CVE-2021-37682

Advisory

Psiz 0.6.0 updates its dependency 'TensorFlow' minimum requirement to v2.4.3 to include a security fixes.

Affected package

Affected versions

Fixed versions

Vulnerability changelog

Breaking Changes
* Removed deprecated classes:
* `GroupLevel`
* `Stimuli`
* `WeightedMinkowski`
* `GroupAttention`
* `GroupAttentionVariational`
* `Kernel`
* `AttentionKernel`
* `SharedEmbedding`
* Removed deprecated arguments for `Behavior` base class:
* n_group
* group_level
* Removed optional argument `verbose` from `load_trials`
* Removed `EmbeddingND` since it causes code coherency issues. This class may be added back in the future.
* Removed `matrix_comparison` and `pairwise_matrix` since these utility functions are incompatible with the more general purpose batch-processing pipeline.
* Stimuli indices are now consistent between PsiZ trial objects and TF dataset versions, calling `as_dataset` no longer increments stimuli indices by one. A `mask_zero` argument has been added to the similarity observations classes. See the docs for a discussion of this masking strategy.
* Move `wpnorm` from `keras.layers.ops` to `psiz.tf.ops`
* Remove `NegLogLikelihood` loss and metric
* Removed alias `psiz.models`, users must use `psiz.keras.models`.
* Change `psiz.datasets.load` to `psiz.datasets.load_dataset`
* Organization of information gain computations has been updated.
* A new submodule has been created: `psiz.trials.information_gain`
* The function `expected_information_gain_rank` has been renamed `ig_categorical` and moved to `psiz.trials.information_gain`.
* The input shape of information gain function has changed from `(n_sample, n_trial, n_outcome)` to `(n_trial, n_sample, n_outcome)`. This change makes the dimension semantics consistent with the output of psiz.keras.models (i.e., `(batch_size, n_sample, n_outcome)`).
* Rename `RandomAttention` initializer to `Dirichlet`.
* For trials.experimental, change `as_dataset` to `export`.
* Update plotter signature in mplot module. Update removes `fig` as an argument and makes `ax` an optional argument.
* remove `verbose` from load_trials

Major Features and Improvements
* Reorganized documentation
* Added Beginner Tutorial
* Add `random_combinations` which handles sampling k-combinations with and without replacement and leans on already existing `choice_wo_replace`.
* Add `ig_model_categorical`, which takes one or more models as an input and computes ensemble-based information gain. Assumes that models generate samples from the posterior on the forward pass (e.g, a variational inference model) and output units are categorical.
* Enhanced `RandomRank` generator:
* Added weighting functionality.
* Added `per_query` functionality.

Bug Fixes and Other Changes
* Multiple changes to docstrings.
* Fix handling of shape argument on call of Dirichlet initializer. Was only using first dimension of shape array.
* Add `docs` section to optional install that includes packages listed in `conf.py` extensions
* Add optional `rng` argument to `choice_wo_replace`.
* Add dynamic version to PsychologicalEmbedding `get_config`
* For trials.experimental make `_save` and `_load` public methods `save` and `load`.
* Add `__all__` definition to `__init__` files satisfying PEP8 and removing linter complaints "imported but unused".

Miscellaneous
* Bump tensorflow-probability requirement to 0.13.0.
* Bump minimum TensorFlow version requirement to v2.4.3 for security fixes.
* Bump maximum TensorFlow version to v2.6.x.
* Add h5py >= 3.0 to setup.cfg so that TrialDataset `_load_h5_group` can safely assume h5py `asstr()` method is available

Resources

• https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-37682