def test_python_rmse(rating_true, rating_pred):
assert (rmse(
rating_true=rating_true,
rating_pred=rating_true,
col_prediction=DEFAULT_RATING_COL,
) == 0)
assert rmse(rating_true, rating_pred) == pytest.approx(7.254309, 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_predict(rating_true):
train_set = cornac.data.Dataset.from_uir(
rating_true.itertuples(index=False), seed=42)
mf = cornac.models.MF(k=100, max_iter=10000, seed=42).fit(train_set)
preds = predict(mf, rating_true)
assert set(preds.columns) == {"userID", "itemID", "prediction"}
assert preds["userID"].dtypes == rating_true["userID"].dtypes
assert preds["itemID"].dtypes == rating_true["itemID"].dtypes
assert 0.02 > mae(rating_true, preds) # ~0.018
assert 0.03 > rmse(rating_true, preds) # ~0.021
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="******",
)