int, slice[int], list[int] Item index. Returns ------- result : numpy 1-D array or numpy 2-D array 1-D array if idx is int, 2-D array if idx is slice or list. z=Sub-classes of lightgbm.Sequence must implement __getitem__()NotImplementedError)r{rs rC __getitem__Sequence.__getitem__s8""abbrBc[S5e)z"Return row count of this sequence.z9Sub-classes of lightgbm.Sequence must implement __len__()rrs rC__len__Sequence.__len__s""]^^rBr:N)r<r=r>r?r@ batch_sizeabcabstractmethodrrslicerr`rrrrAr:rBrCr1r1sp6JcuS%c%:;c cc: ___rBc\\rSrSrSrS\S\\\S\\ \ 4S\ 4Sjr \ SS S\\ \ 4S S4S j5r\ S \\ \4S\\ \ 4S S4S j5rS6SjrS \\ \ 44SjrS7S\S\S\S\ S\ S\ S\ S\ S \\R.\R2R4\\R2R444SjjrS\S\S\S\S \4 SjrS\R.S\S\S\S\\R.S \\R.\44 SjrS\R.S\S\S\S \\R.\44 S jrS!\\R2R@\R2RB4S"\R.S#S$S%S$S&S$S'\S(\S)\ S \\\R2R@\\R2RB44S*jr"S+\R2RBS\S\S\S\\R.S \\R.\44 S,jr#S+\R2RBS\S\S\S \\\\R2R@\\R2RB4\44 S-jr$S+\R2RBS\S\S\S \\R.\44 S.jr%S/\R2R@S\S\S\S \\\\R2R@\\R2RB4\44 S0jr&S/\R2R@S\S\S\S \\R.\44 S1jr'S2\(S\S\S\S \\R.\44 S3jr)S \4S4jr*S5r+g)8_InnerPredictoriz_InnerPredictor of LightGBM. Not exposed to user. Used only for prediction, usually used for continued training. .. note:: Can be converted from Booster, but cannot be converted to Booster. booster_handlerpred_parameter manage_handlecXlX@lX l[U5Ul[ R "S5n[[R"UR[ R"U555 URUl g)aInitialize the _InnerPredictor. Parameters ---------- booster_handle : object Handle of Booster. pandas_categorical : list of list, or None If provided, list of categories for ``pandas`` categorical columns. Where the ``i``th element of the list contains the categories for the ``i``th categorical feature. pred_parameter : dict Other parameters for the prediction. manage_handle : bool If ``True``, free the corresponding Booster on the C++ side when this Python object is deleted. rN) _handle!_InnerPredictor__is_manage_handlerrFrrNrOrPrLGBM_BoosterGetNumClassesrTrU num_class)r{rrrr out_num_classs rCr_InnerPredictor.__init__sg*& "/"40@ Q   * *  ]+  ',,rBboosterr,rEc[R"S5n[[R"UR [R "U555 U"UR URUSS9$)zInitialize an ``_InnerPredictor`` from a ``Booster``. Parameters ---------- booster : Booster Booster. pred_parameter : dict Other parameters for the prediction. rFrrrr)rNrOrPrLGBM_BoosterGetCurrentIterationrrTr)ryrr out_cur_iters rC from_booster_InnerPredictor.from_boosters_||A   0 0 \*  "??&99)   rB model_filec .[R"5n[R"S5n[[R "[ [U55[R"U5[R"U555 U"U[US9USS9$)zInitialize an ``_InnerPredictor`` from a text file containing a LightGBM model. Parameters ---------- model_file : str or pathlib.Path Path to the model file. pred_parameter : dict Other parameters for the prediction. rrTr) rNc_void_prOrPrLGBM_BoosterCreateFromModelfilerSrrTr)ryr rrout_num_iterationss rCfrom_model_file_InnerPredictor.from_model_file sy *#\\!_  0 0s:' /0 ^,  )7*M)   rBNcUR(a*[[R"UR55 gg![ a gf=frG)rrPrLGBM_BoosterFreerAttributeErrorrs rC__del___InnerPredictor.__del__?s> &&400>?'   s:> A  A cURR5nURSS5 URSS5 U$)Nhandler)__dict__rr)r{thiss rC __getstate___InnerPredictor.__getstate__Fs5}}!!# 4  D! rBristart_iteration num_iteration raw_score pred_leaf pred_contribdata_has_headervalidate_featuresc ^ [U[5(a [S5e[U[5(aU(aURV s/sHn [ U 5PM n n [ R[U 5-"5n U V s/sHoRS5PM sn U SS&[[R"URU [ R"[U 5555 [U[5(a[USSUR S9Sn["n U(a[$n U(a[&n U(a[(n [U[ [*45(Ga [-5n [[R."UR[1[ U55[ R"U5[ R"U 5[ R"U5[ R"U5[1UR25[1U R4555 [6R8"U R4[6R:S9nUR<SnSSS5 GO[U[>R@RB5(aUREUUUU S9upGOZ[U[>R@RF5(aURIUUUU S 9upGO[U[6RJ5(aURMUUUU S 9upO[OU5(aURQUUUU S 9upO[U[R5(a,[6RT"U5nURMUUUU S 9upO[U[Z5(a-[]5 URMUR_5UUU S 9upO?[aS 5 [>R@RCU5nUREUUUU S9upU(aWRg[6Rh5n[W[R[>R@Rj45nU(daURlW:wdU(dU(aCURlU-S:XaURoUS5nU$[YSURlSUS35eU$s sn fs sn f!,(df  N=f![Van[YS 5UeSnAff=f![Va'n[S[cU5Rd35UeSnAff=f)aPredict logic. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, pyarrow Table, H2O DataTable's Frame (deprecated) or scipy.sparse Data source for prediction. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). start_iteration : int, optional (default=0) Start index of the iteration to predict. num_iteration : int, optional (default=-1) Iteration used for prediction. raw_score : bool, optional (default=False) Whether to predict raw scores. pred_leaf : bool, optional (default=False) Whether to predict leaf index. pred_contrib : bool, optional (default=False) Whether to predict feature contributions. data_has_header : bool, optional (default=False) Whether data has header. Used only for txt data. validate_features : bool, optional (default=False) If True, ensure that the features used to predict match the ones used to train. Used only if data is pandas DataFrame. .. versionadded:: 4.0.0 Returns ------- result : numpy array, scipy.sparse or list of scipy.sparse Prediction result. Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``). zGCannot use Dataset instance for prediction, please use raw data insteadrNrrirrrrrcsrrr  predict_typecscrr r*)rXrr r*)tablerr r*z(Cannot convert data list to numpy array.z'Converting data to scipy sparse matrix.zCannot predict data for type rzLength of predict result (z) cannot be divide nrow (r)8rr-rr(rrrNr%rr&rPr LGBM_BoosterValidateFeatureNamesrrOrr_C_API_PREDICT_NORMAL_C_API_PREDICT_RAW_SCORE_C_API_PREDICT_LEAF_INDEX_C_API_PREDICT_CONTRIBr rHLGBM_BoosterPredictForFilerSrrr`loadtxtrbrscipysparse csr_matrix_InnerPredictor__pred_for_csr csc_matrix_InnerPredictor__pred_for_cscr_InnerPredictor__pred_for_np2dr'_InnerPredictor__pred_for_pyarrow_tablerr BaseExceptionrr!r\rrrr<rrspmatrixsizereshape)r{rirr r!r"r#r$r%rD data_names ptr_namesr*rNpredsnrowerrr) is_sparses rCpredict_InnerPredictor.predictLsoV dG $ $ef f l + +0A*.,,7,Q#a&,J73z?:=I7ABz!HHW-zBIaL 55LLLLZ1  dL ) )$#$*#'#:#:   D- 3L 4L 1L dS$K ( (33 s4y) _5 \2 _5 ]3t223qvv   166<{{1~ell55 6 6-- /+) .KE4 ell55 6 6-- /+) .KE4 bjj ) ).. /+) /KE4 t $ $77 /+) 8KE4 d # # Vxx~.. /+) /KE4 l + + / 1..MMO /+) /KE4 `FGll--d3-- /+) .KE LL*EutU\\-B-B&CD ejjD0IzzD A% dB/ !#=ejj\Ibcgbhhi!jkk ]8B4\! 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You can split your data into chunks and then concatenate predictions for themr) _MAX_INT32r/rNr!rPrLGBM_BoosterCalcNumPredictrrOrTrU)r{rr rDr*n_predss rC__get_num_preds_InnerPredictor.__get_num_predss * ??IlK<<  ..#  + +  T" \* _- ]+ W%   }}rBrXrCc"[U5upg[U5upn URUUURSUS9n Uc$[R "U [R S9nO3[UR5S:wd[U5U :wa [S5e[R"S5n [[R"URU[R"U 5[R "URS5[R "URS5[R"U5[R"U5[R"U5[R"U5[#UR$5[R&"U 5URR)[R*"[R,555 5 XR.:wa [S5eXQRS4$)Nrrr rDr*rr+Wrong length of pre-allocated predict array Wrong length for predict results)rjr_InnerPredictor__get_num_predsrr`emptyrbrrrNr!rPrLGBM_BoosterPredictForMatrrOrRrSrrTrrrrU) r{rXrr r*rCrirhr type_ptr_data_rL out_num_predss rC__inner_predict_np2d$_InnerPredictor.__inner_predict_np2ds%S) %3D%9"&&+'1% '  =HHWBJJ7E   "c%jG&;JK Kq)   * *  ]+syy|,syy|, V$ \* _- ]+t**+ ]+ $$V^^FOO%DE  )) )?@ @iil""rBc [UR5S:wa [S5eURSnU[:Ga[R "[U[S9n[R "S/[U5-U/-5Vs/sHnURX#Xt5PM nn[R"S/U-[RS9R5n [R"[U5[RS9n [[R "X5[XSS55Hun upUR#U UUUXU S9 M X4$UR#UUUUSS9$s snf) zPredict for a 2-D numpy matrix.rz1Input numpy.ndarray or list must be 2 dimensionalrstartstopsteprrN)rXrr r*rC)rrrrJr`arangediffrrSrintpcumsumrTsumrbr array_split#_InnerPredictor__inner_predict_np2d)r{rXrr r*rDsectionsrrLn_preds_sectionsrCchunkstart_idx_pred end_idx_preds rC__pred_for_np2d_InnerPredictor.__pred_for_np2d/sb syy>Q PQ Qyy| * yyz:NH!tH~!5!>??A$$_QU?  "xxg RWWELLN HHS\   * *  _-""**6>>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ ]+ $$V^^FOO%DE  & )) )?