sgdZddlZddlmZmZddlmZddlmZm Z ddl Z ddl Z ddlmZmZmZmZmZddlmZdd lmZmZdd lmZdd lmZdd lmZmZm Z m!Z!m"Z"dd l#m$Z$m%Z%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,m-Z-m.Z.ddl/m0Z0ddl1m2Z2m3Z3ddl4m5Z5m6Z6m7Z7ddl8m9Z9m:Z:ddgZ;dZ<GddeeZ=Gddee=Z>Gddee=Z?y) zMulti-layer PerceptronN)ABCMetaabstractmethod)chain)IntegralReal) BaseEstimatorClassifierMixinRegressorMixin _fit_context is_classifier)ConvergenceWarning)accuracy_scorer2_score)train_test_split)LabelBinarizer)_safe_indexingcheck_random_state column_or_1d gen_batchesshuffle)IntervalOptions StrOptions)safe_sparse_dot) available_if)_check_partial_fit_first_calltype_of_target unique_labels)_check_optimize_result)check_is_fitted validate_data) ACTIVATIONS DERIVATIVESLOSS_FUNCTIONS) AdamOptimizer SGDOptimizersgdadamcttj||zDcgc]}|jc}Scc}w)z)Pack the parameters into a single vector.)nphstackravel)coefs_ intercepts_ls `/var/www/html/venv/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py_packr30s* 99+)=>Aaggi> ??>s5c eZdZUdZiddeedddgdehd gd ehd gd eed ddgdedheedddgdehdgdeed ddgdeed ddgdeedddgddgddgdeed ddgddgddgdeed ddgddgddgeed ddgeed ddgeed ddgeed ddgeedddeee jhgeedddgd Z e e d!<ed"Zd#Zd$Zd4d&Zd'Zd(Zd)Zd*Zd+Zd5d,Zd-Zd.Zd/Zed%0d1Zd2Zd3Zy)6BaseMultilayerPerceptronzBase class for MLP classification and regression. Warning: This class should not be used directly. Use derived classes instead. .. versionadded:: 0.18 hidden_layer_sizesz array-liker#Nleft)closed activation>relutanhidentitylogisticsolver>r)r*lbfgsalphar batch_sizeauto learning_rate>adaptiveconstant invscalinglearning_rate_initneitherpower_tmax_iterrboolean random_statetolverbose warm_startmomentumbothnesterovs_momentumearly_stopping)validation_fractionbeta_1beta_2epsilonn_iter_no_changemax_fun_parameter_constraintscT||_||_||_||_||_||_||_| |_| |_||_ | |_ | |_ | |_ ||_ ||_||_||_||_||_||_||_||_||_||_yN)r9r>r@rArCrGrIrJlossr6rrLrMrNrOrPrRrSrTrUrVrWrXrY)selfr6r9r>r@rArCrGrIrJr]rrLrMrNrOrPrRrSrTrUrVrWrXrYs r2__init__z!BaseMultilayerPerceptron.__init__as8%  $*"4    "4 ( $  "4,#6    0 ct|jdz D]_}|j|\}}}tj|||||j |<|j |\}}||||j|<ay)z?Extract the coefficients and intercepts from packed_parameters.r#N)range n_layers_ _coef_indptrr,reshaper/_intercept_indptrr0)r^packed_parametersistartendshapes r2_unpackz BaseMultilayerPerceptron._unpackst~~)* ?A $ 1 1! 4 E3ZZ(9%(DeLDKKN//2JE3"3E#">D  Q   ?r`cnt|j}t|jdz D]e}t |||j |||dz<||dzxx|j |z cc<|dz|jdz k7sX|||dzgt|j}||dz|S)a,Perform a forward pass on the network by computing the values of the neurons in the hidden layers and the output layer. Parameters ---------- activations : list, length = n_layers - 1 The ith element of the list holds the values of the ith layer. r#)r$r9rbrcrr/r0out_activation_)r^ activationshidden_activationrhoutput_activations r2 _forward_passz&BaseMultilayerPerceptron._forward_passs(8t~~)* 6A!0QQ!PKA  A $"2"21"5 5 A4>>A-.!+a!e"45  6((<(<=+a!e,-r`TcR|rt||ddgd}|}t|j}t|jdz D]H}t ||j |}||j|z }||jdz k7sA||Jt|j}|||S)a[Predict using the trained model This is the same as _forward_pass but does not record the activations of all layers and only returns the last layer's activation. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data. check_input : bool, default=True Perform input data validation or not. Returns ------- y_pred : ndarray of shape (n_samples,) or (n_samples, n_outputs) The decision function of the samples for each class in the model. csrcscF) accept_sparseresetr#r) r"r$r9rbrcrr/r0rn)r^X check_inputr9rprhrqs r2_forward_pass_fastz+BaseMultilayerPerceptron._forward_pass_fasts& dAeU^5QA (8t~~)* .A(T[[^DJ $**1- -JDNNQ&&!*-  . ((<(<=*%r`ct||j||||<||xx|j|j|zz cc<||xx|zcc<t j ||d||<y)zCompute the gradient of loss with respect to coefs and intercept for specified layer. This function does backpropagation for the specified one layer. rN)rTr@r/r,mean)r^layer n_samplesrodeltas coef_gradsintercept_gradss r2_compute_loss_gradz+BaseMultilayerPerceptron._compute_loss_gradso,K,>,@,@&-P 55TZZ$++e*<<<5Y&!