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"OOF,<,<],KL  & &z* [)!  !&&  ""77 CM &0@0@0O P  **NN:.LL-% zz    & &w /=m  r4argsc|j|j|_t}|D]"}|j|j |$|Sr9)aliasesr<rupdate get_sorted)clsr=ris r5getz_ConfigAliases.get#sL ;; 446CKe *A JJs~~a( ) * r4rc~|j|j|_|jj||gSr9)r?r<rD)rBrs r5rAz_ConfigAliases.get_sorted,s3 ;; 446CK{{tdV,,r4c|j|j|_t|}|D]8}|jjD]}||vs|j |8:|Sr9)r?r<rrr@)rBr=rargr?s r5 get_by_aliasz_ConfigAliases.get_by_alias2sm ;; 446CK$i C;;--/ '>JJw'    r4)r/r0r1r? staticmethodrrsrr< classmethodrrDrArHr3r4r5r(r(sG Dd3i$8  8C-c-d3i--   S  r4r(main_param_name default_valuec~t|}tj|}|Dcgc] }||k7s | }}||jvr|D]}|j |d|S|D]}||jvs||||<n||jvr|D]}|j |d|S|||<|Scc}w)aGet a single parameter value, accounting for aliases. Parameters ---------- main_param_name : str Name of the main parameter to get a value for. One of the keys of ``_ConfigAliases``. params : dict Dictionary of LightGBM parameters. default_value : Any Default value to use for the parameter, if none is found in ``params``. 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(rAkeyspop)rKr>rLr?aparams r5_choose_param_valuerR?s&f F''8G!:QQ/%9q:G:&++-' $E JJud # $  FKKM !&,UmF? #  &++-' $E JJud # $ ,F? 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Fields with bad pandas dtypes: z, )rrrrrr)rs column_name pandas_dtypebad_pandas_dtypess r5rrs|*>)C)C)E %K&|'8'89 -r,( XY]YbYbctYuXv w   s&A target_dtypect|j |j|dS#t$r |j |dj cYSt $r$|j|tjcYSwxYw)NFrr)rOna_value) rrto_numpyr~rrrrRnan)r[rxs r5_pandas_to_numpyr~st!- B}})A>=A> feature_namecategorical_featurepandas_categoricalct|jdk7s|jddkr td|jd}|dk(r"|jDcgc] }t |}}t |j|jd Dcgc]\}}t|ts|}}}|Dcgc]}||jjr| }}|0|Dcgc]$}t||jj&}}nt|t|k7r td t ||d D]U\}}t||jjt|k7s5||jj|||<W|r7||jd j!d t"j$i||<|dk(r|}|jDcgc]}|j&} }| j)t"j*t#j,| } t/|| |||fScc}wcc}}wcc}wcc}wcc}w)Nrrrz/Input data must be 2 dimensional and non empty.F)deepautoTstrictz9train and valid dataset categorical_feature do not match.c.|jjSr9)catcodesr;s r5r/z#_data_from_pandas..s r4rx)rrrrcolumnsrsziprrrrorderedr categoriesset_categoriesapplyreplacerRr}rrrS result_typer~) r[rrrcolrOcat_colscat_cols_not_orderedcategory df_dtypesrxs r5_data_from_pandasrs#  4::!tzz!}q0JKK 99%9 Dv,0LL9SC9 9"$,, DIUZX]_rMsH7?&\sd3immF[F[s&\&\!HPQd49==#;#;<QQ x=C 23 3XY Y +=dK CMCDIMM,,-h? IMM88BS  Ch--.CDLLbRTRXRX\ZXf$2)-55I5 RZZ >>9-L L9  7:']Q6s*H<I-I8II#)I %I file_namectj|t}d|d}|(t|d5}|j |ddd|S|S#1swY|SxYw)Ndefaultz pandas_categorical: rP)r5dumpsropenwrite)rrcategorical_json pandas_strrs r5_dump_pandas_categoricalr"sfzz"4>VW()9(:"=J )S ! Q GGJ  : s AA model_strcpd}t| }|t| }t|d5} t||}|j |t |j }t|dk\rn|dz}G ddddjdj}|j|sK|djdj}n(|&|jdd|}||dj}j|r!tj|t|dSy#1swYxYw) Nzpandas_categorical:rbrrrrr) rr rmaxseekr readlinesrr startswithrfindr5r6) rr pandas_keyoffset max_offsetrlines last_lineidxs r5_load_pandas_categoricalr.s4'J*o Fi(( )T " aVZ0vx( u:?!     "I$$W-335 ##J/b ((1779I  oodAv.cdO))+ J'zz)C O$5677#  s A D,,D5ceZdZdZdZej deee e efde jfdZ ej defdZy)r&a 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. irr7ctd)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)rkrs r5 __getitem__zSequence.__getitem__hs8""abbr4ctd)z"Return row count of this sequence.z9Sub-classes of lightgbm.Sequence must implement __len__()rrks r5__len__zSequence.__len__s""]^^r4N)r/r0r1r2 batch_sizeabcabstractmethodrintslicerrRrrrr3r4r5r&r&Jsp6JcuS%c%:;c cc: ___r4cteZdZdZdedeeedeee fde fdZ e dddeee fd dfd Z e d eeefdeee fd dfd Zd3dZd eee ffdZ d4dededede de de de de d efdZdedededed ef dZdej2dedededeej2d eej2eff dZdej2dededed eej2eff dZd eej<j>ej<j@fd!ej2d"d#d$d#d%d#d&ed'ed(e d e!fd)Z"d*ej<j@dedededeej2d eej2eff d+Z#d*ej<j@dededed ee!eff d,Z$d*ej<j@dededed ee!eff d-Z%d.ej<j>dededed ee!eff d/Z&d.ej<j>dededed ee!eff d0Z'de(jRdededed eej2eff d1Z*d efd2Z+y )5_InnerPredictorz_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_handlec||_||_||_t||_t j d}ttj|jt j||j|_ y)aXInitialize 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_handlerrrr@rArBrLGBM_BoosterGetNumClassesrFrG num_class)rkrrrr out_num_classs r5__init__z_InnerPredictor.__init__sm*& "/"40@ Q   * *  ]+  ',,r4boosterr!r7ctjd}ttj|j tj |||j |j|dS)zInitialize an ``_InnerPredictor`` from a ``Booster``. Parameters ---------- booster : Booster Booster. pred_parameter : dict Other parameters for the prediction. rFrrrr)r@rArBrLGBM_BoosterGetCurrentIterationrrFr)rBrr out_cur_iters r5 from_boosterz_InnerPredictor.from_boosters_||A   0 0 \*  "??&99)   r4 model_filec 0tj}tjd}tt j t t|tj|tj|||t||dS)aInitialize 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) r@rrArBrLGBM_BoosterCreateFromModelfilerErsrFr)rBrrrout_num_iterationss r5from_model_filez_InnerPredictor.from_model_filesy *#\\!_  0 0s:' /0 ^,  )7*M)   r4Nc |jr)ttj|jyy#t $rYywxYwr9)rrBrLGBM_BoosterFreerAttributeErrorrs r5__del__z_InnerPredictor.__del__s> &&400>?'   s48 AAc|jj}|jdd|jdd|S)Nhandler)__dict__rrO)rkthiss r5 __getstate__z_InnerPredictor.__getstate__s5}}!!# 4  D! r4r[start_iteration num_iteration raw_score pred_leaf pred_contribdata_has_headervalidate_featuresc t t|tr tdt|tr|r|jD cgc] } t | } } t jt| z} | D cgc]} | jdc} | ddttj|j| t jt| t|trt|dd|j d}t"} |rt$} |rt&} |rt(} t|t t*frt-5} ttj.|jt1t |t j|t j| t j|t j|t1|j2t1| j4t7j8| j4t6j:}|j<d}dddnqt|t>j@jBr|jE|||| \}}n3t|t>j@jFr|jI|||| \}}nt|t6jJr|jM|||| \}}ntOjP|r,|jStUjV|||| \}}nt|tXr/ t7jZ|}|jM|||| \}}nC tad t>j@jC|}|jE|||| \}}|rjgt6jh}ttXt>j@jjf}|sU|jlk7s|s|rB|jl|zdk(r|jo|d}|St_d|jld|d|Scc} wcc} w#1swYxYw#t\$r}t_d |d}~wwxYw#t\$r'}tdtc|jd|d}~wwxYw)aUPredict logic. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, pyarrow Table or polars DataFrame Data source for prediction. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs 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 insteadrNrr[rrrrrcsrrr predict_typecscrrr)rJrrr)r[rrrz(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"r~rrrsr@rrrrBr LGBM_BoosterValidateFeatureNamesrrArr_C_API_PREDICT_NORMAL_C_API_PREDICT_RAW_SCORE_C_API_PREDICT_LEAF_INDEX_C_API_PREDICT_CONTRIBr rLGBM_BoosterPredictForFilerErrrRloadtxtrTrscipysparse csr_matrix_InnerPredictor__pred_for_csr csc_matrix_InnerPredictor__pred_for_cscr_InnerPredictor__pred_for_np2dnwdis_into_dataframe"_InnerPredictor__pred_for_narwhalsnw from_nativerr BaseExceptionrrrr/rrispmatrixsizereshape)rkr[rrrrrrrr6 data_names ptr_namesrrpredsnrowerrr is_sparses r5predictz_InnerPredictor.predicts-d dG $ef f dL ).?