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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)rrNrfkeyspop)rorIrprdaparams r@_choose_param_valuervs&f F''8G!:QQ/%9q:G:&++-' $E JJud # $  FKKM !&,UmF? #  &++-' $E JJud # $ ,F? 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IMM88BS  C 8}h--.CDLLbRTRXRX\ZXf$2)-55I5 RZZ >>9-L L9  3:p&\Q6s*II+I6I I !)I*I file_namectj|t}d|d}|(t|d5}|j |ddd|S|S#1swY|SxYw)Ndefaultz pandas_categorical: rt)rZdumpsr$openwrite)rrcategorical_json pandas_strr@s r@_dump_pandas_categoricalrksfzz"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 startswithrfindrZr[) rr pandas_keyoffset max_offsetr@lines last_lineidxs r@_load_pandas_categoricalrws4'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)r1a 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. irrBctd)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)rvrs r@ __getitem__zSequence.__getitem__s8""abbr?ctd)z"Return row count of this sequence.z9Sub-classes of lightgbm.Sequence must implement __len__()rrs r@__len__zSequence.__len__s""]^^r?N)r:r;r<r= batch_sizeabcabstractmethodrrslicerr]rrrr>r?r@r1r1sp6JcuS%c%:;c cc: ___r?c0eZdZdZdedeeedeee fde fdZ e dddeee fd dfd Z e d eeefdeee fd dfd Zd4dZd eee ffdZ d5dededede de de de de d eej,ej0j2eej0j2ffdZdedededed ef dZdej,dedededeej,d eej,eff dZdej,dededed eej,eff dZd eej0j>ej0j@fd!ej,d"d#d$d#d%d#d&ed'ed(e d eeej0j>eej0j@ffd)Z!d*ej0j@dedededeej,d eej,eff d+Z"d*ej0j@dededed eeeej0j>eej0j@feff d,Z#d*ej0j@dededed eej,eff d-Z$d.ej0j>dededed eeeej0j>eej0j@feff d/Z%d.ej0j>dededed eej,eff d0Z&d1e'dededed eej,eff d2Z(d efd3Z)y )6_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_handlerr8rrKrLrMrLGBM_BoosterGetNumClassesrQrR num_class)rvrrrr out_num_classs r@rz_InnerPredictor.__init__sm*& "/"40@ Q   * *  ]+  ',,r?boosterr,rBctjd}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)rKrLrMrLGBM_BoosterGetCurrentIterationrrQr)rgrr out_cur_iters r@ from_boosterz_InnerPredictor.from_boosters_||A   0 0 \*  "??&99)   r? 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) rKc_void_prLrMrLGBM_BoosterCreateFromModelfilerPr~rQr)rgrrrout_num_iterationss r@from_model_filez_InnerPredictor.from_model_file sy *#\\!_  0 0s:' /0 ^,  )7*M)   r?Nc |jr)ttj|jyy#t $rYywxYwrD)rrMrLGBM_BoosterFreerAttributeErrorrs r@__del__z_InnerPredictor.__del__?s> &&400>?'   s48 AAc|jj}|jdd|jdd|S)Nhandler)__dict__rrs)rvthiss r@ __getstate__z_InnerPredictor.__getstate__Fs5}}!!# 4  D! r?rfstart_iteration num_iteration raw_score pred_leaf pred_contribdata_has_headervalidate_featuresc  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}dddnt|t>j@jBr|jE|||| \}}nXt|t>j@jFr|jI|||| \}}nt|t6jJr|jM|||| \}}ntO|r|jQ|||| \}}nt|tRr/ t7jT|}|jM|||| \}}nt|tZr1t]|jM|j_||| \}}nC tad t>j@jC|}|jE|||| \}}|rjgt6jh}ttRt>j@jjf}|sU|jlk7s|s|rB|jl|zdk(r|jo|d}|StYd|jld|d|Scc} wcc} w#1swYxYw#tV$r}tYd |d}~wwxYw#tV$r'}tdtc|jd|d}~wwxYw)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 insteadrNrrfrrrrrcsrrr predict_typecscrrr )rUrrr )tablerrr 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(rr~rKrrrrMr LGBM_BoosterValidateFeatureNamesrrLrr_C_API_PREDICT_NORMAL_C_API_PREDICT_RAW_SCORE_C_API_PREDICT_LEAF_INDEX_C_API_PREDICT_CONTRIBr r:LGBM_BoosterPredictForFilerPrrr]loadtxtr_rscipysparse csr_matrix_InnerPredictor__pred_for_csr csc_matrix_InnerPredictor__pred_for_cscr_InnerPredictor__pred_for_np2dr'_InnerPredictor__pred_for_pyarrow_tablerr BaseExceptionrr!rLrrrr:rrspmatrixsizereshape)rvrfrrrrrrrrA data_names ptr_namesr r@predsnrowerrr  is_sparses r@predictz_InnerPredictor.predictLs[V dG $ef f l +0A*.,,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 t $77 /+) 8KE4 d # Vxx~.. /+) /KE4 l + / 1..MMO /+) /KE4 `FGll--d3-- /+) .KE4 LL*EutU\\-B-B&CD ejjD0IzzD A% dB/ !#=ejj\Ibcgbhhi!jkk ]8B4 & &\! V !KLRUU V(! `"?T @S@S?T UV\__ `sHQ94Q>C2R#R*R,R  R) R$$R), S5"SSr'r c |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/rKrrMrLGBM_BoosterCalcNumPredictrrLrQrR)rvrrr'r n_predss r@__get_num_predsz_InnerPredictor.