Quickstart

Our goal here is to help you to get the first practical experience with inne and give you a brief overview on some basic functionalities of inne.

Inne

The inne package inherits from sklearn classes, and thus drops in neatly next to other sklearn with an identical calling API. Similarly it supports input in a variety of formats: an array (or pandas dataframe) of shape (num_samples x num_features).

>>> from inne import IsolationNNE
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [0, 0], [-20, 50], [3, 5]])
>>> clf = IsolationNNE(n_estimators=200, max_samples=16)
>>> clf.fit(X)
>>> clf.predict(X)

Examples

• See IsolationNNE example for an illustration of the use of IsolaitonNNE.

• See Comparing anomaly detection algorithms for outlier detection on toy datasets for a comparison of ensemble.IsolationForest with neighbors.LocalOutlierFactor, svm.OneClassSVM (tuned to perform like an outlier detection method), linear_model.SGDOneClassSVM, and a covariance-based outlier detection with covariance.EllipticEnvelope.