Hypernets: A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

Last update: Dec 23, 2022

Overview

Hypernets

We Are Hiring！

Dear folks, we are opening several precious positions based in Beijing both for professionals and interns avid in AutoML/NAS, please send your resume/cv to [email protected]. (Application deadline: TBD.)

Hypernets: A General Automated Machine Learning Framework

Hypernets is a general AutoML framework, based on which it can implement automatic optimization tools for various machine learning frameworks and libraries, including deep learning frameworks such as tensorflow, keras, pytorch, and machine learning libraries like sklearn, lightgbm, xgboost, etc. We introduced an abstract search space representation, taking into account the requirements of hyperparameter optimization and neural architecture search(NAS), making Hypernets a general framework that can adapt to various automated machine learning needs.

Overview

Conceptual Model

Illustration of the Search Space

Installation

Install Hypernets with pip command:

pip install hypernets

Optional, to run Hypernets in JupyterLab notebooks, install Hypernets and JupyterLab with command:

pip install hypernets[notebook]

Optional, to run Hypernets in distributed Dask cluster, install Hypernets with command:

pip install hypernets[dask]

Optional, to support dataset with simplified Chinese in feature generation, install jieba package before run Hypernets, or install Hypernets with command:

pip install hypernets[zhcn]

Optional, install all Hypernets components and dependencies with one command:

pip install hypernets[all]

Verify installation:

python -m hypernets.examples.smoke_testing

Hypernets related projects

HyperGBM: A full pipeline AutoML tool integrated various GBM models.
HyperDT/DeepTables: An AutoDL tool for tabular data.
HyperKeras: An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras.
Cooka: Lightweight interactive AutoML system.
Hypernets: A general automated machine learning framework.

Documents

Neural Architecture Search

DataCanvas

Hypernets is an open source project created by DataCanvas.

Comments

window pip fail.

ERROR: Complete output from command 'D:\Anaconda\Anaconda3\python.exe' -u -c 'import setuptools, tokenize;file='"'"'C:\Users\John\AppData\Local\Temp\pip-install-4hgmcjg1\python-geohash\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\John\AppData\Local\Temp\pip-wheel-gijlszf9' --python-tag cp37: ERROR: running bdist_wheel running build running build_py creating build creating build\lib.win-amd64-3.7 copying geohash.py -> build\lib.win-amd64-3.7 copying quadtree.py -> build\lib.win-amd64-3.7 copying jpgrid.py -> build\lib.win-amd64-3.7 copying jpiarea.py -> build\lib.win-amd64-3.7 running build_ext building '_geohash' extension error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": https://visualstudio.microsoft.com/downloads/

ERROR: Failed building wheel for python-geohash Running setup.py clean for python-geohash Failed to build python-geohash ERROR: spyder 3.3.6 requires pyqt5<5.13; python_version >= "3", which is not installed. ERROR: spyder 3.3.6 requires pyqtwebengine<5.13; python_version >= "3", which is not installed. ERROR: distributed 2021.11.1 has requirement dask==2021.11.1, but you'll have dask 2.1.0 which is incompatible. ERROR: woodwork 0.9.0 has requirement pandas>=1.3.0, but you'll have pandas 1.2.3 which is incompatible. ERROR: featuretools 1.2.0 has requirement dask[dataframe]>=2021.10.0, but you'll have dask 2.1.0 which is incompatible. ERROR: featuretools 1.2.0 has requirement pandas<2.0.0,>=1.3.0, but you'll have pandas 1.2.3 which is incompatible. Installing collected packages: psutil, pyyaml, tblib, distributed, woodwork, featuretools, python-geohash, hypernets Found existing installation: psutil 5.6.3 Uninstalling psutil-5.6.3: Successfully uninstalled psutil-5.6.3 Found existing installation: PyYAML 5.1.1 ERROR: Cannot uninstall 'PyYAML'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
Installation

opened by zhangxjohn 2
dataset about dsutils

an error about "from hypernets.frameworks.ml.datasets import dsutils", I think it should be "from deeptables.datasets import dsutils".
bug

opened by ninanxiaoguai 2
A suggestion for GreedyEnsemble

On line 107 of voting's code, i.e. if self.ensemble_size <= 0: size = predictions.shape[1]

Is it possible to replace it with self.ensemble_size <= 0 or predictions.shape[1] < self.ensemble_size: size = predictions.shape[1]?

opened by zhangxjohn 1
scorer & calc_score
Please make sure that this is a feature request.

System information

Hypernets version (you are using):

Are you willing to contribute it (Yes/No):

Describe the feature and the current behavior/state. I found that the user-defined evaluation metric function could not be set in the hypernets experiment, and calc_score() contained few metrics, such as no mape.

Will this change the current api? How?

Any Other info.
opened by zhangxjohn 1
Bump url-parse from 1.5.1 to 1.5.3 in /hypernets/hn_widget/js
Bumps url-parse from 1.5.1 to 1.5.3.

Commits

ad44493 [dist] 1.5.3

c798461 [fix] Fix host parsing for file URLs (#210)

201034b [dist] 1.5.2

2d9ac2c [fix] Sanitize only special URLs (#209)

fb128af [fix] Use 'null' as origin for non special URLs

fed6d9e [fix] Add a leading slash only if the URL is special

94872e7 [fix] Do not incorrectly set the slashes property to true

81ab967 [fix] Ignore slashes after the protocol for special URLs

ee22050 [ci] Use GitHub Actions

d2979b5 [fix] Special case the file: protocol (#204)

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dependencies
opened by dependabot[bot] 1
Bump tar from 6.1.0 to 6.1.10 in /hypernets/hn_widget/js
Bumps tar from 6.1.0 to 6.1.10.

