package sklearn

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package sklearn
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Class type
type tag = [
  1. | `MLPClassifier
]
type t = [ `BaseEstimator | `BaseMultilayerPerceptron | `ClassifierMixin | `MLPClassifier | `Object ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_classifier : t -> [ `ClassifierMixin ] Obj.t
val as_multilayer_perceptron : t -> [ `BaseMultilayerPerceptron ] Obj.t
val as_estimator : t -> [ `BaseEstimator ] Obj.t
val create : ?hidden_layer_sizes:Py.Object.t -> ?activation:[ `Identity | `Logistic | `Tanh | `Relu ] -> ?solver:[ `Lbfgs | `Sgd | `Adam ] -> ?alpha:float -> ?batch_size:int -> ?learning_rate:[ `Constant | `Invscaling | `Adaptive ] -> ?learning_rate_init:float -> ?power_t:float -> ?max_iter:int -> ?shuffle:bool -> ?random_state:int -> ?tol:float -> ?verbose:int -> ?warm_start:bool -> ?momentum:float -> ?nesterovs_momentum:bool -> ?early_stopping:bool -> ?validation_fraction:float -> ?beta_1:float -> ?beta_2:float -> ?epsilon:float -> ?n_iter_no_change:int -> ?max_fun:int -> unit -> t

Multi-layer Perceptron classifier.

This model optimizes the log-loss function using LBFGS or stochastic gradient descent.

.. versionadded:: 0.18

Parameters ---------- hidden_layer_sizes : tuple, length = 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

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 L2 penalty (regularization term) parameter.

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 : double, 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 : double, 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 <random_state>`.

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 <warm_start>`.

momentum : float, default=0.9 Momentum for gradient descent update. Should be between 0 and 1. Only used when solver='sgd'.

nesterovs_momentum : boolean, 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. 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. coefs_ : list, length n_layers - 1 The ith element in the list represents the weight matrix corresponding to layer i. intercepts_ : list, length n_layers - 1 The ith element in the list represents the bias vector corresponding to layer i + 1. n_iter_ : int, The number of iterations the solver has ran. n_layers_ : int Number of layers. n_outputs_ : int Number of outputs. out_activation_ : string Name of the output activation function. 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... 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. He, Kaiming, et al. 'Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.' arXiv preprint arXiv:1502.01852 (2015). Kingma, Diederik, and Jimmy Ba. 'Adam: A method for stochastic optimization.' arXiv preprint arXiv:1412.6980 (2014).

val fit : x:[> `ArrayLike ] Np.Obj.t -> y:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> t

Fit 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, shape (n_samples,) or (n_samples, n_outputs) The target values (class labels in classification, real numbers in regression).

Returns ------- self : returns a trained MLP model.

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

val partial_fit : ?classes:Py.Object.t -> x:Py.Object.t -> y:Py.Object.t -> [> tag ] Obj.t -> t

None

val predict : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Predict using the multi-layer perceptron classifier

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The input data.

Returns ------- y : ndarray, shape (n_samples,) or (n_samples, n_classes) The predicted classes.

val predict_log_proba : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Return 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))

val predict_proba : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Probability 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_`.

val score : ?sample_weight:[> `ArrayLike ] Np.Obj.t -> x:[> `ArrayLike ] Np.Obj.t -> y:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> float

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val classes_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute classes_: get value or raise Not_found if None.

val classes_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute classes_: get value as an option.

val loss_ : t -> float

Attribute loss_: get value or raise Not_found if None.

val loss_opt : t -> float option

Attribute loss_: get value as an option.

val coefs_ : t -> Py.Object.t

Attribute coefs_: get value or raise Not_found if None.

val coefs_opt : t -> Py.Object.t option

Attribute coefs_: get value as an option.

val intercepts_ : t -> Py.Object.t

Attribute intercepts_: get value or raise Not_found if None.

val intercepts_opt : t -> Py.Object.t option

Attribute intercepts_: get value as an option.

val n_iter_ : t -> int

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> int option

Attribute n_iter_: get value as an option.

val n_layers_ : t -> int

Attribute n_layers_: get value or raise Not_found if None.

val n_layers_opt : t -> int option

Attribute n_layers_: get value as an option.

val n_outputs_ : t -> int

Attribute n_outputs_: get value or raise Not_found if None.

val n_outputs_opt : t -> int option

Attribute n_outputs_: get value as an option.

val out_activation_ : t -> string

Attribute out_activation_: get value or raise Not_found if None.

val out_activation_opt : t -> string option

Attribute out_activation_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.