Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

• h-anomaly
A small example package Anomaly detection using hierarchical clustering, anomaly detector, classifiers and fast model rebuilding

• keras-complex
Complex values in Keras – Deep learning for humans. (https://…/Convolution#Domain_of_definition ) could provide some interesting results in signal processing-based deep learning. A simple(-ish) idea is including explicit phase information of time series in neural networks. This code enables complex-valued convolution in convolutional neural networks in (https://keras.io ) with the (https://tensorflow.org ) backend. This makes the network modular and interoperable with standard keras layers and operations.

• konduit
konduit: Enterprise Runtime for Machine Learning Models

• pylana
Python API for LANA Process Mining

• waymo-od-tf2-0
Waymo Open Dataset libraries.

• whynotr
Companion package to whynot: A collection of causal estimators in R.

• agnes
AGNES – Flexible Reinforcement Learning Framework with PyTorch

• Chamak
A python implementation of laravel framework for machine learning, AI, datascience and data intensive work.

• drepr
Data Representation Language for Reading Heterogeneous Datasets. This library allows reading heterogeneous datasets of different formats and layouts. For more information, please visit (https://…/d-repr ).

• onpoint
NLP question answering service

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

• dframcy
Pandas Dataframe integration for spaCy. DframCy is a light-weight utility module to integrate Pandas Dataframe to spaCy’s linguistic annotation and training tasks. DframCy provides clean APIs to convert spaCy’s linguistic annotations, Matcher and PhraseMatcher information to Pandas dataframe, also supports training and evaluation of NLP pipeline from CSV/XLXS/XLS without any changes to spaCy’s underlying APIs.

• LPCTorch
LPC Utility for Pytorch Library. LPCTorch is a small pytorch utility for Linear Predictive Coding. It provides a simple way to compute windowed Linear Predictive Coding Coefficients on a input audio signal. The repo uses the Burg’s methods and is heavily inspired from the librosa audio library implementation.

• pydistcore
The interface library for probabilistic modeling in HEP

• waymo-od-tf2-0
Waymo Open Dataset libraries.

• whynot
A framework for benchmarking causal inference.

• autoPyTorch
Auto-PyTorch searches neural architectures using BO-HB

• azureml-contrib-automl-dnn-forecasting
AutoML DNN Forecasting Models

• azureml-contrib-interpret
The azureml-contrib-interpret package contains experimental functionality for the azureml-interpret package, which offers a variety of services for machine learning model interpretability. Eventually, useful pieces of this package will be moved into the full package.

• azureml-interpret
Microsoft Azure Machine Learning Interpret API for Python.

• giotto-learn
Toolbox for Machine Learning using Topological Data Analysis.

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

• bsfc
Bayesian Spectral Fitting Code (BSFC)

• dbscan1d
dbscan1d is a package for DBSCAN on 1D arrays. (https://…/sklearn.cluster.DBSCAN.html ) does not have a special case for 1D, where calculating the full distance matrix is wasteful. It is much better to simply sort the input array and performing efficient bisects for finding closest points. Here are the results of running the simple profile script included with the package. In every case DBSCAN1D is much faster than scikit learn’s implementation.

• kinlin
WIP: PyTorch based framework

• macad-gym
Learning environments for Multi-Agent Connected Autonomous Driving (MACAD) with OpenAI Gym compatible interfaces

• pyTokenizer
A streaming tokenizer.

• syllogio-identifyPropositions
Identify natural language propositions in a written argument.

• TeaML
Automated Modeling in Financial Domain. TeaML is a simple and design friendly automatic modeling learning framework.

• timeseriesql
A Pythonic query language for time series data

• trendln
Support and Resistance Trend lines Calculator for Financial Analysis

• deepstack
DeepStack: Ensembling Keras Deep Learning Models into the next Performance Level

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

• tempeh
Machine Learning Performance Testing Framework

• torch3d
Datasets and network architectures for 3D deep learning in PyTorch

• trains-agent
Trains-Agent DevOps for deep learning (DevOps for TRAINS)

• tree-lstm
pytorch tree lstm package. This repository contains a Pytorch Implementation of ‘Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks ‘ (https://…/1503.00075 ). This contains two type of tree-lstm (Child sum, N-ary). This was tested by Python 3.6, Pytorch 1.3.0., and this internally uses dgl 0.4.0 This repository referenced https://…/tree_lstm.py

• trelawney
Generic Interpretability package. Trelawney is a general interpretability package that aims at providing a common api to use most of the modern interpretability methods to shed light on sklearn compatible models (support for Keras and XGBoost are tested).

• arus
Activity Recognition with Ubiquitous Sensing

• cma-es
Covariance Matrix Adaptation Evolution Strategy (CMA-ES) implemented with TensorFlow v2. The CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is an evolutionary algorithm for difficult non-linear non-convex black-box optimisation problems in continuous domain. It is considered as state-of-the-art in evolutionary computation and has been adopted as one of the standard tools for continuous optimisation in many (probably hundreds of) research labs and industrial environments around the world.

• mozilla-fldp
Federated Learning Experimentation

• musco-pytorch
MUSCO: Multi-Stage COmpression of Neural Networks

• waymo-od-tf2-0
Waymo Open Dataset libraries.

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

• GN
A hierachical community detection algorithm by Girvan Newman. A Girvan Newman step is defined as a couple of successive edge removes such that a new community occurs.

• meditorch
A PyTorch package for biomedical image processing

• metriculous
Very unstable library containing utilities to measure and visualize statistical properties of machine learning models.

• podworld
2D partially observable dynamic world for RL experiments. PodWorld is (https://gym.openai.com ) environment for (http://…/the-book-2nd.html ) experimentations. PodWorld is specifically designed to be partially observable and dynamic (hence abbreviation P.O.D.). We emphasize these two attributes to force agents learn spatial as well as temporal representations that must go beyond simple memorization. In addition, all entities in PodWorld must obey laws of physics allowing for long tail of emergent observations that may not appear in games designed with arbitrary hand crafted rules for human entertainment. PodWorld is designed to be highly customizable as well as hackable with fairly simple and minimal code base. PodWorld is designed to be fast (>500 FPS on usual laptops) without needing GPU and run cross platform in headless mode or with render.

• sensplit
Splits a dataset (in Pandas dataframe format) to train/test sets.

• simple-gpu-scheduler
A simple scheduler for running commands on multiple GPUs. A simple scheduler to run your commands on individual GPUs. Following the (https://…/KISS_principle ), this script simply accepts commands via `stdin` and executes them on a specific GPU by setting the `CUDA_VISIBLE_DEVICES` variable.

• simple-s3
Simple S3 uploads

• sparkle-hypothesis
Use the power of hypothesis property based testing in PySpark tests. Data heavy tests benefit from Hypothesis to generate your data and desinging your tests. Sparkle-hypothesis makes it easy to use Hypothesis strategies to generate dataframes.

• sstk
A systematic strategy toolkit.

• TeaML
Automated Modeling in Financial Domain. TeaML is a simple and design friendly automatic modeling learning framework. It can automatically model from beginning to end, and in the end, it will also help you output a model report about the model.

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

• spacy-transformers
spaCy pipelines for pre-trained BERT and other transformers

• stackerpy
Model Stacking for scikit-learn models for Machine Learning (including blending)

• tabnet
Tensorflow 2.0 implementation of TabNet of any configuration. A Tensorflow 2.0 port for the paper (https://…/1908.07442 ), whose original codebase is available at https://…/tabnet.

• TeaML
Automated Modeling in Financial Domain. TeaML is a simple and design friendly automatic modeling learning framework.

• traintorch
Package for live visualization of metrics during training of a machine learning model

• trax
Trax

• VoicePy
A Python Library to interface with Alexa, Dialogflow, and Google Actions.

• aiPool
Framework for Advanced Statistics and Data Sciences

• cprior
Fast Bayesian A/B and multivariate testing

• elasticdl
A Kubernetes-native Deep Learning Framework

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

• adafdr
A fast and covariate-adaptive method for multiple hypothesis testing

• linear
A straightforward package for linear regression with Gaussian priors.

• lm-decoder
KenLM decoder for Non-Autoregressive Models.

• meepo2
event sourcing for databases.

• neural-wrappers
Generic neural networks high level wrapper for PyTorch

• ngboost
Library for probabilistic predictions via gradient boosting.

• numba-scipy
numba-scipy extends Numba to make it aware of SciPy

• numpy-financial
The numpy-financial package contains a collection of elementary financial functions.

• object-detection-core
Tensorflow Object Detection Library

• olr
olr: Optimal Linear Regression. The olr function runs all the possible combinations of linear regressions with all of the dependent variables against the independent variable and returns the statistical summary of either the greatest adjusted R-squared or R-squared term.

