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Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/14934 rzqSamples \[(\d )*\d\] can not be inverted when drop=None and handle_unknown='error' because they contain all zerosrr/N)rr1rr2r3r4r)rX_transrrrs r#?test_one_hot_encoder_inverse_transform_raise_error_with_unknownrAs]& g . 2 21 5C A $Wh7 z - g& . - -s A"" A0c[R"SS/SS/SS//[S9n[SSS 9nUR U5n[ UR U5U5 g) Nr`rrbrrrI if_binaryFrr)rrrrrr r)rohers r#&test_one_hot_encoder_inverse_if_binaryrasV 61+!}xmC   Q Ds,,T2A6r%)rrN reset_dropc~[R"SS/SS/SS//[S9n[USS9nUR U5 UR U5nUR 5nURUS 9 [URU5U5 [UR U5U5 [UR 5U5 g) Nr`rrbrrrIFrr) rrrrr1r5rrr rr )rrrrrrs r#test_one_hot_encoder_drop_resetrhs 61+!}xms((+335s; ??1  $ $ &/ 99 9 ==+112:: > >> > 1a&A I; /C 1C z - ! . - -s "E== F Encoderc4[R"S[RS/5/nU"US9n[R"SS//[S9Rn[ R "[SS9 URU5 SSS5 g!,(df  g=f)zTest encoder for specified categories that nan is at the end. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/27088 rrrrIzNan should be the last elementr/N) rrrrrNr2r3r4r1rr rrs r#,test_encoder_nan_ending_specified_categoriesrsl HHa^ $ %D T "C 1a&(**A z)I J   K J Js .B  Bc[R"SS/SS//[S9Rn[ /SQ/SQ/S9n[R"/S Q/S Q/5n[ UR U5R5U5 URSR5/SQ:Xde[R"URSR[R5(deURS R5/SQ:Xde[R"URS R[R5(deg) NrSrTrrrIr)rrrr)rrrrrr)rrrrrrr) rrrrNrr rrrrrrJr rrrs r#7test_one_hot_encoder_specified_categories_mixed_columnsr$s 3*q!f%V466A OY#? @C ((24RS TCs((+335s; ??1  $ $ &/ 99 9 ==+112:: > >> > ??1  $ $ &) 33 3 ==+112:: > >> >r%c[R"S5nURSS/SS/S.5n[U5n[ U/SQ/SQ/5 g) NrRrSrTrrrUrXrY)r2rZr[rr )r\r]rs r#test_one_hot_encoder_pandasr1sF   X &B <C   q !Fs}}&78F% ###7Axx#sc3Z#s<=H!T+ [ >C   q !Fs}}&78F%r%)rdrr)r$rr)rrrc[5n[R"/SQ/SQ/SS9n[UR U5UR S55 [SS9n[UR U5U5 g)NrrrrrrrrIfloat64)rrrr rastypers r#test_ordinal_encoderr2ds^  C ((Iy) 9Cs((+SZZ -BC w 'Cs((+S1r%)rrzobject-string-catc [US9n[R"S/S//5n[UR U5U5 [ UR S5[ US5:XdeURSR5[ US5:XdeURSRU:Xde[US9n[R"[SS9 URU5 SSS5 g!,(df  g=f)Nrrrrr.r/)rrrr rrrLrrrJr2r3r4r1)rr8r rrrs r#)test_ordinal_encoder_specified_categoriesr4us2 D )C ((SEC5> "Cs((+S1 q! 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A<c[R"/SQ/[S9Rn/SQn[ US9nSn[ R "[US9 URU5 SSS5 g!,(df  g=f)N)LowMediumHighrSrRrI)rRrSrTrz*Shape mismatch: if categories is an array,r/) rrrrNrr2r3r4r1)rr rrs r#+test_ordinal_encoder_raise_categories_shaperUsU <=VLNNA $D D )C 6C z -   . - -s A** A8c [SS9n[R"/SQ/SQ/SS9n[R"SS/S S //S S9[R"SS/S S //SS9[R"S S /SS//5[R"SS/SS//5[R"SS /S S //SS94HnURU5 [ [ S5Vs/sH(o0R URUR:HPM* sn5(de[URU5R5U5 M SS/S S //nURU5 [ [ S5Vs/sH?n[R"UR UR[R5PMA sn5(de[URU5R5U5 SS /S S //nURU5 [ [ S5Vs/sHo0R URS:HPM sn5(de[URU5R5U5 gs snfs snfs snf)NrKr)rrrr)rrrrr0rIrrrr+rrSrTrrabcdr) rrrr1allrangerrJr r5rrinteger)rrris r#test_encoder_dtypesr_s 6 *C (((*>?y QC 1a&1a&!1 1a&1a&!3 3*sCj)* 4,t -. 