Struct EconomicsMetric 

pub struct EconomicsMetric {

    pub id: String,
    pub title: String,
    pub class: EconomicsClass,
    pub inputs: Option<EconomicsClass>,
    pub unit: String,
    pub formula: String,
    pub not: Option<String>,
    pub value: Option<f64>,
    pub status: EconomicsMetricStatus,
    pub n: usize,
    pub distribution: Option<EconomicsDistribution>,
    pub interval: Option<EconomicsInterval>,
    pub suite: String,
    pub evidence: Vec<String>,
    pub warnings: Vec<String>,
}

One metric: its value, where it comes from, and how much it can bear.

use majordomus_cli::economics::model::{EconomicsClass, EconomicsMetric, EconomicsMetricStatus};
let m: EconomicsMetric = serde_json::from_value(serde_json::json!({
    "id": "effective_token_reduction", "title": "Effective token reduction",
    "class": "derived", "inputs": "observed", "unit": "ratio",
    "formula": "median of 1 - treatment / control", "status": "not_measured", "n": 0,
    "suite": "pilot"
}))
.unwrap();
assert_eq!(m.inputs, Some(EconomicsClass::Observed));
assert!(m.value.is_none() && m.status == EconomicsMetricStatus::NotMeasured);
assert!(serde_json::to_value(&m).unwrap().get("value").is_none(), "no value, not a zero");

Fields

id: String

The metric’s id.

title: String

A name.

class: EconomicsClass

The measurement class.

inputs: Option<EconomicsClass>

For a derived metric, the class of what it was derived from: a reduction derived from observed usage is not the same evidence as one derived from counted bytes.

unit: String

ratio, tokens, calls.

formula: String

The formula, in words.

not: Option<String>

What the metric is not, where it is easily mistaken for something else.

value: Option<f64>

The headline value; absent when nothing was measured.

status: EconomicsMetricStatus

Where it stands.

n: usize

How many values it rests on.

distribution: Option<EconomicsDistribution>

Their distribution.

interval: Option<EconomicsInterval>

The interval, when enough values exist.

suite: String

The suite.

evidence: Vec<String>

The runs, pairs or seeds it rests on.

warnings: Vec<String>

What a reader must know before using it.

Trait Implementations

impl Clone for EconomicsMetric

fn clone(&self) -> EconomicsMetric

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for EconomicsMetric

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for EconomicsMetric

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl JsonSchema for EconomicsMetric

fn schema_name() -> Cow<'static, str>

The name of the generated JSON Schema.

fn schema_id() -> Cow<'static, str>

Returns a string that uniquely identifies the schema produced by this type.

fn json_schema(generator: &mut SchemaGenerator) -> Schema

Generates a JSON Schema for this type.

fn inline_schema() -> bool

Whether JSON Schemas generated for this type should be included directly in parent schemas, rather than being re-used where possible using the $ref keyword.

impl PartialEq for EconomicsMetric

fn eq(&self, other: &EconomicsMetric) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for EconomicsMetric

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for EconomicsMetric