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/*
* Hanzo Cloud API
*
* The Hanzo Cloud API as a customer calls it: every operation under /v1/ except the operator's admin product, relay routes, legacy spellings and capabilities still reached by flag. Tagged by product: the first path segment after /v1/.
*
* The version of the OpenAPI document: v1
*
* Generated by: https://openapi-generator.tech
*/
use crate::models;
use serde::{Deserialize, Serialize};
#[derive(Clone, Default, Debug, PartialEq, Serialize, Deserialize)]
pub struct RiskSplitCounts {
/// Judged is how many rows carry a disposition. It is zero until a label plane writes one, and reporting it plainly is what lets a model plane refuse to rank rather than name a winner it cannot justify.
#[serde(rename = "judged", skip_serializing_if = "Option::is_none")]
pub judged: Option<i32>,
/// Productive is how many judged rows carry the one disposition.
#[serde(rename = "productive", skip_serializing_if = "Option::is_none")]
pub productive: Option<i32>,
/// Rows is how many rows the version holds across every split. It is the size of the version, not of the source window — the horizon, the cuts and the row cap all bind before this number.
#[serde(rename = "rows", skip_serializing_if = "Option::is_none")]
pub rows: Option<i32>,
/// Subjects is how many distinct subjects the rows belong to. Every row of one subject is in ONE split, so this is the real sample size — the row count flatters it whenever a subject is active.
#[serde(rename = "subjects", skip_serializing_if = "Option::is_none")]
pub subjects: Option<i32>,
/// Test is how many fall after the second cut — the LATEST slice, and the only one a score is honest about, since the split is temporal.
#[serde(rename = "test", skip_serializing_if = "Option::is_none")]
pub test: Option<i32>,
/// Train is how many rows fall before the first cut — the EARLIEST slice of the window, which is what a model is fitted on.
#[serde(rename = "train", skip_serializing_if = "Option::is_none")]
pub train: Option<i32>,
/// Unproductive is how many carry the other. With Productive it accounts for Judged, so the class imbalance is visible before anyone trains on it; both stay 0 while Judged is 0.
#[serde(rename = "unproductive", skip_serializing_if = "Option::is_none")]
pub unproductive: Option<i32>,
/// Val is how many fall between the two cuts, held out for tuning.
#[serde(rename = "val", skip_serializing_if = "Option::is_none")]
pub val: Option<i32>,
}
impl RiskSplitCounts {
pub fn new() -> RiskSplitCounts {
RiskSplitCounts {
judged: None,
productive: None,
rows: None,
subjects: None,
test: None,
train: None,
unproductive: None,
val: None,
}
}
}