East Data Science

Data science and ML platform functions for the East language

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

Open source use: AGPL-3.0
Commercial use: Available for proprietary use - contact support@elara.ai

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.
- @elaraai/east-node-std: Console, FileSystem, Fetch, Crypto, Time, Path, Random
- @elaraai/east-node-io: SQLite, PostgreSQL, MySQL, MongoDB, Redis, S3, FTP, SFTP, XLSX, XML, compression
- @elaraai/east-node-cli: CLI for running East IR programs in Node.js
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.
- @elaraai/east-ui: 50+ typed UI components for layouts, forms, charts, tables, dialogs
- @elaraai/east-ui-components: React renderer with Chakra UI v3 styling
- @elaraai/e3-ui: e3 + UI bridge — Data bindings, e3.ui() task, manifest
- @elaraai/e3-ui-components: React Query hooks and preview components for the e3 API
- east-ui-preview: VS Code extension for live East UI component 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.
- @elaraai/e3: SDK for authoring e3 packages with typed tasks and pipelines
- @elaraai/e3-core: Object store, dataflow orchestrator, execution state
- @elaraai/e3-types: Shared type definitions for e3 packages
- @elaraai/e3-cli: e3 repo, e3 package, e3 workspace, e3 dataflow run, e3 watch, e3 task logs commands
- @elaraai/e3-api-client: HTTP client for remote e3 repositories
- @elaraai/e3-api-server: REST API server for e3 repositories
- @elaraai/e3-api-tests: Shared API compliance test suites

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 1.0.89

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Source distribution (sdist)

Source distribution for elaraai-east-py-datascience 1.0.89
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Built distributions (wheels)

Table of built distributions (wheels) for elaraai-east-py-datascience 1.0.89
File	Interpreter	ABI	Platform	Reset
elaraai_east_py_datascience-1.0.89-cp313-cp313-win_amd64.whl 245.3 kB Oct 14, 2026	CPython 3.13	CPython 3.13	Windows x86-64	Details
elaraai_east_py_datascience-1.0.89-cp313-cp313-win32.whl 242.3 kB Oct 14, 2026	CPython 3.13	CPython 3.13	Windows x86-32	Details
elaraai_east_py_datascience-1.0.89-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl 242.8 kB Oct 14, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64	Details
elaraai_east_py_datascience-1.0.89-cp313-cp313-macosx_11_0_arm64.whl 240.8 kB Oct 14, 2026	CPython 3.13	CPython 3.13	macOS 11.0+ ARM64	Details
elaraai_east_py_datascience-1.0.89-cp313-cp313-macosx_10_13_x86_64.whl 241.5 kB Oct 14, 2026	CPython 3.13	CPython 3.13	macOS 10.13+ x86-64	Details
elaraai_east_py_datascience-1.0.89-cp312-cp312-win_amd64.whl 245.3 kB Oct 14, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details
elaraai_east_py_datascience-1.0.89-cp312-cp312-win32.whl 242.4 kB Oct 14, 2026	CPython 3.12	CPython 3.12	Windows x86-32	Details
elaraai_east_py_datascience-1.0.89-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl 242.8 kB Oct 14, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64	Details
elaraai_east_py_datascience-1.0.89-cp312-cp312-macosx_11_0_arm64.whl 240.8 kB Oct 14, 2026	CPython 3.12	CPython 3.12	macOS 11.0+ ARM64	Details
elaraai_east_py_datascience-1.0.89-cp312-cp312-macosx_10_13_x86_64.whl 241.5 kB Oct 14, 2026	CPython 3.12	CPython 3.12	macOS 10.13+ x86-64	Details
elaraai_east_py_datascience-1.0.89-cp311-cp311-win_amd64.whl 245.1 kB Oct 14, 2026	CPython 3.11	CPython 3.11	Windows x86-64	Details
elaraai_east_py_datascience-1.0.89-cp311-cp311-win32.whl 242.3 kB Oct 14, 2026	CPython 3.11	CPython 3.11	Windows x86-32	Details
elaraai_east_py_datascience-1.0.89-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl 242.7 kB Oct 14, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.17+ x86-64, Linux glibc 2.28+ x86-64	Details
elaraai_east_py_datascience-1.0.89-cp311-cp311-macosx_11_0_arm64.whl 240.6 kB Oct 14, 2026	CPython 3.11	CPython 3.11	macOS 11.0+ ARM64	Details
elaraai_east_py_datascience-1.0.89-cp311-cp311-macosx_10_9_x86_64.whl 241.3 kB Oct 14, 2026	CPython 3.11	CPython 3.11	macOS 10.9+ x86-64	Details

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Release files / elaraai_east_py_datascience-1.0.89-cp311-cp311-macosx_11_0_arm64.whl

Download URL	elaraai_east_py_datascience-1.0.89-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	`3d8906f338646243a6028f8a96c13857ff7b6ef471e56113656ff7b2f48c053c`
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Release files / elaraai_east_py_datascience-1.0.89-cp311-cp311-macosx_10_9_x86_64.whl

Download URL	elaraai_east_py_datascience-1.0.89-cp311-cp311-macosx_10_9_x86_64.whl
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Tags	CPython 3.11 macOS 10.9+ x86-64
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