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Each carrier’s rater defines its own input schema and premium outputs. A book stores household and vehicle facts in a shared schema. A program pairs a rater with the Schema of the book it accepts and a prepare function that builds the rater’s inputs from that book.

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The schema is the program’s shape

program.schema is a Schema, the same type as book.schema. It lists each table and column the program accepts, with its type and whether it is required. A line’s schema, such as ppa.SCHEMA, accepts the line’s core columns. To read a column of your own, declare it with with_columns:
Read the schema in pandas with program.schema.to_pandas(), or print it.

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prepare holds each carrier-specific rule

prepare is a function from a book to {submission_id: json_in}. Each json_in has the shape of rater.inputs, and it depends only on that submission’s rows. rate gives prepare the book in chunks, and a failing submission is left out of the book, so this rule keeps each input the same however the book is split. Relativities converts its values to JSON: dates become ISO text, numpy numbers become Python numbers, a missing field is left out, and a missing list element stays as None. The book stays the same for each carrier, and a change to one carrier touches its own prepare. For a carrier’s own estimate of an empty fact, write a new prepare that fills the fact and calls the old one. The book keeps the fact as reported, so one carrier’s estimate stays out of another carrier’s input.

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What a program guarantees

  • The schema is enforced. rate and explain check the book against the program’s schema. A missing required column, or a value of the wrong type, raises BookError before prepare runs.
  • The schema is all prepare reads. prepare gets a copy of the book with only the schema’s tables and columns. A column outside the schema raises KeyError in prepare, so program.schema lists every column the program can depend on.
  • One bad submission fails only itself. When prepare raises, Relativities halves the book until it finds each submission that prepare fails on alone. Those rows get the status mapping_failed and the reason code PREPARE_FAILED, with the function’s name and the exception as the reason. prepare then runs again on the rest of the book, and the other rows rate. A clean book runs prepare once.
  • A submission that prepare leaves out stays in the results. Its status is mapping_failed, with the reason code NOT_PREPARED.
  • Explain matches rate. explain runs prepare on the chunk that rate gives it, so its json_in is the exact input rated and its row is the row rate gives. Its book holds the submission’s facts, with the schema’s columns.
Keep failed mappings visible in your analysis and investigate the underlying data or prepare logic.

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Result columns carry the rater’s own names

rate returns the rater’s RatingOutcomes as columns, with the names the rater gives them. The rater defines what it returns, so the result shows exactly that. A total is the carrier’s own total, and a premium carries the carrier’s own term. In a market, two programs share a column when their raters give an outcome the same name. Carrier A’s six-month premium and carrier B’s twelve-month annual_premium stay in separate columns. The choice of a comparison basis, such as an annual premium, stays with you, in pandas.

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Each layer adds its key

Analysis code for one program works on a market’s table too: it groups by one more column.

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One rating path

program.rate and market.rate use the same path as rater.rate: parallel native batches. The unit of work is one program and one chunk of up to 10,000 submissions from one version of the book. Units run on two threads, and a study builds one version at a time, so memory holds a few chunks, plus the result rows. Each input depends only on its own submission’s rows, so a chunk gives the same rows as the whole book. explain runs prepare on the chunk that holds the submission, then rates the one submission again with each calculation kept. Rating is deterministic, so the explanation matches the rated row. market.explain gives each program’s explanation side by side.

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Mapping quality is part of rating quality

A correctly implemented algorithm still needs the right inputs. Vehicle symbols, territory assignments, discounts, and coverage selections can change a comparison materially. explain exposes the inputs passed to RSL and its calculations. Review the Python prepare function to establish each mapping’s source and assumptions. Use Validate a program to reconcile independent cases before analyzing the full book.