Skip to main content

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.

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

Metadata

Release files for elaraai-east-py-datascience 1.0.91

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for elaraai-east-py-datascience 1.0.91
File Size Uploaded
elaraai_east_py_datascience-1.0.91.tar.gz 3.8 MB Details

Built distributions (wheels)

Table of built distributions (wheels) for elaraai-east-py-datascience 1.0.91
File
elaraai_east_py_datascience-1.0.91-cp313-cp313-win_amd64.whl CPython 3.13 CPython 3.13 Windows x86-64 Details
elaraai_east_py_datascience-1.0.91-cp313-cp313-win32.whl CPython 3.13 CPython 3.13 Windows x86-32 Details
elaraai_east_py_datascience-1.0.91-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64 Details
elaraai_east_py_datascience-1.0.91-cp313-cp313-macosx_11_0_arm64.whl CPython 3.13 CPython 3.13 macOS 11.0+ ARM64 Details
elaraai_east_py_datascience-1.0.91-cp313-cp313-macosx_10_13_x86_64.whl CPython 3.13 CPython 3.13 macOS 10.13+ x86-64 Details
elaraai_east_py_datascience-1.0.91-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
elaraai_east_py_datascience-1.0.91-cp312-cp312-win32.whl CPython 3.12 CPython 3.12 Windows x86-32 Details
elaraai_east_py_datascience-1.0.91-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64 Details
elaraai_east_py_datascience-1.0.91-cp312-cp312-macosx_11_0_arm64.whl CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details
elaraai_east_py_datascience-1.0.91-cp312-cp312-macosx_10_13_x86_64.whl CPython 3.12 CPython 3.12 macOS 10.13+ x86-64 Details
elaraai_east_py_datascience-1.0.91-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
elaraai_east_py_datascience-1.0.91-cp311-cp311-win32.whl CPython 3.11 CPython 3.11 Windows x86-32 Details
elaraai_east_py_datascience-1.0.91-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64 Details
elaraai_east_py_datascience-1.0.91-cp311-cp311-macosx_11_0_arm64.whl CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
elaraai_east_py_datascience-1.0.91-cp311-cp311-macosx_10_9_x86_64.whl CPython 3.11 CPython 3.11 macOS 10.9+ x86-64 Details

Total release size: 7.5 MB

Release files / elaraai_east_py_datascience-1.0.91.tar.gz

Download URL elaraai_east_py_datascience-1.0.91.tar.gz
Size 3.8 MB
Tags Source
SHA-256 checksum
How to use checksums
3d8658e06530892da36286b771d0756a3f47874022f85d01fb7e1db3d3f94d61
BLAKE2b-256 checksum
How to use checksums
add4ffdce1001260b54bec0e9cc374424191a09323f514b9819004526169a224
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp313-cp313-win_amd64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp313-cp313-win_amd64.whl
Size 245.3 kB
Tags CPython 3.13 Windows x86-64
SHA-256 checksum
How to use checksums
81b2800e62927e84d6653ce82f787c04493364bb89281f953be8d2ce0fdbf352
BLAKE2b-256 checksum
How to use checksums
3a6d85a04fa4f50874269c35a271d3cd4d39fa7928190c76902caa02f31d9994
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp313-cp313-win32.whl

Download URL elaraai_east_py_datascience-1.0.91-cp313-cp313-win32.whl
Size 242.3 kB
Tags CPython 3.13 Windows x86-32
SHA-256 checksum
How to use checksums
a203d037e373c160f12a91016d1fcb67f93f62b50403062382d0b2f503ef20ea
BLAKE2b-256 checksum
How to use checksums
efc803ddeb3d8ae39e6d5df9f8264483ebfe21dfa6b87fd4c25c76f40c2fc2ff
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Size 242.8 kB
Tags CPython 3.13 Linux glibc 2.17+ x86-64 Linux glibc 2.28+ x86-64
SHA-256 checksum
How to use checksums
9a8d546920e7f8a8784fe7c3792d943f7ea01f2e78ee8f547af81bfcf0634c22
BLAKE2b-256 checksum
How to use checksums
646b52eb5706cc16c8a217904e8a9b70ca2fc55ce0f9bb981d5102e0ceb9b67c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp313-cp313-macosx_11_0_arm64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp313-cp313-macosx_11_0_arm64.whl
Size 240.8 kB
Tags CPython 3.13 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
13f4925120e6cec6de9495c57b229242e21684493d04b1a6da203d991c70c588
BLAKE2b-256 checksum
How to use checksums
8a170f245d942918f3dc74c91620adcc8217288065b7611842fddb0a92b9e7f6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp313-cp313-macosx_10_13_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp313-cp313-macosx_10_13_x86_64.whl
Size 241.5 kB
Tags CPython 3.13 macOS 10.13+ x86-64
SHA-256 checksum
How to use checksums
2fc5e8aa6d56e5c8b4355d0c4f66c317119b92fc57500dce4b59a4cf43b4f34e
BLAKE2b-256 checksum
How to use checksums
554bd9740cd67c2104c6472057d7649d6573e9522ab4a00f0ad63cb14c45d42c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp312-cp312-win_amd64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp312-cp312-win_amd64.whl
Size 245.3 kB
Tags CPython 3.12 Windows x86-64
SHA-256 checksum
How to use checksums
08198b0be02d499a36426196072710542d232dac4cf89a2223d6d9392a0368db
BLAKE2b-256 checksum
How to use checksums
cb88246ab865606a1436ad85bb1cf9585ef4aee4ac1bebb3bbc37375de54d640
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp312-cp312-win32.whl

