A configurable-on-the-fly HTTP Proxy

Tools for debugging your node.js modules and event loop

Base stream implementations for winston@3 and up.

Generate HEX colors for a given namespace.

Safer Node.js Buffer API

HTTP and HTTPS modules that follow redirects.

Simply swizzle your arguments

Jupyter telemetry library

This package provides a spawner for Jupyterhub to spawn notebooks using batch resource managers.

An extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture.

The SudoSpawner enables JupyterHub to spawn single-user servers without being root, by spawning an intermediate process via sudo, which takes actions on behalf of the user.

This package includes WrapSpawner and ProfilesSpawner, which provide mechanisms for runtime configuration of spawners. The inspiration for their development was to allow users to select from a range of pre-defined batch job profiles, but their operation is completely generic.

The systemdspawner enables JupyterHub to spawn single-user notebook servers using systemd.

JupyterHub: A multi-user server for Jupyter notebooks

LDAP Authenticator for JupyterHub

skops is a Python library helping you share your scikit-learn based models and put them in production.

imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance.

PyDMD is a Python package designed for Dynamic Mode Decomposition (DMD), a data-driven method used for analyzing and extracting spatiotemporal coherent structures from time-varying datasets. It provides a comprehensive and user-friendly interface for performing DMD analysis, making it a valuable tool for researchers, engineers, and data scientists working in various fields.

Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized.

This package provides a neural network library for PyTorch compatible with the scikit-learn API.

sklearn-compat is a small Python package that help developer writing scikit-learn compatible estimators to support multiple scikit-learn versions.

The library classeval is developed to evaluate the models performance of any kind of two-class or multi-class model. classeval computes many scoring measures in case of a two-class clasification model. Some measures are utilized from sklearn, among them AUC, MCC, Cohen kappa score, matthews correlation coefficient, whereas others are custom. This library can help to consistenly compare the output of various models. In addition, it can also give insights in tuning the models performance as the the threshold being used can be adjusted and evaluated. The output of classeval can subsequently plotted in terms of ROC curves, confusion matrices, class distributions, and probability plots. Such plots can help in better understanding of the results.

PyThresh is a comprehensive and scalable Python toolkit for thresholding outlier detection likelihood scores in univariate/multivariate data. It has been written to work in tandem with PyOD and has similar syntax and data structures. However, it is not limited to this single library.

PyThresh is meant to threshold likelihood scores generated by an outlier detector. It thresholds these likelihood scores and replaces the need to set a contamination level or have the user guess the amount of outliers that may exist in the dataset beforehand. These non-parametric methods were written to reduce the user's input/guess work and rather rely on statistics instead to threshold outlier likelihood scores. For thresholding to be applied correctly, the outlier detection likelihood scores must follow this rule: the higher the score, the higher the probability that it is an outlier in the dataset. All threshold functions return a binary array where inliers and outliers are represented by a 0 and 1 respectively.

PyThresh includes more than 30 thresholding algorithms. These algorithms range from using simple statistical analysis like the Z-score to more complex mathematical methods that involve graph theory and topology.

Datasets is a lightweight library providing access to major public datasets (image, audio, text, etc.), as well as enabling efficient data preparation for inspection and ML model evaluation and training.