Define the model-run dictionary input contract

The accepted input remains: named fields for one model run, including an optional version. Keep parsing and normalization in functions that do not score, train, or call a model. That split lets a test fail the boundary without blaming the core logic. The user-facing decision stays: store one training run so later code can read named fields without positional guessing.

REQUIRED={'name','accuracy','ready','owner'}
def validate(run):
    missing=REQUIRED-set(run)
    if missing: raise KeyError(f'missing {sorted(missing)}')
    if not isinstance(run['accuracy'], float): raise TypeError('accuracy must be float')
    return run
print(validate({'name':'ready-v1','accuracy':0.91,'ready':True,'owner':'ml-lab'}))

Expected evidence: validated run dict. If the contract is silent on a bad value, later debugging will look like an algorithm bug when it is really a data bug.

Write one assertion for a neighboring valid input to the model-run dictionary so tightening the boundary does not over-reject. Document field names and types the way a teammate would need them on day two of python-dictionaries—not as comments you plan to delete.

List every field in name='ready-v1', accuracy=0.91, ready=True, owner='ml-lab' and mark each as required, optional, or forbidden. Required fields must fail loudly when missing; optional fields need defaults you can quote in a test; forbidden fields (secrets, raw PII, path escapes) must never be accepted silently. This list is the contract for the model-run dictionary.

Add one sentence about encoding, units, or timezones if relevant to named fields for one model run, including an optional version. Contracts that ignore units create “correct” programs that still ship wrong decisions when someone tries to store one training run so later code can read named fields without positional guessing.

Write the error string you want for the most likely bad input. Prefer ValueError('threshold out of range')-style messages over generic invalid input. The contract’s job is to make KeyError on missing version, or silent overwrite of accuracy with a string harder to confuse with a model or algorithm bug later.

At the data contract stage for python-dictionaries, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about name='ready-v1', accuracy=0.91, ready=True, owner='ml-lab' that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: a handwritten field list before coding the dict.

For this page specifically, success looks like malformed inputs rejected with field-named errors while still centering the user decision to store one training run so later code can read named fields without positional guessing. If you cannot point to a file, command, or assertion that proves that for the model-run dictionary, stay on this page instead of advancing.

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