Tools for Optuna, MLflow and the integration of both.

Overview

HPOflow - Sphinx DOC

DOC MIT License Contributor Covenant Python Version pypi
pytest status Static Code Checks status Build & Deploy Doc GitHub issues

Tools for Optuna, MLflow and the integration of both.

Detailed documentation with examples can be found here: Sphinx DOC

Table of Contents

Maintainers

One Conversation
This project is maintained by the One Conversation team of Deutsche Telekom AG.

The main components are:

Installation

HPOflow is available at the Python Package Index (PyPI). It can be installed with pip:

$ pip install hpoflow

Some additional dependencies might be necessary.

To use hpoflow.optuna_mlflow.OptunaMLflow:

$ pip install mlflow GitPython

To use hpoflow.optuna_transformers.OptunaMLflowCallback:

$ pip install mlflow GitPython transformers

To install all optional dependencies use:

$ pip install hpoflow[optional]

Support and Feedback

The following channels are available for discussions, feedback, and support requests:

Reporting Security Vulnerabilities

This project is built with security and data privacy in mind to ensure your data is safe. We are grateful for security researchers and users reporting a vulnerability to us, first. To ensure that your request is handled in a timely manner and non-disclosure of vulnerabilities can be assured, please follow the below guideline.

Please do not report security vulnerabilities directly on GitHub. GitHub Issues can be publicly seen and therefore would result in a direct disclosure.

Please address questions about data privacy, security concepts, and other media requests to the [email protected] mailbox.

Contribution

Our commitment to open source means that we are enabling - in fact encouraging - all interested parties to contribute and become part of our developer community.

Contribution and feedback is encouraged and always welcome. For more information about how to contribute, as well as additional contribution information, see our Contribution Guidelines.

Code of Conduct

This project has adopted the Contributor Covenant as our code of conduct. Please see the details in our Contributor Covenant Code of Conduct. All contributors must abide by the code of conduct.

Licensing

Copyright (c) 2021 Philip May, Deutsche Telekom AG
Copyright (c) 2021 Philip May
Copyright (c) 2021 Timothy Wolff-Piggott

Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License by reviewing the file LICENSE in the repository.

Comments
  • review README.md and CONTRIBUTING.md

    review README.md and CONTRIBUTING.md

    Review README.md and CONTRIBUTING.md

    • is there something missing? maybe compare with optuna and transformers
    • spelling
    • idiomatic english
    • consistency
    • correctness
    • links ok?
    • ...

    PS: The real documentation is still missing and a know issue.

    opened by PhilipMay 12
  • add typing in optuna_transformers

    add typing in optuna_transformers

    @twolffpiggott can you please tell me the type of this?

    https://github.com/telekom/HPOflow/blob/e2b0943218af419a79ce95e60b67c9a4c2477349/hpoflow/optuna_transformers.py#L47

    opened by PhilipMay 6
  • add `transformers.py`

    add `transformers.py`

    @twolffpiggott should we add this here or to an other project we open source?

    https://github.com/PhilipMay/mltb/blob/master/mltb/integration/transformers.py

    enhancement 
    opened by PhilipMay 6
  • Create Sphinx documentation page

    Create Sphinx documentation page

    • [x] setup
    • [x] make GH action
    • [x] setup page
    • [x] change styling to telekom style
    • switch to MD
    • [x] add more content
    • [x] link from README to page
    • [x] link from pypi to GH page
    • [x] add impressum
    • [x] remove strange mouse over image effect
    • add version info
    documentation 
    opened by PhilipMay 4
  • Problems with direct `_imports.check()` call

    Problems with direct `_imports.check()` call

    When the __init__.py imports OMLflowCallback the optuna_transformers.py script is executed. That executes the _imports.check() call which then throws an exception if transformers or mlflow is not installed. But that should be avoided.

    See here: https://github.com/telekom/HPOflow/blob/d1cce5cbc2a84634d1484a053286000dda05b681/hpoflow/optuna_transformers.py#L11-L17

    The solution would be to put the _imports.check() call into the constructor. But that is not possible because OMLflowCallback inherits from transformers.

    The only solution I have is to put OMLflowCallback into an factory function that creates an OMLflowCallback and does the _imports.check() in there.

    @twolffpiggott what do you think?

    bug 
    opened by PhilipMay 3
  • Flake8 ignore list for Black compatibility

    Flake8 ignore list for Black compatibility

    Flake8 raises a warning for "E203" when it encounters a Black decision to insert whitespace before : in slicing syntax.

    Black's behaviour is more correct here, so my suggestion is to add "E203" to the flake8 config ignore list.

    i.e. in setup.cfg:

    [flake8]
    ...
    extend-ignore = E203
    opened by twolffpiggott 3
  • Simple Example?

    Simple Example?

