Python exp_var示例

示例#1

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="******")

示例#2

文件： test_python_evaluation.py 项目： Globalync/globalynk-r

def test_python_errors(python_data):
    rating_true, rating_pred, _ = python_data(binary_rating=False)

    with pytest.raises(ValueError):
        rmse(rating_true, rating_true, col_user="******")

    with pytest.raises(ValueError):
        mae(rating_pred,
            rating_pred,
            col_rating=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=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=PREDICTION_COL,
                 col_user="******")

示例#3

文件： test_python_evaluation.py 项目： bethz/try_template

def test_python_exp_var(python_data, target_metrics):
    rating_true, rating_pred, _ = python_data

    assert exp_var(
        rating_true=rating_true, rating_pred=rating_true, col_prediction="rating"
    ) == pytest.approx(1.0, TOL)
    assert exp_var(rating_true, rating_pred) == target_metrics["exp_var"]

示例#4

def test_python_exp_var(rating_true, rating_pred):
    assert exp_var(
        rating_true=rating_true,
        rating_pred=rating_true,
        col_prediction=DEFAULT_RATING_COL,
    ) == pytest.approx(1.0, TOL)
    assert exp_var(rating_true, rating_pred) == pytest.approx(-6.4466, TOL)

示例#5

文件： test_python_evaluation.py 项目： David-Li-L/recommenders

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="******"
        )

示例#6

文件： test_python_evaluation.py 项目： David-Li-L/recommenders

def test_python_exp_var(rating_true, rating_pred):
    assert exp_var(
        rating_true=rating_true,
        rating_pred=rating_true,
        col_prediction=DEFAULT_RATING_COL,
    ) == pytest.approx(1.0, TOL)
    assert exp_var(rating_true, rating_pred) == pytest.approx(-6.4466, TOL)

示例#7

文件： test_python_evaluation.py 项目： Globalync/globalynk-r

def test_python_exp_var(python_data, target_metrics):
    rating_true, rating_pred, _ = python_data(binary_rating=False)

    assert exp_var(rating_true=rating_true,
                   rating_pred=rating_true,
                   col_prediction=DEFAULT_RATING_COL) == pytest.approx(
                       1.0, TOL)
    assert exp_var(rating_true, rating_pred) == target_metrics["exp_var"]

示例#8

文件： benchmark_utils.py 项目： mindis/recommender

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),
    }

示例#9

文件： benchmark_utils.py 项目： David-Li-L/recommenders

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)
    }

示例#10

文件： rec-nb-sar.py 项目： sparsh-ai/reco-base

                 col_user='******',
                 col_item='itemID',
                 col_rating='rating')
eval_mae = mae(test,
               top_k,
               col_user='******',
               col_item='itemID',
               col_rating='rating')
eval_rsquared = rsquared(test,
                         top_k,
                         col_user='******',
                         col_item='itemID',
                         col_rating='rating')
eval_exp_var = exp_var(test,
                       top_k,
                       col_user='******',
                       col_item='itemID',
                       col_rating='rating')

positivity_threshold = 2
test_bin = test.copy()
test_bin['rating'] = binarize(test_bin['rating'], positivity_threshold)

top_k_prob = top_k.copy()
top_k_prob['prediction'] = minmax_scale(top_k_prob['prediction'].astype(float))

eval_logloss = logloss(test_bin,
                       top_k_prob,
                       col_user='******',
                       col_item='itemID',
                       col_rating='rating')

示例#11

# calculate some regression metrics
eval_r2 = rsquared(test_df,
                   scores,
                   col_user=USER,
                   col_item=ITEM,
                   col_rating=RATING,
                   col_prediction=PREDICTION)
eval_rmse = rmse(test_df,
                 scores,
                 col_user=USER,
                 col_item=ITEM,
                 col_rating=RATING,
                 col_prediction=PREDICTION)
eval_mae = mae(test_df,
               scores,
               col_user=USER,
               col_item=ITEM,
               col_rating=RATING,
               col_prediction=PREDICTION)
eval_exp_var = exp_var(test_df,
                       scores,
                       col_user=USER,
                       col_item=ITEM,
                       col_rating=RATING,
                       col_prediction=PREDICTION)

# print("Model:\t" + learn.__class__.__name__,
#       "RMSE:\t%f" % eval_rmse,
#       "MAE:\t%f" % eval_mae,
#       "Explained variance:\t%f" % eval_exp_var,
#       "R squared:\t%f" % eval_r2, sep='\n')