e!h0hddlmZddlmZmZddlZddlmZm Z m Z m Z m Z ddl mZddlmZmZddlmZmZdd lmZmZdd lmZmZmZmZdd lmZdd lm Z m!Z!m"Z"m#Z#dd l$m%Z%ddl&m'Z'ddl(m)Z)m*Z*ddl+m,Z,m-Z-m.Z.m/Z/m0Z0ddl1m2Z2m3Z3dZ4Gdde e eZ5Gdde5Z6dZ7dZ8Gdde5Z9y))MutableMapping)IntegralRealN) BaseEstimatorClassifierMixinMetaEstimatorMixin _fit_contextclone)NotFittedError) check_scoringget_scorer_names) _CurveScorer!_threshold_scores_to_class_labels)_safe_indexingget_tags) HasMethodsInterval RealNotInt StrOptions)_get_response_values_binary)MetadataRouter MethodMapping_raise_for_paramsprocess_routing) available_if)type_of_target)Paralleldelayed)_check_method_params_estimator_has _num_samplescheck_is_fitted indexable)StratifiedShuffleSplitcheck_cvcf t|jy#t$rt|dYywxYw)N estimator_)r# estimatorr )r*s y/var/www/html/diagnosisapp-backend/venv/lib/python3.12/site-packages/sklearn/model_selection/_classification_threshold.py_check_is_fittedr,/s.1 ++, 1 <01s 00c&eZdZUdZeddgeddggehdgdZeed<dd d Z d Z e d dZ e dZeeddZeeddZeeddZfdZxZS)BaseThresholdClassifieraBase class for binary classifiers that set a non-default decision threshold. In this base class, we define the following interface: - the validation of common parameters in `fit`; - the different prediction methods that can be used with the classifier. .. versionadded:: 1.5 Parameters ---------- estimator : estimator instance The binary classifier, fitted or not, for which we want to optimize the decision threshold used during `predict`. response_method : {"auto", "decision_function", "predict_proba"}, default="auto" Methods by the classifier `estimator` corresponding to the decision function for which we want to find a threshold. It can be: * if `"auto"`, it will try to invoke, for each classifier, `"predict_proba"` or `"decision_function"` in that order. * otherwise, one of `"predict_proba"` or `"decision_function"`. If the method is not implemented by the classifier, it will raise an error. fit predict_probadecision_function>autor0r1r*response_method_parameter_constraintsr2r4c ||_||_yNr3)selfr*r4s r+__init__z BaseThresholdClassifier.__init__Ys".cH|jdk(rddg}|S|j}|S)zDefine the response method.r2r0r1r6)r9r4s r+_get_response_methodz,BaseThresholdClassifier._get_response_method]s6   6 ).0CDO#22Or;F)prefer_skip_nested_validationc jt||dt||\}}t|d}|dk7rtd||j||fi|t |j dr|j j|_t |j dr|j j|_|S)Fit the classifier. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) Target values. **params : dict Parameters to pass to the `fit` method of the underlying classifier. Returns ------- self : object Returns an instance of self. Ny) input_namebinaryz=Only binary classification is supported. Unknown label type: n_features_in_feature_names_in_) rr$r ValueError_fithasattrr)rDrE)r9XrAparamsy_types r+r/zBaseThresholdClassifier.fites0 &$-A1c2 X OPVxX   !Q!&! 4??$4 5"&//"@"@D  4??$7 8%)__%F%FD " r;c.|jjS)zClasses labels.)r)classes_)r9s r+rMz BaseThresholdClassifier.classes_s'''r;cht|t|d|j}|j|S)aPredict class probabilities for `X` using the fitted estimator. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- probabilities : ndarray of shape (n_samples, n_classes) The class probabilities of the input samples. r))r,getattrr*r0r9rIr*s r+r0z%BaseThresholdClassifier.predict_probas/ D,? &&q))r;predict_log_probacht|t|d|j}|j|S)aPredict logarithm class probabilities for `X` using the fitted estimator. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- log_probabilities : ndarray of shape (n_samples, n_classes) The logarithm class probabilities of the input samples. r))r,rOr*rQrPs r+rQz)BaseThresholdClassifier.predict_log_proba/ D,? **1--r;cht|t|d|j}|j|S)aDecision function for samples in `X` using the fitted estimator. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- decisions : ndarray of shape (n_samples,) The decision function computed the fitted estimator. r))r,rOr*r1rPs r+r1z)BaseThresholdClassifier.decision_functionrSr;ct|}d|j_t |j j j|j _|S)NF)super__sklearn_tags__classifier_tags multi_classrr* input_tagssparse)r9tags __class__s r+rWz(BaseThresholdClassifier.