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Data science and machine learning platform functions for the East language (TypeScript types). Use when writing East programs that need optimization (MADS, Optuna, SimAnneal, Scipy, Optimization, GoogleOr), machine learning (XGBoost, LightGBM, NGBoost, Torch MLP, Lightning, GP), Bayesian inference (PyMC), simulation (Simulation DES), ML utilities (Sklearn preprocessing, metrics, splits), conformal prediction (MAPIE), or model explainability (SHAP). Triggers for: (1) Writing East programs with @elaraai/east-py-datascience, (2) Derivative-free optimization with MADS, (3) Bayesian optimization with Optuna, (4) Discrete/combinatorial optimization with SimAnneal, (5) Gradient boosting with XGBoost or LightGBM, (6) Probabilistic predictions with NGBoost or GP, (7) Neural networks with Torch MLP or Lightning, (8) Data preprocessing and metrics with Sklearn, (9) Conformal prediction intervals with MAPIE, (10) Model explainability with Shap, (11) Iterative coordinate descent with Optimization, (12) Constraint programming, vehicle routing, LP/MIP, or graph algorithms with GoogleOr, (13) Bayesian regression, hierarchical models, and multi-layer estimation with PyMC, (14) Economic ontology simulation via discrete event simulation with Simulation.
How this skill is triggered — by the user, by Claude, or both
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/east:east-py-datascienceThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Data science and machine learning platform functions for the East language. Provides optimization, ML models, preprocessing, and explainability.
Data science and machine learning platform functions for the East language. Provides optimization, ML models, preprocessing, and explainability.
import { East, FloatType, variant } from "@elaraai/east";
import { MADS } from "@elaraai/east-py-datascience";
// Define objective function
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));
});
Task → What do you need?
│
├─ MADS (derivative-free continuous optimization)
│ └─ .optimize()
│
├─ Optuna (Bayesian hyperparameter tuning)
│ └─ .optimize()
│
├─ SimAnneal (discrete/combinatorial optimization)
│ └─ .optimize(), .optimizePermutation(), .optimizeSubset()
│
├─ ALNS (adaptive large neighborhood search)
│ └─ .optimize([SolutionType], initial, objective, destroys, repairs, config)
│ └─ Generic over solution type S - define your own struct
│
├─ Optimization (iterative coordinate descent)
│ └─ .iterative(objective, paramSpaces, config)
│
├─ GoogleOr (Google OR-Tools)
│ ├─ CP-SAT → .cpsatSolve(), .cpsatSolveAll()
│ ├─ Routing → .routingSolve() (TSP, CVRP, VRPTW, VRPPD)
│ ├─ Linear → .linearSolve() (LP, MIP)
│ └─ Graph → .minCostFlow(), .maxFlow(), .assignment()
│
├─ Scipy
│ ├─ Optimization → .optimizeMinimize(), .optimizeMinimizeQuadratic(), .optimizeDualAnnealing()
│ ├─ Statistics → .statsDescribe(), .statsPearsonr(), .statsSpearmanr(), .statsPercentile(), .statsPercentileOfScore(), .statsIqr(), .statsMedian(), .statsMad(), .statsRobust()
│ ├─ Histogram/KDE → .histogram(), .kdeFit(), .kdeEvaluate()
│ ├─ Curve Fitting → .curveFit()
│ └─ Interpolation → .interpolate1dFit(), .interpolate1dPredict()
│
├─ XGBoost (gradient boosting)
│ ├─ Train → .trainRegressor(), .trainClassifier(), .trainQuantile()
│ └─ Predict → .predict(), .predictClass(), .predictProba(), .predictQuantile()
│
├─ LightGBM (fast gradient boosting)
│ ├─ Train → .trainRegressor(), .trainClassifier()
│ └─ Predict → .predict(), .predictClass(), .predictProba()
