Learning to rank metrics.

letor_metrics.py

# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause

import numpy as np


def ranking_precision_score(y_true, y_score, k=10):
    """Precision at rank k

    Parameters
    ----------
    y_true : array-like, shape = [n_samples]
        Ground truth (true relevance labels).

    y_score : array-like, shape = [n_samples]
        Predicted scores.

    k : int
        Rank.

    Returns
    -------
    precision @k : float
    """
    unique_y = np.unique(y_true)

    if len(unique_y) > 2:
        raise ValueError("Only supported for two relevance levels.")

    pos_label = unique_y[1]
    n_pos = np.sum(y_true == pos_label)

    order = np.argsort(y_score)[::-1]
    y_true = np.take(y_true, order[:k])
    n_relevant = np.sum(y_true == pos_label)

    # Divide by min(n_pos, k) such that the best achievable score is always 1.0.
    return float(n_relevant) / min(n_pos, k)


def average_precision_score(y_true, y_score, k=10):
    """Average precision at rank k

    Parameters
    ----------
    y_true : array-like, shape = [n_samples]
        Ground truth (true relevance labels).

    y_score : array-like, shape = [n_samples]
        Predicted scores.

    k : int
        Rank.

    Returns
    -------
    average precision @k : float
    """
    unique_y = np.unique(y_true)

    if len(unique_y) > 2:
        raise ValueError("Only supported for two relevance levels.")

    pos_label = unique_y[1]
    n_pos = np.sum(y_true == pos_label)

    order = np.argsort(y_score)[::-1][:min(n_pos, k)]
    y_true = np.asarray(y_true)[order]

    score = 0
    for i in xrange(len(y_true)):
        if y_true[i] == pos_label:
            # Compute precision up to document i
            # i.e, percentage of relevant documents up to document i.
            prec = 0
            for j in xrange(0, i + 1):
                if y_true[j] == pos_label:
                    prec += 1.0
            prec /= (i + 1.0)
            score += prec

    if n_pos == 0:
        return 0

    return score / n_pos


def dcg_score(y_true, y_score, k=10, gains="exponential"):
    """Discounted cumulative gain (DCG) at rank k

    Parameters
    ----------
    y_true : array-like, shape = [n_samples]
        Ground truth (true relevance labels).

    y_score : array-like, shape = [n_samples]
        Predicted scores.

    k : int
        Rank.

    gains : str
        Whether gains should be "exponential" (default) or "linear".

    Returns
    -------
    DCG @k : float
    """
    order = np.argsort(y_score)[::-1]
    y_true = np.take(y_true, order[:k])

    if gains == "exponential":
        gains = 2 ** y_true - 1
    elif gains == "linear":
        gains = y_true
    else:
        raise ValueError("Invalid gains option.")

    # highest rank is 1 so +2 instead of +1
    discounts = np.log2(np.arange(len(y_true)) + 2)
    return np.sum(gains / discounts)


def ndcg_score(y_true, y_score, k=10, gains="exponential"):
    """Normalized discounted cumulative gain (NDCG) at rank k

    Parameters
    ----------
    y_true : array-like, shape = [n_samples]
        Ground truth (true relevance labels).

    y_score : array-like, shape = [n_samples]
        Predicted scores.

    k : int
        Rank.

    gains : str
        Whether gains should be "exponential" (default) or "linear".

    Returns
    -------
    NDCG @k : float
    """
    best = dcg_score(y_true, y_true, k, gains)
    actual = dcg_score(y_true, y_score, k, gains)
    return actual / best


# Alternative API.

def dcg_from_ranking(y_true, ranking):
    """Discounted cumulative gain (DCG) at rank k

    Parameters
    ----------
    y_true : array-like, shape = [n_samples]
        Ground truth (true relevance labels).

    ranking : array-like, shape = [k]
        Document indices, i.e.,
            ranking[0] is the index of top-ranked document,
            ranking[1] is the index of second-ranked document,
            ...

    k : int
        Rank.

