:iMASrSSKrSSKrSSKrSSKrSSKrSSKJr SSK J r SSK J r SSK Jr SSKJr SSKJr SS KJr SS KJr SS KJrJr SSKJr SSKJ r SSK!J"r" SSK#J$r$ SSK%J&r&J'r'J(r( SSK)J*r* SSK+J,r,J-r- SSK.J/r/J0r0J1r1 SSK2J3r3 SSK4J5r5J6r6 \"\Rn5r8\&"\$Rr\,\-S9"SS\\ 55r:g!\\4a SS KJr SS KJr Nf=f)z9Bagging classifier trained on balanced bootstrap samples.N)clone)BaggingClassifier)_parallel_decision_function)_partition_estimators)NotFittedError)DecisionTreeClassifier) parse_version)check_is_fitted)Paralleldelayed)r )r )_ParamsValidationMixin)Pipeline)RandomUnderSampler)BaseUnderSampler) Substitutioncheck_sampling_strategycheck_target_type) available_if)_n_jobs_docstring_random_state_docstring) HasMethodsInterval StrOptions) _fit_context)_bagging_parameter_constraints_estimator_has)sampling_strategyn_jobs random_statec^\rSrSrSr\\"S5:a\R"\ R5r O\R"\ 5r \ R\ "\RSSSS9\"1Sk5\\/S /\"S /5S /S .5 S \ ;a\ S SSSSSSSSSS S SS S. U4SjjjrU4Sjr\"54Sjr\S5r\"SS9U4Sj5rS U4Sjjr\"\"S55S5r\S5r U4Sjr!Sr"U=r#$)!BalancedBaggingClassifier+uA Bagging classifier with additional balancing. This implementation of Bagging is similar to the scikit-learn implementation. It includes an additional step to balance the training set at fit time using a given sampler. This classifier can serves as a basis to implement various methods such as Exactly Balanced Bagging [6]_, Roughly Balanced Bagging [7]_, Over-Bagging [6]_, or SMOTE-Bagging [8]_. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object, default=None The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a decision tree. .. versionadded:: 0.10 n_estimators : int, default=10 The number of base estimators in the ensemble. max_samples : int or float, default=1.0 The number of samples to draw from X to train each base estimator. - If int, then draw ``max_samples`` samples. - If float, then draw ``max_samples * X.shape[0]`` samples. max_features : int or float, default=1.0 The number of features to draw from X to train each base estimator. - If int, then draw ``max_features`` features. - If float, then draw ``max_features * X.shape[1]`` features. bootstrap : bool, default=True Whether samples are drawn with replacement. .. note:: Note that this bootstrap will be generated from the resampled dataset. bootstrap_features : bool, default=False Whether features are drawn with replacement. oob_score : bool, default=False Whether to use out-of-bag samples to estimate the generalization error. warm_start : bool, default=False When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble. {sampling_strategy} replacement : bool, default=False Whether or not to randomly sample with replacement or not when `sampler is None`, corresponding to a :class:`~imblearn.under_sampling.RandomUnderSampler`. {n_jobs} {random_state} verbose : int, default=0 Controls the verbosity of the building process. sampler : sampler object, default=None The sampler used to balanced the dataset before to bootstrap (if `bootstrap=True`) and `fit` a base estimator. By default, a :class:`~imblearn.under_sampling.RandomUnderSampler` is used. .. versionadded:: 0.8 Attributes ---------- estimator_ : estimator The base estimator from which the ensemble is grown. .. versionadded:: 0.10 n_features_ : int The number of features when `fit` is performed. .. deprecated:: 1.0 `n_features_` is deprecated in `scikit-learn` 1.0 and will be removed in version 1.2. When the minimum version of `scikit-learn` supported by `imbalanced-learn` will reach 1.2, this attribute will be removed. estimators_ : list of estimators The collection of fitted base estimators. sampler_ : sampler object The validate sampler created from the `sampler` parameter. estimators_samples_ : list of ndarray The subset of drawn samples (i.e., the in-bag samples) for each base estimator. Each subset is defined by a boolean mask. estimators_features_ : list of ndarray The subset of drawn features for each base estimator. classes_ : ndarray of shape (n_classes,) The classes labels. n_classes_ : int or list The number of classes. oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. oob_decision_function_ : ndarray of shape (n_samples, n_classes) Decision function computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, ``oob_decision_function_`` might contain NaN. n_features_in_ : int Number of features in the input dataset. .. versionadded:: 0.9 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during `fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 0.9 See Also -------- BalancedRandomForestClassifier : Random forest applying random-under sampling to balance the different bootstraps. EasyEnsembleClassifier : Ensemble of AdaBoost classifier trained on balanced bootstraps. RUSBoostClassifier : AdaBoost classifier were each bootstrap is balanced using random-under sampling at each round of boosting. Notes ----- This is possible to turn this classifier into a balanced random forest [5]_ by passing a :class:`~sklearn.tree.DecisionTreeClassifier` with `max_features='auto'` as a base estimator. See :ref:`sphx_glr_auto_examples_ensemble_plot_comparison_ensemble_classifier.py`. References ---------- .. [1] L. Breiman, "Pasting small votes for classification in large databases and on-line", Machine Learning, 36(1), 85-103, 1999. .. [2] L. Breiman, "Bagging predictors", Machine Learning, 24(2), 123-140, 1996. .. [3] T. Ho, "The random subspace method for constructing decision forests", Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998. .. [4] G. Louppe and P. Geurts, "Ensembles on Random Patches", Machine Learning and Knowledge Discovery in Databases, 346-361, 2012. .. [5] C. Chen Chao, A. Liaw, and L. Breiman. "Using random forest to learn imbalanced data." University of California, Berkeley 110, 2004. .. [6] R. Maclin, and D. Opitz. "An empirical evaluation of bagging and boosting." AAAI/IAAI 1997 (1997): 546-551. .. [7] S. Hido, H. Kashima, and Y. Takahashi. "Roughly balanced bagging for imbalanced data." Statistical Analysis and Data Mining: The ASA Data Science Journal 2.5‐6 (2009): 412-426. .. [8] S. Wang, and X. Yao. "Diversity analysis on imbalanced data sets by using ensemble models." 2009 IEEE symposium on computational intelligence and data mining. IEEE, 2009. Examples -------- >>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import train_test_split >>> from sklearn.metrics import confusion_matrix >>> from imblearn.ensemble import BalancedBaggingClassifier >>> X, y = make_classification(n_classes=2, class_sep=2, ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) >>> print('Original dataset shape %s' % Counter(y)) Original dataset shape Counter({{1: 900, 0: 100}}) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, ... random_state=0) >>> bbc = BalancedBaggingClassifier(random_state=42) >>> bbc.fit(X_train, y_train) BalancedBaggingClassifier(...) >>> y_pred = bbc.predict(X_test) >>> print(confusion_matrix(y_test, y_pred)) [[ 23 0] [ 2 225]] z1.4rrright)closed>allautomajority not majority not minorityboolean fit_resampleN)r replacementsamplerbase_estimatorg?TFr() max_samples max_features bootstrapbootstrap_features oob_score warm_startrr.r r!verboser/c b>[TU]UUUUUUUU U U S9 XlXlXlXlg)N) n_estimatorsr1r2r3r4r5r6r r!r7)super__init__ estimatorrr.r/)selfr<r9r1r2r3r4r5r6rr.r r!r7r/ __class__s ]/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/imblearn/ensemble/_bagging.pyr;"BalancedBaggingClassifier.