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)
My project contrasts K-Nearest Neighbors and Random Forrest Regressors on Real World data

kNN-vs-RFR My project contrasts K-Nearest Neighbors and Random Forrest Regressors on Real World data In many areas, rental bikes have been launched to

1 Oct 28, 2021
A data preprocessing package for time series data. Design for machine learning and deep learning.

A data preprocessing package for time series data. Design for machine learning and deep learning.

Allen Chiang 152 Jan 07, 2023
Python module for machine learning time series:

seglearn Seglearn is a python package for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extr

David Burns 536 Dec 29, 2022
CVXPY is a Python-embedded modeling language for convex optimization problems.

CVXPY The CVXPY documentation is at cvxpy.org. We are building a CVXPY community on Discord. Join the conversation! For issues and long-form discussio

4.3k Jan 08, 2023
A flexible CTF contest platform for coming PKU GeekGame events

Project Guiding Star: the Backend A flexible CTF contest platform for coming PKU GeekGame events Still in early development Highlights Not configurabl

PKU GeekGame 14 Dec 15, 2022
PyPOTS - A Python Toolbox for Data Mining on Partially-Observed Time Series

A python toolbox/library for data mining on partially-observed time series, supporting tasks of forecasting/imputation/classification/clustering on incomplete multivariate time series with missing va

Wenjie Du 179 Dec 31, 2022
Machine-learning-dell - Repositório com as atividades desenvolvidas no curso de Machine Learning

📚 Descrição Neste curso da Dell aprofundamos nossos conhecimentos em Machine Learning. 🖥️ Aulas (Em curso) 1.1 - Python aplicado a Data Science 1.2

Claudia dos Anjos 1 Jan 05, 2022
Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

Linear Models Implementations of LinearRegression, LassoRegression and RidgeRegression with appropriate Regularizers and Optimizers. Linear Regression

Keivan Ipchi Hagh 1 Nov 22, 2021
BigDL: Distributed Deep Learning Framework for Apache Spark

BigDL: Distributed Deep Learning on Apache Spark What is BigDL? BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can w

4.1k Jan 09, 2023
Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...

Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...

Thoughtworks 318 Jan 02, 2023
Simple data balancing baselines for worst-group-accuracy benchmarks.

BalancingGroups Code to replicate the experimental results from Simple data balancing baselines achieve competitive worst-group-accuracy. Replicating

Facebook Research 29 Dec 02, 2022
Real-time stream processing for python

Streamz Streamz helps you build pipelines to manage continuous streams of data. It is simple to use in simple cases, but also supports complex pipelin

Python Streamz 1.1k Dec 28, 2022
A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

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

Daniel Formoso 5.7k Dec 30, 2022
Distributed Computing for AI Made Simple

Project Home Blog Documents Paper Media Coverage Join Fiber users email list Uber Open Source 997 Dec 30, 2022

All-in-one web-based development environment for machine learning

All-in-one web-based development environment for machine learning Getting Started • Features & Screenshots • Support • Report a Bug • FAQ • Known Issu

3 Feb 03, 2021
A Python Module That Uses ANN To Predict A Stocks Price And Also Provides Accurate Technical Analysis With Many High Potential Implementations!

Stox A Module to predict the "close price" for the next day and give "technical analysis". It uses a Neural Network and the LSTM algorithm to predict

Stox 31 Dec 16, 2022
Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

Responsible AI Workshop Responsible innovation is top of mind. As such, the tech industry as well as a growing number of organizations of all kinds in

Microsoft 9 Sep 14, 2022
Data from "Datamodels: Predicting Predictions with Training Data"

Data from "Datamodels: Predicting Predictions with Training Data" Here we provid

Madry Lab 51 Dec 09, 2022
This handbook accompanies the course: Machine Learning with Hung-Yi Lee

This handbook accompanies the course: Machine Learning with Hung-Yi Lee

RenChu Wang 472 Dec 31, 2022
Predicting job salaries from ads - a Kaggle competition

Predicting job salaries from ads - a Kaggle competition

Zygmunt Zając 57 Oct 23, 2020