@ @{rBc[UR5upVn[UR5upn URR [ RSS9n [n U[:Xa*[R"[R5"5n O)[R"[R5"5n [R"[R5"5nU [:Xa*[R"[R5"5nO)[R"[R 5"5n[ R""S[ R$S9n['[(R*"UR,U[R."U5U RR1[R"[R55U[R."U 5[R"[3UR55[R"[3UR55[R"UR4S5[R."U5[R."U5[R."U5[7UR85[R."U 5URR1[R"[R55[R:"U 5[R:"U5[R:"U555 UR=UUU UUUU SS9n[3UR5S- nUU4$)NFrrrrTrnrorprqrrrsrtru)rrwrrirrr`r_C_API_MATRIX_TYPE_CSRrrNrrRr!rrrrTrrPrLGBM_BoosterPredictSparseOutputrrOrrrrSrrT%_InnerPredictor__create_sparse_native)r{r)rr r*rr__rrVrWr matrix_typerprqrrromatricesrDs rC__inner_predict_csr_sparse*_InnerPredictor.__inner_predict_csr_sparsesC+7szz*B' R%3CHH%="kk(((> , 0 0#^^FNN;=N#^^FNN;=N ..8: 0 0!>>&..9;L!>>&//:>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ [)  (()GH ^, _- \*%  ,..)+%'#/  3::"~rBc U[:XaURUUUUS9$[UR5S- nU[:GaS/[ [ R"[U[S95-U/-n[ R"U5Vs/sHopRX#Xt5PM nn[ R"S/U-[ RS9R5n [ R"[U5[ RS9n [![!XfSS5[!XSS55H!uupupUR#XU UUUXUS9 M# X4$UR#UUUUSS9$s snf)zPredict for a CSR data.r(rrr\rN)r)rr r*rC)r2)_InnerPredictor__inner_predict_csr_sparserrwrJrr`r`rarSrrbrcrTrdrbr"_InnerPredictor__inner_predict_csr)r{r)rr r*rDrgrrLrhrC start_idxend_idxrjrks rC__pred_for_csr_InnerPredictor.__pred_for_csrs~ 1 122 /+) 3  3::" * sT"))*4j"YZZ^b]ccHfhfmfmnvfwxfwab++OA\fwGx!xxg RWWELLN HHS\+7szz*B' R%3CHH%="kk(((> , 0 0#^^FNN;=N#^^FNN;=N ..8: 0 0!>>&..9;L!>>&//:>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ [)  (()GH ^, _- \*%  ,..)+%'#/  yy|~rBcURSnU[:a URUR5UUUS9$U[:XaUR UUUUS9$UR UUUUS9n[R"U[RS9n[R"S5n[UR5upn [UR5upnURS[::deUR R#[R$SS9n['[(R*"UR,U [R."U 5URR1[R2"[R455U [R."U 5[R"[7UR55[R"[7UR55[R"URS5[R."U5[R."U5[R."U5[9UR:5[R<"U5URR1[R2"[R>5555 XhR@:wa [CS5eXu4$) zPredict for a CSC data.rr(r+rPrFrrR)"rrJr8tocsrr2)_InnerPredictor__inner_predict_sparse_cscrSr`rTrbrNr!rrwrrirrrrPrLGBM_BoosterPredictForCSCrrOrrrRrrSrrTrrUr)r{r,rr r*rDrLrCrXrrrrrVrWrs rC__pred_for_csc_InnerPredictor.__pred_for_cschs)yy| * &&IIK /+) '  1 122 /+) 3  &&+'% '   3q) *6szz*B' R%3CHH%="yy|z)))kk(((>   * *  _-""**6>>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ ]+ $$V^^FOO%DE  & )) )?@ @{rBr-c[(a [(d [S5e[SURR 55(d [ S5eURUUURUS9n[R"U[RS9n[R"S5n[U5n[[ R""UR$[R"UR&5[R("UR*5[R("UR,5[R."U5[R."U5[R."U5[1UR25[R4"U5URR7[R8"[R:555 5 XWR<:wa [ S5eXaR4$)zPredict for a PyArrow table.zACannot predict from Arrow without 'pyarrow' and 'cffi' installed.c3# UH4n[U5=(d [U5=(d [U5v M6 g7frGrrrrts rCr;_InnerPredictor.__pred_for_pyarrow_table..3r_qZ[#A&U*;A*>UBRSTBUU_q<>=Arrow table may only have integer or floating point datatypesrPrrrR)rrr/rrtypesrrSnum_rowsr`rTrbrNr!r rPrLGBM_BoosterPredictForArrowrrrrrrOrSrrTrrrrU) r{r-rr r*rLrCrXc_arrays rC__pred_for_pyarrow_table(_InnerPredictor.__pred_for_pyarrow_tablest"!nn cd dr_d_k_k_q_qrrr\] ]&&+'% '   3q) %U+  , , w//0 2 23 2 23 \* _- ]+t**+ ]+ $$V^^FOO%DE   )) )?@ @nn$$rBc[R"S5n[[R"UR [R "U555 UR$zpGet the index of the current iteration. Returns ------- cur_iter : int The index of the current iteration. rrNrOrPrrrrTrUr{r s rCcurrent_iteration!_InnerPredictor.current_iterationH||A   0 0  \*  !!!rB)__is_manage_handlerrrrrEN)rrFFFFF),r<r=r>r?r@_BoosterHandlerrrrrboolrrr rr rrr_LGBM_PredictDataTyperr`rr5r6r>rGrSrrfr;r9r7rrrr8rr:r&r<rrAr:rBrCrrs+!-&!-%T$Z0!-S#X !-  !-F  S#X     8 #t)$ S#X     <d38n !" %"'\#\\ \  \  \\\ \ rzz5<<00$u||7L7L2MM N\|    8'# ZZ'#'# '#  '#  # '# rzz3 '#R( ZZ(( (  ( rzz3  (T<" %,,))5<<+B+BB C<"::<"* <" + <" ( <"<"<"<" tELL++,d5<<3J3J.KK L<"|0 \\ $ $00 0  0  # 0 rzz3 0d8 \\ $ $88 8  8 uT%,,112D9P9P4QQRTWW X 8t) \\ $ $)) )  ) rzz3  )V8 \\ $ $88 8  8 uT%,,112D9P9P4QQRTWW X 8t; \\ $ $;; ;  ; rzz3  ;z+%+%+% +%  +% rzz3  +%Z"3"rBrcT\rSrSrSrSWS\S\\S\SS\\S\\ S \\ S \ S \ S \\ \\4S \S\\4SjjrSXSjrS\S\R,4SjrS\S\SS4SjrS\\R,S\\R,S\S\SS4 SjrS\R,SS4SjrS\ \\44SjrSYSjrS\\S\S\\ \\\R,4SS4Sjr!S\\S\\S\SS\\S\\ S \\ S\\S \ S \ S \\ \\4S\\SS4S jr"\#S!\\$S"\%\S\&\R,4S#j5r'S!\\$S\S\(\\R,\\R,44S$jr)S!\\$S\\SS4S%jr*S&\R,S'\S\\SS4S(jr+S)\\R,S'\S\\SS4S*jr,S+\-R\R^S'\S\\SS4S,jr0S-\-R\RbS'\S\\SS4S.jr2S/\3S'\S\\SS4S0jr4\#S \ \\4S1\ \\4S2\5\S\4S3j5r6SYS4jr7SZS\S\\S\\S\\ S \\ S \\ \\4S\\SS4S5jjr8S[S\\S \\ \\4SS4S6jjr9S7\ \\:4SS4S8jr;S \\ \\4SS4S9jrSS4S<jr?S;\S\\R,4S=jr@S \ SS4S>jrAS\\SS4S?jrBS\S@jrCS \ SS4SAjrDS\\SS4SBjrES\\SS4SCjrFS \\ SS4SDjrGS\\ SS4SEjrHS\\SS4SFjrIS\\4SGjrJS\\4SHjrKS\\4SIjrLS\\ 4SJjrMS\\4SKjrNS\\ 4SLjrOS\\4SMjrPS\4SNjrQS\4SOjrRSP\ \\4S\4SQjrSS]SR\S\5S4SSjjrTS^STjrUS7\ \\:4SS4SUjrVSVrWg)_r-ia7 Dataset in LightGBM. LightGBM does not train on raw data. It discretizes continuous features into histogram bins, tries to combine categorical features, and automatically handles missing and infinite values. This class handles that preprocessing, and holds that alternative representation of the input data. Nrir referencerrrrrrL free_raw_datarc  SUlXlX lX0lX@lXPlXlX`lXplXl [U 5Ul Xl SUl SUlSUlSUlSUlSUlSUlg)aD Initialize Dataset. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame (deprecated), scipy.sparse, Sequence, list of Sequence, list of numpy array or pyarrow Table Data source of Dataset. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file. label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Label of the data. reference : Dataset or None, optional (default=None) If this is Dataset for validation, training data should be used as reference. weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Weight for each instance. Weights should be non-negative. group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow Array, pyarrow ChunkedArray, pyarrow Table (for multi-class task) or None, optional (default=None) Init score for Dataset. feature_name : list of str, or 'auto', optional (default="auto") Feature names. If 'auto' and data is pandas DataFrame or pyarrow Table, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default="auto") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. params : dict or None, optional (default=None) Other parameters for Dataset. free_raw_data : bool, optional (default=True) If True, raw data is freed after constructing inner Dataset. position : numpy 1-D array, pandas Series or None, optional (default=None) Position of items used in unbiased learning-to-rank task. NTr)rrirrrrrrrrrrLr used_indices _need_slice _predictorr_params_back_upversion _start_row) r{rirrrrrrrrLrrs rCrDataset.__init__sn26   "   $";4'Q)QQ'tA~##ryy0 <s'+.:N:N9O~!^__ &&"((2 ?1#W^=N=T=Tq AN !-n.?!@!CO hh?1A?rxxP ,[ 9A( '(9:   4 4 NFNN6>>&//;Z,[\ OV^^FNN6>>TR$b/[R&"TR$S[RS 9nOT$TR)U5 T$) NFc3\># UH!nTRRUS5v M# g7f)FN)rLrz)raliasr{s rCr7Dataset._set_init_score_by_predictor..s%!jMiE$++//%"?"?Mis),rT)rir!r$rrg) fill_valuer])rrr rLanyr^rznum_datarGrgrr`rTrrbrrr?r full_likeset_init_score) r{rrirr$rrsub_init_scorerjnew_init_scores ` rC_set_init_score_by_predictor$Dataset._set_init_score_by_predictor s   dS$K ( (T[[-D!!j^M_M_`hMi!jjO==?  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Please use z< argument of the Dataset constructor to pass this parameter.rz) or unknown name(z) in categorical_featurerz in param dict is overridden.categorical_columnz4Reference dataset should be None or dataset instancerz,Data list can only be of ndarray or SequencezCannot initialize Dataset from zLabel should not be NonezBThe init_score will be overridden by the prediction of init_model.rrirzWrong predictor type )Grrrrr(rr column_namesinspect signature __class__ _lazy_init parametersrrrrArraddrrrr<r^rzrsortedrFrLr- constructr rNrrPrLGBM_DatasetCreateFromFilerSrTr5r6r7_Dataset__init_from_csrr9_Dataset__init_from_cscr`r_Dataset__init_from_np2d!_Dataset__init_from_pyarrow_tablerr_Dataset__init_from_list_np2dr_Dataset__init_from_seqsr1r!r\rr= set_label get_labelr set_weight set_group set_positionrrrrset_feature_name)r{rirrrrrrrrrLr args_namesrEcategorical_indices feature_dictrr cat_aliasrrr)rEs rCr%Dataset._lazy_init6s[ <DLK  &/&B&BD #"+"?"?  