def test_python_rsquared(rating_true, rating_pred):
assert (rsquared(
rating_true=rating_true,
rating_pred=rating_true,
col_prediction=DEFAULT_RATING_COL,
) == pytest.approx(1.0, TOL))
assert rsquared(rating_true, rating_pred) == pytest.approx(-31.699029, TOL)
def rating_metrics_python(test, predictions):
return {
"RMSE": rmse(test, predictions, **COL_DICT),
"MAE": mae(test, predictions, **COL_DICT),
"R2": rsquared(test, predictions, **COL_DICT),
"Explained Variance": exp_var(test, predictions, **COL_DICT),
}
def test_python_errors(rating_true, rating_pred):
with pytest.raises(ValueError):
rmse(rating_true, rating_true, col_user="******")
with pytest.raises(ValueError):
mae(
rating_pred,
rating_pred,
col_rating=DEFAULT_PREDICTION_COL,
col_user="******",
)
with pytest.raises(ValueError):
rsquared(rating_true, rating_pred, col_item="not_item")
with pytest.raises(ValueError):
exp_var(
rating_pred,
rating_pred,
col_rating=DEFAULT_PREDICTION_COL,
col_item="not_item",
)
with pytest.raises(ValueError):
precision_at_k(rating_true, rating_pred, col_rating="not_rating")
with pytest.raises(ValueError):
recall_at_k(rating_true, rating_pred, col_prediction="not_prediction")
with pytest.raises(ValueError):
ndcg_at_k(rating_true, rating_true, col_user="******")
with pytest.raises(ValueError):
map_at_k(
rating_pred,
rating_pred,
col_rating=DEFAULT_PREDICTION_COL,
col_user="******",
)