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dgg}t|ddgddgg|d}||}tj|td}d|d<d|dddf<t|||tgdgdg}td}t j t |d||WddS1swYdS)Nrr)rr rrrrDr rrr rF)rrGrrr)6r8)rr$rG)r rrr)rrGr$r rrrrr)Shape of the passed X data is not correctr,) rrrrrr inverse_transformreescaper/r0r1)r$rrrrX_trexpmsgr!r!r"test_one_hot_encoder_inversesJ        "rz X, X_transr rrrrrrrsrtrurNrrrrr)rrrrr)rrrrrcCs`t|d|}d}|rt|d}tjt|d||WddS1s)wYdS)zCheck that `inverse_transform` raise an error with unknown samples, no dropped feature, and `handle_unknow="error`. 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Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/27088 rrrrDzNan should be the last elementr,N) rrrrrIr/r0r1r.rrrrr!r!r",test_encoder_nan_ending_specified_categoriess   "rcCstjddgddggtdj}tgdgdgd}tgd gd g}t||||jd gdks;Jt |jdj tj sHJ|jd  gdksUJt |jd j tj sbJdS) NrMrNrr rDr)rrr r)rrrrrr)rrrrrrr) rrrrIrr rrrrrrErrrrr!r!r"7test_one_hot_encoder_specified_categories_mixed_columns$srcCsDtd}|ddgddgd}t|}t|gdgdgdS) NrLrMrNrr rOrRrS)r/rTrUrr)rVrWrr!r!r"test_one_hot_encoder_pandas1s rzdrop, expected_namesx0_cx2_br)rx1_2r)rr rNx0_bx2_a)rbinarymanualcCs:gdgdg}t|d}|||}t||dS)N)rr rM)rNr rNr)rr.rr )rexpected_namesrrrr!r!r"'test_one_hot_encoder_feature_names_drop:s   rcCsddgddgddgg}tgdgdgdg}tdd g}td d d }||}t|j|t||d dgddgddgg}tddgddgddgg}td dg}td d d }||}t|j|t||dS)Nr]yesnorc)rrrrr?)rrrrrrFrtruerMfalserr)rrrrr drop_idx_r)rexpectedexpected_drop_idxrresultr!r!r"*test_one_hot_encoder_drop_equals_if_binaryLs        r )r]r r)rrr)rrrcCsTt}tjgdgdgdd}t|||dtdd}t|||dS)NrrrrrrrrDfloat64)rrrr rastyperr!r!r"test_ordinal_encoderds   r)rrzobject-string-catcCst|d}tdgdgg}t|||t|jdt|dks%J|jdt|dks4J|jdj |ks>Jt|d}t j t dd| |WddS1s[wYdS)Nrrrrr+r,)rrrr rrrGrrrEr/r0r1r.)rr5rrrrr!r!r")test_ordinal_encoder_specified_categoriesus   "rcCsgdgdg}t}||}tj|td}t|||tgdgdg}td}t j t |d||WddS1sGwYdS)NrrrD)rrrr rRrr,) rrrrrr rrrr/r0r1)rrrrrr!r!r"test_ordinal_encoder_inverses   "rcCstddd}tjddgddgdd ggtd }tjdd gd dgddggtd }||||}tjd dgddgddggdd }t||||}tjddgddgddggtd }t||dS)Nuse_encoded_valuer$ unknown_valuerMxrNyrrrDxyblar rrr)rrrrr.r2r r)rX_fitr X_trans_encr X_trans_invinv_expr!r!r"+test_ordinal_encoder_handle_unknowns_strings         r!rEcCstddd}tjddgddgdd gg|d }tjdd gd dgddgg|d }||||}tjddgddgd d ggdd }t||||}tjddgddgddggtd }t||dS)Nrrrr r rDr`rr)rrrr.r2r rr)rErrrrrrr r!r!r",test_ordinal_encoder_handle_unknowns_numerics         r'cCs`tdtjd}tdgdgdgg}|||dgdgdgg}t|dgdgtjggdS)Nrrrr r r(r)rrrrr.r2r )rrrr!r!r"(test_ordinal_encoder_handle_unknowns_nans  r(cCsdtdtjtd}tdgdgdgg}tjtdd||WddS1s+wYdS)Nr)r$rrErr r z'dtype parameter should be a float dtyper,) rrrintrr/r0r1r.)rrr!r!r"8test_ordinal_encoder_handle_unknowns_nan_non_float_dtypes "r*cCsjtjgdgtdj}gd}t|d}d}tjt|d||WddS1s.wYdS)N)LowMediumHighr,r+rD)r+r,r-rz*Shape mismatch: if categories is an array,r,) rrrrIrr/r0r1r.)rrrrr!r!r"+test_ordinal_encoder_raise_categories_shapes  "r.csxtddtjgdgdgdd}tjddgd d ggd dtjddgd d ggddtd d gddggtddgddggtjdd gd d ggddfD]!tfddtdDscJt|qLddgd d ggtfddtdDsJt|dd gd d ggtfddtdDsJt|dS)NrFr)rrrr)rrrrrrDrr r r(rrMrNrrabcdrcsg|] }j|jjkqSr!rrE.0irrr!r" sz'test_encoder_dtypes..cs"g|] }tj|jtjqSr!)rrrrEintegerr4rr!r"r8s"cg|] }j|jdkqS)rr3r4r:r!r"r8) rrrr.allranger r2r)rr!r7r"test_encoder_dtypess&     r?cstd}tddtjgdgdgdd}|jdd gd d gd d gddd}|tfddtd Ds.rMrNrrrPrQrAcs g|] }j|j|kqSr!r3r4X_typerr!r"r8s ) r/rTrrrrUr.r=r>r r2rrE)rVrrr!rBr"test_encoder_dtypes_pandass  "   rDcCsXt}ddgddgg}ttd||WddS1s%wYdS)NrYrr[r r))rwarningscatch_warnings simplefilterr)rrr!r!r"test_one_hot_encoder_warnings    "rHcCsddgddgddgg}t|ddddgddggd}||d dgg}tddgg}d }tjt|d  ||}Wd n1sDwYt||d S) z,Check handle_unknown='warn' works correctly.rMrrNr rFr'rrr$rGrqFound unknown categories in columns \[0\] during transform. These unknown categories will be encoded as all zerosr,N rr.rrr/warns UserWarningr2r)rrrX_testrJwarn_msgrr!r!r"test_ohe_handle_unknown_warn%s    rP missing_valuec Csndddd|g}t|d}gdgddddd|gg}||}gdgd gd g}t|||j|us8Jd d t|j|jD}||}t j |t d }t |drt|dd|ddt |dskJt |dssJt|ddddf|ddddft|dddf|dddft |dsJt |dsJdSt||t||dS)Nrr`r rr)rr`r rrM)rr`rrrM)rrrrr)rrrrrrcSsg|]\}}||qSr!r!)r5rrr!r!r"r8Ms z4test_one_hot_encoder_drop_manual..rDr=)r=r=) rrrr rrrr rrrrr) rQ cats_to_droprrtransr dropped_cats X_inv_transX_arrayr!r!r" test_one_hot_encoder_drop_manual?s2      *" rW)rr r\rMcCs^t|d}d}tjt|d|gdgdgdgWddS1s(wYdS)Nrz-`drop` should have length equal to the numberr,rr)rr ;)rr/r0r1r.)rrrr!r!r"test_invalid_drop_lengthds "rYdensityrdenserMr rNrcCst|d}t||d}gdgdg}||||t|j|j|dkr/t|jdnt||j|jD]\}}}|t||ksFJq7t|jtj sPJ|jj t ksXJdS)Nrr)rrrMr\rr) rr.r rr rr)rrndarrayrEr)rZrohe_baseohe_testrdrop_catdrop_idxcat_listr!r!r"test_categoriesls     rccCs|jjs JdS)N)__sklearn_tags__ input_tags categorical)rr!r!r""test_encoders_has_categorical_tagssrgkwargsmax_categories min_frequency g(\?r)rirjr`rGrFrMrNrrc Cstdgddgddgddgdgj}td|d d d ||}t|jgd gdgdgdgdgd gg}tddgddgddgddgddgg}||}t||dddgdgdD}| |}t||| } tddg| dS)zpTest that different parameters for combine 'a', 'c', and 'd' into the infrequent category works as expected.rMrrNrrr]rr r&F)rGr$rrMrrerrcSg|]}|gqSr!r!r5colr!r!r"r8z2test_ohe_infrequent_two_levels..infrequent_sklearnr(rx0_infrequent_sklearnNr! rrrIrr.r infrequent_categories_r2rrr) rhrGX_trainrrNr r expected_invX_invrr!r!r"test_ohe_infrequent_two_levelss(2(    rzcCstdgddgddgddgdgj}td d d |d |}|jd |jd dks2Jtdgdgg}||}td gdgg|| }t dg|| |}t dgdgg|dS)z3Test two levels