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-1.0.60.tar.gz (660.9 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-1.0.60-cp313-cp313-win_amd64.whl (246.6 kB view details)

Uploaded CPython 3.13Windows x86-64

elaraai_east_py_datascience-1.0.60-cp313-cp313-win32.whl (244.3 kB view details)

Uploaded CPython 3.13Windows x86

elaraai_east_py_datascience-1.0.60-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-1.0.60-cp313-cp313-macosx_11_0_arm64.whl (242.4 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

elaraai_east_py_datascience-1.0.60-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-1.0.60-cp312-cp312-win_amd64.whl (246.7 kB view details)

Uploaded CPython 3.12Windows x86-64

elaraai_east_py_datascience-1.0.60-cp312-cp312-win32.whl (244.3 kB view details)

Uploaded CPython 3.12Windows x86

elaraai_east_py_datascience-1.0.60-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-1.0.60-cp312-cp312-macosx_11_0_arm64.whl (242.5 kB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

elaraai_east_py_datascience-1.0.60-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-1.0.60-cp311-cp311-win_amd64.whl (246.4 kB view details)

Uploaded CPython 3.11Windows x86-64

elaraai_east_py_datascience-1.0.60-cp311-cp311-win32.whl (243.9 kB view details)

Uploaded CPython 3.11Windows x86

elaraai_east_py_datascience-1.0.60-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-1.0.60-cp311-cp311-macosx_11_0_arm64.whl (242.3 kB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

elaraai_east_py_datascience-1.0.60-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-1.0.60.tar.gz.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60.tar.gz
Algorithm Hash digest
SHA256 48c9d5dda32d415d91c3dc23db6ddb8faa12fa718249327bd60a316f03141b87
MD5 acf108210a127182a363d45b965e551b
BLAKE2b-256 2e95acfb6b93f30394bcfa48dcba8d6d4ff9d87cefd18beeb8011947d391d021

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 822482bed943802cf74f20ec2775cb2f346fb6433f99bbd1186f4b03d7fa44e0
MD5 f380bb37a297b7a372186ba87c060949
BLAKE2b-256 f5252ce63a12596179481822093a40a259682acfdbd8a7b16f6e9a5b1f13d767

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp313-cp313-win32.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp313-cp313-win32.whl
Algorithm Hash digest
SHA256 76dd40b4b96de5f33539eb3a19b3d325f24146aaff4c6fa3b4ca9d3e6cb593d5
MD5 10a89d07e90dd0f1ac331a951094a442
BLAKE2b-256 c2e50855eb412e92ad6af09879fe8823791ff75b1f6cad5e7bf4fc6f731e8e99

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-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-1.0.60-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 613096d0fdd20d4e8ea40de6c2c7addf0edac10d9c8d3056e05007c85a2e47e8
MD5 f1c1e6e06b995d0ac56fe9406ebdc893
BLAKE2b-256 63db01dbac760032bb9cc4cbbba2d85ae231e3f9f55d59a35194e643cd90675f

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9fa6ce23e0b416167bc3942bbe1ede5f502288523d10c5ac5b3a0253bcfd3a13
MD5 13c128ac909905f9155949e9519e02a0
BLAKE2b-256 a440ec27f8ce0decebf2b399e802ed76e16974998fcad945e03cde978d24bde5

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp313-cp313-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp313-cp313-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 8a5370e5f7714029dd1aed1dd97ad080f6ce3aa224f3f3b5b96ab9f84b843ad0
MD5 2afb1031a72c8c2d10d269e3f8562975
BLAKE2b-256 c4d9daebe8f487a00247901c0dc1871870012044872c75124376685eb7d2f207

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 3f9f36ef66bb1be2f87ba253262b2879a65680ec4be058847696c43925cf277e
MD5 c02cec491e0a8551189988a61cf7a0a9
BLAKE2b-256 7a0cf2a014255f4552cda46eb3b6063dc292397dc3f45ec159389fdc47488e67

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp312-cp312-win32.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp312-cp312-win32.whl
Algorithm Hash digest
SHA256 ccbb57028d1a79e4b7051c9c6fcb97206ab772ad9ad9802d7b4f805eee798e55
MD5 326bad57f8fece65ffdc670bcaf0260f
BLAKE2b-256 767cdaa7e7fb80bd33b01f79af1776deeab4ea118fa86bc1e6bd1461009e3269

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-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-1.0.60-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 f781cd5b2e8ebc3bde1741ff6690540c9de41788609622ff6c88a5906c3f9561
MD5 7e0c4d15b4c43c55638721c7d37d135f
BLAKE2b-256 b1add81442fc0bfbee12b6025b959be0e7cf11664e604e99b5fe1a94b6df1c20

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 109ed67258bea7d5aa088379494dadcda2bfc7a33a8af9f9368827676e56aa5f
MD5 7d50fc64f239dbc448965ebbf48a9ed2
BLAKE2b-256 70404b89ed8a36b8bcce8f05b3dcd833b0b7705b86b2aa2b2daa7f13a88cfc1f

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp312-cp312-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp312-cp312-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 a4180266913cbe3d7b3de2be466394240b3c21d0755d235952d5b8bc2ffe156d
MD5 de049eba603e824db8f62f13a30d5a12
BLAKE2b-256 b27786ba96196069cd1d6ccada6e1c579242b90c0d526ed93d1ecf68e9b7316b

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 c7b724cfdeac3542f53542950b89d6e2b6714a8a58654a6eef9064f16eab4427
MD5 bdf094589b61ddc4da7544274764db1e
BLAKE2b-256 adb6aa9b7f40f12ae712c7cd7af33142bf5bdda2039d47f699ec63e2f4e15858

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp311-cp311-win32.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 bf89b8f5a419035ef06b27c3f8bd0e6e304eabad454591dbee962dbeb7c18d23
MD5 8c45cca4d27830d06d56932924b1e771
BLAKE2b-256 0a227e6a6e6877b5701a27f6d848bf4d955ec592bd46564c683f7b2cc7c93060

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-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-1.0.60-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 300f591611dc0f0be205bebf21f10f2bfb6c261e004c19e63f97d9ba0f8dc0d0
MD5 d6462446ffd4479e205d76457c1803ae
BLAKE2b-256 f5594a860dae21f3aa7ce062bbef8006da5e09d99532ec32a3161aa135c414d6

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ea8d14bf1c13b571fdd0ef89519c832296a1bec4370d89793c1c25b02f750cab
MD5 9a0fa23cbd2cb7384a6cd1e36a80c087
BLAKE2b-256 7772d807183d1a718236e5049ebff2720c56488bcee856e008dbfa87714b3014

See more details on using hashes here.

File details

Details for the file elaraai_east_py_datascience-1.0.60-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for elaraai_east_py_datascience-1.0.60-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a893602390e3dd422d0b0f5fa9d39974f2fd5e81464c1405ec7b27519b11b610
MD5 5f36812448f2ba900382bd3a150bb295
BLAKE2b-256 b8c43b73ea5dc8c4ae2cd5455b68ba87f27d1bd45620a4eb9dfb81357d300dc4

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

1.0.61

16 files

This release

1.0.60 This release

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