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

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16 files

1.0.6

16 files

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16 files

1.0.4

10 files

1.0.3

10 files

1.0.2

10 files

1.0.1

10 files

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