and dropping the frequent category.rMrrNrrr]rr r&Fr r$rrirrrrtrsN) rrrIrr.rr r2rrr r)rrwrrNrr X_inverser!r!r",test_ohe_infrequent_two_levels_drop_frequents"2   r}cCstdgddgddgddgdgj}td d d |d }d |dd}tjt|d||WddS1sAwYdS)z_Test two levels and dropping any infrequent category removes the whole infrequent category.rMrrNrrr]rr r&Fr r{Unable to drop category r( from feature 0 because it is infrequentr,NrrrIrr/r0r1r.rrwrrr!r!r"5test_ohe_infrequent_two_levels_drop_infrequent_errorss2 "rr%gQ?g{Gz?r$c Cstdgddgddgddgdgj}tdd d d ||}t|jddggdgdgdgdgd gg}tgd gdgdgdgdg}||}t||dgdgdgdgdgg}| |}t||| }tgd|dS)zkTest that different parameters for combing 'a', and 'd' into the infrequent category works as expected.rMrrNrrr]rr r&Fr$rrnrrrrrrs)rrrtNr!ru) rhrwrrNr rrxryrr!r!r" test_ohe_infrequent_three_levelss.2(    rcCstdgddgddgddgdgj}td d d|d |}tdgdgdgg}td d gd d gd d gg|||jdd|d}tj t |d|dgdgg}Wdn1sfwYtd d gd d gg|dS)z5Test three levels and dropping the frequent category.rMrrNrrr]rr r&Fr{rrr%r*r+r,rnN) rrrIrr.rr2rr/rLrM)rrwrrNrrr!r!r".test_ohe_infrequent_three_levels_drop_frequents"2"rcCstdgddgddgddgdgj}td d d|d }d |d d}tjt|d||WddS1sAwYdS)z7Test three levels and dropping the infrequent category.rMrrNrrr]rr r&Fr{r~rrr,Nrrr!r!r"7test_ohe_infrequent_three_levels_drop_infrequent_errorss2 "rcCstdgddgddgddgdgj}td d dd |}t|jddggdgdgdgdgg}tgd gd gdgd g}||}t||dgg}d}t j t |d||WddS1sowYdS)zmTest that different parameters for combining 'a', and 'd' into the infrequent category works as expected.rMrrNrrr]rr r)F)r$rrirrrbadz.Found unknown categories \['bad'\] in column 0r,N) rrrIrr.r rvr2rr/r0r1)rwrrNr rrr!r!r"(test_ohe_infrequent_handle_unknown_error's 2"   "rcCstjdgddgdgtdj}tdgdgddd ||}dgd gd gd gdgg}td dgdd gdd gdd gdd gg}||}t||dddgg}dgd gg}|D]}|j|d|tdgd gg||qZdS)zG'a' is the only frequent category, all other categories are infrequent.rMrrnrcrDrrrMrNFr&rGrr$rNrrrrrrrNr!) rrrrIrr.r2rr)rhrwrrNr rdropsrr!r!r"5test_ohe_infrequent_two_levels_user_cats_one_frequent?s("(    rcCstjdgddgddgddgdgtd j}tgd gd d d d|}t|jgdgdgdgdgdgdgg}tddgddgddgddgddgg}||}t ||dddgdgdD}| |}t||dS)zFTest that the order of the categories provided by a user is respected.rMrrNrrr]rr rDrFr&r rGrr$ri)rrrMrnrrcSror!r!rpr!r!r"r8qrrz.rsr(N rrrrIrr.r rvr2rrrwrrNr rrxryr!r!r"(test_ohe_infrequent_two_levels_user_cats[s**(   rcCstjdgddgddgddgdgtd j}tgd gd d dd |}t|jddggdgdgdgdgdgg}tgdgdgdgdgdg}||}t ||dgdgdgdgdgg}| |}t||dS)zTest that the order of the categories provided by a user is respected. In this case 'c' is encoded as the first category and 'b' is encoded as the second one.rMrrNrrr]rr rDrrrNrMFr&rrnrrrrsNrrr!r!r"*test_ohe_infrequent_three_levels_user_catsvs4*(   rcCsbtjgdgdf}tdddd}||ddgddgg}||}t|gd gd gd S) zaTest