__init__sM$ %#%1!%  #!2& c>[TU]U5n[UR[5(aUR R S:wa}[URUUR R 5R5VVs0sH/up4[R"URU:H5SSU_M1 snnUl U$URUl U$s snnf)Nbypassr) r: _validate_y isinstancerdictsampler__sampling_typeritemsnpwhereclasses__sampling_strategy)r=y y_encodedkeyvaluer>s r?rD%BalancedBaggingClassifier._validate_y4sG'* t--t 4 4 ,,8#:**MM00#%' #'#JC#-.q1!4e;#'D #'+&<&> %dnn-IgI == ' '8 3 MM $ $t7N7N $ O" ', )B C rAcP[R"S[5 UR$)z-Number of features when ``fit`` is performed.z`n_features_` was deprecated in scikit-learn 1.0. This attribute will not be accessible when the minimum supported version of scikit-learn is 1.2.)warningswarn FutureWarningn_features_in_)r=s r? n_features_%BalancedBaggingClassifier.n_features_Vs(      """rA)prefer_skip_nested_validationcB>UR5 [TU] X5$)aBuild a Bagging ensemble of estimators from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. y : array-like of shape (n_samples,) The target values (class labels in classification, real numbers in regression). Returns ------- self : object Fitted estimator. )_validate_paramsr:fit)r=XrNr>s r?rdBalancedBaggingClassifier.fitas ( w{1  rAc>[U5 URc[URS9UlO[ UR5Ul[ TU]XUR5$)N)r.) rr/rr.rGrr:_fitr1)r=rerNr1 max_depth sample_weightr>s r?rhBalancedBaggingClassifier._fitxsW! << . ,,DM"$,,/DMw|A$"2"233rAdecision_functionc ^^^[T5 TRTSS/SSSS9m[TRTR5up#m[ UTR S9"UUU4Sj[U555n[U5TR- nU$)aDAverage of the decision functions of the base classifiers. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- score : ndarray of shape (n_samples, k) The decision function of the input samples. The columns correspond to the classes in sorted order, as they appear in the attribute ``classes_``. Regression and binary classification are special cases with ``k == 1``, otherwise ``k==n_classes``. csrcscNF) accept_sparsedtypeforce_all_finitereset)r r7c 3># UHHn[[5"TRTUTUS-TRTUTUS-T5v MJ g7f)rN)r r estimators_estimators_features_).0irer=startss r? >BalancedBaggingClassifier.decision_function..shF # / 0  VAE];))&)fQUmD   #sAA) r _validate_datarr9r r r7rangesum)r=rer _ all_decisions decisionsrys`` @r?rl+BalancedBaggingClassifier.decision_functions$      %."  2$2C2CT[[Q6  EF 6] F   &):):: rAc[URRS35n[[ S5:a[ U5 UR $Ue![a Uef=f)z2Attribute for older sklearn version compatibility.z+ object has no attribute 'base_estimator_'.z1.2)AttributeErrorr>__name__sklearn_versionr r rVr)r=errors r?base_estimator_)BalancedBaggingClassifier.base_estimator_sg~~&&''R S  ]51 1 %& "   s A AcV>[TU]5nSnSnSnX!;a XAUU'U$X40X'U$)N _xfail_checkscheck_estimators_nan_infz9Fails because the sampler removed infinity and NaN values)r: _more_tags)r=tagstags_key failing_testreasonr>s r?r$BalancedBaggingClassifier._more_tagssIw!#"1 L  +1N< ( +3DN rA)rMr<rVr.r/rGr)N )NNN)$r __module__ __qualname____firstlineno____doc__rr copydeepcopyr_parameter_constraintsrupdaternumbersRealrrFcallablerr;rDrrXpropertyr_rrdrhrrrlrr__static_attributes__ __classcell__)r>s@r?r#r#+sf HV-..!%/@/W/W!X!%/M!N!!q!G<VW " &;"N#34d;  11 "#3 4!   !!!F$+A*B  ##6!7!, 4 .!456+7+Z    rAr#);rrrr[numpyrJsklearn sklearn.basersklearn.ensemblersklearn.ensemble._baggingrsklearn.ensemble._basersklearn.exceptionsr sklearn.treersklearn.utils.fixesr sklearn.utils.validationr sklearn.utils.parallelr r ImportErrorModuleNotFoundErrorjoblibbaserpipelinerunder_samplingrunder_sampling.baserutilsrrrutils._available_ifrutils._docstringrrutils._param_validationrrr utils.fixesr_commonrr __version__r_sampling_strategy_docstringr#rAr?rs?  .A8-/-4,8 */2LL.IFF&C 3 34&CC ( _ 68I_  _/ (),+,sC C#"C#