Tirsvad
769c777b48
- Add scripts for generating booking data, building metadata, and updating holidays - Implement public_holidays.py service with holiday lookup functionality - Update CSV data with expanded hotel booking records - Add holidays.json dataset for public holiday dates - Enhance LLM report service with improved formatting - Update metadata domain model and API dependencies - Add test coverage for dataset and public holidays
50 lines
2.1 KiB
Python
50 lines
2.1 KiB
Python
"""Guards the bundled synthetic dataset against the hotel's real constraints."""
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from pathlib import Path
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import pandas as pd
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import pytest
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from nf_hotel_api.core.config import get_settings
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from nf_hotel_api.repositories.booking_repository import CsvBookingRepository
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from nf_hotel_api.repositories.metadata_repository import JsonHotelMetadataRepository
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from nf_hotel_api.services.cleaning import DataCleaningService
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@pytest.fixture(scope="module")
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def metadata():
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return JsonHotelMetadataRepository(get_settings().metadata_path).load()
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@pytest.fixture(scope="module")
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def clean_bookings(metadata) -> pd.DataFrame:
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settings = get_settings()
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raw = CsvBookingRepository(settings.default_data_path, settings.csv_separator).load()
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return DataCleaningService(metadata).clean(raw)
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def test_arrivals_are_within_2022_to_2025(clean_bookings):
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assert clean_bookings["arrival_date"].min() >= pd.Timestamp(2022, 1, 1)
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assert clean_bookings["arrival_date"].max() <= pd.Timestamp(2025, 12, 31)
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def test_only_known_room_types_are_used(clean_bookings, metadata):
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assert set(clean_bookings["assigned_room_type"]) <= set(metadata.room_types)
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def test_hotel_is_never_overbooked(clean_bookings, metadata):
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"""Non-cancelled bookings must fit in the rooms the hotel actually has."""
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stays = clean_bookings[clean_bookings["is_canceled"] == 0]
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for code, room in metadata.room_types.items():
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of_type = stays[stays["assigned_room_type"] == code]
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nights = (of_type["stays_in_weekend_nights"] + of_type["stays_in_week_nights"]).astype(int)
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# +1 on arrival, -1 on the day after the last night; the running sum is the occupancy.
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starts = of_type["arrival_date"].value_counts()
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ends = (of_type["arrival_date"] + pd.to_timedelta(nights, unit="D")).value_counts()
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change = starts.sub(ends, fill_value=0).sort_index()
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assert change.cumsum().max() <= room.room_count, f"room type {code} overbooked"
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def test_prices_are_present_and_non_negative(clean_bookings):
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assert clean_bookings["prize_per_nigth"].notna().all()
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assert (clean_bookings["prize_per_nigth"] >= 0).all()
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