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-1.0.67.tar.gz (695.7 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-1.0.67-cp313-cp313-win_amd64.whl (246.6 kB view details)

Uploaded CPython 3.13Windows x86-64

elaraai_east_py_datascience-1.0.67-cp313-cp313-win32.whl (244.3 kB view details)

Uploaded CPython 3.13Windows x86

elaraai_east_py_datascience-1.0.67-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-1.0.67-cp313-cp313-macosx_11_0_arm64.whl (242.4 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

elaraai_east_py_datascience-1.0.67-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-1.0.67-cp312-cp312-win_amd64.whl (246.7 kB view details)

Uploaded CPython 3.12Windows x86-64

elaraai_east_py_datascience-1.0.67-cp312-cp312-win32.whl (244.3 kB view details)

Uploaded CPython 3.12Windows x86

elaraai_east_py_datascience-1.0.67-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-1.0.67-cp312-cp312-macosx_11_0_arm64.whl (242.5 kB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

elaraai_east_py_datascience-1.0.67-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-1.0.67-cp311-cp311-win_amd64.whl (246.4 kB view details)

Uploaded CPython 3.11Windows x86-64

elaraai_east_py_datascience-1.0.67-cp311-cp311-win32.whl (243.9 kB view details)

Uploaded CPython 3.11Windows x86

elaraai_east_py_datascience-1.0.67-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-1.0.67-cp311-cp311-macosx_11_0_arm64.whl (242.3 kB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

elaraai_east_py_datascience-1.0.67-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-1.0.67.tar.gz.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67.tar.gz
Algorithm Hash digest
SHA256 8f4acfbe02fb1a06eec89df3e36ffd96b842ec05a835a5b5fd8fafd5a49117b0
MD5 0ae1334252e391c577dee605fbbc007d
BLAKE2b-256 859313750153a2f26cd0994c0d6e1ef8f68a34cdcce7b08661b4d7f3fbee0a07

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 271b264665df927547fe410676fc01f4bcc3197dfe1bb87eaaf95be438ab7327
MD5 dec3d662404946a05a1cb73ea1308792
BLAKE2b-256 2bc5038f344c8ab0be753425a3df6bd40ed6bea9353ddb5e728d6fcadfd9adad

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp313-cp313-win32.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp313-cp313-win32.whl
Algorithm Hash digest
SHA256 a9b2e32dcb6a128b0480e3d5263cd6418922848b3d0dea950c5980d7c4554da1
MD5 1e650f6a86817ee558572af1195b4621
BLAKE2b-256 72e034f868f7a7302ba4593ecd5d1681d43961b783c948de31a0fbc4016da9f5

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-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-1.0.67-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 5cf037beb54edccf42456ebe2579d751f94b81243318d65d9f19d8808f7f4073
MD5 3554f1823a8fe9e412a805b702964b37
BLAKE2b-256 e9506e5aa731c6827209926a0ac5cc3effe8eac1117bcc7e9ac9c1997e55d3e2

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 586c52f29e12b03743af183083197641d7de328a285065a6f136e428c721efd7
MD5 c51d879dfa99c1dfa08fad7fcf895e1e
BLAKE2b-256 721c61262528f6066a1d38801912a04aef322b49e906a0cb701b1d17b43c28b6

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp313-cp313-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp313-cp313-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 b074bd08affb9c7c5828b3d5b9f6a0bb4385150a11ccac4533bd30d6dec87104
MD5 6e419b250b86c462874d87c91634ad6f
BLAKE2b-256 af798c3d1ad536e6716391c24cbfbf5df9b64a3fcc085c22d0244f3db01fc877

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 f9c65be478eb1c7f198e6b43450272ddf7c010647cbe974ef24f963c7b80679d
MD5 5958eafdf3802a5c852123bd4df6840d
BLAKE2b-256 3c4f235bff5a0355ae878db999247e3ad42f21f5908dbc97b03564d3e4b5c42e

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp312-cp312-win32.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp312-cp312-win32.whl
Algorithm Hash digest
SHA256 806f8fdf094ebaebf6100f81a1ddb2a8a933097ae8946dadf97fdbb2c9b07a73
MD5 50fde7f191548620ca575d781de1dbf9
BLAKE2b-256 0ac0701bd20f472273dcfb9541fcdb5d693be9ee9cfd48abc6064639e217ea2b

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-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-1.0.67-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 26f4ec3f724a7f206774cb89448866d7ca056a5257312283e189a544a168ead0
MD5 403def48ca62a5fcaa95db12cf267b17
BLAKE2b-256 353a14b106408e2a403c58619df0eda4187cb505a694b1ab67695d57132e871d

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 4cc332648be38683db8a3e6405886dac762cc72f5ca57f0be5383c8e58b8c01a
MD5 fbe1cddb594bb115feb28de21788cea0
BLAKE2b-256 6771d9e3590b45a86b50511a5c7c176b2d2a615e71f6e015fff522296eb5086a

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp312-cp312-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp312-cp312-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 9248231941453b42644f7443bf0823aaec8f56e120f3320cdd1b6151dc71a388
MD5 635a888bd465bb9e2c493f3ee8001495
BLAKE2b-256 066554291009a8c2b42511b47b37dbc9c6743514517d9227e078ab9c7631eda8

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 e3c4efd8d3e08f302a9ab7fb6925149b7bad0666416d294192ac962a6e60addb
MD5 fcae7724086cea2add75e8f43281684d
BLAKE2b-256 a818a02763d201373cace80dd0c329539acd05fda9e4a8764dd234ccfd0c9496

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp311-cp311-win32.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 792b57b0e0a4811bfcdb1e0d5d84d5af0662c0adb7f0270e148d00c1858a9d74
MD5 fde48c81eb19004580eedc14a1aa4a12
BLAKE2b-256 9d10d91ceebb6c17c88c63e27039a36c212488e3c4da9bcc6c5a60fb8bd15e14

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-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-1.0.67-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 84189d0bddd4517af32108c50ddcf7a08c04ce62b63f11b8037bb17815abc591
MD5 dff0f5dcd8b95a4450b62375abd9d712
BLAKE2b-256 6abea1cf56bf4d10dedfeaaf12b177371954c25204333951ab33ac140c3404c0

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 4b90c24d2721e788f3abd341ca10b52a0b8fba22bb8a87442dfd9ca6d3f390dd
MD5 fac0d16d2b8cdbc15af2d96c2862d4be
BLAKE2b-256 94f92ec7f37da6dba9ec99f06db75714292736d2ebc7fae2f43b81e0dedc23c2

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.67-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.67-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 cc48a85b317c03f19e4ecfe8f9e9bd101e4538fb7a443b8f082ca89c1d899e86
MD5 bd0e7aae2fa3c4d1817fd1fd5b6a7be4
BLAKE2b-256 36ae22341901466e5fb31571762c79275d0587bb2d085fc9901f0ad9e4d5a2f6

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

This release

1.0.67 This release

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

1.0.61

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