# 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>): 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<f64>, yhat: Vec<f64>): 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<f64>, yhat: Vec<f64>): f64
```

The mean of the absolute differences between `y` and `yhat`.

Effects: fail.

### `meanSquaredError`

```gizlang
function meanSquaredError(y: Vec<f64>, yhat: Vec<f64>): f64
```

The mean of the squared differences between `y` and `yhat`.

Effects: fail.

### `classMetrics`

```gizlang
function classMetrics(y: Vec<f64>, yhat: Vec<f64>, 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<f64>, yhat: Vec<f64>): 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<f64>, yhat: Vec<f64>): 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<f64>, score: Vec<f64>): 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<f64>, yhat: Vec<f64>): Vec<f64>
```

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<f64>, score: Vec<f64>): 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<f64>, p: Vec<f64>): f64
```

linfa's log loss: each probability clamped to [ε, 1 − ε] of f32, in f32.

Effects: fail.

### `silhouette`

```gizlang
function silhouette(x: Vec<f64>, n: i64, d: i64, labels: Vec<f64>): f64 throws string
```

The mean silhouette coefficient (Euclidean); 2 to n - 1 distinct labels.

Effects: fail.

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>): 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<f64>, yhat: Vec<f64>): 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<f64>, yhat: Vec<f64>): f64

The mean of the absolute differences between y and yhat.

Effects: fail.

`meanSquaredError`

gizlang

function meanSquaredError(y: Vec<f64>, yhat: Vec<f64>): f64

The mean of the squared differences between y and yhat.

Effects: fail.

`classMetrics`

gizlang

function classMetrics(y: Vec<f64>, yhat: Vec<f64>, 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<f64>, yhat: Vec<f64>): 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<f64>, yhat: Vec<f64>): 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<f64>, score: Vec<f64>): 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<f64>, yhat: Vec<f64>): Vec<f64>

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<f64>, score: Vec<f64>): 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<f64>, p: Vec<f64>): f64

linfa's log loss: each probability clamped to [ε, 1 − ε] of f32, in f32.

Effects: fail.

`silhouette`

gizlang

function silhouette(x: Vec<f64>, n: i64, d: i64, labels: Vec<f64>): f64 throws string

The mean silhouette coefficient (Euclidean); 2 to n - 1 distinct labels.

Effects: fail.