#!:r`cx|j||j||||||\}}}t||} || fS)aRCompute the MLP loss function and its corresponding derivatives with respect to the different parameters given in the initialization. Returned gradients are packed in a single vector so it can be used in lbfgs Parameters ---------- packed_coef_inter : ndarray A vector comprising the flattened coefficients and intercepts. X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data. y : ndarray of shape (n_samples,) The target values. activations : list, length = n_layers - 1 The ith element of the list holds the values of the ith layer. deltas : list, length = n_layers - 1 The ith element of the list holds the difference between the activations of the i + 1 layer and the backpropagated error. More specifically, deltas are gradients of loss with respect to z in each layer, where z = wx + b is the value of a particular layer before passing through the activation function coef_grads : list, length = n_layers - 1 The ith element contains the amount of change used to update the coefficient parameters of the ith layer in an iteration. intercept_grads : list, length = n_layers - 1 The ith element contains the amount of change used to update the intercept parameters of the ith layer in an iteration. Returns ------- loss : float grad : array-like, shape (number of nodes of all layers,) )rl _backpropr3) r^packed_coef_interrxyrorrrr]grads r2_loss_grad_lbfgsz)BaseMultilayerPerceptron._loss_grad_lbfgssLV &',0NN q+vz?- )j/Z1Tzr`c |jd}|j|}|j}|dk(r|jdk(rd}t |||d} d} |j D]+} | j } | tj| | z } -| d|jz| z|z z } |jdz } |d|z || <|j| |||||t|j} t|jdz ddD]Y}t|||j |j ||dz <| ||||dz |j|dz |||||[| ||fS) aCompute the MLP loss function and its corresponding derivatives with respect to each parameter: weights and bias vectors. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data. y : ndarray of shape (n_samples,) The target values. activations : list, length = n_layers - 1 The ith element of the list holds the values of the ith layer. deltas : list, length = n_layers - 1 The ith element of the list holds the difference between the activations of the i + 1 layer and the backpropagated error. More specifically, deltas are gradients of loss with respect to z in each layer, where z = wx + b is the value of a particular layer before passing through the activation function coef_grads : list, length = n_layers - 1 The ith element contains the amount of change used to update the coefficient parameters of the ith layer in an iteration. intercept_grads : list, length = n_layers - 1 The ith element contains the amount of change used to update the intercept parameters of the ith layer in an iteration. Returns ------- loss : float coef_grads : list, length = n_layers - 1 intercept_grads : list, length = n_layers - 1 rlog_lossr=binary_log_loss?rr#)rkrrr]rnr&r/r.r,dotr@rcrr%r9rbrr|)r^rxrrorrrrloss_func_namer]valuesslastinplace_derivativerhs r2rz"BaseMultilayerPerceptron._backpropsHGGAJ ((5  Z 'D,@,@J,N.Nn-aRA #A A bffQl "F # tzz!V+i77~~! #2*t   )[&*o )9t~~)1b1 A+F1It{{1~7G7GHF1q5M {1~va!e} =  # #Ay+vz?   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Kfg%6 ++33ZKWU$$U$7 '..u5.A.((r`c |j}t|ds|g}t|}tjtj |dkrt d|zt|d xs|j xr| }|j||||\}}|j\}}|jdk(r|jd}|jd|_ |g|z|jgz}t|j|_|r|j!|||j"|gdgt%|dz zz} dgt%| dz z} t'|dd|ddD cgc](\} } tj(| | f|j" *} } } |ddD cgc]#} tj(| |j" %}} |j*t,vr|j/||| | | |||n&|j*d k(r|j1||| | | ||t3|j4|j6}t9d |Ds t d |Scc} } wcc} w) N__iter__rz'hidden_layer_sizes must be > 0, got %s.r/)rwr#)rr#rrr?c3bK|]'}tj|j)ywr\)r,isfiniteall).0ws r2 z0BaseMultilayerPerceptron._fit..s!9A2;;q>%%'9s-/zrSolver produced non-finite parameter weights. 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It is going to be clipped)rLgzIteration %d, loss = %.8fzLValidation score did not improve more than tol=%f for %d consecutive epochs.zITraining loss did not improve more than tol=%f for %d consecutive epochs.z`Stochastic Optimizer: Maximum iterations (%d) reached and the optimization hasn't converged yet.zTraining interrupted by user.);r/r0rr>r(rGrCrPrRrIrr'rUrVrWrSrr rrrrTrinverse_transformrkr,arangeintrAminwarningswarncliprrbrJrrrrstopri update_paramsrrrrrNprint_update_no_improvement_countiteration_endsrrXrMtrigger_stoppingrKeyboardInterruptrr)r^rxrrorrrrrparamsrSshould_stratifyrX_valy_valr sample_idxrAitaccumulated_loss batch_sliceX_batchy_batch batch_lossgradsmsg is_stoppings r2rz(BaseMultilayerPerceptron._fit_stochastic&s\t///'$ "={{e#".