__get_num_predss * ??IlK<<  ..#  + +  T" \* _- ]+ W%   }}r?rUr&ct|\}}t|\}} } |j|||jd|} |&t j | tj }n1t|jdk7st|| k7r tdtjd} ttj|j|tj| tj |jdtj |jdtj|tj|tj|tj|t#|j$tj&| |jj)tj*tj, | | j.k7r td||jdfS)Nrrrr'r rr+Wrong length of pre-allocated predict array Wrong length for predict results)rgr_InnerPredictor__get_num_predsrr]emptyr_rrrKrrMrLGBM_BoosterPredictForMatrrLrOrPrrQrrrrR) rvrUrrr r&rfrer type_ptr_data_r. out_num_predss r@__inner_predict_np2dz$_InnerPredictor.__inner_predict_np2ds%S) f%3D%9"-&&+'1% '  =HHWBJJ7E   "c%jG&;JK Kq)   * *  ]+syy|,syy|, V$ \* _- ]+t**+ ]+ $$V^^FOO%DE  m)) )?@ @ciil""r?c t|jdk7r td|jd}|tkDrt j t|t}t j dgt|z|gzDcgc]}|j||||}}t jdg|ztjj} t jt|tj} tt j ||t| | ddD]!\} \} } |j#| |||| | | #| |fS|j#||||dScc}w) zPredict for a 2-D numpy matrix.rz1Input numpy.ndarray or list must be 2 dimensionalrstartstopsteprrN)rUrrr r&)rrrr,r]arangediffrr4rintpcumsumr5sumr_r array_split#_InnerPredictor__inner_predict_np2d)rvrUrrr r'sectionsrr.n_preds_sectionsr&chunkstart_idx_pred end_idx_preds r@__pred_for_np2dz_InnerPredictor.__pred_for_np2d/ss syy>Q PQ Qyy| * yyz:NH!tH~!5!>?$$_mQ UG "xxg RWWELLN HHS\   * *  _-""**6>>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ ]+ $$V^^FOO%DE  & m)) )?@ @d{r?ct|j\}}}t|j\}} } |jj t jd} t} |tk(r)tjtj} n(tjtj} tjtj}| tk(r)tjtj}n(tjtj }t j"dt j$}t't)j*|j,|tj.|| jj1tjtj|tj.| tjt3|jtjt3|jtj|j4dtj.|tj.|tj.|t7|j8tj.| |jj1tjtjtj:| tj:|tj:||j=||| |||| d}t3|jdz }||fS)NFrrrrTrMrNrOrPrQrRrSrT)rrVrrfrgrr]r_C_API_MATRIX_TYPE_CSRrrKrrOrrrrr5rrMrLGBM_BoosterPredictSparseOutputrrLrrrrPrrQ%_InnerPredictor__create_sparse_native)rvr rrr rirj__rr7r8rk matrix_typerOrPrQrNmatricesr's r@__inner_predict_csr_sparsez*_InnerPredictor.__inner_predict_csr_sparsesQ+7szz*B' OR%3CHH%="-kk(((> , 0 0;V^^FNN;=N;V^^FNN;=N8&..8: 0 096>>&..9;L:6>>&//:>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ [)  (()GH ^, _- \*%  ,..)+%'#/  3::"~r?c |tk(r|j||||St|jdz }|tkDrdgt t jt|tz|gz}t j|Dcgc]}|j||||}}t jdg|zt jj} t jt|t j} t!t!||ddt!| | ddD]'\\} } \} }|j#|| | |||| | |)| |fS|j#||||dScc}w)zPredict for a CSR data.r rrr<rN)r rrr r&)r)_InnerPredictor__inner_predict_csr_sparserrVr,rr]r@rAr4rrBrCr5rDr_r"_InnerPredictor__inner_predict_csr)rvr rrr r'rGrr.rHr& start_idxend_idxrJrKs r@__pred_for_csrz_InnerPredictor.__pred_for_csrs 1 122 /+) 3  3::" * sT"))*4j"YZZ^b]ccHfhfmfmnvfwxabt++O]A|\xGx!xxg RWWELLN HHS\ , 0 0;V^^FNN;=N;V^^FNN;=N8&..8: 0 096>>&..9;L:6>>&//:>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ [)  (()GH ^, _- \*%  ,..)+%'#/  yy|~r?c|jd}|tkDr#|j|j|||S|tk(r|j ||||S|j ||||}tj|tj}tjd}t|j\} } } t|j\} } }|jdtksJ|j j#tj$d}t't)j*|j,| tj.| |jj1tj2tj4| tj.| tjt7|jtjt7|jtj|jdtj.|tj.|tj.|t9|j:tj<||jj1tj2tj>||j@k7r tCd||fS) zPredict for a CSC data.rr rr1rFrr3)"rr,rtocsrr)_InnerPredictor__inner_predict_sparse_cscr4r]r5r_rKrrrVrrfrgrrrMrLGBM_BoosterPredictForCSCrrLrrrOrrPrrQrrRr)rvrrrr r'r.r&r9rirjrrrr7r8r~s r@__pred_for_cscz_InnerPredictor.__pred_for_cschs1yy| * &&IIK /+) '  1 122 /+) 3  &&+'% '   3q) *6szz*B' OR%3CHH%="-yy|z)))kk(((>   * *  _-""**6>>&..+IJ ]+s3::/s388}-syy|, \* _- ]+t**+ ]+ $$V^^FOO%DE  & m)) )?@ @d{r?rctrts tdtd|jj Ds t d|j|||j|}tj|tj}tjd}t|}tt!j"|j$tj|j&tj(|j*tj(|j,tj.|tj.|tj.|t1|j2tj4||jj7tj8tj: ||j<k7r t d||jfS)zPredict for a PyArrow table.zACannot predict from Arrow without 'pyarrow' and 'cffi' installed.c3fK|])}t|xst|xs t|+ywrDrrrrts r@rz;_InnerPredictor.__pred_for_pyarrow_table..1rZ[#A&U*;A*>UBRSTBUUr/1=Arrow table may only have integer or floating point datatypesr1rrr3)rrr/rrtypesrr4num_rowsr]r5r_rKrrrMrLGBM_BoosterPredictForArrowrrrrrrLrPrrQrrrrR) rvrrrr r.r&r9c_arrays r@__pred_for_pyarrow_tablez(_InnerPredictor.__pred_for_pyarrow_tablesp"n cd dr_d_k_k_q_qrr\] ]&&+'% '   3q) %U+  , , w//0 2 23 2 23 \* _- ]+t**+ ]+ $$V^^FOO%DE   m)) )?@ @enn$$r?ctjd}ttj|j tj ||jSzGet the index of the current iteration. Returns ------- cur_iter : int The index of the current iteration. rrKrLrMrrrrQrRrvrs r@current_iterationz!