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 1.0.77

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 1.0.77
File Size Uploaded
elaraai_east_py_datascience-1.0.77.tar.gz 795.7 kB Details

Built distributions (wheels)

Table of built distributions (wheels) for elaraai-east-py-datascience 1.0.77
File
elaraai_east_py_datascience-1.0.77-cp313-cp313-win_amd64.whl CPython 3.13 CPython 3.13 Windows x86-64 Details
elaraai_east_py_datascience-1.0.77-cp313-cp313-win32.whl CPython 3.13 CPython 3.13 Windows x86-32 Details
elaraai_east_py_datascience-1.0.77-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-1.0.77-cp313-cp313-macosx_11_0_arm64.whl CPython 3.13 CPython 3.13 macOS 11.0+ ARM64 Details
elaraai_east_py_datascience-1.0.77-cp313-cp313-macosx_10_13_x86_64.whl CPython 3.13 CPython 3.13 macOS 10.13+ x86-64 Details
elaraai_east_py_datascience-1.0.77-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
elaraai_east_py_datascience-1.0.77-cp312-cp312-win32.whl CPython 3.12 CPython 3.12 Windows x86-32 Details
elaraai_east_py_datascience-1.0.77-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-1.0.77-cp312-cp312-macosx_11_0_arm64.whl CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details
elaraai_east_py_datascience-1.0.77-cp312-cp312-macosx_10_13_x86_64.whl CPython 3.12 CPython 3.12 macOS 10.13+ x86-64 Details
elaraai_east_py_datascience-1.0.77-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
elaraai_east_py_datascience-1.0.77-cp311-cp311-win32.whl CPython 3.11 CPython 3.11 Windows x86-32 Details
elaraai_east_py_datascience-1.0.77-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.28+ x86-64, Linux glibc 2.17+ x86-64 Details
elaraai_east_py_datascience-1.0.77-cp311-cp311-macosx_11_0_arm64.whl CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
elaraai_east_py_datascience-1.0.77-cp311-cp311-macosx_10_9_x86_64.whl CPython 3.11 CPython 3.11 macOS 10.9+ x86-64 Details

Total release size: 4.4 MB

Release files / elaraai_east_py_datascience-1.0.77.tar.gz

Download URL elaraai_east_py_datascience-1.0.77.tar.gz
Size 795.7 kB
Tags Source
SHA-256 checksum
How to use checksums
c906162a9d1b21099291511ec7d0e71a3f0d9d09b82a8e6d2fd6d64d64ebcb3e
BLAKE2b-256 checksum
How to use checksums
fe2b178e7652fc922ed0e55e7739d060dd4c9cd5aa1f140e2e7afd90de4a8b9d
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-1.0.77-cp313-cp313-win_amd64.whl

Download URL elaraai_east_py_datascience-1.0.77-cp313-cp313-win_amd64.whl
Size 245.3 kB
Tags CPython 3.13 Windows x86-64
SHA-256 checksum
How to use checksums
a5a342c58f2aaa9fa62d5f1c00e3b7bf66b08b1c61b1a17daeecdb892864a203
BLAKE2b-256 checksum
How to use checksums
1c0bf1730e142fcd89a0f65922c3a9046c9ba75b7cecbde6bb55a27548fb49b9
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-1.0.77-cp313-cp313-win32.whl

Download URL elaraai_east_py_datascience-1.0.77-cp313-cp313-win32.whl
Size 242.3 kB
Tags CPython 3.13 Windows x86-32
SHA-256 checksum
How to use checksums
1c54015e1df6b8913b042660334c0b22639634c1934f9fecab024d0d9440b5fa
BLAKE2b-256 checksum
How to use checksums
b293e1cb751fdd355ae170b2f7631e591d5e252c06859da13481727c9a1922a1
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-1.0.77-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.77-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
0062518f93a1bdf67beabff174faa1af10a2bc9282551b281ebc8ef8ddacd668
BLAKE2b-256 checksum
How to use checksums
21602c97d0b3a50763f414b4c4d9f54605834e4a086b17a1c2fec2b8508841da
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-1.0.77-cp313-cp313-macosx_11_0_arm64.whl

Download URL elaraai_east_py_datascience-1.0.77-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
8626a3d2aaeee1f9a7148edfc02d72646e00570d4c5263c4ed0c9f709d28255a
BLAKE2b-256 checksum
How to use checksums
9cc7c988e2efce6834b77f9c4674a348055e7f6bdca132ab401c249d81ebaaf1
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-1.0.77-cp313-cp313-macosx_10_13_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.77-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
fc6e1aa186c8c814d66c3a1e2ed876ff8ab76f863be972c047a2d64444f70a03
BLAKE2b-256 checksum
How to use checksums
da54695d1b81c934687e8454f0594de1c3354fc2509f677c2827ec298ce03189
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-1.0.77-cp312-cp312-win_amd64.whl

