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expected.r3rr4rrrRrr r"F)r6r rer rcSsg|] }|gqSrrrcolrrrrwsz2test_ohe_infrequent_two_levels..infrequent_sklearnr#rx0_infrequent_sklearnN rrrCr r)rinfrequent_categories_r-rrwr[) rr6X_trainrX_testrr} expected_invX_invr\rrrtest_ohe_infrequent_two_levelsYs(2(    rcCstdgddgddgddgdgj}td d d |d |}t|jd gtdgdgg}||}td gdgg|| }tdg|| |}tdgdgg|dS)z3Test two levels and dropping the frequent category.r3rr4rrrRrr r"Fr r rrrprr rrN) rrrCr r)rrr-rr[rw)rprrrr}r\ X_inverserrr,test_ohe_infrequent_two_levels_drop_frequents"2   rc Csztdgddgddgddgdgj}td d d |d }d |dd}tjt|d||W5QRXdS)z_Test two levels and dropping any infrequent category removes the whole infrequent category.r3rr4rrrRrr r"Fr rUnable to drop category r( from feature 0 because it is infrequentr'NrrrCr r*r+r,r)rprrr8rrr5test_ohe_infrequent_two_levels_drop_infrequent_errorss2rrgQ?g{Gz?rc Cstdgddgddgddgdgj}tfd d d ||}t|jddggdgdgdgdgd gg}td ddgddd gdd dgddd gddd gg}||}t||dgdgdgdgdgg}| |}t||| }tdddg|dS)zkTest that different parameters for combing 'a', and 'd' into the infrequent category works as expected.r3rr4rrrRrr r"Fr rrr rrrrrNr) rrrrrr}rrr\rrr test_ohe_infrequent_three_levelss022    rc Cstdgddgddgddgdgj}td d d|d |}tdgdgdgg}td d gd d gd d gg|||jdd|d}tj t |d|dgdgg}W5QRXtd d gd d gg|dS)z5Test three levels and dropping the frequent category.r3rr4rrrRrr r"Frrr r!r%r&r'rN) rrrCr r)rr-r`r*warns UserWarning)rprrrr8r}rrr.test_ohe_infrequent_three_levels_drop_frequents 2"rc Csztdgddgddgddgdgj}td d d|d }d |d d}tjt|d||W5QRXdS)z7Test three levels and dropping the infrequent category.r3rr4rrrRrr r"Frrrrr'Nrrrrr7test_ohe_infrequent_three_levels_drop_infrequent_errorss2rc Cstdgddgddgddgdgj}td d dd |}t|jddggdgdgdgdgg}td d d gd d d gd d d gd d d gg}||}t||dgg}d}t j t |d||W5QRXdS)zmTest that different parameters for combining 'a', and 'd' into the infrequent category works as expected.r3rr4rrrRrr r$F)r rrr rbadz.Found unknown categories \['bad'\] in column 0r'N) rrrCr r)rrr-rr*r+r,)rrrrr}r8rrr(test_ohe_infrequent_handle_unknown_errors"2*  rcCstjdgddgdgtdj}tfddddggd d d ||}dgdgdgdgdgg}td d gd d gd d gd d gd d gg}||}t||dddgg}dgdgg}|D].}|j|d|td gd gg||qdS)zG'a' is the only frequent category, all other categories are infrequent.r3rrrWr?rrr4Fr"r6rr r rrqrrN) rrr^rCr r)r-rr`)rrrrrr}Zdropsrprrr5test_ohe_infrequent_two_levels_user_cats_one_frequents&" (    r cCstjdgddgddgddgdgtd j}tddddggd d d d |}t|jdddggdgdgdgdgdgg}tddgddgddgddgddgg}||}t ||dddgdgdD}| |}t||dS)zFTest that the order of the categories provided by a user is respected.r3rr4rrrRrr r?Fr"r r6rr rrr rcSsg|] }|gqSrrrrrrrEsz.rr#N rrr^rCr r)rrr-rrwrrrrr}rrrrr(test_ohe_infrequent_two_levels_user_cats/s(( (   r c Cstjdgddgddgddgdgtd j}tddddggd d dd |}t|jddggdgdgdgdgd gg}tdddgdddgdddgdddgdddgg}||}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.r3rr4rrrRrr r?Fr"r rrr rNr r rrr*test_ohe_infrequent_three_levels_user_catsJs2( 2   rc Cstjdddddddddg dddddddddg