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.

• nervaluate
NER evaluation done right. nervaluate is a python module for evaluating Named Entity Recognition (NER) models as defined in the SemEval 2013 – 9.1 task. The evaluation metrics output by nervaluate go beyond a simple token/tag based schema, and consider diferent scenarios based on wether all the tokens that belong to a named entity were classified or not, and also wether the correct entity type was assigned.

• ocr-file
Google OCR package

• odahu-flow-jupyterlab-plugin
A JupyterLab Notebook server extension for jupyter_odahuflow

• SDI-PANDAS
List of operators using the pandas module for processing the input

• alo
A tool to combine function with DAG

• deeptranslit
Transliteration with Deep Learning

• FairAutoML
A Fairness-aware AutoML System

• kdap
KDAP is a package to analyze knowledge data. This library contains a collection of utilities for efficiently processing and anlyzing the data of Wiki-based and QnA-based portals (eg., Wikipedia, Wikia, Stack Exchange, etc.). The function takes Knol-ML files as input. Most of the functions of this library are implemented in such a way that parallel processing can be achieved. The library is currently in the development phase, feel free to raise an issue. We welcome the community to contribute towards KDAP code base and ARK dataset. Please check our webpage for more details (https://…/)

• mgc
A comprehensive independence testing package

• nn-numpy
A package which contains a simple implementation of neural network with numpy

• pandas-htools
Tools for Exploratory data analysis with Pandas

• pybats
Bayesian Forecasting of Time Series

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.

• essay-eval
Evaluation of essays using NLP

• genclf
Gender Classifier ML Package for classifying gender using firstname

• pyTsetlinMachineParallel
Multi-threaded implementation of the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features and multigranularity.

• resystools
a lib with some recommendation algorithms

• seqvec
Embeeder tool for ‘Modelling the Language of Life – Deep Learning Protein Sequences’. One common task in Computational Biology is the prediction of aspects of protein function and structure from their amino acid sequence. For 26 years, most state-of-the-art approaches toward this end have been marrying machine learning and evolutionary information. The retrieval of related proteins from ever growing sequence databases is becoming so time-consuming that the analysis of entire proteomes becomes challenging. On top, evolutionary information is less powerful for small families, e.g. for proteins from the Dark Proteome.

• sinabs
A spiking deep neural network simulator, and neuromoprhic hardware emulator. sinabs (pytorch based library) is developed to design and implement Spiking Convolutional Neural Networks (SCNNs). The library implements several layers that are spiking equivalents of CNN layers. In addition it provides support to import CNN models implemented in keras conveniently to test their spiking equivalent implementation.

• dm-memorytasks
DeepMind Memory Tasks, a set of Unity-based machine-learning research tasks.

• gplpy
Grammar Guided Genetic Programming Library

• mskit
A tool kit for MS data post-processing

• mxnet-osx
mxnet-osx

• nbeats-keras
N-Beats: Neural basis expansion analysis for interpretable time series forecasting
• Implementation in Pytorch
• Implementation in Keras

• nbeats-pytorch
N-Beats: Neural basis expansion analysis for interpretable time series forecasting
• Implementation in Pytorch
• Implementation in Keras

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.

• git-hammer
Statistics tool for git repositories

• LAD-SOMPY
Self Organizing Maps Package

• n2dit
Deep learning-based Night-to-Day image-translation software

• pysparkcli
PySpark Project Buiding Tool. Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

• serial-visualisation
A package for visualisation of serial data in a grid.

• topsis-jamesfallon
Implementation of TOPSIS decision making

• wf-smc-kalman
Sequential Monte Carlo modeling for linear Gaussian systems

• woe-monotonic-binning
Optimal binning algorithm and function to apply on a pandas DataFrame

• arboretum-dev
Gradient Boosting powered by GPU(NVIDIA CUDA)

• args-topic-modeling
modeling the topic of arguments

• deepl-pro
A wrapper for the DeepL Pro API.

• dj-kaggle-pipeline
Pipelines and utility classes for Kaggle and data science!

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.

• deepforest
Tree crown prediction using deep learning retinanets

• distilkobert
Distillation of KoBERT

• ohsome2label
Historical OpenStreetMap Objects to Machine Learning Training Samples

• pyapprox
High-dimensional function approximation and estimation

• sc2rl
A python-sc2 wrapper for Reinforcement Learning

• valkyrie
Implementation of machine learning algorithm.

• yao-framework
Extensible Efficient Quantum Algorithm Design in Python

• do-py
Standardized data structures for Python.

• drlkit
A High Level Python Deep Reinforcement Learning library. Great for beginners, for prototyping and quickly comparing algorithms

• exkaldi
ExKaldi Automatic Speech Recognition Toolkit

• gdpr-prov-neo4j-api
This project exports a set of methods that enable graph-based database management.

• gimbiseo
A system of Human-Machine Dialogue

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.

• PyPDataHub
This project is used for data development & data processing flow.