*.,,7Q#a&7J73z?:=I7AB!AHHW-BIaL 55LLLLZ1  dL )$#$*#'#:#:   D- 3L 4L 1L dS$K ( &33 s4y) _5 \2 _5 ]3t223qvv   166<{{1~ & &ell55 6-- /+) .KE4 ell55 6-- /+) .KE4 bjj ).. /+) /KE4  " "4 (22^^D) /+) 3KE4 d # Vxx~.. /+) /KE4 `FGll--d3-- /+) .KE4 LL*EutU\\-B-B&CD ejjD0IzzD A% dB/ !#=ejj\Ibcgbhhi!jkk S8B4 & &`! V !KLRUU V! `"?T @S@S?T UV\__ `sHQ4QC2Q?Q*.*RQ'* R3 Q??R R7"R22R7r rc |tkDrtdtdtjd}t t j |jtj|tj|tj|tj|tj||jS)zGet size of prediction result.zXLightGBM cannot perform prediction for data with number of rows greater than MAX_INT32 (zP). You can split your data into chunks and then concatenate predictions for themr) _MAX_INT32r$r@rrBrLGBM_BoosterCalcNumPredictrrArFrG)rkrrr rn_predss r5__get_num_predsz_InnerPredictor.__get_num_predss * ??IlK<<  ..#  + +  T" \* _- ]+ W%   }}r4rJr ct|\}}t|\}} } |j|||jd|} |&t j | tj }n1t|jdk7st|| k7r tdtjd} ttj|j|tj| tj |jdtj |jdtj|tj|tj|tj|t#|j$tj&| |jj)tj*tj, | | j.k7r td||jdfS)Nrrrr rrr+Wrong length of pre-allocated predict array Wrong length for predict results)r\rg_InnerPredictor__get_num_predsrrRemptyrTrrr@rrBrLGBM_BoosterPredictForMatrrArDrErrFrarrrG) rkrJrrrr r[rZre type_ptr_data_r out_num_predss r5__inner_predict_np2dz$_InnerPredictor.__inner_predict_np2ds%S) f%3D%9"-&&+'1% '  =HHWBJJ7E   "c%jG&;JK Kq)   * *  ]+syy|,syy|, V$ \* _- ]+t**+ ]+ $$V^^FOO%DE  m)) )?@ @ciil""r4c t|jdk7r td|jd}|tkDr,t j t|t}t j dgt|z|gzDcgc] }|j||t||"}}t jdg|ztjj} t jt|tj} t!t j"||t!| | ddd d D]!\} \} } |j%| |||| | |  #| |fS|j%||||d Scc}w) zPredict for a 2-D numpy matrix.rz1Input numpy.ndarray or list must be 2 dimensionalrrrrNTr)rJrrrr )rrrrrRarangediffrrrrintpcumsumrsumrTr array_split#_InnerPredictor__inner_predict_np2d)rkrJrrrr sectionsrCrn_preds_sectionsr chunkstart_idx_pred end_idx_preds r5__pred_for_np2dz_InnerPredictor.__pred_for_np2ds syy>Q PQ Qyy| * yyT:>H!tH~!5!>? $$$3"/Q!- %G "xxg RWWELLN HHS\   * *  _-""**6>>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ ]+ $$V^^FOO%DE  & m)) )?@ @d{r4ct|j\}}}t|j\}} } |jj t jd} t} |tk(r)tjtj} n(tjtj} tjtj}| tk(r)tjtj}n(tjtj }t j"dt j$}t't)j*|j,|tj.|| jj1tjtj|tj.| tjt3|jtjt3|jtj|j4dtj.|tj.|tj.|t7|j8tj.| |jj1tjtjtj:| tj:|tj:||j=||| |||| d}t3|jdz }||fS)NFrzrrrTr-r.r/r0r1r2r3r4)rmr6rgr[rGrrRri_C_API_MATRIX_TYPE_CSRrjr@rrDrrbrrrrkrBrLGBM_BoosterPredictSparseOutputrrArarrrErrF%_InnerPredictor__create_sparse_native)rkrrrrrIrJ__rerrrK matrix_typer/r0r1r.matricesr s r5__inner_predict_csr_sparsez*_InnerPredictor.__inner_predict_csr_sparsesQ+7szz*B' OR%3CHH%="-kk(((> , 0 0;V^^FNN;=N;V^^FNN;=N8&..8: 0 096>>&..9;L:6>>&//:>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ [)  (()GH ^, _- \*%  ,..)+%'#/  3::"~r4c |tk(r|j||||St|jdz }|tkDr-dgt t jt|tz|gz}t j|Dcgc] }|j||t||"}}t jdg|zt jj} t jt|t j } t#t#||dddt#| | ddddD]'\\} } \} }|j%|| | |||| | | )| |fS|j%||||d Scc}w) zPredict for a CSR data.rrrrrNTr)rrrrr )r)_InnerPredictor__inner_predict_csr_sparserr6rrrRr r!rrrr"r#rr$rTr"_InnerPredictor__inner_predict_csr)rkrrrrr r'rCrr(r  start_idxend_idxr*r+s r5__pred_for_csrz_InnerPredictor.__pred_for_csrs 1 122 /+) 3  3::" * sT"))Jj"IJJdVSH* $$$3"/Q!- %G "xxg RWWELLN HHS\ , 0 0;V^^FNN;=N;V^^FNN;=N8&..8: 0 096>>&..9;L:6>>&//:>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ [)  (()GH ^, _- \*%  ,..)+%'#/  yy|~r4c|jd}|tkDr#|j|j|||S|tk(r|j ||||S|j ||||}tj|tj}tjd}t|j\} } } t|j\} } }|jdtksJ|j j#tj$d}t't)j*|j,| tj.| |jj1tj2tj4| tj.| tjt7|jtjt7|jtj|jdtj.|tj.|tj.|t9|j:tj<||jj1tj2tj>||j@k7r tCd||fS) zPredict for a CSC data.rrrrrFrzr)"rrrtocsrr)_InnerPredictor__inner_predict_sparse_cscrrRrrTr@rrmr6rgr[rGrrirBrLGBM_BoosterPredictForCSCrrArarrDrrErrFrrGr)rkrrrrr rr rrIrJrRrerrr^s r5__pred_for_cscz_InnerPredictor.__pred_for_csc1s1yy| * &&IIK /+) '  1 122 /+) 3  &&+'% '   3q) *6szz*B' OR%3CHH%="-yy|z)))kk(((>   * *  _-""**6>>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ ]+ $$V^^FOO%DE  & m)) )?@ @d{r4c|j|||jd|}tj|tj}t j d}|j}ttj|jt|t j|t j|t j|t|jt j ||j j#t j$t j&||j(k7r t+d||jdfS)z!Predict for a narwhals DataFrame.rrrr)rrrRrrTr@r__arrow_c_stream__rBr!LGBM_BoosterPredictForArrowStreamrrrArErrFrarrrGr) rkr[rrrrr rrs r5__pred_for_narwhalsz#_InnerPredictor.__pred_for_narwhalsns&&+'A% '   3q) ++-   2 2 5i@ \* _- ]+t**+ ]+ $$V^^FOO%DE   m)) )?@ @djjm##r4ctjd}ttj|j tj ||jSzGet the index of the current iteration. Returns ------- cur_iter : int The index of the current iteration. rr@rArBrrrrFrGrkrs r5current_iterationz!_InnerPredictor.current_iterationH||A   0 0  \*  !!!r4r7N)rrFFFFF),r/r0r1r2_BoosterHandlerrrrsrboolrrJrrr rrr_LGBM_PredictDataTyper_LGBM_PredictReturnTyperrrRrrr&rrrrr_LGBM_PredictSparseReturnTyperQrXrWrrbrr DataFramerrmr3r4r5rrs!-&!-%T$Z0!-S#X !-  !-F  S#X     8 #t)$ S#X     <d38n !" %"'^#^^ ^  ^  ^^^ ^ !^@      :'# ZZ'#'# '#  '#  # '# rzz3 '#R- ZZ-- -  - rzz3  -^<" %,,))5<<+B+BB C<"::<"* <" + <" ( <"<"<"<" '<"|0 \\ $ $00 0  0  # 0 rzz3 0d8 \\ $ $88 8  8 ,c1 2 8t3 \\ $ $33 3  3 ,c1 2 3j8 \\ $ $88 8  8 ,c1 2 8t; \\ $ $;; ;  ; ,c1 2 ;z"$ll"$"$ "$  "$ rzz3  "$H"3"r4rceZdZdZ dTdedeededdeedeedee d e d e d ee e efd ed eefdZdUdZdedej*fdZdededdfdZdeej*deej*dededdf dZdej*ddfdZde e effdZdVdZdeededeeeeej*fddfdZ deedeededdeedeedee deed e d e d ee e efd eeddfdZ!e"d ee#d!e$ede%ej*fd"Z&d ee#dede'eej*eej*ffd#Z(d ee#deeddfd$Z)d%ej*d&e deeddfd'Z*d(eej*d&e deeddfd)Z+d*e,jZj\d&e deeddfd+Z/d,e,jZj`d&e deeddfd-Z1de2jfd&e deeddfd.Z4e"d e e efd/e e efd0e5e defd1Z6dVd2Z7 dWdedeedeedeedee d ee e efd eeddfd3Z8 dXdeed ee e efddfd4Z9d5ee e:fddfd6Z;d ee e efddfd7Zddfd:Z?d9e deej*fd;Z@d e ddfd<ZAdeeddfd=ZBdYd>ZCd e ddfd?ZDdeeddfd@ZEdeeddfdAZFdee ddfdBZGdeeddfdCZHd eeddfdDZIdee fdEZJdeefdFZKdeefdGZLdee fdHZMdeefdIZNdeefdJZOdeefdKZPdefdLZQdefdMZRdNeee fdefdOZSdZdPede5dfdQZTd[dRZUd5ee e:fddfdSZVy)\r"aO 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. Nr[rS referencerTrVrUrrr> free_raw_datarWc .d|_||_||_||_||_||_| |_||_||_||_ t| |_ | |_ d|_ d|_d|_d|_d|_d|_d|_d|_y)aInitialize Dataset. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array, pyarrow Table or polars DataFrame 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. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow ChunkedArray, polars Series or None, optional (default=None) Label of the data. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs 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 ChunkedArray, polars Series or None, optional (default=None) Weight for each instance. Weights should be non-negative. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs group : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series 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. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None, optional (default=None) Init score for Dataset. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs feature_name : list of str, or 'auto', optional (default="auto") Feature names. If 'auto' and data is pandas DataFrame, pyarrow Table, or polars DataFrame, 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, pyarrow ChunkedArray, polars Series or None, optional (default=None) Position of items used in unbiased learning-to-rank task. NTrF)rr[rSrwrTrVrWrUrrrr>rx used_indices _need_slice _predictorr_params_back_upversion _start_row_has_non_default_feature_names) rkr[rSrwrTrVrUrrr>rxrWs r5rzDataset.__init__st26   "    $";4'Q)QQ't eA1~##ryy0 <s'+a.:N:N9O~!^__a &&"((2 ?1#W^A=N=T=Tq AN1 !-nQ.?!@!COA  Dhh?1A?rxxP ,[ 9A( '(9:   4 4 NFNN6>>&//;Z,[\ OV^^FNN6>>r(rDr)rkdataset_paramsr-r.s r5rzDataset.get_paramss ;; "+//*%"$%"$!&!+N.&*[[%6%6%8PTQArD)raliasrks r5rz7Dataset._set_init_score_by_predictor..s !jE$++//%"?!js&)rT)r[rrrrg) fill_valuerO)rrsr r>anyr(rDnum_datarrYr{rRrrrTrrrrU full_likeset_init_score) rkrr[rzrrrUsub_init_scorerCjnew_init_scores ` r5_set_init_score_by_predictorz$Dataset._set_init_score_by_predictors    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 instance)rrr)rrr)rJrr)r[rrr)matsrr)seqsrz,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.rr[rzzWrong predictor type )Irrrrrrrrrrschemanamesrinspect signature __class__ _lazy_init parametersrNrrr enumeratersaddrr~rr/r(rDrOsortedrr>r" constructr r@rrBrLGBM_DatasetCreateFromFilerErFrrr_Dataset__init_from_csrr_Dataset__init_from_cscrRr_Dataset__init_from_np2d_Dataset__init_from_narwhalsrr_Dataset__init_from_list_np2dr_Dataset__init_from_seqsr&r set_label get_labelr set_weight set_group set_positionrr|rrset_feature_name)rkr[rSrwrTrVrUrrrr>rW args_namesrcategorical_indices feature_dictrCr cat_aliasrrrr s r5rzDataset._lazy_init sb <DLK  &/&B&BD #"+"?"?  dL )O`)$7#'#:#: P LD, 3T5L   &<6+A>>$/66<<>L /;f.D+~6&&t~~'@'@ALLQQS ;;= Cj e""%&bd  )4 0"%% L,-7@7NOGAtaO O+ ydC(T\-A'++L,>?c*'++D1#k$t*2E2E1FFXY]X^^v$wxx  y#!/!3!34I!J4I F* *6)+>$+?Jdo % p d #D A '-**Yd*e&t,%%4[%I NOO h '  ! !v; ! G bll--d3$$Q\$]   NN5 ! >>  #78 8   OOF #   NN5 !     h ' i 1&:+Aab  - - [_ - `  #    +  "3DO4L4L3MNO O$$\22] Pr! b"A$t*BUBUAV WX^aa bs V93V?? W/"W**W/rrGc#Kd}d}||}|D]v}||k\sJd||t|zk\r*|t|z }|dz }||}||t|zk\r*||z }||}|jdr|n|jxyw)Nrz+sample indices are expected to be monotonicrOWNDATA)rrUr)rrGrseq_idseqrow_id id_in_seqrows r5_yield_row_from_seqlistzDataset._yield_row_from_seqlists6l >FV# R%R R#FSX--#c("! 6lFSX--Ii.C9-#388: = >s AB0Bc|j|}tjt|j ||}|j }g}g}tj t|tj}|D]`}tj|tkDtj|z} || } || } |j| |j| b||fS)zSample data from seqs. Mimics behavior in c_api.cpp:LGBM_DatasetCreateFromMats() Returns ------- sampled_rows, sampled_row_indices )r=r) rrRrrrTr rriabsr:isnanr) rkrr=rGsampledfiltered filtered_idxsampled_row_ranger col_predicate filtered_colfiltered_row_idxs r5__samplezDataset.__samples---D((4 < 9RXXc]JM}-L0?  OOL )    0 1  2%%r4ctd|D}||j||nOt|j}t ||}|j ||\}}|j |||||D]a}t|} t|ddxstj} td| | D]%} t| | z| } |j|| | 'c|S)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 c32K|]}t|ywr9r)rrs r5rz+Dataset.__init_from_seqs..s2cS2rN)rr=rr)r$rrrrI_Dataset__samplerrryr&rrminr) rkrrr=rrHr col_indicesrr rstartends r5__init_from_seqszDataset.__init_from_seqss2T22   "  ' ' K @*4??+<=I*:yAJ'+}}$:}'V $K  " "; Z T 0Cs8D lD9PX=P=PJq$ 3 0%*,d3E#/ 0 0  r4rJrct|jdk7r tdtj|_t |\}}t|\}}}ttj|tj|tj|jdtj|jdtj|t||tj|j |S)z(Initialize data from a 2-D numpy matrix.r)Input numpy.ndarray must be 2 dimensionalrr)rrrr@rrr\rgrBrLGBM_DatasetCreateFromMatrArDrErF) rkrJrrr[rZrerrs r5__init_from_np2dzDataset.__init_from_np2ds syy>Q HI I( $S) f%3D%9"-  * * ]+syy|,syy|, V$z" T\\*   r4rc|djd}tjt|ftj}|dj tj k(r5tjtjt|z}n4tjtjt|z}tjt|z}g}d} t|D]\} } t| jdk7r td| jd|k7r td| jd|| <t| \} } t| \} }}| dk7r|| k7r td| || <| || <|} |j!|tj"|_t't)j*tj,t|tj.|tjtjtjtj| |jj1tjtj,tj,||t3||tj4|j$ |S)z2Initialize data from a list of 2-D numpy matrices.rrrrrz-Input arrays must have same number of columnsz Input chunks must have same type)rrRrrrirOrTr@rrrrArrr\rgrrrrBrLGBM_DatasetCreateFromMatsrDrrarErF)rkrrrrr relayoutsholdersrrCrJrZchunk_ptr_datachunk_type_ptr_dataholders r5__init_from_list_np2dzDataset.__init_from_list_np2d sAw}}QxxT bhh/ 7==BJJ &Cv7#d)CFHBv~~6TBEH<<#d)+. o #FAs399~" !LMMyy|t# !PQQiilDG',KC:H:M 7N/"':m'K !CDD(HQKGAJ/M NN6 "# #&(   + +s4y) HfnnV^^FOO5T&UV ]+ ##FNN6>>$BCt$z" T\\*   r4rct|jt|jk7r7tdt|jdt|jt j |_t|j\}}}t|j\}}} |jdtksJ|jjtjd} tt!j"|t j$|| jj't j(tj*|t j$|t j,t|jt j,t|jt j,|jdt/||t j0|j  |S)z"Initialize data from a CSR matrix.Length mismatch:  vs rFrz)rrGr[rr@rrrmr6rgrrrrRrirBrLGBM_DatasetCreateFromCSRrArarrDrrErF) rkrrrrIrJrRrerrrKs r5__init_from_csrzDataset.__init_from_csrA f s{{ s388} ,0S[[1A0B$s388}oVW W( *6szz*B' OR%3CHH%="-yy|z)))kk(((>   * * _-""**6>>&..+IJ ]+s3::/s388}-syy|,z" T\\*   r4rct|jt|jk7r7tdt|jdt|jt j |_t|j\}}}t|j\}}} |jdtksJ|jjtjd} tt!j"|t j$|| jj't j(tj*|t j$|t j,t|jt j,t|jt j,|jdt/||t j0|j  |S)z"Initialize data from a CSC matrix.rrrFrz)rrGr[rr@rrrmr6rgrrrrRrirBrLGBM_DatasetCreateFromCSCrArarrDrrErF) rkrrrrIrJrRrerrr^s r5__init_from_csczDataset.__init_from_cscd rr4c |j}tj|_t t j t|t||tj|j|S)z*Initialize data from a narwhals DataFrame.) rfr@rrrBr!LGBM_DatasetCreateFromArrowStreamrrErF)rkr[rrrs r5__init_from_narwhalszDataset.