relativities.models

Generated from the package’s Python API. Small, inspectable statistical models with explicit actuarial meanings. Poisson models predict counts, optionally over a supplied exposure. Gamma models predict positive individual claim severities. Both use a log link and numeric features. Both predict individual records; aggregate loss and parameter uncertainty are outside their scope.

ConditionalDistribution

class ConditionalDistribution(
    family: Literal['poisson', 'gamma'],
    means: tuple[tuple[RecordId, float], ...],
    dispersion: float = 1.0,
)

One distribution per successfully predicted record, aligned by identity. Poisson values describe counts over each record’s exposure. Gamma values describe a positive individual severity, one claim at a time.

ConditionalDistribution.mean

def mean(self) -> dict[RecordId, float]

Each predicted record’s conditional mean, by identity.

ConditionalDistribution.quantile

def quantile(self, probability: float) -> dict[RecordId, float]

Return outcome quantiles of each record’s distribution, as opposed to confidence limits for the fitted mean.

ConditionalDistribution.cdf

def cdf(self, value: float) -> dict[RecordId, float]

Return P(outcome <= value) for every predicted record.

ConditionalDistribution.survival

def survival(self, value: float) -> dict[RecordId, float]

Return P(outcome > value), retaining accuracy in the upper tail.

ConditionalDistribution.sample

def sample(
    self,
    n: int,
    *,
    seed: int | None = None,
) -> dict[RecordId, tuple[float, ...]]

Return independent conditional draws; tuple position is the draw ID. Independence is a sampling convention, separate from any estimated portfolio dependence. Seeds reproduce draws for the same ordered inputs and numerical-library versions. Draws are simulated values, separate from the observed records.

LossEvaluationReport

class LossEvaluationReport(
    target: str,
    unit: str,
    family: str,
    exposure: str | None,
    total_count: int,
    evaluated_count: int,
    observed_total: float,
    expected_total: float,
    mean_absolute_error: float | None,
    mean_deviance: float | None,
    failures: tuple[ResultRow, ...],
)

Holdout diagnostics for one loss model, from evaluate_loss. Totals, mean absolute error and mean deviance cover the records that could be evaluated, unweighted. Every excluded record is in failures with its reason.

LossEvaluationReport.summary

def summary(self) -> dict[str, object]

The report as a plain dict, with actual-to-expected, the weighting and the metric denominator spelled out.

LossModel

class LossModel(
    *,
    family: Literal['poisson', 'gamma'],
    features: Sequence[str],
    target: str,
    unit: str,
    output: str | None = None,
    exposure: str | None = None,
)

A Poisson frequency or Gamma severity GLM with a fixed log link. fit returns a new model. Configuration and fitted parameters are immutable. Predictors are explicit numeric columns, used as given, with formulas and learned preprocessing out of scope. Everything a prediction needs lives in the persisted model.

LossModel.fitted

@property
def fitted(self) -> bool

Whether the model has fitted parameters.

LossModel.metadata

@property
def metadata(self) -> dict[str, object]

What the model is and what its predictions mean: configuration, link, prediction meaning, uncertainty and fit.

LossModel.input_schema

@property
def input_schema(self) -> dict[str, object]

The JSON Schema of one prediction input: each feature, and the exposure.

LossModel.output_schema

@property
def output_schema(self) -> dict[str, object]

The JSON Schema of one prediction: the conditional expected value, in unit.

LossModel.coefficients

@property
def coefficients(self) -> dict[str, float]

The fitted coefficients by name, intercept first.

LossModel.fit

def fit(self, training: Dataset) -> LossModel

Fit unweighted observations; exposure is an offset, and every observation weighs the same. Missing/invalid observations fail fitting rather than silently changing the training population. Gamma dispersion is the Pearson estimate.

LossModel.call

def __call__(self, inputs: Inputs) -> LossResult

Predict every input, keeping its ID; a bad input is prediction_failed.

LossModel.save

def save(self, path: str | os.PathLike[str]) -> Path

Save parameters and configuration as plain JSON data, free of executable code.

LossModel.load

@classmethod
def load(cls, path: str | os.PathLike[str]) -> LossModel

Load a local JSON artifact from a file path.

LossResult

class LossResult(RatingResult)(
    rows: Iterable[ResultRow],
    *,
    distributions: Mapping[str, ConditionalDistribution],
    metadata: Mapping[str, object] | None = None,
)

Predicted means and conditional distributions, with every input retained.

LossResult.distributions

@property
def distributions(self) -> Mapping[str, ConditionalDistribution]

Each output’s conditional distribution, by output name.

evaluate_loss

def evaluate_loss(model: LossModel, data: Dataset) -> LossEvaluationReport

Evaluate against caller-selected holdout data; the model keeps its fitted parameters.

Get started

Comparative pricing

Prepare and validate

Concepts

Rater development

Python reference

Data reference

relativities.models

ConditionalDistribution

ConditionalDistribution.mean

ConditionalDistribution.quantile

ConditionalDistribution.cdf

ConditionalDistribution.survival

ConditionalDistribution.sample

LossEvaluationReport

LossEvaluationReport.summary

LossModel

LossModel.fitted

LossModel.metadata

LossModel.input_schema

LossModel.output_schema

LossModel.coefficients

LossModel.fit

LossModel.call

LossModel.save

LossModel.load

LossResult

LossResult.distributions

evaluate_loss

​ConditionalDistribution

​ConditionalDistribution.mean

​ConditionalDistribution.quantile

​ConditionalDistribution.cdf

​ConditionalDistribution.survival

​ConditionalDistribution.sample

​LossEvaluationReport

​LossEvaluationReport.summary

​LossModel

​LossModel.fitted

​LossModel.metadata

​LossModel.input_schema

​LossModel.output_schema

​LossModel.coefficients

​LossModel.fit

​LossModel.call

​LossModel.save

​LossModel.load

​LossResult

​LossResult.distributions

​evaluate_loss

ConditionalDistribution

ConditionalDistribution.mean

ConditionalDistribution.quantile

ConditionalDistribution.cdf

ConditionalDistribution.survival

ConditionalDistribution.sample

LossEvaluationReport

LossEvaluationReport.summary

LossModel

LossModel.fitted

LossModel.metadata

LossModel.input_schema

LossModel.output_schema

LossModel.coefficients

LossModel.fit

LossModel.call

LossModel.save

LossModel.load

LossResult

LossResult.distributions

evaluate_loss