1c(QH%X6   qJAOOA&,,7JKKKK3==+335s; Q!QAGGAJ USTXVX cooa066 CXV W WW Ws}}Q'//137 SAs8AGGAJ eAhGh"((H4hG H HH Hs}}Q'//137K W Hs/I+ &AI0%I5c[R"S5n[SS9n[R"/SQ/SQ/SS9nUR SS /S S /S S /S.SS9nUR U5 [[S 5Vs/sHoARURS:HPM sn5(de[URU5R5U5 UR SS /SS/SS/S.5n/SQnUR U5 [[S 5Vs/sH oARURXT:HPM" sn5(de[URU5R5U5 gs snfs snf)NrRrKr)rrrrrr)rrrrrrr0rIrrrr+rrrVrWCrrSrTrr)rrr0)r2rZrrrr[r1r[r\rrJr r5r)r\rrrr^expected_cat_types r#test_encoder_dtypes_pandasrdsO   X &B 6 *C (( ')GH C Aq6AaV T "C=G z 1 @A 2 1 1s A  AdensityrdenserSrrTrc[US9n[XS9n/SQ/SQ/nURU5 URU5 [URUR5 US:Xa[URS5 O>[ XRUR5HupVnU[ U5U:XaMe [UR[R5(deURR[:Xdeg)Nrr)rrrSrrr) rr1r rr(rrOrrndarrayrJr)rrohe_baseohe_testrdrop_catdrop_idxcat_lists r#test_categoriesrls73H7>H  &A LLO LLOx++X-A-AB w8--q1,/ $$h&:&:- (HCM*h6 66-  h(("** 5 55 5    # #v -- -r%cdU"5R5RR(deg)N)__sklearn_tags__ input_tags categorical)rs r#"test_encoders_has_categorical_tagsrs" 9 % % ' 2 2 > >> >r%kwargsmax_categories min_frequency g(\?r)rrrgrLrKrSrTrrc2[R"S/S-S/S--S/S--S/S--/5Rn[SUS S S .UD6R U5n[ UR /S Q/5 S/S/S/S/S //n[R"SS/SS/SS/SS/SS//5nURU5n[XV5 S/S/S--Vs/sHow/PM nnURU5n [ X5 UR5n [ SS/U 5 gs snf)zlTest that different parameters for combine 'a', 'c', and 'd' into the infrequent category works as expected.rSrrTr$rrdrrr(F)rLr&rrSrrerrinfrequent_sklearnr+rx0_infrequent_sklearnNr rrrNrr1r infrequent_categories_r5r rr) rrLX_trainrrpr)rcol expected_invX_invrs r#test_ohe_infrequent_two_levelsrsFhh SEBJ.#;seaiGHIKKG  ,      c'l  s11O3DEecUSEC53% 0Fxx!Q!Q!Q!Q!Q@AHmmF#GH&&)U.B-Ca-G%GH%GcE%GLH  ! !' *E|+--/M 78-H Is Dc[R"S/S-S/S--S/S--S/S--/5Rn[S S S US 9R U5nUR S UR S S:Xde[R"S/S//5nURU5n[S /S//U5 UR5n[S/U5 URU5n[S/S//U5 g)z3Test two levels and dropping the frequent category.rSrrTr$rrdrrr(Frr&rrrrrrrN) rrrNrr1rr(r5r rr r)rrrrprr X_inverses r#,test_ohe_infrequent_two_levels_drop_frequentrshh SEBJ.#;seaiGHIKKG ,     c'l  ??1 cmmA. /3 66 6 XXusen %FmmF#GaS1#J(--/M/0-@%%g.I 456 Br%c,[R"S/S-S/S--S/S--S/S--/5Rn[S S S US 9nS US<S3n[R "[ US9 URU5 SSS5 g!,(df  g=f)z[Test two levels and dropping any infrequent category removes the whole infrequent category.rSrrTr$rrdrrr(FrrUnable to drop category r( from feature 0 because it is infrequentr/NrrrNrr2r3r4r1rrrrs r#5test_ohe_infrequent_two_levels_drop_infrequent_errorsrs hh SEBJ.