++&&MM++LL #&"/++KKKKLL #   ;OP P,, +D1Jdoo6JO+qH!1!//22! " AuaT"--??FEEGGAJ YYy4 ??f $S),J* =!Y?JS ;DLDMM*O <<")$BTBT!UJ#& #.y*#EAK||"0J{4K"L"#J{$;"<"#K."#K.%,KN>Bnn#"' ?;J O% #((;+<+<<)$ '8EOO11&%@/A2 ! - : 9$  '' 3<<5tzz8RRS11.%O..tww7--0E0EE&@#xx)>)>?@:#xx)>)>?@ #'//"B"B3 "UK"562<<4==0MMM--(+ UO f **DK#44D  ! ; MM9 : ;s3IR*RR76R7c(|r|j||}|jj||jrt d|jdz|jd}||j |j zkr|xjdz c_nd|_||j kDrb||_|jDcgc]}|jc}|_ |jDcgc]}|jc}|_ yy|jd|j|j z kDr|xjdz c_nd|_|jd|jkr|jd|_yycc}wcc}w)NzValidation score: %frr#r)_scorerrrNrrrMrr/rrr0rrr)r^rSrr val_scorelast_valid_scorerrhs r2rz5BaseMultilayerPerceptron._update_no_improvement_countsU  E51I  # # * *9 5||,t/F/Fr/JJK $66r: 4#>#>#IJ**a/*-.*$"="==.>+6:kk#BAFFH#B ;?;K;K(La(L%> #doo&@@**a/*-.*#doo5"&"2"22"66$C(Ls F 1Fprefer_skip_nested_validationc*|j||dS)aFit the model to data matrix X and target(s) y. Parameters ---------- X : ndarray or sparse matrix of shape (n_samples, n_features) The input data. y : ndarray of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels in classification, real numbers in regression). Returns ------- self : object Returns a trained MLP model. Frrr^rxrs r2fitzBaseMultilayerPerceptron.fits$yyA5y11r`cX|jtvrtd|jzy)NzNpartial_fit is only available for stochastic optimizers. %s is not stochastic.T)r>rAttributeError)r^s r2 _check_solverz&BaseMultilayerPerceptron._check_solvers3 ;;1 1 57;{{C r`c|j|d}tj|js#tj|jrtj S|||S)z.Private score method without input validation.Fry)_predictr,isnanrisinfnan)r^rxrscore_functiony_preds r2_score_with_functionz-BaseMultilayerPerceptron._score_with_functionsUqe4 88F    !RXXf%5%9%9%;66Ma((r`T)F) __name__ __module__ __qualname____doc__rrrrrr,rrZdict__annotations__rr_rlrrrzrrrrrrrrrr rrrr`r2r5r55s!$  Xq$v 6 !$ z"JKL !$ :678 !$ (4D89!$  x Xq$v 6 !$ *%KLM!$ xaiHI!$ HT1d6:;!$ Xh4?@!$ I;!!$" (#!$$ q$v67%!$& I;'!$( yk)!$* XdAq89+!$, yk-!$. 9+/!$0!)q!F CDD!Qv67D!Qv67T1d9=> Xq$v 6 D266( # Xq$v>?A!$D!F22h?2#J ;0dM1^,3\)"JX- ^Y5v7>5262&)r`r5) metaclassceZdZdZ ddddddddd d dd d d d d d d dddddfd ZdZdZddZfdZe de d d dZ dZ dZ fdZxZS)! MLPClassifiera+Multi-layer Perceptron classifier. This model optimizes the log-loss function using LBFGS or stochastic gradient descent. .. versionadded:: 0.18 Parameters ---------- hidden_layer_sizes : array-like of shape(n_layers - 2,), default=(100,) The ith element represents the number of neurons in the ith hidden layer. activation : {'identity', 'logistic', 'tanh', 'relu'}, default='relu' Activation function for the hidden layer. - 'identity', no-op activation, useful to implement linear bottleneck, returns f(x) = x - 'logistic', the logistic sigmoid function, returns f(x) = 1 / (1 + exp(-x)). - 'tanh', the hyperbolic tan function, returns f(x) = tanh(x). - 'relu', the rectified linear unit function, returns f(x) = max(0, x) solver : {'lbfgs', 'sgd', 'adam'}, default='adam' The solver for weight optimization. - 'lbfgs' is an optimizer in the family of quasi-Newton methods. - 'sgd' refers to stochastic gradient descent. - 'adam' refers to a stochastic gradient-based optimizer proposed by Kingma, Diederik, and Jimmy Ba For a comparison between Adam optimizer and SGD, see :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py`. Note: The default solver 'adam' works pretty well on relatively large datasets (with thousands of training samples or more) in terms of both training time and validation score. For small datasets, however, 'lbfgs' can converge faster and perform better. alpha : float, default=0.0001 Strength of the L2 regularization term. The L2 regularization term is divided by the sample size when added to the loss. For an example usage and