B 0d#@sdZddlmZddlmZddlZddlmZddlmZdd lm Z dd lm Z dd lm Z dd l m Z dd lmZddlmZddlmZddlZddlZddlmZddlmZddlmZddlmZddlmZddlmZddlmZm Z ddl!m"Z"ddl m#Z#ddl m$Z$ddl m%Z%ddl&m'Z'm(Z(ddl)m*Z*ddl+m,Z,GdddZ-Gd d!d!eed"Z.Gd#d$d$ee.Z/Gd%d&d&e e.Z0dS)'a?Gradient Boosted Regression Trees. This module contains methods for fitting gradient boosted regression trees for both classification and regression. The module structure is the following: - The ``BaseGradientBoosting`` base class implements a common ``fit`` method for all the estimators in the module. Regression and classification only differ in the concrete ``LossFunction`` used. - ``GradientBoostingClassifier`` implements gradient boosting for classification problems. - ``GradientBoostingRegressor`` implements gradient boosting for regression problems. )ABCMeta)abstractmethodN) BaseEnsemble)ClassifierMixin)RegressorMixin) BaseEstimator) is_classifier) deprecated)predict_stages) predict_stage)_random_sample_mask) csc_matrix) csr_matrix)issparse)time)train_test_split)DecisionTreeRegressor)DTYPEDOUBLE) _gb_losses)check_random_state) check_array) column_or_1d)check_is_fitted_check_sample_weight)check_classification_targets)NotFittedErrorc@s*eZdZdZddZd ddZddZd S) VerboseReporteraReports verbose output to stdout. Parameters ---------- verbose : int Verbosity level. If ``verbose==1`` output is printed once in a while (when iteration mod verbose_mod is zero).; if larger than 1 then output is printed for each update. cCs ||_dS)N)verbose)selfr r"F/var/www/html/venv/lib/python3.7/site-packages/sklearn/ensemble/_gb.py__init__FszVerboseReporter.__init__rcCsddg}ddg}|jdkr.|d|d|d|d td d t|dt|d ||_d|_t|_ ||_ d S)zInitialize reporter Parameters ---------- est : Estimator The estimator begin_at_stage : int, default=0 stage at which to begin reporting ZIterz Train Lossz {iter:>10d}z{train_score:>16.4f}rz OOB Improvez{oob_impr:>16.4f}zRemaining Timez{remaining_time:>16s}z%10s z%16s  N) subsampleappendprintlentuplejoin verbose_fmt verbose_modr start_timebegin_at_stage)r!estr/Z header_fieldsr,r"r"r#initIs        zVerboseReporter.initcCs|jdk}||j}|d|jdkr|r4|j|nd}|j|dt|jt|d}|dkrvd|d}n d|}t |j j|d|j |||d|j dkr|d|jddkr|jd9_d S) zUpdate reporter with new iteration. Parameters ---------- j : int The new iteration. est : Estimator The estimator. rr<z{0:.2f}mgN@z{0:.2f}s)iterZ train_scoreoob_imprremaining_time N) r&r/r-oob_improvement_ n_estimatorsrr.floatformatr(r, train_score_r )r!jr0do_oobir4r5r"r"r#updategs  &   zVerboseReporter.updateN)r)__name__ __module__ __qualname____doc__r$r1r?r"r"r"r#r;s  rc @seZdZdZeddddddddd d Zed3d d Zd4d dZddZddZ ddZ ddZ ddZ ddZ eddZd5ddZd6dd Zd7d"d#Zd$d%Zd&d'Zd8d(d)Zed*d+Zd,d-Zd.d/Zed0ed1d2ZdS)9BaseGradientBoostingz*Abstract base class for Gradient Boosting.g?rNFg?g-C6?)alphar max_leaf_nodes warm_startvalidation_fractionn_iter_no_changetolcCs||_||_||_||_||_||_||_| |_| |_||_ | |_ | |_ | |_ ||_ ||_||_||_||_||_||_||_dS)N)r8 learning_rateloss criterionmin_samples_splitmin_samples_leafmin_weight_fraction_leafr& max_features max_depthmin_impurity_decrease ccp_alphar1 random_staterEr rFrGrHrIrJ)r!rLrKr8rMrNrOrPrRrSr1r&rQrTrUrEr rFrGrHrIrJr"r"r#r$s*zBaseGradientBoosting.__init__cCsdS)zCalled by fit to validate y.Nr")r!y sample_weightr"r"r# _validate_ysz BaseGradientBoosting._validate_yc Cs|jtkst|j} |} |} xt| jD]} | jrJtj | | ktj d}| j || | |d}t |j d|j|j|j|j|j|j|j||jd }|jdkr||tj }| dk r| n|}|j|||dd| j|j|||||||j| d ||j|| f<q,W|S) z@Fit another stage of ``_n_classes`` trees to the boosting model.)dtype)krWbest) rMZsplitterrRrNrOrPrSrQrFrUrTg?NF)rW check_input)rKrZ)rYboolAssertionErrorloss_copyrangeKZis_multi_classnparrayfloat64Znegative_gradientrrMrRrNrOrPrSrQrFrTr&astypefitZupdate_terminal_regionstree_rK estimators_)r!r>XrVraw_predictionsrW sample_maskrUX_cscX_csrrLZ original_yZraw_predictions_copyrZZresidualtreer"r"r# _fit_stagesJ  zBaseGradientBoosting._fit_stagecCs|jdkrtd|j|jdkr0td|j|j|jksH|jtjkrXtd|j|jdkrpt dt n|jdkrt d t |jd krt |j d krtj ntj}n tj|j}t|r||j|_n |jd kr||j|_n||_d|jkrd ksntd|j|jdk rlt|jtrB|j|jn*t|jtr\|jdksltd|jd|jkrd ksntd|jt|jtr2|jdkrt|rtdtt|j}n|j}nV|jdkrtdtt|j}n2|jdkr"tdtt|j}ntd|jnj|jdkrF|j}nVt|jt j!r^|j}n>d|jkrxd krnntt|j|jd}ntd||_"t|j#t j!t$dfstd|j#dS)z?Check validity of parameters and raise ValueError if not valid.rz.n_estimators must be greater than 0 but was %rgz/learning_rate must be greater than 0 but was %rzLoss '{0:s}' not supported. lszqThe loss 'ls' was deprecated in v1.0 and will be removed in version 1.2. 