#;seaiGHIKKG ,   C %T!WK/W XC z -  . - - *B BrIgQ?g{Gz?rHc [R"S/S-S/S--S/S--S/S--/5Rn[SS S S .UD6R U5n[ UR SS//5 S/S/S/S/S //n[R"/S Q/SQ/SQ/SQ/SQ/5nURU5n[XE5 S/S/S/S/S//nURU5n[ Xg5 UR5n[ /SQU5 g)zgTest that different parameters for combing 'a', and 'd' into the infrequent category works as expected.rSrrTr$rrdrrr(Fr&rrr/rrrr.r)rrrNrr) rrrrpr)rrrrs r# test_ohe_infrequent_three_levelsrs# hh SEBJ.#;seaiGHIKKG  ,E EK  c'ls11S#J<@ecUSEC53% 0FxxIy)YOPHmmF#GH&      L  ! !' *E|+--/M@-Pr%c"[R"S/S-S/S--S/S--S/S--/5Rn[S S SUS 9R U5n[R"S/S/S//5n[ S S /S S /S S //UR U55 URSS9R U5 Sn[R"[US9 UR S/S//5nSSS5 [ S S /S S //W5 g!,(df  N!=f)z5Test three levels and dropping the frequent category.rSrrTr$rrdrrr(Frrrr'r-r.r/rN) rrrNrr1r r5rr2rnro)rrrrprrs r#.test_ohe_infrequent_three_levels_drop_frequentrshh SEBJ.#;seaiGHIKKG ,     c'l XXusecU+ ,FaVaVaV,cmmF.CDNN(N+//8 $C k ---#/ .aVaV$g. . -s D Dc,[R"S/S-S/S--S/S--S/S--/5Rn[S S SUS 9nS US <S3n[R "[ US9 URU5 SSS5 g!,(df  g=f)z7Test three levels and dropping the infrequent category.rSrrTr$rrdrrr(Frrrrr/Nrrs r#7test_ohe_infrequent_three_levels_drop_infrequent_errorsrshh SEBJ.#;seaiGHIKKG ,   C %T!WK/W XC z -  . - -rc[R"S/S-S/S--S/S--S/S--/5Rn[S S SS 9R U5n[ UR SS//5 S/S/S/S//n[R"/S Q/S Q/SQ/S Q/5nURU5n[X45 S//nSn[R"[US9 URU5 SSS5 g!,(df  g=f)ziTest that different parameters for combining 'a', and 'd' into the infrequent category works as expected.rSrrTr$rrdrrr,F)r&rrr/rr.badz.Found unknown categories \['bad'\] in column 0r/N) rrrNrr1r rr5r r2r3r4)rrrpr)rrs r#(test_ohe_infrequent_handle_unknown_errorr'shh SEBJ.#;seaiGHIKKG eA  c'ls11S#J<@ecUSEC5 )FxxIy)DEHmmF#GH&gYF ;C z - f . - -s C00 C>c[R"S/S-S/S--/[S9Rn[ S/SQ/SSS .UD6R U5nS/S /S /S /S//n[R"S S/SS /SS /SS /SS //5nUR U5n[XE5 SSS//nS/S //nUH@nURUS9R U5 [S/S //UR U55 MB g)zG'a' is the only frequent category, all other categories are infrequent.rSrrrjrIrrrSrTFr(rLrr&rTrrrrrrrNr) rrrrNrr1r5r r)rrrrpr)rdropsrs r#5test_ohe_infrequent_two_levels_user_cats_one_frequentr?s hh SEBJ./v>@@G  (),      c'l ecUSEC53% 0Fxx!Q!Q!Q!Q!Q@AHmmF#GH&kC5 )EecU^F D!%%g.!qc CMM&$9:r%c[R"S/S-S/S--S/S--S/S--/[S 9Rn[ /S Q/S S S S9R U5n[ UR/SQ/5 S/S/S/S/S//n[R"SS/SS/SS/SS/SS//5nURU5n[X45 S/S/S--Vs/sHoU/PM nnURU5n[ Xg5 gs snf)zFTest that the order of the categories provided by a user is respected.rSrrTr$rrdrrrIrFr(rrLrr&r)rrrSrrrrr+N rrrrNrr1r rr5r r)rrrpr)rrrrs r#(test_ohe_infrequent_two_levels_user_catsr[s(hh cURZ 3%"* ,uqy 89a  (),    c'l s11O3DEecUSEC53% 