│
├─ NGBoost (probabilistic gradient boosting)
│ ├─ Train → .trainRegressor()
│ └─ Predict → .predict(), .predictDist()
│
├─ Torch (neural networks)
│ ├─ Train → .mlpTrain(), .mlpTrainMulti()
│ ├─ Predict → .mlpPredict(), .mlpPredictMulti()
│ └─ Embeddings → .mlpEncode(), .mlpDecode()
│
├─ Lightning (PyTorch Lightning neural networks)
│ ├─ Train → .train(X, y, config, masks, group_weights, conditions)
│ ├─ Predict → .predict(model, X, masks, conditions)
│ ├─ Embeddings → .encode(), .decode(), .decodeConditional() (autoencoder only)
│ ├─ Architectures:
│ │ ├─ mlp: simple feedforward
│ │ ├─ autoencoder: encoder → latent → decoder
│ │ ├─ conv1d: 1D convolutional autoencoder (temporal)
│ │ ├─ sequential: LSTM/GRU autoencoder (temporal)
│ │ └─ transformer: attention-based autoencoder (temporal)
│ ├─ Output modes:
│ │ ├─ regression: MSE loss
│ │ ├─ binary: BCE loss, per-position pos_weights (VectorType), masks
│ │ └─ multi_head: N independent CE heads, per-head class_weights, masks
│ ├─ Conditional generation: condition_dim in temporal architectures
│ └─ Features: early stopping, gradient clipping, epoch callbacks, group_weights
│
├─ GP (Gaussian Process regression)
│ ├─ Train → .train()
│ └─ Predict → .predict(), .predictStd()
│
├─ MAPIE (conformal prediction intervals)
│ ├─ Regression → .trainConformalRegressor(), .trainCQR()
│ ├─ Classification → .trainConformalClassifier()
│ ├─ Predict → .predictInterval(), .predictSet()
│ └─ SHAP integration → .uncertaintyPredictorRegressor(), .uncertaintyPredictorClassifier()
│
├─ Sklearn (preprocessing, metrics & clustering)
│ ├─ Splitting → .split() (N-way with stratify, overlap, multi_overlap)
│ ├─ Overlap filtering → .overlap()
│ ├─ Scaling → .standardScalerFit/Transform(), .minMaxScalerFit/Transform(), .robustScalerFit/Transform()
│ ├─ Encoding → .labelEncoderFit/Transform/InverseTransform(), .ordinalEncoderFit/Transform()
│ ├─ Class weights → .computeClassWeight()
│ ├─ Regression metrics → .computeMetrics(), .computeMetricsMulti()
│ ├─ Classification metrics → .computeClassificationMetrics(), .computeClassificationMetricsMulti()
│ ├─ Probability metrics → .rocAucScore(), .logLoss(), .confusionMatrix()
│ ├─ Multi-target → .regressorChainTrain(), .regressorChainPredict()
│ ├─ GMM clustering → .gmmFit(), .gmmPredict(), .gmmPredictProba(), .gmmScoreSamples(), .gmmSample(), .gmmBic(), .gmmAic()
│ └─ Clustering evaluation → .silhouetteScore()
│
├─ PyMC (Bayesian inference)
│ ├─ Train → .trainRegression(), .trainHierarchical(), .trainMultiLayer()
│ ├─ Predict → .predict(), .predictDistribution()
│ ├─ Posterior → .posteriorSummary(), .posteriorSamples()
│ └─ Diagnostics → .diagnostics(), .posteriorPredictiveCheck()
│
├─ Simulation (economic ontology simulation via DES)
│ ├─ Single run → .run([R, E], initialState, initialEvents, process, config)
│ └─ Monte Carlo → .runTrajectories([R, E], initialState, initialEvents, process, config)
│
└─ Shap (model explainability)
├─ Create → .treeExplainerCreate() (XGBoost only), .kernelExplainerCreate() (any model)
├─ Compute → .computeValues(), .featureImportance()
└─ Supports → TreeExplainer: XGBoost; KernelExplainer: XGBoost, LightGBM, NGBoost, GP, Torch, RegressorChain, MAPIE
| Type | Definition | Description |
|---|---|---|
VectorType | ArrayType(FloatType) | 1D array of floats (e.g., [1.0, 2.0, 3.0]) |
MatrixType | ArrayType(ArrayType(FloatType)) | 2D array of floats (e.g., [[1.0, 2.0], [3.0, 4.0]]) |
LabelVectorType | ArrayType(IntegerType) | Class labels as integers (e.g., [0n, 1n, 0n, 2n]) |