visualization of varying regularization, see :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py`. batch_size : int, default='auto' Size of minibatches for stochastic optimizers. If the solver is 'lbfgs', the classifier will not use minibatch. When set to "auto", `batch_size=min(200, n_samples)`. learning_rate : {'constant', 'invscaling', 'adaptive'}, default='constant' Learning rate schedule for weight updates. - 'constant' is a constant learning rate given by 'learning_rate_init'. - 'invscaling' gradually decreases the learning rate at each time step 't' using an inverse scaling exponent of 'power_t'. effective_learning_rate = learning_rate_init / pow(t, power_t) - 'adaptive' keeps the learning rate constant to 'learning_rate_init' as long as training loss keeps decreasing. Each time two consecutive epochs fail to decrease training loss by at least tol, or fail to increase validation score by at least tol if 'early_stopping' is on, the current learning rate is divided by 5. Only used when ``solver='sgd'``. learning_rate_init : float, default=0.001 The initial learning rate used. It controls the step-size in updating the weights. Only used when solver='sgd' or 'adam'. power_t : float, default=0.5 The exponent for inverse scaling learning rate. It is used in updating effective learning rate when the learning_rate is set to 'invscaling'. Only used when solver='sgd'. max_iter : int, default=200 Maximum number of iterations. The solver iterates until convergence (determined by 'tol') or this number of iterations. For stochastic solvers ('sgd', 'adam'), note that this determines the number of epochs (how many times each data point will be used), not the number of gradient steps. shuffle : bool, default=True Whether to shuffle samples in each iteration. Only used when solver='sgd' or 'adam'. random_state : int, RandomState instance, default=None Determines random number generation for weights and bias initialization, train-test split if early stopping is used, and batch sampling when solver='sgd' or 'adam'. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. tol : float, default=1e-4 Tolerance for the optimization. When the loss or score is not improving by at least ``tol`` for ``n_iter_no_change`` consecutive iterations, unless ``learning_rate`` is set to 'adaptive', convergence is considered to be reached and training stops. verbose : bool, default=False Whether to print progress messages to stdout. warm_start : bool, default=False When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See :term:`the Glossary `. momentum : float, default=0.9 Momentum for gradient descent update. Should be between 0 and 1. Only used when solver='sgd'. nesterovs_momentum : bool, default=True Whether to use Nesterov's momentum. Only used when solver='sgd' and momentum > 0. early_stopping : bool, default=False Whether to use early stopping to terminate training when validation score is not improving. If set to true, it will automatically set aside 10% of training data as validation and terminate training when validation score is not improving by at least ``tol`` for ``n_iter_no_change`` consecutive epochs. The split is stratified, except in a multilabel setting. If early stopping is False, then the training stops when the training loss does not improve by more than tol for n_iter_no_change consecutive passes over the training set. Only effective when solver='sgd' or 'adam'. 