__init_from_narwhals sb++- (   2 25i@z" T\\*    r4 other_params ignore_keysc|jD]\}}||vs ||vs |||k7sy|jD]\}}||vs ||vs |||k7syy)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. FTr,)r>rrr-r.s r5_compare_params_for_warningz#Dataset._compare_params_for_warning sz2!&&( !DAq #F?fQi1n  !LLN !DAq #L(Aa,@  !r4c @ |j|j|jj}|j}||k7rC|j||tj ds t d|j||j|j|j|j|j|j|j|j|j|j |j"d|j$ n[t'|jt(j*d}|j,j.sJ|jjt)j0|jjj3t(j*d }t)j4t)j6t9t;|| |jd \}|_ t=j>|_tA|j$}tCtEjF|jjIj|j<jKt=jLt<jNt=jN|jPdtS|t=jT|j|jVs|jY|j|j[|j|j|j]|j|j_ tadtc|j tdr|j |jj ur|jY|jg|j |j|n|j|j|jd|j|j|j|j |j"|jh|j$|j |jVrd|_ |jk|_|S)zsLazy init. Returns ------- self : Dataset Constructed Dataset object. Nr)r>rrz1Overriding the parameters from Reference Dataset.r) r[rSrwrTrVrWrUrrrr>rzrFrz)repeatsT) return_countsrzLabel should not be None.r) r[rSrwrTrVrUrrrr>rW)6rrwrrr(rDr_update_paramsrzrr[rSrTrVrWrUr|rr>rrRrirUr\rruniquerepeatrrr@rrrBrLGBM_DatasetGetSubsetrrarrDrrErFrxget_datarrrrrrrrget_feature_name)rkreference_paramsr>rz group_inforrs r5rzDataset.construct sl << ~~)#'>>#<#<#> *--;;%%5$2$6$67L$M< %%XY''(89$$,OO!YY"jj"&..#{{"jj!%#'??"&//%)%6%6,2#{{$ $5!.. hh+$L (--::::~~++7%'XXdnn.B.B%C%J%J288Z_%J%` (* IIeC O&>(//77v~~8VW"NN<+=+=a+@A":."LL6  -- zz-tzz2}}0))$--8~~'/()DEE"4??OD OO4>>3L3LL 99&*ooDIIT`: **";;**#"oo!%!2!2(,(@(@;;!]] !!  $ 5 5 7D  r4c t|||||||||j }|j|_|j|_|S)aJ Create validation data align with current Dataset. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array, pyarrow Table or polars DataFrame 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. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow ChunkedArray, polars Series or None, optional (default=None) Label of the data. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs weight : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None, optional (default=None) Weight for each instance. Weights should be non-negative. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs group : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series 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. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None, optional (default=None) Init score for Dataset. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs params : dict or None, optional (default=None) Other parameters for validation Dataset. position : numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars 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. )rSrwrTrVrWrUr>rx)r"rxr|r) rkr[rSrTrVrUr>rWrs r5 create_validzDataset.create_valid sPT !,,  !%!8!8 r4c| |j}td||j|j||j}|j |_|j |_t||_|S)aGet 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)rwrrr>rx) r>r"rrrxr|rrrz)rkrzr>rs r5subsetzDataset.subsety sm& >[[F ** $ 8 8,,  !%!8!8!,/ r4filenamec ttj|jjt t ||S)aSave 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. )rBrLGBM_DatasetSaveBinaryrrrErsrkr-s r5 save_binaryzDataset.save_binary s=$   ' ' ((s8}%   r4csStdfd }j |Stjt t j t t }|dk7rQj|jSttjjdS)Ncjs_ytj_jjyr9)r>rr}r@)r>rksr5r@z&Dataset._update_params..update s4;;$ '/ '<$ ""6*r4rrro) rrrLGBM_DatasetUpdateParamCheckingrErr>r[rr$rr)rkr>r@rs`` r5r!zDataset._update_params sK&! + <<  H  66)$++67)&12Cax99(H%%' ((>(>(@(G(G(PQQ r4c`|j!t|j|_d|_|Sr9)rrr}r>rs r5_reverse_update_paramszDataset._reverse_update_params s+ << "4#7#78DK#'D  r4 field_namec |jtd|d|dttj|jt |dt jdt jt||Stj|stj|rt|ttfsxtj |dj#}ttj$|jt |t'||xj(dz c_|S|dk(rat*j,}t/|rt1||| }njt3|r!t5||| }|j7d }n>t9d |d vrt*j:}nt*j<}t1||| }|j>t*j<k(s|j>t*j,k(rtA|\}}}nF|j>t*j:k(rtC|\}}}nt9d|j>d|t|k7r t9dttj|jt ||t jtE|t j||xj(dz c_|S)aSet property into the Dataset. Parameters ---------- field_name : str The field name of the information. data : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None The data to be set. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs Returns ------- self : Dataset Dataset with set property. Nz Cannot set z before construct datasetrT) allow_seriesrrUrrMrQzinit_score must be list, numpy 1-D array or pandas Series. In multiclass classification init_score can also be a list of lists, numpy 2-D array or pandas DataFrame.>rVrWz-Expected np.float32/64 or np.int32, met type(r_zInput type error for set_field)#r ExceptionrBrLGBM_DatasetSetFieldrEr@rA_FIELD_TYPE_MAPPERrris_into_seriesrrrrrrf#LGBM_DatasetSetFieldFromArrowStreamrr~rRrTrrrrrYr~rirSrOrgrmr)rkr7r[rrOrerfrs r5 set_fieldzDataset.set_field sm2 << k*5NOP P < ))LL:&LLOLL!3J!?@  K  ! !$ '3+=+=d+CZ <+N t$?RRTI 88LL:&9)D  LLA LK  %JJE &(d%jQ"4((d%jQzzz,@ 22 $$e*MD :: #tzzRZZ'?%3D%9 "Hi ZZ288 #%1$%7 "HiKDJJ>/ ; ;=> >    ! >>/ /([[fnnV^^&DE"((C^^3 3*[[fnnV^^&DE"((C^^3 3*[[fnnV__&EF"((C N+ +  %}}H((h.KQkk8["9kE r4c |j|k(r|S|jg|j||_|jS|dk(r|S|jdk7rtdt |||_|jSt d)a&Set categorical features. Parameters ---------- categorical_feature : list of str or int, or 'auto' Names or indices of categorical features. Returns ------- self : Dataset Dataset with set categorical features. rzIcategorical_feature in Dataset is overridden. New categorical_feature is zrCannot set categorical feature after freed raw data, set free_raw_data=False when construct Dataset to avoid this.)rr[rrrr$)rkrs r5set_categorical_featurezDataset.set_categorical_featuret s  # #': :K 99 ''/+>(((**$. ++v5 66:;N6O5PR,?(((**P r4c||j|St|trVt|jtr<||jk(r-|j|jjk(r|S|j ||_|S|j 1||_|j |j|j d|S|jg|j[|jj E||_|j |j|jj |j|Std)zSet predictor for continued training. It is not recommended for user to call this function. Please use init_model argument in engine.train() or engine.cv() instead. NrzhCannot set predictor after freed raw data, set free_raw_data=False when construct Dataset to avoid this.) r|rrrmrr[rrzrwr$)rkrs r5_set_predictorzDataset._set_predictor s.  !8K  ? 3 4??Tc8dT__,++-1R1R1TT << 'DO( 'YY "'DO  - -//YY! . "    *t~~/IdnnNaNaNm'DO  - -//^^((!.. .   P r4cV|j|jj|jj |j |j j|j r|S|j||_ |jStd)aSet reference Dataset. Parameters ---------- reference : Dataset Reference that is used as a template to construct the current Dataset. Returns ------- self : Dataset Dataset with set reference. zhCannot set reference after freed raw data, set free_raw_data=False when construct Dataset to avoid this.) rGrrrrIr| get_ref_chain intersectionr[rwrr$)rkrws r5 set_referencezDataset.set_reference s $$Y%B%BCTT  " " .