deepod.models.DeepSVDD

class deepod.models.DeepSVDD(epochs=100, batch_size=64, lr=0.001, rep_dim=128, hidden_dims='100,50', act='ReLU', bias=False, epoch_steps=-1, prt_steps=10, device='cuda', verbose=2, random_state=42)

Deep One-class Classification for Anomaly Detection (ICML’18)

[BRVG+18]

Parameters:

• epochs (int, optional (default=100)) – Number of training epochs

• batch_size (int, optional (default=64)) – Number of samples in a mini-batch

• lr (float, optional (default=1e-3)) – Learning rate

• rep_dim (int, optional (default=128)) – Dimensionality of the representation space

• hidden_dims (list, str or int, optional (default='100,50')) –

  Number of neural units in hidden layers

  • If list, each item is a layer

  • If str, neural units of hidden layers are split by comma

  • If int, number of neural units of single hidden layer

• act (str, optional (default='ReLU')) – activation layer name choice = [‘ReLU’, ‘LeakyReLU’, ‘Sigmoid’, ‘Tanh’]

• bias (bool, optional (default=False)) – Additive bias in linear layer

• epoch_steps (int, optional (default=-1)) –

  Maximum steps in an epoch

  • If -1, all the batches will be processed

• prt_steps (int, optional (default=10)) – Number of epoch intervals per printing

• device (str, optional (default='cuda')) – torch device,

• verbose (int, optional (default=1)) – Verbosity mode

• int (random_state：) – the seed used by the random

• (default=42) (optional) – the seed used by the random

decision_scores_

The outlier scores of the training data. The higher, the more abnormal. Outliers tend to have higher scores. This value is available once the detector is fitted.

Type:

numpy array of shape (n_samples,)

threshold_

The threshold is based on contamination. It is the n_samples * contamination most abnormal samples in decision_scores_. The threshold is calculated for generating binary outlier labels.

Type:

float

labels_

The binary labels of the training data. 0 stands for inliers and 1 for outliers/anomalies. It is generated by applying threshold_ on decision_scores_.

Type:

int, either 0 or 1

Methods

__init__([epochs, batch_size, lr, rep_dim, ...])
decision_function(X[, return_rep])	Predict raw anomaly scores of X using the fitted detector.
decision_function_update(z, scores)	for any updating operation after decision function
epoch_update()	for any updating operation after each training epoch
fit(X[, y])	Fit detector.
fit_auto_hyper(X[, y, X_test, y_test, ...])	Fit detector.
inference_forward(batch_x, net, criterion)	define forward step in inference
inference_prepare(X)	define test_loader
load_model(path)
load_ray_checkpoint(best_config, best_checkpoint)
predict(X[, return_confidence])	Predict if a particular sample is an outlier or not.
save_model(path)
set_seed(seed)
set_tuned_net(config)
set_tuned_params()
training_forward(batch_x, net, criterion)	define forward step in training
training_prepare(X, y)	define train_loader, net, and criterion

decision_function(X, return_rep=False)

Predict raw anomaly scores of X using the fitted detector.

The anomaly score of an input sample is computed based on the fitted detector. For consistency, outliers are assigned with higher anomaly scores.

Parameters:

• X (numpy array of shape (n_samples, n_features)) – The input samples. Sparse matrices are accepted only if they are supported by the base estimator.

• return_rep (boolean, optional, default=False) – whether return representations

Returns:

anomaly_scores – The anomaly score of the input samples.

Return type:

numpy array of shape (n_samples,)

decision_function_update(z, scores)

for any updating operation after decision function

epoch_update()

for any updating operation after each training epoch

fit(X, y=None)

Fit detector. y is ignored in unsupervised methods.

Parameters:

• X (numpy array of shape (n_samples, n_features)) – The input samples.

• y (numpy array of shape (n_samples, )) – Not used in unsupervised methods, present for API consistency by convention. used in (semi-/weakly-) supervised methods

Returns:

self – Fitted estimator.

Return type:

object

fit_auto_hyper(X, y=None, X_test=None, y_test=None, n_ray_samples=5, time_budget_s=None)

Fit detector. y is ignored in unsupervised methods.

Parameters:

• X (numpy array of shape (n_samples, n_features)) – The input samples.

• y (numpy array of shape (n_samples, )) – Not used in unsupervised methods, present for API consistency by convention. used in (semi-/weakly-) supervised methods

• X_test (numpy array of shape (n_samples, n_features), default=None) – The input testing samples for hyper-parameter tuning.

• y_test (numpy array of shape (n_samples, ), default=None) – Label of input testing samples for hyper-parameter tuning.

• n_ray_samples (int, default=5) – Number of times to sample from the hyperparameter space

• time_budget_s (int, default=None) – Global time budget in seconds after which all trials of Ray are stopped.

Returns:

config – tuned hyper-parameter

Return type:

dict

inference_forward(batch_x, net, criterion)

define forward step in inference

inference_prepare(X)

define test_loader

predict(X, return_confidence=False)

Predict if a particular sample is an outlier or not.

Parameters:

• X (numpy array of shape (n_samples, n_features)) – The input samples.

• return_confidence (boolean, optional(default=False)) – If True, also return the confidence of prediction.

Returns:

• outlier_labels (numpy array of shape (n_samples,)) – For each observation, tells whether it should be considered as an outlier according to the fitted model. 0 stands for inliers and 1 for outliers.

• confidence (numpy array of shape (n_samples,).) – Only if return_confidence is set to True.

training_forward(batch_x, net, criterion)

define forward step in training

training_prepare(X, y)

define train_loader, net, and criterion