0Fxx!Q!Q!Q!Q!Q@AHmmF#GH&'*U.B-Ca-G%GH%GcE%GLH  ! !' *E|+Is C9c[R"S/S-S/S--S/S--S/S--/[S 9Rn[ /S Q/S S SS 9R U5n[ URSS//5 S/S/S/S/S//n[R"/SQ/SQ/SQ/SQ/SQ/5nURU5n[X45 S/S/S/S/S//nURU5n[ XV5 g)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.rSrrTr$rrdrrrIrrrTrSFr(rrr.rr/rNr)rrrpr)rrrs r#*test_ohe_infrequent_three_levels_user_catsrvs hh cURZ 3%"* ,uqy 89a  (),    c'l s11S#J<@ecUSEC53% 0FxxIy)YOPHmmF#GH&      L  ! !' *E|+r%c[R/SQ/SQ4n[SSSS9nURU5 SS/SS//nUR U5n[ U/S Q/S Q/5 g ) z]Test infrequent categories where feature 0 has infrequent categories, and feature 1 does not. rrrrrrrrr rrrrrrrrrrrF)rrrrrrrrr)rrrrN)rc_rr1r5r )rrrprs r#test_ohe_infrequent_mixedrsc )+FFGA q{% PCGGAJ!fq!f FmmF#GGlL9:r%c J[R/SQ/SQ/SQ4n[SSSS9nURU5R 5n[ UR SS S /5 [ UR S S S /5 [ UR S S 5 UR5n[ /S QU5 /SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/ n[XB5 /SQ/SQ/nURU5n/SQ/SQ/n[XFR 55 URU5n[R"/SQ/SQ/[S9n[ X5 [SSSS9RU5n[R"[ SS9 URU5 S S S 5 /S Q/S!Q/nURU5n/S"Q/SQ/n[XFR 55 URU5n[R"/S#Q/S$Q/[S9n[ X5 g !,(df  N=f)%z?Test infrequent categories with feature matrix with 3 features.r) rrrrrrdrrr) rrrrrrrrrrKrr(rLrr&rrrrdN)x0_0x0_3rx1_0x1_5x1_infrequent_sklearnx2_0x2_1)rrrrrrrr)rrrrrrrr)rrrrrrrr)rrrrrrrr)rrrrrrrr)rrrrrrrr)rrrrrrrr)rrrrrrrr)rrr)r+rr)rrrrrrrr)rrrrrrrr)rrN)rrNrIr,r.r/)rrr)rrdr)rrrrrrrr)rrr)rrr)rrrrrr rrr r5rrrr1r2r3r4) rrrrr)rp X_test_transrrs r#'test_ohe_infrequent_multiple_categoriesrs #$# % A !s11!4d; --/M    !         HH& #F==(L)*BCHH2245  ! !, /E88 (*IJRXL|+ !G  c!f z)C D f E $F==(L(*BCHH2245  ! !, /E88 8:VWL|+! E Ds H H"c [R"S5nUR/SQ/SQS.SS/S9n[SS S S 9nUR U5R 5n[ URS S S/5 [ URS/SQ5 /SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/ n[XC5 URSS/SS/S.SS/S9n/SQ/SQ/nURU5n[XFR 55 URU5n[R"SS/SS//[S9n[ X5 URSS/SS/S.SS/S9nURU5R 5n/SQ/SQ/n[XF5 URU5n[R"SS/SS//[S9n[ X5 g)zHTest infrequent categories with a pandas dataframe with multiple dtypes.rR rSfrrrrSrrTrT rrrrdrdrgrrr)strrOrrOcolumnsrKrr(rrrSrTrrrrg)rrrrrr)rrrrrr)rrrrrr)rrrrrr)rrrrrrrrgrrIrrN)r2rZr[rrrr rr r5rrrr) r\rrrr)rprrrs r#.test_ohe_infrequent_multiple_categories_dtypesrs   X &B @1   A !PU\ WF"$67H==(LH2245  ! !, /E88  4 5=Q7RSL|+\\3*b!W=u~\ VF==(002L"$67HH+  ! !, /E88 # $';Q&?@L|+r%ri)rrc[R"S/S-S/S--S/S--S/S--/5Rn[SS S S .UD6nUR U5 UR S//5n[ US //5 g ),All user provided categories are infrequent.rSrrTr$rrdrrr(FrrNr)rrrNrr1r5r rrrrs r#$test_ohe_infrequent_one_level_errorsrHshh SEBJ.