infrequent categories where feature 0 has infrequent categories, and feature 1 does not. rrr r r r r rr rrrrrrrrrr rF)rirrrrrrrr)rrrrN)rc_rr.r2r)rrrNrr!r!r"test_ohe_infrequent_mixeds   rc Cstjgdgdgdf}tdddd}||}t|jdd d gt|jd d d gt|jd d |}tgd |gdgdgdgdgdgdgdgdgdg }t||gdgdg}| |}gdgdg}t||| |}tj gdgdgt d}t||tdddd |}tjtdd | |Wd n1swYgd gd!g}| |}gd"gdg}t||| |}tj gd#gd$gt d}t||d S)%z?Test infrequent categories with feature matrix with 3 features.r) rrrrrr]rrr) rrrrrrrrrrFr r&rGrir$rrr r]N)x0_0x0_3rtx1_0x1_5x1_infrequent_sklearnx2_0x2_1)rrrrrrrr)rrrrrrrr)rrrrrrrr)rrrrrrrr)rrrrrrrr)rrrrrrrr)rrrrrrrr)rrrrrrrr)r rr )r(rr )rrrrrrrr)rrrrrrrr)r rsN)rsrNrDr)r+r,)rrr)r r]r)rrrrrrrr)rsrsr)r rsr)rrrrrr rvrrr2rrrr.r/r0r1) rrrrr rN X_test_transryrxr!r!r"'test_ohe_infrequent_multiple_categoriessp        rc Cstd}|jgdgddddgd}tdd d d }||}t|jd d dgt|jdgdgdgdgdgdgdgdgdgdgdg }t|||jddgddgdddgd}gdgdg}| |}t||| |}t j ddgddggt d}t|||jddgddgdddgd}| |}gdgdg}t||| |}t j ddgddggt d}t||dS)zHTest infrequent categories with a pandas dataframe with multiple dtypes.rL rMfrrrrMrrNrN rr rr]r]r`rr r)strr)rr)columnsrFr r&rrrMrNrrr r`)rrrrrr)rrrrrr)rrrrrr)rrrrrr)rrrrrrrr`rsrDrrN)r/rTrUrrrr rvrr2rrrr) rVrrrr rNrryrxr!r!r".test_ohe_infrequent_multiple_categories_dtypessV         rrb)rjricCsptdgddgddgddgdgj}tdd d d |}|||dgg}t|d ggd S),All user provided categories are infrequent.rMrrNrrr]rr r&FrrNr!)rrrIrr.r2rrhrwrrr!r!r"$test_ohe_infrequent_one_level_errorsHs2 rcCsbtjdgdgtdj}td gdgddd||}|dgdgg}t|d gd ggd S) rrnr rDrFr&rrMrNr!)rrrrIrr.r2rrr!r!r"5test_ohe_infrequent_user_cats_unknown_training_errorsVsrzinput_dtype, category_dtype)OOOUUOUUSOSUSS array_type)rr dataframec Cstjdgdgg|d}tjddg|dg}t|dd|}tdgdgdgdgg||d}||}tddgddgddgddgg}t||t|d|} | |}tdgdgdgdgg}t||d S) a"Check that encoding work with object, unicode, and byte string dtypes. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/15616 https://github.com/scikit-learn/scikit-learn/issues/15726 https://github.com/scikit-learn/scikit-learn/issues/19677 rNrMrDFrrrrN) rrrr.rr2rrr ) rCcategory_dtyperrrGrrNrr oer!r!r"test_encoders_string_categoriesgs  "  rcCs~tjdgdggdd}tjddgddg}t|dd}td}tjt|d ||Wd d S1s8wYd S) zCheck that this mixture of predefined categories and X raises an error. Categories defined as bytes can not easily be compared to data that is a string. rNrMUrDSFrzjIn column 0, the predefined categories have type 'bytes' which is incompatible with values of type 'str_'.r,N) rrrrrr/r0r1r.)rrGrrr!r!r"$test_mixed_string_bytes_categoricalss  "rcCsPtjdd|d|ggtdj}tddd|}|}t|ddd |gdS) NrMrNrDFr%rr$x0_arx0_)rrrrIrr.rr )rQrrnamesr!r!r")test_ohe_missing_values_get_feature_namessrcCsrtd}|jgdtjdddtjgtdddd gd }tgd gd gd gdg}t|}t||dS)NrL)dogrNrr