• optima
PyTorch Optimization Framework for Researchers

• ox_mon
Monitor status, security, and robustness of your machines. Basically, there are lots of little monitoring and checking tasks you may find yourself needing to do. You could write a separate script for each such task, but it would be nice to have some basic scaffolding for things like notifications, logging, testing, and so on to simplify machine monitoring. Better yet, by having a common framework many developers can contribute small snippets of such tools that work in a similar way to simplify life for everyone.

• pennpaper
Set of utilities for ploting results of non-deterministic experiments, e.g. machine learning, optimization, genetic algorithms. Pen’n’paper is a package to easily collect the data about (noisy) processes and plot them for comparison. This package is not aiming at feature completeness. Instead it should give you an easy start during the phase of the project when you want to just concentrate on an experimental idea.

• rlcard
A Toolkit for Reinforcement Learning in Card Games

• soms
A package to check system related metrics and values for simpler blackbox monitoring

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

• starline
Unofficial python library for StarLine API

• adafdr
A fast and covariate-adaptive method for multiple hypothesis testing. Software accompanying the paper ‘AdaFDR: a Fast, Powerful and Covariate-Adaptive Approach to Multiple Hypothesis Testing’, 2018.

• Alexa-Payloads
A Python Library to interface with Alexa

• antspyx
Advanced Normalization Tools in Python

• deepevent
Deep Learning to identify gait events. We propose a new application (_DeepEvent_) of long short term memory recurrent neural network to the automatic detection of gait events. The 3D position and velocity of the markers on the heel, toe and lateral malleolus were used by the network to determine Foot Strike (FS) and Foot Off (FO). The method was developed from 10526 FS and 9375 FO from 226 children. _DeepEvent_ predicted **FS** within **5.5 ms** and **FO** within **10.7 ms** of the gold standard (automatic determination using force platform data) and was more accurate than common heuristic marker trajectory-based methods proposed in the literature and another deep learning method.

• GoogleActions
A Python Library to interface with Alexa

• gsclight
A simplified package for Google Search Console API. A simplified, object-oriented helper library for the (https://…/search-console-api-original ). This package only covers basic API calls and was mainly created for practice purposes. To get the full Google Search Console API experience see Josh Carty’s (https://…/google-searchconsole ).

• numba-scipy
numba-scipy extends Numba to make it aware of SciPy. numba-scipy extends Numba to make it aware of SciPy. Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax.

• pyus
GPU-accelerated Python package for ultrasound imaging.

• sparkle_test
A small and simple base class for fast and clean PySpark unit tests. Unit testing in Spark is made easier with sparkle-test, the settings are tuned for performance and your unit tests don’t leave any files in your workspace. There is one convenience method for asserting dataframe equality.

• Swarmrob
An Orchestration Tool for Container-based Robot Applications. Because of the very heterogeneous composition of software and hardware in robotics, the reproduction of experiments is a common problem. SwarmRob is a python framework that uses container technologies and orchestration to enable the simple sharing of experimental artifacts and improve the reproducibility in robotics research.

• Swarmrob-Worker
An Orchestration Tool for Container-based Robot Applications. Because of the very heterogeneous composition of software and hardware in robotics, the reproduction of experiments is a common problem. SwarmRob is a python framework that uses container technologies and orchestration to enable the simple sharing of experimental artifacts and improve the reproducibility in robotics research.

• trvae
trVAE – Regularized Conditional Variational Autoencoders

• airtunnel
Airtunnel – tame your Airflow

• deepposekit
A toolkit for pose estimation using deep learning. DeepPoseKit is a software toolkit with a high-level API for 2D pose estimation of user-defined keypoints using deep learning – written in Python and built using [Tensorflow](https://…/tensorflow ) and [Keras](https://…/keras ). Use DeepPoseKit if you need:
• tools for annotating images or video frames with user-defined keypoints
• a straightforward but flexible data augmentation pipeline using the [imgaug package](https://…/imgaug )
• a Keras-based interface for initializing, training, and evaluating pose estimation models
• easy-to-use methods for saving and loading models and making predictions on new data

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

• pyphinb
Python library for computing integrated information.

• strsimpy
A library implementing different string similarity and distance measures. A dozen of algorithms (including Levenshtein edit distance and sibblings, Jaro-Winkler, Longest Common Subsequence, cosine similarity etc.) are currently implemented. Check the summary table below for the complete list…

• sword
A fast, sharp, Python C extension for data mining. ‘sword’ is a Python C Extension for making data mining and data searching super fast. It’s designed to give you amazing performance in the comfort and simplicity of Python. sword offers a wide array of tools to parse and search tabular data, csv, xml, and much more.

