# gizai:ml/data the data (row-major f64 matrices), the estimators and the model that the other parts share. ```gizlang import * as Data from "gizai:ml/data"; ``` ## Types ### `Data` ```gizlang type Data = { rows: i64; features: i64; x: Vec; targets: i64; y: Vec; } ``` A training set: rows × features in x, rows × targets in y (labels for classification, none for clustering). - `rows`: `i64` - `features`: `i64` - `x`: `Vec` - `targets`: `i64` - `y`: `Vec` ### `Split` ```gizlang type Split = { train: Data; validation: Data; } ``` Two disjoint copies of one dataset, preserving each row's feature/target pairing. - `train`: `M37.Data` - `validation`: `M37.Data` ## Functions ### `tabular` ```gizlang function tabular(features: Vec>, targets: Vec> = []): Data throws string ``` Build a row-major dataset without a database. Labels are optional for clustering or PCA. Every feature row and target row must have one consistent width. Effects: fail. ### `param` ```gizlang function param(h: Json, key: string): Option ``` The hyperparameter `key` of `h`, if set. Effects: fail. ### `number` ```gizlang function number(h: Json, key: string, d: f64): f64 ``` The number under `key` in the hyperparameters `h`, else `d`. Effects: fail. ### `integer` ```gizlang function integer(h: Json, key: string, d: i64): i64 ``` The integer under `key` in the hyperparameters `h`, else `d`. Effects: fail. ### `text` ```gizlang function text(h: Json, key: string, d: string): string ``` The text under `key` in the hyperparameters `h`, else `d`. Effects: fail. ### `flag` ```gizlang function flag(h: Json, key: string, d: bool): bool ``` The boolean hyperparameter KEY of H, or D when H does not have it. Effects: fail. ### `isNull` ```gizlang function isNull(h: Json, key: string): bool ``` Whether the hyperparameter `key` of `h` is set to null. Effects: fail. ### `maxFeatures` ```gizlang function maxFeatures(h: Json, d: i64, fallback: string): i64 ``` max_features: "sqrt", "log2", an int, a fraction, or null (all). Effects: fail. ### `textual` ```gizlang function textual(v: Json): bool ``` Whether `v` is a JSON string. ### `column` ```gizlang function column(y: Vec, n: i64, t: i64, j: i64): Vec ``` Column `j` of the `n` by `t` row-major matrix `y`. Effects: fail. ## Methods ### `Data.validate` ```gizlang function Data.validate(self: Data, task: string): bool throws string ``` Validate the physical data contract before any algorithm indexes a row. Effects: fail. ### `Data.split` ```gizlang function Data.split(self: Data, validationRows: i64, seed: u64): Split throws string ``` Deterministic shuffled holdout. Fit preprocessing on train, never on validation. For class-stratified or time-series splits, select rows in the caller instead. Effects: fail. ### `Data.folds` ```gizlang function Data.folds(self: Data, k: i64, seed: u64): Vec throws string ``` K disjoint validation folds: each row appears in exactly one validation set. Each fold trains on the remaining rows; deterministic for a given seed. Effects: fail. ### `Data.stratifiedFolds` ```gizlang function Data.stratifiedFolds(self: Data, k: i64, seed: u64): Vec throws string ``` Stratified K-folds for classification: preserve class proportions in every fold. Require at least K examples per class to keep minority classes evaluable. Effects: fail. ### `Model.predictChecked` ```gizlang function Model.predictChecked(self: Model, row: Vec): Vec throws string ``` Safe single-row inference. Use this at untrusted API and file boundaries. Effects: fail. ### `Model.predictRowsChecked` ```gizlang function Model.predictRowsChecked(self: Model, x: Vec, rows: i64): Vec throws string ``` Safe inference of a row-major batch without a database. Effects: fail. ### `Model.probaChecked` ```gizlang function Model.probaChecked(self: Model, row: Vec): Vec throws string ``` Safe probability inference for one row. Effects: fail. ### `Model.outputs` ```gizlang function Model.outputs(self: Model, row: Vec): Vec ``` The raw outputs of a row: the concatenation of the outputs of each part. Effects: fail. ### `Model.predict` ```gizlang function Model.predict(self: Model, row: Vec): Vec ``` The prediction of a row: each target (regression), the label (classification), the cluster (clustering), the components (decomposition). Effects: fail. ### `Model.predictRows` ```gizlang function Model.predictRows(self: Model, x: Vec, n: i64): Vec ``` The predictions of `n` rows of `x` (row-major, `features` wide), one row after another, on all threads. Effects: fail. ### `Model.probaRows` ```gizlang function Model.probaRows(self: Model, x: Vec, n: i64): Vec throws string ``` The class probabilities of `n` rows of `x`, one row after another, on all threads. Effects: fail. ### `Model.proba` ```gizlang function Model.proba(self: Model, row: Vec): Vec throws string ``` The probability of each class, in the order of the classes. Effects: fail. ## Constants ### `ML_FORMAT` ```gizlang const ML_FORMAT = 1 ``` The version of the model file format.