rr(rD)col1col2rrr)rrrrrrr)rrrrrrr)rrrrrrr)rrrrrrr) r/rTrUrrrfloatrr)rVdfexpected_df_transrr!r!r"%test_ohe_missing_value_support_pandass  r pd_nan_typepd.NAznp.nanc Cstd}|dkr |jntj}|d|jdd|ddgddi}tgd gd gd gd gd g}td |d}| |}t ||t |j dksMJt |j dddgdt|j ddsgJdS)NrLrrrrMrNrrD)rrrr)rrrr)rrrrrFrrrr=r)r/rTNArrrUrrrrrlenrr isnan)rr$rVpd_missing_valuerrrdf_transr!r!r"1test_ohe_missing_value_support_pandas_categoricals(   rcCsddgddgddgg}tdd|d}||}tgd gd gd g}t||d d gg}tgd g}d}tjt|d ||}Wdn1sPwYt||| |}t |tjddggt ddS)zZCheck drop='first' and handle_unknown='ignore'/'infrequent_if_exist' during transform.rMrrNr rrFrrr$rr)rrrrr tFound unknown categories in columns \[0, 1\] during transform. These unknown categories will be encoded as all zerosr,NrD rrrrrr/rLrMr2rr rr$rrrrJrNrOryr!r!r"/test_ohe_drop_first_handle_unknown_ignore_warnss,      rcCsddgddgddgg}tdd|d}||}tgd gd gd g}t||d d gg}tgdg}d}tjt|d ||}Wdn1sPwYt||| |}t |tjddggt ddS)zDCheck drop='if_binary' and handle_unknown='ignore' during transform.rMrrNr rrFrrrrRrr )rrrrrr,NrDrrr!r!r"3test_ohe_drop_if_binary_handle_unknown_ignore_warnss,      rcCsddgddgddgg}tdd|ddgddggd}||d dgg}tddgg}d }tjt|d  ||}Wd n1sDwYt||d S) znCheck drop='first' and handle_unknown='ignore'/'infrequent_if_exist' during fit with categories passed in.rMrrNr rrFrIrrJr,NrK)r$rrrNrJrOrr!r!r"'test_ohe_drop_first_explicit_categories&s    rcCstd}|jgdgddddgd}tdd }|jdd d }tjt|d  ||Wd n1s9wY||tjt|d | |Wd d S1s[wYd S)zJRaise informative error message when pandas output and sparse_output=True.rLr)rrNrN)rMrNrMrNrTrr2zxPandas output does not support sparse data. Set sparse_output=False to output pandas dataframes or disable Pandas outputr,N) r/rTrUr set_outputr0r1rr.r2)rVrrrr!r!r"'test_ohe_more_informative_error_messageAs       "rcCsnttjdddggj}ttjd}dtj}tjt|d| |WddS1s0wYdS)zDTest ordinal encoder with nan passthrough fails when dtype=np.int32.rrrDzdThere are missing values in features \[0\]. For OrdinalEncoder to encode missing values with dtype: r,N) rrrrIrint32r/r0r1r.)rrrr!r!r"Btest_ordinal_encoder_passthrough_missing_values_float_errors_dtypeUs  "rencoded_missing_valuercCstjtjdddggtjdj}t|d|}t|jdks Jt |jdddtjg| |}t ||gdgdgdgg| |}t ||dS) z.Test ordinal encoder with nan on float dtypes.rrrDrrrrN) rrrrrIrr.rrrr2r)rrrrr|r!r!r"5test_ordinal_encoder_passthrough_missing_values_floatcs  rc Cs td}|dkr |jntj}|d|jdd|ddgddi}t|d |}t |j d ks1Jt |j d d d gdt |j d dsKJ| |}t|dgdg|gdgdgg||}|jdkskJt |d dd fddgt |d d d fddgt |dsJd S)z0Check ordinal encoder is compatible with pandas.rLrrrrMrNrrDrrrNr rr=@rr)rrr r)r/rTrrrrUrrr.rrr rr2rrr)rrrVrrrrr|r!r!r"=test_ordinal_encoder_missing_value_support_pandas_categoricalus"   rr)zobject-None-missing-valuezobject-nan-missing_valueznumeric-missing-valuecCst|d}tdgtjgg}t||||jdj|ks!Jt|d}tj t dd| |WddS1s>wYdS)z.Test ordinal encoder for specified categories.rrrr+r,N) rrrrr rrrEr/r0r1r.)rr5rrrrr!r!r"=test_ordinal_encoder_specified_categories_missing_passthroughs &  "rcCsrtjgdtdg}||d}tjddggtdj}tjtdd||WddS1s2wYdS) zTest encoder for specified categories have duplicate values. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/27088 )rMrNrMrDrrMrNz5the predefined categories contain duplicate elements.r,N)rrrrIr/r0r1r.rr!r!r"+test_encoder_duplicate_specified_categoriess  "rzX, expected_X_trans, X_testrr)rrr)rrrr)rrMrN)rrrcCs8tddd}||}t||t||dggdS)z>Test the interaction between missing values and handle_unknownrr=rgN)rrrr2)rexpected_X_transrNrrr!r!r"/test_ordinal_encoder_handle_missing_and_unknowns   r csr_containercCstgdgdg}||}t}d}tjt|d ||Wdn1s+wYtjt|d ||Wdn1sGwY||}||}tjt|d||WddS1smwYdS)zCheck that we raise proper error with sparse input in OrdinalEncoder. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/19878 r rz2Sparse data was passed, but dense data is requiredr,N) rrrr/r0rr.rr)rrX_sparseencoderrrrr!r!r"test_ordinal_encoder_sparses    "rcCstgdddtjf}tgdgddd}||tgdgdd}tjtd d ||WddS1s>wYdS) zCheck OrdinalEncoder.fit works with unseen category when `handle_unknown="use_encoded_value"`. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/19872 )rrrrr rN)r=rrrr")rGr$rr)rr+r,)rrnewaxisrr.r/r0r1)rrr!r!r"-test_ordinal_encoder_fit_with_unseen_categorys   "rrwAAOrrNcCs4tddd}||||}t|ddggdS)zChecks that `OrdinalEncoder` transforms string dtypes. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/19872 rirrN)rr.r2r)rwrNrrr!r!r"1test_ordinal_encoder_handle_unknown_string_dtypes&s   rcCsbtgddd}t|}t|jtj|ddj| |}t|dgdgdgdggdS) zCheck that `OrdinalEncoder` accepts Python integers that are potentially larger than 64 bits. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/20721 )l HP 1&l H]viel :?i}GalIRK2e6kr=rr)axisr r N) rrr@rr.r rsortrIr2)rrrr!r!r"#test_ordinal_encoder_python_integerBs  rcCsHtd}gd}|jgdg|d}t|}|}t||dS)z-Check feature names out is same as the input.rL)rNrrMrrN)r/rTrUrr.rr )rVrrrfeature_names_outr!r!r".test_ordinal_encoder_features_names_out_pandasVs  rcCstjdgdgtjggtd}tdtjdd|}||}t|dgdgdggtjd gtjggtd}||}t|tjgdgg||}|ddd usSJt |dds^Jd S) zECheck interactions between encode_unknown and missing value encoding.rMrNrDrr$rrrrrN) rrrrrr.r2rrr)rrrrNr X_roundtripr!r!r"0test_ordinal_encoder_unknown_missing_interactionbs    r with_pandascCstjddgddgdtjggtd}d}|r(td}|j|d d gd }|d }n|d }tdd}tjt |d| |WddS1sIwYdS)zXCheck OrdinalEncoder errors when encoded_missing_value is used by an known category.rMrrNrrrDzTencoded_missing_value \(1\) is already used to encode a known category