_InnerPredictor.current_iterationH||A   0 0  \*  !!!r?rBN)rrFFFFF)*r:r;r<r=_BoosterHandlerrrr~rboolrrnrrr rrr_LGBM_PredictDataTyperr]rrrr!r*r4rrFrrrrqrxrwrrrr&rrr>r?r@rrs+!-&!-%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; \\ $ $;; ;  ; rzz3  ;z+%+%+% +%  +% rzz3  +%Z"3"r?rceZdZdZ dUdedeededdeedeedee d e d e d ee e efd ed eefdZdVdZdedej*fdZdededdfdZdeej*deej*dededdf dZdej*ddfdZde e effdZdWdZdeededeeeeej*fddfdZ deedeededdeedeedee deed e d e d ee e efd eeddfdZ!e"d ee#d!e$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-Z1d.e2d&e deeddfd/Z3e"d e e efd0e e efd1e4e defd2Z5dWd3Z6 dXdedeedeedeedee d ee e efd eeddfd4Z7 dYdeed ee e efddfd5Z8d6ee e9fddfd7Z:d ee e efddfd8Z;dWd9Zd:e deej*fd<Z?d e ddfd=Z@deeddfd>ZAdZd?ZBd e ddfd@ZCdeeddfdAZDdeeddfdBZEdee ddfdCZFdeeddfdDZGd eeddfdEZHdee fdFZIdeefdGZJdeefdHZKdee fdIZLdeefdJZMdeefdKZNdeefdLZOdefdMZPdefdNZQdOeee fdefdPZRd[dQede4dfdRZSd\dSZTd6ee e9fddfdTZUy)]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. Nrfrw referencerxrzryrrrI free_raw_datar{c  d|_||_||_||_||_||_| |_||_||_||_ t| |_ | |_ d|_ d|_d|_d|_d|_d|_d|_y)a| 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)rrfrwrrxrzr{ryrrrrIr used_indices _need_slice _predictorr_params_back_upversion _start_row) rvrfrwrrxrzryrrrIrr{s r@rzDataset.__init__sn26   "    $";4'Q)QQ't eA1~##ryy0 <s'+a.:N:N9O~!^__a &&"((2 ?1#W^A=N=T=Tq AN1 !-nQ.?!@!COA  Dhh?1A?rxxP ,[ 9A( '(9:   4 4 NFNN6>>&//;Z,[\ OV^^FNN6>>.s !jE$++//%"?!js&)rT)rfrrrrg) fill_valuerZ)rr~r rIanyrNrhnum_datar*rdrr]r5rr_rrr"ry full_likeset_init_score) rvrrfrrrrysub_init_scorerjnew_init_scores ` r@_set_init_score_by_predictorz$Dataset._set_init_score_by_predictor s    dS$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.rrfrzWrong predictor type )Grrrrr(rr column_namesinspect signature __class__ _lazy_init parametersrrrrr3rr~addrrrr:rNrhrssortedr8rIr- constructr rKrrMrLGBM_DatasetCreateFromFilerPrQrrr_Dataset__init_from_csrr_Dataset__init_from_cscr]r_Dataset__init_from_np2d!_Dataset__init_from_pyarrow_tablerr_Dataset__init_from_list_np2dr_Dataset__init_from_seqsr1r!rLrr  set_label get_labelr set_weight set_group set_positionrrrrset_feature_name)rvrfrwrrxrzryrrrrIr{ args_namesr7categorical_indices feature_dictrr cat_aliasrrr r(s r@rzDataset._lazy_init6s9 <DLK  &/&B&BD #"+"?"?  dL )O`)$7#'#:#: P LD, 3T5L t $)?,,L~6&&t~~'@'@ALLQQS ;;= Cj e""%&bd  )4 0"%% L,-7@7NOGAtaO O+ ydC(T\-A'++L,>?c*'++D1#k$t*2E2E1FFXY]X^^v$wxx  y#!/!3!34I!J4I F* *6)+>  #78 8   OOF #   NN5 !     h ' i 1&:+Aab  - - [_ - `  #    +  "3DO4L4L3MNO O$$\22c Px! b"A$t*BUBUAV WX^aa bs( V>2V## W,"WWseqsrgc#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)rr`r)rrgrseq_idseqrow_id id_in_seqrows r@_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) rr]rrrTr@rrabsrEisnanr) rvrrHrgsampledfiltered filtered_idxsampled_row_ranger col_predicate filtered_colfiltered_row_idxs r@__samplezDataset.__samples--j9((4 < 9RXXc]JM}-L0?  OOL )    0 1  2%%r?ctd|D}||j||nNt|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|ywrDr)rr s r@rz+Dataset.__init_from_seqs..s2cS2r)Nrr)rDrr8rrT_Dataset__samplerrrr1rrminr) rvrrrHrrSr col_indicesr r'rr=ends r@__init_from_seqszDataset.__init_from_seqss2T22   "  ' ' K @*4??+<=I*:yAJ'+}}T:'F $K  " "; Z T 0Cs8D lD9PX=P=PJq$ 3 0%*,d3E#/ 0 0  r?rUrct|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)rrrrKrrrgrrMrLGBM_DatasetCreateFromMatrLrOrPrQ) rvrUrrrfrerr7r8s r@__init_from_np2dzDataset.__init_from_np2d s syy>Q HI I( $S) f%3D%9"-  * * ]+syy|,syy|, V$z" T\\*   r?matsc|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.rrrrr#z-Input arrays must have same number of columnsz Input chunks must have same type)rr]r5rrrZr_rKrrrrLrrrgrrrrrMrLGBM_DatasetCreateFromMatsrOrrrPrQ)rvr&rrrr'rlayoutsholdersr7rrUrechunk_ptr_datachunk_type_ptr_dataholders r@__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\\*   r?r ct|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 rFr)rrgrfrrKrrrrVrrr,rr]rrMrLGBM_DatasetCreateFromCSRrLrrrOrrPrQ) rvr rrrirjrrrr7r8rks r@__init_from_csrzDataset.