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 early_stopping is True. beta_1 : float, default=0.9 Exponential decay rate for estimates of first moment vector in adam, should be in [0, 1). Only used when solver='adam'. beta_2 : float, default=0.999 Exponential decay rate for estimates of second moment vector in adam, should be in [0, 1). Only used when solver='adam'. epsilon : float, default=1e-8 Value for numerical stability in adam. Only used when solver='adam'. n_iter_no_change : int, default=10 Maximum number of epochs to not meet ``tol`` improvement. Only effective when solver='sgd' or 'adam'. .. versionadded:: 0.20 max_fun : int, default=15000 Only used when solver='lbfgs'. Maximum number of loss function calls. The solver iterates until convergence (determined by 'tol'), number of iterations reaches max_iter, or this number of loss function calls. Note that number of loss function calls will be greater than or equal to the number of iterations for the `MLPClassifier`. .. versionadded:: 0.22 Attributes ---------- classes_ : ndarray or list of ndarray of shape (n_classes,) Class labels for each output. loss_ : float The current loss computed with the loss function. best_loss_ : float or None The minimum loss reached by the solver throughout fitting. If `early_stopping=True`, this attribute is set to `None`. Refer to the `best_validation_score_` fitted attribute instead. loss_curve_ : list of shape (`n_iter_`,) The ith element in the list represents the loss at the ith iteration. validation_scores_ : list of shape (`n_iter_`,) or None The score at each iteration on a held-out validation set. The score reported is the accuracy score. Only available if `early_stopping=True`, otherwise the attribute is set to `None`. best_validation_score_ : float or None The best validation score (i.e. accuracy score) that triggered the early stopping. Only available if `early_stopping=True`, otherwise the attribute is set to `None`. t_ : int The number of training samples seen by the solver during fitting. coefs_ : list of shape (n_layers - 1,) The ith element in the list represents the weight matrix corresponding to layer i. intercepts_ : list of shape (n_layers - 1,) The ith element in the list represents the bias vector corresponding to layer i + 1. 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_iter_ : int The number of iterations the solver has run. n_layers_ : int Number of layers. n_outputs_ : int Number of outputs. out_activation_ : str Name of the output activation function. See Also -------- MLPRegressor : Multi-layer Perceptron regressor. BernoulliRBM : Bernoulli Restricted Boltzmann Machine (RBM). Notes ----- MLPClassifier trains iteratively since at each time step the partial derivatives of the loss function with respect to the model parameters are computed to update the parameters. It can also have a regularization term added to the loss function that shrinks model parameters to prevent overfitting. This implementation works with data represented as dense numpy arrays or sparse scipy arrays of floating point values. References ---------- Hinton, Geoffrey E. "Connectionist learning procedures." Artificial intelligence 40.1 (1989): 185-234. Glorot, Xavier, and Yoshua Bengio. "Understanding the difficulty of training deep feedforward neural networks." International Conference on Artificial Intelligence and Statistics. 2010. :arxiv:`He, Kaiming, et al (2015). "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." <1502.01852>` :arxiv:`Kingma, Diederik, and Jimmy Ba (2014) "Adam: A method for stochastic optimization." <1412.6980>` Examples -------- >>> from sklearn.neural_network import MLPClassifier >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import train_test_split >>> X, y = make_classification(n_samples=100, random_state=1) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, ... random_state=1) >>> clf = MLPClassifier(random_state=1, max_iter=300).fit(X_train, y_train) >>> clf.predict_proba(X_test[:1]) array([[0.038..., 0.961...]]) >>> clf.predict(X_test[:5, :]) array([1, 0, 1, 0, 1]) >>> clf.score(X_test, y_test) 0.8... r*-C6?rBrEMbP?rrTNF?皙?+?:0yE> :r>r@rArCrGrIrJrrLrMrNrOrPrRrSrTrUrVrWrXrYcTt|||||||||| d| | | | ||||||||||y)Nrr6r9r>r@rArCrGrIrJr]rrLrMrNrOrPrRrSrTrUrVrWrXrYsuperr_r^r6r9r>r@rArCrGrIrJrrLrMrNrOrPrRrSrTrUrVrWrXrY __class__s r2r_zMLPClassifier.