Download URL elaraai_east_py_datascience-1.0.91-cp312-cp312-win32.whl
Size 242.4 kB
Tags CPython 3.12 Windows x86-32
SHA-256 checksum
How to use checksums
ad2da8671143527a371f3e2904a1358d5eceac84a88e00d5f2c057da73ffc95b
BLAKE2b-256 checksum
How to use checksums
6a2efd0390974c1b4cdf1fd7471b77464d97c8ac06a50f22811742a44f6652ae
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Size 242.8 kB
Tags CPython 3.12 Linux glibc 2.17+ x86-64 Linux glibc 2.28+ x86-64
SHA-256 checksum
How to use checksums
2c1a4ec2d07770f65cb7fe59e0b164e42e69edafb8d68164bdb087d300012443
BLAKE2b-256 checksum
How to use checksums
bbb5790366971c1a98449bc2f74e468abef0192117c08bd51540c98de5096b2c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp312-cp312-macosx_11_0_arm64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp312-cp312-macosx_11_0_arm64.whl
Size 240.8 kB
Tags CPython 3.12 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
739fae3eabe4583302726f64ced6607cf19df11982bf113d581720a5393fd7d4
BLAKE2b-256 checksum
How to use checksums
a84c5f0be14e552a24b8ce5265167acedb6847970767d3de97bfc91856516dcf
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp312-cp312-macosx_10_13_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp312-cp312-macosx_10_13_x86_64.whl
Size 241.5 kB
Tags CPython 3.12 macOS 10.13+ x86-64
SHA-256 checksum
How to use checksums
82cff88b49e7779c1de52dbe24962d55047916f0664f175f6991bd10fc5ec925
BLAKE2b-256 checksum
How to use checksums
313743195c7bd4dc055b465419d1cb0fe4a5bf6ed59225d9a3f01b0fe8817558
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp311-cp311-win_amd64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp311-cp311-win_amd64.whl
Size 245.1 kB
Tags CPython 3.11 Windows x86-64
SHA-256 checksum
How to use checksums
2d757d40da662c759b2b3a4145e2fae2d8903eb60fc38113739ad7e289df0cb8
BLAKE2b-256 checksum
How to use checksums
328e0d749c072d66323e08ca327e3638f003e1b73541b2c0276749fdf14ad609
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp311-cp311-win32.whl

Download URL elaraai_east_py_datascience-1.0.91-cp311-cp311-win32.whl
Size 242.3 kB
Tags CPython 3.11 Windows x86-32
SHA-256 checksum
How to use checksums
6b620e476104319a293e27e791a09d1e19e4865430d5055fcc7fddbe97bc90d8
BLAKE2b-256 checksum
How to use checksums
c68b6b343a01440feac901f7656858e61bbe41cf829997b0e969364767e181d2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Size 242.7 kB
Tags CPython 3.11 Linux glibc 2.17+ x86-64 Linux glibc 2.28+ x86-64
SHA-256 checksum
How to use checksums
fc9bfb58ce1710cbac6c517e17550ec002fdae196623fe0e9a25de5346b77a78
BLAKE2b-256 checksum
How to use checksums
b64cb9f9a182c39acde722586078d0666ce651ef4b0b0bcc2775042c25f0e31e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp311-cp311-macosx_11_0_arm64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp311-cp311-macosx_11_0_arm64.whl
Size 240.6 kB
Tags CPython 3.11 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
5381621b5154459164e392e22e67c28ef939393f69c53e07befb6a37257bfe1a
BLAKE2b-256 checksum
How to use checksums
f11960f995a83677fb4686d1a9cffb0f3a4ae33e9ce050906c965fcd56d3ea00
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release files / elaraai_east_py_datascience-1.0.91-cp311-cp311-macosx_10_9_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.91-cp311-cp311-macosx_10_9_x86_64.whl
Size 241.3 kB
Tags CPython 3.11 macOS 10.9+ x86-64
SHA-256 checksum
How to use checksums
b5c92127714a26cdcb50f253da75050ed122427f388945e56944329d20a4eea0
BLAKE2b-256 checksum
How to use checksums
52aca1cc82c9f4e1a1f25ceab628023d9608ee5a7f084e174acb86524f2bc578
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.14

Release history Release notifications | RSS feed

This release

1.0.91 This release

16 release files

1.0.9

16 release files

1.0.8

16 release files

1.0.7

16 release files

1.0.6

16 release files

1.0.5

16 release files

1.0.4

10 release files

1.0.3

10 release files

1.0.2

10 release files

1.0.1

10 release 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