dL ) )O`)$7#'#:#: P LD 35L t $ $)?,,L~6&&t~~'@'@ALLQQS ;;=C e""%&bd! )4 0 0"%% L,--7@7NO7NGAta7N O+dC((T\-A'++L,>?c**'++D1#k$t*2E2E1FFXY]X^^v$wxx ,#!/!3!34I!JI F* *6)+>  #78 8   OOF #   NN5 !     h ' i 1 1&:+Aab  - - [_ - `  #    +  "3DO4L4L3MNO O$$\22c Px! b"A$t*BUBUAV WX^aa bsX42X## Y-"YYseqsrc## SnSnXnUHznXR:dS5eXR[U5-:a*U[U5- nUS- nXnXR[U5-:aM*XR- nXFnURS(aUOUR5v M| g7f)Nrz+sample indices are expected to be monotonicrOWNDATA)rrcr)r<rrseq_idseqrow_id id_in_seqrows rC_yield_row_from_seqlistDataset._yield_row_from_seqlistslF# R%R R#SX--#c("! lSX--I.C9-#388: =s AB 5B cURU5n[R"[UR X555nUR n/n/n[R "[U5[RS9nUHan[R"U5[:[R"U5-n Xn Xyn URU 5 URU 5 Mc XV4$)zSample data from seqs. Mimics behavior in c_api.cpp:LGBM_DatasetCreateFromMats() Returns ------- sampled_rows, sampled_row_indices r) rr`rrrDTr`rrabsrHisnanr) r{r<rKrsampledfiltered filtered_idxsampled_row_ranger col_predicate filtered_colfiltered_row_idxs rC__sampleDataset.__samples--j9((4 < 9RXXc]JM-L0?  OOL )    0 1 %%rBc[SU55nUbURX25 OI[UR55n[ X45nUR X5upgUR XgXS5 UHfn[U5n [USS5=(d [Rn [SX5H$n [X-U 5n URXU 5 M& Mh U$)z Initialize data from list of Sequence objects. Sequence: Generic Data Access Object Supports random access and access by batch if properly defined by user Data scheme uniformity are trusted, not checked c38# UHn[U5v M g7frGr))rr@s rCr+Dataset.__init_from_seqs..s2TcSTr7Nrr)rdrrFrrW_Dataset__samplerrrr1rrminr) r{r<rrKrrVr col_indicesr@rDrr]ends rC__init_from_seqsDataset.__init_from_seqss2T22   "  ' ' @*4??+<=I*:AJ'+}}T'F $K  " ";Z TCs8D lD9PX=P=PJq$3%,d3#/4  rBrXrc&[UR5S:wa [S5e[R"5Ul[ U5upE[U5upgn[[R"U[R"U5[R"URS5[R"URS5[R"U5[U5U[R"UR 555 U$)z(Initialize data from a 2-D numpy matrix.r)Input numpy.ndarray must be 2 dimensionalrr)rrrrNrrrjrrPrLGBM_DatasetCreateFromMatrOrRrSrT) r{rXrrrirhrrVrWs rC__init_from_np2dDataset.__init_from_np2d s syy>Q HI I( $S) %3D%9"  * * ]+syy|,syy|, V$z" T\\*   rBmatscUSRSn[R"[U54[R5nUSR [R :Xa6[R"[R5[U5-"5nO5[R"[R5[U5-"5n[R[U5-"5n/nSn [U5Hup[U R5S:wa [S5eU RSU:wa [S5eU RSXZ'[U 5up[U 5upnU S:waX:wa [S5eXU 'XU 'Un UR!U5 M [R""5Ul['[(R*"[R,"[U55[R."U[R"[R"[R555[R"U 5URR1[R"[R,55[R,"U5U[3U5U[R4"UR$55 5 U$)z2Initialize data from a list of 2-D numpy matrices.rrrrr]z-Input arrays must have same number of columnsz Input chunks must have same type)rr`rTrrr]rbrNrrrrOrrrjrrrrrPrLGBM_DatasetCreateFromMatsrRrrrSrT)r{rarrrrDrlayoutsholdersrVrrXrhchunk_ptr_datachunk_type_ptr_dataholders rC__init_from_list_np2dDataset.__init_from_list_np2d sAw}}QxxT bhh/ 7==BJJ &v7#d)CFHv~~6TBEH<<#d)+. oFA399~" !LMMyy|t# !PQQiilDG',KC:H:M 7N"':'K !CDD(QKAJ/M NN6 "#&&(   + +s4y) HfnnV^^FOO5T&UV ]+ ##FNN6>>$BCt$z" T\\*   rBr)c&[UR5[UR5:wa7[S[UR5S[UR535e[R "5Ul[UR5upEn[UR5upxn URS[::deURR[RSS9n [[ R""U[R$"U5U RR'[R("[R*55U[R$"U5[R,"[UR55[R,"[UR55[R,"URS5[/U5U[R0"UR 55 5 U$)z"Initialize data from a CSR matrix.Length mismatch:  vs rFr)rrrirrNrrrrwrrrJrr`rrPrLGBM_DatasetCreateFromCSRrOrrrRr!rSrT) r{r)rrrrrrrVrWrs rC__init_from_csrDataset.__init_from_csrR b s{{ s388} ,0S[[1A0B$s388}oVW W( *6szz*B' R%3CHH%="yy|z)))kk(((>   * * _-""**6>>&..+IJ ]+s3::/s388}-syy|,z" T\\*   rBr,c&[UR5[UR5:wa7[S[UR5S[UR535e[R "5Ul[UR5upEn[UR5upxn URS[::deURR[RSS9n [[ R""U[R$"U5U RR'[R("[R*55U[R$"U5[R,"[UR55[R,"[UR55[R,"URS5[/U5U[R0"UR 55 5 U$)z"Initialize data from a CSC matrix.rlrmrFr)rrrirrNrrrrwrrrJrr`rrPrLGBM_DatasetCreateFromCSCrOrrrRr!rSrT) r{r,rrrrrrrVrWrs rC__init_from_cscDataset.__init_from_csct rqrBr-c H[(a [(d [S5e[SURR 55(d [ S5e[U5n[R"5Ul [[R"[R"UR5[R"UR 5[R"UR"5[%U5U[R&"UR555 U$)z%Initialize data from a PyArrow table.zFCannot init Dataset from Arrow without 'pyarrow' and 'cffi' installed.c3# UH4n[U5=(d [U5=(d [U5v M6 g7frGrrs rCr4Dataset.__init_from_pyarrow_table.. rrr)rrr/rrrrr rNrrrPrLGBM_DatasetCreateFromArrowr!rrrrSrT)r{r-rrrs rC__init_from_pyarrow_table!Dataset.__init_from_pyarrow_table s"!nn hi ir_d_k_k_q_qrrr\] ]%U+(   , ,w//0 2 23 2 23z" T\\*    rB other_params ignore_keyscUR5Hup4X2;dM X0;d XU:wdM g UR5Hup4X2;dM X1;d XAU:wdM g g)aCompare two dictionaries with params ignoring some keys. It is only for the warning purpose. Parameters ---------- params : dict One dictionary with parameters to compare. other_params : dict Another dictionary with parameters to compare. ignore_keys : set Keys that should be ignored during comparing two dictionaries. Returns ------- compare_result : bool Returns whether two dictionaries with params are equal. FTrb)rLr|r}rcrds rC_compare_params_for_warning#Dataset._compare_params_for_warning s`0!&&(DA#?fi1n )LLNDA#(Aa,@ #rBc` URGcURGbURR5nUR5nX!:waEURUU[R S5S9(d [ S5 URU5 URcURURURURURURURURUR UR"SUR$S9 GOd['UR[(R*SS9nUR,R.(deURRb[(R0"URR5R3[(R*S S 9n[(R4"[(R6"[9[;U55US 9URS S 9uoPl [<R>"5Ul[AUR$5n[C[DRF"URRI5RUR<RK[<RL"[<RN55[<RN"URPS5[SU5[<RT"UR555 URV(dURY5 URbUR[UR5 URbUR]UR5 UR_5c [aS5e[cUR [d5(aXUR URR La5URY5 URgUR URUS9 O}URURURSURURURUR UR"URhUR$URS9 URV(aSUl URk5UlU$)zKLazy init. Returns ------- self : Dataset Constructed Dataset object. Nr)rLr|r}z1Overriding the parameters from Reference Dataset.r) rirrrrrrrrrrLrr]rFr)repeatsT) return_countsrzLabel should not be None.r ) rirrrrrrrrrLr)6rrrrr^rzr_update_paramsrr%rirrrrrrrrLrr`rrcrrruniquerepeatrrrNrrFrPrLGBM_DatasetGetSubsetr)rrrRrrSrTrget_datar4r5r2rrrrrget_feature_name)r{reference_paramsrLr group_inforWrs rCr)Dataset.construct se << ~~)#'>>#<#<#> *-;;%%5$2$6$67L$M< %%XY''(89$$,OO!YY"jj"&..#{{"jj!%#'??"&//%)%6%6,2#{{$ $5T5F5Fbhh]k#lL'--::::~~++7%'XXdnn.B.B%C%J%J288Z_%J%` (* IIeC O&>(//77v~~8VW"NN<+=+=a+@A":."LL6  -- zz-tzz2}}0))$--8~~'/()DEE"4??ODD OO4>>3L3LL 99&*ooDIIT`: **";;**#"oo!%!2!2(,(@(@;;!]] !!  $ 5 5 7D  rBc [UUUUUUUUURS9 nURUlURUlU$)aCreate validation data align with current Dataset. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame (deprecated), scipy.sparse, Sequence, list of Sequence or list of numpy array Data source of Dataset. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file. label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Label of the data. weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Weight for each instance. Weights should be non-negative. group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow Array, pyarrow ChunkedArray, pyarrow Table (for multi-class task) or None, optional (default=None) Init score for Dataset. params : dict or None, optional (default=None) Other parameters for validation Dataset. position : numpy 1-D array, pandas Series or None, optional (default=None) Position of items used in unbiased learning-to-rank task. Returns ------- valid : Dataset Validation Dataset with reference to self. )rrrrrrrLr)r-rrr) r{rirrrrrLrrs rC create_validDataset.create_valid0 sPN !,,  !%!8!8 rBc Uc URn[SUURURUURS9nUR UlUR Ul[U5UlU$)a(Get subset of current Dataset. Parameters ---------- used_indices : list of int Indices used to create the subset. params : dict or None, optional (default=None) These parameters will be passed to Dataset constructor. Returns ------- subset : Dataset Subset of the current Dataset. N)rrrrLr) rLr-rrrrrr(r)r{rrLrs rCsubsetDataset.subsetf sm& >[[F ** $ 8 8,,  !