-i <SSKrSSKJr SSKrSSKJr SSKJr SSK J r SSK J r SSK Jr SS KJrJr "S S 5rS rS rSrSrSrSrSrg)N)Mapping)check_consistent_length)check_matplotlib_support)_get_response_values_binary) parse_version)type_of_target)_check_pos_label_consistency _num_samplesc\rSrSrSrSSS.Sjr\SSSS.Sj5r\SSSS .S j5r\S 5r \ S 5r \ S 5r Sr g)"_BinaryClassifierCurveDisplayMixinzMixin class to be used in Displays requiring a binary classifier. The aim of this class is to centralize some validations regarding the estimator and the target and gather the response of the estimator. N)axnamec [URRS35 SSKJn UcUR 5upAUc[ US[ USS55nXRU4$)Nz.plotrestimator_namer)r __class____name__matplotlib.pyplotpyplotsubplotsgetattrfigure)selfrrplt_s J/var/www/html/venv/lib/python3.13/site-packages/sklearn/utils/_plotting.py_validate_plot_params8_BinaryClassifierCurveDisplayMixin._validate_plot_paramss_ DNN$;$;#!AB/3|y""++7  +  $&&r ) sample_weightr#rc[URS35 [U5S:wa[S[U5S35e[ XU5 [ XA5nUbUOSnXE4$)Nz.from_predictionsbinaryz The target y is not binary. Got  type of target. Classifier)rrr ValueErrorrr )r%y_truer)r,r#rs r!_validate_from_predictions_paramsD_BinaryClassifierCurveDisplayMixin._validate_from_predictions_params5st !CLL>1B!CD & !X -2>&3I2JK   >0C 't\r c&^[URS35 SS1n[U4SjU55(d[SUS35e[ TSSS5[ TSS S5p[ U5Xx-:wa[S Xx-S [ U5S 35e[ U5S :wa[S[ U5S35e[X#U5 [XS5nU$![a)n [[U 5RSS55eSn A ff=f)Nz.from_cv_resultsr&indicesc3,># UH oT;v M g7f)N).0key cv_resultss r V_BinaryClassifierCurveDisplayMixin._validate_from_cv_results_params..Us> *$ szB`cv_results` does not contain one of the following required keys: zr. Set explicitly the parameters `return_estimator=True` and `return_indices=True` to the function`cross_validate`.trainrtestz=`X` does not contain the correct number of samples. Expected z, got .r.z"The target `y` is not binary. Got r/r2r() rrallr1lenr r rr strreplace) r%r;r'r(r,r# required_keys train_size test_sizees ` r _validate_from_cv_results_paramsC_BinaryClassifierCurveDisplayMixin._validate_from_cv_results_paramsHsE !CLL>1A!BC$i0 > >>>T /"$$   9%g.q1 2  9%f-a0 1 ?j4 4&236,q/9J!M  !  (4^A5F4GGWX  m4 <4YBI   <SV^^Hc:; ; `curve_kwargs` must be None, a dictionary or a list of length z. Got: r@rzfTo avoid labeling individual curves that have the same appearance, `curve_kwargs` should be a list of z dictionaries. Alternatively, set `name` to `None` or a single string to label a single legend entry with mean ROC AUC score of all curves.Ng?z--blue)alpha linestylecolormeanrstdz +/- rLrMmetricrQ)r1warningswarn FutureWarning isinstancelistrBrCrr rRextendzipappend enumerate_validate_style_kwargs)n_curvesr legend_metricrP curve_kwargskwargsdefault_multi_curve_kwargslabelslabel_aggregaterNrOfold_idxrQ curve_kwargs_s r_validate_curve_kwargs9_BinaryClassifierCurveDisplayMixin._validate_curve_kwargss~J F?   MMR  "L lD ) )c,.?8.KP*GL>4  tT " "D Q|T2266>Z@OO  dC 6D dD ! !c$i1n?D$(Lv d lG , ,(>H4L/2PV%W"  !| :;hF "t  ] "@RRf%tAw0BO U#/7&',}U7KD6QQR/SS$ (E-2Ft1L*MM$ MM?+tf1 .EE F36}X7NPT3U/# 6HH+9K4V$-V#4 #4 #GU#3\5K L#4    s1Hr8)r __module__ __qualname____firstlineno____doc__r classmethodr*r3rI staticmethodrRrp__static_attributes__r8r rr r s +/T #174d'' .2d$++Z  vvr r c6UbU$Uc U(aS$S$[U5(a UROUnU(a"URS5(aUSSnO$SU3nOURS5(aSUSS3nURSS5nUR 5$) aValidate the `score_name` parameter. If `score_name` is provided, we just return it as-is. If `score_name` is `None`, we use `Score` if `negate_score` is `False` and `Negative score` otherwise. If `score_name` is a string or a callable, we infer the name. We replace `_` by spaces and capitalize the first letter. We remove `neg_` and replace it by `"Negative"` if `negate_score` is `False` or just remove it otherwise. NzNegative scoreScoreneg_z Negative r )callabler startswithrD capitalize) score_namescoring negate_scores r_validate_score_namers #/ .224 %%'  %;L;Q 37 8&7&<  # ( r cSH!nURURS5 M# SH"nURURSS5 M$ g)zRemove the top and right spines of the plot. Parameters ---------- ax : matplotlib.axes.Axes The axes of the plot to despine. )toprightF)bottomleftrrN)spines set_visible set_bounds)rss r_despinerbsF !   '  ! 1% r c[U5nURSURS-3nUS:wa=U(a[SUSUS35e[R "SUSUS3[ 5 U$U$) z/Deprecate `estimator_name` in favour of `name`.r@ deprecatedzSCannot provide both `estimator_name` and `name`. `estimator_name` is deprecated in z and will be removed in z. Use `name` only.z"`estimator_name` is deprecated in z. Use `name` instead.)rmajorminorr1r]r^r_)rrversionversion_removes r_deprecate_estimator_namerpsG$G a (9':;N% $$+9,D^DTU##   0 9Q3 5   Kr c>Ucg[U[5(aU$U/$)z3Convert parameters to a list, leaving `None` as is.N)r`ra)params r_convert_to_list_leaving_noners# }% 7Nr c 0nUR5H upE[U[5(dMXSU'M" 0UEUEn[UR 5Vs1sHn[U5iM sn5S:aUR 5Vs/sHowPM nnSR SR USS5US/5n Sn SU;aSn SR S UR555n [U S US U S U 35egs snfs snf) z>Check required and optional parameters are of the same length.rrz, Nr[z'name' (or self.name)z (or `plot`)c3F# UHupUS[U53v M g7f)z: N)rB)r9r:values rr<'_check_param_lengths..s%& 5Gzsse2c%j\ "5Gs!z from `z` initializationz/, should all be lists of the same length. Got: )rr`rarBvaluesrjoinr1) requiredoptional class_nameoptional_providedrr all_paramsr: param_keysparams_formattedor_plotlengths_formatteds r_check_param_lengthsrs*~~'  eT " "&+d #(3H2 12J J$5$5$7 8$75CJ$7 89A=%/__%67%6cc%6 7#<<:cr?)CZPR^(TU "j 0$G II& 5?5E5E5G&    |3CG9M< 87s D D)r]collections.abcrnumpyrrr_optional_dependenciesr _responserfixesr multiclassr validationr r r rrrfrrrrr8r rrsS#%<2 &BjjZ'6#<~ &( r