mlresearch.neural_network.OneClassMLP

class mlresearch.neural_network.OneClassMLP(hidden_layer_sizes=(500, 500), activation='relu', nu=0.01, *, solver='adam', alpha=0.0001, batch_size=64, learning_rate='constant', learning_rate_init=1e-05, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=15, max_fun=15000)

Unsupervised One-Class neural network. Can be used to project n-dimensional data into a single dimension and for outlier detection. Also used to calculate the $alpha$ and $beta$ supports for the performance metrics used to assess synthetic data quality.

Uses the loss function proposed in [1], which is optimized using LBFGS, stochastic gradient descent, or adam. This implementation refers to the architecture described in [2] using the soft-boundary objective.

Parameters:

hidden_layer_sizesarray-like of shape(n_layers - 2,), default=(500, 500)

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)

nufloat, default=0.01

Hyperparameter used in the loss function.

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.

alphafloat, 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_sizeint, default=64

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, the current learning rate is divided by 5.

Only used when solver='sgd'.

learning_rate_initfloat, default=1e-5

The initial learning rate used. It controls the step-size in updating the weights. Only used when solver=’sgd’ or ‘adam’.

power_tfloat, 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_iterint, 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.

shufflebool, default=True

Whether to shuffle samples in each iteration. Only used when solver=’sgd’ or ‘adam’.

random_stateint, 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.

tolfloat, default=0

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.

verbosebool, default=False

Whether to print progress messages to stdout.

warm_startbool, default=False

When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.

momentumfloat, default=0.9

Momentum for gradient descent update. Should be between 0 and 1. Only used when solver=’sgd’.

nesterovs_momentumbool, default=True

Whether to use Nesterov’s momentum. Only used when solver=’sgd’ and momentum > 0.

beta_1float, 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_2float, default=0.999

Exponential decay rate for estimates of second moment vector in adam, should be in [0, 1). Only used when solver=’adam’.

epsilonfloat, default=1e-8

Value for numerical stability in adam. Only used when solver=’adam’.

n_iter_no_changeint, default=15

Maximum number of epochs to not meet tol improvement. Only effective when solver=’sgd’ or ‘adam’.

max_funint, 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 OneClassMLP.

References

[1]

Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C. Estimating the support of a high- dimensional distribution. Neural computation, 13(7): 1443–1471, 2001.

[2]

Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Muller, E., and Kloft, M. Deep one-class classification. In International conference on machine learning, pp. 4393–4402. PMLR, 2018.

Attributes:

center_float

The center of the euclidean ball used to calculate the loss.

radius_float

The radius of the euclidean ball used to calculate the loss.

loss_float

The current loss computed with the loss function.

best_loss_float or None

The minimum loss reached by the solver throughout fitting.

loss_curve_list of shape (n_iter_,)

The ith element in the list represents the loss at the ith iteration.

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 fit.

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.

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.

fit(X, y=None)

Fit the model to data matrix X.

Parameters:

Xndarray or sparse matrix of shape (n_samples, n_features)

The input data.

Returns:

selfobject

Returns a trained MLP model.

fit_predict(X, y=None, **kwargs)

Perform fit on X and returns labels for X.

Returns -1 for outliers and 1 for inliers.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The input samples.

yIgnored

Not used, present for API consistency by convention.

**kwargsdict

Arguments to be passed to fit.

New in version 1.4.

Returns:

yndarray of shape (n_samples,)

1 for inliers, -1 for outliers.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

partial_fit(X, y=None)

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.

yndarray of shape (n_samples,)

The target values.

Returns:

selfobject

Trained MLP model.

predict(X)

Predict using the multi-layer perceptron model.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The input data.

Returns:

yndarray of shape (n_samples, n_outputs)

The predicted values.

set_params(**params)

Set the parameters of this estimator.

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

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.