• pytolemaic
Package for ML model analysis. Pytolemaic package analyzes your model and dataset and measure their quality. The package supports classification/regression models built for tabular datasets (e.g. sklearn’s regressors/classifiers), but will also support custom made models as long as they implement sklearn’s API. The package is aimed for personal use and comes with no guarantees. I hope you will find it useful. I will appreciate any feedback you have.

• RobotFrameworkElasticSearchLibrary
Robot Framework ElasticSearch library.

• royal
A financial market modeling library

• sqfuffle
Quickly solves differentiable multi-objective optimization problems.

• text2vec
Text to Vector Tool, encode text

• TextExtract
Tesseract Optimize is a optimize version of Tesseract to provide better text recognition.

• utensor-cgen
C code generation program for uTensor

• wwl
Wasserstein Weisfeiler-Lehman Graph Kernels

• xain-fl
XAIN is an open source framework for federated learning. The XAIN project is building a GDPR-compliance layer for machine learning. The approach relies on federated machine learning (FedML) as enabling technology that removes compliance-related adoption barriers of AI applications used in production. At present, the source code in this project demonstrates the effectiveness of our FedML implementation on well known benchmarks using a realistic deep learning model structure. We will soon add a link to details on those experiments.

• xxzlib
An feature extraction algorithm

• DDPG-TF
DDPG implimentaion in Tensorflow-2.0

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.

• classy-vision
An end-to-end PyTorch framework for image and video classification.

• cnnutils
A set of utility function which help build convolutional neural networks

• colin-mico
MICO: Mutual Information and Conic Optimization for feature selection. MICO is a Python package that implements a conic optimization based feature selection method with mutual information (MI) measure. The idea behind the approach is to measure the features’relevance and redundancy using MI, and then formulate a feature selection problem as a pure-binary quadratic optimization problem, which can be heuristically solved by an efficient randomization algorithm via semidefinite programming. Optimization software Colin is used for solving the underlying conic optimization problems.

• DataScienceHelpers
Helper modules for doing data cleaning, training, exploration, etc. A set of modules that contain helper methods, e.g. the old fastai ‘structered.py’ file which contains functions that split a date into parts and automatically convert dataframe object columns to categorical variables.

• dial
A Deep Learning GUI-based Framework

• dm-env-rpc
A networking protocol for agent-environment communication.

• fastcore
Python supercharged for fastai development

• giganticode-langmodels
A toolkit for applying machine learning to large source code corpora

• mlpl
A data science pipeline tool to speed up data science life cycle. Using this library, you can:
• Test new experiments easily and keep track of their results.
• Keep details of each preprocessing/FE step easily accessible in collapsibles.
• Do hyperparameter search. (Bayesian search, quick linear search)
• Create a pipeline that consists of useful steps and save/load it.
• Automatically try different processing steps and use useful ones. (imputations, binning, one-hot encoding, …)
• Make your predictions more reliable by averaging results obtained from different CV splits and random seeds.

• nproc
Neyman-Pearson (NP) Classification Algorithms and NP Receiver Operating Characteristic (NP-ROC) Curves

• ontogram
Ontogram is an OWL ontology diagram generator.

• paddy
Optimize hyperparameters using the Paddy field algorithm

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.

• vertica-ml-python
Vertica-ML-Python simplifies data exploration, data cleaning and machine learning in Vertica.

• yale-dhlab-keras-preprocessing
Easy data preprocessing and data augmentation for deep learning models

• cadishi
High performance distance histogram calculation framework for CPUs and GPUs. CADISHI \– CAlculation of DIStance HIstograms \– is a software package that enables scientists to compute (Euclidean) distance histograms efficiently. Any sets of objects that have 3D Cartesian coordinates may be used as input, for example, atoms in molecular dynamics datasets or galaxies in astrophysical contexts. CADISHI drives the high-performance kernels pydh (CPU) and cudh (GPU, optional) to do the actual histogram computation. The kernels pydh and cudh are part of CADISHI and are written in C++ and CUDA.

• EduSim
This project aims to provide some offline simulators for training and testing recommender systems of education.

• enlp
Python library of NLP functions originally collated by Equinor Knowledge and AI Data Science team.

• farm-haystack
Neural Question Answering at Scale. Use modern transformer based models like BERT to find answers in large document collections

• lets-plot
An open source library for statistical plotting

• nbtrash
Add Recycle bin to your jupyter notebook.

• paddlecraft
Make Neural Models as APIs for solving more complicated AI problems

• Tanystes
The Tanystes Deep Learning Framework

• transfertools
A compact Python toolbox for transfer learning. transfertools is a small Python package containing recent transfer learning algorithms. Transfer learning strives to transfer information from one dataset, the source domain, to a related dataset, the target domain. Several constraints and assumptions can be placed on the domains, inspiring different algorithms to do the information transfer. The package contains four transfer learning algorithms.

• uncover-ml
Machine learning tools for the Geoscience Australia uncover project

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.