• audlib
Audio and Speech Processing Library. pyaudlib (name subject to change) provides a collection of utilities for developing speech-related applications using both signal processing and deep learning.

• deepdos
A machine learning/AI based approach to protecting your devices against ddos attacks. Welcome to deepdos, the python program written to monitor and potentially secure your network from ddos attacks! While not currently utilizing deep learning to classify packets, deepdos currently utilizes logistic regression in order to classify packets and has so far been trained on 200,000 packets from all sorts of DDOS attacks and normal traffic setup. This project couldn’t have been done without the help of the Canadian Institute for Cybersecurity with providing both the original flow dataset and tool to create flow csvs from .pcap files. Their site and all resources have been linked at the bottom.

• disarray
Calculate confusion matrix metrics from your pandas DataFrame. This package calculates metrics derived from a confusion matrix and makes them directly accessible from a pandas DataFrame. Simply install and import `disarray`.

• shyft.time-series
An OpenSource toolbox providing tools for advanced time-series

• skprofiling
Profiling of machine learning models based on scikit-learning interface

• Tasmanian
UQ library for sparse grids and Bayesian inference. The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN is a collection of robust libraries for high dimensional integration and interpolation as well as parameter calibration. This documentation focuses on the libraries and the software API, refer to the PDF document on the project web-page for specifics about the mathematics of the implemented methods.

• DtwSom
Implementation of the Dynamic Time Warping Self Organizing Maps (DTW-SOM)

• fairkit-learn
A machine learning fairness toolkit. Fairkit-learn is an open-source, publicly available Python toolkit designed to help data scientists evaluate and explore machine learning models with respect to quality and fairness metrics simultaneously. Fairkit-learn builds on top of [scikit-learn](https://…/stable ), the state-of-the-art tool suite for data mining and data analysis, and [AI Fairness 360](https://aif360.mybluemix.net ), the state-of-the-art Python toolkit for examining, reporting, and mitigating machine learning bias in individual models. Fairkit-learn supports all metrics and learning algorithms available in scikit-learn and AI Fairness 360, and all of the bias mitigating pre- and post-processing algorithms available in AI Fairness 360, and provides extension points to add more metrics and algorithms.

• mastml
MAterials Simulation Toolkit – Machine Learning

• network-automation-simplifed
Network Automation Simplified. Suppose you must implement a network change. You probably have a standard sequence of steps you typically follow. You may be able to predict the steps you’ll need to perform solely from experience. You may have a formalized document detailing the sequence of tasks consisting of 4, 6 or 10 steps, depending on what you’re doing.

• yayes
Repair erroneous entries in data streams while maintaining accuracy of overall trends. Yayes patching was designed to repair erroneous entries in data streams while maintaining accuracy of overall trends. The yayes package is most powerful in situations where users see sudden, temporary drops in variables’ values that then return to correct variable values (i.e. discrepancies in variable values that may be caused by missing data or data input errors). This method was inspired by the logic behind max-pooling insofar as high values of data often contain more meaningful information and that lower values of data may more often be discarded without losing relevant information.

• adamod
AdaMod optimization algorithm, build on PyTorch.

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

• bhcd
Bayesian Hierarchical Community Discovery. An efficient Bayesian nonparametric model for discovering hierarchical community structure in social networks.

• fstringen
A text generator based on f-strings

• MICA
Mutual Information based Clustering Analysis

• nb2pdf
Jupyter Notebook to PDF Converter. This package converst Jupyter Notebooks (‘.ipynb’ files) to PDFs using nbconvert. It is designed to create manual submissions for the [Python local grader](https://…/local-grader ) being developed by the UC Berkeley Division of Data Science and Information.

• otter-grader
Jupyter Notebook Autograder

• pymetalog
A python package that generates functions for the metalog distribution. The metalog distribution is a highly flexible probability distribution that can be used to model data without traditional parameters. This repo is a working project for a python package (pymetalog) that generates functions for the metalog distribution. The metalog distribution is a highly flexible probability distribution that can be used to model data without traditional parameters.

• scikit-ika
real-time lifelong machine learning

• tensorsketch
Lightweight deep learning library for TensorFlow 2.0

• tic
TIC is a library that acts as a Toolbox for Interpretability Comparison.

• tomaster
Topological Mode Analysis on Steroids

• adafdr
A fast and covariate-adaptive method for multiple hypothesis testing

• bert-document-classification
long document classification with language models

• brainnet
A library for Artificial Intelligence

• diversify
Playlist generator based on artificial intelligence

• indoorplants
Tools for data analysis and model validation