in features: rLletterpetrz \['pet'\]z\[1\]rrr,N) rrrrr/rTrUrr0r1r.)rr error_msgrVrr!r!r"0test_ordinal_encoder_encoded_missing_value_errors"    "rz4X_train, X_test_trans_expected, X_roundtrip_expected1c Cstdtjtjd|}tdgtjgdgg}||}t||||}|jd}t |D]+}||df} ||df} | durI| dusHJq0t | rUt | sTJq0| | ks[Jq0dS)znCheck transform when unknown_value and encoded_missing_value is nan. Non-regression test for #24082. rrrrNrN) rrrr.rr2rrrr>rr) rwX_test_trans_expectedX_roundtrip_expectedrrNrr n_samplesr6 expected_valvalr!r!r"9test_ordinal_encoder_unknown_missing_interaction_both_nans*       rcCstd}|ddgddgd}t}|jddd}tjt|d  ||Wd n1s3wYtd d jd d}td d jdd}||}||}t| |t | |j d S)z*Check OneHotEncoder works with set_output.rLrMrNrr rOrzCPandas output does not support sparse data. Set sparse_output=Falser,NFrdefault) r/rTrUrrr0r1rrto_numpyr rr)rVrWrr- ohe_default ohe_pandas X_defaultX_pandasr!r!r"test_one_hot_encoder_set_outputs     r cCsttd}|ddgddgd}tjdd}tjdd}||}||}t||t| |j d S) z+Check OrdinalEncoder works with set_output.rLrMrNrr rOrrN) r/rTrUrrrrrr rr)rVrW ord_default ord_pandasr r r!r!r"test_ordinal_set_outputs   rcCstgdddgg}t|d}|ddggt|t|jks Jt|jD]\}}|jtks0Jt|||q%dS)zjCheck that the categories_ dtype is `object` for string categories Regression test for gh-25171. )asmmaseasrasacsr2rrN)rr.rr enumeraterErr )rGrnrr!r!r" test_predefined_categories_dtypes rcCs~tjdgdgtjggtd}tdd|}t|dgdgdggtddd |}td gg}||}t|dggd S) zBCheck missing value or unknown encoding can equal the cardinality.rrrDr rrrrrsnakeN) rrrrrrrr.r2)rrrrNr!r!r"1test_ordinal_encoder_missing_unknown_encoding_maxs rcCstjdgddgddgddgddgdgtdj}tdd d d |}t|gd |jd |j d dksAJtjdgddgddgdgtdj}tdd dd |}t|dg|jd |j d dkswJtjdgddgddgddgddgdgtdj}tdd dgd |}t|gd|jd |j d dksJtdd dd |}t|gd|j dusJdS)zkCheck drop_idx is defined correctly with infrequent categories. Non-regression test for gh-25550. rMr rNr(rrrnrDFr)rjrr)rx0_dx0_ertrr]rrt)rrrrtN)rrrrrt) rrrrIrr.r rrr )rrr!r!r"#test_drop_idx_infrequent_categoriess<4 ,4 rcCstdgddgddgddgdgj}tdd d d ||}t|jgd gt|jddggdgdgdgdgd gg}dgdgdgdgd gg}||}t ||| |}dgdgdgdgdgg}t||dS)zGTest parameters for grouping 'a', and 'd' into the infrequent category.rMrrNrrr]rr rr=rrlrr rrrsNr!) rrrIrr.r rrvr2rr)rhrwordinalrNexpected_transrr|expected_inverser!r!r",test_ordinal_encoder_infrequent_three_levels6s,2   r!cCstjdgddgddgddgdgtd j}tgd gdd d d |}t|jgd gt|jddggdgdgdgdgdgg}dgdgdgdgd gg}| |}t ||| |}dgdgdgdgdgg}t||dS)zTest that the order of the categories provided by a user is respected. In this case 'c' is encoded as the first category and 'b' is encoded as the second one. rMrrNrrr]rr rDrrr=)rGrir$rrr rrrsN) rrrrIrr.r rrvr2rr)rwrrNrrr|r r!r!r"6test_ordinal_encoder_infrequent_three_levels_user_cats]s6*   