ModelBlobType | BlobType | Serialized model (opaque, pass to predict functions) |
| Module | Import | Purpose |
|---|---|---|
| MADS | import { MADS } from "@elaraai/east-py-datascience" | Derivative-free blackbox optimization |
| Optuna | import { Optuna } from "@elaraai/east-py-datascience" | Bayesian optimization (hyperparameter tuning) |
| SimAnneal | import { SimAnneal } from "@elaraai/east-py-datascience" | Simulated annealing (permutation/subset) |
| ALNS | import { ALNS } from "@elaraai/east-py-datascience" | Adaptive Large Neighborhood Search (generic over solution type) |
| Scipy | import { Scipy } from "@elaraai/east-py-datascience" | Statistics, optimization, interpolation |
| XGBoost | import { XGBoost } from "@elaraai/east-py-datascience" | Gradient boosting (regression/classification/quantile) |
| LightGBM | import { LightGBM } from "@elaraai/east-py-datascience" | Fast gradient boosting |
| NGBoost | import { NGBoost } from "@elaraai/east-py-datascience" | Probabilistic gradient boosting |
| Torch | import { Torch } from "@elaraai/east-py-datascience" | Neural networks (MLP) |
| Lightning | import { Lightning } from "@elaraai/east-py-datascience" | PyTorch Lightning neural networks |
| GP | import { GP } from "@elaraai/east-py-datascience" | Gaussian Process regression |
| MAPIE | import { MAPIE } from "@elaraai/east-py-datascience" | Conformal prediction intervals |
| Sklearn | import { Sklearn } from "@elaraai/east-py-datascience" | Preprocessing, metrics, data splitting |
| Shap | import { Shap } from "@elaraai/east-py-datascience" | Model explainability (SHAP values) |
| Optimization | import { Optimization } from "@elaraai/east-py-datascience" | Iterative coordinate descent optimization |
| GoogleOr | import { GoogleOr } from "@elaraai/east-py-datascience" | OR-Tools: CP-SAT, routing, LP/MIP, graph algorithms |
| PyMC | import { PyMC } from "@elaraai/east-py-datascience" | Bayesian regression, hierarchical models, multi-layer estimation |
| Simulation | import { Simulation } from "@elaraai/east-py-datascience" | Economic ontology simulation via DES (single run, Monte Carlo trajectories) |
import { MADS, Optuna, Sklearn, XGBoost, ALNS } from "@elaraai/east-py-datascience";
// Access types via Module.Types.TypeName
MADS.Types.VectorType // ArrayType(FloatType)
MADS.Types.BoundsType // StructType({ lower, upper })
MADS.Types.ResultType // StructType({ x_best, f_best, ... })
Optuna.Types.ParamSpaceType // Parameter definition
Optuna.Types.StudyResultType // Optimization result
ALNS.Types.ConfigType // ALNS configuration
ALNS.Types.ResultType // Result with "S" placeholder for solution type
Sklearn.Types.SplitConfigType // Train/test split config
XGBoost.Types.ModelBlobType // Trained model
// 1. Prepare data
const X = $.let([[...], [...], ...]);
const y = $.let([...]);
// 2. Configure and train
const config = $.let({ /* options with variant('some', value) or variant('none', null) */ });
const model = $.let(Module.train(X, y, config));
// 3. Predict
const predictions = $.let(Module.predict(model, X_test));
// 1. Define objective function
const objective = East.function([VectorType], FloatType, ($, x) => {
// compute and return objective value
});
// 2. Set bounds and config
const bounds = $.let({ lower: [...], upper: [...] });
const config = $.let({ /* options */ });
// 3. Optimize
const result = $.let(Module.optimize(objective, x0, bounds, config));
// result.x_best, result.f_best
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