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You can construct Pool from list, numpy.ndarray, pandas.DataFrame, pandas.Series. Parameters ---------- data : list or numpy.ndarray or pandas.DataFrame or pandas.Series or FeaturesData or string or pathlib.Path Data source of Pool. If list or numpy.ndarrays or pandas.DataFrame or pandas.Series, giving 2 dimensional array like data. If FeaturesData - see FeaturesData description for details, 'cat_features' and 'feature_names' parameters must be equal to None in this case If string or pathlib.Path, giving the path to the file with data in catboost format. If string starts with "quantized://", the file has to contain quantized dataset saved with Pool.save(). label : list or numpy.ndarrays or pandas.DataFrame or pandas.Series, optional (default=None) Labels data. If not None, can be a single- or two- dimensional array with either: - numerical values - for regression (including multiregression), ranking and binary classification problems - class labels (boolean, integer or string) - for classification (including multiclassification) problems If `data` parameter points to a file, Label data is loaded from it as well. This parameter must be None in this case. cat_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Categ features indices or names. If it contains feature names, Pool's feature names must be defined: either by passing 'feature_names' parameter or if data is pandas.DataFrame (feature names are initialized from it's column names) Must be None if 'data' parameter has FeaturesData type text_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Text features indices or names. If it contains feature names, Pool's feature names must be defined: either by passing 'feature_names' parameter or if data is pandas.DataFrame (feature names are initialized from it's column names) Must be None if 'data' parameter has FeaturesData type embedding_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Embedding features indices or names. If it contains feature names, Pool's feature names must be defined: either by passing 'feature_names' parameter or if data is pandas.DataFrame (feature names are initialized from it's column names) Must be None if 'data' parameter has FeaturesData type embedding_features_data : list or dict, optional (default=None) If not None, giving the data of Embedding features (instead of data in main 'data' parameter). If list - list containing 2d arrays (lists or numpy.ndarrays or scipy.sparse.spmatrix) with [n_data_size x embedding_size] elements If dict - dict containing 2d arrays (lists or numpy.ndarrays or scipy.sparse.spmatrix) with [n_data_size x embedding_size] elements Dict keys must be the same as specified in 'embedding_features' parameter column_description : string or pathlib.Path, optional (default=None) ColumnsDescription parameter. There are several columns description types: Label, Categ, Num, Auxiliary, DocId, Weight, Baseline, GroupId, Timestamp. All columns are Num as default, it's not necessary to specify this type of columns. Default Label column index is 0 (zero). If None, Label column is 0 (zero) as default, all data columns are Num as default. If string or pathlib.Path, giving the path to the file with ColumnsDescription in column_description format. pairs : list or numpy.ndarray or pandas.DataFrame or string or pathlib.Path The pairs description. If list or numpy.ndarrays or pandas.DataFrame, giving 2 dimensional. The shape should be Nx2, where N is the pairs' count. The first element of the pair is the index of winner object in the training set. The second element of the pair is the index of loser object in the training set. If string or pathlib.Path, giving the path to the file with pairs description. graph: list or numpy.ndarray or pandas.DataFrame or string or pathlib.Path The graph description. ... delimiter : string, optional (default=' ') Delimiter to use for separate features in file. Should be only one symbol, otherwise would be taken only the first character of the string. has_header : bool optional (default=False) If True, read column names from first line. ignore_csv_quoting : bool optional (default=False) If True ignore quoting '"'. weight : list or numpy.ndarray, optional (default=None) Weight for each instance. If not None, giving 1 dimensional array like data. group_id : list or numpy.ndarray, optional (default=None) group id for each instance. If not None, giving 1 dimensional array like data. group_weight : list or numpy.ndarray, optional (default=None) Group weight for each instance. If not None, giving 1 dimensional array like data. subgroup_id : list or numpy.ndarray, optional (default=None) subgroup id for each instance. If not None, giving 1 dimensional array like data. pairs_weight : list or numpy.ndarray, optional (default=None) Weight for each pair. If not None, giving 1 dimensional array like pairs. baseline : list or numpy.ndarray, optional (default=None) Baseline for each instance. If not None, giving 2 dimensional array like data. timestamp: list or numpy.ndarray, optional (default=None) Timestamp for each instance. Should be a non-negative integer. Useful for sorting a learning dataset by this field during training. feature_names : list or string or pathlib.Path, optional (default=None) If list - list of names for each given data_feature. If string or pathlib.Path - path with scheme for feature names data to load. If this parameter is None and 'data' is pandas.DataFrame feature names will be initialized from DataFrame's column names. Must be None if 'data' parameter has FeaturesData type feature_tags : json, optional (default=None) Format: {'tag1': { 'features': [], 'cost': } 'tag2': { ... } ... } thread_count : int, optional (default=-1) Thread count for data processing. If -1, then the number of threads is set to the number of CPU cores. log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Nz3data and pairs parameters should be the same types.z3data and graph parameters should be the same types.z\data should be the string or pathlib.Path type if column_description parameter is specified.c3$K|]}|du ywr(rrvs rrz Pool.__init__.. s `3_Q1D=3_zcat_features, text_features, embedding_features, embedding_features_data, weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, label should have the None type when the pool is read from the file.zbfeature_names should have None or string or pathlib.Path type when the pool is read from the file.c3$K|]}|du ywr(rrGs rrz Pool.__init__..s Q7Pq}7PrIzcat_features, text_features, embedding_features, embedding_features_data, feature_names should have the None type when 'data' parameter has FeaturesData typerrz'data' is numpy array of floating point numerical type, it means no categorical features, but 'cat_features' parameter specifies nonzero number of categorical featuresz'data' is numpy array of floating point numerical type, it means no text features, but 'text_features' parameter specifies nonzero number of text featuresOz'data' is numpy array of non-object type, it means no embedding features, but 'embedding_features' parameter specifies nonzero number of embedding featuresz'data' is scipy.sparse.spmatrix of floating point numerical type, it means no categorical features, but 'cat_features' parameter specifies nonzero number of categorical featuresz'data' is scipy.sparse.spmatrix, it means no text features, but 'text_features' parameter specifies nonzero number of text featuresz'data' is scipy.sparse.spmatrix and 'embedding_features_data' is None, it means no embedding features, but 'embedding_features' parameter specifies nonzero number of embedding featureszZ'embedding_features_data' is not None, but 'embedding_features' parameter is not specifiedzX'embedding_features_data' and 'embedding_features' contain different numbers of featureszPkeys of 'embedding_features_data' dict do not correspond to 'embedding_features'z@'embedding_features_data' must have either 'list' or 'dict' typez`feature_names must be None or have non-string type when the pool is created from python objects.z'data' parameter can't be None)re_check_data_type_check_data_emptyrgrsrDr_read FeaturesDatarrdtypekindr_scipysparsespmatrixrhr'rrkeys_initsuperrDr4)r3datar  cat_features text_featuresembedding_featuresembedding_features_datacolumn_descriptionpairsgraph delimiter has_headerignore_csv_quotingweightgroup_id group_weight subgroup_id pairs_weightbaseline timestampr feature_tags thread_countrWrXdata_can_be_none __class__s rr4z Pool.__init__Ys9Jx *%%d+&&t,$D*)ETY[eIf)f'(]^^$D*)ETY[eIf)f'(]^^%1*T::V')GHHdJ/`<Pbd{~DFNP\3> hX]3_``+m&1J}V`&3%y'"!"$D%,,*?*?@ JJOOs29QX[\hXilmXm"/!q#*5C U:VV&3$~'"!"((?F"#56#>U>Z>Z>\:]]&3$v'"!"#0 b#"-<+. 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Must be int indices. z1Invalid {}[{}] = {} value type={}: must be int().z.Invalid {}[{}] = {} value: index must be < {}.N)rirgrmrDrr)r3rfeatures_countryindxfeatures r_check_string_feature_valuez Pool._check_string_feature_value|s'x0MD'g}5#$W$^$^_lnrt{~BCJ~K%LMM.(#$T$[$[\ikoqx{I%JKK 1rct|ttjtfs#t dj t|y)z0 Check type of pairs parameter. zDInvalid pairs type={}: must be list(), np.ndarray() or pd.DataFrame.N)rgrhrrrrDrrr3r^s r_check_pairs_typezPool._check_pairs_types=%$ I!>? f m mnrsxny z{ {@rc  t|D]v\}}t|dk7rtdj|t|D];\}}t |t rtdj|||t |xy)zG Check values in pairs parameter. Must be int indices. rz%Length of pairs[{}] isn't equal to 2.z8Invalid pairs[{}][{}] = {} value type={}: must be int().N)rir_rDrrgrmr)r3r^pair_idpairirws r_check_pairs_valuezPool._check_pairs_values'u-MGTD Q#$K$R$RSZ$[\\%dO5!%7'(b(i(ijqstv{~BCH~I)JKK,.rct|ttttfs#t dj t|y)z% Check type of data. zInvalid data type={}: data must be list(), np.ndarray(), DataFrame(), Series(), FeaturesData scipy.sparse matrix or filename str() or pathlib.Path().N)rgrsrkSPARSE_MATRIX_TYPESrOrDrr)r3rXs rrLzPool._check_data_typesB$[:M| \]MNTfUYZ^U_N` ^rc Xt|tr|s tdyt|ttfrt|t r/t jt j|t}nt j|}t|dk(rX|ddkDrPt|dtr%t|tt|dgz}nt|tdgz}t|dk(stdj||ddk(r tdyy) z Check that data is not empty (0 objects is ok). note: already checked if data is FeatureType, so no need to check again zFeatures filename is empty.rPrrrz7Input data has invalid shape: {}. Must be 2 dimensionalz)Input data must have at least one featureN)rgrsrDrkrrhrshapeasarrayobjectr_rrqr)r3rX data_shapes rrMzPool._check_data_emptys dJ '#$ABB {,?@ A$%XXbjjV&DE XXd^ :!# 1 (9d1gx0!&zE3tAw<.4I'I!JJ!&zE1#J'>!?Jz?a'#$]$d$deo$pqq!}!#$OPP"Brcjt|ts#tdjt |y)z& Check type of label. z*Invalid label type={}: must be array like.NrgrkrDrrr3r s r_check_label_typezPool._check_label_types1%- L S STXY^T_ `a a.rc6t|dk(r tdy)z+ Check label is not empty. rzLabels variable is empty.N)r_rDrs r_check_label_emptyzPool._check_label_emptys  u:? ;< < rcht||k7r$tdjt||y)z3 Check label length and dimension. z6Length of label={} and length of data={} is different.Nr_rDr)r3r  samples_counts r_check_label_shapezPool._check_label_shapes6 u: & X _ _`cdi`jly z{ { 'rcjt|ts#tdjt |y)z3 Check type of baseline parameter. z-Invalid baseline type={}: must be array like.Nrr3rhs r_check_baseline_typezPool._check_baseline_type1(K0 O V VW[\dWe fg g1rc"t||k7r$tdjt||t|dtrt|dt r td t j|jt jdt jdt jdfvr ty#t$r8tdjt j|jwxYw) z6 Check baseline length and dimension. z:Length of baseline={} and length of data={} are different.rz=Baseline must be 2 dimensional data, 1 column for each class.rpfloat32rnz5Invalid baseline value type={}: must be float or int.N) r_rDrrgrrtrarrayrP)r3rhrs r_check_baseline_shapezPool._check_baseline_shapes x=M ) \ c cdghpdqtA!BC C(1+x0Jx{L4Y _` ` zxx!''0A288ICVXZX`X`afXg/hh#o%i z W ^ ^_a_g_ghp_q_w_w xy y zs%A'C ADcjt|ts#tdjt |y)z1 Check type of weight parameter. z+Invalid weight type={}: must be array like.Nrr3rcs r_check_weight_typezPool._check_weight_types1&+. M T TUYZ`Ua bc c/rct||k7r$tdjt||t|dtt fs&tdjt |dy)z& Check weight length. z8Length of weight={} and length of data={} are different.rzWInvalid weight value type={}: must be 1 dimensional data with int, float or long types.N)r_rDrrgrmror)r3rcrs r_check_weight_shapezPool._check_weight_shapes v;- ' Z a abeflbmo| }~ ~&)m[%AB y!A!ABFGMNOGPBQ!RS SCrcjt|ts#tdjt |y)z3 Check type of group_id parameter. z-Invalid group_id type={}: must be array like.Nrr3rds r_check_group_id_typezPool._check_group_id_typerrcht||k7r$tdjt||y)z( Check group_id length. z:Length of group_id={} and length of data={} are different.Nr)r3rdrs r_check_group_id_shapezPool._check_group_id_shapesC x=M ) \ c cdghpdqtA!BC C *rcjt|ts#tdjt |y)z7 Check type of group_weight parameter. z1Invalid group_weight type={}: must be array like.Nrr3res r_check_group_weight_typezPool._check_group_weight_types1, 4 S Z Z[_`l[m no o5rct||k7r$tdjt||t|dts&tdjt |dy)z, Check group_weight length. z>Length of group_weight={} and length of data={} are different.rzPInvalid group_weight value type={}: must be 1 dimensional data with float types.N)r_rDrrgror)r3rers r_check_group_weight_shapezPool._check_group_weight_shape s |  - ` g ghklxhy|I!JK K,q/K9 r y yz~@LMN@O{P!QR R:rcjt|ts#tdjt |y)z6 Check type of subgroup_id parameter. z0Invalid subgroup_id type={}: must be array like.Nrr3rfs r_check_subgroup_id_typezPool._check_subgroup_id_types1+{3 R Y YZ^_jZk lm m4rcht||k7r$tdjt||y)z+ Check subgroup_id length. z=Length of subgroup_id={} and length of data={} are different.Nr)r3rfrs r_check_subgroup_id_shapezPool._check_subgroup_id_shapesD { } , _ f fgjkvgwzG!HI I -rcjt|ts#tdjt |y)z4 Check type of timestamp parameter. z.Invalid timestamp type={}: must be array like.Nrr3ris r_check_timestamp_typezPool._check_timestamp_type s1)[1 P W WX\]fXg hi i2rc t|ts#tdjt ||j D]\}}t|ts$tdj|t |d|vrtdj|t|dt s'tdj|t |dd|vrd|d<nQt|dttfs'tdj|t |dt|d|d<tt|dD]{}t|d|trt|d|tr#|! |j|d|}||d|<Std j|t |d||S#t$r"td j||d|wxYw) Nz0Invalid feature_tags type={}: must be dict like.zUInvalid type of value in feature_tags by key {}, value type is {}: must be dict like.rzBInvalid value in feature_tags by key {}, key 'features' is needed.zbInvalid type of value in feature_tags by key {}, value type of features is {}: must be array like.cost?z[Invalid type of value in feature_tags by key {}, value type of cost is {}: must be integer.zUnknown feature in tag {}: {}zQInvalid type of feature in tag {}, value type is {}: must be int or feature name.)rgr'rDrritemsrkrmrrnranger_rw ValueError)r3tagsrtag_name tag_featuresr5 feature_ids r_check_transform_tagszPool._check_transform_tags's$% R Y YZ^_cZd ef f&*jjl "HllD1#${%C%CDLNRS_N`%abb-#$h$o$opx$yzzl:6 D#%I%P%PQY[_`lmw`x[y%z{{\)'* V$!,v"68LM')F)M)MNVX\]ijp]qXr)stt'*<+?'@ V$Sj!9:;l:6s;]K Z 8 =sC Ha}%2%8%8j9QRU9V%W 5?L,S1'({)C)CDLNRS_`jSkloSpNq)rss<'32 &}+,K,R,RS[]ijt]uvy]z,{||}s <G  +G7cht||k7r$tdjt||y)z) Check timestamp length. z;Length of timestamp={} and length of data={} are different.Nr)r3rirs r_check_timestamp_shapezPool._check_timestamp_shapeEsC y>] * ] d dehiresvC!DE E +rc||j}t|ts#tdj t |t ||k7r$tdj t ||y)Nz,Invalid feature_names type={} : must be listzVInvalid length of feature_names={} : must be equal to the number of columns in data={})num_colrgrrDrrr_)r3rrs r_check_feature_nameszPool._check_feature_namesLs ?llnG-2 N U UVZ[hVi jk k }  ( x  ADERASU\!]^ ^ )rcjt|ts#tdjt |y)Nz*Invalid thread_count type={} : must be int)rgrmrDrr)r3rks r_check_thread_countzPool._check_thread_countTs/, 6 L S STXYeTf gh h7rct|ts#tdjt |t dd}|j |||S)Nz6Invalid rindex type={} : must be list or numpy.ndarrayTrl)rgrkrDrrrD _take_slice)r3rindex slicedPools rslicez Pool.sliceXsL&+. X _ _`dek`l mn n$6 tV,rcjtdd}tdd}|j||||||||fS)NTr)rD_train_eval_split)r3has_timeis_classification eval_fractionsave_eval_pool train_pool eval_pools rtrain_eval_splitzPool.train_eval_split_s@$6 5  z9h@QS`bpq9$$rc|j|t|tr |j}|j ||j ||Sr()rrgrvaluesr _set_pairsrs r set_pairszPool.set_pairsesB u% eY 'LLE &  rcJ|j||j||Sr()r_set_feature_namesr3rs rset_feature_nameszPool.set_feature_namesms# !!-0  . rc|j||j|}tj||j df}|j ||j |j ||SrI)r_if_pandas_to_numpyrreshapenum_rowr _set_baseliners r set_baselinezPool.set_baselinersd !!(+++H5::h(<= ""8T\\^< 8$ rc|j||j|}|j||j|j ||Sr()rrrr _set_weightrs r set_weightzPool.set_weightzsI '))&1   8   rc|j||j|}|j||j|j ||Sr()rrrr _set_group_idrs r set_group_idzPool.set_group_idsI !!(+++H5 ""8T\\^< 8$ rc|j||j|}|j||j|j ||Sr()rrrr_set_group_weightrs rset_group_weightzPool.set_group_weightsI %%l3// =  &&|T\\^D |, rc|j||j|}|j||j|j ||Sr()rrrr_set_subgroup_idrs rset_subgroup_idzPool.set_subgroup_idsI $$[1..