%!8!8!,/ rBfilenamec [[R"UR5R[ [ U5555 U$)a.Save Dataset to a binary file. .. note:: Please note that `init_score` is not saved in binary file. If you need it, please set it again after loading Dataset. Parameters ---------- filename : str or pathlib.Path Name of the output file. Returns ------- self : Dataset Returns self. )rPrLGBM_DatasetSaveBinaryr)rrSrr{rs rC save_binaryDataset.save_binary s=$   ' ' ((s8}%   rBc^^T(dT$[T5mSUU4SjjnTRc U"5 T$Tb[R"[ [ TR 55[ [ T555nUS:waSTRbU"5 TR5 T$[[R"5RS55eT$)Nc>TR(dTTlg[TR5TlTRRT5 grG)rLrrrw)rLr{srCrw&Dataset._update_params..update s4;;$ '/ '<$ ""6*rBrrr) rrrLGBM_DatasetUpdateParamCheckingrSrFrLrirr/rr)r{rLrwrs`` rCrDataset._update_params sK&! + + <<  H  66)$++67)&12Cax99(H%%' ((>(>(@(G(G(PQQ rBcbURc![UR5UlSUlU$rG)rrrrLrs rC_reverse_update_paramsDataset._reverse_update_params s+ << "4#7#78DK#'D  rB field_namec URc[SUS35eUcg[[R"UR[ U5S[ R"S5[ R"[U555 U$[U5(d[U5(Ga[U5(aQUS:wa[SUS35e[URVVs/sHnURHnUPM M snn5n[U5n[[R "UR[ U5[ R""UR$5[ R&"UR(5[ R&"UR*555 U=R,S- slU$US:Xa`[.R0n[3U5(a [5X&US 9nOf[7U5(a[9X&US 9nUR;S S 9nO<[=S 5eUS ;a[.R>nO[.R@n[5X&US 9nURB[.R@:XdURB[.R0:Xa[EU5upxn OFURB[.R>:Xa[GU5upxn O[=SURBS35eU[U:wa [=S5e[[R"UR[ U5U[ R"[IU55[ R"U555 U=R,S- slU$s snnf)aiSet property into the Dataset. Parameters ---------- field_name : str The field name of the information. data : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow Array, pyarrow ChunkedArray or None The data to be set. Returns ------- self : Dataset Dataset with set property. Nz Cannot set z before construct datasetrrz,pyarrow tables are not supported for field ''rrr[r_zinit_score must be list, numpy 1-D array or pandas Series. In multiclass classification init_score can also be a list of lists, numpy 2-D array or pandas DataFrame.>rrz-Expected np.float32/64 or np.int32, met type(rzInput type error for set_field)%r ExceptionrPrLGBM_DatasetSetFieldrSrNrO_FIELD_TYPE_MAPPERrrrr#rrr LGBM_DatasetSetFieldFromArrowr!rrrrrr`rbrrrr rgrrrar]rrr) r{rrirrirr]rrrWs rC set_fieldDataset.set_field s& << k*5NOP P < ))LL:&LLOLL!3J!?@  K T " "&7&=&=!&&-$'ST^S__`%abb'&*\\%1E%*\\E%1%1).G 22LL:&NN7#3#34OOG$6$67OOG$6$67   LLA LK  %JJE &&(L"4(((Lzzz,@ 22 $TZHD :: #tzzRZZ'?%3D%9 "H ZZ288 #%1$%7 "HKDJJ>/ ; ;=> >    ! >>/ /([[fnnV^^&DE"((C^^3 3*[[fnnV^^&DE"((C^^3 3*[[fnnV__&EF"((C N+ +  %}}H((h.KQkk8"9kE rBc&URU:XaU$URbhURcXlUR5$US:XaU$URS:wa[S[ U535 XlUR5$[ S5e)zSet categorical features. Parameters ---------- categorical_feature : list of str or int, or 'auto' Names or indices of categorical features. Returns ------- self : Dataset Dataset with set categorical features. rzIcategorical_feature in Dataset is overridden. New categorical_feature is zrCannot set categorical feature after freed raw data, set free_raw_data=False when construct Dataset to avoid this.)rrirrrr/)r{rs rCset_categorical_featureDataset.set_categorical_featureh s  # #': :K 99 ''/+>(((**$. ++v5 66:;N6O5PR,?(((**P rBcUcURcU$[U[5(a\[UR[5(a=XR:Xa.UR5URR5:XaU$URcXlU$UR b-XlUR URUR SS9 U$URbeURbXURR bAXlUR URURR URS9 U$[S5e)zSet predictor for continued training. It is not recommended for user to call this function. Please use init_model argument in engine.train() or engine.cv() instead. Nr zhCannot set predictor after freed raw data, set free_raw_data=False when construct Dataset to avoid this.) rrrrrrirrrr/)r{rs rC_set_predictorDataset._set_predictor s,  !8K  ? 3 3 4??Tc8d8d__,++-1R1R1TT << 'O( 'YY "'O  - -//YY! . "    *t~~/IdnnNaNaNm'O  - -//^^((!.. .   P rBc`URUR5RUR5R UR 5 UR 5RUR 55(aU$URbXl UR5$[S5e)zSet reference Dataset. Parameters ---------- reference : Dataset Reference that is used as a template to construct the current Dataset. Returns ------- self : Dataset Dataset with set reference. zhCannot set reference after freed raw data, set free_raw_data=False when construct Dataset to avoid this.) rrr6rrr get_ref_chain intersectionrirrr/)r{rs rC set_referenceDataset.set_reference s $$Y%B%BCTT  " " .-- .     , ,Y-D-D-F G GK 99 &N$$& &P rBc US:waXlURbUbUS:wa[U5UR5:wa)[ S[U5SUR5S35eUVs/sHn[ U5PM nn[ [R"UR[[RU5[R"[U5555 U$s snf)zSet feature name. Parameters ---------- feature_name : list of str Feature names. Returns ------- self : Dataset Dataset with set feature name. rzLength of feature_name(z) and num_feature( ) don't match) rrr num_featurerrSrPrLGBM_DatasetSetFeatureNamesr*rNr%rO)r{rrc_feature_names rCr6Dataset.set_feature_name s 6 ! ,  << # (@\U[E[< D$4$4$66 -c,.?-@@RSWScScSeRffst8DD|tfTl|ND 00LLV__n=LL\!23  Es-C&cXlURb[U[5(aQ[ UR 5S:a [ S5e[R"[U[RS95nO,[U5(aUnO[U[RSS9nURSU5 URS5UlU$)a Set label of Dataset. Parameters ---------- label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None The label information to be set into Dataset. Returns ------- self : Dataset Dataset with set label. rz0DataFrame for label cannot have multiple columnsrrr)rrrr(rrrr`rgrrarrrr)r{r label_arrays rCr1Dataset.set_label s << #%..u}}%)$%WXX hh'7BJJ'WX "5))# /RZZgV NN7K 00DJ rBcUbq[U5(aA[R"[R"US55R 5(aSnO [ R"US:H5(aSnXlURbTUbQ[U5(d[U[ RSS9nURSU5 URS5UlU$)aSet weight of each instance. Parameters ---------- weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None Weight to be set for each data point. Weights should be non-negative. Returns ------- self : Dataset Dataset with set weight. Nrrr) rr%requalas_pyr`rrrrarr)r{rs rCr3Dataset.set_weight s"   ((>>*"2"261"=>DDFF!F! $$  << #(:$V,,*6(S NN8V ,..2DK rBcXlURb+Ub(URSU5 URS5UlU$)amSet init score of Booster to start from. Parameters ---------- init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow Array, pyarrow ChunkedArray, pyarrow Table (for multi-class task) or None Init score for Booster. Returns ------- self : Dataset Dataset with set init score. r)rrrr)r{rs rCrDataset.set_init_score+ s; % << # (> NN< 4"nn\:DO rBcXlURbmUbj[U5(d[U[R SS9nUR SU5 URS5nUb[R"U5UlU$)aDSet group size of Dataset (used for ranking). Parameters ---------- group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. Returns ------- self : Dataset Dataset with set group. rr) rrrrr`rrrra)r{rconstructed_groups rCr4Dataset.set_groupA sn( << #(9$U++)%rxxgN NN7E * $w 7  ,WW%67  rBcXlURb.Ub+[U[RSS9nUR SU5 U$)a Set position of Dataset (used for ranking). Parameters ---------- position : numpy 1-D array, pandas Series or None, optional (default=None) Position of items used in unbiased learning-to-rank task. Returns ------- self : Dataset Dataset with set position. rr)rrrr`rr)r{rs rCr5Dataset.set_position` s= ! << #(<( SH NN:x 0 rBc URc [S5eUR5n[R"S5nSn[R "S5n[ U5Vs/sHn[R"U5PM nn[RU-"[[RU56n[[R"UR[R"U5[R"U5[R "U5[R"U5U55 XR:wa [!S5eURnX8:a[ U5Vs/sHn[R"U5PM nn[RU-"[[RU56n[[R"UR[R"U5[R"U5[R "U5[R"U5U55 [ U5V s/sH oU RR#S5PM" sn $s snfs snfs sn f)zGet the names of columns (features) in the Dataset. Returns ------- feature_names : list of str The names of columns (features) in the Dataset. z0Cannot get feature_name before construct datasetr6Length of feature names doesn't equal with num_featurer)rr/rrNrOc_size_trrir%rBrrPrLGBM_DatasetGetFeatureNamesrTrUrr r{rrnreserved_string_buffer_sizerequired_string_buffer_sizerWstring_buffersptr_string_buffersactual_string_buffer_sizers rCrDataset.get_feature_namev s <<  RS S&&( ll1o &)#&,ooa&8#\abm\no\nWX&556QR\no$oo ;c&BRBRTb>cd  , ,  [) [) ;< 89"    ++ +UV V$?$E$E! & B^cdo^pq^pYZf99:ST^pNq"(//K"?#fFVFVXfBg!h  00LLLL-LL-OO$=>LL!<=&  BG{ASTASAq!''