__init_from_csrR f s{{ s388} ,0S[[1A0B$s388}oVW W( *6szz*B' OR%3CHH%="-yy|z)))kk(((>   * * _-""**6>>&..+IJ ]+s3::/s388}-syy|,z" T\\*   r?rct|jt|jk7r7tdt|jdt|jt j |_t|j\}}}t|j\}}} |jdtksJ|jjtjd} tt!j"|t j$|| 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.r0r1rFr)rrgrfrrKrrrrVrrr,rr]rrMrLGBM_DatasetCreateFromCSCrLrrrOrrPrQ) rvrrrrirjrrrr7r8r~s r@__init_from_csczDataset.__init_from_csct r4r?rc trts tdtd|jj Ds t dt|}tj|_ ttjtj|jtj|j tj|j"t%||tj&|j|S)z%Initialize data from a PyArrow table.zFCannot init Dataset from Arrow without 'pyarrow' and 'cffi' installed.c3fK|])}t|xst|xs t|+ywrDrrs r@rz4Dataset.__init_from_pyarrow_table.. rrr)rrr/rrrrrrKrrrMrLGBM_DatasetCreateFromArrowrrrrrPrQ)rvrrrrs r@__init_from_pyarrow_tablez!Dataset.__init_from_pyarrow_table s"n hi ir_d_k_k_q_qrr\] ]%U+(   , ,w//0 2 23 2 23z" T\\*    r? 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. FTrR)rIr<r=rSrTs r@_compare_params_for_warningz#Dataset._compare_params_for_warning sz0!&&( !DAq #F?fQi1n  !LLN !DAq #L(Aa,@  !r?c @ |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)rIr<r=z1Overriding the parameters from Reference Dataset.r) rfrwrrxrzr{ryrrrrIrrZrFr)repeatsT) return_countsrzLabel should not be None.r) rfrwrrxrzryrrrrIr{)6rrrr?rNrhr_update_paramsrrrfrwrxrzr{ryrrrIrr]rr`rrruniquerepeatrrrKrr8rMrLGBM_DatasetGetSubsetrrrrOrrPrQrget_datarrrrrrrrget_feature_name)rvreference_paramsrIr group_infor8rs r@rzDataset.construct sf << ~~)#'>>#<#<#> *--;;%%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??OD OO4>>3L3LL 99&*ooDIIT`: **";;**#"oo!%!2!2(,(@(@;;!]] !!  $ 5 5 7D  r?c t|||||||||j }|j|_|j|_|S)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. )rwrrxrzr{ryrIr)r-rrr) rvrfrwrxrzryrIr{rs r@ create_validzDataset.create_valid0 sPN !,,  !%!8!8 r?c| |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)rrrrIr) rIr-rrrrrrr)rvrrIrs r@subsetzDataset.subsetf sm& >[[F ** $ 8 8,,  !%!8!8!,/ r?filenamec 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. )rMrLGBM_DatasetSaveBinaryrrrPr~rvrPs r@ save_binaryzDataset.save_binary s=$   ' ' ((s8}%   r?csStdfd }j |Stjt t j t t }|dk7rQj|jSttjjdS)Ncjs_ytj_jjyrD)rIrrre)rIrvsr@rez&Dataset._update_params..update s4;;$ '/ '<$ ""6*r?rrr) rrrLGBM_DatasetUpdateParamCheckingrPr8rIrfrr/rr)rvrIrers`` r@rDzDataset._update_params sK&! + <<  H  66)$++67)&12Cax99(H%%' ((>(>(@(G(G(PQQ r?c`|j!t|j|_d|_|SrD)rrrrIrs r@_reverse_update_paramszDataset._reverse_update_params s+ << "4#7#78DK#'D  r? field_namec |jtd|d|dttj|jt |dt jdt jt||St|s t|rt|rJ|dk7rtd|dt|jDcgc]}|jD]}|c}}}t|}ttj |jt |t j"|j$t j&|j(t j&|j*|xj,dz c_|S|dk(rat.j0}t3|rt5||| }njt7|r!t9||| }|j;d }n>t=d |d vrt.j>}nt.j@}t5||| }|jBt.j@k(s|jBt.j0k(rtE|\}}} nF|jBt.j>k(rtG|\}}} nt=d|jBd|t|k7r t=dttj|jt ||t jtI|t j||xj,dz c_|Scc}}w)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 Array, pyarrow ChunkedArray or None The data to be set. Returns ------- self : Dataset Dataset with set property. Nz Cannot set z before construct datasetrryz,pyarrow tables are not supported for field ''rrArXr\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.>rzr{z-Expected np.float32/64 or np.int32, met type(rzInput type error for set_field)%r ExceptionrMrLGBM_DatasetSetFieldrPrKrL_FIELD_TYPE_MAPPERrrrr#rrrLGBM_DatasetSetFieldFromArrowrrrrrrr]r_rrrrrdrrr^rZrrr) rvrZrfrrIrrZrrr8s r@ set_fieldzDataset.set_field s& << k*5NOP P < ))LL:&LLOLL!3J!?@  K T "&7&=!&-$'ST^S__`%abb'&*\\!%*\\").G 22LL:&NN7#3#34OOG$6$67OOG$6$67   LLA LK  %JJE &(UL"4((ULzzz,@ 22 $TZHD :: #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 r?c |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.)rrfrrrr/)rvrs r@set_categorical_featurezDataset.set_categorical_featureh s  # #': :K 99 ''/+>(((**$. ++v5 66:;N6O5PR,?(((**P r?c||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.) rrrrrrfrrrr/)rvrs r@_set_predictorzDataset._set_predictor s.  !8K  ? 