-- .     , ,Y-D-D-F GK 99 &DN$$& &P r4c |dk7r||_d|_|j||dk7rt||j k7r)t dt|d|j d|Dcgc] }t |}}ttj|jttj|tjt||Scc}w)zSet feature name. Parameters ---------- feature_name : list of str Feature names. Returns ------- self : Dataset Dataset with set feature name. rTzLength of feature_name(z) and num_feature( ) don't match)rrrr num_featurerrErBrLGBM_DatasetSetFeatureNamesrr@rrA)rkrrc_feature_names r5rzDataset.set_feature_name s 6 ! ,D 26D / << # (@\U[E[< D$4$4$66 -c,.?-@@RSWScScSeRffst8DDtfTlDND 00LLV__n=LL\!23  Es0C$c||_|jt|trRt |j dkDr t dtjt|tj}nYtj|stj|rt|ts|}nt|tjd}|j!d||j#d|_|S)aSet label of Dataset. Parameters ---------- label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow ChunkedArray, polars Series or None The label information to be set into Dataset. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs Returns ------- self : Dataset Dataset with set label. rz0DataFrame for label cannot have multiple columnsrrSr)rSrrrrrrrRrYr~rSrrr=rrr?rE)rkrS label_arrays r5rzDataset.set_label s& << #%.u}}%)$%WXX hh'7BJJ'WX ''.#2D2DU2KU_`egpUq# /U"**SZ[ NN7K 00DJ r4c|Ztj|r+tj|ddk(j rd}nt j|dk(rd}||_|jk|it|tstj|st|t jd}|jd||jd|_|S)aSet weight of each instance. Parameters ---------- weight : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None Weight to be set for each data point. Weights should be non-negative. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs Returns ------- self : Dataset Dataset with set weight. NT) series_onlyrrTr)rr=rrrrRrTrrrrrSr?rE)rkrTs r5rzDataset.set_weight s.  !!&)NN6t<AFFH!F! $  << #(:&),C4F4Fv4N*bjjxX NN8V ,..2DK r4c||_|j*|(|jd||jd|_|S)aSet 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 ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None Init score for Booster. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs Returns ------- self : Dataset Dataset with set init score. rU)rUrr?rE)rkrUs r5rzDataset.set_init_scoreD s=,% << # (> NN< 4"nn\:DO r4c0||_|j|t|tst j |st |tjd}|jd||jd}|tj||_|S)aSSet group size of Dataset (used for ranking). Parameters ---------- group : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series 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. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs Returns ------- self : Dataset Dataset with set group. rVr) rVrrrrr=rrRrir?rEr!)rkrVconstructed_groups r5rzDataset.set_group` s|4 << #(9%+33E3Ee3L)uBHH7S NN7E * $w 7  ,WW%67  r4c||_|jk|it|tst j |st |tjd}|jd||jd|_|S)axSet position of Dataset (used for ranking). Parameters ---------- position : numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None, optional (default=None) Position of items used in unbiased learning-to-rank task. Returns ------- self : Dataset Dataset with set position. rWr) rWrrrrr=rrRrir?rE)rkrWs r5rzDataset.set_position sg ! << #(<(I.c6H6H6R,("((Q[\ NN:x 0 NN:6DM r4c |j td|j}tjd}d}tj d}t |Dcgc]}tj|}}tj|zttj|}ttj|jtj|tj|tj |tj||||jk7r t!d|j}||krt |Dcgc]}tj|}}tj|zttj|}ttj|jtj|tj|tj |tj||t |D cgc] } || jj#d"c} Scc}wcc}wcc} w)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$rPr@rAc_size_trr2rrr3rBrLGBM_DatasetGetFeatureNamesrFrGrr rkrPr8reserved_string_buffer_sizerequired_string_buffer_sizerstring_buffersptr_string_buffersactual_string_buffer_sizerCs r5r&zDataset.get_feature_name s <<  RS S&&( ll1o &)#&,ooa&8#\abm\noWX&556QRoo$oo ;c&BRBRTb>cd  , ,  [) [) ;< 89"    +++ +UV V$?$E$E! &)B B^cdo^pqYZf99:STqNq"(//K"?#fFVFVXfBg!h  00LLLL-LL-OO$=>LL!<=&  BG{ASTAq!''..w7TT;p"rUs!II.%I c^|j|jd|_|jS)aGet the label of the Dataset. Returns ------- label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow ChunkedArray, polars Series or None The label information from the Dataset. For a constructed ``Dataset``, this will only return a numpy array. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs rS)rSrErs r5rzDataset.get_label s( :: 0DJzzr4c^|j|jd|_|jS)aGet the weight of the Dataset. Returns ------- weight : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars 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. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs rT)rTrErs r5 get_weightzDataset.get_weight s( ;; ..2DK{{r4c^|j|jd|_|jS)avGet 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), pyarrow ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None Init score of Booster. For a constructed ``Dataset``, this will only return ``None`` or a numpy array. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs rU)rUrErs r5get_init_scorezDataset.get_init_score s( ?? ""nn\:DOr4c(|j td|jrL|j?|j2|jj |_|j t |j tjtjjfr$|j |jddf|_nt |j tr8|j j|jj|_nOt |j tr |j |j|_nt!j"|j rUtj$|j}t'j(|j |j+|_nt-|j r`t/|j dkDrHtj$t1|j3|j |j|_n,t5dt7|j j8dd|_|j t;d|j S)aGet the raw data of the Dataset. Returns ------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array, pyarrow Table, polars DataFrame, or None Raw data used in the Dataset construction. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs 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.)rr:r{rzrwr[rrRrrrrrilocrr&rrrrr to_nativerrrrrrr/r$)rkrGs r5r%zDataset.get_data s << FG G    1 1 =$..B\++DIyy$dii"**ell6K6K)LM $ $*;*;Q*> ?DI <8 $ t/@/@ A F F HDI 84 $ $*;*; >D)ny(r4c |j |j tdttj|j|j|j du}t |j j}|j d|_n|j t|j tjr#t|j tjr2tj|j |j f|_nBt|j tjjr@tj|j |j jf|_nt|j t rtjj|j |j f||_nt|j t rHtjj|j |j j"f||_nNd|_nEt|j t r#t&s t)dt|j tjr3t+|j t!|j fdd|_nt|j tjjrAt+|j t!|j jfdd|_nSt|j t r*t+|j |j fdd|_nd|_nd|_|j :dt |j jd |d }||rd nd z }t-||j/|_t-d d|_d|_|S)a^Add 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. zQUY ZDI $DI  99 +D,<,E,E+FF[%&&:<  RZN`r G  ! 