{;  %%k4<<>B k* rc|j||j|}|j||j|j ||Sr()rrr num_pairs_set_pairs_weight)r3rgs rset_pairs_weightzPool.set_pairs_weightsJ  -// =    t~~/?@ |, rc|j||j|}|j||j|j ||Sr()rrrr_set_timestamprs r set_timestampzPool.set_timestampsI ""9-,,Y7  ##It||~> I& rc|js tdt|ts#tdj t ||j |y)z Save the quantized pool to a file. Parameters ---------- fname : string or pathlib.Path Output file name. zPool is not quantized7Invalid fname type={}: must be str() or pathlib.Path().N) is_quantizedrDrgrsrr_saver3fnames rsavez Pool.savesP  " 78 8%, Y ` `aefkal mn n 5rc  H|jr tdi} t| ||}| jdd}| jdd}| r(tdj | j t | ||||||||| | | | |j| y)a Quantize this pool Parameters ---------- pool : catboost.Pool Dataset to quantize. ignored_features : list, [default=None] Indices or names of features that should be excluded when training. per_float_feature_quantization : list of strings, [default=None] List of float binarization descriptions. Format : described in documentation on catboost.ai Example 1: ['0:1024'] means that feature 0 will have 1024 borders. Example 2: ['0:border_count=1024', '1:border_count=1024', ...] means that two first features have 1024 borders. Example 3: ['0:nan_mode=Forbidden,border_count=32,border_type=GreedyLogSum', '1:nan_mode=Forbidden,border_count=32,border_type=GreedyLogSum'] - defines more quantization properties for first two features. border_count : int, [default = 254 for training on CPU or 128 for training on GPU] The number of partitions in numeric features binarization. Used in the preliminary calculation. range: [1,65535] on CPU, [1,255] on GPU max_bin : float, synonym for border_count. feature_border_type : string, [default='GreedyLogSum'] The binarization mode in numeric features binarization. Used in the preliminary calculation. Possible values: - 'Median' - 'Uniform' - 'UniformAndQuantiles' - 'GreedyLogSum' - 'MaxLogSum' - 'MinEntropy' sparse_features_conflict_fraction : float, [default=0.0] CPU only. Maximum allowed fraction of conflicting non-default values for features in exclusive features bundle. Should be a real value in [0, 1) interval. nan_mode : string, [default=None] Way to process missing values for numeric features. Possible values: - 'Forbidden' - raises an exception if there is a missing value for a numeric feature in a dataset. - 'Min' - each missing value will be processed as the minimum numerical value. - 'Max' - each missing value will be processed as the maximum numerical value. If None, then nan_mode=Min. input_borders : string or pathlib.Path, [default=None] input file with borders used in numeric features binarization. task_type : string, [default=None] The calcer type that will be used to train the model after quantization. Possible values: - 'CPU' - 'GPU' used_ram_limit=None random_seed : int, [default=None] The random seed used for data sampling. If None, 0 is used. zPool is already quantizedNrrz'got an unexpected keyword arguments: {})rrD_process_synonymsr`rrUr _quantize)r3rrrmax_binrrrrrrrkwargsrrrs rquantizez Pool.quantizesB     ;< <&!  "L$jj)>E06 ;bdh0i-  I P PQWQ\Q\Q^ _` `$V-=?]_k%8:[]p%-}iYd%J L vrct|tr |j}t|tr"t j |jd}|S)Nr)rgrrrr transpose)r3rs rrzPool._if_pandas_to_numpy s; eV $LLE eY 'LL.q1E rcvt|tr |j}t|tr |j}|Sr()rgrrrrs r_label_if_pandas_to_numpyzPool._label_if_pandas_to_numpys- eV $LLE eY 'LLE rc 6t| | 5|j|||||j||d}n|j||||fD]} | d} |j | |j ||||||d||| | dddy#1swYyxYw)z& Read Pool from file. Nrr)rersrurwr _read_pool)r3 pool_filer]r^r_feature_names_pathr`rarbrkquantization_paramsrWrXrrs rrNz Pool._reads&x *   i);UE J  ! !) ,!)%'"334FG 2E:<D;  $ $\ 2 OO""! "# + * *sAB2BBc 0t|j}|t|trBt|j }|"t |t |k7r t d||zS| t dt|Dcgc]}t|tc}s t dt |}g}d} tt|t|zD]4} | |vr|jd| z|j|| | dz } 6|S|Scc}w)NzDkeys of embedding_features_data and embedding_features are differentz`embedding_features is not specified but embedding_features_data without feature names is presentzeembedding_features contain feature names but embedding_features_data without feature names is presentrz_embedding_feature_%ir) rhcolumnsrgr'rUrrrDallrmrr_rV) r3data_as_data_framer\r[ non_embedding_data_feature_namesembedding_feature_namesembedding_feature_idembedding_features_setrnon_embedding_feature_idx feature_idxs r_infer_feature_nameszPool._infer_feature_namesEsI+/0B0J0J+K( " .148*./F/K/K/M*N'%1-.#6M2NN+,rss7:QQQ%-')KLLhz{hzPdJ';]Khz{|')PQQ),-?)@& " ,-)#(-M)NQTUgQh)h#iK"&<<%,,-D{-RS%,,-MNg-hi1Q61 $j%$3 3!|sDc t|tr||j|||}t|tr|jj }t|t r!|j}|j}n||zt|trtj|t}ttj|dk(rtj|d}tj|\}}|Zt|z }t|t rt|j }n|}|D]}t|k7st#dd}|||j%||j'||j)|}ttj|dk(rtj|d}|j+|||j-||1t/||}|j1|d|j3|d|1t/||}|j1|d|j3|d|1t/||}|j1|d|j3|d|V|j5|t|tr |j}|j7|tj|d}|>|j5|t|tr |j}|j7|| 4|j9| |j;| } |j=| | 4|j?| |j;| } |jA| | 4|jC| |j;| } |jE| | 4|jG| |j;| } |jI| | 4|j9| |j;| } |j=| ||L|jK||j;|}tjL|d f}|jO|||4|jQ||j;|}|jS|||jU||}|jW||||||||| | | | | |||||y) z7 Initialize Pool from array like data. Nrrz]samples count in 'embeddings_features_data' does not correspond to samples count in main datarrYrZr[rJ),rgrrrrtolistrOget_object_countget_feature_countrhrrrr_r expand_dimsr'rDrrr rrrrzrrrrrrrrrrrrrrrrrr _init_pool)r3rXr rYrZr[r\r^r_rcrdrerfrgrhrirrjrkrr|embedding_features_data_valuesembedding_feature_data pairs_lens rrVz Pool._initds, dI &$ $ 9 9$@WYk l dF #;;%%'D dL ) 113M!335N  $%zz$f5288D>"a'~~dA.,.HHTN )M> " . c"9: :N148156M6T6T6V1W.1H.*H&-.-?'w+I     " "5 )  # #E *2259E288E?#q(ua0  # #E= 9  $  % %m^ D  #0}ML  + +L. I  , ,\>> Z  $1-OM  + +M? 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N)rWrr4)r3rrms rr4zCatBoost.__init__s h&v.rct|dddk(xs%tj|jdd}|j |jdd||d|S) z returns: train_pool, eval_pool eval_pool will be uninitialized if save_eval_pool is false _estimator_typeNrvrcr{rFr)rrr2rr)r3rrrrs r_dataset_train_eval_splitz"CatBoost._dataset_train_eval_splits%T+.( s'lG~Fxylpqrls~Fsrz1'eval_set' tuple contains at least one None value)rYrZr[zEmpty 'eval_set' in tuplez(Multiple eval set shall not contain Nonez[Invalid type of 'eval_set': {}, while expected Pool or (X, y) or filename, or list thereof.zFTo employ param {'use_best_model': True} provide non-empty 'eval_set'.z"Error while loading init_model: {})r eval_setsrrr)%rrrrgrOrrrr- is_empty_rDrDrrr_TrainCallbacksWrapperrr_check_train_paramsrrrhrVrrsrrqr_rrrrr get_paramr load_modelr)%r3r+rrYrZr[r^r_r,rdrerfrgrhreval_setrrrArBr]rrrrYrrrrrrrr eval_set_listreval_total_row_countr0s% r_prepare_train_paramszCatBoost._prepare_train_paramss $++, >F&! a & MMV W0 aX 06?[ 56H!VUij&q!\=J\^cej'4h k[g'/1CE     . .)!T22">  D+T 2 #  1A 7M<1I- w   $&3F? #  $&3F? #   'F9   %'5F# $ , &F9  5F9 F"9%  $&3F? #  $&3F? #  (*;F& '  "8"CF; 6""6*F# ::os +s 2##$OPP#'#A#A*fei#A#j J$.x$>XJ   %H(D)  *$ " (=(=(??$R=((*a/'(BCCHj1  hCU!VW$ " (=(=(??$R=((*a/'(E(L(LX(VWWHe,x=A%'(Z(a(abefklG~FlGgGcH)IJJA;&(1+*='([\\      %/%G%G%I&0&I&I&K+5+S+S+U % " (=(=(??$R=((*a/'(CDD!}%)'(RSS#%B%I%IJNOWJX%YZZC&F >>* +0D0I hi i  " :z(J T%Z22:> %" 0$    T#$H$O$OPQ$RSS TsO?? P'P""P'c t||5| td|#t|ttfzs td|j d,id|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|d|d|d|} | d }!| d!}"| d"}#t ||d#t|jg5|j|"| d$|!|#| dddd|jj}$|$r t|$rnt|jd%kDrn|jjsh|#rDt!||||||||| | | | | |}"|!j#d&d'd'k7r|j%|"|!d()\}"}%|j'|"t(j**n |j't(j*+ddd|S#1swYxYw#1swY|SxYw)-NX must not be NonezCy may be None only when X is an instance of catboost.Pool or stringr+rrYrZr[r^r_r,rdrerfrgrhrrrrrArBr]rrrrYrrrrrrrrzTraining plotsrrrrFr)rXrrr)rerDrgrsrDrrDr7 get_paramsrr_get_loss_function_nameis_groupwise_metricr_r_has_leaf_weights_in_modelr-rrget_feature_importancerr)&r3r+rrYrZr[r^r_r,rdrerfrgrhrrrrrArBr]rrrrYrrrrrrWrX train_paramsrrrlossrs& r_fitz CatBoost._fitO s x *y#$899yAzTG/C!D#$ijj5455   '3 CP ew  #( 8E PX gs ( 7C NV gu " ,3 CP W[ gp  $6  EQ  an    6K  [h , @Q ^h $ L"(+F%l3J+,>? dI/?.QUQ`Q`QbBcAde  -$ . f<<779D+D1d88:;a?||>>@'%6().!!)$('($.& "::os;sB,0,J,J:W]ns,J,tMJ//ZiFfFf/g//Y5U5U/Vy+| Sfe++| s%CG=G18C/G=1G: 6G==HFc |jg||||||||| | | | | ||||||||||||||||||S)a> Fit the CatBoost model. Parameters ---------- X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series or string. If not catboost.Pool or catboost.FeaturesData it must be 2 dimensional Feature matrix or string - file with dataset. Must be non-empty (contain > 0 objects) y : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Labels of the training data. If not None, can be a single- or two- dimensional array with either: - numerical values - for regression (including multiregression), ranking and binary classification problems - class labels (boolean, integer or string) - for classification (including multiclassification) problems Use only if X is not catboost.Pool and does not point to a file. cat_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Categ columns indices. Use only if X is not catboost.Pool and not catboost.FeaturesData text_features: list or numpy.ndarray, optional (default=None) If not none, giving the list of Text columns indices. Use only if X is not catboost.Pool and not catboost.FeaturesData embedding_features: list or numpy.ndarray, optional (default=None) If not none, giving the list of Embedding columns indices. Use only if X is not catboost.Pool and not catboost.FeaturesData pairs : list or numpy.ndarray or pandas.DataFrame The pairs description. If list or numpy.ndarrays or pandas.DataFrame, giving 2 dimensional. The shape should be Nx2, where N is the pairs' count. The first element of the pair is the index of the winner object in the training set. The second element of the pair is the index of the loser object in the training set. graph : list or numpy.ndarray or pandas.DataFrame The graph edges list description. If list or numpy.ndarrays or pandas.DataFrame, giving 2 dimensional. sample_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Instance weights, 1 dimensional array like. group_id : list or numpy.ndarray, optional (default=None) group id for each instance. If not None, giving 1 dimensional array like data. Use only if X is not catboost.Pool. group_weight : list or numpy.ndarray, optional (default=None) Group weight for each instance. If not None, giving 1 dimensional array like data. subgroup_id : list or numpy.ndarray, optional (default=None) subgroup id for each instance. If not None, giving 1 dimensional array like data. Use only if X is not catboost.Pool. pairs_weight : list or numpy.ndarray, optional (default=None) Weight for each pair. If not None, giving 1 dimensional array like pairs. baseline : list or numpy.ndarray, optional (default=None) If not None, giving 2 dimensional array like data. Use only if X is not catboost.Pool. use_best_model : bool, optional (default=None) Flag to use best model eval_set : catboost.Pool or list of catboost.Pool or tuple (X, y) or list [(X, y)], optional (default=None) Validation dataset or datasets for metrics calculation and possibly early stopping. logging_level : string, optional (default=None) Possible values: - 'Silent' - 'Verbose' - 'Info' - 'Debug' metric_period : int Frequency of evaluating metrics. verbose : bool or int If verbose is bool, then if set to True, logging_level is set to Verbose, if set to False, logging_level is set to Silent. If verbose is int, it determines the frequency of writing metrics to output and logging_level is set to Verbose. silent : bool If silent is True, logging_level is set to Silent. If silent is False, logging_level is set to Verbose. verbose_eval : bool or int Synonym for verbose. Only one of these parameters should be set. plot : bool, optional (default=False) If True, draw train and eval error in Jupyter notebook plot_file : file-like or str, optional (default=None) If not None, save train and eval error graphs to file early_stopping_rounds : int Activates Iter overfitting detector with od_wait parameter set to early_stopping_rounds. save_snapshot : bool, [default=None] Enable progress snapshotting for restoring progress after crashes or interruptions snapshot_file : string or pathlib.Path, [default=None] Learn progress snapshot file path, if None will use default filename snapshot_interval: int, [default=600] Interval between saving snapshots (seconds) init_model : CatBoost class or string or pathlib.Path, [default=None] Continue training starting from the existing model. If this parameter is a string or pathlib.Path, load initial model from the path specified by this string. callbacks : list, optional (default=None) List of callback objects that are applied at end of each iteration. log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Returns ------- model : CatBoost )r) r3r+rrYrZr[r^r_r,rdrerfrgrhrrrrrArBr]rrrrYrrrrrrWrXs rfitz CatBoost.fit sLRtyyuuAu|u]ujnPZ[_amPn4#F#F#Htx) D%%%rct|ts#tdjt |||vr*tdj|dj |y)Nz/Invalid prediction_type type={}: must be str().z0Invalid value of prediction_type={}: must be {}.rF)rgrtrDrrr/)r3rvalid_prediction_typess r_validate_prediction_typez"CatBoost._validate_prediction_type2 sp/<8 Q X XY]^mYn op p "8 8 R Y YZikoktktvLlM!NO O 9rc |xs|jd}|d}|j|||\}} |j||j|||||||} | r| dS| SNrFr)rrrr) r3rXrrrrkrrrdata_is_single_object predictionss r_predictzCatBoost._predict8 sy6T^^I6 ?G&*&F&FtM_am&n## &&7(( YXdfmoxy !6{1~GKGrrJc 2|j||||||d|S)a Predict with data. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. prediction_type : string, optional (default='RawFormulaVal') Can be: - 'RawFormulaVal' : return raw value. - 'Class' : return class label. - 'Probability' : return probability for every class. - 'Exponent' : return Exponent of raw formula value. - 'RMSEWithUncertainty': return standard deviation for RMSEWithUncertainty loss function (logarithm of the standard deviation is returned by default). ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool, optional (default=False) If True, writes the evaluation metric measured set to stderr. task_type : string, [default=None] The evaluator type. Possible values: - 'CPU' - 'GPU' (models with only numerical features are supported for now) Returns ------- prediction : If data is for a single object, the return value depends on prediction_type value: - 'RawFormulaVal' : return raw formula value. - 'Class' : return class label. - 'Probability' : return one-dimensional numpy.ndarray with probability for every class. otherwise numpy.ndarray, with values that depend on prediction_type value: - 'RawFormulaVal' : one-dimensional array of raw formula value for each object. - 'Class' : one-dimensional array of class label for each object. - 'Probability' : two-dimensional numpy.ndarray with shape (number_of_objects x number_of_classes) with probability for every class for each object. predictrr3rXrrrrkrrs rrzCatBoost.predictB s%n}}T?KLZaclnwxxrcT|xs|jd}|d}|j|||\}}|j|ddg|dk(r |j}|j ||||||} |dk(r1| j } | j | d|t| d|z } |r| dS| S)NrF VirtEnsemblesTotalUncertaintyrr)rrrrGrrrrn) r3rXrrrrkrrrrrs r_virtual_ensembles_predictz#CatBoost._virtual_ensembles_predict{ s6T^^I6 ?G&*&F&FtM_am&n## &&J\8]^ >((I::4R[]twCELM o -%%E%--eAh8OQTUZ[\U]`wUwQxyK!6{1~GKGrc 0|j||||||dS)a Predict with data. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. prediction_type : string, optional (default='RawFormulaVal') Can be: - 'VirtEnsembles': return V (virtual_ensembles_count) predictions. k-th virtEnsemle consists of trees [0, T/2] + [T/2 + T/(2V) * k, T/2 + T/(2V) * (k + 1)] * constant. - 'TotalUncertainty': see returned predictions format in 'Returns' part ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. virtual_ensembles_count: int, optional (default=10) virtual ensembles count for 'TotalUncertainty' and 'VirtEnsembles' prediction types. thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool, optional (default=False) If True, writes the evaluation metric measured set to stderr. Returns ------- prediction : (with V as virtual_ensembles_count and T as trees count, k-th virtEnsemle consists of trees [0, T/2] + [T/2 + T/(2V) * k, T/2 + T/(2V) * (k + 1)] * constant) If data is for a single object, return 1-dimensional array of predictions with size depends on prediction type, otherwise return 2-dimensional numpy.ndarray with shape (number_of_objects x size depends on prediction type); Returned predictions depends on prediction type: If loss-function was RMSEWithUncertainty: - 'VirtEnsembles': [mean0, var0, mean1, var1, ..., vark-1]. - 'TotalUncertainty': [mean_predict, KnowledgeUnc, DataUnc]. otherwise for regression: - 'VirtEnsembles': [mean0, mean1, ...]