3 4??Tc8dT__,++-1R1R1TT << 'DO( 'YY "'DO  - -//YY! . "    *t~~/IdnnNaNaNm'DO  - -//^^((!.. .   P r?cV|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.) rirrrrkr get_ref_chain intersectionrfrrr/)rvrs r@ set_referencezDataset.set_reference s $$Y%B%BCTT  " " .-- .     , ,Y-D-D-F GK 99 &DN$$& &P r?c |dk7r||_|j||dk7rt||jk7r)t dt|d|jd|Dcgc] }t |}}t tj|jttj|tjt||Scc}w)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_featurerrPrMrLGBM_DatasetSetFeatureNamesrrKrrL)rvrrc_feature_names r@rzDataset.set_feature_name s 6 ! ,D  << # (@\U[E[< D$4$4$66 -c,.?-@@RSWScScSeRffst8DDtfTlDND 00LLV__n=LL\!23  Es)Cc||_|jt|trRt |j dkDr t dtjt|tj}n*t|r|}nt|tjd}|jd||jd|_|S)aTSet 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 columnsrrwrA)rwrrr(rrrr]rdrr^rrrarg)rvrw label_arrays r@rzDataset.set_label s << #%.u}}%)$%WXX hh'7BJJ'WX "5)# /RZZgV NN7K 00DJ r?c|_t|r:tjtj|dj rd}nt j|dk(rd}||_|jQ|Ot|st|t jd}|jd||jd|_|S)a^Set 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. NrrxrA) rr%requalas_pyr]rxrrr^rarg)rvrxs r@rzDataset.set_weight s"   (>>*"2"261"=>DDF!F! $  << #(:$V,*6(S NN8V ,..2DK r?c||_|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 Array, pyarrow ChunkedArray, pyarrow Table (for multi-class task) or None Init score for Booster. Returns ------- self : Dataset Dataset with set init score. ry)ryrrarg)rvrys r@rzDataset.set_init_score+ s= % << # (> NN< 4"nn\:DO r?c||_|jh|ft|st|tj d}|j d||jd}|t j||_|S)aSet 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. rzrA) rzrrrr]rrargrA)rvrzconstructed_groups r@rzDataset.set_groupA sm( << #(9$U+)%rxxgN NN7E * $w 7  ,WW%67  r?c||_|j0|.t|tjd}|j d||S)aSSet 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. r{rA)r{rrr]rra)rvr{s r@rzDataset.set_position` s? ! << #(<( SH NN:x 0 r?c |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/rrrKrLc_size_trrXrr4rrMrLGBM_DatasetGetFeatureNamesrQrRrr rvrrr]reserved_string_buffer_sizerequired_string_buffer_sizer8string_buffersptr_string_buffersactual_string_buffer_sizers r@rIzDataset.get_feature_namev 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 or None The label information from the Dataset. For a constructed ``Dataset``, this will only return a numpy array. rw)rwrgrs r@rzDataset.get_label s( :: 0DJzzr?c^|j|jd|_|jS)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. rx)rxrgrs r@ get_weightzDataset.get_weight s( ;; ..2DK{{r?c^|j|jd|_|jS)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. ry)ryrgrs r@get_init_scorezDataset.get_init_score s( ?? ""nn\:DOr?c|j td|jr|j|j|jj |_|j t |j tjtjjfr$|j |jddf|_nst |j tr8|j j|jj|_n!t |j tr-t!|j |jddf|_nt |j t"r|j |j|_nt%|j r`t'|j dkDrHtj(t+|j-|j |j|_n,t/dt1|j j2dd|_|j t5d|j S)a.Get 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.)rr]rrrrfrr]rrrr!r(ilocrr!rLr1rrrrrrrr:r/rs r@rHzDataset.get_data s << FG G    1 1 =$..B\++DIyy$dii"**ell6K6K)LM $ $*;*;Q*> ?DI <8 $ t/@/@ A F F HDI <879 $ $*;*;Q*> ?DI 84 $ $*;*; >D)ny(r?c"|j |j tdttj|j|j|j du}t |j j}|j d|_n|j t|j tjrt|j tjr2tj|j |j f|_nzt|j tjjr@tj|j |j jf|_n t|j t rtjj|j |j f||_nt|j t rHtjj|j |j j"f||_n"t|j t$rVt'tjj|j |j j)f||_nd|_nt|j t rt,s t/dt|j tjr4t1|j t!|j fdd|_n%t|j tjjrBt1|j t!|j jfdd|_nt|j t r+t1|j |j fdd|_npt|j t$rLt't1|j t!|j j)fdd|_n d|_nt|j t$rt't|j tjrIt%tj|j j)|j f|_not|j tjjrVt%tj|j j)|j jf|_nt|j t rRt%tj|j j)|j j"f|_nt|j t$rVt%tj|j j)|j j)f|_nd|_nd|_|j :dt |j jd |d }||rd nd z }t3||j5|_t3d 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 L979 & < @S@S@U3V'W^_nr sDI $DIDII|435ejj"**5 ,RYY 8J8J8Lejj7Y-Z [DI ELL,A,AB ,RYY 8J8J8LejjN`N`Nb7c-d eDI L9 ,RYY 8J8J8LejjN_N_7`-a bDI L9 ,RYY 8J8J8LejjNaNaNc7d-e fDI $DI  99 +D,<,E,E+FF[%&&:<  RZN`r G  ! 