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OneClassSVM

One-class SVM for anomaly detection.

This is a stochastic implementation of the one-class SVM algorithm, and will not exactly match its batch formulation.

It is encouraged to scale the data upstream with preprocessing.StandardScaler, as well as use feature_extraction.RBFSampler to capture non-linearities.

Parameters

  • nu

    Default → 0.1

    An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. You can think of it as the expected fraction of anomalies.

  • optimizer

    Type → optim.base.Optimizer | None

    Default → None

    The sequential optimizer used for updating the weights.

  • intercept_lr

    Type → optim.base.Scheduler | float

    Default → 0.01

    Learning rate scheduler used for updating the intercept. A optim.schedulers.Constant is used if a float is provided. The intercept is not updated when this is set to 0.

  • clip_gradient

    Default → 1000000000000.0

    Clips the absolute value of each gradient value.

  • initializer

    Type → optim.base.Initializer | None

    Default → None

    Weights initialization scheme.

Attributes

  • weights

Examples

from river import anomaly
from river import compose
from river import datasets
from river import metrics
from river import preprocessing

model = anomaly.QuantileFilter(
    anomaly.OneClassSVM(nu=0.2),
    q=0.995
)

auc = metrics.ROCAUC()

for x, y in datasets.CreditCard().take(2500):
    score = model.score_one(x)
    is_anomaly = model.classify(score)
    model = model.learn_one(x)
    auc = auc.update(y, is_anomaly)

auc

ROCAUC: 74.68%

You can also use the evaluate.progressive_val_score function to evaluate the model on a data stream.

from river import evaluate

model = model.clone()

evaluate.progressive_val_score(
    dataset=datasets.CreditCard().take(2500),
    model=model,
    metric=metrics.ROCAUC(),
    print_every=1000
)

[1,000] ROCAUC: 74.40%
[2,000] ROCAUC: 74.60%
[2,500] ROCAUC: 74.68%
ROCAUC: 74.68%

Methods

learn_many
learn_one

Update the model.

Parameters

  • x — 'dict'

Returns

AnomalyDetector: self

score_one

Return an outlier score.

A high score is indicative of an anomaly. A low score corresponds to a normal observation.

Parameters

  • x

Returns

An anomaly score. A high score is indicative of an anomaly. A low score corresponds a