. - 'TotalUncertainty': [mean_predicts, KnowledgeUnc]. otherwise for binary classification: - 'VirtEnsembles': [ApproxRawFormulaVal0, ApproxRawFormulaVal1, ..., ApproxRawFormulaValk-1]. - 'TotalUncertainty': [DataUnc, TotalUnc]. virtual_ensembles_predict)r)r3rXrrrrkrs rrz"CatBoost.virtual_ensembles_predict s0f..t_iQhjvxB]^ ^rc #K|xs|jd}|d}|j|||\}} |j||dk(r |j}|j |||||||} | D] } | r| dn| ywr)rrrrGr) r3rXrrrrrkrrrstaged_predict_iteratorrs r_staged_predictzCatBoost._staged_predict s6T^^I6 ?G&*&F&FtM_am&n## &&7 >((I"&"?"?oWbdmoz}IKR#S2K$9+a.{ J3sAc 2|j|||||||dS)a Predict target at each stage for data. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. prediction_type : string, optional (default='RawFormulaVal') Can be: - 'RawFormulaVal' : return raw formula value. - 'Class' : return class label. - 'Probability' : return probability for every class. - 'RMSEWithUncertainty': return standard deviation for RMSEWithUncertainty loss function (logarithm of the standard deviation is returned by default). ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. eval_period: int, optional (default=1) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool If True, writes the evaluation metric measured set to stderr. Returns ------- prediction : generator for each iteration that generates: If data is for a single object, the return value depends on prediction_type value: - 'RawFormulaVal' : return raw formula value. - 'Class' : return class label. - 'Probability' : return one-dimensional numpy.ndarray with probability for every class. otherwise numpy.ndarray, with values that depend on prediction_type value: - 'RawFormulaVal' : one-dimensional array of raw formula value for each object. - 'Class' : one-dimensional array of class label for each object. - 'Probability' : two-dimensional numpy.ndarray with shape (number_of_objects x number_of_classes) with probability for every class for each object. staged_predictrr3rXrrrrrkrs rrzCatBoost.staged_predict s2f##D/; S^`lnuxHI Irc#K|dk(r |j}|j|dd\}}|j|||}|D]}|yw)Nriterate_leaf_indexesrJrk)rGrr)r3rXrrrleaf_indexes_iterator leaf_indexs r_iterate_leaf_indexeszCatBoost._iterate_leaf_indexes sZ >((I2249O^`2aa $ ; ;D+y Y/J 0sAA c(|j|||S)a Returns indexes of leafs to which objects from pool are mapped by model trees. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. ntree_start: int, optional (default=0) Index of first tree for which leaf indexes will be calculated (zero-based indexing). ntree_end: int, optional (default=0) Index of the tree after last tree for which leaf indexes will be calculated (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. Returns ------- leaf_indexes : generator. For each object in pool yields one-dimensional numpy.ndarray of leaf indexes. )r)r3rXrrs rrzCatBoost.iterate_leaf_indexes s.))$ YGGrcz|dk(r |j}|j|d|\}}|j|||||S)Nrcalc_leaf_indexes)rGrr)r3rXrrrkrrs r_calc_leaf_indexeszCatBoost._calc_leaf_indexes# sG >((I2249Ll[a++D+y,X_``rc,|j|||||S)a Returns indexes of leafs to which objects from pool are mapped by model trees. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. ntree_start: int, optional (default=0) Index of first tree for which leaf indexes will be calculated (zero-based indexing). ntree_end: int, optional (default=0) Index of the tree after last tree for which leaf indexes will be calculated (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool (default=False) Enable debug logging level. Returns ------- leaf_indexes : 2-dimensional numpy.ndarray of numpy.uint32 with shape (object count, ntree_end - ntree_start). i-th row is an array of leaf indexes for i-th object. )r)r3rXrrrkrs rrzCatBoost.calc_leaf_indexes) s@&&t[)\SZ[[rcX|js td|jSNzModel is not fitted)rrDrr@s rrz CatBoost.get_cat_feature_indicesK s'~~ 56 6,,..rcX|js td|jSr)rrDrr@s rrz!CatBoost.get_text_feature_indicesP s'~~ 56 6--//rcX|js td|jSr)rrDrr@s rrz&CatBoost.get_embedding_feature_indicesU s'~~ 56 62244rc &|js tdt|ts#tdj t ||j r tdt|tsCt|ts3t|ts#tdj t |ttd|s td|tj}t|tst|tr|g}t|}t| | 5t!| | d|g5|j#||||||||\} }ddddddt%t' S#1swY&xYw#1swY*xYw)NbThere is no trained model to evaluate metrics on. Use fit() to train model. Then call this method.,Invalid data type={}, must be catboost.Pool.zData is empty.zdInvalid metrics type={}, must be list(), str() or one of builtin catboost.metrics.* class instances.cFt|txst|tSr()rgr r#)metrics rz(CatBoost._eval_metrics..c sj&F&k*U[]jJk&krz[Invalid metric type: must be string() or one of builtin catboost.metrics.* class instances.zEval metrics plot)rrDrgrDrrrrkrtr#rrtempfilemkdtemprprerDrr'ro)r3rXrtrrrrkres_dirrrArBrWrX metrics_score metric_namess r _eval_metricszCatBoost._eval_metricsZ s~~!EF F$% N U UVZ[_V` ab b >> 01 1';/ 7L8YblmtwDcE!G!N!NOST[O\!]^ ^3kmtuv }~ ~ ?&&(G g| , 7M0RiG09 x *LyJ]`g_h,i*.*A*A$Q\^gitwCELNU+V 'M<-j *C m455-j,i * *s$*F:E;F;F FFc h|j||||||t|j||| | | S)a] Calculate metrics. Parameters ---------- data : catboost.Pool Data to evaluate metrics on. metrics : list of strings or catboost.metrics.BuiltinMetric List of evaluated metrics. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. eval_period: int, optional (default=1) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. tmp_dir : string or pathlib.Path (default=None) The name of the temporary directory for intermediate results. If None, then the name will be generated. plot : bool, optional (default=False) If True, draw train and eval error in Jupyter notebook plot_file : file-like or str, optional (default=None) If not None, save train and eval error graphs to file log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Returns ------- prediction : dict: metric -> array of shape [(ntree_end - ntree_start) / eval_period] )rr7r) r3rXrtrrrrkrrArBrWrXs r eval_metricszCatBoost.eval_metricsp sl^!!$iVbdrswtCtCtEeFHOQUW`bjltu urc 8| td| td| tdd} |d} tj}tjj |d} tjj |d}t |t | t |td| d | |g 5|j||||||| |dd| | |j||||||||dd| | ddd| rtj|yy#1swY"xYw) a Draw train and eval errors in Jupyter notebook for both models Parameters ---------- model: CatBoost model Another model to draw metrics data : catboost.Pool Data to evaluate metrics on. metrics : list of strings or catboost.metrics.BuiltinMetric List of evaluated metrics. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. eval_period: int, optional (default=1) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. tmp_dir : string or pathlib.Path (default=None) The name of the temporary directory for intermediate results. If None, then the name will be generated. plot_file : file-like or str, optional (default=None) If not None, save eval error graphs to file log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Nz(You should provide model for comparison.z'You should provide data for comparison.z*You should provide metrics for comparison.FT first_model second_modelzCompare modelsrBrCr;)rArBrWrX) rDr r r)r*r/r-rDrshutilrmtree)r3rrXrtrrrrkrrBrWrXneed_to_remove first_dir second_dirs rcomparezCatBoost.compare s3X = JK K < IJ J ? LM M ?!N&&(GGGLL-8 WW\\'>: ( * + $)@P^gis]t u   tWk9kS_ajls$)THW_  a   g{I{T`blnu%*dXX`  bv  MM' "  v us 4;DDc n|js tdt|j||||||S)a* Create batch metric calcer. Could be used to aggregate metric on several pools Parameters ---------- Same as in eval_metrics except data Returns ------- BatchMetricCalcer object Usage example ------- # Large dataset is partitioned into parts [part1, part2] model.fit(params) batch_calcer = model.create_metric_calcer(['Logloss']) batch_calcer.add(part1) batch_calcer.add(part2) metrics = batch_calcer.eval_metrics() r)rrDBatchMetricCalcerr)r3rtrrrrkrs rcreate_metric_calcerzCatBoost.create_metric_calcer sA&~~!EF F w YP[]ikrssrc|jj}|r+t|r tjt |ddStjt |ddS)Nrr)rrrrrr)r3rs rfeature_importances_zCatBoost.feature_importances_ sQ||335 '-88GD*>EF F88GD*EtLM MrAutoRawriTc$ t||5t|tst|ts t dt|}|dkr t d||}t t |}|t jk(rH|jj}|rt|rt j}nt j}|t jk(rR|j|d|\}}|jdk7r^t dj!||j|3t|t"s#t dj!t%||t jk(}|xr|jj'xs|t j(k(}|s@|'|r t d t d j!||j*r t d t t,| j.} |j1||| ||||| | | | | \}}|t jt jt jt j2fvr|Dcgc]}|d }}d}|t jk(rd }|t jk(rd }|r t5||||r;t7t9||t;dd}ddg}t=||cdddSt?j@|cdddS|t jBk(rt|ddtDrtt?j@|Dcgc]H}t?j@|Dcgc]%}t?j@|Dcgc]}|c}'c}}Jc}}}cdddS|Dcgc]}|Dcgc]}|c}}}}|rt=|cdddSt?j@|cdddS|t jFk(r%t|ddtDrtt?j@|Dcgc]H}t?j@|Dcgc]%}t?j@|Dcgc]}|c}'c}}Jc}}}cdddSt?j@|Dcgc]m}t?j@|Dcgc]H}t?j@|Dcgc]%}t?j@|Dcgc]}|c}'c}}Jc}}}oc}}}}cdddS|t j(k(rg|D cgc]"} t| dt| d| dg$}} |rgd}t=||cdddSt?j@|cdddS dddycc}wcc}wcc}}wcc}}}wcc}wcc}}wcc}wcc}}wcc}}}wcc}wcc}}wcc}}}wcc}}}}wcc} w#1swYyxYw)a Parameters ---------- data : Data to get feature importance. If type in ('LossFunctionChange', 'ShapValues', 'ShapInteractionValues') data must of Pool type. For every object in this dataset feature importances will be calculated. if type == 'SageValues' data must of Pool type. For every feature in this dataset importance will be calculated. If type == 'PredictionValuesChange', data is None or a dataset of Pool type Dataset specification is needed only in case if the model does not contain leaf weight information (trained with CatBoost v < 0.9). If type == 'PredictionDiff' data must contain a matrix of feature values of shape (2, n_features). Possible types are catboost.Pool or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData or pandas.SparseDataFrame or scipy.sparse.spmatrix If type == 'FeatureImportance' See 'PredictionValuesChange' for non-ranking metrics and 'LossFunctionChange' for ranking metrics. If type == 'Interaction' This parameter is not used. type : EFstrType or string (converted to EFstrType), optional (default=EFstrType.FeatureImportance) Possible values: - PredictionValuesChange Calculate score for every feature. - LossFunctionChange Calculate score for every feature by loss. - FeatureImportance PredictionValuesChange for non-ranking metrics and LossFunctionChange for ranking metrics - ShapValues Calculate SHAP Values for every object. - ShapInteractionValues Calculate SHAP Interaction Values between each pair of features for every object - Interaction Calculate pairwise score between every feature. - PredictionDiff Calculate most important features explaining difference in predictions for a pair of documents. - SageValues Calculate SAGE value for every feature prettified : bool, optional (default=False) change returned data format to the list of (feature_id, importance) pairs sorted by importance thread_count : int, optional (default=-1) Number of threads. If -1, then the number of threads is set to the number of CPU cores. verbose : bool or int If False, then evaluation is not logged. If True, then each possible iteration is logged. If a positive integer, then it stands for the size of batch N. After processing each batch, print progress and remaining time. fstr_type : string, deprecated, use type instead shap_mode : string, optional (default="Auto") used only for ShapValues type Possible values: - "Auto" Use direct SHAP Values calculation only if data size is smaller than average leaves number (the best of two strategies below is chosen). - "UsePreCalc" Calculate SHAP Values for every leaf in preprocessing. Final complexity is O(NT(D+F))+O(TL^2 D^2) where N is the number of documents(objects), T - number of trees, D - average tree depth, F - average number of features in tree, L - average number of leaves in tree This is much faster (because of a smaller constant) than direct calculation when N >> L - "NoPreCalc" Use direct SHAP Values calculation calculation with complexity O(NTLD^2). Direct algorithm is faster when N < L (algorithm from https://arxiv.org/abs/1802.03888) shap_calc_type : EShapCalcType or string, optional (default="Regular") used only for ShapValues type Possible values: - "Regular" Calculate regular SHAP values - "Approximate" Calculate approximate SHAP values - "Exact" Calculate exact SHAP values interaction_indices : list of int or string (feature_idx_1, feature_idx_2), optional (default=None) used only for ShapInteractionValues type Calculate SHAP Interaction Values between pair of features feature_idx_1 and feature_idx_2 for every object reference_data: catboost.Pool or None Reference data for Independent Tree SHAP values from https://arxiv.org/abs/1905.04610v1 if type == 'ShapValues' and reference_data is not None, then Independent Tree SHAP values are calculated sage_n_samples: int, optional (default=32) Number of outer samples used in SAGE values approximation algorithm sage_batch_size: int, optional (default=min(512, number of samples in dataset)) Number of samples used on each step of SAGE values approximation algorithm sage_detect_convergence: bool, optional (default=False) If set True, sage values calculation will be stopped either when sage values converge or when sage_n_samples iterations of algorithm pass log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Returns ------- depends on type: - FeatureImportance See PredictionValuesChange for non-ranking metrics and LossFunctionChange for ranking metrics. - PredictionValuesChange, LossFunctionChange, PredictionDiff, SageValues with prettified=False (default) list of length [n_features] with feature_importance values (float) for feature - PredictionValuesChange, LossFunctionChange, PredictionDiff, SageValues with prettified=True list of length [n_features] with (feature_id (string), feature_importance (float)) pairs, sorted by feature_importance in descending order - ShapValues np.ndarray of shape (n_objects, n_features + 1) with Shap values (float) for (object, feature). In case of multiclass the returned value is np.ndarray of shape (n_objects, classes_count, n_features + 1). For each object it contains Shap values (float). Values are calculated for RawFormulaVal predictions. - ShapInteractionValues np.ndarray of shape (n_objects, n_features + 1, n_features + 1) with Shap interaction values (float) for (object, feature(i), feature(j)). In case of multiclass the returned value is np.ndarray of shape (n_objects, classes_count, n_features + 1, n_features + 1). For each object it contains Shap interaction values (float). Values are calculated for RawFormulaVal predictions. - Interaction list of length [n_features] of 3-element lists of (first_feature_index, second_feature_index, interaction_score (float)) verbose should be bool or int.rverbose should be non-negative.Nrrz){} requires a pair of documents, found {}rzModel has no meta information needed to calculate feature importances. Pass training dataset to this function.zoFeature importance type {} requires training dataset to be passed to this function.zdata is empty.rrrT)rzreversez Feature Id Importances)r)zFirst Feature IndexzSecond Feature Indexr)$rergrlrnrDrrrrrrrrrrrrrD_typeofrrrrrvrrrsortedrorrrrrrkr)!r3rXr prettifiedrkr fstr_typerrrrrrrrrWrXrrneed_meta_infoempty_data_is_okfstrrrvfeature_importancesattribute_namerdoc dimensionrAfeature1feature2rows! rrzCatBoost.get_feature_importance s@x *gt,Z5M#$DEE'lG{#$EFF$ (D9Dy222||;;=/5$77D$;;Dy///::4AY[gha<<>Q&'(S(Z(Z[_aeamamao(pqq#JtT,B'(V(](]^efj^k(lmm!Y%E%EEN-[$,,2Y2Y2[|_cgpg|g|_| #<%+EFF,<>'(8992=.QWWN"&//$nl\ceqs|R2@.Racz#| D- 88):V:V!00)2F2FHH=A&BTEuQxT#&B!%9;;;%@N9777%9N!&+ *0]DW1X^hij^kuy*z'+];G$%8'J}+ *@88$78A+ *B---d1gaj+688Y]%_Y]RU&(XXHK/MHK902xx+496+4%99607HK/M&NY]%_`G+ *LCGG$3#6#u#6$FG!