deepod.models.DeepIsolationForest
- class deepod.models.DeepIsolationForest(epochs=100, batch_size=1000, lr=0.001, rep_dim=128, hidden_dims='100,50', act='ReLU', bias=False, n_ensemble=50, n_estimators=6, max_samples=256, n_jobs=1, epoch_steps=-1, prt_steps=10, device='cuda', verbose=2, random_state=42)[source]
Deep Isolation Forest for Anomaly Detection
- Parameters:
Methods
__init__([epochs, batch_size, lr, rep_dim, ...])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)define train_loader, net, and criterion
- decision_function(X)[source]
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.
- 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)[source]
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.