113 Z $* "& r?c 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. )rMrLGBM_DatasetDumpTextrrrPr~rSs r@ _dump_textzDataset._dump_text s=   % % ((s8}%   r?) NNNNNrrNTNr)rBr-)NNNNNNrD)rr-rBr-)d)rr-rBr-)Vr:r;r<r=_LGBM_TrainDataTyper_LGBM_LabelType_LGBM_WeightType_LGBM_GroupType_LGBM_InitScoreType_LGBM_FeatureNameConfiguration%_LGBM_CategoricalFeatureConfigurationrr~rr_LGBM_PositionTyperrrr]rr_DatasetHandlerrrrrrrrrrrmr1rrrrrrrrrrrrrrr&rrr?rrMrOr rTrDrY_LGBM_SetFieldTyperargrirkrorrrrrrrIrrrrHrrrrrrrmrrr>r?r@r-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^ed ede e!jDe7jpjree7jpjrffd?Z: dgde;d@edEee?dFe@dGeAdHeeeefdIed>ed eddfdJZBdKedLede(fdMZCdKedLedNe(ddfdOZDdefdPZEdeefdQZF dhd2edReede!jDfdSZG didTe eefdUee eefdVede eHe!jDe!jDfe!jDeffdWZIdXedYed+ee e+ee+fdee,fdZZJdYede!jDfd[ZKd^d\ZLy)jr,zBooster in LightGBM.NrI 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||jq|||_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 machinesrorIrpr&zInvalid machines in params. num_machineslocal_listen_portp0time_outx)rrlisten_time_outrrrrz;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):rKrr_network_Booster__need_reload_eval_info_train_data_name_Booster__set_objective_to_nonebest_iteration best_scorerrr-rrr:rvrsr~rr|rr3rr set_networkrhrrerr8rMrLGBM_BoosterCreaterPrQr valid_setsname_valid_sets_Booster__num_datasetr_Booster__init_predictorLGBM_BoosterMergerLrrR_Booster__num_class_Booster__inner_predict_buffer_Booster__is_predicted_cur_iter_Booster__get_eval_inforrtrain_set_versionrrr_get_loaded_parammodel_from_stringrI) rvrIrrrrnum_machines_from_machine_listrrrs r@rzBooster.__init__s((  '+$ *',$ 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 ": 9 #!'a  443z?+LL!34LL. #LLOM ..LLLL/  -22D &>&TD #Z[++-F  "  " "9 -  m r?rBc |jr|j |j)t t j |jyy#t$rYBwxYw#t$rYywxYwrD)r free_networkrrrMrrrs r@rzBooster.__del__sl }}!!# ||'400>?(       s"A4A$ A! A!$ A0/A0c$|jdSrD) __deepcopy__rs r@__copy__zBooster.__copy__s  &&r?rbkwargsc>|jd}t|S)Nrrr)model_to_stringr,)rvrbrrs r@rzBooster.__deepcopy__s ((r(: ++r?c|jj}|d}|jdd|jdd||jd|d<|S)Nrrrrr)rrrsr)rvrrs r@rzBooster.__getstate__s[}}!!#i d# t$  "222DDO r?statec ||jd|jdd}|}tj}tjd}t t j t|tj|tj|||d<|jj|y)Nrrr) rhrKrrLrMrLGBM_BoosterLoadModelFromStringrPrQrre)rvrrrrs r@ __setstate__zBooster.__setstate__sIIi8T)BC  __&F!'a  449%LL!34LL(  &E)  U#r?c d}tjd}tj|}tjtj|}t t j|jtj|tj|||j}||kDrtj|}tjtj|}t t j|jtj|tj||tj|jjdS)NrPrr)rKrrXrrrMrLGBM_BoosterGetLoadedParamrrQrRrZr[r)rvr\r]r^r_r`s r@rzBooster._get_loaded_params nnQ' 33J? "OOF,<,<],KL  + + z* [)!   !&&  ""77 CM &0@0@0O P  //LLNN:.LL-%  zz---44W=>>r?c|jjdd|jjddd|_|S)zFree Booster's Datasets. Returns ------- self : Booster Booster without Datasets. rNrr)rrsrrs r@ free_datasetzBooster.free_datasets9 +t, ,- r?c"g|_g|_|SrD)rrrs r@ _free_bufferzBooster._free_buffers&(#')$ r?rrrrc (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. r&T) rrr3rrMrLGBM_NetworkInitrPrKrLr)rvrrrrs r@rzBooster.set_networksq2 hs ,xx)H  ! !x  ./ _- \*     r?cNttjd|_|S)zFree Booster's network. Returns ------- self : Booster Booster with freed network. F)rMrLGBM_NetworkFreerrs r@rzBooster.free_network s! 4((*+  r?cts 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_node<s DIIK/ /r? 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)NrrrBcn||dnd}|}|rdnd}|j|rdndd}|||S)N-rSLr leaf_indexr)rh)rrtree_numis_split node_typenode_numrs r@_get_node_indexzOBooster.trees_to_dataframe..create_node_record.._get_node_indexFsS0:/Ej\+2)$/#+C 88XM)rrrrs r@_get_split_featurezRBooster.trees_to_dataframe..create_node_record.._get_split_featureQsK"$'$0'4T/5J'K $#(,O'< $#$(L##r?c>t|jddhk(S)N leaf_value leaf_count)r3rrrs r@_is_single_node_treezTBooster.trees_to_dataframe..create_node_record.._is_single_node_tree^s499;'L,+GGGr?r node_index left_child right_child parent_indexr split_gain threshold decision_typemissing_direction missing_typerRrxcount default_leftleftrightinternal_valueinternal_weightinternal_countr leaf_weightr)rr~rrrrrr) rrrrrrrrnoders r@create_node_recordz6Booster.trees_to_dataframe..create_node_record?s  :38n :$SM : : $38n $'S 2 $# $ H4S> Hd H6A]D!+D !+D !0z!BD !%D "&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 $%5 6W K !%\ 2W +D1%)-%8DN$($6DMKr?c  |||||}|g} |r2ddg}|D])} |||dz|||d} |j| +|S)N)rrrrrrrrr)extend) rrrrrr'reschildrenchild subtree_listrr(tree_dict_to_node_lists r@r/z:Booster.trees_to_dataframe..tree_dict_to_node_lists&%%+' D&Cd#(-8% -E#9!