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, Randomeast-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 logscommands - @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
Links
- Website: https://elaraai.com/
- East Repository: https://github.com/elaraai/east-workspace/tree/main/libs/east
- Issues: https://github.com/elaraai/east-workspace/issues
- Email: support@elara.ai
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
1 maintainer
Metadata
1 maintainer
Release files for elaraai-east-py-datascience 1.0.88
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| elaraai_east_py_datascience-1.0.88.tar.gz | 3.8 MB | Details |
Built distributions (wheels)
Total release size: 7.5 MB
Release files / elaraai_east_py_datascience-1.0.88.tar.gz
| Download URL | elaraai_east_py_datascience-1.0.88.tar.gz |
|---|---|
| Size | 3.8 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a3a012a5dd9642821fb36c178e90ab2f5788468596da620bdb08c8a28d693b55
|
|
BLAKE2b-256 checksum How to use checksums |
3395ed6bc211a21b1feb933f82bdf6c01f84e2ebf8673cd0747efd127317c03c
|
| 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-1.0.88-cp313-cp313-win_amd64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-cp313-cp313-win_amd64.whl |
|---|---|
| Size | 245.3 kB |
| Tags | CPython 3.13 Windows x86-64 |
|
SHA-256 checksum How to use checksums |
13a42113d44defdd4997831cf64dc5de44f16f390d80ce9ad32349130dbf31a5
|
|
BLAKE2b-256 checksum How to use checksums |
85ab947d0638e37a4ff95e6f57e5a66e0421f1f9c56f62a10148aa6dbe320660
|
| 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-1.0.88-cp313-cp313-win32.whl
| Download URL | elaraai_east_py_datascience-1.0.88-cp313-cp313-win32.whl |
|---|---|
| Size | 242.3 kB |
| Tags | CPython 3.13 Windows x86-32 |
|
SHA-256 checksum How to use checksums |
a2160730accbc27245e0d906f5d6b453ee10bfce0a8b78290fdca3bcf3de1dc3
|
|
BLAKE2b-256 checksum How to use checksums |
d6f6869441a61ab9fb241e2fc78bfdca5603ca993eabbd3c727cdc077596dd24
|
| 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-1.0.88-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-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 |
94041feec47bfb149307079ae4b86203fbd7c1b989aeaaf2414944d381606ede
|
|
BLAKE2b-256 checksum How to use checksums |
0e6b5a277dd5146e17d8db42d6bd43aee9a904d177f6dadda860d532f82ea7ca
|
| 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-1.0.88-cp313-cp313-macosx_11_0_arm64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-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 |
942995a552442862ab98689581d97875278ae37366f4c3c5cf00a457335f1e61
|
|
BLAKE2b-256 checksum How to use checksums |
133c102ff4a8f8eacd5d860d5b46f1a136be387dc8f8c377872165f92cc0cfc7
|
| 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-1.0.88-cp313-cp313-macosx_10_13_x86_64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-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 |
9006487f2247effffc1787ba6fb1c98c4b5e592c18053d558c6bb85e800760ca
|
|
BLAKE2b-256 checksum How to use checksums |
d583c2ebafdea956204d92a744e5dafbfc0ef8cf2ce25f3bde3075220dbf1adb
|
| 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-1.0.88-cp312-cp312-win_amd64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-cp312-cp312-win_amd64.whl |
|---|---|
| Size | 245.3 kB |
| Tags | CPython 3.12 Windows x86-64 |
|
SHA-256 checksum How to use checksums |
88b4deb41546ed13b04751551e80c111ceceb0fcfe3e68d2ee404b19de7b702c
|
|
BLAKE2b-256 checksum How to use checksums |
263b7fac42281067378fe758c3cb657f02514a6207df28826133a30b6c9fd8e0
|
| 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-1.0.88-cp312-cp312-win32.whl
| Download URL | elaraai_east_py_datascience-1.0.88-cp312-cp312-win32.whl |
|---|---|
| Size | 242.4 kB |
| Tags | CPython 3.12 Windows x86-32 |
|
SHA-256 checksum How to use checksums |
cdac52a202e3421feede9feb3e96cc8314ea248a12e2ca8f754615fa07439322
|
|
BLAKE2b-256 checksum How to use checksums |
2b0ba7de04b495044667a0664999714032ff096d31121a50e7f1d3370536bfbf
|
| 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-1.0.88-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-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 |
db5e6a7f6e8c5163d3d5693c65d7652328b2e4fe934bdbb3b9316bb7e5f337b8
|
|
BLAKE2b-256 checksum How to use checksums |
4fac3311889530bb7762bb206ffee2a31b13bf890c0e624119af0441610906f7
|
| 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-1.0.88-cp312-cp312-macosx_11_0_arm64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-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 |
f29f1ebef79522b5845e3e7c99d250b9149e9b7f853f54fa2bb21b1266837e83
|
|
BLAKE2b-256 checksum How to use checksums |
d9d7aa7041e6957259c91a192bea7ad2e0f4948dd63014c96c6258b598f8f56a
|
| 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-1.0.88-cp312-cp312-macosx_10_13_x86_64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-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 |
63ff277a2a93f21b902b7f8dcfd692e99f33c520a35457377fa47a3bbe890c83
|
|
BLAKE2b-256 checksum How to use checksums |
a8289fb1e45e8d9629e485d6914c4bd0a9e36ffaca87c49498f6ba12242e01c8
|
| 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-1.0.88-cp311-cp311-win_amd64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-cp311-cp311-win_amd64.whl |
|---|---|
| Size | 245.1 kB |
| Tags | CPython 3.11 Windows x86-64 |
|
SHA-256 checksum How to use checksums |
3ac15a03e9c8ea4f7b668d29704c91707e02cdf34afdc566eac511f11dff6134
|
|
BLAKE2b-256 checksum How to use checksums |
5ec87778982c51b242d9cf18acf6a0fabdafd4021bfd02b7aa8867706c3722bf
|
| 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-1.0.88-cp311-cp311-win32.whl
| Download URL | elaraai_east_py_datascience-1.0.88-cp311-cp311-win32.whl |
|---|---|
| Size | 242.3 kB |
| Tags | CPython 3.11 Windows x86-32 |
|
SHA-256 checksum How to use checksums |
68d027a6e63485df8af5d4a0098ff43d8c49dfccb7d630c60f8c651d8c3fee73
|
|
BLAKE2b-256 checksum How to use checksums |
d940c82cdc72501c3ddb8eff48011c985aca565b5019d3a9c67e9921c9821a86
|
| 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-1.0.88-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-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 |
658c3e7acf49d28e429826bb65ce07f8e0ba781c196b5f136c17186fc50f95d4
|
|
BLAKE2b-256 checksum How to use checksums |
25f9b18956659a0434fa000cf9c7b6b2320e347d2e136686fa066cc9988f3f7b
|
| 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-1.0.88-cp311-cp311-macosx_11_0_arm64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-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 |
987f3c8eff571e98be8f6665e3db966cdc3ca1604f0ac5886b3ffcbcef0f8fc8
|
|
BLAKE2b-256 checksum How to use checksums |
542e848a79e31808233659dc9a554519bcb9c15243d932444fccac782333a262
|
| 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-1.0.88-cp311-cp311-macosx_10_9_x86_64.whl
| Download URL | elaraai_east_py_datascience-1.0.88-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 |
7c19ba5919187baa26c17b425b04ae0e2a4171f76481f6860f67d60c11f3abb9
|
|
BLAKE2b-256 checksum How to use checksums |
547d6781cfe0916be173027a73024688e950b867d333a90fe92671d817661cee
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
