Rate submissions with a rater

from pathlib import Path

import pandas as pd

from relativities import Dataset, Rater

rater = Rater.load(Path("relativities-examples/example-auto.zip"))
submissions = pd.DataFrame(
    {
        "submission_id": ["S-1", "S-2", "S-3"],
        "drivers": [[{"age": 42}], [{"age": 19}], [{"age": 50}]],
        "vehicles": [[{"annual_mileage": 5_000}], [{"annual_mileage": 12_000}], [{"annual_mileage": 45_000}]],
        "coverages": [{"collision": False}, {"collision": True}, {"collision": False}],
    }
)
results = rater.rate(Dataset.from_pandas(submissions, key="submission_id"))
results  # RatingResult(3 rows: 2 succeeded, 1 refused)

catalog_id = "your-rater-id"  # replace with an ID available to your API key
latest = Rater.load(catalog_id)
bundle_id = latest.bundle_id

fourth = Rater.load(catalog_id, revision=4)  # choose a published release number
exact = Rater.load(
    catalog_id,
    revision=bundle_id,
    local_files_only=True,  # read the cache only
)
exact.bundle_id == latest.bundle_id  # True

folder = Rater.download(catalog_id, Path("raters/study-rater"), revision=bundle_id)
sorted(path.name for path in folder.iterdir())  # ['rater-manifest.json', 'rater.py', 'rater.rsl']
local = Rater.load(folder)  # offline
local.bundle_id == latest.bundle_id  # True

rater.inputs.values.tolist()
# [['coverages.collision', 'bool', '', '', True, ''],
#  ['drivers[].age', 'int', '', '16 to 99', True, ''],
#  ['vehicles[].annual_mileage', 'int', '', '0 to 30000', True, '']]
rater.outputs["path"].tolist()  # ['annual_premium']

one = rater.rate({"drivers": [{"age": 30}], "vehicles": [{"annual_mileage": 9_000}], "coverages": {"collision": True}})
one.to_pandas()["annual_premium"].item()  # 650.0
several = rater.rate([{"drivers": [{"age": 30}], "vehicles": [{"annual_mileage": 9_000}], "coverages": {"collision": False}}] * 2)
several.to_pandas()["annual_premium"].tolist()  # [500.0, 500.0]

frame = results.to_pandas()
frame.columns.tolist()  # ['submission_id', 'status', 'reason_code', 'reason', 'annual_premium']
frame["annual_premium"].tolist()  # [400.0, 812.5, nan]
frame.groupby("reason_code").size()  # MILEAGE_OUT_OF_RANGE: 1
frame.loc[frame["status"] == "succeeded", "annual_premium"].mean()  # 606.25

results.rows[1].outputs["Policy"]["annual_premium"]  # Decimal('812.5')
results.rows[2].error.code  # 'MILEAGE_OUT_OF_RANGE'

for loaded in (rater, latest, fourth, exact, local):
    loaded.close()