Search Results for "bandit"

Bandit is a tool designed to find common security issues in Python code. To do this, Bandit processes each file, builds an AST from it, and runs appropriate plugins against the AST nodes. Once Bandit has finished scanning all the files, it generates a report. Bandit was originally developed within the OpenStack Security Project and later rehomed to PyCQA.

...The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and then experiment with multiple strategies—evolutionary algorithms, Bayesian optimization, bandit methods, genetic algorithms, etc. Nevergrad supports parallelization, budget scheduling, and multiple cost/resource constraints, allowing it to scale to nontrivial optimization problems. It includes visualization tools and diagnostic metrics to compare strategy performance, track parameter evolution, and detect stagnation.

Plug is a specification and set of utilities for building composable modules in Elixir web applications. It defines a standard connection interface, allowing developers to create “plugs” that act as middleware for handling requests and responses. Examples include parsing parameters, managing sessions, logging, or authentication, all of which can be plugged into a pipeline. Plug serves as the foundation for the Phoenix framework, which builds on it to deliver a full-featured web stack. The...

...The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks.