East Data Science

Data science and ML platform functions for the East language

TypeScript: AGPL-3.0 Python: BSL 1.1 Node Version

East Data Science provides machine learning and optimization platform functions for the East language.

Installation

npm install @elaraai/east-py-datascience @elaraai/east

Python Optional Dependencies

Each module has its own optional Python dependencies to avoid installing unnecessary packages. Install only the extras you need:

# Single extra
pip install "east-py-datascience[scipy]"

# Multiple extras
pip install "east-py-datascience[scipy,sklearn,xgboost]"

# All extras
pip install "east-py-datascience[all]"

When using a git dependency in pyproject.toml:

"east-py-datascience[scipy] @ git+https://github.com/elaraai/east-workspace@main#subdirectory=libs/east-py/packages/east-py-datascience"
Module Extra Python Packages
MADS mads PyNomadBBO
Optuna optuna optuna
SimAnneal simanneal simanneal
Scipy scipy scipy, cloudpickle
Optimization (none) (core only — numpy)
GoogleOr google-or ortools
Sklearn sklearn scikit-learn, skl2onnx, onnxruntime, cloudpickle
XGBoost xgboost xgboost, cloudpickle
LightGBM lightgbm lightgbm, cloudpickle
NGBoost ngboost ngboost, cloudpickle
Torch torch torch, cloudpickle
GP gp scikit-learn, cloudpickle
Lightning lightning torch, pytorch-lightning, cloudpickle
Shap shap shap, cloudpickle
MAPIE mapie mapie, cloudpickle
ALNS alns alns
PyMC pymc pymc, cloudpickle
Simulation (none) (core only — numpy)
Causal causal dowhy, econml, PyALE, pandas, matplotlib, scikit-learn, cloudpickle

Quick Start

import { East, FloatType, variant } from "@elaraai/east";
import { MADS } from "@elaraai/east-py-datascience";

// Define objective function: minimize sum of squares
const objective = East.function([MADS.Types.VectorType], FloatType, ($, x) => {
    const x0 = $.let(x.get(0n));
    const x1 = $.let(x.get(1n));
    return $.return(x0.multiply(x0).add(x1.multiply(x1)));
});

// Optimize
const optimize = East.function([], MADS.Types.ResultType, $ => {
    const x0 = $.let([0.5, 0.5]);
    const bounds = $.let({
        lower: [-1.0, -1.0],
        upper: [1.0, 1.0],
    });
    const config = $.let({
        max_bb_eval: variant('some', 100n),
        display_degree: variant('some', 0n),
        direction_type: variant('none', null),
        initial_mesh_size: variant('none', null),
        min_mesh_size: variant('none', null),
        seed: variant('some', 42n),
    });

    return $.return(MADS.optimize(objective, x0, bounds, variant('none', null), config));
});

Modules

Optimization

Module Description Use Cases
MADS Derivative-free blackbox optimization using NOMAD algorithm Functions without derivatives, expensive evaluations, noisy/discontinuous objectives
Optuna Bayesian optimization with TPE sampler Hyperparameter tuning, mixed-type parameters, efficient search with few evaluations
SimAnneal Simulated annealing for discrete optimization TSP, scheduling, subset selection, knapsack, assignment problems
Scipy Scientific optimization and curve fitting Gradient-based minimization, curve fitting, interpolation, statistics
Optimization Iterative coordinate descent optimization Parameter tuning, sequential optimization across parameter groups
GoogleOr Google OR-Tools constraint programming, routing, LP, and graph algorithms CP-SAT, vehicle routing (TSP/VRP), linear/mixed-integer programming, min-cost flow, max flow, assignment, sparse min-cost assignment

Machine Learning

Module Description Use Cases
Sklearn Core ML utilities from scikit-learn N-way splits, preprocessing (Standard/MinMax/RobustScaler), encoding (Label/Ordinal), metrics, GMM clustering, multi-target regression
XGBoost Gradient boosting with XGBoost Regression, classification, feature importance, fast training
LightGBM Fast gradient boosting with leaf-wise growth Large datasets, high cardinality features, faster than XGBoost on big data
NGBoost Natural gradient boosting with uncertainty Probabilistic predictions, confidence intervals, uncertainty quantification
Torch Neural networks with PyTorch MLP regression/classification, deep learning, custom architectures
Lightning PyTorch Lightning neural networks MLP, autoencoder, conv1d, sequential, transformer architectures
GP Gaussian Process regression Small datasets, uncertainty quantification, Bayesian optimization surrogate
MAPIE Conformal prediction intervals Prediction intervals, prediction sets, uncertainty quantification

Bayesian Inference

Module Description Use Cases
PyMC Bayesian inference with PyMC Bayesian linear regression, hierarchical models, multi-layer joint estimation, posterior analysis

Causal Inference

Module Description Use Cases
Causal One declarative causal experiment (Causal.experiment) over DoWhy / EconML / PyALE internals, plus Causal.designValidation (statsmodels power) Naive vs adjusted effect, confounder balance, propensity overlap, placebo/E-value robustness, and an honesty verdict (refuses when the data can't support an answer); and the real controlled-trial recipe that would confirm it — sample size, split, match-on categories, and a power curve

Simulation

Module Description Use Cases
Simulation Economic ontology simulation via DES Simulating economic resources, events, and processes; single deterministic runs, Monte Carlo trajectories

Explainability

Module Description Use Cases
Shap SHAP values for model interpretation Feature importance, model explanations, debugging predictions

Documentation

See USAGE.md for detailed API reference with examples.

Development

npm run build     # Compile TypeScript
npm run test      # Run test suite
npm run lint      # Check code quality

Claude Code plugin

The East ecosystem also ships a Claude Code plugin — East language skills, example search, and preemptive diagnostics for East code — installed separately from the elaraai marketplace:

# Inside Claude Code
/plugin marketplace add elaraai/east-workspace
/plugin install east@elaraai

# From a terminal
claude plugin marketplace add elaraai/east-workspace
claude plugin install east@elaraai

License

This package has different licenses for TypeScript and Python code:

TypeScript (type definitions): Dual AGPL-3.0 / Commercial

Python (runtime implementations): BSL 1.1 (Business Source License)

  • Non-production use (evaluation, testing, development) is free
  • Production use by or on behalf of for-profit entities requires a commercial license
  • Code becomes AGPL-3.0 four years after each release

See LICENSE.md for full details.

Commercial licensing: support@elara.ai

Ecosystem

  • East: Statically typed, expression-based language with serializable IR. Run portable logic across TypeScript, Python, C, and other runtimes.

    • @elaraai/east: Core language SDK with type system, expressions, and reference JS compiler

  • East Node: Node.js platform functions for I/O, databases, and system operations.

  • East C: C11 native runtime for executing East IR. Distributed via npm (launcher + per-platform optional dependencies) and as tarballs on each GitHub Release.

    • @elaraai/east-c-cli: npm launcher — installs the matching native binary as an optional dependency
    • east-c: Core runtime — type system, IR interpreter, builtins, serialization (Beast2, JSON, CSV, East text)
    • east-c-std: Console, FileSystem, Fetch, Crypto, Time, Path, Random
    • east-c-cli: CLI for running East IR programs natively

  • East Python: Python runtime, standard platform, I/O, and data-science platform functions. Published to PyPI.

    • east-py: Core Python runtime — type system, IR compiler, 212+ builtins, Cython-accelerated hot paths
    • east-py-std: Console, FileSystem, Fetch, Crypto, Time, Path, Random
    • east-py-io: SQLite, PostgreSQL, MySQL, MongoDB, Redis, S3, FTP, SFTP, XLSX, XML, compression
    • east-py-cli: CLI for running East IR programs in Python
    • east-py-datascience (PyPI) + @elaraai/east-py-datascience (npm): Optimization (MADS, Optuna, ALNS, GoogleOR), ML (XGBoost, LightGBM, NGBoost, PyTorch, Lightning, GP), Bayesian inference (PyMC), explainability (SHAP), conformal prediction (MAPIE)

  • East UI: Typed UI component definitions and React renderer, plus VS Code preview.

  • e3 — East Execution Engine: Durable execution engine for running East pipelines at scale. Git-like content-addressable storage, automatic memoization, reactive dataflow, real-time monitoring.

Links

About Elara

East is developed by Elara AI Pty Ltd, an AI-powered platform that creates economic digital twins of businesses that optimize performance. Elara combines business objectives, decisions and data to help organizations make data-driven decisions across operations, purchasing, sales and customer engagement, and project and investment planning. East powers the computational layer of Elara solutions, enabling the expression of complex business logic and data in a simple, type-safe and portable language.

Developed by Elara AI Pty Ltd.

Developed by Elara AI Pty Ltd

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.0.70)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for elaraai from gravatar.com elaraai

License

BSL-1.1

Requires

Python >=3.11

Provides Extra

mads optuna simanneal sklearn scipy xgboost lightgbm ngboost shap torch gp lightning mapie alns google-or pymc causal all dev

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

elaraai_east_py_datascience-1.0.61.tar.gz (661.3 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

elaraai_east_py_datascience-1.0.61-cp313-cp313-win_amd64.whl (246.6 kB view details)

Uploaded CPython 3.13Windows x86-64

elaraai_east_py_datascience-1.0.61-cp313-cp313-win32.whl (244.3 kB view details)

Uploaded CPython 3.13Windows x86

elaraai_east_py_datascience-1.0.61-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (244.4 kB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

elaraai_east_py_datascience-1.0.61-cp313-cp313-macosx_11_0_arm64.whl (242.4 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

elaraai_east_py_datascience-1.0.61-cp313-cp313-macosx_10_13_x86_64.whl (243.2 kB view details)

Uploaded CPython 3.13macOS 10.13+ x86-64

elaraai_east_py_datascience-1.0.61-cp312-cp312-win_amd64.whl (246.7 kB view details)

Uploaded CPython 3.12Windows x86-64

elaraai_east_py_datascience-1.0.61-cp312-cp312-win32.whl (244.3 kB view details)

Uploaded CPython 3.12Windows x86

elaraai_east_py_datascience-1.0.61-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (244.4 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

elaraai_east_py_datascience-1.0.61-cp312-cp312-macosx_11_0_arm64.whl (242.5 kB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

elaraai_east_py_datascience-1.0.61-cp312-cp312-macosx_10_13_x86_64.whl (243.3 kB view details)

Uploaded CPython 3.12macOS 10.13+ x86-64

elaraai_east_py_datascience-1.0.61-cp311-cp311-win_amd64.whl (246.4 kB view details)

Uploaded CPython 3.11Windows x86-64

elaraai_east_py_datascience-1.0.61-cp311-cp311-win32.whl (243.9 kB view details)

Uploaded CPython 3.11Windows x86

elaraai_east_py_datascience-1.0.61-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (244.2 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

elaraai_east_py_datascience-1.0.61-cp311-cp311-macosx_11_0_arm64.whl (242.3 kB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

elaraai_east_py_datascience-1.0.61-cp311-cp311-macosx_10_9_x86_64.whl (243.0 kB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

File details

Details for the file elaraai_east_py_datascience-1.0.61.tar.gz.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61.tar.gz
Algorithm Hash digest
SHA256 d12bc2b1863187d6393c7354097ce9482a15c85b0a224e0d1dd3895e5381ca95
MD5 c4946f9b883af723e52a63ef87dcafe4
BLAKE2b-256 24a38ff21779f8bb0cb3faab9911475d44f0ba8fbbdd55d7e1548884f7a606ac

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 397935f3f56dcf95e0ebfa0050c65d93da1eeddf34c9c266f960aa0177d4a958
MD5 9494a81cdf84d6f9704b3bd5fb91c474
BLAKE2b-256 3db61837b4725ec9500ee859022dbf91135d4c2cc222ee18d408c55747dff50a

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp313-cp313-win32.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp313-cp313-win32.whl
Algorithm Hash digest
SHA256 2c76657f2fd0843e7cc01b518d8b9f77110218251d8cab62c3765f30ebbe0e42
MD5 90f9c97fcc4be9bec4c5c13c49f63759
BLAKE2b-256 ed23886942ca063b235aa1ea58f7960425f86c588ee8140c8e664ac9a5553602

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 7ac460213aaaa84b5871c05420e2465f0232760cd346b6a7c4a3fba00aae6507
MD5 cba18513bc8545d96c52fc2516eac254
BLAKE2b-256 4dcf320f2be7a7851d9c5328c51d73247d4f635afe8a43e2122db1b15184c16b

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 bbf7e1846eaa9917a77400c2ae9a611b66b24bae4fdc8bcafecb44f8d0766120
MD5 418a8a9d1cc43482dd564bd97c7d6237
BLAKE2b-256 31f80cb1c59a5ba333c4b68ec01b8bb0ec94727e7214c1312370fd2e8e65fa55

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp313-cp313-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp313-cp313-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 0bfbb159441a9516a6c6e994d28b1a1e1e9ff4e23c3fcc3bdce129cc4f977f2d
MD5 effee8a2d48b7ab61f879abe19943f35
BLAKE2b-256 5d9b8b8b8a0d3bae8cdd90b59a351047a6a9c58bd03fe778529b6a5b6857de8c

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 8fb703b9849c1779db72d767cbd08670d92e3df7387c6d9b6292ab0a25802a22
MD5 e9ff222ee17069118f1a075d43acc974
BLAKE2b-256 e64ee3d41172d62b9465bfb8114f72016ce0c9f638d5caafce083abd6516839c