r"cCstgdgdf}tdd|}t|jdddg|jddus&Jddgddgg}ddgddgg}||}t||||}tj ddgd dggt d }t||dS) zETest when feature 0 has infrequent categories and feature 1 does not.rrr rirrr NrsrD) r column_stackrr.r rvr2rrrr)rrrNrrr|r r!r!r"%test_ordinal_encoder_infrequent_mixeds   r%cCs td}|gd}|jgdgd|jdgddgdd gd g|d d gd d }tdd|}t|jdddgt|jdgdt|jdd d g|jgdgd|jdgd gd gdg|d d gd d }gdgdgdgdg}| |}t ||dS)zHTest infrequent categories with a pandas DataFrame with multiple dtypes.rL)birdrrrrrrr(rr rr&rD)rr)rfrr#rrMrNrrr )rMrNrr)r`rr]r)r r r)r r r )rrr rN) r/rTCategoricalDtyperUrrr.r rvr2r)rVcategorical_dtyperrrNrrr!r!r":test_ordinal_encoder_infrequent_multiple_categories_dtypess:    r)cCstjdgddgddgddgdtjggtd j}td d d dd |}t|jgd gtjdgdgdgdgdgtjggtd }dgdgdgdgd gdgg}| |}t ||dS)zJCheck behavior of unknown_value and encoded_missing_value with infrequent.rMrrNrrr]rr rDrr )r$rrirrmrnrrN) rrrrrIrr.r rvr2r)rwrrNrrr!r!r".test_ordinal_encoder_infrequent_custom_mappings$2( r*cCstjdgddgddgddgdgtd j}tdi|d d d |}td d d |}dgdgdgdgd gg}t||||dS)zMAll categories are considered frequent have same encoding as default encoder.rMrrNrrr]rr rDrr=rrnNr!rrrrIrr.rr2)rhrwadjusted_encoderdefault_encoderrNr!r!r"!test_ordinal_encoder_all_frequents* *r.dcCstjdgddgddgddgdgtd j}tdi|d d d |}dgdgdgdgd gg}t||dgdgdgdgd ggdS)zAWhen all categories are infrequent, they are all encoded as zero.rMrrNrrr]rr rDrr=rrnrNr!r+)rhrwrrNr!r!r"#test_ordinal_encoder_all_infrequents *(r0cCstjtjgddgddgddgdggtdj}td d |}tjdddtjggtdj}||}t|d gd gd gtjggdS)z5Check behavior when missing value appears frequently.rrr]rrrdeerrDr r#r rrN rrrrrIrr.r2rrrrNrr!r!r"-test_ordinal_encoder_missing_appears_frequent s,  r4cCstjtjgdgddgddgdgdgdd gdgtd j}td d |}tjddgdd gtjd gdd gddggtd }||}t|d dgd dgtjdgddgddggdS)z7Check behavior when missing value appears infrequently.rr]rrrr1redr%greenrDr()rjr rrNr2r3r!r!r"/test_ordinal_encoder_missing_appears_infrequent s(& .r7cCsdtjdgdgdggtd}|gdgd}tt||WddS1s+wYdS)a!Check that we raise a `NotFittedError` by calling transform before fit with the encoders. One could expect that the passing the `categories` argument to the encoder would make it stateless. However, `fit` is making a couple of check, such as the position of `np.nan`. rPrQrArDr@rN)rrrr/r0rr2)rrrr!r!r"test_encoder_not_fitted3 s    "r8)rrEnumpyrr/scipyrsklearn.exceptionsrsklearn.preprocessingrrsklearn.utils._missingrsklearn.utils._testingrrr sklearn.utils.fixesr r#mark parametrizer8rArfloat32rrKrXrrrrrrrrrrrrrrrrrr9str_rrIrrrrrrrr rrrr!r)r'r(r*r.r?rDrHrPrWrYrcrgrzr}rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrHrr rrrrr!r"r%r)r*r.r0r4r7r8r!r!r!r"s        <      /*       & &*  !& 1                $          $[A      % $     "        !    $  $  0