#;seaiGHIKKG  ,E EK CGGGmmcUG$GGqcU#r%c[R"S/S-/[S9Rn[ S /SQ/SSS.UD6R U5nUR S/S//5n[US /S //5 g ) rrrrIrFr(rrSrNr)rrrrNrr1r5r rs r#5test_ohe_infrequent_user_cats_unknown_training_errorsrVshh {&133G  (),      c'l mmcUSEN+GGqcA3Z(r%zinput_dtype, category_dtype)OOOUUOUUSOSUSS array_type)rr dataframec[R"S/S//US9n[R"SS/US9/n[USS9RU5n[ S/S/S/S//X S9nUR U5n[R"SS/SS/SS/SS//5n[ Xx5 [US9RU5n U R U5n[R"S/S/S/S//5n[Xx5 g ) aCheck 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 rTrSrIFrrrrN) rrrr1rr5r rr ) rGcategory_dtyperrrLrrprr)oes r#test_encoders_string_categoriesrgs 3%#{3A((C:^<=J :U C G G JC  use$jFmmF#Gxx!Q!Q!Q!Q89HG& : . 2 21 5Bll6"Gxx!qcA3,-Hw)r%c.[R"S/S//SS9n[R"SS/SS9/n[USS9n[R"S5n[ R "[US 9 URU5 S S S 5 g !,(df  g =f) zCheck that this mixture of predefined categories and X raises an error. Categories defined as bytes can not easily be compared to data that is a string. rTrSUrISFrzjIn column 0, the predefined categories have type 'bytes' which is incompatible with values of type 'str_'.r/N) rrrrrr2r3r4r1)rrLrrs r#$test_mixed_string_bytes_categoricalsrsy 3%#s+A((C:S12J :U CC )) ' C z -   . - -s +B Bc[R"SSUSU//[S9Rn[ SSS9R U5nUR 5n[USSS U3/5 g) NrSrTrIFr'rr&x0_arx0_)rrrrNrr1rr )rtrrnamess r#)test_ohe_missing_values_get_feature_namesrse 3]C?@OQQA eH E I I! LC  % % 'Euvv]O/DEFr%c  [R"S5nUR/SQ[R"SSS[R /[ S9S.SS /S 9n[R"/S Q/S Q/S Q/SQ/5n[U5n[X25 g)NrR)dogrNrrrr+rI)col1col2rrr)rrrrrrr)rrrrrrr)rrrrrrr)rrrrrrr) r2rZr[rrrfloatrr )r\dfexpected_df_transrs r#%test_ohe_missing_value_support_pandasrs   X &B /HHaArvv.e<   B ! ! ! !   #2 &CC+r% pd_nan_typepd.NAznp.nanc "[R"S5nUS:Xa URO[RnUR SUR SSUSS/SS905n[R"/S Q/S Q/S Q/S Q/S Q/5n[S US9nURU5n[XW5 [UR5S:Xde[URSSS/SQ5 [R"URSS5(deg)NrRrrrrSrTrrI)rrrr)rrrr)rrrrrFrrrrAr)r2rZNArrr[rrrrr lenrr isnan)rr&r\pd_missing_valuerrrdf_transs r#1test_ohe_missing_value_support_pandas_categoricalr s   X &B +w 6ruuBFF  BIIsC)93DJIW  B         eN KC  $H%0 s 1 $$ $sq)#2.@ 88COOA&r* + ++ +r%cSS/SS/SS//n[SSUS9nURU5n[R"/S Q/S Q/S Q/5n[ X45 S S //n[R"/S Q/5nSn[ R "[US9 URU5nSSS5 [ X45 URU5n[U[R"SS//[S95 g!,(df  NQ=f)zVCheck drop='first' and handle_unknown='ignore'/'infrequent_if_exist' during transform.rSrrTrrrFrrr&rr)rrrrrtFound unknown categories in columns \[0, 1\] during transform. These unknown categories will be encoded as all zerosr/NrI rrrrr r2rnror5rr rr&rrrrOrprqrs r#/test_ohe_drop_first_handle_unknown_ignore_warnsrs qC8c1X&A  E. C"G    JG(AhZF9+&J  k 2--' 3G(  ! !* -Eubhhaz@A 3 2 C(( C6cSS/SS/SS//n[SSUS9nURU5n[R"/S Q/S Q/S Q/5n[ X45 S S //n[R"/SQ/5nSn[ R "[US9 URU5nSSS5 [ X45 URU5n[U[R"SS//[S95 g!,(df  NQ=f)zDCheck drop='if_binary' and handle_unknown='ignore' during transform.rSrrTrrrFr rrrXrr)rrrrr r/NrIrrs r#3test_ohe_drop_if_binary_handle_unknown_ignore_warnsrs qC8c1X&A  n C"G    JG(AhZF<.)J  k 2--' 3G(  ! !