iAddlZddlmZddlZddlmZddlmZddl m Z ddl m Z ddl mZddlmZmZGd d Zd Zd Zd ZdZdZdZdZdZy)N)Mapping)check_consistent_length)check_matplotlib_support)_get_response_values_binary) parse_version)type_of_target)_check_pos_label_consistency _num_samplesceZdZdZddddZedddddZeddddd Zed Ze d Z e d d Z y)"_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 t|jjdddlm}||j \}}|t |dt |dd}||j|fS)Nz.plotrestimator_namer)r __class____name__matplotlib.pyplotpyplotsubplotsgetattrfigure)selfr rplt_s l/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/sklearn/utils/_plotting.py_validate_plot_paramsz8_BinaryClassifierCurveDisplayMixin._validate_plot_paramssc DNN$;$;#!AB/3|y""++7  +   y$&&r) sample_weightr rct|jdt|dk7rtdt|dt |||t ||}||nd}||fS)Nz.from_predictionsbinaryz The target y is not binary. Got  type of target. Classifier)rrr ValueErrorrr )r"y_truer&r(r rs r!_validate_from_predictions_paramszD_BinaryClassifierCurveDisplayMixin._validate_from_predictions_params5sz !CLL>1B!CD & !X -2>&3I2JK   >0FC 't\$rct|jdddh}tfd|Dstd|dt dddt dd d}}t |||zk7rtd ||zd t |d t |d k7rtdt |dt|||y)Nz.from_cv_resultsr#indicesc3&K|]}|v yw)N).0key cv_resultss r zV_BinaryClassifierCurveDisplayMixin._validate_from_cv_results_params..Ts> 3*$ 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+)rrallr-lenr rr)r"r6r$r%r( required_keys train_size test_sizes ` r _validate_from_cv_results_paramszC_BinaryClassifierCurveDisplayMixin._validate_from_cv_results_paramsHs !CLL>1A!BC$i0 > >>T /"$$   9%g.q1 2  9%f-a0 1 ?j94 4&236,q/9J!M  !  (4^A5F4GGWX  1m4rcR|||d|d|dd}|S| |d|d}|S||}|Sd}|S)z;Helper to get legend label using `name` and `legend_metric`Nz (z = 0.2f)r3)curve_legend_metric curve_namelegend_metric_namelabels r_get_legend_labelz4_BinaryClassifierCurveDisplayMixin._get_legend_labelmsz  *z/E!l"%7$8`curve_kwargs` must be None, a dictionary or a list of length z. Got: r:zfTo 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.Nmeanrstdz +/- rBrCmetricrG)r-warningswarn FutureWarning isinstancelistr<strrupdater rHextendzipappend enumerate_validate_style_kwargs)n_curvesr legend_metricrF curve_kwargsdefault_curve_kwargsdefault_multi_curve_kwargskwargslabelslabel_aggregaterDrEfold_idxrG curve_kwargs_s r_validate_curve_kwargsz9_BinaryClassifierCurveDisplayMixin._validate_curve_kwargszs^ F?   MMR  "L lD )c,.?8.KP*GL>4  tT "D Q|T266>Z@OO  dC 6D dD !c$i1n(?D$(Lv d lG ,(>H4L  !4(?L  '#% % -)+ & a< ' '(B C ] "@RRf%tAw0BO U#/7&',}U7KD6QQR/SS$ (E-2Ft1L*MM$ MM?+tf1 .EE F36}X7NPT3U/#Z 6HH+Z9K4V$-V#4  $5% #%8#78,x:P $5    s G,)NN) r __module__ __qualname____doc__r classmethodr'r/r@ staticmethodrHrer3rrr r s +/T #174d'' .2d$"5"5H  "#'EErr c||S||rdSdSt|r |jn|}|r|jdr|dd}nd|}n|jdrd|dd}|jdd}|j S) 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 startswithreplace capitalize) score_namescoring negate_scores r_validate_score_namerws #/ .224 %%' % %;LS;Q 237 8&7&< s # ( rcdD] }|j|jd"dD]!}|j|jdd#y)zRemove the top and right spines of the plot. Parameters ---------- ax : matplotlib.axes.Axes The axes of the plot to despine. )toprightF)bottomleftrrJN)spines set_visible set_bounds)r ss r_despinerhsF !   '  ! 1% rct|}|jd|jdz}|dk7r7|rtd|d|dt j d|d|dt |S|S) z/Deprecate `estimator_name` in favour of `name`.r: deprecatedzSCannot provide both `estimator_name` and `name`. `estimator_name` is deprecated in  and will be removed in z. Use `name` only.z"`estimator_name` is deprecated in z. Use `name` instead.)rmajorminorr-rOrPrQ)rrversionversion_removes r_deprecate_estimator_namervsG$G a (9':;N% $$+9,D^DTU##   0 9Q3 5   Krc2|yt|tr|S|gS)z3Convert parameters to a list, leaving `None` as is.N)rRrS)params r_convert_to_list_leaving_noners  }% 7Nrc i}|jD]\}}t|ts|||<i||}t|j Dchc] }t|c}dkDr|j Dcgc]}|}}dj dj |dd|dg} d} d|vrd} dj d |jD} t| d |d | d | ycc}wcc}w) z>Check required and optional parameters are of the same length.rJrz, NrMz'name' (or self.name)z (or `plot`)c3BK|]\}}|dt|yw)z: N)r<)r4r5values rr7z'_check_param_lengths..s(& 5GzsEse2c%j\ "5Gsz from `z` initializationz/, should all be lists of the same length. Got: )rrRrSr<valuesrjoinr-) requiredoptional class_nameoptional_providedrr all_paramsr5 param_keysparams_formattedor_plotlengths_formatteds r_check_param_lengthsrs,~~' e 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 C4< C9ct|}|jd|jdz}|'t|tr|dk(st d|d|dt|tr|dk(s#t jd|d|dt|S|S)z-Deprecate `y_pred` in favour of of `y_score`.r:rrzi`y_pred` and `y_score` cannot be both specified. Please use `y_score` only as `y_pred` was deprecated in rzy_pred was deprecated in z. Please use `y_score` instead.) rrrrRrTr-rOrPrQ)y_scorer&rrs r_deprecate_y_pred_parameterrsG$G a (9':;NJvs$;,@V 33:)<() ,  vs #,(> +G95"##BD    Nr)rOcollections.abcrnumpyry sklearn.utilsr$sklearn.utils._optional_dependenciesrsklearn.utils._responsersklearn.utils.fixesrsklearn.utils.multiclassrsklearn.utils.validationr r r rwrrZrrrrrr3rrrsX#1I?-3Oppf'6#<~ &( 4r