(0Q+ *T "xx/U+ *V888d1gaj+688]a%c]aVY&(XXLO/QLO02xx199;19X9;0XWV=5 V8$> V= WX#X( W1 W :W?XX6?X5W W& W$/ W8 W XX*W9W2W,*5 W'4> W,* W2 W9 X/X'X-X X3X8V==WX WXWW  X'W,*,W22W99 XXc |jr%|jjs tdt |t st |t s tdt |}|dkr td| | }tjdt| | 5|j||||||||} ddd| S#1swY SxYw)a This is the implementation of the LeafInfluence algorithm from the following paper: https://arxiv.org/pdf/1802.06640.pdf Parameters ---------- pool : Pool The pool for which you want to evaluate the object importances. train_pool : Pool The pool on which the model has been trained. top_size : int (default=-1) Method returns the result of the top_size most important train objects. If -1, then the top size is not limited. type : string, optional (default='Average') Possible values: - Average (Method returns the mean train objects scores for all input objects) - PerObject (Method returns the train objects scores for every input object) importance_values_sign : string, optional (default='All') Method returns only Positive, Negative or All values. Possible values: - Positive - Negative - All update_method : string, optional (default='SinglePoint') Possible values: - SinglePoint - TopKLeaves (It is posible to set top size : TopKLeaves:top=2) - AllPoints Description of the update set methods are given in section 3.1.3 of the paper. thread_count : int, optional (default=-1) Number of threads. If -1, then the number of threads is set to the number of CPU cores. verbose : bool or int If False, then evaluation is not logged. If True, then each possible iteration is logged. If a positive integer, then it stands for the size of batch N. After processing each batch, print progress and remaining time. ostr_type : string, deprecated, use type instead log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Returns ------- object_importances : tuple of two arrays (indices and scores) of shape = [top_size] z:Object importance is not supported for non symmetric treesr%rr&NzK'ostr_type' parameter will be deprecated soon, use 'type' parameter instead) rr _is_obliviousrDrgrlrnrrrer) r3rrrrrrrkrrrWrXrAs rget_object_importancezCatBoost.get_object_importance sz >> DLL$>$>$@ \] ]'4(GS1I @A Ag, Q; AB B  D MMg h x *__Zx}Vlnz}DEF+ + s !CCcH||kDr td|j||y)af Shrink the model. Parameters ---------- ntree_end: int Leave the trees with indices from the interval [ntree_start, ntree_end) (zero-based indexing). ntree_start: int, optional (default=0) Leave the trees with indices from the interval [ntree_start, ntree_end) (zero-based indexing). z*ntree_start should be less than ntree_end.N)rDr )r3rrs rshrinkzCatBoost.shrink/ s(  " LM M +y1rc$|jy)z= Drop unused features information from model N)r r@s rdrop_unused_featureszCatBoost.drop_unused_features> s '')rc |js tdt|ts#tdj t ||t|t sot |t|ts|jndt|ts|jndt|ts|jnd}|j||||y)a Save the model to a file. Parameters ---------- fname : string Output file name. format : string Possible values: * 'cbm' for catboost binary format, * 'coreml' to export into Apple CoreML format * 'onnx' to export into ONNX-ML format * 'pmml' to export into PMML format * 'cpp' to export as C++ code * 'python' to export as Python code. export_parameters : dict Parameters for CoreML export: * prediction_type : string - either 'probability' or 'raw' * coreml_description : string * coreml_model_version : string * coreml_model_author : string * coreml_model_license: string Parameters for PMML export: * pmml_copyright : string * pmml_description : string * pmml_model_version : string pool : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Training pool. z^There is no trained model to use save_model(). Use fit() to train model. Then use this method.rN)rXrYrZr[) rrDrgrsrrrDrOrrrr)r3rrrrs r save_modelzCatBoost.save_modelD s<~~!AB B%, Y ` `aefkal mn n  JtT$:DNtUaDbT::jnPZ[_amPn4#F#F#Htx D (94@rc|du|duz|duzdk7r td||j|||S||j||S||j||S)z Load model from a file, stream or blob. Parameters ---------- fname : string Input file name. Nrz:Exactly one of fname/stream/blob arguments mustn't be None)rDrr r)r3rrr!blobs rrzCatBoost.load_modelo s TMfn - >! C \] ]     UF +     " "6 *    " "4 ( rcL|j}|iS|j|S)a Get param value from CatBoost model. Parameters ---------- key : string The key to get param value from. Returns ------- value : The param value of the key, returns None if param do not exist. )rr)r3rzrs rrzCatBoost.get_param s(" >Izz#rcT|jj}|r t|S|S)z Get all params from CatBoost model. Returns ------- result : dict Dictionary of {param_key: param_value}. )rrr)r3deeprs rrzCatBoost.get_params s+""'') F# #Mrcl|js td|jjS)ad Get all params (specified by user and default params) that were set in training from CatBoost model. Full parameters documentation could be found here: https://catboost.ai/docs/concepts/python-reference_parameters-list.html Returns ------- result : dict Dictionary of {param_key: param_value}. zbThere is no trained model to use get_all_params(). Use fit() to train model. Then use this method.)rrDr_get_plain_paramsr@s rget_all_paramszCatBoost.get_all_params s6~~!EF F||--//rct|ts#tdjt ||j |y)z Save the model borders to a file. Parameters ---------- fname : string or pathlib.Path Output file name. rN)rgrsrDrrr)rs r save_borderszCatBoost.save_borders s:%, Y ` `aefkal mn n 5!rc"|jS)zH Return map feature_index: borders for float features. )r+r@s r get_borderszCatBoost.get_borders s   ""rc |jr tdt|D]\}}||j|<d|jvr-|jddk(r|jj d|S)z Set parameters into CatBoost model. Parameters ---------- **params : key=value format List of key=value paris. Example: model.set_params(iterations=500, thread_count=2). z(You can't change params of fitted model.rkrJ)rrDr rr`)r3rrzrvs r set_paramszCatBoost.set_params sw >>  JK K#F+JC%*D  c ", T.. .43D3D^3TXZ3Z    ! !. 1 rcfd}d} ddlm}j } j|dd\}} g} ig|jz} j} |D]"}t|tsPj|jvrtd j|jj|}n|}j|}|| vsJd | |}t!|dk(r<|j"j%d dg }| |j'g|||| z } |j"j%d dt)t+t!|dzdj|dgt-|dd|ddDcgc]\}}dj||c}}zdj|dgzd}g}t/|j1D]x\}}||||| |\}}|| ||<|j3|j5|ddj||j3|j5|g||gdz||||}| |j'|| gz } %|r t7| |r t9|d| | | fS#t$r.}tjdtt |d}~wwxYwcc}}w)a* To use this function, you should install plotly. data: numpy.ndarray or pandas.DataFrame or catboost.Pool features_to_change: list-like with int (for indices) or str (for names) elements Numerical features indices or names in `data` for which you want to vary prediction value. plot: bool Plot predictions. plot_file: str Output file for plot predictions. Returns ------- List of list of predictions for all buckets for all samples in data ctd|dzd}td|dzd}|g|z|gz}g}g}d} tj||r|dk(r t |nd} t t |dz D]P} ||| || dzzdz gz }| |||| dzkr| } ||} |d||<| j |gz }| ||<R| t |} || fS)NrrrJrAsTrueg@)minmaxrisnanr_rr) r2rborders nan_treatmentleft_extend_borderright_extend_borderextended_borderspointsr border_idxrbufr3s rrz*CatBoost.plot_predictions..predict s-!$Q^R!8 "%a'"+oq"9  23g=AT@UU FKJxxK()-:h-FS\A 3/0145,Q/2B1q52IIROPP%#k*:=MaRSe=T*T!"J+&#)":K  S 122 #&K 6! \  * *rcP|jdj|dddd|S)Nz%Prediction variation for feature '{}' Predictionrrrrrrrr)Layoutr)r/r~rs r get_layoutz-CatBoost.plot_predictions..get_layout s999=DDWM)""&  rrNrvary_feature_value_and_applyrJrNo feature named "{}" in model)only float features indexes are supportedBinsrtickvalsrXrrr(-inf, {:.4f}]({:.4f}, {:.4f}]({:.4f}, +inf)FrtickmodergticktextrrzDocument {} predictions)rrr)rrrzPredictions for all buckets)rrrrrrr+rrr-rgrnrQrDrrwr_rXAxisr rhrrori get_featuresrVr#r r)r3rXfeatures_to_changerArBrrar/r0 model_bordersrfigsall_predictionsnan_treatmentsr~rrTrval_1val_2tracer5rrrZrs` rplot_predictionszCatBoost.plot_predictions s  +*  & * ))+ 2249Wfh2ia$/113)Ggs+&&.'ATAT2T'(H(O(OPW(XYY"1177@ % --k:-/ \1\ \/#K0G7|q  fsC r*Re2T UUIIOO eCL1$456*11'!*=>.1'#2, .LN.LleU-33E5A.LNN+11'"+>?@ %$ EE!*4+<+<+>!? X*1(KR`alRm*n' Z4?$[1 JJ?IcIjIjknIoJp RZZ:,;z;R:S`eZfg"@ GU3F RYYE&Y9: :DK*N  T "  9&CT J$$o & MMD Ec!f% % &<NsJ&K & K/)KKc ddlmfdfd}ddfd}fd} ||} | D cgc]} j | } } t| d vr#td jt| t| d k(} j|d | \}}tjjj|| |}| r6|j| Dcgc]}t|dzc}}|| | |}n| | d| d|}|r t!||rt#|dj||||fS#t$r.}tjdtt |d}~wwxYwcc} wcc}w)a To use this function, you should install plotly. data: numpy.ndarray or pandas.DataFrame or catboost.Pool features: int, str, list, tuple, list, tuple Float features to calculate partial dependence for. Number of features should be 1 or 2. plot: bool Plot predictions. plot_file: str Output file for plot predictions. thread_count: int Number of threads to use. If -1 use maximum available number of threads. Returns ------- If number of features is one - 1d numpy array and figure with line plot. If number of features is two - 2d numpy array and figure with 2d heatmap. rNrcht|tsPj|jvrtdj |jj |}n|}|j vsJdtj |dkDsJdj ||S)Nrcrdrz(feature with idx {} is not used in model)rgrnrQrDrrwr+r_)r~rr3s r getFeatureIdxz7CatBoost.plot_partial_dependence..getFeatureIdxZsgs+&&.'ATAT2T'(H(O(OPW(XYY"1177@ % $"3"3"55 b7b b5t((*;781< |>h>o>op{>| |< rct|tst|tr|Dcgc] }| }}|St|tst|tr |g}|St dcc}w)NzyUnsupported type for argument 'features'. Must be one of: int, string, list, list, tuple, tuple)rgrhrqrnrrD)rr~ features_idxsr|s rgetFeatureIndicesz;CatBoost.plot_partial_dependence..getFeatureIndiceses~(D)Z%-HGO PxGw!7x P ! Hc*j3.G!.x!8 9 ! $%bcc !QsA/c >|dndj|dttt|dzdj|dgt |dd|ddDcgc]\}}dj||c}}zd j|dgzd d Scc}}w) NrezBins of feature '{}'rrrirrJrjrkFrl)rrhrr_ro)rT feature_namervrws r getAxisParamsz7CatBoost.plot_partial_dependence..getAxisParamsns#/#7=U=\=\]i=j# s7|a'7!89-44WQZ@A14WSb\712;1OQ1O066ueD1OQQ.44WR[ABC#() ) QsBc jjd i|d|d}jjd i|d|d}jdj dj dj t t|||}jj|| }|S) Nr)rrz'Partial dependence plot for features {}z'{}'z', 'r_)zrhr) rroYAxisr`rr/rrr Heatmap) rrTrrrrr3rr/s rplot2dz0CatBoost.plot_partial_dependence..plot2dys#BIIOO_mGAJ][\M]&^_E#BIIOO_mGAJ][\M]&^_EYY?FFxW_WdWdehiln{e|W}G~F ))k!:6)JCJrcjjd i|}ddd}jdj|||}j j |d|}|S) NzMean Predictionr)rrz(Partial dependence plot for feature '{}'r_r)rrrhr)rror`rr r#) r~rTrrrrr3rr/s rplot1dz0CatBoost.plot_partial_dependence..plot1ds|#BIIOO=mG&<=E*EYY@GGPF ))k!PY_)`CJr)rrz-Number of 'features' should be 1 or 2, got {}rplot_partial_dependencerrz)Partial dependence plot for features '{}'r()rrrrrrr+r_rDrrrrr_calc_partial_dependencerr r)r3rXrrArBrkr0rrr features_idxr5rT is_2d_plotrrtrr3rr|r/s` @@@rrz CatBoost.plot_partial_dependenceBs$ & *  ! )  )2 7CD|4$$&s+|D | F * Q X XY\]iYj kl l&!+ 2249Ram2na((4<<#H#H|]i#jk -5576S7as1vz76STOwACa'!*oFC  S !  9&Q&X&XYa&bdg h##c & MMD Ec!f% % &zE7Ts"E F$F  F)E<<Fc ` |du} t|tsd|i}t|tsd|i}| st|t|k(sJd| s(|j|jk(sJd|jD]/} |j || d||j | d\|| <} 1t |jt |j} }||jddvrdnd }|d vrtd j|| |j}|i}n/t|tst|t r td ||i}t|tst|tr|g}d }n|}d}g}g}g}g}i}|D]}t|tsP|j||jvrtdj||jj|}n|}|j|}||vrz||vr||||<|j||||<|j j#|\}}|dk(r#|j||jd|j||jd|Dcgc]!}|j j%|||||#}}t't }t|}d|}}t)|D]z\}}||} || }|dk(rA| |vr/|j j+|d|}!|!D"cgc]}"|"}!}"n|| }!t|!t,stdj|t}#|!D]$}"|j j/|"|||#|"<&|#j1D"$cic]\}"}$|$|" }%}"}$t)|D]t\}}&t3j4t7|%jDcgc]}|%| c}|&|d<|&|j9dd||j|&|v|dz }X|D]}&||j|&||dz }}|s| 5t;|| |j|}'|r t=|'| t?| d|'g|jD]} t|| dk(s|| d|| <!|r |||dSi}(|D]&}t|tr |||(|<||||(|<(|(Scc}wcc}"wcc}$}"wcc}w)a Get statistics for the feature using the model, dataset and target. To use this function, you should install plotly. The catboost model has borders for the float features used in it. The borders divide feature values into bins, and the model's prediction depends on the number of the bin where the feature value falls in. For float features this function takes model's borders and computes 1) Mean target value for every bin; 2) Mean model prediction for every bin; 3) The number of objects in dataset which fall into each bin; 4) Predictions on varying feature. For every object, varies the feature value so that it falls into bin #0, bin #1, ... and counts model predictions. Then counts average prediction for each bin. For categorical features (only one-hot supported) does the same, but takes feature values provided in cat_feature_values instead of borders. Parameters ---------- data: numpy.ndarray or pandas.DataFrame or catboost. Pool or dict {'pool_name': pool} if you want several pools Data to compute statistics on target: numpy.ndarray or pandas.Series or dict {'pool_name': target} if you want several pools or None Target corresponding to data Use only if data is not catboost.Pool. feature: None, int, string, or list of int or strings Features indexes or names in pd.DataFrame for which you want to get statistics. None, if you need statistics for all features. prediction_type: str Prediction type used for counting mean_prediction: 'Class', 'Probability' or 'RawFormulaVal'. If not specified, is derived from the model. cat_feature_values: list or numpy.ndarray or pandas.Series or dict: int or string to list or numpy.ndarray or pandas.Series Contains categorical feature values you need to get statistics on. Use dict, when parameter 'feature' is a list to specify cat values for different features. When parameter 'feature' is int or str, you can just pass list of cat values. plot: bool Plot statistics. max_cat_features_on_plot: int If categorical feature takes more than max_cat_features_on_plot different unique values, output result on several plots, not more than max_cat_features_on_plot feature values on each. Used only if plot=True or plot_file is not None. thread_count: int Number of threads to use for getting statistics. plot_file: str Output file for plot statistics. Returns ------- dict if parameter 'feature' is int or string, else dict of dicts: For each unique feature contain python dict with binarized feature statistics. For float feature, includes 'borders' -- borders for the specified feature in model 'binarized_feature' -- numbers of bins where feature values fall 'mean_target' -- mean value of target over each bin 'mean_prediction' -- mean value of model prediction over each bin 'objects_per_bin' -- number of objects per bin 'predictions_on_varying_feature' -- averaged over dataset predictions for varying feature (see above) For one-hot feature, returns the same, but with 'cat_values' instead of 'borders' Nrz$inconsistent size of data and targetz*inconsistent pool_names of data and targetget_binarized_statisticsrc) CrossEntropyre Probability RawFormulaVal)ClassrrExponentzUnknown prediction type "{}"z9cat_feature_values should be dict when features is a listTFrcrcatrprzfFeature '{}' is categorical. Please provide values for which you need statistics in cat_feature_values cat_valuesrTrzCatboost metrics graph) rgr'r_rUrrrhrrrDrrQrrnrwrVr$_get_feature_type_and_internal_index_get_binarized_statisticsr ri_get_cat_feature_valuesrk_calc_cat_feature_perfect_hashrrrr*r`_plot_feature_statisticsr r))r3rXtargetr~rcat_feature_valuesrAmax_cat_features_on_plotrkrBtarget_is_nonerzr pool_namesris_for_one_featurecat_features_numsfloat_features_numsfeature_type_mapperrfeature_name_to_num feature_num feature_typefeature_internal_index data_itemresultsstatistics_by_featureto_float_offset cat_index float_indexrrrcat_feature_values_rq val_to_hashhash hash_to_valresr3 return_statss) rcalc_feature_statisticsz CatBoost.calc_feature_statisticssD 4$%:D&$'&\FTc&k!9a;aa9$))+!=k?kk=99;C;;DIGacoqwq{q{|BFrGHLDIq .TYY[0Aj  "/3~~o/NRm/mm$  "W W > E Eo VW W ?))G  %!# 0$7gt,'(cdd*13E)F& gs #z'3'?yH!% H!&     Ggs+&&.'ATAT2T'(H(O(OPW(XYY"1177@ % --k:-'00.@.M"7+   )+6  (37<<3d3dep3q 0L0},!(()?@#**51#**+AB#**73) 6 !  i <<99        !!,D 1/0!"O;  !45GAt(+L-l;Ku}'99*.,,*N*NtTUwXc*d':M*N:M33:M'*N*<\*J'!"5{C'd #f .C'+||'R'RSVXijsXt'uK$/:E:K:K:MN:MYS$tSy:M N'0FAs3588U[\g\l\l\nUoNO#q 76> 9(*%##( *C  %$y*BSEJ(--/C(-.!3-B3-G-J%c*0 ()<]1=M)NO O G'3'(=g(F W%(=>QRY>Z([ W%  O!$+OOborder=blackellipseredrectutf-8rrrYesNo) graphvizrrr_replacedecodernoderedge)r3splits leaf_valuesrr_ layer_size current_size split_numnode_num node_labelrrparent edge_labels r_plot_oblivious_treezCatBoost._plot_oblivious_treess3$   s6{QB7I!*->!' !2!:!:68Q!O!W!WXackmn!oJ#E%E!,X!6J!E"E!+!2!27!;J 3|,jU S!#*Q.14F*6*:a*?TJJJs6{C ,=zJ! -.0 !OJ386 !s7C44 D?DcZddlm}|}fdfdd||S)Nrrcjdj|}|}|j||dd|S)Nzleaf_{}rrr)rr)node_idxr_cur_idrr node_to_leafs r plot_leafz3CatBoost._plot_nonsymmetric_tree..plot_leafs@%%l8&<=F$\(%;.plot_subtrees(#v- 511"))(3#H-55fhJRRS\^fhij  6:WI Nh'*a/(59H+Hz(7KA7N,NPUVH 68T2h'*a/(59H+Hz(7KA7N,NPUVH 68U3Mr)rr) r3rrrrrr_rrs ```` @@r_plot_nonsymmetric_treez CatBoost._plot_nonsymmetric_trees($    * Q rcB||j|ddnd\}}|j||}|j|}|jj r|j ||S|j |}|j|}|j||||S)N plot_treerJrra) rr\r_rr8rrbrdr)r3r]rrrrrrs rrzCatBoost.plot_treesZ^Zj$224SU2Vp|a&&x600: << % % ',,V[A A228rorrcrzBParameter '{}' currently is not supported in hyperparaneter searchrzNy may be None only when X is an instance of catboost.Pool, str or pathlib.Pathz-Parameter grid is not a dict or a list ({!r}))r+rrrrc__iter__splitaMcv should be one of possible things: - None, to use the default 3-fold cross validation, - integer, to specify the number of folds in a (Stratified)KFold - one of the scikit-learn splitter classes (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) - An iterable yielding (train, test) splits as arrays of indicesFzHyperparameters search plotrT)rr)rrDrergrrir`rrrsrDr TypeErrorrrmrrtis_cv_stratified_objectiverCAttributeErrorrDr7r_tune_hyperparamsrMr)r3 param_gridr+rcvn_iterpartition_random_seedcalc_cv_statisticssearch_by_train_test_splitrefitshuffle stratified train_sizerrArBrWrXcurrently_not_supported_paramsgrid_numgridparamrr custom_folds fold_countrc cv_results rrzCatBoost._tune_hyperparamssv T^^%!DE E x *. * *g.(\ "+J"7$(.(.;E}+,p,w,wx},~< #8y#$899yAzTG/C!D#$tuuj7H*=> O V VWa bcc55Q5?L!(+FLJ"m, & ?D A %!