__sklearn_tags__sDw')+0(!)$..!9!D!D!K!K r;)__name__ __module__ __qualname____doc__rrr5dict__annotations__r:r=r r/propertyrMrr!r0rQr1rW __classcell__r]s@r+r.r.6s8 / 0 23 4 ''UVW $D6</&+% %N((.12*3*$.!456.7.$.!456.7.$r;r.ceZdZUdZiej edhegeeddgdZe e d<ddddfd Z e d Z d Zd Zd ZxZS) FixedThresholdClassifieraBinary classifier that manually sets the decision threshold. This classifier allows to change the default decision threshold used for converting posterior probability estimates (i.e. output of `predict_proba`) or decision scores (i.e. output of `decision_function`) into a class label. Here, the threshold is not optimized and is set to a constant value. Read more in the :ref:`User Guide `. .. versionadded:: 1.5 Parameters ---------- estimator : estimator instance The binary classifier, fitted or not, for which we want to optimize the decision threshold used during `predict`. threshold : {"auto"} or float, default="auto" The decision threshold to use when converting posterior probability estimates (i.e. output of `predict_proba`) or decision scores (i.e. output of `decision_function`) into a class label. When `"auto"`, the threshold is set to 0.5 if `predict_proba` is used as `response_method`, otherwise it is set to 0 (i.e. the default threshold for `decision_function`). pos_label : int, float, bool or str, default=None The label of the positive class. Used to process the output of the `response_method` method. When `pos_label=None`, if `y_true` is in `{-1, 1}` or `{0, 1}`, `pos_label` is set to 1, otherwise an error will be raised. response_method : {"auto", "decision_function", "predict_proba"}, default="auto" Methods by the classifier `estimator` corresponding to the decision function for which we want to find a threshold. It can be: * if `"auto"`, it will try to invoke `"predict_proba"` or `"decision_function"` in that order. * otherwise, one of `"predict_proba"` or `"decision_function"`. If the method is not implemented by the classifier, it will raise an error. Attributes ---------- estimator_ : estimator instance The fitted classifier used when predicting. classes_ : ndarray of shape (n_classes,) The class labels. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. See Also -------- sklearn.model_selection.TunedThresholdClassifierCV : Classifier that post-tunes the decision threshold based on some metrics and using cross-validation. sklearn.calibration.CalibratedClassifierCV : Estimator that calibrates probabilities. Examples -------- >>> from sklearn.datasets import make_classification >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.metrics import confusion_matrix >>> from sklearn.model_selection import FixedThresholdClassifier, train_test_split >>> X, y = make_classification( ... n_samples=1_000, weights=[0.9, 0.1], class_sep=0.8, random_state=42 ... ) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, stratify=y, random_state=42 ... ) >>> classifier = LogisticRegression(random_state=0).fit(X_train, y_train) >>> print(confusion_matrix(y_test, classifier.predict(X_test))) [[217 7] [ 19 7]] >>> classifier_other_threshold = FixedThresholdClassifier( ... classifier, threshold=0.1, response_method="predict_proba" ... ).fit(X_train, y_train) >>> print(confusion_matrix(y_test, classifier_other_threshold.predict(X_test))) [[184 40] [ 6 20]] r2booleanN) threshold pos_labelr5)rjrkr4cDt|||||_||_yNr3)rVr:rkrj)r9r*rjrkr4r]s r+r:z!FixedThresholdClassifier.__init__3s% 9oN""r;ct|ddx}r |jS t|j|jjS#t$rt dtwxYw)Nr)z+The underlying estimator is not fitted yet.)rOrMr#r*r AttributeError)r9r*s r+rMz!FixedThresholdClassifier.classes_?selD9 99 9%% % " DNN +>>** * " =! " "s *AA"c t|dfi|}t|jj||fi|jj|_|S)r@r/)rr r*r/r))r9rIrArJ routed_paramss r+rGzFixedThresholdClassifier._fitKsJ((e>v> 3%/33AqXM>V #(<(OUX !