* -Eubhhd }FCD 3 2rc@SS/SS/SS//n[SSUSS/SS//S9nURU5 S S//n[R"SS//5nS n[R "[ US 9 URU5nS S S 5 [WU5 g !,(df  N=f) zjCheck drop='first' and handle_unknown='ignore'/'infrequent_if_exist' during fit with categories passed in.rSrrTrrrFrkrrlr/Nrm)r&rrrprOrqrs r#'test_ohe_drop_first_explicit_categoriesr&s qC8c1X&A  %#JA'  C GGAJAhZFAq6(#J A  k 2--' 3GZ( 3 2rsc[R"S5nUR/SQ/SQS.SS/S9n[SS 9nUR SS 9 S n[R "[ US 9 URU5 S S S 5 URU5 [R "[ US 9 URU5 S S S 5 g !,(df  NS=f!,(df  g =f)zJRaise informative error message when pandas output and sparse_output=True.rRr)rrTrT)rSrTrSrTrTrr5zxPandas output does not support sparse data. Set sparse_output=False to output pandas dataframes or disable Pandas outputr/N) r2rZr[r set_outputr3r4rr1r5)r\rrrs r#'test_ohe_more_informative_error_messagerAs   X &B IO 88BNN1%b) * ** *||BHHuse.C-DsecUST$$X.I ??f $$ $y!Q'#s4yQ'#s4 88IdO $ $$ $r%r")zobject-None-missing-valuezobject-nan-missing_valueznumeric-missing-valuect[US9n[R"S/[R//5n[ UR U5U5 UR SRU:Xde[US9n[R"[SS9 URU5 SSS5 g!,(df  g=f)z.Test ordinal encoder for specified categories.rrrr.r/N) rrrrr rrrJr2r3r4r1)rr8r rrrs r#=test_ordinal_encoder_specified_categories_missing_passthroughr%sL 4 (B ((SEBFF8$ %Cr''*C0 >>!  " "i // / 4 (B z)C D r  E D Ds B)) B7c[R"/SQ[S9/nU"US9n[R"SS//[S9Rn[R "[ SS9 URU5 SSS5 g!,(df  g=f) zTest encoder for specified categories have duplicate values. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/27088 )rSrTrSrIrrSrTz5the predefined categories contain duplicate elements.r/N)rrrrNr2r3r4r1rs r#+test_encoder_duplicate_specified_categoriesr'sl HH_F 3 4D T "C 3*V,..A Q      s "A== B zX, expected_X_trans, X_testrr)rrr)rr"rr)rrSrT)r"rrc[SSS9nURU5n[XA5 [URU5S//5 g)z>Test the interaction between missing values and handle_unknownr8rAr:gN)rrr r5)rexpected_X_transrprrs r#/test_ordinal_encoder_handle_missing_and_unknownr*s@8 ':" MBq!GG.BLL(D6(3r% csr_containerc.[R"/SQ/SQ/5nU"U5n[5nSn[R"[ US9 UR U5 SSS5 [R"[ US9 URU5 SSS5 URU5nU"U5n[R"[ US9 URU5 SSS5 g!,(df  N=f!,(df  Nl=f!,(df  g=f)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) rrrr2r3rr1rr)r+rX_sparseencoderrrr!s r#test_ordinal_encoder_sparser/s )Y'(AQHGBG y 0 H 1 y 0h' 1##A&G"7+N y 0!!.1 1 0 1 0 0 0 1 0s$ C$=C5 D$ C25 D Dc>[R"/SQ5SS2[R4n[/SQ/SSS9nUR U5 [/SQ/SS9n[ R "[S S 9 UR U5 SSS5 g!,(df  g=f) 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 )rrrrrrN)rArrr8rF)rLr&r;r,r r.r/)rrnewaxisrr1r2r3r4)rrs r#-test_ordinal_encoder_fit_with_unseen_categoryr2sw #$Q ]3A <0CSW BFF1I J< HB z)C D q  E D Ds 3B BrAAOrrpcz[SSS9nURU5 URU5n[USS//5 g)zChecks that `OrdinalEncoder` transforms string dtypes. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/19872 r8ir:rN)rr1r5r )rrprrs r#1test_ordinal_encoder_handle_unknown_string_dtypesr6&s;* (;2 NCGGGmmF#GGr1gY'r%c6[R"/SQ5RSS5n[5R U5n[ UR [R"USS9R5 URU5n[ US/S/S/S//5 g) 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}GalIRK2e6krArr)axisrrN) rrrDrr1r rsortrNr5)rr.rs r##test_ordinal_encoder_python_integerr:Bs   gb!n""1%Gw**BGGAA,>,@,@A"Gw!qcA3 45r%c[R"S5n/SQnUR/SQ/US9n[5R U5nUR 5n[ X5 g)z-Check feature names out is same as the input.rR)rTrrSrrN)r2rZr[rr1rr )r\rrrfeature_names_outs r#.test_ordinal_encoder_features_names_out_pandasr=VsU   X &B E i[% 0A    q !C113u0r%c&[R"S/S/[R//[S9n[ S[RSS9R U5nUR U5n[US/S/S//5 [R"S /[R//[S9nUR U5n[U[R/S//5 URU5nUSSbe[R"USS5(deg ) zECheck interactions between encode_unknown and missing value encoding.rSrTrIr8r&r;rrrrN) rrrrrr1r5r rr)rrrrpr X_roundtrips r#0test_ordinal_encoder_unknown_missing_interactionrBbs 3%#)8A *ff   c!f  ll1oGGqcA3-.XXurvvh'v 6F<<'LLBFF8bT"23&&|4K q>!  $$ $ 88KN1% & && &r% with_pandascv[R"SS/SS/S[R//[S9nSnU(a.[R "S5nUR US S /S 9nUS -nOUS -n[SS9n[R"[US9 URU5 SSS5 g!,(df  g=f)zTCheck OrdinalEncoder errors when encoded_missing_value is used by an known category.rSrrTrrrIzTencoded_missing_value \(1\) is already used to encode a known category in features: rRletterpetrz \['pet'\]z\[1\]rrr/N) rrrrr2rZr[rr3r4r1)rCr error_msgr\rs r#0test_ordinal_encoder_encoded_missing_value_errorrHs 3,e sBFFm>>RJ))$/I''-HH%%'3z7798;K;KL 0 /s #D  Dc|[R"S5nURSS/SS/S.5n[5R SS9n[5R SS9nUR U5nUR U5n[ UR5U5 [UR5UR5 g ) z+Check OrdinalEncoder works with set_output.rRrSrTrrrUrRrN) r2rZr[rrrr rSr rr)r\r] ord_default ord_pandasrVrWs r#test_ordinal_set_outputr\s   X &B <)B C6Ry3r%c \[R"S5nUR/SQ5nUR/SQ/SQUR S/S-S/S--S /-S /-US 9S ./S QS 9n[ SS9R U5n[URSSS/5 [URS/SQ5 [URSS S /5 UR/SQ/SQUR S/S /-S /-S/-US 9S ./S QS 9n/SQ/SQ/SQ/SQ/nURU5n[Xe5 g)zHTest infrequent categories with a pandas DataFrame with multiple dtypes.rR)birdrrrhrrrr+rrrhrzrI)rrOrrrvrrSrTrrr)rSrTrr)rgrrdr)rrr)rrr)rrrrN) r2rZCategoricalDtyper[rrr1r rr5r )r\categorical_dtyperrorprprs r#:test_ordinal_encoder_infrequent_multiple_categories_dtypesr}sY   X &B++,KL @199! ugk)WI5@'% .  