f}tdddd}||ddgddgg}||}t|ddddgddddggdS) zaTest infrequent categories where feature 0 has infrequent categories, and feature 1 does not.rr r r rF)rrprN)rc_r r)r-r)rrrr}rrrtest_ohe_infrequent_mixedns 2  rc Cstjdddddddddg dddddddddg dddddddddg f}tdddd }||}t|jdddgt|jdddgt|jdd |}td d d dddddg|ddddddddgddddddddgddddddddgddddddddgddddddddgddddddddgddddddddgddddddddgddddddddgg }t||dddgdddgg}| |}ddddddddgddddddddgg}t||| |}tj ddd gddd ggt d}t||tdddd  |}tjtdd| |W5QRXdddgdddgg}| |}ddddddddgddddddddgg}t||| |}tj dddgdddggt d}t||d S)z?Test infrequent categories with feature matrix with 3 features.rr r r rrRrAr"r6rr NZx0_0Zx0_3rZx1_0Zx1_5Zx1_infrequent_sklearnZx2_0Zx2_1r#rr?r$r&r')rrr rrrrr[rr-rwrr^r)r*r+r,) rrr}r\rr X_test_transrrrrr'test_ohe_infrequent_multiple_categoriess   (   ( rc Cs(td}|jdddddddddg dddd d d dddg d d d gd}tdddd}||}t|jdddgt|jdddd gddddddgddddddgddddddgddddddgddddddgddddddgddddddgddddddgddddddgg }t|||jddgdd gd d d gd}ddddddgddddddgg}| |}t||| |}t j ddgddggt d}t|||jddgd dgd d d gd}| |}ddddddgddddddgg}t||| |}t j ddgddggt d}t||dS)zHTest infrequent categories with a pandas dataframe with multiple dtypes.rFr3frr4rr rrRrT)strrrrcolumnsrAr"rr rr?N)r*rJrKr rrrrrr-rwrrr^) rLrrr}rrrrrrrr.test_ohe_infrequent_multiple_categories_dtypess\            rrV)rrcCsptdgddgddgddgdgj}tfd d d |}|||dgg}t|d ggd S),All user provided categories are infrequent.r3rr4rrrRrr r"Frr N)rrrCr r)r-rrrrr}rrr$test_ohe_infrequent_one_level_errorss2 rcCsftjdgdgtdj}tfddddggdd d ||}|dgdgg}t|d gd ggd S) rrr r?rrr3r4Fr"rr N)rrr^rCr r)r-rrrrr5test_ohe_infrequent_user_cats_unknown_training_errors*s rc CsHddgddgddgg}d}tjt|dtdd |W5QRXdS) NrOr rPr r z'`sparse` was renamed to `sparse_output`r'Fr)r*r FutureWarningr r))rr8rrr&test_one_hot_encoder_sparse_deprecated;srzinput_dtype, category_dtypeZOOZOUZUOZUUSOZSUZSS array_typerrZ 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 r4r3r?Frcrr r5N) rrr r)rr-rr r) r>Zcategory_dtyper!rr6rrr}roerrrtest_encoders_string_categoriesDs  "  r#c Cshtjdgdggdd}tjddgddg}t|dd}td}tjt|d ||W5QRXd 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. r4r3Ur?SFrczjIn column 0, the predefined categories have type 'bytes' which is incompatible with values of type 'str_'.r'N) rrr rxryr*r+r,r))rr6rr8rrr$test_mixed_string_bytes_categoricalscs r&cCsPtjdd|d|ggtdj}tddd|}|}t|ddd |gdS) Nr3r4r?Fr!rr Zx0_arZx0_)rrr^rCr r)r[r)rrrnamesrrr)test_ohe_missing_values_get_feature_namesxsr)c Cstd}|jddddgtjdddtjgtddd d gd }tdd ddd ddgd ddd dddgddd ddd dgd dddddd gg}t|}t||dS) NrFdogrhr rr#r?)col1col2r+r,rr ) r*rJrKrrrifloatrdr)rLdfexpected_df_transrmrrr%test_ohe_missing_value_support_pandass    r0 pd_nan_typepd.NAznp.nanc Cstd}|dkr|jntj}|d|jdd|ddgddi}td d d d gd d d d gd d d d gd d d d gd d d d gg}td |d }| |}t ||t |j d kst t|j d dd dddgt|j d d st dS)NrFr2r+rr3r4categoryr?rr Fr'r:)r*rJNArrirKrrr rrlenrrrisnan)r1r rLpd_missing_valuer.r/rdf_transrrr1test_ohe_missing_value_support_pandas_categoricals*         r9c Csddgddgddgg}tdd|d}||}tdddgdddgdddgg}t||d d gg}tdddgg}d }tjt|d ||}W5QRXt||| |}t |tjddggt d dS)zZCheck drop='first' and handle_unknown='ignore'/'infrequent_if_exist' during transform.r3rr4r r rqFrprr rr tFound unknown categories in columns \[0, 1\] during transform. These unknown categories will be encoded as all zerosr'r?N r rrrrr*rrr-rwrr^r rrr}rDrwarn_msgrrrr/test_ohe_drop_first_handle_unknown_ignore_warnss.     