113 Z $* "& r4c ttj|jjt t ||S)aKSave 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. )rBrLGBM_DatasetDumpTextrrrErsr0s r5 _dump_textzDataset._dump_text s=   % % ((s8}%   r4) NNNNNrrNTNro)r7r")NNNNNNr9)rwr"r7r")d)rr"r7r")Wr/r0r1r2_LGBM_TrainDataTyper_LGBM_LabelType_LGBM_WeightType_LGBM_GroupType_LGBM_InitScoreType_LGBM_FeatureNameConfiguration%_LGBM_CategoricalFeatureConfigurationrrsrrq_LGBM_PositionTyperrrrRrr_DatasetHandlerrrrrrrrrrrIr&rrrrrrrrrrrrrrrrurrrrr*r,r r1r!r6_LGBM_SetFieldTyper?rErGrIrMrrrrrrr&rrhrjr%rorqrrPr|rKrrr3r4r5r"r"s,0)--1+/487=EK+/"15s:!s:(s:I& s: )* s: ( s:01s:5s:Cs:c3h(s:s:-.s:j C BJJ D$  :>"**%>RZZ(> >  >  >@rzzi<#DcN#J)O,)")uT#Y %:;< )  )V3*+3(3I& 3 )* 3 ( 3013O,353C3c3h(3-.3 3B >d8n >x} >QYZ\ZdZdQe > >&X&C&E$rzzJZ\`acakak\lJlDm&8 8n n-   DZZ  n-   852::5 5 n- 5  5n!\\ $ $! ! n- !  !F!\\ $ $! ! n- !  !Fll  n-   . S#X 38n X    D]D,0-1+/48+/15W!W(W)* W ( W 01 Wc3h(W-.W Wx,0 3i c3h(   DE#t)$44Xd38n%=)8 ^^)*^  ^@;C;HRZZ,@;z$B$ $L%O,% %N8-KPY>x8YB%)*% %N01 8#(# #J-. 0+U$s)+UZ8O4&H%56&)< =&((#67(T8O44 h'9: P#P(SSS(WuS#XW3W@sS^ed ede7fd?Z8 dgde9d@e:dAe(dBeedCee;dDee<dEee=dFe>dGe?dHeeeefdIed>ed eddfdJZ@dKedLede(fdMZAdKedLedNe(ddfdOZBdefdPZCdeefdQZD dhd2edReede!jDfdSZE didTe eefdUee eefdVede eFe!jDe!jDfe!jDeffdWZGdXedYed+ee e+ee+fdee,fdZZHdYede!jDfd[ZId^d\ZJy)jr!zBooster in LightGBM.Nr> train_setrrc tj|_d|_d|_d|_d|_d|_i|_|in t|}|t|ts!tdt|jtd|d}|d|j!ddn|d}t|t"rt%|j'd }n>t|t(t*frt%|}d j-|}n t/d td ||}td |d }|j1||d |j3dd|d |j5|j7|j9t;|}t=t?j@|jtC|tjD|j||_#g|_$g|_%d|_&|jN|_(|jP=t=t?jR|j|jPjtjTd}t=t?jV|jtjD||jX|_-dg|_.dg|_/|ja|jb|_1|jd|_3||_9y|tjTd} t=t?jhtCt#|tjD| tjD|jtjTd}t=t?jV|jtjD||jX|_-tk||_1|r tmd|jo}||_9y|6|jq||r tmd|jo}||_9ytd)aInitialize 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 machinesrKr>rLrzInvalid machines in params. num_machineslocal_listen_portp0time_outx)rrlisten_time_outrrrrz;Ignoring params argument, using parameters from model file.z=Ignoring params argument, using parameters from model string.z[Need at least one training dataset or model file or model string to create Booster instance):r@rr_network_Booster__need_reload_eval_info_train_data_name_Booster__set_objective_to_nonebest_iteration best_scorerrr"r~rr/rRrOrsrrXrrrr set_networkrDrr@rrrBrLGBM_BoosterCreaterErFr valid_setsname_valid_sets_Booster__num_datasetr|_Booster__init_predictorLGBM_BoosterMergerArrG_Booster__num_class_Booster__inner_predict_buffer_Booster__is_predicted_cur_iter_Booster__get_eval_inforr~train_set_versionrrr_get_loaded_parammodel_from_stringr>) rkr>rrrrnum_machines_from_machine_listrrrs r5rzBooster.__init__ks((  '+$ *',$ 68~8F+;  i1"PQUV_Q`QiQiPj kll( *"F j!) :t,!*-h,589L5M24+658]2"xx1H$%BCC,$2!"@ -$7!"'   %&,-@&A$*JJz3$?!'!7 !    ! MM)..0 1+F3J ''%%:&LL. 'DN-/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 -\]++-F  m r4r7c |jr|j |j)t t j |jyy#t$rYBwxYw#t$rYywxYwr9)r free_networkrrrBrrrs r5rzBooster.__del__sl }}!!# ||'400>?(       s"A4A$ A! A!$ A0/A0c$|jdSr9) __deepcopy__rs r5__copy__zBooster.__copy__s  &&r4r=kwargsc>|jd}t|S)Nrrr)model_to_stringr!)rkr=rrs r5rzBooster.__deepcopy__s ((r(: ++r4c|jj}|d}|jdd|jdd||jd|d<|S)Nrrrrr)rrrOr)rkrrs r5rzBooster.__getstate__s[}}!!#i d# t$  "222DDO r4statec ||jd|jdd}|}tj}tjd}t t j t|tj|tj|||d<|jj|y)Nrrr) rDr@rrArBrLGBM_BoosterLoadModelFromStringrErFrr@)rkrrrrs r5 __setstate__zBooster.__setstate__sIIi8T)BC  __&F!'a  449%LL!34LL(  &E)  U#r4c d}tjd}tj|}tjtj|}t t j|jtj|tj|||j}||kDrtj|}tjtj|}t t j|jtj|tj||tj|jjdS)Nr*rr)r@rr2rr3rBrLGBM_BoosterGetLoadedParamrrFrGr5r6r)rkr7r8r9r:r;s r5rzBooster._get_loaded_param s nnQ' 33J? "OOF,<,<],KL  + + z* [)!   !&&  ""77 CM &0@0@0O P  //LLNN:.LL-%  zz---44W=>>r4c|jjdd|jjddd|_|S)zFree Booster's Datasets. Returns ------- self : Booster Booster without Datasets. rNrr)rrOrrs r5 free_datasetzBooster.free_dataset<s9 +t, ,- r4c"g|_g|_|Sr9)rrrs r5 _free_bufferzBooster._free_bufferIs&(#')$ r4rrrrc (t|ttfrdj|}t t j t|tj|tj|tj|d|_ |S)a1Set 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. rT) rrrrrBrLGBM_NetworkInitrEr@rAr)rkrrrrs r5rzBooster.set_networkNsq2 hs ,xx)H  ! !x  ./ _- \*     r4cNttjd|_|S)zFree Booster's network. Returns ------- self : Booster Booster with freed network. F)rBrLGBM_NetworkFreerrs r5rzBooster.free_networkts! 4((*+  r4cts td|jdk(r tddttt fdt fd ddttt fdtdttd tttd ttdttt ff fd  ddttt fdtdttd tttd ttdtttt ff fd |j}|d }g}|d D]"}|j|d|d|$t||djS)aParse 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_nodes DIIK/ /r4 node_depth tree_index feature_names parent_nodecdtttfdttdtf fd }dtttfdtt tdttf fd }dtttfdt fd}t}||d<||d<||||d <d|d <d|d <||d <||| |d<d|d<d|d<d|d<d|d<d|d<d|d<d|d<d|d< |rd||d ||d <||d ||d <|d|d<|d|d<|d|d<|drdnd|d<|d|d<|d|d<|d|d<|d|d<|S|d|d<||s|d|d<|d|d<|S) Nrrr7cn||dnd}|}|rdnd}|j|rdndd}|||S)N-rSLr leaf_indexr)rD)rrtree_numis_split node_typenode_numrs r5_get_node_indexzOBooster.trees_to_dataframe..create_node_record.._get_node_indexsS0:/Ej\+2)$/#+C 88XM.create_node_record.._get_split_featuresK "$'$0'4T/5J'K $#(,O'< $#$(L##r4c>t|jddhk(S)N leaf_value leaf_count)rrNrs r5_is_single_node_treezTBooster.trees_to_dataframe..create_node_record.._is_single_node_trees499;'L,+GGGr4r node_index left_child right_child parent_index)rrr split_gain threshold decision_typemissing_direction missing_typerGrTcount default_leftleftrightinternal_valueinternal_weightinternal_countr leaf_weightr)rrsrrrrrqr) rrrrrrrrnoders r5create_node_recordz6Booster.trees_to_dataframe..create_node_records# :38n :$SM : : $38n $ (S 2 $# $ H4S> Hd H6A]D!+D !+D !0z!BD !%D "&D #.D $6DP]$^D !!%D  $D $(D !(,D$ %#'D  DM!DN DMd#%4T,5G%T\"&5d=6I:&V]#%),%7\"$($5[!(,_(=_%6:>6JFPW()'+N';^$ $%5 6W !%&7!8X $%5 6W K !%\ 2W +D1%)-%8DN$($6DMKr4c  |||||}|g} |r2ddg}|D])} |||dz|||d} |j| +|S)N)rrrrrrrrr)extend) rrrrrrreschildrenchild subtree_listrrtree_dict_to_node_lists r5rz:Booster.trees_to_dataframe..tree_dict_to_node_lists&%%+' D&Cd#(-8% -E#9!%[#->#-&3$($6 $LJJ|, -Jr4 tree_infotree_structure)rrrr)rNNN)rr$ num_treesrrsrrqrrr dump_modelrrrN)rk model_dictr model_listrrrrs @@@r5trees_to_dataframezBooster.trees_to_dataframes8 W  >> q  ^_ _ 0c3h 0D 0  (,15)- G sCx.G G ! G $DI. G  "# G  #s(^ G V (,15)-  sCx.  !  $DI.   "#   $sCx. !  