Spaces:
Sleeping
Sleeping
File size: 1,641 Bytes
52cc99a 0269b4b 52cc99a 0269b4b 52cc99a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | import numpy as np
import pandas as pd
import xgboost as xgb
from src.training import train as train_module
class DummyRegressor:
def __init__(self, **kwargs):
self.kwargs = kwargs
def fit(self, X, y):
self.columns_ = list(X.columns)
return self
def predict(self, X):
return np.log1p(np.full(len(X), 200.0))
def sample_training_frame() -> pd.DataFrame:
dates = pd.date_range("2015-01-01", periods=90, freq="D")
return pd.DataFrame(
{
"Store": 1,
"DayOfWeek": dates.dayofweek + 1,
"Date": dates,
"Sales": np.linspace(1000, 1500, len(dates)),
"Customers": 500,
"Open": 1,
"Promo": 0,
"StateHoliday": "0",
"SchoolHoliday": 0,
"StoreType": "a",
"Assortment": "a",
"CompetitionDistance": 1000.0,
"Promo2": 0,
"Promo2SinceWeek": 0,
"Promo2SinceYear": 0,
}
)
def test_run_training_saves_model_and_reports_metric(monkeypatch, tmp_path):
monkeypatch.setattr(train_module, "load_raw_data", lambda *args, **kwargs: sample_training_frame())
model_path = tmp_path / "model.json"
metadata_path = tmp_path / "model_metadata.json"
metrics = train_module.run_training(str(model_path), metadata_path=str(metadata_path))
assert model_path.exists()
assert metadata_path.exists()
assert "validation_rmspe" in metrics
assert metrics["validation_rows"] == 42
loaded = xgb.Booster()
loaded.load_model(str(model_path))
assert loaded.num_boosted_rounds() > 0
|