Download URL elaraai_east_py_datascience-1.0.77-cp312-cp312-win_amd64.whl
Size 245.3 kB
Tags CPython 3.12 Windows x86-64
SHA-256 checksum
How to use checksums
fd648183ecdee1e7ee67e5e3c66fb67e94f4b661787e1f23d6df414c2a53b1d2
BLAKE2b-256 checksum
How to use checksums
959087d3186c58dc3a035440eab60d7bbba8db8d223e45c97499be5618755759
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-1.0.77-cp312-cp312-win32.whl

Download URL elaraai_east_py_datascience-1.0.77-cp312-cp312-win32.whl
Size 242.4 kB
Tags CPython 3.12 Windows x86-32
SHA-256 checksum
How to use checksums
19531d7e54dc48c21a26a84d23f4c401438bd428263a1ecf6f16a2f95a7c3342
BLAKE2b-256 checksum
How to use checksums
7fc5618f7a3458fd17569a92042626521fa4ad0f37c72b2b24a58b34267f4f79
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-1.0.77-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.77-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
ea1d33670f9b4096c665933b66cde825888df3acccca5798c73354ee01c02aac
BLAKE2b-256 checksum
How to use checksums
bc952ed5a16df2c27ca51c2d4286ab667a6e86bbec6b1b536807776df95946f8
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-1.0.77-cp312-cp312-macosx_11_0_arm64.whl

Download URL elaraai_east_py_datascience-1.0.77-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
263d065e9576b3ef5e58c6ff877872a5dc61675a8eb62b60c6b5e783b15edcde
BLAKE2b-256 checksum
How to use checksums
2cef82821924f43d32a2a8e8bbd2834acd3150f7a31060b96a97b5483ec569bd
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-1.0.77-cp312-cp312-macosx_10_13_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.77-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
ab38249c95658db9998f205c7fc5234e08e25053901770614946e7680115e794
BLAKE2b-256 checksum
How to use checksums
51e9082d3fb9682da6382889612468035812a6c690311fa34c4cf347a2f780a0
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-1.0.77-cp311-cp311-win_amd64.whl

Download URL elaraai_east_py_datascience-1.0.77-cp311-cp311-win_amd64.whl
Size 245.1 kB
Tags CPython 3.11 Windows x86-64
SHA-256 checksum
How to use checksums
d73c988443f51c0baba28917a2eb83570472ca846f5ea065027dc75e1fa23f8e
BLAKE2b-256 checksum
How to use checksums
4e6e93ab1f77c4c16f7a26ff20d9511584d16198eb07b58bd58846fa42570a19
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-1.0.77-cp311-cp311-win32.whl

Download URL elaraai_east_py_datascience-1.0.77-cp311-cp311-win32.whl
Size 242.3 kB
Tags CPython 3.11 Windows x86-32
SHA-256 checksum
How to use checksums
eca180e5a6d1ccb3f2b06e2b2ec4bec14f036568d11b57466d60ced0d55bf38e
BLAKE2b-256 checksum
How to use checksums
f952407388e0c2c0c899fda243a87caf4b6e039ee54bde6f8729dc97ebd09711
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-1.0.77-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.77-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
0ab69697e710cf944e3ccec86739446980ef11045f2969a5810079f93e15ccc1
BLAKE2b-256 checksum
How to use checksums
a65fbfe80953bb060e03a20bc766c8a067f7c98588037cefb96aeebec470f19f
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-1.0.77-cp311-cp311-macosx_11_0_arm64.whl

Download URL elaraai_east_py_datascience-1.0.77-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
bef995d3397b62152e2eee8f002d81e7710b6e84515b7bb6d886a61238e5d341
BLAKE2b-256 checksum
How to use checksums
f2428c746dd48f85ba7869032cf972ec4287865a2c826382d162ab16968b66a8
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-1.0.77-cp311-cp311-macosx_10_9_x86_64.whl

Download URL elaraai_east_py_datascience-1.0.77-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
0b114a0808b839889b419138c42553612b741949178a5e4356b6901ebb380945
BLAKE2b-256 checksum
How to use checksums
368fce29ab3113efdad9865c80af20a7606b4e26024dc797d043bb7cf901aef2
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

1.0.77 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