r?c Csddgddgddgg}tdd|d}||}tddddgddddgddddgg}t||d d gg}tddddgg}d }tjt|d ||}W5QRXt||| |}t |tjdd ggt dd S)zDCheck drop='if_binary' and handle_unknown='ignore' during transform.r3rr4r r rFr:rr r;r'Nr?r<r=rrr3test_ohe_drop_if_binary_handle_unknown_ignore_warnss.        r@c Csddgddgddgg}tdd|ddgddggd}||d dgg}tddgg}d }tjt|d ||}W5QRXt||d S) znCheck drop='first' and handle_unknown='ignore'/'infrequent_if_exist' during fit with categories passed in.r3rr4r r rqF)rprr r6rzqFound unknown categories in columns \[0\] during transform. These unknown categories will be encoded as all zerosr'N) r r)rrr*rrr-r)r rrrrDr>r}rrr'test_ohe_drop_first_explicit_categoriess  rAc CsXttjdddggj}ttjd}dtj}tjt|d| |W5QRXdS)zDTest ordinal encoder with nan passthrough fails when dtype=np.int32.rrr?zdThere are missing values in features \[0\]. For OrdinalEncoder to encode missing values with dtype: r'N) rrrirCr int32r*r+r,r))rr"r8rrrBtest_ordinal_encoder_passthrough_missing_values_float_errors_dtypes   rCencoded_missing_valuercCstjtjdddggtjdj}t|d|}t|jdks@t t |jdddtjg| |}t ||gdgdgdgg| |}t ||dS) z.Test ordinal encoder with nan on float dtypes.rrr?rDr rrN) rrrirrCr r)r5rrrr-rw)rDrr"r}rrrr5test_ordinal_encoder_passthrough_missing_values_float,s  rFc Cs$td}|dkr|jntj}|d|jdd|ddgddi}t|d |}t |j d ksbt t |j d d d dddgt |j d dst ||}t|dgdg|gdgdgg||}|jdkst t |d dd fddgt |d d d fddgt |ds t d S)z0Check ordinal encoder is compatible with pandas.rFr2r+rr3r4r3r?rEr rNr r:@rr)rr r ru)r*rJr4rrirKrr r)r5rrrr6r-rrwr)r1rDrLr7r.r"r8rrrr=test_ordinal_encoder_missing_value_support_pandas_categorical>s$   rHrGzobject-None-missing-valuezobject-nan-missing_valueznumeric-missing-valuec Cstt|d}tdgtjgg}t||||jdj|ksBtt|d}t j t dd| |W5QRXdS)z.Test ordinal encoder for specified categories.r5rrr&r'N) r rrrirrrr@rr*r+r,r))rr0rrr"r{rrr=test_ordinal_encoder_specified_categories_missing_passthrough_s&  rIzX, expected_X_trans, X_testrrcCs8tddd}||}t||t||dggdS)z>Test the interaction between missing values and handle_unknownrr:rgN)r rrr-)rZexpected_X_transrr"r}rrr/test_ordinal_encoder_handle_missing_and_unknowns   rJc Cstdddgdddgg}t|}t}d}tjt|d||W5QRXtjt|d| |W5QRX| |}t|}tjt|d| |W5QRXdS)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 r r rz6A sparse matrix was passed, but dense data is requiredr'N) rrrZ csr_matrixr r*r+ TypeErrorr)rrw)rZX_sparseencoderrr}rrrrtest_ordinal_encoder_sparses   rMc Cstddddddgddtjf}tdddggddd }||tdddggd d }tjtd d ||W5QRXdS)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 rr r rNr:rr)r6r rr$rr&r')rrZnewaxisr r)r*r+r,)rr"rrr-test_ordinal_encoder_fit_with_unseen_categorys$  rNrZAAOr$rcCs4tddd}||||}t|ddggdS)zChecks that `OrdinalEncoder` transforms string dtypes. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/19872 rirrN)r r)r-r)rrr7r}rrr1test_ordinal_encoder_handle_unknown_string_dtypess   rPcCsftddddgdd}t|}t|jtj|ddj| |}t|dgd gd gdggd S) 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:r r)Zaxisr r N) rrr;r r)rrsortrCr-)rrLr}rrr#test_ordinal_encoder_python_integers  rRcCsLtd}dddg}|jdddgg|d}t|}|}t||d S) z-Check feature names out is same as the input.rFr4rr3r r r rN)r*rJrKr r)r[r)rLr(rr7Zfeature_names_outrrr.test_ordinal_encoder_features_names_out_pandass    rScCstjdgdgtjggtd}tdtjdd|}||}t|dgdgdggtjd gtjggtd}||}t|tjgdgg||}|ddd kst t |ddst d S) zECheck interactions between encode_unknown and missing value encoding.r3r4r?rr rrDrr rN) rrrir^r r)r-rrwrr6)rr"r}rr X_roundtriprrr0test_ordinal_encoder_unknown_missing_interactions    rW with_pandasc Cstjddgddgdtjggtd}d}|rPtd}|j|d d gd }|d }n|d }tdd}tjt |d| |W5QRXdS)zXCheck OrdinalEncoder errors when encoded_missing_value is used by an known category.r3r*r4rhrr?zTencoded_missing_value \(1\) is already used to encode a known category in features: rFletterZpetrz \['pet'\]z\[1\]r rEr'N) rrrir^r*rJrKr r+r,r))rXr error_msgrLr"rrr0test_ordinal_encoder_encoded_missing_value_error8s"   r[z4X_train, X_test_trans_expected, X_roundtrip_expected1c Cstdtjtjd|}tdgtjgdgg}||}t||||}|jd}t |D]V}||df} ||df} | dkr| dkst q`t | rt | st q`| | ks`t q`dS)znCheck transform when unknown_value and encoded_missing_value is nan. Non-regression test for #24082. rrUr\r4rN) r rrir)rr-rrwrrrrr6) rZX_test_trans_expectedZX_roundtrip_expectedr"rrrVZ n_samplesrZ expected_valvalrrr9test_ordinal_encoder_unknown_missing_interaction_both_nanRs(       r^c Cstd}|ddgddgd}t}|jddd}tjt|d ||W5QRXtd d jd d}td d jdd}||}||}t| |t | |j d S)z*Check OneHotEncoder works with set_output.rFr3r4r r rGr-z*Pandas output does not support sparse datar'FrdefaultN) r*rJrKr set_outputr+r,rrto_numpyrr[r)rLrMrr(Z ohe_defaultZ ohe_pandas X_defaultX_pandasrrrtest_one_hot_encoder_set_outputs    recCsttd}|ddgddgd}tjdd}tjdd}||}||}t||t| |j d S) z+Check OrdinalEncoder works with set_output.rFr3r4r r rGr`r_N) r*rJrKr rarrrbrr[r)rLrMZ ord_defaultZ ord_pandasrcrdrrrtest_ordinal_set_outputs   rfcCszdddddgddgg}t|d}|ddggt|t|jksFtt|jD]$\}}|jtksftt|||qPd S) zjCheck that the categories_ dtype is `object` for string categories Regression test for gh-25171. asZmmasZeasZrasZacsr\2r5N) r r)r5rr enumerater@r^r)r6r7nrhrrr test_predefined_categories_dtypes rk)qrxZnumpyrZscipyrr*Zsklearn.exceptionsrZsklearn.utils._testingrrrZ sklearn.utilsrZsklearn.preprocessingr r rmarkZ parametrizer2r9r<rBZfloat32rrErNr]r_rbrdrr^rir-rnr|r~rrrrrrZstr_Zfloat_rrCrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr r rrrrrrrr#r&r)r0r9r?r@rArCrFrHrIrJrMrNrPrRrSrWr[rBr^rerfrkrrrrs             <       /0&     & &* !       -8               $           $[A       % $     "       !