__init__s_6 1!!'1%!1) 3-1  r`c  t|||ddgdtjtjf|\}}|jdk(r|j ddk(r t |d}t|dr|jsH|sFt|_ |jj||jj|_ nt|}|jr>r`c0t|||tSN)r)r5rrr^rxrr7s r2rzMLPClassifier._scoresw+Aq+PPr`c"|jSr\rests r2zMLPClassifier.sc//1r`r ct||r`t|_t|j dr|jj |n|jj ||j ||dS)a(Update the model with a single iteration over the given data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data. y : array-like of shape (n_samples,) The target values. classes : array of shape (n_classes,), default=None Classes across all calls to partial_fit. Can be obtained via `np.unique(y_all)`, where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn't need to contain all labels in `classes`. Returns ------- self : object Trained MLP model. multilabelTr)rrrr startswithrr)r^rxrrDs r2 partial_fitzMLPClassifier.partial_fitsi4 )w 7$2$4D !a ++L9%%))!,%%))'2yyA4y00r`cR|j|}tj||S)aReturn the log of probability estimates. Parameters ---------- X : ndarray of shape (n_samples, n_features) The input data. Returns ------- log_y_prob : ndarray of shape (n_samples, n_classes) The predicted log-probability of the sample for each class in the model, where classes are ordered as they are in `self.classes_`. Equivalent to `log(predict_proba(X))`. )out) predict_probar,log)r^rxy_probs r2predict_log_probazMLPClassifier.predict_log_probas%##A&vvf&))r`ct||j|}|jdk(r|j}|jdk(r$t j d|z |gjS|S)aProbability estimates. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data. Returns ------- y_prob : ndarray of shape (n_samples, n_classes) The predicted probability of the sample for each class in the model, where classes are ordered as they are in `self.classes_`. r#)r!rzrr.rr,vstackr|)r^rxrs r2r[zMLPClassifier.predict_probasb ((+ ??a \\^F ;;! 99a&j&1244 4Mr`cFt|}d|j_|S)NT)r5__sklearn_tags__classifier_tags multi_label)r^tagsr7s r2rbzMLPClassifier.__sklearn_tags__s#w')+/( r`)dr:rr\)rr r!r"r_rrHrrrr rXr^r[rb __classcell__r7s@r2r(r(sTp"4    34 l5n ?Q1251631B*$2r`r(ceZdZdZ ddddddddd d dd d d d d d d dddddfd ZdZddZfdZdZe de d dZ xZ S) MLPRegressora+Multi-layer Perceptron regressor. This model optimizes the squared error using LBFGS or stochastic gradient descent. .. versionadded:: 0.18 Parameters ---------- hidden_layer_sizes : array-like of shape(n_layers - 2,), default=(100,) The ith element represents the number of neurons in the ith hidden layer. activation : {'identity', 'logistic', 'tanh', 'relu'}, default='relu' Activation function for the hidden layer. - 'identity', no-op activation, useful to implement linear bottleneck, returns f(x) = x - 'logistic', the logistic sigmoid function, returns f(x) = 1 / (1 + exp(-x)). - 'tanh', the hyperbolic tan function, returns f(x) = tanh(x). - 'relu', the rectified linear unit function, returns f(x) = max(0, x) solver : {'lbfgs', 'sgd', 'adam'}, default='adam' The solver for weight optimization. - 'lbfgs' is an optimizer in the family of quasi-Newton methods. - 'sgd' refers to stochastic gradient descent. - 'adam' refers to a stochastic gradient-based optimizer proposed by Kingma, Diederik, and Jimmy Ba For a comparison between Adam optimizer and SGD, see :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py`. Note: The default solver 'adam' works pretty well on relatively large datasets (with thousands of training samples or more) in terms of both training time and validation score. For small datasets, however, 'lbfgs' can converge faster and perform better. alpha : float, default=0.0001 Strength of the L2 regularization term. The L2 regularization term is divided by the sample size when added to the loss. batch_size : int, default='auto' Size of minibatches for stochastic optimizers. If the solver is 'lbfgs', the regressor will not use minibatch. When set to "auto", `batch_size=min(200, n_samples)`. learning_rate : {'constant', 'invscaling', 'adaptive'}, default='constant' Learning rate schedule for weight updates. - 'constant' is a constant learning rate given by 'learning_rate_init'. - 'invscaling' gradually decreases the learning rate ``learning_rate_`` at each time step 't' using an inverse scaling exponent of 'power_t'. effective_learning_rate = learning_rate_init / pow(t, power_t) - 'adaptive' keeps the learning rate constant to 