    I don't understand how to use this package. Could you provide a basic example? I don't understand the import_structure and how it relates to importing the modules? Thanks

    opened by jmrichardson 2
  • WIP prefix in contrib file

    WIP prefix in contrib file

    Should this

    Create Work In Progress [WIP] pull requests only if you need clarification or an explicit review before you can continue your work item.

    be more like this

    Add a [WIP] prefix on your pull request name if you need clarification or an explicit review before you can continue your work item.

    documentation 
    opened by PhilipMay 2
Releases(0.1.4)
Owner
Telekom Open Source Software
published by Deutsche Telekom AG and partner companies
Telekom Open Source Software
A collection of Scikit-Learn compatible time series transformers and tools.

tsfeast A collection of Scikit-Learn compatible time series transformers and tools. Installation Create a virtual environment and install: From PyPi p

Chris Santiago 0 Mar 30, 2022
About Solve CTF offline disconnection problem - based on python3's small crawler

About Solve CTF offline disconnection problem - based on python3's small crawler, support keyword search and local map bed establishment, currently support Jianshu, xianzhi,anquanke,freebuf,seebug

天河 32 Oct 25, 2022
Machine Learning e Data Science com Python

Machine Learning e Data Science com Python Arquivos do curso de Data Science e Machine Learning com Python na Udemy, cliqe aqui para acessá-lo. O prin

Renan Barbosa 1 Jan 27, 2022
Transpile trained scikit-learn estimators to C, Java, JavaScript and others.

sklearn-porter Transpile trained scikit-learn estimators to C, Java, JavaScript and others. It's recommended for limited embedded systems and critical

Darius Morawiec 1.2k Jan 05, 2023
ClearML - Auto-Magical Suite of tools to streamline your ML workflow. Experiment Manager, MLOps and Data-Management

ClearML - Auto-Magical Suite of tools to streamline your ML workflow Experiment Manager, MLOps and Data-Management ClearML Formerly known as Allegro T

ClearML 4k Jan 09, 2023
#30DaysOfStreamlit is a 30-day social challenge for you to build and deploy Streamlit apps.

30 Days Of Streamlit 🎈 This is the official repo of #30DaysOfStreamlit — a 30-day social challenge for you to learn, build and deploy Streamlit apps.

Streamlit 53 Jan 02, 2023
CD) in machine learning projectsImplementing continuous integration & delivery (CI/CD) in machine learning projects

CML with cloud compute This repository contains a sample project using CML with Terraform (via the cml-runner function) to launch an AWS EC2 instance

Iterative 19 Oct 03, 2022
A naive Bayes model for cancer classification using a set of documents

Naivebayes text classifcation model for cancer and noncancer documents Author: Alex King Purpose Requirements/files included How to use 1. Purpose The

Alex W King 1 Nov 24, 2021
Timeseries analysis for neuroscience data

=================================================== Nitime: timeseries analysis for neuroscience data ===============================================

NIPY developers 212 Dec 09, 2022
A high-performance topological machine learning toolbox in Python

giotto-tda is a high-performance topological machine learning toolbox in Python built on top of scikit-learn and is distributed under the G

giotto.ai 632 Dec 29, 2022
Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python

Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python Overview Bank Jago has attracted investors' attention since the end

Najibulloh Asror 3 Feb 10, 2022
Automated Time Series Forecasting

AutoTS AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale. There are dozens of forecasting mod

Colin Catlin 652 Jan 03, 2023
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

CatBoost 6.9k Jan 05, 2023
Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing

Parallelized symbolic regression built on Julia, and interfaced by Python. Uses regularized evolution, simulated annealing, and gradient-free optimization.

Miles Cranmer 924 Jan 03, 2023
Can a machine learning project be implemented to estimate the salaries of baseball players whose salary information and career statistics for 1986 are shared?

END TO END MACHINE LEARNING PROJECT ON HITTERS DATASET Can a machine learning project be implemented to estimate the salaries of baseball players whos

Pinar Oner 7 Dec 18, 2021
[DEPRECATED] Tensorflow wrapper for DataFrames on Apache Spark

TensorFrames (Deprecated) Note: TensorFrames is deprecated. You can use pandas UDF instead. Experimental TensorFlow binding for Scala and Apache Spark

Databricks 757 Dec 31, 2022
flexible time-series processing & feature extraction

A corona statistics and information telegram bot.

PreDiCT.IDLab 206 Dec 28, 2022
Machine Learning Techniques using python.

👋 Hi, I’m Fahad from TEXAS TECH. 👀 I’m interested in Optimization / Machine Learning/ Statistics 🌱 I’m currently learning Machine Learning and Stat

FAHAD MOSTAFA 1 Jan 19, 2022
Time-series momentum for momentum investing strategy

Time-series-momentum Time-series momentum strategy. You can use the data_analysis.py file to find out the best trigger and window for a given asset an

Victor Caldeira 3 Jun 18, 2022
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Little Ball of Fur is a graph sampling extension library for Python. Please look at the Documentation, relevant Paper, Promo video and External Resour

Benedek Rozemberczki 619 Dec 14, 2022