% 0 '  r;ct|jjj|jt jdd}|S)KGet metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. Returns ------- routing : MetadataRouter A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. ownerr/calleecallerr*method_mapping)rr]r^addr*rr9routers r+get_metadata_routingz-FixedThresholdClassifier.get_metadata_routingsL dnn&=&=>BBnn(?..eE.JC  r;)r^r_r`rar.r5rrstrrbrcr:rdrMrGrzrrerfs@r+rhrhs{Un$ ! 8 8$ &*D1CD1$D # " "0  Dr;rhc|at||t||} }t||t||} } t|||} t|||} |j|| fi| n|||} } } ||| | fi| S)aFit a classifier and compute the scores for different decision thresholds. Parameters ---------- classifier : estimator instance The classifier to fit and use for scoring. If `classifier` is already fitted, it will be used as is. X : {array-like, sparse matrix} of shape (n_samples, n_features) The entire dataset. y : array-like of shape (n_samples,) The entire target vector. fit_params : dict Parameters to pass to the `fit` method of the underlying classifier. train_idx : ndarray of shape (n_train_samples,) or None The indices of the training set. If `None`, `classifier` is expected to be already fitted. val_idx : ndarray of shape (n_val_samples,) The indices of the validation set used to score `classifier`. If `train_idx`, the entire set will be used. curve_scorer : scorer instance The scorer taking `classifier` and the validation set as input and outputting decision thresholds and scores as a curve. Note that this is different from the usual scorer that output a single score value: * when `score_method` is one of the four constraint metrics, the curve scorer will output a curve of two scores parametrized by the decision threshold, e.g. TPR/TNR or precision/recall curves for each threshold; * otherwise, the curve scorer will output a single score value for each threshold. score_params : dict Parameters to pass to the `score` method of the underlying scorer. Returns ------- scores : ndarray of shape (thresholds,) or tuple of such arrays The scores computed for each decision threshold. When TPR/TNR or precision/ recall are computed, `scores` is a tuple of two arrays. potential_thresholds : ndarray of shape (thresholds,) The decision thresholds used to compute the scores. They are returned in ascending order. indices)rr r/) classifierrIrA fit_params train_idxval_idx curve_scorer score_paramsX_trainX_valy_trainy_valfit_params_trainscore_params_vals r+_fit_and_score_over_thresholdsrsz'95~a7Q'95~a7Q/:yQ/<Q w<+;<)*A|&u  E5 E4D EEr;c tjt||Dcgc]\}}tj|||c}}dScc}}w)alCompute the mean interpolated score across folds by defining common thresholds. Parameters ---------- target_thresholds : ndarray of shape (thresholds,) The thresholds to use to compute the mean score. cv_thresholds : ndarray of shape (n_folds, thresholds_fold) The thresholds used to compute the scores for each fold. cv_scores : ndarray of shape (n_folds, thresholds_fold) The scores computed for each threshold for each fold. Returns ------- mean_score : ndarray of shape (thresholds,) The mean score across all folds for each target threshold. r)axis)npmeanzipinterp)target_thresholds cv_thresholds cv_scoressplit_thresholds split_scores r+_mean_interpolated_scorersR& 7725]I1N - + II')9; G    s!A c eZdZUdZiej eeee e ge e ddddgdedhe e d d d gd ge dgd gd gdZeed<dddddddddfd ZdZdZdZdZxZS)TunedThresholdClassifierCVa8Classifier that post-tunes the decision threshold using cross-validation. This estimator post-tunes the decision threshold (cut-off point) that is used for converting posterior probability estimates (i.e. output of `predict_proba`) or decision scores (i.e. output of `decision_function`) into a class label. The tuning is done by optimizing a binary metric, potentially constrained by a another metric. Read more in the :ref:`User Guide `. .. versionadded:: 1.5 Parameters ---------- estimator : estimator instance The classifier, fitted or not, for which we want to optimize the decision threshold used during `predict`. scoring : str or