# gizai:ml/metrics metrics of models. ```gizlang import * as Metrics from "gizai:ml/metrics"; ``` ## Types ### `ClassMetrics` ```gizlang type ClassMetrics = { f1: f64; f1Micro: f64; precision: f64; recall: f64; accuracy: f64; mcc: f64; } ``` The confusion metrics of PostgresML (src/metrics.rs): f1 macro, precision, recall and accuracy, f32; mcc as linfa's multiclass formula. y and yhat are labels rounded to integers. - `f1`: `f64` - `f1Micro`: `f64` - `precision`: `f64` - `recall`: `f64` - `accuracy`: `f64` - `mcc`: `f64` ## Functions ### `sum32` ```gizlang function sum32(v: Vec): f64 ``` The f32 sum of ndarray (linfa's): eight running sums over blocks of eight, joined pairwise, then the rest in order. Effects: fail. ### `r2` ```gizlang function r2(y: Vec, yhat: Vec): f64 ``` R², mean absolute and mean squared error in f32, as linfa computes them for PostgresML over the flattened targets. Effects: fail. ### `meanAbsoluteError` ```gizlang function meanAbsoluteError(y: Vec, yhat: Vec): f64 ``` The mean of the absolute differences between `y` and `yhat`. Effects: fail. ### `meanSquaredError` ```gizlang function meanSquaredError(y: Vec, yhat: Vec): f64 ``` The mean of the squared differences between `y` and `yhat`. Effects: fail. ### `classMetrics` ```gizlang function classMetrics(y: Vec, yhat: Vec, classes: i64): ClassMetrics throws string ``` The confusion metrics of predictions YHAT for labels Y of CLASSES classes. It fails when the lengths differ. Effects: fail. ### `sklearnScores` ```gizlang function sklearnScores(y: Vec, yhat: Vec): ClassMetrics throws string ``` scikit-learn's classification scores of PostgresML's sklearn_classification_metrics, in f32: f1, precision and recall of class 1 (average "binary") for at most two labels, else their mean over the labels (average "macro"). Effects: fail. ### `sklearnF1` ```gizlang function sklearnF1(y: Vec, yhat: Vec): f64 throws string ``` scikit-learn's f1_score with average "binary": an error for more than two labels. Effects: fail. ### `sklearnRocAuc` ```gizlang function sklearnRocAuc(y: Vec, score: Vec): f64 throws string ``` scikit-learn's roc_auc_score of two classes, the larger one positive: the Mann-Whitney statistic, ties as halves. Effects: fail. ### `sklearnRegression` ```gizlang function sklearnRegression(y: Vec, yhat: Vec): Vec ``` scikit-learn's r2_score, mean_squared_error and mean_absolute_error, in f32: [r2, mse, mae]. A constant truth gives an r2 of 1 for an exact prediction, else 0. Effects: fail. ### `rocAuc` ```gizlang function rocAuc(y: Vec, score: Vec): f64 ``` linfa's area under the ROC curve (PostgresML's roc_auc): the scores of 0 or more in ascending order, a point at each new score, the (tp, fp) rates of the points integrated by the trapezoid rule over tp, in f32. Effects: fail. ### `logLoss` ```gizlang function logLoss(y: Vec, p: Vec): f64 ``` linfa's log loss: each probability clamped to [ε, 1 − ε] of f32, in f32. Effects: fail. ### `silhouette` ```gizlang function silhouette(x: Vec, n: i64, d: i64, labels: Vec): f64 throws string ``` The mean silhouette coefficient (Euclidean); 2 to n - 1 distinct labels. Effects: fail.