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Summary:
Semi-supervised Pseudo Labeler Anomaly Detection with Ensembling (SPADE) is a semi-supervised anomaly detection method that uses an ensemble of one class classifiers as the pseudo-labelers and supervised classifiers to achieve state of the art results especially on datasets with distribution mismatch between labeled and unlabeled samples.
Latest version:
0.4.0
Required dependencies:
absl-py
|
cloudml-hypertune
|
fastavro
|
frozendict
|
google-cloud-bigquery
|
google-cloud-bigquery-storage
|
google-cloud-storage
|
joblib
|
pandas
|
parameterized
|
pyarrow
|
pytest
|
retry
|
scikit-learn
|
tensorflow
|
tensorflow-datasets
|
tensorflow_decision_forests
Optional dependencies:
pyink
|
pylint
|
pytest
|
pytest-xdist
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