B__& "?3  {+ D   &./Dr@)rkr>rs r5reset_parameterzBooster.reset_parameterNsO(/  //LL:&  6" r4fobjcj|2|j|jjk7r|j}d}n)||juxr|j|jk(}||st|ts!t dt |j|j|jur td||_ttj|j|jjjd|j d<|jj|_t#j$d}||j&r tdttj(|jt#j*|t-|j.Dcgc]}dc}|_|j2dk(S|j&sd|j5d d i_||j7d |j\}}|j9|| Scc}w) aUpdate Booster for one iteration. Parameters ---------- train_set : Dataset or None, optional (default=None) Training data. If None, last training data is used. fobj : callable or None, optional (default=None) Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess). preds : numpy 1-D array or numpy 2-D array (for multi-class task) The predicted values. Predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task. train_data : Dataset The training dataset. grad : numpy 1-D array or numpy 2-D array (for multi-class task) The value of the first order derivative (gradient) of the loss with respect to the elements of preds for each sample point. hess : numpy 1-D array or numpy 2-D array (for multi-class task) The value of the second order derivative (Hessian) of the loss with respect to the elements of preds for each sample point. For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes], and grad and hess should be returned in the same format. Returns ------- produced_empty_tree : bool ``True`` if the tree(s) produced by this iteration did not have any splits. This usually means that training is "finished" (calling ``update()`` again will not change the model's predictions). However, that is not always the case. For example, if you have added any randomness (like column sampling by setting ``feature_fraction_bynode < 1.0``), it is possible that another call to ``update()`` would produce a non-empty tree. NFrzJReplace training data failed, you should use same predictor for these datarz-Cannot update due to null objective function.rT objectivenonedata_idx)gradhess)rrr~rr"r~rr/r|rr$rBrLGBM_BoosterResetTrainingDatarrrr@rArLGBM_BoosterUpdateOneIterrFrrrrGr-_Booster__inner_predict_Booster__boost)rkrr.is_the_same_train_setproduced_empty_treerr4r5s r5r@zBooster.updatefsX  !7!74>>;Q;Q!QI$) !$-$?$oDDZDZ^g^o^oDo !  )>i1"PQUV_Q`QiQiPj kll##4+@+@@#$pqq&DN 22LLNN,,.66  .2D ' ' *%)^^%;%;D "$ll1o <++#$STT ..LLLL!45  OJD$<>$BC ##FNN6>>$BC 01   8=T=O=O7P'Q!'Q$"((A--(Rs H>cttj|jt |j Dcgc]}dc}|_|Scc}w)zRollback one iteration. Returns ------- self : Booster Booster with rolled back one iteration. F)rBrLGBM_BoosterRollbackOneIterrrrr)rkrs r5rollback_one_iterzBooster.rollback_one_itersD 433DLLAB7> !H3t/0 4??1-- 1uH  r> NN4 &))A-H  4(% PPr4c>|j|jd|S)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 (metric_name, metric_value, maximize) 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. metric_name : str Unique identifier for the metric (e.g. "custom_adjusted_mse"). metric_value : float Value of the evaluation metric. maximize : bool Are higher values better? e.g. ``True`` for AUC and ``False`` for binary error. Returns ------- result : list[EvalResult] List of ``lightgbm.EvalResult`` objects, named tuples of the form (dataset_name, metric_name, metric_value, maximize). rrU)rWr)rkrSs r5 eval_trainzBooster.eval_trains$B  4+@+@1TY ZZr4c td|jDcgc]-}|j|j|dz ||D]}|/c}}Scc}}w)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 (metric_name, metric_value, maximize) 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. metric_name : str Unique identifier for the metric (e.g. "custom_adjusted_mse"). metric_value : float Value of the evaluation metric. maximize : bool Are higher values better? e.g. ``True`` for AUC and ``False`` for binary error. Returns ------- result : list List of ``lightgbm.EvalResult`` objects, named tuples of the form (dataset_name, metric_name, metric_value, maximize). rrU)rrrWr)rkrSrCrs r5 eval_validzBooster.eval_validsjF1d001 ))D4H4HQ4OZ[ch)i      s2Ar-rrimportance_typec N| |j}t|}ttj|j t j|t j|t j|tt|t|j||S)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. ) r_FEATURE_IMPORTANCE_TYPE_MAPPERrBrLGBM_BoosterSaveModelrr@rArErsrr)rkr-rrr]importance_type_ints r5 save_modelzBooster.save_models:   //M=oN  & &  _- ]+ 01s8}%    !!8!8(C r4 end_iterationc ttj|jt j |t j ||S)aShuffle 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. )rBrLGBM_BoosterShuffleModelsrr@rA)rkrrcs r5shuffle_modelszBooster.shuffle_modelss@(   * *  _- ]+   r4c jttj|j|j t j |_t jd}ttjt|t j|t j|jt jd}ttj|jt j||j|_ t||_|S)zLoad Booster from a string. Parameters ---------- model_str : str Model will be loaded from this string. Returns ------- self : Booster Loaded Booster object. rr)rBrrrrr@rrArrErFrrGrrr)rkrrrs r5rzBooster.model_from_string!s 4((67 ( #\\!_  0 0y! /0 T\\*   Q   * *  ]+  )..":Y"O r4c | |j}t|}d}tjd}tj|}tj tj |}ttj|jtj|tj|tj|tj|tj|||j} | |kDrtj| }tj tj |}ttj|jtj|tj|tj|tj| tj|||jjd} | t|j z } | S)aNSave 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)rr_r@rr2rr3rBrLGBM_BoosterSaveModelToStringrrArFrGrrr) rkrrr]rar7r8r9r:r;rs r5rzBooster.model_to_stringFs4   //M=oN nnQ' 33J? "OOF,<,<],KL  . .  _- ]+ 01z* [)!  !&&  ""77 CM &0@0@0O P  22LLLL1LL/LL!45NN:.LL-% !!((1 '(?(?@@ r4r1c | |j}t|}d}tjd}tj|}tj tj |} ttj|jtj|tj|tj|tj|tj|| |j} | |kDrtj| }tj tj |} ttj|jtj|tj|tj|tj| tj|| tj|jj!d|} tjtj"|j$t&| d<| S)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*rrr0rr)rr_r@rr2rr3rBrLGBM_BoosterDumpModelrrArFrGr5r6rrrr) rkrrr]r1rar7r8r9r:r;rs r5r"zBooster.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 %  !  r4rrrrrc tj|t| } ||dkr |j}nd}| j ||||||||S)a Make a prediction. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, pyarrow Table or polars DataFrame Data source for prediction. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs 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)r[rrrrrrr)rrrrr) rkr[rrrrrrrrrs r5rzBooster.predictsu@$00#F+1   !# $ 3 3 "   +'%+/!  r4rS decay_raterwrTrVrUrrrrxc |jr td| i} tj|t | }|j |dd| }|j \}}tjd}ttj|jtj|td|jd }t!|j"|d<|j%| td |d }|j'd }|r| d k(r|} n|| k7r d }t|t)|||||||| || }||d<t+||}ttj,|j|j|j/d}t1|\}}}ttj2|j|tj4|tj4||j6|_|S)aRefit the existing Booster by new data. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array, pyarrow Table or polars DataFrame Data source for refit. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow ChunkedArray, polars Series or None Label for refit. .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs 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 ChunkedArray, polars Series or None, optional (default=None) Weight for each ``data`` instance. Weights should be non-negative. .. versionadded:: 4.0.0 .