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F IyAN'GG,r%c[R"S/S-S/S--S/S--S/S--[R/-/[S 9Rn[ S S S SS 9R U5n[UR/S Q/5 [R"S/S/S/S/S/[R//[S 9nS/S/S/S/S /S//nURU5n[XC5 g)zJCheck behavior of unknown_value and encoded_missing_value with infrequent.rSrrTr$rrdrrrIr8r)r&r;rrrrrrN) rrrrrNrr1r rr5r )rrorprprs r#.test_ordinal_encoder_infrequent_custom_mappingrshh cURZ 3%"* ,uqy 8BFF8 CDFa *   c'l  w557HI XXusecUSEC5266(C6 RFcA3aS1#s3N'GG,r%cZ[R"S/S-S/S--S/S--S/S--/[S 9Rn[ S0UDS S S .D6R U5n[ S S S 9R U5nS/S/S/S/S //n[ URU5URU55 g)zMAll categories are considered frequent have same encoding as default encoder.rSrrTr$rrdrrrIr8rAr:rNrrrrrNrr1r r5)rradjusted_encoderdefault_encoderrps r#!test_ordinal_encoder_all_frequentrshh cURZ 3%"* ,uqy 89a & !4B c'l%*" c'lecUSEC53% 0F""6*O,E,Ef,Mr%dc[R"S/S-S/S--S/S--S/S--/[S 9Rn[ S0UDS S S .D6R U5nS/S/S/S/S //n[ URU5S/S/S/S/S //5 g)zAWhen all categories are infrequent, they are all encoded as zero.rSrrTr$rrdrrrIr8rAr:rrNrr)rrr.rps r##test_ordinal_encoder_all_infrequentrshh cURZ 3%"* ,uqy 89a  !4B c'l ecUSEC53% 0FG%%f-aS1#sRD/IJr%c[R"[R/S-S/S--S/S--S/-S/-/[S9Rn[ S S 9R U5n[R"SSS[R//[S9RnURU5n[US /S /S /[R//5 g)z5Check behavior when missing value appears frequently.r$rrdrrrhdeerrIrrvrrrN rrrrrNrr1r5r rrorprs r#-test_ordinal_encoder_missing_appears_frequentr s  &&B%2 %! 3wi ?6( JK aA.2215G XXrvv67v F H HF'GGqcA3bffX67r%c [R"[R/S/S--S/S--S/-S/-S/S-S /S--/[S 9Rn[ S S 9R U5n[R"SS/SS /[RS /SS /SS//[S 9nURU5n[US S/S S/[RS/SS/SS//5 g)z7Check behavior when missing value appears infrequently.rrdrrrhrredrIgreenrIr+)rrrrNrrs r#/test_ordinal_encoder_missing_appears_infrequentr s  VVHw| #ugk 1WI = H GaK7)a- '    a 1-11!4G XX e  W  VVW  G  EN   F'GGq!fq!frvvqkAq6Aq6JKr%c[R"S/S/S//[S9nU"/SQ/S9n[R"[ 5 UR U5 SSS5 g!,(df  g=f)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`. rVrWrbrIrarN)rrrr2r3rr5)rrr.s r#test_encoder_not_fittedr3 sT 3%#&f5A/!23G ~ &! ' & &s A A,)rrfnumpyrr2scipyrsklearn.exceptionsrsklearn.preprocessingrrsklearn.utils._missingrsklearn.utils._testingrr r sklearn.utils.fixesr r$mark parametrizer;rErfloat32r0rPr^rrrrrrrrrrrrrrrrr r]str_rrNrr rrrrr!r,r2r4r6rDrOrKrMrPrUr_rdrirrr{r~rrrrrrrrrrrrrrrrrrrrrr rrrrrr r#r%r'r*r/r2r6r:r=rBrHrMrPrXr\rfrirmrrrtrxr}rrrrrrrr%r#rsc  -?0 /@.)+TU#V#.)+TU&V&$"((BJJ )KL2::rzz(JK ;LM ;"((BJJ )KL AM A92xJ$4 9  ( +z*+ #%67vF /C#78G /Cuu#=>fM "O4FC /C#67vF /Cut#<=VL  .G/.G)+TUUDM2$1,$23V,$^UDM2 b'Ar7QG $y)Y&GHS\E3<'3% F o ?  ' 3'*7!=>'CD CE? CE?#;<1vrxxc ';<=>=E?#;<= "+r{ #uenrd%;RZZH Aq6Aq6" #q!fqc]BJJ? HHsElS%L1 @3Z% ! 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