rtjddlmZddlmZmZddlZddlmZm Z m Z m Z m Z ddl mZddlmZmZddlmZmZddlmZmZdd lmZmZdd lmZmZmZmZdd l m!Z!dd l"m#Z#dd l$m%Z%m&Z&m'Z'm(Z(ddl)m*Z*ddl+m,Z,ddl-m.Z.m/Z/ddl0m1Z1m2Z2m3Z3m4Z4m5Z5dZ6Gdde e eZ7Gdde7Z8dZ9dZ:Gdde7Z;dS))MutableMapping)IntegralRealN) BaseEstimatorClassifierMixinMetaEstimatorMixin _fit_contextclone)NotFittedError) check_scoringget_scorer_names) _CurveScorer!_threshold_scores_to_class_labels)StratifiedShuffleSplitcheck_cv)_safe_indexingget_tags) HasMethodsInterval RealNotInt StrOptions) _VisualBlock)_get_response_values_binary)MetadataRouter MethodMapping_raise_for_paramsprocess_routing) available_if)type_of_target)Paralleldelayed)_check_method_params_estimator_has _num_samplescheck_is_fitted indexablecr t|jdS#t$rt|dYdSwxYw)N estimator_)r% estimatorr )r)s o/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/model_selection/_classification_threshold.py_check_is_fittedr+*sR1 +,,,,, 111 <0000001s 66ceZdZUdZeddgeddggehdgdZeed<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>autor/r0r)response_method_parameter_constraintsr1r3c"||_||_dSNr2)selfr)r3s r*__init__z BaseThresholdClassifier.__init__Ts".c4|jdkrddg}n|j}|S)zDefine the response method.r1r/r0r5)r8r3s r*_get_response_methodz,BaseThresholdClassifier._get_response_methodXs+  6 ) ).0CDOO"2Or:F)prefer_skip_nested_validationc Vt||dt||\}}t|d}|dkrtd||j||fi|t |jdr|jj|_t |jdr|jj|_|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(rCrD)r8Xr@paramsy_types r*r.zBaseThresholdClassifier.fit`s0 &$---A1c222 X  XPVXX   !Q!!&!!! 4?$4 5 5 A"&/"@D  4?$7 8 8 G%)_%FD " r:c|jjS)zClasses labels.)r(classes_)r8s r*rLz BaseThresholdClassifier.classes_s''r:cvt|t|d|j}||S)aPredict class probabilities for `X` using the fitted estimator. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- probabilities : ndarray of shape (n_samples, n_classes) The class probabilities of the input samples. r()r+getattrr)r/r8rHr)s r*r/z%BaseThresholdClassifier.predict_probas9 D,?? &&q)))r:predict_log_probacvt|t|d|j}||S)aPredict logarithm class probabilities for `X` using the fitted estimator. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- log_probabilities : ndarray of shape (n_samples, n_classes) The logarithm class probabilities of the input samples. r()r+rNr)rPrOs r*rPz)BaseThresholdClassifier.predict_log_proba9 D,?? **1---r:cvt|t|d|j}||S)aDecision function for samples in `X` using the fitted estimator. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. Returns ------- decisions : ndarray of shape (n_samples,) The decision function computed the fitted estimator. r()r+rNr)r0rOs r*r0z)BaseThresholdClassifier.decision_functionrRr:ct}d|j_t |jjj|j_|S)NF)super__sklearn_tags__classifier_tags multi_classrr) input_tagssparse)r8tags __class__s r*rVz(BaseThresholdClassifier.__sklearn_tags__sBww''))+0(!)$.!9!9!D!K r:)__name__ __module__ __qualname____doc__rrr4dict__annotations__r9r<r r.propertyrLrr#r/rPr0rV __classcell__r\s@r*r-r-1s8 J/ 0 0 J23 4 4 'J'U'U'UVVW $$D6</////\&+%% %N((X(\..1122**32*$\..!45566..76.$\..!45566..76.