'learning_rate_init' as long as training loss keeps decreasing. Each time two consecutive epochs fail to decrease training loss by at least tol, or fail to increase validation score by at least tol if 'early_stopping' is on, the current learning rate is divided by 5. Only used when solver='sgd'. learning_rate_init : float, default=0.001 The initial learning rate used. It controls the step-size in updating the weights. Only used when solver='sgd' or 'adam'. power_t : float, default=0.5 The exponent for inverse scaling learning rate. It is used in updating effective learning rate when the learning_rate is set to 'invscaling'. Only used when solver='sgd'. max_iter : int, default=200 Maximum number of iterations. The solver iterates until convergence (determined by 'tol') or this number of iterations. For stochastic solvers ('sgd', 'adam'), note that this determines the number of epochs (how many times each data point will be used), not the number of gradient steps. shuffle : bool, default=True Whether to shuffle samples in each iteration. Only used when solver='sgd' or 'adam'. random_state : int, RandomState instance, default=None Determines random number generation for weights and bias initialization, train-test split if early stopping is used, and batch sampling when solver='sgd' or 'adam'. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. tol : float, default=1e-4 Tolerance for the optimization. When the loss or score is not improving by at least ``tol`` for ``n_iter_no_change`` consecutive iterations, unless ``learning_rate`` is set to 'adaptive', convergence is considered to be reached and training stops. verbose : bool, default=False Whether to print progress messages to stdout. warm_start : bool, default=False When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See :term:`the Glossary `. momentum : float, default=0.9 Momentum for gradient descent update. Should be between 0 and 1. Only used when solver='sgd'. nesterovs_momentum : bool, default=True Whether to use Nesterov's momentum. Only used when solver='sgd' and momentum > 0. early_stopping : bool, default=False Whether to use early stopping to terminate training when validation score is not improving. If set to True, it will automatically set aside ``validation_fraction`` of training data as validation and terminate training when validation score is not improving by at least ``tol`` for ``n_iter_no_change`` consecutive epochs. Only effective when solver='sgd' or 'adam'. 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 early_stopping is True. beta_1 : float, default=0.9 Exponential decay rate for estimates of first moment vector in adam, should be in [0, 1). Only used when solver='adam'. beta_2 : float, default=0.999 Exponential decay rate for estimates of second moment vector in adam, should be in [0, 1). Only used when solver='adam'. epsilon : float, default=1e-8 Value for numerical stability in adam. Only used when solver='adam'. n_iter_no_change : int, default=10 Maximum number of epochs to not meet ``tol`` improvement. Only effective when solver='sgd' or 'adam'. .. versionadded:: 0.20 max_fun : int, default=15000 Only used when solver='lbfgs'. Maximum number of function calls. The solver iterates until convergence (determined by ``tol``), number of iterations reaches max_iter, or this number of function calls. Note that number of function calls will be greater than or equal to the number of iterations for the MLPRegressor. .. versionadded:: 0.22 Attributes ---------- loss_ : float The current loss computed with the loss function. best_loss_ : float The minimum loss reached by the solver throughout fitting. If `early_stopping=True`, this attribute is set to `None`. Refer to the `best_validation_score_` fitted attribute instead. Only accessible when solver='sgd' or 'adam'. loss_curve_ : list of shape (`n_iter_`,) Loss value evaluated at the end of each training step. The ith element in the list represents the loss at the ith iteration. Only