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Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. y : array-like of shape (n_samples,) Target values (strings or integers in classification, real numbers in regression) For classification, labels must correspond to classes. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node. monitor : callable, default=None The monitor is called after each iteration with the current iteration, a reference to the estimator and the local variables of ``_fit_stages`` as keyword arguments ``callable(i, self, locals())``. If the callable returns ``True`` the fitting procedure is stopped. The monitor can be used for various things such as computing held-out estimates, early stopping, model introspect, and snapshoting. Returns ------- self : object Fitted estimator. )absolute_errorZmaeZmsezCriterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.csrZcscZcooT) accept_sparserYZ multi_outputN)r})rUZ test_sizestratifyrzhThe training data after the early stopping split is missing some classes. Try using another random seed.rv)rrYz9The initial estimator {} does not support sample weights.)rWzPpass parameters to specific steps of your pipeline using the stepname__parameterzXn_estimators=%d must be larger or equal to estimators_.shape[0]=%d when warm_start==TrueC)rYorderrr7)1rMrr|r}r~rGr_validate_datarrrr rXrIrrUrH _n_classesrcuniquerrzrrrrrr_rbrergr: __class__r@ TypeErrorrget_init_raw_predictionsrrr8rir _raw_predictr _fit_stagesr;rr7Z n_estimators_)r!rjrVrWmonitorZsample_weight_is_nonerX_valy_valsample_weight_valrkmsger/Zn_stagesr"r"r#rgs$               zBaseGradientBoosting.fitc  Cs|jd} |jdk} tj| ftd} tdt|j| }|j}|jr\t |jd}| || t |rlt |nd}t |rt |nd}|jdk rt|jtj}|j|dd}| }x:t| |jD](}| rt| ||} ||| || || }||||||| ||| }| r\||| || || |j|<|||| || || |j|<n|||||j|<|jdkr|||| dk r| ||t}|rP|jdk r||t||}t||j|kr|||t|<qPqW|dS) zIteratively fits the stages. For each stage it computes the progress (OOB, train score) and delegates to ``_fit_stage``. Returns the number of stages fit; might differ from ``n_estimators`` due to early stopping. rg?)rYr)r NF)r\)rr&rcZonesr]rrr_r rr1rrrrIfullinf_staged_raw_predictrar8rrpr;r7r?localsnextanyrJr))r!rjrVrkrWrUrrrr/rZ n_samplesr=rlZn_inbagr_Zverbose_reporterrmrnZ loss_historyZy_val_pred_iterr>Z old_oob_scoreZearly_stoppingZvalidation_lossr"r"r#rasj           z BaseGradientBoosting._fit_stagesTcCs tdS)N)NotImplementedError)r!r'r"r"r#_make_estimatorsz$BaseGradientBoosting._make_estimatorcCsb||jdj|dd}|jdkrFtj|jd|jjftj d}n|j ||j tj }|S)z>Check input and compute raw predictions of the init estimator.)rrT)r\rvr)rrY) rri_validate_X_predictrrcrrr_rbrerrf)r!rjrkr"r"r#_raw_predict_inits z&BaseGradientBoosting._raw_predict_initcCs ||}t|j||j||S)z?Return the sum of the trees raw predictions (+ init estimator).)rr rirK)r!rjrkr"r"r#rs z!BaseGradientBoosting._raw_predictccs^|r|j|tdddd}||}x6t|jjdD]"}t|j|||j||Vq4WdS)aCompute raw predictions of ``X`` for each iteration. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. check_input : bool, default=True If False, the input arrays X will not be checked. Returns ------- raw_predictions : generator of ndarray of shape (n_samples, k) The raw predictions of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. Regression and binary classification are special cases with ``k == 1``, otherwise ``k==n_classes``. rrF)rYrrresetrN) rrrrarirr rKr`)r!rjr\rkr>r"r"r#rs z(BaseGradientBoosting._staged_raw_predictcCs\|dd|jD}|s.tj|jtjdSdd|D}tj|dtjd}|t|S)a The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance. Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:`sklearn.inspection.permutation_importance` as an alternative. Returns ------- feature_importances_ : ndarray of shape (n_features,) The values of this array sum to 1, unless all trees are single node trees consisting of only the root node, in which case it will be an array of zeros. cSs&g|]}|D]}|jjdkr |q qS)r)rhZ node_count).0stageror"r"r# sz=BaseGradientBoosting.feature_importances_..)rrYcSsg|]}|jjddqS)F) normalize)rhZcompute_feature_importances)rror"r"r#r%sr)axisrY)rrircrrreZmeansum)r!Zrelevant_treesZrelevant_feature_importancesZavg_feature_importancesr"r"r#feature_importances_s z)BaseGradientBoosting.feature_importances_c Cs|jdk rtd|jttj|tdd}|jj\}}tj ||jdftj dd}xDt |D]8}x2t |D]&}|j||fj }| ||||qlWq^W||j9}|S)ajFast partial dependence computation. Parameters ---------- grid : ndarray of shape (n_samples, n_target_features) The grid points on which the partial dependence should be evaluated. target_features : ndarray of shape (n_target_features,) The set of target features for which the partial dependence should be evaluated. Returns ------- averaged_predictions : ndarray of shape (n_trees_per_iteration, n_samples) The value of the partial dependence function on each grid point. NzxUsing recursion method with a non-constant init predictor will lead to incorrect partial dependence values. Got init=%s.r)rYrr)r1r|r} UserWarningrcZasarrayrrirrrerarhZcompute_partial_dependencerK) r!gridZtarget_featuresr8Zn_trees_per_stageZaveraged_predictionsrrZror"r"r#%_compute_partial_dependence_recursion-s    z:BaseGradientBoosting._compute_partial_dependence_recursioncCs||jdj|dd}|jj\}}t|jd||f}xLt|D]@}x:t|D].}|j||f}|j|dd|dd||f<qVWqHW|S)aApply trees in the ensemble to X, return leaf indices. .. versionadded:: 0.17 Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted to a sparse ``csr_matrix``. Returns ------- X_leaves : array-like of shape (n_samples, n_estimators, n_classes) For each datapoint x in X and for each tree in the ensemble, return the index of the leaf x ends up in each estimator. In the case of binary classification n_classes is 1. )rrT)r\rFN)rrirrrcrraapply)r!rjr8Z n_classesleavesr>r<Z estimatorr"r"r#rVs $zBaseGradientBoosting.applyzoAttribute `n_features_` was deprecated in version 1.0 and will be removed in 1.2. Use `n_features_in_` instead.cCs|jS)N)r)r!r"r"r# n_features_{sz BaseGradientBoosting.n_features_)N)NN)NN)rN)T)T)r@rArBrCrr$rXrprrrrrrrrgrrrrrpropertyrrrr rr"r"r"r#rDsD  >e    ? _  ! ))%rD) metaclasscseZdZdZdZddddddd d d d d d d d d ddd dd dfdd ZddZddZddZddZ ddZ ddZ dd Z d!d"Z d#d$ZZS)%GradientBoostingClassifierat5Gradient Boosting for classification. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage ``n_classes_`` regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced. Read more in the :ref:`User Guide `. Parameters ---------- loss : {'deviance', 'exponential'}, default='deviance' The loss function to be optimized. 'deviance' refers to deviance (= logistic regression) for classification with probabilistic outputs. For loss 'exponential' gradient boosting recovers the AdaBoost algorithm. learning_rate : float, default=0.1 Learning rate shrinks the contribution of each tree by `learning_rate`. There is a trade-off between learning_rate and n_estimators. n_estimators : int, default=100 The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance. subsample : float, default=1.0 The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample < 1.0` leads to a reduction of variance and an increase in bias. criterion : {'friedman_mse', 'squared_error', 'mse', 'mae'}, default='friedman_mse' The function to measure the quality of a split. Supported criteria are 'friedman_mse' for the mean squared error with improvement score by Friedman, 'squared_error' for mean squared error, and 'mae' for the mean absolute error. The default value of 'friedman_mse' is generally the best as it can provide a better approximation in some cases. .. versionadded:: 0.18 .. deprecated:: 0.24 `criterion='mae'` is deprecated and will be removed in version 1.1 (renaming of 0.26). Use `criterion='friedman_mse'` or `'squared_error'` instead, as trees should use a squared error criterion in Gradient Boosting. .. deprecated:: 1.0 Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent. min_samples_split : int or float, default=2 The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. .. versionchanged:: 0.18 Added float values for fractions. min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for fractions. min_weight_fraction_leaf : float, default=0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. max_depth : int, default=3 The maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables. min_impurity_decrease : float, default=0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 init : estimator or 'zero', default=None An estimator object that is used to compute the initial predictions. ``init`` has to provide :meth:`fit` and :meth:`predict_proba`. If 'zero', the initial raw predictions are set to zero. By default, a ``DummyEstimator`` predicting the classes priors is used. random_state : int, RandomState instance or None, default=None Controls the random seed given to each Tree estimator at each boosting iteration. In addition, it controls the random permutation of the features at each split (see Notes for more details). It also controls the random splitting of the training data to obtain a validation set if `n_iter_no_change` is not None. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. max_features : {'auto', 'sqrt', 'log2'}, int or float, default=None The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each split. - If 'auto', then `max_features=sqrt(n_features)`. - If 'sqrt', then `max_features=sqrt(n_features)`. - If 'log2', then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Choosing `max_features < n_features` leads to a reduction of variance and an increase in bias. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. verbose : int, default=0 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree. max_leaf_nodes : int, default=None Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. 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 erase the previous solution. See :term:`the Glossary `. validation_fraction : float, default=0.1 The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if ``n_iter_no_change`` is set to an integer. .. versionadded:: 0.20 n_iter_no_change : int, default=None ``n_iter_no_change`` is used to decide if early stopping will be used to terminate training when validation score is not improving. By default it is set to None to disable early stopping. If set to a number, it will set aside ``validation_fraction`` size of the training data as validation and terminate training when validation score is not improving in all of the previous ``n_iter_no_change`` numbers of iterations. The split is stratified. .. versionadded:: 0.20 tol : float, default=1e-4 Tolerance for the early stopping. When the loss is not improving by at least tol for ``n_iter_no_change`` iterations (if set to a number), the training stops. .. versionadded:: 0.20 ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details. .. versionadded:: 0.22 Attributes ---------- n_estimators_ : int The number of estimators as selected by early stopping (if ``n_iter_no_change`` is specified). Otherwise it is set to ``n_estimators``. .. versionadded:: 0.20 feature_importances_ : ndarray of shape (n_features,) The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance. Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:`sklearn.inspection.permutation_importance` as an alternative. oob_improvement_ : ndarray of shape (n_estimators,) The improvement in loss (= deviance) on the out-of-bag samples relative to the previous iteration. ``oob_improvement_[0]`` is the improvement in loss of the first stage over the ``init`` estimator. Only available if ``subsample < 1.0`` train_score_ : ndarray of shape (n_estimators,) The i-th score ``train_score_[i]`` is the deviance (= loss) of the model at iteration ``i`` on the in-bag sample. If ``subsample == 1`` this is the deviance on the training data. loss_ : LossFunction The concrete ``LossFunction`` object. init_ : estimator The estimator that provides the initial predictions. Set via the ``init`` argument or ``loss.init_estimator``. estimators_ : ndarray of DecisionTreeRegressor of shape (n_estimators, ``loss_.K``) The collection of fitted sub-estimators. ``loss_.K`` is 1 for binary classification, otherwise n_classes. classes_ : ndarray of shape (n_classes,) The classes labels. n_features_ : int The number of data features. .. deprecated:: 1.0 Attribute `n_features_` was deprecated in version 1.0 and will be removed in 1.2. Use `n_features_in_` instead. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_classes_ : int The number of classes. max_features_ : int The inferred value of max_features. See Also -------- HistGradientBoostingClassifier : Histogram-based Gradient Boosting Classification Tree. sklearn.tree.DecisionTreeClassifier : A decision tree classifier. RandomForestClassifier : A meta-estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. AdaBoostClassifier : A meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases. Notes ----- The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and ``max_features=n_features``, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed. References ---------- J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001. J. Friedman, Stochastic Gradient Boosting, 1999 T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009. Examples -------- The following example shows how to fit a gradient boosting classifier with 100 decision stumps as weak learners. >>> from sklearn.datasets import make_hastie_10_2 >>> from sklearn.ensemble import GradientBoostingClassifier >>> X, y = make_hastie_10_2(random_state=0) >>> X_train, X_test = X[:2000], X[2000:] >>> y_train, y_test = y[:2000], y[2000:] >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, ... max_depth=1, random_state=0).fit(X_train, y_train) >>> clf.score(X_test, y_test) 0.913... )rsZ exponentialrsg?dg? friedman_mserrgNrFg-C6?)rLrKr8r&rMrNrOrPrRrSr1rUrQr rFrGrHrIrJrTcs8tj|||||||| | || | ||| |||||ddS)N)rLrKr8rMrNrOrPrRr1r&rQrUr rFrSrGrHrIrJrT)superr$)r!rLrKr8r&rMrNrOrPrRrSr1rUrQr rFrGrHrIrJrT)rr"r#r$s*z#GradientBoostingClassifier.__init__cCsZt|tj|dd\|_}tt||}|dkrBtd|t|j|_|j|_ |S)NT)Zreturn_inverserzuy contains %d class after sample_weight trimmed classes with zero weights, while a minimum of 2 classes are required.) rrcrrZ count_nonzeroZbincountrzr)rr)r!rVrWZn_trim_classesr"r"r#rXs z&GradientBoostingClassifier._validate_ycCstdtdS)Nzcriterion='mae' was deprecated in version 0.24 and will be removed in version 1.1 (renaming of 0.26). Use criterion='friedman_mse' or 'squared_error' instead, as trees should use a squared error criterion in Gradient Boosting.)r|r}r~)r!r"r"r#rsz2GradientBoostingClassifier._warn_mae_for_criterioncCs8|j|tdddd}||}|jddkr4|S|S)a Compute the decision function of ``X``. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- score : ndarray of shape (n_samples, n_classes) or (n_samples,) The decision function of the input samples, which corresponds to the raw values predicted from the trees of the ensemble . The order of the classes corresponds to that in the attribute :term:`classes_`. Regression and binary classification produce an array of shape (n_samples,). rrF)rYrrrr)rrrrravel)r!rjrkr"r"r#decision_function s  z,GradientBoostingClassifier.decision_functionccs||EdHdS)aCompute decision function of ``X`` for each iteration. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Yields ------ score : generator of ndarray of shape (n_samples, k) The decision function of the input samples, which corresponds to the raw values predicted from the trees of the ensemble . The classes corresponds to that in the attribute :term:`classes_`. Regression and binary classification are special cases with ``k == 1``, otherwise ``k==n_classes``. N)r)r!rjr"r"r#staged_decision_function(sz3GradientBoostingClassifier.staged_decision_functioncCs&||}|j|}|jj|ddS)aPredict class for X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- y : ndarray of shape (n_samples,) The predicted values. r)r)rr__raw_prediction_to_decisionrtake)r!rjrkencoded_labelsr"r"r#predict@s  z"GradientBoostingClassifier.predictccs6x0||D]"}|j|}|jj|ddVq WdS)a>Predict class at each stage for X. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Yields ------- y : generator of ndarray of shape (n_samples,) The predicted value of the input samples. r)rN)rr_rrr)r!rjrkrr"r"r#staged_predictSs z)GradientBoostingClassifier.staged_predictc Csb||}y |j|Stk r,Yn2tk r\}ztd|j|Wdd}~XYnXdS)aPredict class probabilities for X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- p : ndarray of shape (n_samples, n_classes) The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. Raises ------ AttributeError If the ``loss`` does not support probabilities. z&loss=%r does not support predict_probaN)rr__raw_prediction_to_probarAttributeErrorrL)r!rjrkrr"r"r# predict_probais   z(GradientBoostingClassifier.predict_probacCs||}t|S)aPredict class log-probabilities for X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- p : ndarray of shape (n_samples, n_classes) The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. Raises ------ AttributeError If the ``loss`` does not support probabilities. )rrclog)r!rjZprobar"r"r#predict_log_probas z,GradientBoostingClassifier.predict_log_probac csry&x ||D]}|j|VqWWnFtk r<Yn2tk rl}ztd|j|Wdd}~XYnXdS)aKPredict class probabilities at each stage for X. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Yields ------ y : generator of ndarray of shape (n_samples,) The predicted value of the input samples. z&loss=%r does not support predict_probaN)rr_rrrrL)r!rjrkrr"r"r#staged_predict_probas z/GradientBoostingClassifier.staged_predict_proba)r@rArBrCr{r$rXrrrrrrrr __classcell__r"r")rr#rsB= rcseZdZdZdZddddddd d d d d d d d dd ddd dd dfdd Zd!ddZddZddZddZ fddZ e de dd Z ZS)"GradientBoostingRegressorai5Gradient Boosting for regression. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function. Read more in the :ref:`User Guide `. Parameters ---------- loss : {'squared_error', 'absolute_error', 'huber', 'quantile'}, default='squared_error' Loss function to be optimized. 'squared_error' refers to the squared error for regression. 'absolute_error' refers to the absolute error of regression and is a robust loss function. 'huber' is a combination of the two. 'quantile' allows quantile regression (use `alpha` to specify the quantile). .. deprecated:: 1.0 The loss 'ls' was deprecated in v1.0 and will be removed in version 1.2. Use `loss='squared_error'` which is equivalent. .. deprecated:: 1.0 The loss 'lad' was deprecated in v1.0 and will be removed in version 1.2. Use `loss='absolute_error'` which is equivalent. learning_rate : float, default=0.1 Learning rate shrinks the contribution of each tree by `learning_rate`. There is a trade-off between learning_rate and n_estimators. n_estimators : int, default=100 The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance. subsample : float, default=1.0 The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample < 1.0` leads to a reduction of variance and an increase in bias. criterion : {'friedman_mse', 'squared_error', 'mse', 'mae'}, default='friedman_mse' The function to measure the quality of a split. Supported criteria are "friedman_mse" for the mean squared error with improvement score by Friedman, "squared_error" for mean squared error, and "mae" for the mean absolute error. The default value of "friedman_mse" is generally the best as it can provide a better approximation in some cases. .. versionadded:: 0.18 .. deprecated:: 0.24 `criterion='mae'` is deprecated and will be removed in version 1.1 (renaming of 0.26). The correct way of minimizing the absolute