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These unknown categories will be encoded as all zerosr)Nr< rrrrrr,rrr/rr rfr!rrrrBrrr0rrr/test_ohe_drop_first_handle_unknown_ignore_warnss,      r|cCsddgddgddgg}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.rErrFr rrFrxrLrrJrr )rrrrryr)Nr<rzr{rrr3test_ohe_drop_if_binary_handle_unknown_ignore_warnss,      r}cCsddgddgddgg}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.rErrFr rrFrrrr)Nr)r!rrrrBrrrrr'test_ohe_drop_first_explicit_categories&s    r~cCstd}|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.rDr)rrFrF)rErFrErFrVTrr/zxPandas output does not support sparse data. 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Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/19872 )rrrrr rN)r6rrrr)r?r!rr&rr(r))rrZnewaxisrr+r,r-r.)rrarrr-test_ordinal_encoder_fit_with_unseen_categorys   "rr.ZAAOrcrcCs4tddd}||||}t|ddggdS)zChecks that `OrdinalEncoder` transforms string dtypes. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/19872 rirrN)rr+r/r)r.rrbrrrr1test_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}GalIRK2e6kr6rr)Zaxisr r N) rrr8rr+r rsortrAr/)rrrrrr#test_ordinal_encoder_python_integerBs  rcCsHtd}gd}|jgdg|d}t|}|}t||dS)z-Check feature names out is same as the input.rD)rFrrErrVN)r,rLrMrr+rar )rNrgrrbZfeature_names_outrrr.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.rErFr<rr!rrrrrN) rrrrfrr+r/rrrt)rrarrrP X_roundtriprrr0test_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.rErirFrrr<zTencoded_missing_value \(1\) is already used to encode a known category in features: rDletterZpetrVz \['pet'\]z\[1\]rrr)N) rrrrfr,rLrMrr-r.r+)rr error_msgrNrarrr0test_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. rrrrFrN) rrrr+rr/rrrrrrt) r.ZX_test_trans_expectedZX_roundtrip_expectedrarrPrZ n_samplesrZ expected_valvalrrr9test_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.rDrErFrr rGrzCPandas output does not support sparse data. Set sparse_output=Falser)NFrdefault) r,rLrMrrr-r.rrto_numpyr rarW)rNrOrr*Z ohe_defaultZ ohe_pandas X_defaultX_pandasrrrtest_one_hot_encoder_set_outputs     rcCsttd}|ddgddgd}tjdd}tjdd}||}||}t||t| |j d S) z+Check OrdinalEncoder works with set_output.rDrErFrr rGrrN) r,rLrMrrrrrr rarW)rNrOZ ord_defaultZ ord_pandasrrrrrtest_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. )asZmmasZeasZrasZacsr2rsrN)rr+rsr enumerater=rfr )r?rbnrrrr test_predefined_categories_dtypes rcCs~tjdgdgtjggtd}tdd|}t|dgdgdggtddd |}td gg}||}t|dggd S) zBCheck missing value or unknown encoding can equal the cardinality.rirr<r rrrrrsnakeN) rrrrfrrrr+r/)rrrbrrrr1test_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. rEr rFr%rrr%r<Fr)r!rr)rx0_dx0_er+rrUrr+)rrrr+N)rrrrr+) rrrfrArr+r rarr)rrrrr#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.rErrFrrrUrr rr6rr#rr rrr*Nr) rrrArr+r rr-r/rr)rr.ordinalrexpected_transrr3expected_inverserrr,test_ordinal_encoder_infrequent_three_levels6s,2   rcCstjdgddgddgddgdgtd 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. rErrFrrrUrr r<rHrr6)r?r r!rrr rrr*N) rrrfrArr+r rr-r/rr)r.rrrrr3rrrr6test_ordinal_encoder_infrequent_three_levels_user_cats]s6*   rcCstgdgdf}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.rJrKr r rrr Nr*r<) rZ column_stackrr+r r-r/rrrrf)rrrrrr3rrrr%test_ordinal_encoder_infrequent_mixeds   rcCs 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.rD)birdrrirrRrTrir%rr rrr<)rUrrrVrrrErFrrXr )rErFrSr)rXrrUr)r r r)r r r )rrr r;N) r,rLZCategoricalDtyperMrrr+r r-r/r)rNZcategorical_dtyperrrrrrrr:test_ordinal_encoder_infrequent_multiple_categories_dtypess:    rcCstjdgddgddgddgdtjggtd j}td d d dd |}t|jgd gtjdgdgdgdgdgtjggtd }dgdgdgdgd gdgg}| |}t ||dS)zJCheck behavior of unknown_value and encoded_missing_value with infrequent.rErrFrrrUrr r<rr )r!rr rr$r%rrN) rrrrfrArr+r r-r/r)r.rrrrrrr.test_ordinal_encoder_infrequent_custom_mappings$2( rcCstjdgddgddgddgdgtd j}tdi|d d d |}td d d |}dgdgdgdgd gg}t||||dS)zMAll categories are considered frequent have same encoding as default encoder.rErrFrrrUrr r<rr6rr%NrrrrfrArr+rr/)rr.Zadjusted_encoderZdefault_encoderrrrr!test_ordinal_encoder_all_frequents* *rdcCstjdgddgddgddgdgtd j}tdi|d d d |}dgdgdgdgd gg}t||dgdgdgdgd ggdS)zAWhen all categories are infrequent, they are all encoded as zero.rErrFrrrUrr r<rr6rr%rNrr)rr.rrrrr#test_ordinal_encoder_all_infrequents *(rcCstjtjgddgddgddgdggtdj}td d |}tjdddtjggtdj}||}t|d gd gd gtjggdS)z5Check behavior when missing value appears frequently.rrirUrrrdeerr<r rr rrN rrrrfrArr+r/rrrrrrrr-test_ordinal_encoder_missing_appears_frequent s,  rcCstjtjgdgddgddgdgdgdd gdgtd j}td d |}tjddgdd gtjd gdd gddggtd }||}t|d dgd dgtjdgddgddggdS)z7Check behavior when missing value appears infrequently.rirUrrrrredrgreenr<r%)r!r rrNrrrrr/test_ordinal_encoder_missing_appears_infrequent s(& .rcCsdtjdgdgdggtd}|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`. rHrIrr<rrsN)rrrfr,r-rr/)rrrrrrtest_encoder_not_fitted3 s    "r)rr numpyrr,ZscipyrZsklearn.exceptionsrZsklearn.preprocessingrrZsklearn.utils._missingrZsklearn.utils._testingrrr Zsklearn.utils.fixesr r markZ parametrizer4r9rZfloat32rrCrPrdrgrrrvryrrfrrlrrrrrrrrrZstr_rrArrrrrrrrrrrrrrrrrrr r rrrrrr1r4r9r<r=r>r@rCrGrIrNrQrZr]r^rbrerhrorwr|r}r~rrrrrrrrrrrrrrr@rrrrrrrrrrrrrrrrrrrrs        <      /*       & &*  !& 1                $          $[A      % $     "        !    $  $  0