%[#->#-&3$($6 $LJJ|, -Jr? tree_infotree_structure)rrrr)rNNN)rr/ num_treesrr~rrrrr dump_modelr*r(rr)rv model_dictr model_listrrr(r/s @@@r@trees_to_dataframezBooster.trees_to_dataframes8 W  >> q  ^_ _ 0c3h 0D 0  (,15)- F sCx.F F ! F $DI. F  "# F  #s(^ F T (,15)-  sCx.  !  $DI.   "#   $sCx. !  B__& "?3  {+ D   &./D>;Q;Q!QI$) !$-$?$oDDZDZ^g^o^oDo !  )>i1"PQUV_Q`QiQiPj kll##4+@+@@#$pqq&DN 22LLNN,,.66  .2D ' ' *%)^^%;%;D "ll1o <++#$STT ..LLLL-  >$BC ##FNN6>>$BC [)   8=T=O=O7P'Q!'Q$  A%%(Rs H>cttj|jt |j Dcgc]}dc}|_|Scc}w)zRollback one iteration. Returns ------- self : Booster Booster with rolled back one iteration. F)rMrLGBM_BoosterRollbackOneIterrrrr)rvr8s r@rollback_one_iterzBooster.rollback_one_itersD 433DLLAB7> !H3t/0 4??1-- 1uH  r> NN4 &))A-H  x77r?c<|j|jd|S)aOEvaluate 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)rer)rvrcs r@ eval_trainzBooster.eval_trains @  !6!65AAr?c td|jDcgc],}|j|j|dz ||D]}|.c}}Scc}}w)aXEvaluate 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)rrrer)rvrcrr"s r@ eval_validzBooster.eval_valid:seD1d001 ))$*>*>q1u*Eq%P      s1ArPrrimportance_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_MAPPERrMrLGBM_BoosterSaveModelrrKrLrPr~rr)rvrPrrrlimportance_type_ints r@ save_modelzBooster.save_model`s:   //M=oN  & &  _- ]+ 01s8}%    !!8!8(C r? 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. )rMrLGBM_BoosterShuffleModelsrrKrL)rvrrrs r@shuffle_modelszBooster.shuffle_modelss@(   * *  _- ]+   r?c 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)rMrrrrrKrrLrrPrQrrRrrr)rvrrrs r@rzBooster.model_from_strings 4((67 ( #\\!_  0 0y! /0 T\\*   Q   * *  ]+  )..":Y"O r?c | |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. rPrr)rrnrKrrXrrrMrLGBM_BoosterSaveModelToStringrrLrQrRrrr) rvrrrlrpr\r]r^r_r`rs r@rzBooster.model_to_strings4   //M=oN nnQ' 33J? "OOF,<,<],KL  . .  _- ]+ 01z* [)!  !&&  ""77 CM &0@0@0O P  22LLLL1LL/LL!45NN:.LL-% !!((1 '(?(?@@ r?rWc | |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. rPrrrVrr)rrnrKrrXrrrMrLGBM_BoosterDumpModelrrLrQrRrZr[rrrr$) rvrrrlrWrpr\r]r^r_r`rs r@r4zBooster.dump_modelsH   //M=oN nnQ' 33J? "OOF,<,<],KL  & &  _- ]+ 01z* [)!  !&&  ""77 CM &0@0@0O P  **LLLL1LL/LL!45NN:.LL-% jj,,33G<+V$(JJ JJ''0 %  !  r?rrrrrc tj|t| } ||dkr |j}nd}| j ||||||||S)a Make 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)rfrrrrrrr)rrrrr*) rvrfrrrrrrrrrs r@r*zBooster.predict]sur$00#F+1   !# $ 3 3 "   +'%+/!  r?rw decay_raterrxrzryrrrrc |jr td| i} tj|t | }|j |dd| }|j \}}tjd}ttj|jtj|td|jd }t!|j"|d<|j%| t'|||||||| || }||d <t)||}ttj*|j|j|j-d}t/|\}}}ttj0|j|tj2|tj2||j4|_|S) arRefit 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.Nr|rT)rfrrrrrr) rfrwrrxrzryrrrIrrefit_decay_rate)rr/rrrr*rrKrLrMrLGBM_BoosterGetLinearrrQrvrIrrRrer-r,rr#rLGBM_BoosterRefitrOr)rvrfrwr}rrxrzryrrrrrrr leaf_predsr'r out_is_linear new_paramsr new_boosterrr8s r@refitz Booster.refits@  ' ' NO O  !N#00hW]N^0_ !*!2!2/ "3"  %% d Q   & &  ]+  ));; %))<)<$= =!.)!% 3'  *4 %&j)4   " "##!!   ''+ %j1!Q  " "##t$t$    $}} r?tree_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) rKrrMrLGBM_BoosterGetLeafValuerrLrQrR)rvrrrs r@get_leaf_outputzBooster.get_leaf_outputFs_ooa   ) )  W% W% S!   yyr?rRc 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. )rMrLGBM_BoosterSetLeafValuerrKrLr)rvrrrRs r@set_leaf_outputzBooster.set_leaf_output`sL0   ) )  W% W%&    r?ctjd}ttj|j tj ||jS)zGet number of features. Returns ------- num_feature : int The number of features. r)rKrLrMrLGBM_BoosterGetNumFeaturerrQrR)rvout_num_features r@rrzBooster.num_featuresH!,,q/  * *  _-  $$$r?c |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. rrrr)rrrKrLrrrXrr4rrMrLGBM_BoosterGetFeatureNamesrrQrRrrrs r@rzBooster.feature_names&&( 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)rrnr]r5rrr_rMrLGBM_BoosterFeatureImportancerrKrLrrr_C_API_FEATURE_IMPORTANCE_SPLITrr)rvrlrrpresults r@feature_importancezBooster.feature_importances.  ++I=oN$**,BJJ?  . .  Y' 01 %%fnnV__&EF    "A A==* *Mr?rbins 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. rootrBNcd|vrnttr |d}n|d}|k(r2t|dtr tdj|d|d|dyy)zRecursively add thresholds.rNrrz@Cannot compute split value histogram for the categorical featurerr)rr~r/r)rrrrrrs r@rz.Booster.get_split_value_histogram..adds$ ,GS1I$1$2G$HM$($9M G+!${"3S9+,noo d;&78D&'D'(%r?rr0r1r)rr SplitValueCountr2)rr~rr4rhrrrr]rErr histogram column_stackrr()rvrrrmodel tree_infosr0n_uniquehist bin_edgesrrrrs ` @@@r@get_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 ? "r? data_namerfc @||jk\r td|jg}|jdkDr?t j |jtj }tjd}ttj|jtj|tj||jjtjtj |j"|jk7r tdt%|jD]4}|j'||j(||||j*|f6t-|r|g}||dk(r |j.}n|j0|dz }|D]i} | | |j3||} t5| t6r!| D]\} } } |j'|| | | fO| \} } } |j'|| | | fk|S)z%Evaluate training or validation data.1Data_idx should be smaller than number of datasetrrzWrong length of eval resultsr)rrr_Booster__num_inner_evalr]r5r_rKrLrMrLGBM_BoosterGetEvalrrQrrrrRrr_Booster__name_inner_eval"_Booster__higher_better_inner_evalrrrrFrr)rvrrfrcrrr]rcur_data eval_function feval_ret eval_namer(is_higher_betters r@ __inner_evalzBooster.__inner_eval2s t)) )PQ Q   1 $XXd332::FF ,,q/K ((LLLL*LL-MM))&..*IJ    D$9$99 !?@@4001 r It'='=a'@&)TMlMlmnMopq r E?GE  1}>>??8a<8!& N  ()$*>*>x*H(S i..>c^JF r?c |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.Frrz1Length of eval names doesn't equal with num_evalsr)auczndcg@zmap@average_precisionN)rrKrLrMrLGBM_BoosterGetEvalCountsrrQrRrrrrXrr4rLGBM_BoosterGetEvalNamesrrrrr) rv out_num_evalr]rrr8rrrrrs r@__get_eval_infozBooster.__get_eval_info~s  ' '+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$QR3/S) ( "&& *y3s L;.M%MM )NNNNr)rBr,)rrrNNrD)Nrr|)rr)Nrr|N)rNFFFFF) g?NNNNrrNTF)r|N)NF)Mr:r;r<r=rrr~rr-rr rrrrrrrrrrrrrrr(r7r9r<r?_LGBM_CustomObjectiveFunctionrrer]rrGrTrrYr3rr^rb_LGBM_CustomEvalFunction!_LGBM_BoosterEvalMethodResultTypergrirkrqrurrrr4rrrr!r*rrrrrrrrrrrrrrrrrerFrr>r?r@r,r,s<,0'+15#' Ec3h(EG$EU39-. E C= EN ',#,,,d38n $$sCx. $T $?4S>?8 "'" $S 3s8S01$$ $  $  $L [FL[Fz  gSY@d38n4(,8<J,G$J,45J,  J,X6&jj6&jj6&  6&p "3""$$"3"U"U*\` 5858586=U8VVWX 58 / 0 58r\` B6=U8VVWX B / 0 BH\`$ 6=U8VVWX$  / 0$ R(, & *T "* }* *  *  *\ !  :#3#9#N(, & >}>> > >D(, &LP M}MM M hS#X'7c3h'GHI M c3h Md !'+" %"'K #K K  } K  K  K K K  K K  rzz5<<00$u||7L7L2MM NK b '+-1+/487=EK37""'Z!ZZ Z G$ Z )* Z(Z01Z5ZCZ!c3h0ZZ ZZ ZxsSU4      D%S%"*Ud3i*U\ '#'&&C=&  &V+/# I#sCxI#uS#X'I# I# uRZZ+,bjj,F G I#V+++6=U8VVWX + / 0 +Z >8r?r,)rwr}rrDr)r=libpathrrrKrrZr{ collectionsrrrenumr functoolsrosr r os.pathr pathlibr tempfiler typingrrrrrrrrrrrnumpyr] scipy.sparsercompatrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+ ImportErrortyping_extensions__all__rrr_ctypes_int_ptr_ctypes_int_array_ctypes_float_ptr_ctypes_float_arrayr~rr_LGBM_EvalFunctionResultType_LGBM_BoosterBestScoreTyper6_LGBM_BoosterEvalMethodResultWithStandardDeviationTyperrrrrrrrr!rrrrrrE_MULTICLASS_OBJECTIVESr]r/rGrTrgrirqrrrrrr0rrrrbytesrrhrrrestype CFUNCTYPEcallbackLGBM_RegisterLogCallbackrr5rMrrrrrrrrrrrrrrrrrr rrrrPrArrayrr$r-r8r: FutureWarningr.rLrNrvr,rrrrrarbrrrrror}r_C_API_FEATURE_IMPORTANCE_GAINr_rnrrrrrrrrrABCr1rr-rrr,r>r?r@rs/ $  # 'llll(0$  '5(6 %S%%56!#tCJ'7"78$)#sE4*?$@!9>sCPTV[?[9\6(-d3icDU.U(V%!&tCy2C'C!DKIJJ   JJ KeJJ    JJ LL   KIJJ   JJ LL KIJJ   ecOKIJJ     c I 22$2 # s s +rzz+eBJJO&<+"4 ))~ 88#8$8 #(/ /// /28SE4K#8XseTk=R"-3-4-0c0d0-S-T--*u** w{{'/%+__D"vfoo6H]+DM$t$$T]]3q82D224;;GDEEud# FC FD FSTASATA ---1bjj19N1SUS]S]1IcIdI S3S+HSQQ(CQxCxDx      ZZ  .WSWTW McMdM [C[D[ 9C9$Q9 &C&D& >>.5X5,58   ZZ &5&75556&76665$55s5rzz55$55s5rzz53336??3 *D*$s)* * ##%UC-.3 Xd38n5 #     ] F::z,,d38n,UX,]abegjbj]k,^ 8.>,1"#!" !"& " 8 , *# bjj RZZ ''.?bjj.P(Q'*'rzz'eOS"**,L&M'*x4xDx  y  T  B B'BZZB"- -0-?-!d, -  2::tCy%S 49(<"=tDz IJ -d-1  d, c4i()   -1#c4i()}d4j8?_sww?_DL "L "^KK\8!)ZZ "**bjj !#!! 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