error is to use `loss='absolute_error'` instead. .. deprecated:: 1.0 Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent. min_samples_split : int or float, default=2 The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. .. versionchanged:: 0.18 Added float values for fractions. min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. .. versionchanged:: 0.18 Added float values for fractions. min_weight_fraction_leaf : float, default=0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. max_depth : int, default=3 Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables. min_impurity_decrease : float, default=0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. .. versionadded:: 0.19 init : estimator or 'zero', default=None An estimator object that is used to compute the initial predictions. ``init`` has to provide :term:`fit` and :term:`predict`. If 'zero', the initial raw predictions are set to zero. By default a ``DummyEstimator`` is used, predicting either the average target value (for loss='squared_error'), or a quantile for the other losses. random_state : int, RandomState instance or None, default=None Controls the random seed given to each Tree estimator at each boosting iteration. In addition, it controls the random permutation of the features at each split (see Notes for more details). It also controls the random splitting of the training data to obtain a validation set if `n_iter_no_change` is not None. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. max_features : {'auto', 'sqrt', 'log2'}, int or float, default=None The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=n_features`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Choosing `max_features < n_features` leads to a reduction of variance and an increase in bias. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. alpha : float, default=0.9 The alpha-quantile of the huber loss function and the quantile loss function. Only if ``loss='huber'`` or ``loss='quantile'``. verbose : int, default=0 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree. max_leaf_nodes : int, default=None Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. 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 erase the previous solution. See :term:`the Glossary `. validation_fraction : float, default=0.1 The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if ``n_iter_no_change`` is set to an integer. .. versionadded:: 0.20 n_iter_no_change : int, default=None ``n_iter_no_change`` is used to decide if early stopping will be used to terminate training when validation score is not improving. By default it is set to None to disable early stopping. If set to a number, it will set aside ``validation_fraction`` size of the training data as validation and terminate training when validation score is not improving in all of the previous ``n_iter_no_change`` numbers of iterations. .. versionadded:: 0.20 tol : float, default=1e-4 Tolerance for the early stopping. When the loss is not improving by at least tol for ``n_iter_no_change`` iterations (if set to a number), the training stops. .. versionadded:: 0.20 ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details. .. versionadded:: 0.22 Attributes ---------- feature_importances_ : ndarray of shape (n_features,) The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance. Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:`sklearn.inspection.permutation_importance` as an alternative. oob_improvement_ : ndarray of shape (n_estimators,) The improvement in loss (= deviance) on the out-of-bag samples relative to the previous iteration. ``oob_improvement_[0]`` is the improvement in loss of the first stage over the ``init`` estimator. Only available if ``subsample < 1.0`` train_score_ : ndarray of shape (n_estimators,) The i-th score ``train_score_[i]`` is the deviance (= loss) of the model at iteration ``i`` on the in-bag sample. If ``subsample == 1`` this is the deviance on the training data. loss_ : LossFunction The concrete ``LossFunction`` object. init_ : estimator The estimator that provides the initial predictions. Set via the ``init`` argument or ``loss.init_estimator``. estimators_ : ndarray of DecisionTreeRegressor of shape (n_estimators, 1) The collection of fitted sub-estimators. n_classes_ : int The number of classes, set to 1 for regressors. .. deprecated:: 0.24 Attribute ``n_classes_`` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26). n_estimators_ : int The number of estimators as selected by early stopping (if ``n_iter_no_change`` is specified). Otherwise it is set to ``n_estimators``. n_features_ : int The number of data features. .. deprecated:: 1.0 Attribute `n_features_` was deprecated in version 1.0 and will be removed in 1.2. Use `n_features_in_` instead. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 max_features_ : int The inferred value of max_features. See Also -------- HistGradientBoostingRegressor : Histogram-based Gradient Boosting Classification Tree. sklearn.tree.DecisionTreeRegressor : A decision tree regressor. sklearn.ensemble.RandomForestRegressor : A random forest regressor. Notes ----- The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and ``max_features=n_features``, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed. References ---------- J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001. J. Friedman, Stochastic Gradient Boosting, 1999 T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009. Examples -------- >>> from sklearn.datasets import make_regression >>> from sklearn.ensemble import GradientBoostingRegressor >>> from sklearn.model_selection import train_test_split >>> X, y = make_regression(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> reg = GradientBoostingRegressor(random_state=0) >>> reg.fit(X_train, y_train) GradientBoostingRegressor(random_state=0) >>> reg.predict(X_test[1:2]) array([-61...]) >>> reg.score(X_test, y_test) 0.4... ) squared_errorrqrrrrtrurg?rg?rrrgrNg?rFg-C6?)rLrKr8r&rMrNrOrPrRrSr1rUrQrEr rFrGrHrIrJrTcs:tj|||||||| | || | | ||||||||ddS)N)rLrKr8rMrNrOrPrRr1r&rQrSrUrEr rFrGrHrIrJrT)rr$)r!rLrKr8r&rMrNrOrPrRrSr1rUrQrEr rFrGrHrIrJrT)rr"r#r$s,z"GradientBoostingRegressor.__init__cCs|jjdkr|t}|S)NO)rYkindrfr)r!rVrWr"r"r#rX7s  z%GradientBoostingRegressor._validate_ycCstdtdS)Nzcriterion='mae' was deprecated in version 0.24 and will be removed in version 1.1 (renaming of 0.26). The correct way of minimizing the absolute error is to use loss='absolute_error' instead.)r|r}r~)r!r"r"r#r<sz1GradientBoostingRegressor._warn_mae_for_criterioncCs"|j|tdddd}||S)aPredict regression target for X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- y : ndarray of shape (n_samples,) The predicted values. rrF)rYrrr)rrrr)r!rjr"r"r#rFsz!GradientBoostingRegressor.predictccs"x||D]}|Vq WdS)aIPredict regression target at each stage for X. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Yields ------ y : generator of ndarray of shape (n_samples,) The predicted value of the input samples. N)rr)r!rjrkr"r"r#r[sz(GradientBoostingRegressor.staged_predictcs*t|}||jd|jjd}|S)aApply trees in the ensemble to X, return leaf indices. .. versionadded:: 0.17 Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted to a sparse ``csr_matrix``. Returns ------- X_leaves : array-like of shape (n_samples, n_estimators) For each datapoint x in X and for each tree in the ensemble, return the index of the leaf x ends up in each estimator. r)rrZreshaperri)r!rjr)rr"r#rps zGradientBoostingRegressor.applyzdAttribute `n_classes_` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26).c CsHy t|Wn6tk rB}ztd|jj|Wdd}~XYnXdS)Nz&{} object has no n_classes_ attribute.r)rrrr:rr@)r!Znfer"r"r#rs z$GradientBoostingRegressor.n_classes_)N)r@rArBrCr{r$rXrrrrr rrrr"r")rr#rsB>    r)1rCabcrrr|_baserbaserrr r utilsr Z_gradient_boostingr r rrnumpyrcZ scipy.sparserrrrZmodel_selectionrrorZ tree._treerrrrrrZutils.validationrrZutils.multiclassr exceptionsrrrDrrr"r"r"r#sT                       O=