A benchmark of data-centric tasks from across the machine learning lifecycle.

Overview
banner

GitHub Workflow Status GitHub Documentation Status pre-commit PyPI - Python Version codecov

A benchmark of data-centric tasks from across the machine learning lifecycle.

Getting Started | What is dcbench? | Docs | Contributing | Website | About

⚡️ Quickstart

pip install dcbench

Optional: some parts of Meerkat rely on optional dependencies. If you know which optional dependencies you'd like to install, you can do so using something like pip install dcbench[dev] instead. See setup.py for a full list of optional dependencies.

Installing from dev: pip install "dcbench[dev] @ git+https://github.com/data-centric-ai/[email protected]"

Using a Jupyter notebook or some other interactive environment, you can import the library and explore the data-centric problems in the benchmark:

import dcbench
dcbench.tasks

To learn more, follow the walkthrough in the docs.

💡 What is dcbench?

This benchmark evaluates the steps in your machine learning workflow beyond model training and tuning. This includes feature cleaning, slice discovery, and coreset selection. We call these “data-centric” tasks because they're focused on exploring and manipulating data – not training models. dcbench supports a growing list of them:

dcbench includes tasks that look very different from one another: the inputs and outputs of the slice discovery task are not the same as those of the minimal data cleaning task. However, we think it important that researchers and practitioners be able to run evaluations on data-centric tasks across the ML lifecycle without having to learn a bunch of different APIs or rewrite evaluation scripts.

So, dcbench is designed to be a common home for these diverse, but related, tasks. In dcbench all of these tasks are structured in a similar manner and they are supported by a common Python API that makes it easy to download data, run evaluations, and compare methods.

✉️ About

dcbench is being developed alongside the data-centric-ai benchmark. Reach out to Bojan Karlaš (karlasb [at] inf [dot] ethz [dot] ch) and Sabri Eyuboglu (eyuboglu [at] stanford [dot] edu if you would like to get involved or contribute!)

You might also like...
Data science, Data manipulation and Machine learning package.
Data science, Data manipulation and Machine learning package.

duality Data science, Data manipulation and Machine learning package. Use permitted according to the terms of use and conditions set by the attached l

Data Version Control or DVC is an open-source tool for data science and machine learning projects
Data Version Control or DVC is an open-source tool for data science and machine learning projects

Continuous Machine Learning project integration with DVC Data Version Control or DVC is an open-source tool for data science and machine learning proj

A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

A toolkit for making real world machine learning and data analysis applications in C++

dlib C++ library Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real worl

A library of extension and helper modules for Python's data analysis and machine learning libraries.
A library of extension and helper modules for Python's data analysis and machine learning libraries.

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks. Sebastian Raschka 2014-2021 Links Doc

A machine learning toolkit dedicated to time-series data

tslearn The machine learning toolkit for time series analysis in Python Section Description Installation Installing the dependencies and tslearn Getti

A machine learning toolkit dedicated to time-series data

tslearn The machine learning toolkit for time series analysis in Python Section Description Installation Installing the dependencies and tslearn Getti

Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way
Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way

Apache Liminals goal is to operationalise the machine learning process, allowing data scientists to quickly transition from a successful experiment to an automated pipeline of model training, validation, deployment and inference in production. Liminal provides a Domain Specific Language to build ML workflows on top of Apache Airflow.

Meerkat provides fast and flexible data structures for working with complex machine learning datasets.
Meerkat provides fast and flexible data structures for working with complex machine learning datasets.

Meerkat makes it easier for ML practitioners to interact with high-dimensional, multi-modal data. It provides simple abstractions for data inspection, model evaluation and model training supported by efficient and robust IO under the hood.