callable, default="balanced_accuracy" The objective metric to be optimized. Can be one of: * a string associated to a scoring function for binary classification (see :ref:`scoring_parameter`); * a scorer callable object created with :func:`~sklearn.metrics.make_scorer`; response_method : {"auto", "decision_function", "predict_proba"}, default="auto" Methods by the classifier `estimator` corresponding to the decision function for which we want to find a threshold. It can be: * if `"auto"`, it will try to invoke, for each classifier, `"predict_proba"` or `"decision_function"` in that order. * otherwise, one of `"predict_proba"` or `"decision_function"`. If the method is not implemented by the classifier, it will raise an error. thresholds : int or array-like, default=100 The number of decision threshold to use when discretizing the output of the classifier `method`. Pass an array-like to manually specify the thresholds to use. cv : int, float, cross-validation generator, iterable or "prefit", default=None Determines the cross-validation splitting strategy to train classifier. Possible inputs for cv are: * `None`, to use the default 5-fold stratified K-fold cross validation; * An integer number, to specify the number of folds in a stratified k-fold; * A float number, to specify a single shuffle split. The floating number should be in (0, 1) and represent the size of the validation set; * An object to be used as a cross-validation generator; * An iterable yielding train, test splits; * `"prefit"`, to bypass the cross-validation. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. warning:: Using `cv="prefit"` and passing the same dataset for fitting `estimator` and tuning the cut-off point is subject to undesired overfitting. You can refer to :ref:`TunedThresholdClassifierCV_no_cv` for an example. This option should only be used when the set used to fit `estimator` is different from the one used to tune the cut-off point (by calling :meth:`TunedThresholdClassifierCV.fit`). refit : bool, default=True Whether or not to refit the classifier on the entire training set once the decision threshold has been found. Note that forcing `refit=False` on cross-validation having more than a single split will raise an error. Similarly, `refit=True` in conjunction with `cv="prefit"` will raise an error. n_jobs : int, default=None The number of jobs to run in parallel. When `cv` represents a cross-validation strategy, the fitting and scoring on each data split is done in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. random_state : int, RandomState instance or None, default=None Controls the randomness of cross-validation when `cv` is a float. See :term:`Glossary `. store_cv_results : bool, default=False Whether to store all scores and thresholds computed during the cross-validation process. Attributes ---------- estimator_ : estimator instance The fitted classifier used when predicting. best_threshold_ : float The new decision threshold. best_score_ : float or None The optimal score of the objective metric, evaluated at `best_threshold_`. cv_results_ : dict or None A dictionary containing the scores and thresholds computed during the cross-validation process. Only exist if `store_cv_results=True`. The keys are `"thresholds"` and `"scores"`. classes_ : ndarray of shape (n_classes,) The class labels. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. See Also -------- sklearn.model_selection.FixedThresholdClassifier : Classifier that uses a constant threshold. sklearn.calibration.CalibratedClassifierCV : Estimator that calibrates probabilities. Examples -------- >>> from sklearn.datasets import make_classification >>> from sklearn.ensemble import RandomForestClassifier >>> from sklearn.metrics import classification_report >>> from sklearn.model_selection import TunedThresholdClassifierCV, train_test_split >>> X, y = make_classification( ... n_samples=1_000, weights=[0.9, 0.1], class_sep=0.8, random_state=42 ... ) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, stratify=y, random_state=42 ... ) >>> classifier = RandomForestClassifier(random_state=0).fit(X_train, y_train) >>> print(classification_report(y_test, classifier.predict(X_test))) precision recall f1-score support 0 0.94 0.99 0.96 224 1 0.80 0.46 0.59 26 accuracy 0.93 250 macro avg 0.87 0.72 0.77 250 weighted avg 0.93 0.93 0.92 250 >>> classifier_tuned = TunedThresholdClassifierCV( ... classifier, scoring="balanced_accuracy" ... ).fit(X_train, y_train) >>> print( ... f"Cut-off point found at {classifier_tuned.best_threshold_:.3f}" ... ) Cut-off point found at 0.342 >>> print(classification_report(y_test, classifier_tuned.predict(X_test))) precision recall f1-score support 0 0.96 0.95 0.96 224 1 0.61 0.65 0.63 26 accuracy 0.92 250 macro avg 0.78 0.80 0.79 250 weighted avg 0.92 0.92 0.92 250 r%Nleft)closedz array-like cv_objectprefitrug?neitherri random_state)scoring thresholdscvrefitn_jobsrstore_cv_resultsr5balanced_accuracyr2dTF)rr4rrrrrrct |||||_||_||_||_||_||_| |_yrm) rVr:rrrrrrr) r9r*rr4rrrrrrr]s r+r:z#TunedThresholdClassifierCV.__init__sJ 9oN $  ( 0r;c tjtr;djcxkrdkr&nn#tdjjnjdk(r=j dur t d tjdjnDtjd j d urjdkDr t d td fi|j_dk(r6j_jd t!t#fg}n t%j_t%jj&fij(j&}j rjj*}}}nnt-j&fij(j&\} } t/| }t/| }t1jj*| }jj*||fi|t3t5j6fd|D\} } t9d| Dr t dt;d| D} t=d| D}tj>t@r#tCjD| |j>}ntCjFj>}tI|| | }|jK}||_&||_'jPr ||d_)S#t$r}td|d }~wwxYw)aFit the classifier and post-tune the decision threshold. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) Target values. **params : dict Parameters to pass to the `fit` method of the underlying classifier and to the `scoring` scorer. Returns ------- self : object Returns an instance of self. rr%)n_splits test_sizerrTz'When cv='prefit', refit cannot be True.rMz-When cv='prefit', `estimator` must be fitted.N)rArFz1When cv has several folds, refit cannot be False.r/r)rc 3K|]f\}}ttdk7r tnjj||j j jhyw)r)rrrrrN)rrr r*r/ _curve_scorerscorerscore) .0rrrIrrrqr9rAs r+ z2TunedThresholdClassifierCV._fit..st *'Iw867)+xE*%Z,66::'#!%!3!3!.!5!5!;!;   *sA,A/c3TK|] }tj|d|d"yw)rN)risclose)rths r+rz2TunedThresholdClassifierCV._fit..s#ARrzz"Q%B(As&(zrThe provided estimator makes constant predictions. Therefore, it is impossible to optimize the decision threshold.c3<K|]}|jywr8)minrrs r+rz2TunedThresholdClassifierCV._fit..% '7  " c3<K|]}|jywr8)maxrs r+rz2TunedThresholdClassifierCV._fit..(rr)num)rscores)* isinstancerrr&rrrFr#r*r r' get_n_splitsr_get_curve_scorerrr)ranger"r splitsplitterr/nextrr rrranyrrrrrlinspaceasarrayrargmax best_score_best_threshold_r cv_results_)r9rIrArJexcsplitsrrrrrwrr min_threshold max_thresholddecision_thresholdsobjective_scoresbest_idxrrrqs``` @@@r+rGzTunedThresholdClassifierCV._fits=( dggt $TWWq'dggDv> !335 >"nnDOJU<?345F#DNN3DOt~~.JRXXaCm&<&<&B&BCFzz56=;R;R;V;V"2 $HBHHQ$R]5K5K5Q5Q$RS 1(I6(I6#7}..22I$  DOO   E4D E#& )XT[[ ) * *+1 * $  = A=A AA   ;H   ;H   doox 0"$++}$//# #%**T__"= 3   $**,+H528<  1* D   q" $G sM,, N5 NNct|d|jj}t|j||j |\}}t ||j|j|S)rsr))rk) r#r_get_pos_labelrr)r=rrrM)r9rIrkrvrws r+rzz"TunedThresholdClassifierCV.predict@sk l+&&557 0 OO  % % '  1 T))4==)  r;ct|jjj|jt jddj|j t jddj|jt jdd}|S) r|r}r/rrr)rrr)rr)rr]r^rr*rrrrs r+rz/TunedThresholdClassifierCV.get_metadata_routingZs !8!8 9 S..,22%2NS,22'%2PS--/,22'%2P  r;ct|j|j}tj||j |j }|S)z8Get the curve scorer based on the objective metric used.)r)r r*rr from_scorerr=r)r9rrs r+rz,TunedThresholdClassifierCV._get_curve_scorerwsB E#// T..0$// r;)r^r_r`rar.r5rsetrcallablerrrrrbrcr:rGrzrrrerfs@r+rrs`D$ ! 8 8$ s+-. /    !T&A<P  z " Zc) < T"'(&K!$D.$ 1,un 4:r;r):collections.abcrnumbersrrnumpyrbaserrr r r exceptionsr metricsr rmetrics._scorerrrutilsrrutils._param_validationrrrrutils._responserutils.metadata_routingrrrrutils.metaestimatorsrutils.multiclassrutils.parallelrrutils.validationr r!r"r#r$_splitr&r'r,r.rhrrrr;r+rs+"(-RR9 0-.51\o/A=\~@6@FFFR8@!8@r;