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs group : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series 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 .. versionadded:: 4.7.0 Support for ``polars`` inputs init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None, optional (default=None) Init score for ``data``. .. versionadded:: 4.0.0 .. versionadded:: 4.2.0 Support for ``pyarrow`` inputs .. versionadded:: 4.7.0 Support for ``polars`` inputs 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.NrmrT)r[rrrrrrrrar'categorical_feature' value passed to Booster.refit() is different from 'categorical_feature' value found in Booster.params. Preferring the value passed via keyword argument. Using refit() to change which columns are treated as categorical is not supported. If you have a valid use case for this, please open an issue at https://github.com/lightgbm-org/LightGBM/issues.) r[rSrwrTrVrUrrr>rxrefit_decay_rate)rr$rrrrrr@rArBrLGBM_BoosterGetLinearrrFrRr>rqrGr@rOr"r!rrrmLGBM_BoosterRefitrDr)rkr[rSrnrwrTrVrUrrrrxrrr leaf_predsr r out_is_linear new_paramscat_features_from_params error_msgr new_boosterrers r5refitz Booster.refit)sz  ' ' NO O  !N#00hW]N^0_ !*!2!2/ "3"  %% d Q   & &  ]+  ));; %))<)<$= =!.) )1 $.>>2G#H  $"f,&>#)-@@F$I..!% 3'  *4 %&j)4   " "##!!   ''+ %j1!Q  " "##t$t$    $}} r4tree_idleaf_idc  tjd}ttj|j tj |tj |tj||jS)a#Get 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) r@rrBrLGBM_BoosterGetLeafValuerrArFrG)rkrzr{rs r5get_leaf_outputzBooster.get_leaf_outputs_ooa   ) )  W% W% S!   yyr4rGc ttj|jt j |t j |t j ||S)aSet 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. )rBrLGBM_BoosterSetLeafValuerr@rAr)rkrzr{rGs r5set_leaf_outputzBooster.set_leaf_outputsL0   ) )  W% W%&    r4ctjd}ttj|j tj ||jS)zGet number of features. Returns ------- num_feature : int The number of features. r)r@rArBrLGBM_BoosterGetNumFeaturerrFrG)rkout_num_features r5rPzBooster.num_feature7sH!,,q/  * *  _-  $$$r4c |j}tjd}d}tjd}t |Dcgc]}tj |}}tj |zttj|}ttj|jtj|tj|tj|tj||||jk7r td|j}||krt |Dcgc]}tj |}}tj |zttj|}ttj|jtj|tj|tj|tj||t |D cgc] } || jj!d"c} Scc}wcc}wcc} w)zGet names of features. Returns ------- result : list of str List with names of features. rr\r]r)rPr@rAr^rr2rrr3rBrLGBM_BoosterGetFeatureNamesrrFrGrrr`s r5rzBooster.feature_nameHs&&( ll1o &)#&,ooa&8#\abm\noWX&556QRoo$oo ;c&BRBRTb>cd  , ,  [) [) ;< 89"    +++ +UV V$?$E$E! &)B B^cdo^pqYZf99:STqNq"(//K"?#fFVFVXfBg!h  00LLLL-LL-OO$=>LL!<=&  BG{ASTAq!''..w7TT;p"rUs H?I%I  iterationc | |j}t|}tj|j tj }t tj|jtj|tj||jjtjtj|tk(r|j!tj"S|S)aGet 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)rr_rRrrPrTrBrLGBM_BoosterFeatureImportancerr@rArarr_C_API_FEATURE_IMPORTANCE_SPLITrri)rkr]rraresults r5feature_importancezBooster.feature_importancets.  ++I=oN$**,BJJ?  . .  Y' 01 %%fnnV__&EF    "A A==* *Mr4rwbins xgboost_stylec dtttfddf  fd |j}|j d |d}g |D] } |d|t |t r:|r8ttj }t| t||n|d}tj | \}} |r@tj| dd|f} | | dddfd kD} trt| d d g S| S|| fS)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. rootr7Ncd|vrnttr |d}n|d}|k(r2t|dtr tdj|d|d|dyy)zRecursively add thresholds.rNrr z@Cannot compute split value histogram for the categorical featurerr)rrsr$r)rrrrwrrs r5rz.Booster.get_split_value_histogram..adds$ ,GS1I$1$2G$HM$($9M G+!${"3S9+,nooMM${"34D&'D'(%r4rrrr)rr SplitValueCountr )rrsrr"rDrrrrRr"rr histogram column_stackrr)rkrwrrmodel tree_infosrn_uniquehist bin_edgesrrrrs ` @@@r5get_split_value_histogramz!Booster.get_split_value_histogramsN )d38n ) ) )! /2 ;'  # -I  *+ , - <:dC0]299V,-Hd.>s8T*HaPD,,vD9i //9QR=$"78Cc!Q$i!m$C#C,1HII ? "r4rVr3c ||jk\r td|jg}|jdkDrHt j |jtj }tjd}ttj|jtj|tj||jjtjtj |j"|jk7r tdt%|jD]=}|j't)||j*||||j,|?t/|r|g}||dk(r |j0}n|j2|dz }|D]} | | |j5||} t7| t8r/| D])} |j't)|| d| d| d+^|j't)|| d| d| d|S) z%Evaluate training or validation data.1Data_idx should be smaller than number of datasetrrzWrong length of eval results)rrrrrr2r)rrr_Booster__num_inner_evalrRrrTr@rArBrLGBM_BoosterGetEvalrrFrarrrGrrr_Booster__name_inner_eval"_Booster__higher_better_inner_evalrxrrr8rr) rkrVr3rSrrr8rCcur_data eval_function feval_ret eval_tuples r5 __inner_evalzBooster.__inner_evals t)) )PQ Q   1 $XXd332::FF ,,q/K ((LLLL*LL-MM))&..*IJ    D$9$99 !?@@4001  %.$($:$:1$=%+AY!%!@!@!C   E?GE  1}>>??8a<8!&   ()$*>*>*>*QS[\ i.&/  &-6,6qM-7])3A JJ")2(1! )21%.q\  . r4c ||jk\r td|j||dk(r(|jj |j z}n-|j |dz j |j z}tj|tj|j|<|j|stjd}|j|jjtjtj}t!t#j$|j&tj(|tj*|||j,t/|j|k7rtd|d|j|<|j|}|j dkDr7|j0|j z}|j3||j d}|S) z,Predict for training and validation dataset.rrrrz)Wrong length of predict results for data TrMrQ)rrrrrrrrRrrTrr@rrarrrBrLGBM_BoosterGetPredictrrArFrGrrr)rkr3rr8rrrs r5__inner_predictzBooster.__inner_predict(s t)) )PQ Q  & &x 0 81}..113d6F6FF//(Q,7@@BTEUEUU46HHWBJJ4WD ' ' 1++H5 ..+K228<CCKKFNN[a[j[jLklH ++LLLL*LL-    C(C(CH(M$NN #LXJ!WXX59D ( ( 2!88B   a {{d&6&66H^^Hd.>.>c^JF r4c |jr,d|_tjd}tt j |j tj||j|_ |jdkDrtjd}d}tjd}t|jDcgc]}tj|}}tj|jzttj|}tt j |j tj|jtj|tj|tj|||j|jk7r t#d|j}||krt|jDcgc]}tj|}}tj|jzttj|}tt j |j tj|jtj|tj|tj||t|jD cgc] } || jj%d"c} |_|j&D cgc]} | j)dc} |_yyycc}wcc}wcc} wcc} w)z%Get inner evaluation count and names.Frr\z1Length of eval names doesn't equal with num_evalsr)auczndcg@zmap@average_precisionN)rr@rArBrLGBM_BoosterGetEvalCountsrrFrGrr^rr2rrr3LGBM_BoosterGetEvalNamesrrrrr) rk out_num_evalr8rarbrrcrdrerCrs r5__get_eval_infozBooster.__get_eval_infoGs  ' '+0D (!<<?L ..LLLL.  %1$6$6D !$$q($ll1o .1+.4ooa.@+V[\`\q\qVr"QRF//0KL""'-oo8M8M&MPSTZTdTdftPu%v"11  T%:%:; [1(CD %@A*  ((K,=,==$%XYY,G,M,M).1JJX]^b^s^sXt&ST334MN&N&+1//D*&55 LL"LL)>)>?"LL5"OO,EF"LL)DE.  \aaeavav[w)xVW.*;*A*A*H*H*Q)x&_c_u_u3W[DOO$QR3/S) ( "&& *y3s L;.M%MM )NNNNro)r7r!)rrrNNr9)NrrX)rr)NrrXN)rNFFFFF) g?NNNNrrNTF)rXN)NF)Kr/r0r1r2rrrsrr"rr rrrrrrrrrrrrrrrr%r'r*r-_LGBM_CustomObjectiveFunctionrqr@rRrr9rDrmrIr!rrNrR_LGBM_CustomEvalFunctionrrXrZr\rbrfrrrr"rrrsrrrrrrrrryr~rrPrrrrrWr8rr3r4r5r!r!hs,0'+15#' Hc3h(HG$HU39-. 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