Comments
  •  No module named 'dcbench.tasks.budgetclean.cpclean'

    No module named 'dcbench.tasks.budgetclean.cpclean'

    After installing dcbench in Google colab environment, the above error was thrown for import dcbench. Full error traceback,

    ---------------------------------------------------------------------------
    ModuleNotFoundError                       Traceback (most recent call last)
    <ipython-input-8-a1030f6d7ef9> in <module>()
          1 
    ----> 2 import dcbench
          3 dcbench.tasks
    
    2 frames
    /usr/local/lib/python3.7/dist-packages/dcbench/__init__.py in <module>()
         13 )
         14 from .config import config
    ---> 15 from .tasks.budgetclean import BudgetcleanProblem
         16 from .tasks.minidata import MiniDataProblem
         17 from .tasks.slice_discovery import SliceDiscoveryProblem
    
    /usr/local/lib/python3.7/dist-packages/dcbench/tasks/budgetclean/__init__.py in <module>()
          3 from ...common import Task
          4 from ...common.table import Table
    ----> 5 from .baselines import cp_clean, random_clean
          6 from .common import Preprocessor
          7 from .problem import BudgetcleanProblem, BudgetcleanSolution
    
    /usr/local/lib/python3.7/dist-packages/dcbench/tasks/budgetclean/baselines.py in <module>()
          6 from ...common.baseline import baseline
          7 from .common import Preprocessor
    ----> 8 from .cpclean.algorithm.select import entropy_expected
          9 from .cpclean.algorithm.sort_count import sort_count_after_clean_multi
         10 from .cpclean.clean import CPClean, Querier
    
    ModuleNotFoundError: No module named 'dcbench.tasks.budgetclean.cpclean'
    

    !pip install dcbench gave the following log

    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. 
    flask 1.1.4 requires click<8.0,>=5.1, but you have click 8.0.3 which is incompatible.
    datascience 0.10.6 requires coverage==3.7.1, but you have coverage 6.2 which is incompatible.
    datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
    coveralls 0.5 requires coverage<3.999,>=3.6, but you have coverage 6.2 which is incompatible.
    Successfully installed SecretStorage-3.3.1 aiohttp-3.8.1 aiosignal-1.2.0 antlr4-python3-runtime-4.8 async-timeout-4.0.2 asynctest-0.13.0 black-21.12b0 cfgv-3.3.1 click-8.0.3 colorama-0.4.4 commonmark-0.9.1 coverage-6.2 cryptography-36.0.1 cytoolz-0.11.2 dataclasses-0.6 datasets-1.17.0 dcbench-0.0.4 distlib-0.3.4 docformatter-1.4 flake8-4.0.1 frozenlist-1.2.0 fsspec-2021.11.1 future-0.18.2 fuzzywuzzy-0.18.0 fvcore-0.1.5.post20211023 huggingface-hub-0.2.1 identify-2.4.1 importlib-metadata-4.2.0 iopath-0.1.9 isort-5.10.1 jeepney-0.7.1 jsonlines-3.0.0 keyring-23.4.0 livereload-2.6.3 markdown-3.3.4 mccabe-0.6.1 meerkat-ml-0.2.3 multidict-5.2.0 mypy-extensions-0.4.3 nbsphinx-0.8.8 nodeenv-1.6.0 omegaconf-2.1.1 parameterized-0.8.1 pathspec-0.9.0 pkginfo-1.8.2 platformdirs-2.4.1 pluggy-1.0.0 portalocker-2.3.2 pre-commit-2.16.0 progressbar-2.5 pyDeprecate-0.3.1 pycodestyle-2.8.0 pyflakes-2.4.0 pytest-6.2.5 pytest-cov-3.0.0 pytorch-lightning-1.5.7 pyyaml-6.0 readme-renderer-32.0 recommonmark-0.7.1 requests-toolbelt-0.9.1 rfc3986-1.5.0 sphinx-autobuild-2021.3.14 sphinx-rtd-theme-1.0.0 torchmetrics-0.6.2 twine-3.7.1 typed-ast-1.5.1 ujson-5.1.0 untokenize-0.1.1 virtualenv-20.12.1 xxhash-2.0.2 yacs-0.1.8 yarl-1.7.2
    WARNING: The following packages were previously imported in this runtime:
      [pydevd_plugins]
    You must restart the runtime in order to use newly installed versions.
    

    python version : 3.7.12 platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic

    opened by mathav95raj 2
  • Slice discovery problem p_72411 misses files

    Slice discovery problem p_72411 misses files

    Hi,

    Thanks for this great tool!

