deepod.models.DevNet
- class deepod.models.DevNet(epochs=100, batch_size=64, lr=0.001, network='MLP', rep_dim=128, hidden_dims='100,50', act='ReLU', bias=False, margin=5.0, l=5000, epoch_steps=-1, prt_steps=10, device='cuda', verbose=2, random_state=42)[source]
Deviation Networks for Weakly-supervised Anomaly Detection (KDD’19) [BPSvdH19]
- Parameters:
epochs (int, optional) – number of training epochs (default: 100).
batch_size (int, optional) – number of samples in a mini-batch (default: 64)
lr (float, optional) – learning rate (default: 1e-3)
rep_dim (int, optional) – it is for consistency, unused in this model.
hidden_dims (list, str or int, optional) – 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 (default: ‘100,50’)
act (str, optional) – activation layer name, choice = [‘ReLU’, ‘LeakyReLU’, ‘Sigmoid’, ‘Tanh’] (default=’ReLU’)
bias (bool, optional) – Additive bias in linear layer (default=False)
margin (float, optional) – margin value used in the deviation loss function (default=5.)
l (int, optional) – the size of samples of the Gaussian distribution used in the deviation loss function (default=5000.)
epoch_steps (int, optional) – Maximum steps in an epoch. If -1, all the batches will be processed (default=-1)
prt_steps (int, optional) – Number of epoch intervals per printing (default=10)
device (str, optional) – torch device (default=’cuda’).
verbose (int, optional) – Verbosity mode (default=1)
random_state (int, optional) – the seed used by the random (default=42)
Methods
__init__([epochs, batch_size, lr, network, ...])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
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
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)- param X:
input data array
- 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:
- 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:
- 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.