accessible when solver='sgd' or 'adam'. validation_scores_ : list of shape (`n_iter_`,) or None The score at each iteration on a held-out validation set. The score reported is the R2 score. Only available if `early_stopping=True`, otherwise the attribute is set to `None`. Only accessible when solver='sgd' or 'adam'. best_validation_score_ : float or None The best validation score (i.e. R2 score) that triggered the early stopping. Only available if `early_stopping=True`, otherwise the attribute is set to `None`. Only accessible when solver='sgd' or 'adam'. t_ : int The number of training samples seen by the solver during fitting. Mathematically equals `n_iters * X.shape[0]`, it means `time_step` and it is used by optimizer's learning rate scheduler. coefs_ : list of shape (n_layers - 1,) The ith element in the list represents the weight matrix corresponding to layer i. intercepts_ : list of shape (n_layers - 1,) The ith element in the list represents the bias vector corresponding to layer i + 1. 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_iter_ : int The number of iterations the solver has run. n_layers_ : int Number of layers. n_outputs_ : int Number of outputs. out_activation_ : str Name of the output activation function. See Also -------- BernoulliRBM : Bernoulli Restricted Boltzmann Machine (RBM). MLPClassifier : Multi-layer Perceptron classifier. sklearn.linear_model.SGDRegressor : Linear model fitted by minimizing a regularized empirical loss with SGD. Notes ----- MLPRegressor trains iteratively since at each time step the partial derivatives of the loss function with respect to the model parameters are computed to update the parameters. It can also have a regularization term added to the loss function that shrinks model parameters to prevent overfitting. This implementation works with data represented as dense and sparse numpy arrays of floating point values. References ---------- Hinton, Geoffrey E. "Connectionist learning procedures." Artificial intelligence 40.1 (1989): 185-234. Glorot, Xavier, and Yoshua Bengio. "Understanding the difficulty of training deep feedforward neural networks." International Conference on Artificial Intelligence and Statistics. 2010. :arxiv:`He, Kaiming, et al (2015). "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." <1502.01852>` :arxiv:`Kingma, Diederik, and Jimmy Ba (2014) "Adam: A method for stochastic optimization." <1412.6980>` Examples -------- >>> from sklearn.neural_network import MLPRegressor >>> from sklearn.datasets import make_regression >>> from sklearn.model_selection import train_test_split >>> X, y = make_regression(n_samples=200, n_features=20, random_state=1) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, ... random_state=1) >>> regr = MLPRegressor(random_state=1, max_iter=2000, tol=0.1) >>> regr.fit(X_train, y_train) MLPRegressor(max_iter=2000, random_state=1, tol=0.1) >>> regr.predict(X_test[:2]) array([ 28..., -290...]) >>> regr.score(X_test, y_test) 0.98... r*r)rBrEr*rrTNFr+r,r-r.r/r0r1cTt|||||||||| d| | | | ||||||||||y)N squared_errorr3r4r6s r2r_zMLPRegressor.__init__s_6 1!!'1 %!1) 3-1  r`c:t||j|S)a;Predict using the multi-layer perceptron model. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data. Returns ------- y : ndarray of shape (n_samples, n_outputs) The predicted values. rFrGs r2rHzMLPRegressor.predictGrIr`cp|j||}|jddk(r|jS|SrK)rzrkr.rLs r2rzMLPRegressor._predictWs8(( (D <<?a <<> ! r`c0t|||tSrN)r5rrrOs r2rzMLPRegressor._score^sw+Aq+JJr`c t|||ddgddtjtjf|\}}|jdk(r|j ddk(r t |d}||fS)NrtruT)rvr9 y_numericrrwrr#r:)r"r,r>r?rrkr)r^rxrrrws r2rzMLPRegressor._validate_inputasi   %.::rzz*  1 66Q;1771:?QT*A!t r`c|jSr\rQrRs r2rTzMLPRegressor.ps c//r`r c*|j||dS)arUpdate the model with a single iteration over the given data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data. y : ndarray of shape (n_samples,) The target values. Returns ------- self : object Trained MLP model. 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