    I'm loading slice discovery problems, however, the problem p_72411 misses files. Can you fix this SD problem?

    FileNotFoundError: [Errno 2] No such file or directory: '/home/user/.dcbench/slice_discovery/problem/artifacts/p_72411/test_predictions.mk/meta.yaml'
    
    opened by duguyue100 0
Releases(v-0.0.1-beta)
Dual Adaptive Sampling for Machine Learning Interatomic potential.

DAS Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hong

6 Jul 06, 2022
Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray

A unified Data Analytics and AI platform for distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray What is Analytics Zoo? Analytics Zo

2.5k Dec 28, 2022
AP1 Transcription Factor Binding Site Prediction

A machine learning project that predicted binding sites of AP1 transcription factor, using ChIP-Seq data and local DNA shape information.

1 Jan 21, 2022
Timeseries analysis for neuroscience data

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

NIPY developers 212 Dec 09, 2022
Add built-in support for quaternions to numpy

Quaternions in numpy This Python module adds a quaternion dtype to NumPy. The code was originally based on code by Martin Ling (which he wrote with he

Mike Boyle 531 Dec 28, 2022
Automated Machine Learning with scikit-learn

auto-sklearn auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. Find the documentation here

AutoML-Freiburg-Hannover 6.7k Jan 07, 2023
Automatically create Faiss knn indices with the most optimal similarity search parameters.

It selects the best indexing parameters to achieve the highest recalls given memory and query speed constraints.

Criteo 419 Jan 01, 2023
Pandas-method-chaining is a plugin for flake8 that provides method chaining linting for pandas code

pandas-method-chaining pandas-method-chaining is a plugin for flake8 that provides method chaining linting for pandas code. It is a fork from pandas-v

Francis 5 May 14, 2022
Magenta: Music and Art Generation with Machine Intelligence

Magenta is a research project exploring the role of machine learning in the process of creating art and music. Primarily this involves developing new

Magenta 18.1k Dec 30, 2022
Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis.

Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis. It is distributed under the MIT License.

Jeong-Yoon Lee 720 Dec 25, 2022
Iris-Heroku - Putting a Machine Learning Model into Production with Flask and Heroku

Puesta en Producción de un modelo de aprendizaje automático con Flask y Heroku L

Jesùs Guillen 1 Jun 03, 2022
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Machine Learning Notebooks, 3rd edition This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code

Aurélien Geron 1.6k Jan 05, 2023
Open source time series library for Python

PyFlux PyFlux is an open source time series library for Python. The library has a good array of modern time series models, as well as a flexible array

Ross Taylor 2k Jan 02, 2023
Code for the TCAV ML interpretability project

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim, Martin Wattenberg, Justin Gilmer, C

552 Dec 27, 2022
PyHarmonize: Adding harmony lines to recorded melodies in Python

PyHarmonize: Adding harmony lines to recorded melodies in Python About To use this module, the user provides a wav file containing a melody, the key i

Julian Kappler 2 May 20, 2022
Painless Machine Learning for python based on scikit-learn

PlainML Painless Machine Learning Library for python based on scikit-learn. Install pip install plainml Example from plainml import KnnModel, load_ir

1 Aug 06, 2022
A scikit-learn based module for multi-label et. al. classification

scikit-multilearn scikit-multilearn is a Python module capable of performing multi-label learning tasks. It is built on-top of various scientific Pyth

802 Jan 01, 2023
Price Prediction model is used to develop an LSTM model to predict the future market price of Bitcoin and Ethereum.

Price Prediction model is used to develop an LSTM model to predict the future market price of Bitcoin and Ethereum.

2 Jun 14, 2022
Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets

Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets Datasets Used: Iris dataset,

Samrat Mitra 2 Nov 18, 2021
Pandas DataFrames and Series as Interactive Tables in Jupyter

Pandas DataFrames and Series as Interactive Tables in Jupyter Star Turn pandas DataFrames and Series into interactive datatables in both your notebook

Marc Wouts 364 Jan 04, 2023