+M>+++6)H"56Ad+A+Bhgm+Bs,AI3EI+H<-AI<I IIc>t|tr|g}|D]d}t|tstdj||D]3}t||trtdj|||f|j ||||d||||| | | | | |||S)aW Exhaustive search over specified parameter values for a model. After calling this method model is fitted and can be used, if not specified otherwise (refit=False). Parameters ---------- param_grid: dict or list of dictionaries Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings. X: numpy.ndarray or pandas.DataFrame or catboost.Pool Data to compute statistics on y: list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Labels of the training data. If not None, can be a single- or two- dimensional array with either: - numerical values - for regression (including multiregression), ranking and binary classification problems - class labels (boolean, integer or string) - for classification (including multiclassification) problems Use only if X is not catboost.Pool and does not point to a file. cv: int, cross-validation generator or an iterable, optional (default=None) Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross validation, - integer, to specify the number of folds in a (Stratified)KFold - one of the scikit-learn splitter classes (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) - An iterable yielding (train, test) splits as arrays of indices. partition_random_seed: int, optional (default=0) Use this as the seed value for random permutation of the data. Permutation is performed before splitting the data for cross validation. Each seed generates unique data splits. Used only when cv is None or int. search_by_train_test_split: bool, optional (default=True) If True, source dataset is splitted into train and test parts, models are trained on the train part and parameters are compared by loss function score on the test part. After that, if calc_cv_statistics=true, statistics on metrics are calculated using cross-validation using best parameters and the model is fitted with these parameters. If False, every iteration of grid search evaluates results on cross-validation. It is recommended to set parameter to True for large datasets, and to False for small datasets. calc_cv_statistics: bool, optional (default=True) The parameter determines whether quality should be estimated. using cross-validation with the found best parameters. Used only when search_by_train_test_split=True. refit: bool (default=True) Refit an estimator using the best found parameters on the whole dataset. shuffle: bool, optional (default=True) Shuffle the dataset objects before parameters searching. stratified: bool, optional (default=None) Perform stratified sampling. True for classification and False otherwise. Currently supported only for final cross-validation. train_size: float, optional (default=0.8) Should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. verbose: bool or int, optional (default=True) If verbose is int, it determines the frequency of writing metrics to output verbose==True is equal to verbose==1 When verbose==False, there is no messages plot : bool, optional (default=False) If True, draw train and eval error for every set of parameters in Jupyter notebook plot_file : file-like or str, optional (default=None) If not None, save train and eval error for every set of parameters to file log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Returns ------- dict with two fields: 'params': dict of best found parameters 'cv_results': dict or pandas.core.frame.DataFrame with cross-validation results columns are: test-error-mean test-error-std train-error-mean train-error-std z#Parameter grid is not a dict ({!r})z;Parameter grid value is not iterable (key={!r}, value={!r})rJrr+rrrrrrrrrrrrArBrWrX)rgrrrrr)r3rr+rrrrrrrrrrrArBrWrXrrzs r grid_searchzCatBoost.grid_searchst j' *$JDdG, E L LT RSS!$s)X6#$a$h$hilnrsvnw$xyy%%!Q!2"7L^'AX_!j'PT`i &  rc4|dkr tdsJt|ts tdsJ|D]C}t||trt ||dr't dj ||||j||||||||| | | | | ||||S)a Randomized search on hyper parameters. After calling this method model is fitted and can be used, if not specified otherwise (refit=False). In contrast to grid_search, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. Parameters ---------- param_distributions: dict Dictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. X: numpy.ndarray or pandas.DataFrame or catboost.Pool Data to compute statistics on y: list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Labels of the training data. If not None, can be a single- or two- dimensional array with either: - numerical values - for regression (including multiregression), ranking and binary classification problems - class labels (boolean, integer or string) - for classification (including multiclassification) problems Use only if X is not catboost.Pool and does not point to a file. cv: int, cross-validation generator or an iterable, optional (default=None) Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross validation, - integer, to specify the number of folds in a (Stratified)KFold - one of the scikit-learn splitter classes (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) - An iterable yielding (train, test) splits as arrays of indices. n_iter: int Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution. partition_random_seed: int, optional (default=0) Use this as the seed value for random permutation of the data. Permutation is performed before splitting the data for cross validation. Each seed generates unique data splits. Used only when cv is None or int. search_by_train_test_split: bool, optional (default=True) If True, source dataset is splitted into train and test parts, models are trained on the train part and parameters are compared by loss function score on the test part. After that, if calc_cv_statistics=true, statistics on metrics are calculated using cross-validation using best parameters and the model is fitted with these parameters. If False, every iteration of grid search evaluates results on cross-validation. It is recommended to set parameter to True for large datasets, and to False for small datasets. calc_cv_statistics: bool, optional (default=True) The parameter determines whether quality should be estimated. using cross-validation with the found best parameters. Used only when search_by_train_test_split=True. refit: bool (default=True) Refit an estimator using the best found parameters on the whole dataset. shuffle: bool, optional (default=True) Shuffle the dataset objects before parameters searching. stratified: bool, optional (default=None) Perform stratified sampling. True for classification and False otherwise. Currently supported only for cross-validation. train_size: float, optional (default=0.8) Should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. verbose: bool or int, optional (default=True) If verbose is int, it determines the frequency of writing metrics to output verbose==True is equal to verbose==1 When verbose==False, there is no messages plot : bool, optional (default=False) If True, draw train and eval error for every set of parameters in Jupyter notebook plot_file : file-like or str, optional (default=None) If not None, save train and eval error for every set of parameters to file log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Returns ------- dict with two fields: 'params': dict of best found parameters 'cv_results': dict or pandas.core.frame.DataFrame with cross-validation results columns are: test-error-mean test-error-std train-error-mean train-error-std rz"n_iter should be a positive numberz*param_distributions should be a dictionaryrvszXParameter grid value is not iterable and do not have 'rvs' method (key={!r}, value={!r})r)rDrgrrrCrrr)r3param_distributionsr+rrrrrrrrrrrrArBrWrXrzs rrandomized_searchzCatBoost.randomized_search{sB Q; !EF FF-w7 !MN NN&C1#6A'RefiRjlqJr |!D!DEHJ]^aJb!cdd'%%*a1F"7L^'AX_!j'PT`i &  rc Z| r|jr td| td|#t|ttfzs tdt ||5|j |||| | }|d}|tj}ntt|j}||d<|tjk(r~t|tr/t|tsdjtt|}| td | td | td | td ||d <||d<nY| tdt|t s td| td| td| td||d<||d<|j#d}|duxrt|t$ }|r td|tt&|j|d<|||d<|tt(|j|d<| rd|d<|d}d}t+|ddkDr td t+|ddk(r|dd!}t-|j/}t1|g}t3|xsdD]@}|j5t6j8j|d"j;|B| r/|j5t6j8j|d#|D] }t1|t=| | d$|%5|j>jA|||}ddd| r|jC| r5tE}|d&jGd'|vr|d'jGdddS#1swY\xYw#1swYSxYw)(a Select best features from pool according to loss value. Parameters ---------- X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series If not catboost.Pool, 2 dimensional Feature matrix or string - file with dataset. y : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Labels of the training data. If not None, can be a single- or two- dimensional array with either: - numerical values - for regression (including multiregression), ranking and binary classification problems - class labels (boolean, integer or string) - for classification (including multiclassification) problems Use only if X is not catboost.Pool and does not point to a file. eval_set : catboost.Pool or list of catboost.Pool or tuple (X, y) or list [(X, y)], optional (default=None) Validation dataset or datasets for metrics calculation and possibly early stopping. features_for_select : str or list of feature indices, names or ranges (for grouping = Individual) Which features should participate in the selection. Format examples: - [0, 2, 3, 4, 17] - [0, "2-4", 17] (both ends in ranges are inclusive) - "0,2-4,20" - ["Name0", "Name2", "Name3", "Name4", "Name20"] num_features_to_select : positive int (for grouping = Individual) How many features to select from features_for_select. algorithm : EFeaturesSelectionAlgorithm or string, optional (default=RecursiveByShapValues) Which algorithm to use for features selection. Possible values: - RecursiveByPredictionValuesChange Use prediction values change as feature strength, eliminate batch of features at once. - RecursiveByLossFunctionChange Use loss function change as feature strength, eliminate batch of features at each step. - RecursiveByShapValues Use shap values to estimate loss function change, eliminate features one by one. steps : positive int, optional (default=1) How many steps should be performed. In other words, how many times a full model will be trained. More steps give more accurate results. shap_calc_type : EShapCalcType or string, optional (default=Regular) Which method to use for calculation of shap values. Possible values: - Regular Calculate regular SHAP values - Approximate Calculate approximate SHAP values - Exact Calculate exact SHAP values train_final_model : bool, optional (default=True) Need to fit model with selected features. verbose : bool or int If verbose is bool, then if set to True, logging_level is set to Verbose, if set to False, logging_level is set to Silent. If verbose is int, it determines the frequency of writing metrics to output and logging_level is set to Verbose. logging_level : string, optional (default=None) Possible values: - 'Silent' - 'Verbose' - 'Info' - 'Debug' plot : bool, optional (default=False) If True, draw train and eval error in Jupyter notebook. plot_file : file-like or str, optional (default=None) If not None, save train and eval error graphs to file log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used grouping : EFeaturesSelectionGrouping or string, optional (default=Individual) Which grouping to use for features selection. Possible values: - Individual Select individual features - ByTags Select feature groups (marked by tags) features_tags_for_select : list of strings (for grouping = ByTags) Which features tags should participate in the selection. num_features_tags_to_select : positive int (for grouping = ByTags) How many features tags to select from features_tags_for_select. Returns ------- dict with fields: 'selected_features': list of selected features indices 'eliminated_features': list of eliminated features indices 'selected_features_tags': list of selected features tags (optional, present if grouping == ByTags) 'eliminated_features_tags': list of selected features tags (optional, present if grouping == ByTags) zQModel was already fitted. Set train_final_model to False or use not fitted model.NrzOy may be None only when X is an instance of catboost.Pool, str or pathlib.Path.)r+rrrrrfeatures_selection_grouping,z&You should specify features_for_selectzKYou should not specify features_tags_for_select when grouping is Individualz)You should specify num_features_to_selectzNYou should not specify num_features_tags_to_select when grouping is Individualfeatures_for_selectnum_features_to_selectz+You should specify features_tags_for_selectz2features_tags_for_select must be a list of stringszBYou should not specify features_for_select when grouping is ByTagsz.You should specify num_features_tags_to_selectzEYou should not specify num_features_to_select when grouping is ByTagsfeatures_tags_for_selectnum_features_tags_to_selectrcz8Custom objective is not supported for features selectionfeatures_selection_algorithmfeatures_selection_stepsrTtrain_final_modelrrrz;Multiple eval sets are not supported for features selectionrzmodel-{}z model-finalzSelect features plotrrr=)$rrDrgrsrDrerrrrrvrrtr/rrrrr rrr_r7rr-rrVr)r*rrDr_select_featuresrrBshow)r3r+rrrr algorithmstepsrr rrrArBrWrXgroupingrrrrrbis_custom_objectiverrr1 plot_dirsstepplot_dirr@figuress rselect_featureszCatBoost.select_featuress^ !1 st t 9 45 5 9Z:+?@ qr r x *55Q[br5AL!(+F5@@01KXV\\8@455@@@18 class_weight. If several of 'classes_count', 'class_weights', 'class_names' parameters are defined the numbers of classes specified by each of them must be equal. auto_class_weights : string [default=None] Enables automatic class weights calculation. Possible values: - Balanced # weight = maxSummaryClassWeight / summaryClassWeight, statistics determined from train pool - SqrtBalanced # weight = sqrt(maxSummaryClassWeight / summaryClassWeight) class_names: list of strings, [default=None] Class names. Allows to redefine the default values for class labels (integer numbers). If several of 'classes_count', 'class_weights', 'class_names' parameters are defined the numbers of classes specified by each of them must be equal. one_hot_max_size : int, [default=None] Convert the feature to float if the number of different values that it takes exceeds the specified value. Ctrs are not calculated for such features. random_strength : float, [default=1] Score standard deviation multiplier. random_score_type : string [default=None] Type of random noise added to scores. Possible values: - 'Gumbel' - Gumbel-distributed - 'NormalWithModelSizeDecrease' - Normally-distributed with deviation decreasing with model iteration count If None than 'NormalWithModelSizeDecrease' will be used by default. name : string, [default='experiment'] The name that should be displayed in the visualization tools. ignored_features : list, [default=None] Indices or names of features that should be excluded when training. train_dir : string or pathlib.Path, [default=None] The directory in which you want to record generated in the process of learning files. custom_metric : string or list of strings, [default=None] To use your own metric function. custom_loss: alias to custom_metric eval_metric : string or object, [default=None] To optimize your custom metric in loss. bagging_temperature : float, [default=None] Controls intensity of Bayesian bagging. The higher the temperature the more aggressive bagging is. Typical values are in range [0, 1] (0 - no bagging, 1 - default). save_snapshot : bool, [default=None] Enable progress snapshotting for restoring progress after crashes or interruptions snapshot_file : string or pathlib.Path, [default=None] Learn progress snapshot file path, if None will use default filename snapshot_interval: int, [default=600] Interval between saving snapshots (seconds) fold_len_multiplier : float, [default=None] Fold length multiplier. Should be greater than 1 used_ram_limit : string or number, [default=None] Set a limit on memory consumption (value like '1.2gb' or 1.2e9). WARNING: Currently this option affects CTR memory usage only. gpu_ram_part : float, [default=0.95] Fraction of the GPU RAM to use for training, a value from (0, 1]. pinned_memory_size: int [default=None] Size of additional CPU pinned memory used for GPU learning, usually is estimated automatically, thus usually should not be set. allow_writing_files : bool, [default=True] If this flag is set to False, no files with different diagnostic info will be created