$r:r-ceZdZUdZiejedhegeeddgdZe 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]] r1booleanN) threshold pos_labelr4)rirjr3cjt||||_||_dSNr2)rUr9rjri)r8r)rirjr3r\s r*r9z!FixedThresholdClassifier.__init__.s4 9oNNN""r:ct|ddx}r|jS t|j|jjS#t$rt dtwxYw)Nr(z+The underlying estimator is not fitted yet.)rNrLr%r)r AttributeErrorr8r)s r*rLz!FixedThresholdClassifier.classes_:swlD99 99 &% % " DN + + +>* * " " " =! " "s < Ac t|dfi|}t|jj||fi|jj|_|S)r?r.)rr r)r.r()r8rHr@rI routed_paramss r*rFzFixedThresholdClassifier._fitFsN((e>>v>> 3%//3AqXXMV # #(<(O(OUX  !% 0 '   r:ct||jtdd}|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)raddr)rr8routers r*get_metadata_routingz-FixedThresholdClassifier.get_metadata_routingsO d+++//n(??..eE.JJ0   r:)r]r^r_r`r-r4rrstrrarbr9rcrLrFrzrrdres@r*rgrgsUUn$ ! 8$ j&**D1CD1$$$D # # # # # # # " "X "0    Dr:rgc|tt||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 outputs a single score value as `curve_scorer` outputs 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. Nindices)rr"r.) classifierrHr@ fit_params train_idxval_idx curve_scorer score_paramsX_trainX_valy_trainy_valfit_params_trainscore_params_vals r*_fit_and_score_over_thresholdsrsp'955~a7Q7Q'955~a7Q7Q/:yQQQ/<QQQ w<<+;<<<<)*A|&u < E5 E E4D E EEr:cdtjfdt||DdS)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. cBg|]\}}tj||S)npinterp).0split_thresholds split_scoretarget_thresholdss r* z,_mean_interpolated_score..s>   - + I')9; G G   r:r)axis)rmeanzip)r cv_thresholds cv_scoress` r*_mean_interpolated_scorersS& 7    14]I1N1N       r:c eZdZUdZiejeeee e ge e ddddgdedhe e d d d gd ge dgd gd gdZeed<dddddddddfd ZdZdZdZdZdZxZS)TunedThresholdClassifierCVaClassifier 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 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: - str: string associated to a scoring function for binary classification, see :ref:`scoring_string_names` for options. - callable: a scorer callable object (e.g., function) with signature ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details. 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 Nleft)closedz array-like cv_objectprefitrug?neitherrh random_state)scoring thresholdscvrefitn_jobsrstore_cv_resultsr4balanced_accuracyr1dTF)rr3rrrrrrct||||_||_||_||_||_||_| |_dSrl) rUr9rrrrrrr) r8r)rr3rrrrrrr\s r*r9z#TunedThresholdClassifierCV.__init__s[ 9oNNN $  ( 0r:c tjtr2djcxkrdkr nntdjjnjdkrYjdurt d tjdn"#t$r}td|d }~wwxYwjnGtjd jd ur' dkrt d td fi| _dkr3j_jd t!t#fg}nt%j_t%jjfijj}jrjj}}}net-jfijj\} } t/| }t/| }t1jj| }jj||fi|t3t5jfd|D\} } t9d| Drt dt;d| D} t=d| D}tjt@rtCj"| |j}ntCj#j}tI|| | }|%}||_&||_'j(r ||d_)S)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.rLz-When cv='prefit', `estimator` must be fitted.N)r@rFz1When cv has several folds, refit cannot be False.r.r)rc 3K|]Z\}}ttdkrtnjj||jjjV[dS)r)rrrrrN)r!rr r)r. _curve_scorerscorerscore) rrrrHrrrqr8r@s r* z2TunedThresholdClassifierCV._fit..s * *'Iw8677)+xE*%%%Z,6:'#!%!3!.!5!;    * * * * * *r:c3XK|]%}tj|d|dV&dS)rN)risclose)rths r*rz2TunedThresholdClassifierCV._fit..s6AARrz"Q%B((AAAAAAr:zrThe provided estimator makes constant predictions. 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