v0.4.3 (2020-10-08)
- Make Prophet an optional dependency
- Extend what is returned by the drift detectors to raw scores
- Add licenses from dependencies
v0.4.2 (2020-09-09)
- Text drift detector functionality for KS and MMD drift detectors
- Add embedding extraction functionality for pretrained HuggingFace transformers models (
alibi_detect.models.embedding) - Add Python 3.8 support
v0.4.1 (2020-05-12)
- Likelihood ratio outlier detector (
alibi_detect.od.llr.LLR) with image and genome dataset examples - Add genome dataset (
alibi_detect.datasets.fetch_genome) - Add PixelCNN++ model (
alibi_detect.models.pixelcnn.PixelCNN)
v0.4.0 (2020-04-02)
- Kolmogorov-Smirnov drift detector (
alibi_detect.cd.ks.KSDrift) - Maximum Mean Discrepancy drift detector (
alibi_detect.cd.mmd.MMDDrift)
v0.3.1 (2020-02-26)
- Adversarial autoencoder detection method (offline method,
alibi_detect.ad.adversarialae.AdversarialAE) - Add pretrained adversarial and outlier detectors to Google Cloud Bucket and include fetch functionality
- Add data/concept drift dataset (CIFAR-10-C) to Google Cloud Bucket and include fetch functionality
- Update VAE loss function and log var layer
- Fix tests for Prophet outlier detector on Python 3.6
- Add batch sizes for all detectors
v0.3.0 (2020-01-17)
- Multivariate time series outlier detection method OutlierSeq2Seq (offline method,
alibi_detect.od.seq2seq.OutlierSeq2Seq) - ECG and synthetic data examples for OutlierSeq2Seq detector
- Auto-Encoder outlier detector (offline method,
alibi_detect.od.ae.OutlierAE) - Including tabular and categorical perturbation functions (
alibi_detect.utils.perturbation)
v0.2.0 (2019-12-06)
- Univariate time series outlier detection methods: Prophet (offline method,
alibi_detect.od.prophet.OutlierProphet) and Spectral Residual (online method,alibi_detect.od.sr.SpectralResidual) - Function for fetching Numenta Anomaly Benchmark time series data (
alibi_detect.datasets.fetch_nab) - Perturbation function for time series data (
alibi_detect.utils.perturbation.inject_outlier_ts) - Roadmap
v0.1.0 (2019-11-19)
- Isolation Forest (Outlier Detection)
- Mahalanobis Distance (Outlier Detection)
- Variational Auto-Encoder (VAE, Outlier Detection)
- Auto-Encoding Gaussian Mixture Model (AEGMM, Outlier Detection)
- Variational Auto-Encoding Gaussian Mixture Model (VAEGMM, Outlier Detection)
- Adversarial Variational Auto-Encoder (Adversarial Detection)