during training. With this flag no snapshotting can be done. Plus visualisation will not work, because visualisation uses files that are created and updated during training. final_ctr_computation_mode : string, [default='Default'] Possible values: - 'Default' - Compute final ctrs for all pools. - 'Skip' - Skip final ctr computation. WARNING: model without ctrs can't be applied. approx_on_full_history : bool, [default=False] If this flag is set to True, each approximated value is calculated using all the preceeding rows in the fold (slower, more accurate). If this flag is set to False, each approximated value is calculated using only the beginning 1/fold_len_multiplier fraction of the fold (faster, slightly less accurate). boosting_type : string, default value depends on object count and feature count in train dataset and on learning mode. Boosting scheme. Possible values: - 'Ordered' - Gives better quality, but may slow down the training. - 'Plain' - The classic gradient boosting scheme. May result in quality degradation, but does not slow down the training. task_type : string, [default=None] The calcer type used to train the model. Possible values: - 'CPU' - 'GPU' device_config : string, [default=None], deprecated, use devices instead devices : list or string, [default=None], GPU devices to use. String format is: '0' for 1 device or '0:1:3' for multiple devices or '0-3' for range of devices. List format is : [0] for 1 device or [0,1,3] for multiple devices. bootstrap_type : string, Bayesian, Bernoulli, Poisson, MVS. Default bootstrap is Bayesian for GPU and MVS for CPU. Poisson bootstrap is supported only on GPU. MVS bootstrap is supported only on GPU. subsample : float, [default=None] Sample rate for bagging. This parameter can be used Poisson or Bernoully bootstrap types. mvs_reg : float, [default is set automatically at each iteration based on gradient distribution] Regularization parameter for MVS sampling algorithm monotone_constraints : list or numpy.ndarray or string or dict, [default=None] Monotone constraints for features. feature_weights : list or numpy.ndarray or string or dict, [default=None] Coefficient to multiply split gain with specific feature use. Should be non-negative. penalties_coefficient : float, [default=1] Common coefficient for all penalties. Should be non-negative. first_feature_use_penalties : list or numpy.ndarray or string or dict, [default=None] Penalties to first use of specific feature in model. Should be non-negative. per_object_feature_penalties : list or numpy.ndarray or string or dict, [default=None] Penalties for first use of feature for each object. Should be non-negative. sampling_frequency : string, [default=PerTree] Frequency to sample weights and objects when building trees. Possible values: - 'PerTree' - Before constructing each new tree - 'PerTreeLevel' - Before choosing each new split of a tree sampling_unit : string, [default='Object']. Possible values: - 'Object' - 'Group' The parameter allows to specify the sampling scheme: sample weights for each object individually or for an entire group of objects together. dev_score_calc_obj_block_size: int, [default=5000000] CPU only. Size of block of samples in score calculation. Should be > 0 Used only for learning speed tuning. Changing this parameter can affect results due to numerical accuracy differences dev_efb_max_buckets : int, [default=1024] CPU only. Maximum bucket count in exclusive features bundle. Should be in an integer between 0 and 65536. Used only for learning speed tuning. sparse_features_conflict_fraction : float, [default=0.0] CPU only. Maximum allowed fraction of conflicting non-default values for features in exclusive features bundle. Should be a real value in [0, 1) interval. grow_policy : string, [SymmetricTree,Lossguide,Depthwise], [default=SymmetricTree] The tree growing policy. It describes how to perform greedy tree construction. min_data_in_leaf : int, [default=1]. The minimum training samples count in leaf. CatBoost will not search for new splits in leaves with samples count less than min_data_in_leaf. This parameter is used only for Depthwise and Lossguide growing policies. max_leaves : int, [default=31], The maximum leaf count in resulting tree. This parameter is used only for Lossguide growing policy. score_function : string, possible values L2, Cosine, NewtonL2, NewtonCosine, [default=Cosine] For growing policy Lossguide default=NewtonL2. GPU only. Score that is used during tree construction to select the next tree split. max_depth : int, Synonym for depth. n_estimators : int, synonym for iterations. num_trees : int, synonym for iterations. num_boost_round : int, synonym for iterations. colsample_bylevel : float, synonym for rsm. random_state : int, synonym for random_seed. reg_lambda : float, synonym for l2_leaf_reg. objective : string, synonym for loss_function. num_leaves : int, synonym for max_leaves. min_child_samples : int, synonym for min_data_in_leaf eta : float, synonym for learning_rate. max_bin : float, synonym for border_count. scale_pos_weight : float, synonym for class_weights. Can be used only for binary classification. Sets weight multiplier for class 1 to scale_pos_weight value. metadata : dict, string to string key-value pairs to be stored in model metadata storage early_stopping_rounds : int Synonym for od_wait. Only one of these parameters should be set. cat_features : list or numpy.ndarray, [default=None] If not None, giving the list of Categ features indices or names (names are represented as strings). If it contains feature names, feature names must be defined for the training dataset passed to 'fit'. text_features : list or numpy.ndarray, [default=None] If not None, giving the list of Text features indices or names (names are represented as strings). If it contains feature names, feature names must be defined for the training dataset passed to 'fit'. embedding_features : list or numpy.ndarray, [default=None] If not None, giving the list of Embedding features indices or names (names are represented as strings). If it contains feature names, feature names must be defined for the training dataset passed to 'fit'. leaf_estimation_backtracking : string, [default=None] Type of backtracking during gradient descent. Possible values: - 'No' - never backtrack; supported on CPU and GPU - 'AnyImprovement' - reduce the descent step until the value of loss function is less than before the step; supported on CPU and GPU - 'Armijo' - reduce the descent step until Armijo condition is satisfied; supported on GPU only model_shrink_rate : float, [default=0] This parameter enables shrinkage of model at the start of each iteration. CPU only. For Constant mode shrinkage coefficient is calculated as (1 - model_shrink_rate * learning_rate). For Decreasing mode shrinkage coefficient is calculated as (1 - model_shrink_rate / iteration). Shrinkage coefficient should be in [0, 1). model_shrink_mode : string, [default=None] Mode of shrinkage coefficient calculation. CPU only. Possible values: - 'Constant' - Shrinkage coefficient is constant at each iteration. - 'Decreasing' - Shrinkage coefficient decreases at each iteration. langevin : bool, [default=False] Enables the Stochastic Gradient Langevin Boosting. CPU only. diffusion_temperature : float, [default=0] Langevin boosting diffusion temperature. CPU only. posterior_sampling : bool, [default=False] Set group of parameters for further use Uncertainty prediction: - Langevin = True - Model Shrink Rate = 1/(2N), where N is dataset size - Model Shrink Mode = Constant - Diffusion-temperature = N, where N is dataset size. CPU only. boost_from_average : bool, [default=True for RMSE, False for other losses] Enables to initialize approx values by best constant value for specified loss function. Available for RMSE, Logloss, CrossEntropy, Quantile and MAE. tokenizers : list of dicts, Each dict is a tokenizer description. Example: ``` [ { 'tokenizer_id': 'Tokenizer', # Tokeinzer identifier. 'lowercasing': 'false', # Possible values: 'true', 'false'. 'number_process_policy': 'LeaveAsIs', # Possible values: 'Skip', 'LeaveAsIs', 'Replace'. 'number_token': '%', # Rarely used character. Used in conjunction with Replace NumberProcessPolicy. 'separator_type': 'ByDelimiter', # Possible values: 'ByDelimiter', 'BySense'. 'delimiter': ' ', # Used in conjunction with ByDelimiter SeparatorType. 'split_by_set': 'false', # Each single character in delimiter used as individual delimiter. 'skip_empty': 'true', # Possible values: 'true', 'false'. 'token_types': ['Word', 'Number', 'Unknown'], # Used in conjunction with BySense SeparatorType. # Possible values: 'Word', 'Number', 'Punctuation', 'SentenceBreak', 'ParagraphBreak', 'Unknown'. 'subtokens_policy': 'SingleToken', # Possible values: # 'SingleToken' - All subtokens are interpreted as single token). # 'SeveralTokens' - All subtokens are interpreted as several token. }, ... ] ``` dictionaries : list of dicts, Each dict is a tokenizer description. Example: ``` [ { 'dictionary_id': 'Dictionary', # Dictionary identifier. 'token_level_type': 'Word', # Possible values: 'Word', 'Letter'. 'gram_order': '1', # 1 for Unigram, 2 for Bigram, ... 'skip_step': '0', # 1 for 1-skip-gram, ... 'end_of_word_token_policy': 'Insert', # Possible values: 'Insert', 'Skip'. 'end_of_sentence_token_policy': 'Skip', # Possible values: 'Insert', 'Skip'. 'occurrence_lower_bound': '3', # The lower bound of token occurrences in the text to include it in the dictionary. 'max_dictionary_size': '50000', # The max dictionary size. }, ... ] ``` feature_calcers : list of strings, Each string is a calcer description. Example: ``` [ 'NaiveBayes', 'BM25', 'BoW:top_tokens_count=2000', ] ``` text_processing : dict, Text processging description. eval_fraction : float, [default=None] Fraction of the train dataset to be used as the evaluation dataset. rvcxi}xgd}yttjD]\}z}{|zyvs {{xz<tt|xyN) not_paramsr3rrm)r rrrWr$r4)}r3rTrKrMrRmodel_size_regrOrcrrrroutput_bordersfold_permutation_blockrrXrrcounter_calc_methodleaf_estimation_iterationsleaf_estimation_methodrkrrbest_model_min_treesrrrrctr_leaf_count_limitstore_all_simple_ctrmax_ctr_complexityrallow_const_labelrw classes_countrfrgrhone_hot_max_sizerandom_strengthrandom_score_typerrr1r[rZrobagging_temperaturerrrfold_len_multiplierr gpu_ram_partpinned_memory_sizeallow_writing_filesfinal_ctr_computation_modeapprox_on_full_history boosting_type simple_ctrcombinations_ctrper_feature_ctrrrr device_configdevicesbootstrap_type subsamplemvs_reg sampling_unitsampling_frequencydev_score_calc_obj_block_sizerrrNrUrVrWrPrQrSrbrLrrdgpu_cat_features_storagedata_partitionmetadatarYrY grow_policyr^r_r\r]score_functionleaf_estimation_backtrackingctr_history_unitrrpenalties_coefficientrrmodel_shrink_ratemodel_shrink_modelangevindiffusion_temperatureposterior_samplingboost_from_averagerZ tokenizers dictionariesfeature_calcerstext_processingr[callbackrrrr'rzrvrms} rr4zCatBoostClassifier.__init__sStB #FHMMO4JC*$):#s 5  $08rc$|jj}t|d|vrtj |d|j g|||||d||dddd|| | | | | ||||||||||||||S)a1 Fit the CatBoostClassifier model. Parameters ---------- X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series If not catboost.Pool, 2 dimensional Feature matrix or string - file with dataset. y : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Labels of the training data. If not None, can be a single- or two- dimensional array with either: - numerical values - for binary classification problems - class labels (boolean, integer or string) Use only if X is not catboost.Pool and does not point to a file. cat_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Categ columns indices. Use only if X is not catboost.Pool. text_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Text columns indices. Use only if X is not catboost.Pool. embedding_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Embedding columns indices. Use only if X is not catboost.Pool. graph : list or numpy.ndarray or pandas.DataFrame The graph edges list description. If list or numpy.ndarrays or pandas.DataFrame, giving 2 dimensional. sample_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Instance weights, 1 dimensional array like. baseline : list or numpy.ndarray, optional (default=None) If not None, giving 2 dimensional array like data. Use only if X is not catboost.Pool. use_best_model : bool, optional (default=None) Flag to use best model eval_set : catboost.Pool or list of catboost.Pool or tuple (X, y) or list [(X, y)], optional (default=None) Validation dataset or datasets for metrics calculation and possibly early stopping. metric_period : int Frequency of evaluating metrics. verbose : bool or int If verbose is bool, then if set to True, logging_level is set to Verbose, if set to False, logging_level is set to Silent. If verbose is int, it determines the frequency of writing metrics to output and logging_level is set to Verbose. silent : bool If silent is True, logging_level is set to Silent. If silent is False, logging_level is set to Verbose. logging_level : string, optional (default=None) Possible values: - 'Silent' - 'Verbose' - 'Info' - 'Debug' plot : bool, optional (default=False) If True, draw train and eval error in Jupyter notebook plot_file : file-like or str, optional (default=None) If not None, save train and eval error graphs to file verbose_eval : bool or int Synonym for verbose. Only one of these parameters should be set. early_stopping_rounds : int Activates Iter overfitting detector with od_wait set to early_stopping_rounds. save_snapshot : bool, [default=None] Enable progress snapshotting for restoring progress after crashes or interruptions snapshot_file : string or pathlib.Path, [default=None] Learn progress snapshot file path, if None will use default filename snapshot_interval: int, [default=600] Interval between saving snapshots (seconds) init_model : CatBoost class or string or pathlib.Path, [default=None] Continue training starting from the existing model. If this parameter is a string or pathlib.Path, load initial model from the path specified by this string. callbacks : list, optional (default=None) List of callback objects that are applied at end of each iteration. log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Returns ------- model : CatBoost rcN)rrrr$_check_is_compatible_lossrr3r+rrYrZr[r_r,rhrrrrrArBr]rrrrYrrrrrrWrXrs rrzCatBoostClassifier.fit ssX""'')&! f $  8 89P Q  M! MQ M  Mm M5G M Mu MVc Mei Mko Mqu Mw{ M~F MHV M M# M%2 M48 M:C MEW MYe Mgt M M/ M1> M@M MO` Mbl Mnw MzB MDL M rc 2|j||||||d|S)aI Predict with data. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. prediction_type : string, optional (default='Class') Can be: - 'RawFormulaVal' : return raw formula value. - 'Class' : return class label. - 'Probability' : return probability for every class. - 'LogProbability' : return log probability for every class. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool, optional (default=False) If True, writes the evaluation metric measured set to stderr. task_type : string, [default=None] The evaluator type. Possible values: - 'CPU' - 'GPU' (models with only numerical features are supported for now) Returns ------- prediction: If data is for a single object, the return value depends on prediction_type value: - 'RawFormulaVal' : return raw formula value. - 'Class' : return class label. - 'Probability' : return one-dimensional numpy.ndarray with probability for every class. - 'LogProbability' : return one-dimensional numpy.ndarray with log probability for every class. otherwise numpy.ndarray, with values that depend on prediction_type value: - 'RawFormulaVal' : one-dimensional array of raw formula value for each object. - 'Class' : one-dimensional array of class label for each object. - 'Probability' : two-dimensional numpy.ndarray with shape (number_of_objects x number_of_classes) with probability for every class for each object. - 'LogProbability' : two-dimensional numpy.ndarray with shape (number_of_objects x number_of_classes) with log probability for every class for each object. rrrs rrzCatBoostClassifier.predicts%r}}T?KLZaclnwxxrc 2|j|d||||d|S)a Predict class probability with X. Parameters ---------- X : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If X is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool If True, writes the evaluation metric measured set to stderr. task_type : string, [default=None] The evaluator type. Possible values: - 'CPU' - 'GPU' (models with only numerical features are supported for now) Returns ------- prediction : If X is for a single object return one-dimensional numpy.ndarray with probability for every class. otherwise return two-dimensional numpy.ndarray with shape (number_of_objects x number_of_classes) with probability for every class for each object. r predict_probar)r3r+rrrkrrs rrbz CatBoostClassifier.predict_probas%T}}Q {I|U\^moxyyrc 2|j|d||||d|S)a Predict class log probability with data. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool If True, writes the evaluation metric measured set to stderr. task_type : string, [default=None] The evaluator type. Possible values: - 'CPU' - 'GPU' (models with only numerical features are supported for now) Returns ------- prediction : If data is for a single object return one-dimensional numpy.ndarray with log probability for every class. otherwise return two-dimensional numpy.ndarray with shape (number_of_objects x number_of_classes) with log probability for every class for each object. rpredict_log_probar)r3rXrrrkrrs rrdz$CatBoostClassifier.predict_log_probas0T}}T#3[)\[bdwzCD Drc 2|j|||||||dS)a Predict target at each stage for data. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. prediction_type : string, optional (default='Class') Can be: - 'RawFormulaVal' : return raw formula value. - 'Class' : return class label. - 'Probability' : return probability for every class. - 'LogProbability' : return log probability for every class. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. eval_period: int, optional (default=1) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool If True, writes the evaluation metric measured set to stderr. Returns ------- prediction : generator for each iteration that generates: If data is for a single object, the return value depends on prediction_type value: - 'RawFormulaVal' : return raw formula value. - 'Class' : return majority vote class. - 'Probability' : return one-dimensional numpy.ndarray with probability for every class. - 'LogProbability' : return one-dimensional numpy.ndarray with log probability for every class. otherwise numpy.ndarray, with values that depend on prediction_type value: - 'RawFormulaVal' : one-dimensional array of raw formula value for each object. - 'Class' : one-dimensional array of class label for each object. - 'Probability' : two-dimensional numpy.ndarray with shape (number_of_objects x number_of_classes) with probability for every class for each object. - 'LogProbability' : two-dimensional numpy.ndarray with shape (number_of_objects x number_of_classes) with log probability for every class for each object. rrrs rrz!CatBoostClassifier.staged_predicts2l##D/; S^`lnuxHI Irc 2|j|d|||||dS)aO Predict classification target at each stage for data. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. eval_period: int, optional (default=1) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool If True, writes the evaluation metric measured set to stderr. Returns ------- prediction : generator for each iteration that generates: If data is for a single object return one-dimensional numpy.ndarray with probability for every class. otherwise return two-dimensional numpy.ndarray with shape (number_of_objects x number_of_classes) with probability for every class for each object. rstaged_predict_probarr3rXrrrrkrs rrgz'CatBoostClassifier.staged_predict_probaMs2N##D-iQ\^jlsvLM Mrc 2|j|d|||||dS)aW Predict classification target at each stage for data. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. eval_period: int, optional (default=1) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool If True, writes the evaluation metric measured set to stderr. Returns ------- prediction : generator for each iteration that generates: If data is for a single object return one-dimensional numpy.ndarray with log probability for every class. otherwise return two-dimensional numpy.ndarray with shape (number_of_objects x number_of_classes) with log probability for every class for each object. rstaged_predict_log_probarrhs rrjz+CatBoostClassifier.staged_predict_log_probavs3N##D*:KT_amovyST Trc t|tr*| td|j}| tdt|trXt |j dk7r-tdjt |j ||j d}n | tdtj|}|j|dddd dd jd }tj|jtjr9tj|jtjrtd |jtj k(r9tj|jtjr}td tj|jtjr tdtj|jtj r tdtj"tj|tj|k(S)a= Calculate accuracy. Parameters ---------- X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series Data to apply model on. y : list or numpy.ndarray True labels. Returns ------- accuracy : float N\Wrong initializing y: X is catboost.Pool object, y must be initialized inside catboost.Pool.Label in X has not initialized.rz=y is DataFrame and has {} columns, but must have exactly one.ry should be specified.rrJscorerrrrkrrzDpredicted classes have numeric type but specified y contains stringszDpredicted classes have boolean type but specified y contains stringsz=predicted classes have string type but specified y is numericz=predicted classes have string type but specified y is boolean)rgrDrDr|rr_rrrrrr issubdtyperPnumber characterbool_mean)r3r+rpredicted_classess rrozCatBoostClassifier.scores a }#%CDD Ay#$EFF a #199~"#$c$j$jknopoxoxky$z{{!))A,A Y 89 9 HHQK MM #&*  '"+  ==*00")) <}}QWWbll3#$jkk  $ $ 0}}QWWbll3#$jkk}}QWWbii0#$cddqww1#$cddwwrxx 12bhhqkABBrc.|js td|j}|d|jvr|d=yyt |t s tdd|cxkr dksJdJdt ||jd<y)z Set a threshold for class separation in binary classification task for a trained model. :param binclass_probability_threshold: float number in [0, 1] or None to discard it z9You can't set probability threshold for not fitted model.Nbinclass_probability_thresholdz3binclass_probability_threshold must have float typerrz^Please provide correct probability for binclass_probability_threshold argument in [0, 1] range)rrDrDrUrgror)r3rxrLs rset_probability_thresholdz,CatBoostClassifier.set_probability_thresholds ~~ [\ \$$& ) 1/8==?B=>Cr?r@rArrrrBrCrDrErFrHrGrIrrrNrUrVrWrPrQrSrbrLrrJrKrLrYrYrMr^r_r\r]rNrOrPrrrQrrrRrSrTrUrVrWrZrXrYrZr[r[rrrr'rzrvrmsv rr4zCatBoostRegressor.__init__sSfB #FHMMO4JC*$):#s 5 /7rct|j}t|d|vrtj |d|j g|||||d||dddd|| | | | | |||||||||||||S)a Fit the CatBoost model. Parameters ---------- X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series If not catboost.Pool, 2 dimensional Feature matrix or string - file with dataset. y : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Labels of the training data. If not None, can be a single- or two- dimensional array with numerical values. Use only if X is not catboost.Pool and does not point to a file. cat_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Categ columns indices. Use only if X is not catboost.Pool. text_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Text columns indices. Use only if X is not catboost.Pool. embedding_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Embedding columns indices. Use only if X is not catboost.Pool. graph : list or numpy.ndarray or pandas.DataFrame The graph edges list description. If list or numpy.ndarrays or pandas.DataFrame, giving 2 dimensional. sample_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Instance weights, 1 dimensional array like. baseline : list or numpy.ndarray, optional (default=None) If not None, giving 2 dimensional array like data. Use only if X is not catboost.Pool. use_best_model : bool, optional (default=None) Flag to use best model eval_set : catboost.Pool or list of catboost.Pool or tuple (X, y) or list [(X, y)], optional (default=None) Validation dataset or datasets for metrics calculation and possibly early stopping. metric_period : int Frequency of evaluating metrics. verbose : bool or int If verbose is bool, then if set to True, logging_level is set to Verbose, if set to False, logging_level is set to Silent. If verbose is int, it determines the frequency of writing metrics to output and logging_level is set to Verbose. silent : bool If silent is True, logging_level is set to Silent. If silent is False, logging_level is set to Verbose. logging_level : string, optional (default=None) Possible values: - 'Silent' - 'Verbose' - 'Info' - 'Debug' plot : bool, optional (default=False) If True, draw train and eval error in Jupyter notebook plot_file : file-like or str, optional (default=None) If not None, save train and eval error graphs to file (requires installed plotly) verbose_eval : bool or int Synonym for verbose. Only one of these parameters should be set. early_stopping_rounds : int Activates Iter overfitting detector with od_wait set to early_stopping_rounds. save_snapshot : bool, [default=None] Enable progress snapshotting for restoring progress after crashes or interruptions snapshot_file : string or pathlib.Path, [default=None] Learn progress snapshot file path, if None will use default filename snapshot_interval: int, [default=600] Interval between saving snapshots (seconds) init_model : CatBoost class or string or pathlib.Path, [default=None] Continue training starting from the existing model. If this parameter is a string or pathlib.Path, load initial model from the path specified by this string. callbacks : list, optional (default=None) List of callback objects that are applied at end of each iteration. log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Returns ------- model : CatBoost rcN)rrrrr^rr_s rrzCatBoostRegressor.fitsV$++,&! f $  7 78O PtyyuuAu|u]uSu'u)6u8IuKUuW`ubjultu urc V||j}|j||||||d|S)aL Predict with data. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. prediction_type : string, optional (default='RawFormulaVal') Can be: - 'RawFormulaVal' : return raw formula value. - 'Exponent' : return Exponent of raw formula value. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool If True, writes the evaluation metric measured set to stderr. task_type : string, [default=None] The evaluator type. Possible values: - 'CPU' - 'GPU' (models with only numerical features are supported for now) Returns ------- prediction : If data is for a single object, the return value is single float formula return value otherwise one-dimensional numpy.ndarray of formula return values for each object. r)_get_default_prediction_typerrs rrzCatBoostRegressor.predicts9X  ""??AO}}T?KLZaclnwxxrc 2|j|||||||dS)a Predict target at each stage for data. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. eval_period: int, optional (default=1) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool If True, writes the evaluation metric measured set to stderr. Returns ------- prediction : generator for each iteration that generates: If data is for a single object, the return value is single float formula return value otherwise one-dimensional numpy.ndarray of formula return values for each object. rrrs rrz CatBoostRegressor.staged_predict&s2H##D/; S^`lnuxHI Irc t|tr*| td|j}|td| tdt j |tj }|j||jddddd }|jj}|d k(r |dddf}|j|jk7r1d }t|j|j|j|j|j}t j||j!d z d z}t j||z d z}d||z z S)a3 Calculate R^2. Parameters ---------- X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series Data to apply model on. y : list or numpy.ndarray True labels. Returns ------- R^2 : float NrlrmrnrrrJrorprzLlabels and predictions should have same size. But y.size={} != preds.size={})axisrr)rgrDrDr|rrfloat64rrrrrrrrrru)r3r+rrrmsgtotal_sum_of_squaresresidual_sum_of_squaress rrozCatBoostRegressor.scoreLs] a }#%CDD Ay#$EFF Y 89 9 HHQbjj )mm  ==?&$ ||335 ( (%ad+K   qvv %`C 166;3C3C DE E IIk'' (!vvq166q6>'9a&?@"$&&!k/a)?"@*-AAAArctj|xs,tj|xstj|}t |t r|st dj|yy)NzInvalid loss_function='{}': for regressor use RMSE, MultiRMSE, SurvivalAft, MAE, Quantile, LogLinQuantile, Poisson, MAPE, Lq or custom objective object)rr5r8r>rgrrDr)rc is_regressions rr^z+CatBoostRegressor._check_is_compatible_lossxs 99-H~HLrLrtAMB~FNFoFop}F~ mS )-!LMSMSTaMbc c3@ )rct|j}t||jd}|r9t |t r)|j ds|j dry|dk(ryy)NrcPoissonTweedierrr)rrrrrgr startswith)r3rrcs rrz.CatBoostRegressor._get_default_prediction_typesa$++,&! ?3 Z s;'' 2m6N6Ny6Y! 55,r)pNNNNNNr{NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNr~)NrrrJNrrr()rrrrrr4rrrrorbr^rr&r's@rrrs$"O  '+# #'#!!!    #'# $&* *. !%"%)!"$(%)" cy8vdh>BlpVZgk$( ruh.y`$IL*BXcc rrcFeZdZdZdZ d fd Z d dZd dZd dZd dZ e dZ xZ S)CatBoostRankera Implementation of the scikit-learn API for CatBoost ranking. Parameters ---------- Like in CatBoostClassifier, except loss_function, classes_count, class_names and class_weights loss_function : string, [default='YetiRank'] 'YetiRank' 'YetiRankPairwise' 'StochasticFilter' 'StochasticRank' 'QueryCrossEntropy' 'QueryRMSE' 'GroupQuantile' 'QuerySoftMax' 'PairLogit' 'PairLogitPairwise' rycqi}qgd}rttjD]\}s}t|srvs ttqs<tt|qyr&)r rrrWrr4rsv rr4zCatBoostRanker.__init__sRfB #FHMMO4JC*$):#s 5 nd,V4rc t|j} t| d| vrtj | d|j g|||||||| || | | | |||||||||||||||||||S)aC Fit the CatBoostRanker model. Parameters ---------- X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series If not catboost.Pool, 2 dimensional Feature matrix or string - file with dataset. y : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Labels of the training data. If not None, can be a single-dimensional array with numerical values. Use only if X is not catboost.Pool and does not point to a file. group_id : numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Ranking groups, 1 dimensional array like. Use only if X is not catboost.Pool. cat_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Categ columns indices. Use only if X is not catboost.Pool. text_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Text columns indices. Use only if X is not catboost.Pool. embedding_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Embedding columns indices. Use only if X is not catboost.Pool. pairs : list or numpy.ndarray or pandas.DataFrame, optional (default=None) The pairs description in the form of a two-dimensional matrix of shape N by 2: N is the number of pairs. The first element of the pair is the zero-based index of the winner object from the input dataset for pairwise comparison. The second element of the pair is the zero-based index of the loser object from the input dataset for pairwise comparison. graph : list or numpy.ndarray or pandas.DataFrame The graph edges list description. If list or numpy.ndarrays or pandas.DataFrame, giving 2 dimensional. sample_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None) Instance weights, 1 dimensional array like. group_weight : list or numpy.ndarray (default=None) The weights of all objects within the defined groups from the input data in the form of one-dimensional array-like data. Used for calculating the final values of trees. By default, it is set to 1 for all objects in all groups. Only a weight or group_weight parameter can be used at a time subgroup_id : list or numpy.ndarray (default=None) Subgroup identifiers for all input objects. Supported identifier types are: int string types (string or unicode for Python 2 and bytes or string for Python 3). pairs_weight : list or numpy.ndarray (default=None) The weight of each input pair of objects in the form of one-dimensional array-like pairs. The number of given values must match the number of specified pairs. This information is used for calculation and optimization of Pairwise metrics . By default, it is set to 1 for all pairs. baseline : list or numpy.ndarray, optional (default=None) If not None, giving 2 dimensional array like data. Use only if X is not catboost.Pool. use_best_model : bool, optional (default=None) Flag to use best model eval_set : catboost.Pool or list of catboost.Pool or tuple (X, y) or list [(X, y)], optional (default=None) Validation dataset or datasets for metrics calculation and possibly early stopping. verbose : bool or int If verbose is bool, then if set to True, logging_level is set to Verbose, if set to False, logging_level is set to Silent. If verbose is int, it determines the frequency of writing metrics to output and logging_level is set to Verbose. logging_level : string, optional (default=None) Possible values: - 'Silent' - 'Verbose' - 'Info' - 'Debug' plot : bool, optional (default=False) If True, draw train and eval error in Jupyter notebook plot_file : file-like or str, optional (default=None) If not None, save train and eval error graphs to file verbose_eval : bool or int Synonym for verbose. Only one of these parameters should be set. metric_period : int Frequency of evaluating metrics. silent : bool If silent is True, logging_level is set to Silent. If silent is False, logging_level is set to Verbose. early_stopping_rounds : int Activates Iter overfitting detector with od_wait set to early_stopping_rounds. save_snapshot : bool, [default=None] Enable progress snapshotting for restoring progress after crashes or interruptions snapshot_file : string or pathlib.Path, [default=None] Learn progress snapshot file path, if None will use default filename snapshot_interval: int, [default=600] Interval between saving snapshots (seconds) init_model : CatBoost class or string or pathlib.Path, [default=None] Continue training starting from the existing model. If this parameter is a string or pathlib.Path, load initial model from the path specified by this string. callbacks : list, optional (default=None) List of callback objects that are applied at end of each iteration. log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Returns ------- model : CatBoost rc)rrrrr^r)!r3r+rrdrYrZr[r^r_r,rerfrgrhrrrrrArBr]rrrrYrrrrrrWrXrs! rrzCatBoostRanker.fits~T$++,&! f $  4 4VO5L M  M! MQ M  Mm M5G M MPU M M!) M+7 M9D MFR MT\ M^l M M# M%2 M48 M:C MEW MYe Mgt M M0 M2? MAN MPa Mcm Mox MzB MDL M rc 0|j|d||||dS)a Predict with data. Parameters ---------- X : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool If True, writes the evaluation metric measured set to stderr. Returns ------- prediction : If data is for a single object, the return value is single float formula return value otherwise one-dimensional numpy.ndarray of formula return values for each object. rrr)r3r+rrrkrs rrzCatBoostRanker.predicts!6}}QiW^`ijjrc 2|j|d|||||dS)a Predict target at each stage for data. Parameters ---------- X : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData Data to apply model on. If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted as a list of features for a single object. ntree_start: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). ntree_end: int, optional (default=0) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. eval_period: int, optional (default=1) Model is applied on the interval [ntree_start, ntree_end) with the step eval_period (zero-based indexing). thread_count : int (default=-1) The number of threads to use when applying the model. Allows you to optimize the speed of execution. This parameter doesn't affect results. If -1, then the number of threads is set to the number of CPU cores. verbose : bool If True, writes the evaluation metric measured set to stderr. Returns ------- prediction : generator for each iteration that generates: If data is for a single object, the return value is single float formula return value otherwise one-dimensional numpy.ndarray of formula return values for each object. rrr)r3r+rrrrkrs rrzCatBoostRanker.staged_predicts1:##A YP[]ikruEF Frc :d} t|tr:| td|j}| td|j }| td| td|j |} t |g| g| |||ggdd||dd| dS) a Calculate NDCG@top Parameters ---------- X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series Data to apply model on. y : list or numpy.ndarrays or pandas.DataFrame or pandas.Series True labels. group_id : list or numpy.ndarray or pandas.DataFrame or pandas.Series Ranking groups. If X is a Pool, group_id must be defined into X top : unsigned integer, up to `pow(2, 32) / 2 - 1` NDCG, Number of top-ranked objects to calculate NDCG type : str NDCG, Metric_type: 'Base' or 'Exp' denominator : str NDCG, Denominator type: 'LogPosition' or 'Position' group_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series Group weights. thread_count : int, optional (default=-1) Number of threads to work with. Returns ------- NDCG@top : float higher is better c tjtj|dryddjt ||Dcgc]\}}| dj ||c}}zScc}}w)NNDCGzNDCG:;z{}={})rrequalr/ror)rnamesrHns rget_ndcg_metric_namez2CatBoostRanker.score..get_ndcg_metric_namesavvbhhvt,-SXXFTYHZ&lHZ1^_^kw~~a';HZ&lmm m&ls  A/ A/ NrlzjWrong initializing group_id: X is catboost.Pool object, group_id must be initialized inside catboost.Pool.zy must be initialized.zPgroup_id must be initialized. If groups are not expected, pass an array of zeros)rr denominatorr)rgrDrDr|get_group_id_hashr_eval_metric_util) r3r+rrdrrrrerkrrs rrozCatBoostRanker.scores4 n a }#%CDD A##%QRR**,H 9 89 9   rs sll1o  !{m5I3PTVaJbeC6DFJLTVbdhjnp|}~@ @rctj|}tj|}|r)tjdj |t |s|stdj |yy)NzHRegression loss ('{}') ignores an important ranking parameter 'group_id'zInvalid loss_function='{}': for ranker use YetiRank, YetiRankPairwise, StochasticFilter, StochasticRank, QueryCrossEntropy, QueryRMSE, GroupQuantile, QuerySoftMax, PairLogit, PairLogitPairwise. It's also possible to use a regression loss)rrAr5rrrRuntimeWarningrD)rc is_rankingrs rr^z(CatBoostRanker._check_is_compatible_losssx33MB  99-H  MMdkklyz}K Lm!NOUfUbNce e, r)pNNNNNNrzNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNr)rrrJNr)NNNNNNrJ) rrrrrr4rrrrorbr^r&r's@rrrs$O   '+# #'#!!!    #'# $&* *. !%"%)!"$(%)" cy5vNR^bOSlpVZgk$( sjk:F>-@^ e errc~| td|||}n td|||}n td|t|}|jd|i| -|jddid|vr|d=|jd| i|| td |}t|}|j |||| | || | | |||||| |S) ai Train CatBoost model. Parameters ---------- params : dict Parameters for CatBoost. If None, all params are set to their defaults. If dict, overriding parameters present in the dict. pool : catboost.Pool or tuple (X, y) Data to train on. iterations : int Number of boosting iterations. Can be set in params dict. evals : catboost.Pool or list of catboost.Pool or tuple (X, y) or list [(X, y)] Synonym for eval_set. Only one of these parameters should be set. dtrain : catboost.Pool or tuple (X, y) Synonym for pool parameter. Only one of these parameters should be set. logging_level : string, optional (default=None) Possible values: - 'Silent' - 'Verbose' - 'Info' - 'Debug' metric_period : int Frequency of evaluating metrics. verbose : bool or int If verbose is bool, then if set to True, logging_level is set to Verbose, if set to False, logging_level is set to Silent. If verbose is int, it determines the frequency of writing metrics to output and logging_level is set to Verbose. verbose_eval : bool or int Synonym for verbose. Only one of these parameters should be set. iterations : int Number of boosting iterations. Can be set in params dict. num_boost_round : int Synonym for iterations. Only one of these parameters should be set. eval_set : catboost.Pool or list of catboost.Pool or tuple (X, y) or list [(X, y)] Validation dataset or datasets for metrics calculation and possibly early stopping. plot : bool, optional (default=False) If True, draw train and eval error in Jupyter notebook ` plot_file : file-like or str, optional (default=None) If not None, save train and eval error graphs to file early_stopping_rounds : int Activates Iter overfitting detector with od_wait set to early_stopping_rounds. save_snapshot : bool, [default=None] Enable progress snapshotting for restoring progress after crashes or interruptions snapshot_file : string or pathlib.Path, [default=None] Learn progress snapshot file path, if None will use default filename snapshot_interval: int, [default=600] Interval between saving snapshots (seconds) init_model : CatBoost class or string or pathlib.Path, [default=None] Continue training starting from the existing model. If this parameter is a string or pathlib.Path, load initial model from the path specified by this string. log_cout: output stream or callback for logging (default=None) If None is specified, sys.stdout is used log_cerr: error stream or callback for logging (default=None) If None is specified, sys.stderr is used Returns ------- model : CatBoost class params should be set.9Only one of the parameters pool and dtrain should be set.HOnly one of the parameters iterations and num_boost_round should be set.rTrrrrXz9Only one of the parameters evals, eval_set should be set.)r+rrrArBrrrrYrrrrrWrX)rDrrrr)rrdtrainrrrTrVevalsrrArBrrrYrrrrrWrXrs rtrainr s#n~344  <D [\ \"  (J jk k&! *  ( v    y! ,      [\ \ V E IIx}4[dnu'}$9)=N[e( 4 Lrc|g}|D]4}t}||_|j|j|6|S)z6 Convert _Catboost instances to Catboost ones )rrrrV)models output_modelsrcb_models r_convert_to_catboostrsDM: ..0X&  rc| dkDsJ|dkDsJ|dkDsJ|dkDsJ|d}tjj|}|jdd|}|jdd|}t |}|| z dz|z }g}t j }|j}t|D]}|j||}t||| |dd| d dd d |z |dd d }|j|||||d |j|d|t|dd5}tj|}ddddD]s}t!t#|d|dD]S\} \}!}"|jtj$|tj$t'd |"z |d| <Uut|d5}tj(||ddd|j+|d|j-\}#}$|j/|#| ztj$| z |$| ztj$| z ||j1|z |j| |z}%t|||| |dd| |dd ||dd d }&|&j||%||||d |j3t5||&gd d g|S#1swYxYw#1swYxYw)a Implementation of Gaussian process sampling (Kernel Gradient Boosting/Algorithm 4) from "Gradient Boosting Performs Gaussian Process Inference" https://arxiv.org/abs/2206.05608 Produces samples from posterior GP with prior assumption f ~ GP(0, sigma ** 2 K + delta ** 2 I) Parameters ---------- X : list or numpy.ndarray or pandas.DataFrame or pandas.Series or catboost.FeaturesData If catboost.FeaturesData it must be 2 dimensional Feature matrix Must be non-empty (contain > 0 objects) y : list or numpy.ndarray or pandas.DataFrame or pandas.Series Labels of the training data. Must be a single-dimensional array with numerical values. eval_set : catboost.Pool or list of catboost.Pool or tuple (X, y) or list [(X, y)], optional (default=None) Validation dataset or datasets for metrics calculation and possibly early stopping in posterior training. cat_features : list or numpy.ndarray, optional (default=None) If not None, giving the list of Categ columns indices. Use only if X is not catboost.FeaturesData text_features : list or numpy.ndarray, optional (default=None) If not none, giving the list of Text columns indices. Use only if X is not catboost.FeaturesData embedding_features : list or numpy.ndarray, optional (default=None) If not none, giving the list of Embedding columns indices. Use only if X is not catboost.FeaturesData random_seed : int, [default=None] Random number seed. If None, 0 is used. range: [0,+inf) samples : int, [default=10] Number of Monte-Carlo samples from GP posterior. Controls how many models this function will return. range: [1,+inf) posterior_iterations : int, [default=900] Max count of trees for posterior sampling step. range: [1,+inf) prior_iterations : int, [default=100] Max count of trees for prior sampling step. range: [1,+inf] learning_rate : float, [default=0.1] Step size shrinkage used in update to prevent overfitting. range: (0,1] depth : int, [default=6] Depth of the trees in the models. range: [1,16] sigma : float, [default=0.1] Scale of GP kernel (lower values lead to lower posterior variance) range: (0,+inf) delta : float, [default=0] Scale of homogenious noise of GP kernel (adjust if target is noisy) range: [0,+inf) random_strength : float, [default=0.1] Corresponds to parameter beta in the paper. Higher values lead to faster convergence to GP posterior. range: (0,+inf) random_score_type : string [default='Gumbel'] Type of random noise added to scores. Possible values: - 'Gumbel' - Gumbel-distributed (as in paper) - 'NormalWithModelSizeDecrease' - Normally-distributed with deviation decreasing with model iteration count (default in CatBoost) eps : float, [default=1e-4] Technical parameter that controls the precision of the prior estimation. range: (0, 1] verbose : bool or int Verbosity of posterior model training output If verbose is bool, then if set to True, logging_level is set to Verbose, if set to False, logging_level is set to Silent. If verbose is int, it determines the frequency of writing metrics to output and logging_level is set to Verbose. Returns ------- models : list of trained CatBoostRegressor models (size = samples parameter value) rNl)lowhighrr)rrr{rFrL2Plain)rrTrKrcrDrMrrOrWr5r6rRrNr>)rYrZr[rr)rrr r)encodingoblivious_treesr leaf_weights)rw)r)rrTrKrRrcrDrMrrOrWr5r6rRrNr>)rrYrZr[r)r%)rrandom default_rngintegersr_r NamedTemporaryFilerrnormalrrr?openrloadrirosqrtrRdumprrrrrV sum_models)'r+rrrYrZr[rsamplesposterior_iterationsprior_iterationsrKrMsigmadeltar5r6epsrrandom_generator prior_seedsposterior_seedsNrRrtmp_fileprior_model_tmp_filesampleprior_yprior prior_file prior_jsontreeindrqrcrr posterior_y posteriors' rsample_gaussian_processrsHT AII aKK a   !GG yy,,[9"++g+NK&//AG'/RO AA(50Q6:M**,H#==."))!)<!#F+' )-$cE/!   %'1   -f1E &g >*:.J?01D&/D4GnI]0^&_"]c6+;+B+BQRUWU\U\]`abdj]kUlQlIm+B+n]#C('`2& , IIj* -- -f=..0 t   9I1J!JTTY\\^\c\cdt\uMuv%--**-=-D-D5WX-D-YY %'/+'/ )-$+/! "  %'1    Z (:QFKLI!L U? > - ,sK ?K K K! c| tdt|}t|t|t ||| |\}}} d|vr tdt d||fDr | td||dk(rd} d}||j d |i| |j d | i||j d |i|-|j d d id|vr|d=|j d|i|||}n td|||}n td||j d|i| | }|||d<|||d<|||d<|S|Q|d}nZt|dr|j}n=t|dr t|}n%t|}t|}n||}n |||k(sJ||dk(rd} n1| /|jdd}t|txr t|} d|vryt|d|j!}t#|j%t#|k7r4tdt'|zdzt'|j%z|d=d|vryt|d|j!}t#|j)t#|k7r4tdt'|zdzt'|j)z|d=d|vryt|d|j!} t#|j+t#| k7r4tdt'| zdzt'|j+z|d=t-|}!t/|!g}"t1|D]@}#|"j3t4j6j9|!d j;|#B|"D] }$t/|$t=||5t?||d!|""5|s(tA|||||| | || ||| cdddcdddStA|||||| | || ||| \}%}&tC|&}'|%|'fcdddcdddS#1swYnxYwdddy#1swYyxYw)#a2 Cross-validate the CatBoost model. Parameters ---------- pool : catboost.Pool Data to cross-validate on. params : dict Parameters for CatBoost. CatBoost has many of parameters, all have default values. If None, all params still defaults. If dict, overriding some (or all) params. dtrain : catboost.Pool or tuple (X, y) Synonym for pool parameter. Only one of these parameters should be set. iterations : int Number of boosting iterations. Can be set in params dict. num_boost_round : int Synonym for iterations. Only one of these parameters should be set. fold_count : int, optional (default=3) The number of folds to split the dataset into. nfold : int Synonym for fold_count. type : string, optional (default='Classical') Type of cross-validation Possible values: - 'Classical' - 'Inverted' - 'TimeSeries' inverted : bool, optional (default=False) Train on the test fold and evaluate the model on the training folds. partition_random_seed : int, optional (default=0) Use this as the seed value for random permutation of the data. Permutation is performed before splitting the data for cross validation. Each seed generates unique data splits. seed : int, optional Synonym for partition_random_seed. This parameter is deprecated. Use partition_random_seed instead. If both parameters are initialised partition_random_seed parameter is ignored. shuffle : bool, optional (default=True) Shuffle the dataset objects before splitting into folds. logging_level : string, optional (default=None) Possible values: - 'Silent' - 'Verbose' - 'Info' - 'Debug' stratified : bool, optional (default=None) Perform stratified sampling. True for classification and False otherwise. as_pandas : bool, optional (default=True) Return pd.DataFrame when pandas is installed. If False or pandas is not installed, return dict. metric_period : int, [default=1] The frequency of iterations to print the information to stdout. The value should be a positive integer. verbose : bool or int If verbose is bool, then if set to True, logging_level is set to Verbose, if set to False, logging_level is set to Silent. If verbose is int, it determines the frequency of writing metrics to output and logging_level is set to Verbose. verbose_eval : bool or int Synonym for verbose. Only one of these parameters should be set. plot : bool, optional (default=False) If True, draw train and eval error in Jupyter notebook plot_file : file-like or str, optional (default=None) If not None, save train and eval error graphs to file early_stopping_rounds : int Activates Iter overfitting detector with od_wait set to early_stopping_rounds. save_snapshot : bool, [default=None] Enable progress snapshotting for restoring progress after crashes or interruptions snapshot_file : string or pathlib.Path, [default=None] Learn progress snapshot file path, if None will use default filename snapshot_interval: int, [default=600] Interval between saving snapshots (seconds) metric_update_interval: float, [default=0.5] Interval between updating metrics (seconds) folds: generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None) If generator or iterator, it should yield the train and test indices for each fold. 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