..w7ASTT;p"rUs$ I/* I4'I9c`URcURS5UlUR$)zGet the label of the Dataset. Returns ------- label : list, numpy 1-D array, pandas Series / one-column DataFrame or None The label information from the Dataset. For a constructed ``Dataset``, this will only return a numpy array. r)rrrs rCr2Dataset.get_label s( :: 0DJzzrBc`URcURS5UlUR$)a Get the weight of the Dataset. Returns ------- weight : list, numpy 1-D array, pandas Series or None Weight for each data point from the Dataset. Weights should be non-negative. For a constructed ``Dataset``, this will only return ``None`` or a numpy array. r)rrrs rC get_weightDataset.get_weight s( ;; ..2DK{{rBc`URcURS5UlUR$)a,Get the initial score of the Dataset. Returns ------- init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None Init score of Booster. For a constructed ``Dataset``, this will only return ``None`` or a numpy array. r)rrrs rCget_init_scoreDataset.get_init_score s( ?? ""nn\:DOrBcURc [S5eUR(Ga<URGb.URGb URR UlUR Gb[ UR [R[RR45(a$UR URSS24UlGO[ UR [5(a8UR RURR5UlGO2[ UR [5(a-[!5 UR URSS24UlO[ UR ["5(aUR URUlO[%UR 5(ab['UR 5S:aI[R("[+UR-UR UR555UlO,[/S[1UR 5R2S35 SUlUR c [5S5eUR $)aGet the raw data of the Dataset. Returns ------- data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame (deprecated), scipy.sparse, Sequence, list of Sequence or list of numpy array or None Raw data used in the Dataset construction. Nz(Cannot get data before construct DatasetrzCannot subset z. type of raw data. Returning original raw dataFzjCannot call `get_data` after freed raw data, set free_raw_data=False when construct Dataset to avoid this.)rrrrrrirr`rr5r6r>r(ilocrr!r\r1rrrrrDrrr<r/rs rCrDataset.get_data s << FG G     1 1 =$..B\++DIyy$dii"**ell6K6K)LMM $ $*;*;Q*> ?DI <88 $ t/@/@ A F F HDI <8879 $ $*;*;Q*> ?DI 844 $ $*;*; UR U5 UR bUR U;a UR nOU$U$[U5U:aMgU$)abGet a chain of Dataset objects. Starts with r, then goes to r.reference (if exists), then to r.reference.reference, etc. until we hit ``ref_limit`` or a reference loop. Parameters ---------- ref_limit : int, optional (default=100) The limit number of references. Returns ------- ref_chain : set of Dataset Chain of references of the Datasets. )rArrr-r'r)r{rhead ref_chains rCrDataset.get_ref_chainW s""%% )ny($(( d#NN.T^^95T>>D)ny(rBcURb URc [S5e[[R"URUR55 UR SLn[ UR 5RnUR c SUlGOZUR GbL[UR [R5(Ga[UR [R5(a3[R"UR UR 45UlGO[UR [RR5(aA[R"UR UR R545UlGOR[UR [ 5(a=[R"UR UR R"45UlGO[UR [$5(aK['5 [R"UR UR R)545UlGOSUlGO[UR [RR5(GawUR R+5n[UR [R[RR45(a;[RRUR UR 4US9UlGO[UR [ 5(aE[RRUR UR R"4US9UlGOS[UR [$5(aS['5 [RRUR UR R)54US9UlGOSUlGO[UR [ 5(Ga[,(d [/S5e[UR [R5(a1[1UR [!UR 54SSS9UlGOH[UR [RR5(a?[1UR [!UR R554SSS9UlGO[UR [ 5(a([1UR UR 4SSS9UlGO[UR [$5(aI['5 [1UR [!UR R)554SSS9UlGO'SUlGO[UR [$5(Ga['5 [UR [R5(aJ[%[R"UR R)5UR 455UlGO[UR [RR5(aW[%[R"UR R)5UR R5455UlO[UR [ 5(aS[%[R"UR R)5UR R"455UlO[UR [$5(aW[%[R"UR R)5UR R)5455UlOSUlOSUlUR c?S[ UR 5RS US 3nUU(aS OS - n[3U5 UR55Ul 5 SUlSUlU$)aAdd features from other Dataset to the current Dataset. Both Datasets must be constructed before calling this method. Parameters ---------- other : Dataset The Dataset to take features from. Returns ------- self : Dataset Dataset with the new features added. NzJBoth source and target Datasets must be constructed before adding features)formatztCannot add features to DataFrame type of raw data without pandas installed. Install pandas and restart your session.rT)axis ignore_indexzCannot add features from z type of raw data to z type of raw data. ztoarrayr(r r!r\r getformatrr/r rrrrr)r{otherwas_noneold_self_data_type sparse_formaterr_msgs rCadd_features_fromDataset.add_features_fromu s~ << 5==#8ij j  , ,    99$!$))_55 :: DI YY "$))RZZ00ejj"**55 " 499ejj*A BDI ELL,A,ABB " 499ejj6H6H6J*K LDI L99 " 499ejj6G6G*H IDI L9979 " 499ejj6I6I6K*L MDI $DIDIIu||'<'<== $ 3 3 5 ejj2::u||7L7L*MNN % 3 3TYY 4KTa 3 bDI L99 % 3 3TYY @Q@Q4R[h 3 iDI L9979 % 3 3TYY @S@S@U4V_l 3 mDI $DIDII|44'''C ejj"**55 & < 3K'LSTcg hDI ELL,A,ABB & < @R@R@T3U'V]^mq rDI L99 & 5::'>QUY ZDI L9979 & < @S@S@U3V'W^_nr sDI $DIDII|4435ejj"**55 ,RYY 8J8J8Lejj7Y-Z [DI ELL,A,ABB ,RYY 8J8J8LejjN`N`Nb7c-d eDI L99 ,RYY 8J8J8LejjN_N_7`-a bDI L99 ,RYY 8J8J8LejjNaNaNc7d-e fDI $DI  99 +D,<,E,E+FF[%&&:<  RZN`r G  ! 113 Z $* "& rBc [[R"UR5R[ [ U5555 U$)zSave Dataset to a text file. This format cannot be loaded back in by LightGBM, but is useful for debugging purposes. Parameters ---------- filename : str or pathlib.Path Name of the output file. Returns ------- self : Dataset Returns self. )rPrLGBM_DatasetDumpTextr)rrSrrs rC _dump_textDataset._dump_text s=   % % ((s8}%   rB)rrrrrrrirrrrrrrLrrrrr) NNNNNrrNTNr)rEr-)NNNNNNrG)rr-rEr-)d)r r-rEr-)Xr<r=r>r?r@_LGBM_TrainDataTyper_LGBM_LabelType_LGBM_WeightType_LGBM_GroupType_LGBM_InitScoreType_LGBM_FeatureNameConfiguration%_LGBM_CategoricalFeatureConfigurationrrrr_LGBM_PositionTyperrrr`rr_DatasetHandlerrrrrrrrrr%rr1rrrDrrVr0r-r/r5r6r7r+r9r,r&r.rrr)rrr rrr_LGBM_SetFieldTyperrrrrr6r1r3rr4r5rr2rrrrrrrrrrrrAr:rBrCr-r-s,0)--1+/487=EK+/"15I!I(II& I )* I ( I01I5ICIc3h(II-.IV   D$  :>"**%>RZZ(> >  >  >@rzzi<#DcN#J)O,)")uT#Y %:;< )  )V}3*+}3(}3I& }3 )* }3 ( }301}3O,}35}3C}3c3h(}3-.}3 }3~ >d8n >x} >QYZ\ZdZdQe > >&T(^&&tBJJGWY]^`^h^hYiGiAj&88nn-  B ZZn-   642::44n- 4  4l  \\ $ $  n-   D  \\ $ $  n-   Dn-   :S#X38nX  BY|,0-1+/48+/154!4(4)* 4 ( 4 01 4c3h(4-.4 4r,0 3i c3h(   DE#t)$44Xd38n%=)8 ee)*e  eN;C;HRZZ,@;z$B$ $L%O,% %N8-KPY<x8Y6)* B01 ,( >-. ,+U$s)+UZ 8O4  H%56  )< = "(#67"H8O4( h'9: P#P(SSS(WuS#XW3W@sS^\ S?\ S \S \ \"RF\8RrRt\\8RrRt44S@jjr;SiS\SE\\?SF\\@SG\ASH\BSI\\\\4SJ\ S?\ S \S S4SKjjrCSL\SM\S \)4SNjrDSL\SM\SO\)S S4SPjrES \4SQjrFS \\4SRjrGSjS3\SS\\S \"RF4STjjrHSkSU\ \\4SV\\ \\4SW\ S \ \I\"RF\"RF4\"RF\44SXjjrJSY\SZ\S,\\ \,\\,4S \\-4S[jrKSZ\S \"RF4S\jrLS`S]jrMS^rNg)lr,izBooster in LightGBM.NrL train_setr rc [R"5UlSUlSUlSUlSUlSUl0UlUc0O [U5nUGb[U[5(d![S[U5R35e[SUSS9nUScUR!SS5 OUSn[U["5(a[%UR'S 55nOC[U[([*45(a[%U5nS R-U5nO [/S 5e[S UUS9n[S US S9nUR1UUS UR3SS5US S9 UR55 UR7UR955 [;U5n[=[>R@"UR[CU5[RD"UR555 X l#/Ul$/Ul%SUl&URNUl(URPb>[=[>RR"URURPR55 [RT"S5n[=[>RV"UR[RD"U555 URXUl-S/Ul.S/Ul/URa5 URbUl1URdUl3GO5UGb[RT"S5n [=[>Rh"[C[#U55[RD"U 5[RD"UR555 [RT"S5n[=[>RV"UR[RD"U555 URXUl-[kUS9Ul1U(a [mS5 URo5nO UbURqU5 O [S5eXl9g)a}Initialize the Booster. Parameters ---------- params : dict or None, optional (default=None) Parameters for Booster. train_set : Dataset or None, optional (default=None) Training dataset. model_file : str, pathlib.Path or None, optional (default=None) Path to the model file. model_str : str or None, optional (default=None) Model will be loaded from this string. FTtrainingrN.Training data should be Dataset instance, met machinesrrLrr3zInvalid machines in params. num_machineslocal_listen_portp0time_outx)r$r'listen_time_outr&rrrz;Ignoring params argument, using parameters from model file.z[Need at least one training dataset or model file or model string to create Booster instance):rNrr_network_Booster__need_reload_eval_info_train_data_name_Booster__set_objective_to_nonebest_iteration best_scorerrr-rrr<rrrrrrrArr set_networkrzr)rwrrFrPrLGBM_BoosterCreaterSrTr  valid_setsname_valid_sets_Booster__num_datasetr_Booster__init_predictorLGBM_BoosterMergerOrrU_Booster__num_class_Booster__inner_predict_buffer_Booster__is_predicted_cur_iter_Booster__get_eval_inforrtrain_set_versionrrr_get_loaded_parammodel_from_stringrL) r{rLr r rr$num_machines_from_machine_listrrrs rCrBooster.__init__s((  '+$ *',$ 68~8F+;  i11"PQUV_Q`QiQiPj kll( *"F j!) :t,!*-h,,589L5M24+6658]2"xx1H$%BCC,$2!"@ -$7!"'   %&,-@&A$*JJz3$?!'!7 !    ! MM)..0 1+F3J ''%%:&LL. 'N-/DO.0D !"D $-$8$8D !$$0** --55 #LLOM ..LLLL/  -22D GKfD ',17D (  "&/&B&BD #%.%6%6D "  #!'a  443z?+LL!34LL. #LLOM ..LLLL/  -22D &>&TD #Z[++-F  "  " "9 -m  rBrEcUR(aUR5 URb*[ [ R "UR55 gg![a NEf=f![a gf=frG)r, free_networkrrrPrrrs rCrBooster.__del__sl }}!!# ||'400>?(       s"!A6A, A)(A), A98A9c$URS5$rG) __deepcopy__rs rC__copy__Booster.__copy__s  &&rBrtkwargsc2URSS9n[US9$)Nrr r)model_to_stringr,)r{rtrIrs rCrFBooster.__deepcopy__s ((r(: ++rBcURR5nUSnURSS5 URSS5 UbURSS9US'U$)Nrr r4rrK)rrrrM)r{rrs rCrBooster.__getstate__s[}}!!#i d# t$  "222DDO rBstatec URSURSS55nUb[R"5n[R"S5n[ [ R "[U5[R"U5[R"U555 X1S'URRU5 g)Nrrr) rzrNrrOrPrLGBM_BoosterLoadModelFromStringrSrTrrw)r{rQrrrs rC __setstate__Booster.__setstate__sIIi8T)BC  __&F!'a  449%LL!34LL(  &)  U#rBc Sn[R"S5n[R"U5n[R"[R"U55n[ [ R"UR[R"U5[R"U5U55 URnXQ:a[R"U5n[R"[R"U55n[ [ R"UR[R"U5[R"U5U55 [R"URRS55$)Nr`rr)rNr!rir%rrPrLGBM_BoosterGetLoadedParamrrTrUrkrlr)r{rmrnrorprqs rCr>Booster._get_loaded_params nnQ' 33J? "OOF,<,<],KL  + + z* [)!   !&&  ""77 CM &0@0@0O P  //LLNN:.LL-%  zz---44W=>>rBcURRSS5 URRSS5 SUlU$)zWFree Booster's Datasets. Returns ------- self : Booster Booster without Datasets. r Nr4r)rrr6rs rC free_datasetBooster.free_datasets9 +t, ,- rBc"/Ul/UlU$rG)r:r;rs rC _free_bufferBooster._free_buffers&(#')$ rBr$r'r+r&c :[U[[45(aSRU5n[ [ R "[U5[R"U5[R"U5[R"U555 SUl U$)aSet the network configuration. Parameters ---------- machines : list, set or str Names of machines. local_listen_port : int, optional (default=12400) TCP listen port for local machines. listen_time_out : int, optional (default=120) Socket time-out in minutes. num_machines : int, optional (default=1) The number of machines for distributed learning application. Returns ------- self : Booster Booster with set network. r3T) rrrArrPrLGBM_NetworkInitrSrNrOr,)r{r$r'r+r&s rCr2Booster.set_networkst2 hs , ,xx)H  ! !x  ./ _- \*     rBcP[[R"55 SUlU$)zXFree Booster's network. Returns ------- self : Booster Booster with freed network. F)rPrLGBM_NetworkFreer,rs rCrCBooster.free_network s! 4((*+  rBc^^^[(d [S5eUR5S:Xa [S5eS[[[ 4S[ 4SjmSS[[[ 4S[S[[S [[[S [[S[[[ 44 U4S jjjmSS[[[ 4S[S[[S [[[S [[S[[[[ 44 UUU4S jjjmUR5nUS n/nUS H nURT"USUSUS95 M" [X3SR5S9$)a"Parse the fitted model and return in an easy-to-read pandas DataFrame. The returned DataFrame has the following columns. - ``tree_index`` : int64, which tree a node belongs to. 0-based, so a value of ``6``, for example, means "this node is in the 7th tree". - ``node_depth`` : int64, how far a node is from the root of the tree. The root node has a value of ``1``, its direct children are ``2``, etc. - ``node_index`` : str, unique identifier for a node. - ``left_child`` : str, ``node_index`` of the child node to the left of a split. ``None`` for leaf nodes. - ``right_child`` : str, ``node_index`` of the child node to the right of a split. ``None`` for leaf nodes. - ``parent_index`` : str, ``node_index`` of this node's parent. ``None`` for the root node. - ``split_feature`` : str, name of the feature used for splitting. ``None`` for leaf nodes. - ``split_gain`` : float64, gain from adding this split to the tree. ``NaN`` for leaf nodes. - ``threshold`` : float64, value of the feature used to decide which side of the split a record will go down. ``NaN`` for leaf nodes. - ``decision_type`` : str, logical operator describing how to compare a value to ``threshold``. For example, ``split_feature = "Column_10", threshold = 15, decision_type = "<="`` means that records where ``Column_10 <= 15`` follow the left side of the split, otherwise follows the right side of the split. ``None`` for leaf nodes. - ``missing_direction`` : str, split direction that missing values should go to. ``None`` for leaf nodes. - ``missing_type`` : str, describes what types of values are treated as missing. - ``value`` : float64, predicted value for this leaf node, multiplied by the learning rate. - ``weight`` : float64 or int64, sum of Hessian (second-order derivative of objective), summed over observations that fall in this node. - ``count`` : int64, number of records in the training data that fall into this node. Returns ------- result : pandas DataFrame Returns a pandas DataFrame of the parsed model. zxThis method cannot be run without pandas installed. You must install pandas and restart your session to use this method.rz._is_split_node<s DIIK/ /rB node_depth tree_index feature_names parent_nodec >S[[[4S[[S[4U 4SjjnS[[[4S[[ [S[[4U 4SjjnS[[[4S[ 4Sjn[5nX(S'XS'U"X5US 'SUS 'SUS 'XHS 'U"X5US 'SUS'SUS'SUS'SUS'SUS'SUS'SUS'SUS'T "U5(aiU"US U5US 'U"US U5US 'USUS'USUS'USUS'US(aSOSUS'USUS'USUS'USUS'USUS'U$USUS'U"U5(dUSUS'USUS'U$)NrfrmrEc>UbUS3OSnT"U5nU(aSOSnURU(aSOSS5nUUU3$)N-rSLrh leaf_indexr)rz)rfrmtree_numis_split node_typenode_numrjs rC_get_node_indexOBooster.trees_to_dataframe..create_node_record.._get_node_indexFsS0:/Ej\+2)$/#+C 88XMT"U5(aUb XSnU$USnU$SnU$)N split_featurer:)rfrnrrjs rC_get_split_featureRBooster.trees_to_dataframe..create_node_record.._get_split_featureQsL"$''$0'4/5J'K $#(,O'< $#$(L##rBc>[UR55SS1:H$)N leaf_value leaf_count)rArris rC_is_single_node_treeTBooster.trees_to_dataframe..create_node_record.._is_single_node_tree^s499;'L,+GGGrBrl node_index left_child right_child parent_indexr} split_gain threshold decision_typemissing_direction missing_typerUrcount default_leftleftrightinternal_valueinternal_weightinternal_countr leaf_weightr)rrrrrrrr) rfrlrmrnrorzr~rnoderjs rCcreate_node_record6Booster.trees_to_dataframe..create_node_record?s :38n :$SM : : $38n $'S 2 $# $ H4S> Hd H6A]D!+ !+ !0!BD !%D "&D #. $6t$KD !!%D  $D $(D !(,D$ %#'D  DM!DN DMd##%4T,5G%T\"&5d=6I:&V]#%),%7\"$($5[!(,_(=_%6:>6JFPW()'+N';^$ $%5 6W !%&7!8X $%5 6W K !%\ 2W +D11%)-%8DN$($6DMKrBc >T "UUUUUS9nU/nT "U5(a0SS/nUH&nT "XUS-UUUSS9n URU 5 M( U$)N)rfrlrmrnrorrrr)extend) rfrlrmrnrorreschildrenchild subtree_listrjrtree_dict_to_node_lists rCr:Booster.trees_to_dataframe..tree_dict_to_node_lists&%%+' D&Cd##(-8%E#9![#->#-&3$($6 $LJJ|,&JrB tree_infotree_structure)rfrmrnr)rNNN)rr/ num_treesrrrrrrr dump_modelrr(r)r{ model_dictrn model_listrfrjrrs @@@rCtrees_to_dataframeBooster.trees_to_dataframes8 W  >> q  ^_ _ 0c3h 0D 0  (,15)- F sCx.F F ! F $DI. F  "# F  #s(^ F F T (,15)-  sCx.  !  $DI.   "#   $sCx. !   B__& "?3  {+D   &./D>;Q;Q!QI$) !$-$?$oDDZDZ^g^o^oDo !  )>i11"PQUV_Q`QiQiPj kll##4+@+@@#$pqq&N 22LLNN,,.66  .2D ' ' *%)^^%;%;D "ll1o <++#$STT ..LLLL-  >$BC ##FNN6>>$BC [)   8=T=O=O7P'Q7P!7P'Q$  A%%(Rs# Ic[[R"UR55 [ UR 5Vs/sHnSPM snUlU$s snf)zdRollback one iteration. Returns ------- self : Booster Booster with rolled back one iteration. F)rPrLGBM_BoosterRollbackOneIterrrr6r;)r{rWs rCrollback_one_iterBooster.rollback_one_itersJ 433DLLAB7> !H3t/0??1-- 1uH1 r> NN4 &))A-H  77rBc<URURSU5$)aEvaluate for training data. Parameters ---------- feval : callable, list of callable, or None, optional (default=None) Customized evaluation function. Each evaluation function should accept two parameters: preds, eval_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. preds : numpy 1-D array or numpy 2-D array (for multi-class task) The predicted values. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. If custom objective function is used, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. eval_data : Dataset The training dataset. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. Returns ------- result : list List with (train_dataset_name, eval_name, eval_result, is_higher_better) tuples. r)rr.)r{rs rC eval_trainBooster.eval_trains @  !6!65AArBc [SUR5VVs/sH/nURURUS- X!5HnUPM M1 snn$s snnf)aEvaluate for validation data. Parameters ---------- feval : callable, list of callable, or None, optional (default=None) Customized evaluation function. Each evaluation function should accept two parameters: preds, eval_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. preds : numpy 1-D array or numpy 2-D array (for multi-class task) The predicted values. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. If custom objective function is used, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. eval_data : Dataset The validation dataset. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. Returns ------- result : list List with (validation_dataset_name, eval_name, eval_result, is_higher_better) tuples. r)rr6rr5)r{rrr/s rC eval_validBooster.eval_valid:s`D1d001 1))$*>*>q1u*EqP P 1   s6Arr rimportance_typec XUc URn[Un[[R"UR [ R"U5[ R"U5[ R"U5[[U5555 [URU5 U$)aSave Booster to file. Parameters ---------- filename : str or pathlib.Path Filename to save Booster. num_iteration : int or None, optional (default=None) Index of the iteration that should be saved. If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. If <= 0, all iterations are saved. start_iteration : int, optional (default=0) Start index of the iteration that should be saved. importance_type : str, optional (default="split") What type of feature importance should be saved. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. Returns ------- self : Booster Returns self. ) r0_FEATURE_IMPORTANCE_TYPE_MAPPERrPrLGBM_BoosterSaveModelrrNrOrSrrr)r{rr rrimportance_type_ints rC save_modelBooster.save_model`s:   //M=oN  & &  _- ]+ 01s8}%    !!8!8(C rB end_iterationc [[R"UR[R "U5[R "U555 U$)aNShuffle models. Parameters ---------- start_iteration : int, optional (default=0) The first iteration that will be shuffled. end_iteration : int, optional (default=-1) The last iteration that will be shuffled. If <= 0, means the last available iteration. Returns ------- self : Booster Booster with shuffled models. )rPrLGBM_BoosterShuffleModelsrrNrO)r{rrs rCshuffle_modelsBooster.shuffle_modelss@(   * *  _- ]+   rBc v[[R"UR55 UR 5 [ R "5Ul[ R"S5n[[R"[U5[ R"U5[ R"UR555 [ R"S5n[[R"UR[ R"U555 URUl [US9UlU$)zLoad Booster from a string. Parameters ---------- model_str : str Model will be loaded from this string. Returns ------- self : Booster Loaded Booster object. rrL)rPrrrr]rNrrOrSrSrTrrUr9rr)r{rrrs rCr?Booster.model_from_strings 4((67 ( #\\!_  0 0y! /0 T\\*   Q   * *  ]+  )..":Y"O rBc :Uc URn[UnSn[R"S5n[R"U5n[R "[R "U55n[[R"UR[R"U5[R"U5[R"U5[R"U5[R"U5U55 URn X:a[R"U 5n[R "[R "U55n[[R"UR[R"U5[R"U5[R"U5[R"U 5[R"U5U55 URRS5n U [UR 5- n U $)aSave Booster to string. Parameters ---------- num_iteration : int or None, optional (default=None) Index of the iteration that should be saved. If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. If <= 0, all iterations are saved. start_iteration : int, optional (default=0) Start index of the iteration that should be saved. importance_type : str, optional (default="split") What type of feature importance should be saved. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. Returns ------- str_repr : str String representation of Booster. r`rr)r0rrNr!rir%rrPrLGBM_BoosterSaveModelToStringrrOrTrUrrr) r{r rrrrmrnrorprqrs rCrMBooster.model_to_strings4   //M=oN nnQ' 33J? "OOF,<,<],KL  . .  _- ]+ 01z* [)!  !&&  ""77 CM &0@0@0O P  22LLLL1LL/LL!45NN:.LL-% !!((1 '(?(?@@ rBrhc Uc URn[UnSn[R"S5n[R"U5n[R "[R "U55n [[R"UR[R"U5[R"U5[R"U5[R"U5[R"U5U 55 URn X:a[R"U 5n[R "[R "U55n [[R"UR[R"U5[R"U5[R"U5[R"U 5[R"U5U 55 [R"URR!S5US9n [R"[R""UR$[&S95U S'U $)aDump Booster to JSON format. Parameters ---------- num_iteration : int or None, optional (default=None) Index of the iteration that should be dumped. If None, if the best iteration exists, it is dumped; otherwise, all iterations are dumped. If <= 0, all iterations are dumped. start_iteration : int, optional (default=0) Start index of the iteration that should be dumped. importance_type : str, optional (default="split") What type of feature importance should be dumped. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. object_hook : callable or None, optional (default=None) If not None, ``object_hook`` is a function called while parsing the json string returned by the C API. It may be used to alter the json, to store specific values while building the json structure. It avoids walking through the structure again. It saves a significant amount of time if the number of trees is huge. Signature is ``def object_hook(node: dict) -> dict``. None is equivalent to ``lambda node: node``. See documentation of ``json.loads()`` for further details. Returns ------- json_repr : dict JSON format of Booster. r`rrrgrr)r0rrNr!rir%rrPrLGBM_BoosterDumpModelrrOrTrUrkrlrrrr1) r{r rrrhrrmrnrorprqrs rCrBooster.dump_modelsH   //M=oN nnQ' 33J? "OOF,<,<],KL  & &  _- ]+ 01z* [)!  !&&  ""77 CM &0@0@0O P  **LLLL1LL/LL!45NN:.LL-% jj,,33G<+V$(JJ JJ''0 %  !  rBr!r"r#r$r%c [RU[U 5S9n UcUS::a URnOSnU R UUUUUUUUS9$)aMake a prediction. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, pyarrow Table, H2O DataTable's Frame (deprecated) or scipy.sparse Data source for prediction. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). start_iteration : int, optional (default=0) Start index of the iteration to predict. If <= 0, starts from the first iteration. num_iteration : int or None, optional (default=None) Total number of iterations used in the prediction. If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; otherwise, all iterations from ``start_iteration`` are used (no limits). If <= 0, all iterations from ``start_iteration`` are used (no limits). raw_score : bool, optional (default=False) Whether to predict raw scores. pred_leaf : bool, optional (default=False) Whether to predict leaf index. pred_contrib : bool, optional (default=False) Whether to predict feature contributions. .. note:: If you want to get more explanations for your model's predictions using SHAP values, like SHAP interaction values, you can install the shap package (https://github.com/slundberg/shap). Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra column, where the last column is the expected value. data_has_header : bool, optional (default=False) Whether the data has header. Used only if data is str. validate_features : bool, optional (default=False) If True, ensure that the features used to predict match the ones used to train. Used only if data is pandas DataFrame. **kwargs Other parameters for the prediction. Returns ------- result : numpy array, scipy.sparse or list of scipy.sparse Prediction result. Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``). rrrr)rirr r!r"r#r$r%)rr rr0rG) r{rirr r!r"r#r$r%rIrs rCrGBooster.predict]sur$00#F+1   !# $ 3 3 "   +'%+/!  rBr decay_raterrrrrrr rc UR(a [S5eU c0n [RU[ U 5S9nUR USSU S9nUR unn[R"S5n[[R"UR[R"U555 [SURSS 9n[!UR"5US'UR%U 5 ['UUUUUUUU UU S 9 nUUS '[)UU5n[[R*"URUR55 UR-S5n[/U5un n[[R0"URU[R2"U5[R2"U555 UR4UlU$) a Refit the existing Booster by new data. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame (deprecated), scipy.sparse, Sequence, list of Sequence or list of numpy array Data source for refit. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array or pyarrow ChunkedArray Label for refit. decay_rate : float, optional (default=0.9) Decay rate of refit, will use ``leaf_output = decay_rate * old_leaf_output + (1.0 - decay_rate) * new_leaf_output`` to refit trees. reference : Dataset or None, optional (default=None) Reference for ``data``. .. versionadded:: 4.0.0 weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Weight for each ``data`` instance. Weights should be non-negative. .. versionadded:: 4.0.0 group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None) Group/query size for ``data``. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. .. versionadded:: 4.0.0 init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow Array, pyarrow ChunkedArray, pyarrow Table (for multi-class task) or None, optional (default=None) Init score for ``data``. .. versionadded:: 4.0.0 feature_name : list of str, or 'auto', optional (default="auto") Feature names for ``data``. If 'auto' and data is pandas DataFrame, data columns names are used. .. versionadded:: 4.0.0 categorical_feature : list of str or int, or 'auto', optional (default="auto") Categorical features for ``data``. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. .. versionadded:: 4.0.0 dataset_params : dict or None, optional (default=None) Other parameters for Dataset ``data``. .. versionadded:: 4.0.0 free_raw_data : bool, optional (default=True) If True, raw data is freed after constructing inner Dataset for ``data``. .. versionadded:: 4.0.0 validate_features : bool, optional (default=False) If True, ensure that the features used to refit the model match the original ones. Used only if data is pandas DataFrame. .. versionadded:: 4.0.0 **kwargs Other parameters for refit. These parameters will be passed to ``predict`` method. Returns ------- result : Booster Refitted Booster. z,Cannot refit due to null objective function.NrrT)rirr"r%rrr%) rirrrrrrrrLrrefit_decay_rate)r/r/rr rrGrrNrOrPrLGBM_BoosterGetLinearrrTrrLrrUrwr-r,r8r@rLGBM_BoosterRefitrRr,)r{rirrrrrrrrr rr%rIr leaf_predsrDr out_is_linear new_paramsr  new_boosterrrWs rCrefit Booster.refits@  ' ' NO O  !N#00hW]N^0_ !*!2!2/ "3"  %% d Q   & &  ]+  ));; %))<)<$= =!.)!% 3'  *4 %&j)4   " "##!!   ''+ %j1!Q  " "##t$t$    $}} rBtree_idleaf_idc [R"S5n[[R"UR [R "U5[R "U5[R"U555 UR$)zGet the output of a leaf. Parameters ---------- tree_id : int The index of the tree. leaf_id : int The index of the leaf in the tree. Returns ------- result : float The output of the leaf. r) rNrrPrLGBM_BoosterGetLeafValuerrOrTrU)r{rrrs rCget_leaf_outputBooster.get_leaf_outputFs_ooa   ) )  W% W% S!   yyrBrUc [[R"UR[R "U5[R "U5[R "U555 U$)a(Set the output of a leaf. .. versionadded:: 4.0.0 Parameters ---------- tree_id : int The index of the tree. leaf_id : int The index of the leaf in the tree. value : float Value to set as the output of the leaf. Returns ------- self : Booster Booster with the leaf output set. )rPrLGBM_BoosterSetLeafValuerrNrOr)r{rrrUs rCset_leaf_outputBooster.set_leaf_output`sL0   ) )  W% W%&    rBc[R"S5n[[R"UR [R "U555 UR$)zWGet number of features. Returns ------- num_feature : int The number of features. r)rNrOrPrLGBM_BoosterGetNumFeaturerrTrU)r{out_num_features rCrBooster.num_featuresH!,,q/  * *  _-  $$$rBc UR5n[R"S5nSn[R"S5n[ U5Vs/sHn[R "U5PM nn[R U-"[[RU56n[[R"UR[R"U5[R"U5[R"U5[R"U5U55 XR:wa [S5eURnX8:a[ U5Vs/sHn[R "U5PM nn[R U-"[[RU56n[[R"UR[R"U5[R"U5[R"U5[R"U5U55 [ U5V s/sH oU RR!S5PM" sn $s snfs snfs sn f)z^Get names of features. Returns ------- result : list of str List with names of features. rrrr)rrNrOrrrir%rBrrPrLGBM_BoosterGetFeatureNamesrrTrUrrrs rCrBooster.feature_names&&( ll1o &)#&,ooa&8#\abm\no\nWX&556QR\no$oo ;c&BRBRTb>cd  , ,  [) [) ;< 89"    ++ +UV V$?$E$E! & B^cdo^pq^pYZf99:ST^pNq"(//K"?#fFVFVXfBg!h  00LLLL-LL-OO$=>LL!<=&  BG{ASTASAq!''..w7ASTT;p"rUs I I-'I! iterationc Uc URn[Un[R"UR 5[R S9n[ [R"UR[R"U5[R"U5URR[R"[R5555 U[:XaUR![R"5$U$)aiGet feature importances. Parameters ---------- importance_type : str, optional (default="split") How the importance is calculated. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. iteration : int or None, optional (default=None) Limit number of iterations in the feature importance calculation. If None, if the best iteration exists, it is used; otherwise, all trees are used. If <= 0, all trees are used (no limits). Returns ------- result : numpy array Array with feature importances. r)r0rr`rTrrbrPrLGBM_BoosterFeatureImportancerrNrOrrr_C_API_FEATURE_IMPORTANCE_SPLITrr)r{rrrresults rCfeature_importanceBooster.feature_importances.  ++I=oN$**,BJJ?  . .  Y' 01 %%fnnV__&EF    "A A==* *MrBrbins xgboost_stylec8^^ ^ ^ S[[[4SS4U UU U 4Sjjm UR5nUR S5m USn/m UHnT "US5 M Ub[ U[ 5(a@U(a9[[R"T 55n[Ub [Xr5OUS5n[R"T US 9upU(aB[R"U SSU45n XSS2S4S :n [(a [U S S /S 9$U $X4$)a\Get split value histogram for the specified feature. Parameters ---------- feature : int or str The feature name or index the histogram is calculated for. If int, interpreted as index. If str, interpreted as name. .. warning:: Categorical features are not supported. bins : int, str or None, optional (default=None) The maximum number of bins. If None, or int and > number of unique split values and ``xgboost_style=True``, the number of bins equals number of unique split values. If str, it should be one from the list of the supported values by ``numpy.histogram()`` function. xgboost_style : bool, optional (default=False) Whether the returned result should be in the same form as it is in XGBoost. If False, the returned value is tuple of 2 numpy arrays as it is in ``numpy.histogram()`` function. If True, the returned value is matrix, in which the first column is the right edges of non-empty bins and the second one is the histogram values. Returns ------- result_tuple : tuple of 2 numpy arrays If ``xgboost_style=False``, the values of the histogram of used splitting values for the specified feature and the bin edges. result_array_like : numpy array or pandas DataFrame (if pandas is installed) If ``xgboost_style=True``, the histogram of used splitting values for the specified feature. rootrENc>SU;azTb[T[5(a TUSnOUSnUT:Xa7[US[5(a [S5eTRUS5 T"US5 T"US5 gg)zRecursively add thresholds.rhNr}rz@Cannot compute split value histogram for the categorical featurerr)rrr/r)r)r}r'rrnr s rCr'.Booster.get_split_value_histogram..adds$ ,GS1I1I$1$2G$HM$($9M G+!${"3S99+,noo d;&78D&'D'(%rBrnrrr)r&r SplitValueCountr)rrrrrzrrrr`rrrW histogram column_stackrr()r{rr&r'model tree_infosrn_uniquehist bin_edgesrr'rnr s ` @@@rCget_split_value_histogram!Booster.get_split_value_histogramsN )d38n ) ) )! /2 ;'  #I  *+ ,$ <:dC00]299V,-Hd.>s8*HaPD,,vD9 //9QR=$"78C!Q$i!m$C#C,1HII ? "rB data_namerc dX R:a [S5eUR5 /nURS:GaC[R "UR[R S9n[R"S5n[[R"UR[R"U5[R"U5URR[R"[R 5555 UR"UR:wa [S5e[%UR5H3nUR'XR(UXWUR*U45 M5 [-U5(aU/nUbUS:Xa UR.nOUR0US- nUHnn U cMU "UR3U5U5n [5U [65(a!U Hupn UR'XX45 M MVU upn UR'XX45 Mp U$)z%Evaluate training or validation data.1Data_idx should be smaller than number of datasetrrzWrong length of eval resultsr)r6rr<_Booster__num_inner_evalr`rTrbrNrOrPrLGBM_BoosterGetEvalrrTrrrrUrr_Booster__name_inner_eval"_Booster__higher_better_inner_evalrr r4rrr)r{r7rrrr#rnrcur_data eval_function feval_ret eval_namer5is_higher_betters rC __inner_evalBooster.__inner_eval2s )) )PQ Q   1 $XXd332::FF ,,q/K ((LLLL*LL-MM))&..*IJ    D$9$99 !?@@4001 I'='=a'@&)TMlMlmnMopq2 E??GE  1}>>??8a<8!&  ()$*>*>x*H(S i...>c^JF rBc UR(GaKSUl[R"S5n[[R "UR [R"U555 URUl URS:Ga[R"S5nSn[R"S5n[UR5Vs/sHn[R"U5PM nn[RUR-"[[RU56n[[R "UR [R"UR5[R"U5[R"U5[R"U5U55 URUR:wa [#S5eURnX8:a[UR5Vs/sHn[R"U5PM nn[RUR-"[[RU56n[[R "UR [R"UR5[R"U5[R"U5[R"U5U55 [UR5V s/sH oU RR%S5PM" sn UlUR&V s/sHoR)S5PM sn Ulgggs snfs snfs sn fs sn f)z%Get inner evaluation count and names.Frrz1Length of eval names doesn't equal with num_evalsr)auczndcg@zmap@average_precisionN)r-rNrOrPrLGBM_BoosterGetEvalCountsrrTrUr:rrrir%rBrLGBM_BoosterGetEvalNamesrrr<rr=) r{ out_num_evalrnrrrWrrrrrs rC__get_eval_infoBooster.__get_eval_info~s  ' ' '+0D (!<<?L ..LLLL.  %1$6$6D !$$q($ll1o .1+.4ooa.@+V[\`\q\qVr"VrQRF//0KLVr"'-oo8M8M&MPSTZTdTdftPu%v"11  T%:%:; [1(CD %@A*  ((K,=,==$%XYY,G,M,M).JX]^b^s^sXt&XtST334MNXt#&+1//D*&55 LL"LL)>)>?"LL5"OO,EF"LL)DE.  \aaeavav[w)x[wVW*;*A*A*H*H*Q[w)x&_c_u_u3_uW[OO$QR_u3/S) ( "&& *y3s M M$='M):M.)__higher_better_inner_eval__init_predictor__inner_predict_buffer__is_predicted_cur_iter__name_inner_eval__need_reload_eval_info __num_class __num_dataset__num_inner_eval__set_objective_to_nonerr,r.r0r1r5rrLr r=r4)NNNNr)rEr,)r(r*rNNrG)Nrr)rr)NrrN)rNFFFFF) g?NNNNrrNTF)rN)NF)Or<r=r>r?r@rrrrr-rr rrrGrFrrTr>rZr]rrrr2rCr(rrrr_LGBM_CustomObjectiveFunctionrrwr`rrrrrrrrr_LGBM_CustomEvalFunction!_LGBM_BoosterEvalMethodResultTyperrrrrr?rMrrrr5r6r>rGrrrrrrrr rrrrr$rr5rrr<rAr:rBrCr,r,s<,0'+15#' Ec3h(EG$EU39-. 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