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp312-cp312-win32.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp312-cp312-win32.whl
Algorithm Hash digest
SHA256 d632ab2271cee292a3c73f1dae7bdd1da777d1c33b241d94ee20e7cb2a7af732
MD5 f86c3fb8cee9c05adb42aa2353edaea4
BLAKE2b-256 404a48a0795783862053648ba0cffef9a79ccf8be3670492ca1c8eaf0039e9a3

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 de84ce59d640385916ce5e4a3f2c6ab1f42b0a0f2e6120427024ca17d2fd8efe
MD5 978794d64af5c7836de11016511f47ff
BLAKE2b-256 be4a00a309d41a94c4207894f5bb69ef8f1817f4d8c914b1ec798d77c25c3358

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 3d08157560316a0c16169bbf4a9a6f98fcb611873fa0ae5587b5a9196e6b54ed
MD5 b30e1b722ee85000b727efd8700dbbd2
BLAKE2b-256 da31a5c7255a1fffd8148342b7c2f00d643abb02694f0e6fc5493bcddea4af1e

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp312-cp312-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp312-cp312-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 bc21ab8cfa4f3b1829a8414606e976e145ac60c7cb37854e54685875856a23a5
MD5 926de6247a1783166470e9bf1b73ac6a
BLAKE2b-256 5c17037529f8b63c24003698f09b7fd219848bd2022b554d9c539175ad945f14

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 086df7647d064ebd2fe5f24de024592d2a5fe8368f9207a249e419093fb4f9c8
MD5 ff19d26006abfd30187846ee2f785c17
BLAKE2b-256 90b2f5647e3fb57241b2d68dcca6f677603d9ac4884ed4500cde2cd8d8a32bef

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp311-cp311-win32.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 3d183833bb427d344fac4d20b692dd8147a00922ac926ad451615af54da7c7f3
MD5 a9ffd7124e9d74b64b33f516f44525de
BLAKE2b-256 beb97eb01fb0d96f5bdf42e5629f89f304680a8105c2e2f59788362479714350

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 88d7e1d60c7f7bf62c4bb0c269f81d039fa6296b2768de1b03a23ce95bc8082a
MD5 57865479db477f88c8bb8d4b682b104f
BLAKE2b-256 445bd50e8e50c171312acc29dbfdfe3291c763ebe0331fa97f56ab27e407b04b

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cc9ba039bb8987c0a61e585d761915830dd496340beb521f0779be57e03054f9
MD5 80fffdb5781d73a601555410b003b8c8
BLAKE2b-256 bf7d92c29e9a8a6400ec30cbb349588ec9ff84f8f9ae098316aced93ae5c0c6d

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.61-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.61-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9ce12dcee9feb432046a2ffc41b935582f5d58a0ef85cdf310d96a071ddb70c0
MD5 04e44f30a4d6a43e7befdc7230e33868
BLAKE2b-256 62bde1414b6a7957a628205b898f05492b02df6a79ae05e317589c63797900dd

See more details on using hashes here.

Release history Release notifications | RSS feed

1.0.70

16 files

1.0.69

16 files

1.0.68

16 files

1.0.67

16 files

1.0.66

16 files

1.0.65

16 files

1.0.64

16 files

1.0.63

16 files

1.0.62

16 files

This release

1.0.61 This release

16 files

1.0.60

16 files

1.0.59

16 files

1.0.58

16 files

1.0.57

16 files

1.0.56

16 files

1.0.55

16 files

1.0.54

16 files

1.0.53

16 files

1.0.52

16 files

1.0.51

16 files

1.0.50

16 files

1.0.49

16 files

1.0.48

16 files

1.0.47

16 files

1.0.46

16 files

1.0.45

16 files

1.0.44

16 files

1.0.43

16 files

1.0.42

16 files

1.0.41

16 files

1.0.40

16 files

1.0.39

16 files

1.0.38

16 files

1.0.37

16 files

1.0.36

16 files

1.0.35

16 files

1.0.34

16 files

1.0.33

16 files

1.0.32

16 files

1.0.31

16 files

1.0.30

16 files

1.0.29

16 files

1.0.28

16 files

1.0.27

16 files

1.0.26

16 files

1.0.25

16 files

1.0.24

16 files

1.0.23

16 files

1.0.22

16 files

1.0.21

16 files

1.0.20

16 files

1.0.19

16 files

1.0.18

16 files

1.0.17

16 files

1.0.16

16 files

1.0.15

16 files

1.0.14

16 files

1.0.13

16 files

1.0.12

16 files

1.0.11

16 files

1.0.10

16 files

1.0.9

16 files

1.0.8

16 files

1.0.7

16 files

1.0.6

16 files

1.0.5

16 files

1.0.4

10 files

1.0.3

10 files

1.0.2

10 files

1.0.1

10 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page