• abao-matrix-operations
Basic Matrix Operations

• ai6
Artificial Intelligence Examples

• DynEnv
Dynamic RL Environments for Autonomous Driving and Robot Soccer

• func-adl.ast
Functional Analysis Description Language – Backend AST Manipulation Packages

• grape-model
GRAPE makes it easy to fit a regression model with hyperparameter optimization.

• pytorch-influence-functions
This package is a plug-n-play PyTorch reimplementation of Influence Functions. Influence Functions were introduced in the paper Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang (ICML2017).

• robotframework-imagelibrary
Image Processing Library For Robot Framework

• tensorcox
Coxs partial likelihood in Tensorflow. Tensorflow implementation of Coxs’ partial likelihood [^1] based on a counting process representation[^2]. Combining Cox’s model with the counting process representation allows for more complex censoring patterns as well as the inclusion of time-varying covariates. Implementing this in Tensorflow with batch optimization methods gives us a powerful approach that can scale to big data problems and make full use of distributed computing environments. Since the algorithm is fully written in Tensorflow it is easy to integrate it into larger workflows and combine it with Neural Networks etc.

• topologika
Localized topological data analysis

• torchtuples
Training neural networks in PyTorch

• trainer-xy
trainer dashboard

• transparentai
Python tool to create an ethic AI from defining users’s need to monitoring 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.

• boltzmannclean
Fill missing values in DataFrames with Restricted Boltzmann Machines. Fill missing values in a pandas DataFrame using a Restricted Boltzmann Machine. Provides a class implementing the scikit-learn transformer interface for creating and training a Restricted Boltzmann Machine. This can then be sampled from to fill in missing values in training data or new data of the same format. Utility functions for applying the transformations to a pandas DataFrame are provided, with the option to treat columns as either continuous numerical or categorical features.

• metaphor-draw
Neural network draw. Metaphor-draw is a drawind too for neural networks. It can be used as well with MLP model and GraphMachine models.

• minipipe
A machine learning mini-batch pipeline for out-of-memory training

• mlwrapper
Wrapper for machine learning experiments

• nb-black
A simple extension for Jupyter Notebook and Jupyter Lab to beautify Python code automatically using Black.

• py-am
Data engineering & Data science Framework

• pycaret
A Python package for supervised and unsupervised machine learning. pycaret is the free software and open source machine learning library for python programming language. It is built around several popular machine learning libraries in python. It’s primary objective is to simplify problem solving through machine learning by providing an easy to use unified API.

• rl-plotter
A plotter for reinforcement learning

• sparktorch
Deep learning on Apache Spark with Pytorch. This is an implementation of Pytorch on Spark. The goal of this library is to provide a simple, understandable interface in using Torch on Spark. With SparkTorch, you can easily integrate your deep learning model with a ML Spark Pipeline. Underneath, SparkTorch uses a parameter server to train the Pytorch network in a distributed manner. Through the api, the user can specify the style of training, whether that is Hogwild or async with locking.

• py-test-utility
useful package for pipelines testing and data mocking for new generation data warehouse

• qjqqjq
An feature extraction algorithm

• reprotools
A set of tools to evaluate the reproducibility of computations

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.

• kili
Python client for Kili Technology labeling tool. Kili Technology is an image, text and voice data annotation tool designed to help companies deploy machine learning applications faster. In a few minutes you can start annotating your data thanks to a catalogue of intuitive and configurable interfaces. You can easily accelerate the labeling process by connecting one of your models to pre annotate the data. The work of the annotators is 2 to 5 times faster. Kili Technology facilitates collaboration between technical teams and the business, but also with outsourced annotation companies. Data governance is managed, and production quality control is facilitated. Kili Technology meets the needs of small teams as well as those of large companies with massive stakes.

• labelyou
Semi-Automatic OCR Annotation Tool

• mcmaxenttest
Statistical test to detect higher-order correlations between count variables.

• notebookc2
A package to convert your Jupyter Notebook

• octavvs
Open Chemometrics Toolkit for Analysis and Visualization of Vibrational Spectroscopy data

• onlineChange
Quickest change detection algorithms for online streaming data in python. The onlineChange python package is designed to quickest detect any change in distributions for online streaming data, supporting any user-specified distributions. It supports sequential Monte Carlo to perform Bayesian change point analysis, where the ‘parameters before or after the change can be unknown’. It also supports likelihood ratio based test to determine the stopping time (i.e. when change occurs).

• openml-keras
Keras extension for Openml python API

• openml-mxnet
MXNet extension for Openml python API

• paddle-fleet
Distributed Training Package Based on PaddlePaddle. Fleet Package is a high level distributed training package. A user can easily define deep learning models with fleet built-in package or generate a new model with API. Online Training is supported in Fleet package.

• plttools
Interact with PLTs

• preprocess1
A package to eliminate multicollinearity

• pyetaler
A high performance implementation of Numenta’s HTM algorithms