Commits

188badd 6.1.10

23312ce drop dirCache for symlink on all platforms

4f1f4a2 6.1.9

875a37e fix: prevent path escape using drive-relative paths

b6162c7 fix: reserve paths properly for unicode, windows

3aaf19b fix: prune dirCache properly for unicode, windows

6a9c51d 6.1.8

dfc5923 fix: skip extract if linkpath is stripped entirely

575a511 fix: reserve paths case-insensitively

d61628c 6.1.7

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dependencies
opened by dependabot[bot] 1
Bump tar from 6.1.0 to 6.1.5 in /hypernets/hn_widget/js
Bumps tar from 6.1.0 to 6.1.5.

Commits

bd4691c 6.1.5

d694c4f ci: test on node 16

84acbd3 fix(unpack): fix hang on large file on open() fail

97c46fc fix(unpack): always resume parsing after an entry error

488ab8c chore: WriteEntry cleaner write() handling

be89aaf WriteEntry backpressure

ba73f5e chore: track fs state on WriteEntry class, not in arguments

bf69383 6.1.4

06cbde5 Avoid an unlikely but theoretically possible redos

0b78386 6.1.3

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dependencies
opened by dependabot[bot] 1
Bump dns-packet from 1.3.1 to 1.3.4 in /js
Bumps dns-packet from 1.3.1 to 1.3.4.

Commits

ebdf849 1.3.4

ac57872 move all allocUnsafes to allocs for easier maintenance

c64c950 1.3.3

0598ba1 fix .. in encodingLength

010aedb 1.3.2

0d0d593 backport encodingLength fix to v1

See full diff in compare view

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dependencies
opened by dependabot[bot] 0
Bump path-parse from 1.0.6 to 1.0.7 in /js
Bumps path-parse from 1.0.6 to 1.0.7.

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dependencies
opened by dependabot[bot] 0
Bump url-parse from 1.5.1 to 1.5.3 in /js
Bumps url-parse from 1.5.1 to 1.5.3.

Commits

ad44493 [dist] 1.5.3

c798461 [fix] Fix host parsing for file URLs (#210)

201034b [dist] 1.5.2

2d9ac2c [fix] Sanitize only special URLs (#209)

fb128af [fix] Use 'null' as origin for non special URLs

fed6d9e [fix] Add a leading slash only if the URL is special

94872e7 [fix] Do not incorrectly set the slashes property to true

81ab967 [fix] Ignore slashes after the protocol for special URLs

ee22050 [ci] Use GitHub Actions

d2979b5 [fix] Special case the file: protocol (#204)

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dependencies
opened by dependabot[bot] 0

Releases(0.2.5.7)

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Fix requirements
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Owner

DataCanvas

We love data science!

GitHub Repository https://hypernets.readthedocs.io/

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Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

Call of Duty World League: Search & Destroy Outcome Predictions Growing up as an avid Call of Duty player, I was always curious about what factors led

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2 May 24, 2022

Hypernets: A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

Related tags

Overview

Hypernets

We Are Hiring！

Hypernets: A General Automated Machine Learning Framework

Overview

Conceptual Model

Illustration of the Search Space

Installation

Related Links

Hypernets related projects

Documents

Neural Architecture Search

DataCanvas

Comments

Releases(0.2.5.7)

0.2.5.7(Nov 24, 2022)

0.2.5.6(Oct 8, 2022)

0.2.5.5(Sep 5, 2022)

0.2.5.4(May 31, 2022)

0.2.5.3(Mar 25, 2022)

0.2.5.2(Mar 8, 2022)

0.2.5.1(Mar 2, 2022)

0.2.5(Feb 28, 2022)

0.2.3.2(Dec 16, 2021)

0.2.3(Aug 20, 2021)

0.1.8(Mar 4, 2021)

0.1.7(Feb 4, 2021)

0.1.5(Nov 30, 2020)

0.1.3(Sep 27, 2020)

0.1.2(Sep 2, 2020)

0.1.1(Jun 24, 2020)

Owner

DataCanvas

Fundamentals of Machine Learning

Regularization and Feature Selection in Least Squares Temporal Difference Learning

Python based GBDT implementation

An implementation of Relaxed Linear Adversarial Concept Erasure (RLACE)

MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees.

Apple-voice-recognition - Machine Learning

XGBoost + Optuna

Automated Machine Learning Pipeline for tabular data. Designed for predictive maintenance applications, failure identification, failure prediction, condition monitoring, etc.

Firebase + Cloudrun + Machine learning

The Ultimate FREE Machine Learning Study Plan

Case studies with Bayesian methods

NumPy-based implementation of a multilayer perceptron (MLP)

K-Means clusternig example with Python and Scikit-learn

Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

Kaggle Tweet Sentiment Extraction Competition: 1st place solution (Dark of the Moon team)

A collection of Machine Learning Models To Web Api which are built on open source technologies/frameworks like Django, Flask.

Anytime Learning At Macroscale

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

BudouX is the successor to Budou, the machine learning powered line break organizer tool.

ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions