Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit the model using X and y as training data. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Randomly draw feature indices. Internal use only. Modified from sklearn/ensemble/bagging.py Parameters ---------- random_state : Ra

Determine optimal number of bins for a histogram using the Birge Rozenblac method (see :cite:`birge2006many` for details.) See https:

Console entry point for `pyod-install-skill` (legacy alias in v3.1.0+).

Compute label-based validation metrics for one detector or consensus. Returns binary precision / recall / F1 (sklearn `'binary'` average,

Apply the robust median absolute deviation (MAD) to measure the inconsistency/variability of the rotation costs. Parameters ---------

Combination method to merge the scores from multiple estimators by taking the median. Parameters ---------- scores : numpy array of s

Selects the most suitable deep learning model based on the dataset tags and model analyses. Returns -------

Utility function to calculate pearson matrix (row-wise). Parameters ---------- mat : numpy array of shape (n_samples, n_features)

Predict if a particular sample is an outlier or not. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Not supported (transductive detector).

Print compact hindsight metrics.

Apply ROD on a 3D subSpace then process it with sigmoid to compare apples to apples Parameters ---------- subspace : array-like,

Resolve a detector from a string name or BaseDetector instance. Parameters ---------- detector : str or BaseDetector If string, c

Turn raw outlier outlier scores to binary labels (0 or 1). Parameters ---------- pred_scores : list or numpy array of shape (n_samples,)

Implementation of Sigmoid function Parameters ---------- x : array-like, decision scores Returns ------- array-like, x

Train the deep learning model. Parameters ---------- train_loader : torch.utils.data.DataLoader The data loader f

AUCP class for Area Under Curve Precentage thresholder. Use the area under the curve to evaluate a non-parametric means to threshold sc

BOOT class for Bootstrapping thresholder. Use a boostrapping based method to find a non-parametric means to threshold scores generated

CHAU class for Chauvenet's criterion thresholder. Use the Chauvenet's criterion to evaluate a non-parametric means to threshold scores

CLF class for Trained Classifier thresholder. Use the trained linear classifier to evaluate a non-parametric means to threshold scores

CLUST class for clustering type thresholders. Use the clustering methods to evaluate a non-parametric means to threshold scores generat

CPD class for Change Point Detection thresholder. Use change point detection to find a non-parametric means to threshold scores generat

DECOMP class for Decomposition based thresholders. Use decomposition to evaluate a non-parametric means to threshold scores generated b

DSN class for Distance Shift from Normal thresholder. Use the distance shift from normal to evaluate a non-parametric means to threshol

EB class for Elliptical Boundary thresholder. Use pseudo-random elliptical boundaries to evaluate a non-parametric means to threshold s

FGD class for Fixed Gradient Descent thresholder. Use the fixed gradient descent to evaluate a non-parametric means to threshold scores

FWFM class for Full Width at Full Minimum thresholder. Use the full width at full minimum (aka base width) to evaluate a non-parametric

GAMGMM class for gammaGMM thresholder. Use a Bayesian method for estimating the posterior distribution of the contamination factor (i.e

GESD class for Generalized Extreme Studentized Deviate thresholder. Use the generalized extreme studentized deviate to evaluate a non-p

HIST class for Histogram based thresholders. Use histograms methods as described in scikit-image.filters to evaluate a non-parametric m

IQR class for Inter-Qaurtile Region thresholder. Use the inter-quartile region to evaluate a non-parametric means to threshold scores g

KARCH class for Riemannian Center of Mass thresholder. Use the Karcher mean (Riemannian Center of Mass) to evaluate a non-parametric me

MAD class for Median Absolute Deviation thresholder. Use the median absolute deviation to evaluate a non-parametric means to threshold

MCST class for Monte Carlo Shapiro Tests thresholder. Use uniform random sampling and statstical testing to evaluate a non-parametric m

MIXMOD class for the Normal & Non-Normal Mixture Models thresholder. Use normal & non-normal mixture models to find a non-parametric means

MOLL class for Friedrichs' mollifier thresholder. Use the Friedrichs' mollifier to evaluate a non-parametric means to threshold scores

MTT class for Modified Thompson Tau test thresholder. Use the modified Thompson Tau test to evaluate a non-parametric means to threshol

OCSVM class for One-Class Support Vector Machine thresholder. Use a one-class svm to evaluate a non-parametric means to threshold score

QMCD class for Quasi-Monte Carlo Discreprancy thresholder. Use the quasi-Monte Carlo discreprancy to evaluate a non-parametric means to

REGR class for Regression based thresholder. Use the regression to evaluate a non-parametric means to threshold scores generated by the

VAE class for Variational AutoEncoder thresholder. Use a VAE to evaluate a non-parametric means to threshold scores generated by the de

WIND class for topological Winding number thresholder. Use the topological winding number (with respect to the origin) to evaluate a no

YJ class for Yeo-Johnson transformation thresholder. Use the Yeo-Johnson transformation to evaluate a non-parametric means to threshold

ZSCORE class for ZSCORE thresholder. Use the zscore to evaluate a non-parametric means to threshold scores generated by the decision_sc

Factory: returns a torch.nn.Module for the AnomalyDAE.

Factory: returns torch.nn.Module for CONAD.

Factory: returns a torch.nn.Module for CoLA. Uses local-context contrastive learning: a GCN encoder produces node embeddings, and a biline

Calculated the Cook's distance Parameters ---------- X : numpy array of shape (n_samples, n_features) The training dataset.

Factory: returns a torch.nn.Module for the DOMINANT AE.

Factory: returns torch.nn.Module for GUIDE dual AE.

Internal function to calculate for Smoothing Factors of data points Repeated n_iter_ of times in randomized mode.

Return up to `top_k` shipped detector names for `data_type`, ranked by the modality-specific benchmark from the KB. Two ranking sourc

Flip the byte order of one field's dtype only. The realignment should accept this as a compatible difference.

Probe whether the optional `mcp` package is importable. Returns the FastMCP class if available, else None. Does NOT call sys.exit — callers d

Internal function to check for and validate class parameters. Also, to return random state instance and the appropriate dissimilarity measure

Check subset size.

Return a list of (field, saved, running, severity) tuples for every recorded dependency that drifted from the running version.

Connectivity-Based Outlier Factor (COF) Algorithm This function is called internally to calculate the Connectivity-Based Outl

Connectivity-Based Outlier Factor (COF) Algorithm This function is called internally to calculate the Connectivity-Based Outl

Compute the Matrix Profile of a 1-D time series using STOMP. Parameters ---------- T : np.ndarray of shape (n,) Input time series

Full SCAN procedure: similarity, cores, clustering, scoring. Parameters ---------- edge_index : np.ndarray of shape (2, n

Discover the running sklearn's Tree node dtype dynamically. Reads `sklearn.tree._tree.NODE_DTYPE` when available (sklearn >= 1.0). Falls back

Internal method for predicting outlier scores using default ABOD. Parameters ---------- X : numpy array of shape (n_samples,

Internal method for predicting outlier scores using Fast ABOD. Parameters ---------- X : numpy array of shape (n_samples, n_f

Predict raw anomaly score of X using the fitted detector. For consistency, outliers are assigned with larger anomaly scores. Paramete

Predict raw anomaly score of X using the fitted detector. For consistency, outliers are assigned with larger anomaly scores. Paramete