    Returns
    -------
    DCG @k : float
    """
    y_true = np.asarray(y_true)
    ranking = np.asarray(ranking)
    rel = y_true[ranking]
    gains = 2 ** rel - 1
    discounts = np.log2(np.arange(len(ranking)) + 2)
    return np.sum(gains / discounts)


def ndcg_from_ranking(y_true, ranking):
    """Normalized discounted cumulative gain (NDCG) at rank k

    Parameters
    ----------
    y_true : array-like, shape = [n_samples]
        Ground truth (true relevance labels).

    ranking : array-like, shape = [k]
        Document indices, i.e.,
            ranking[0] is the index of top-ranked document,
            ranking[1] is the index of second-ranked document,
            ...

    k : int
        Rank.

    Returns
    -------
    NDCG @k : float
    """
    k = len(ranking)
    best_ranking = np.argsort(y_true)[::-1]
    best = dcg_from_ranking(y_true, best_ranking[:k])
    return dcg_from_ranking(y_true, ranking) / best


if __name__ == '__main__':

    # Check that some rankings are better than others
    assert dcg_score([5, 3, 2], [2, 1, 0]) > dcg_score([4, 3, 2], [2, 1, 0])
    assert dcg_score([4, 3, 2], [2, 1, 0]) > dcg_score([1, 3, 2], [2, 1, 0])

    assert dcg_score([5, 3, 2], [2, 1, 0], k=2) > dcg_score([4, 3, 2], [2, 1, 0], k=2)
    assert dcg_score([4, 3, 2], [2, 1, 0], k=2) > dcg_score([1, 3, 2], [2, 1, 0], k=2)

    # Perfect rankings
    assert ndcg_score([5, 3, 2], [2, 1, 0]) == 1.0
    assert ndcg_score([2, 3, 5], [0, 1, 2]) == 1.0
    assert ndcg_from_ranking([5, 3, 2], [0, 1, 2]) == 1.0

    assert ndcg_score([5, 3, 2], [2, 1, 0], k=2) == 1.0
    assert ndcg_score([2, 3, 5], [0, 1, 2], k=2) == 1.0
    assert ndcg_from_ranking([5, 3, 2], [0, 1]) == 1.0

    # Check that sample order is irrelevant
    assert dcg_score([5, 3, 2], [2, 1, 0]) == dcg_score([2, 3, 5], [0, 1, 2])

    assert dcg_score([5, 3, 2], [2, 1, 0], k=2) == dcg_score([2, 3, 5], [0, 1, 2], k=2)

    # Check equivalence between two interfaces.
    assert dcg_score([5, 3, 2], [2, 1, 0]) == dcg_from_ranking([5, 3, 2], [0, 1, 2])
    assert dcg_score([1, 3, 2], [2, 1, 0]) == dcg_from_ranking([1, 3, 2], [0, 1, 2])
    assert dcg_score([1, 3, 2], [0, 2, 1]) == dcg_from_ranking([1, 3, 2], [1, 2, 0])
    assert ndcg_score([1, 3, 2], [2, 1, 0]) == ndcg_from_ranking([1, 3, 2], [0, 1, 2])

    assert dcg_score([5, 3, 2], [2, 1, 0], k=2) == dcg_from_ranking([5, 3, 2], [0, 1])
    assert dcg_score([1, 3, 2], [2, 1, 0], k=2) == dcg_from_ranking([1, 3, 2], [0, 1])
    assert dcg_score([1, 3, 2], [0, 2, 1], k=2) == dcg_from_ranking([1, 3, 2], [1, 2])
    assert ndcg_score([1, 3, 2], [2, 1, 0], k=2) == \
            ndcg_from_ranking([1, 3, 2], [0, 1])

    # Precision
    assert ranking_precision_score([1, 1, 0], [3, 2, 1], k=2) == 1.0
    assert ranking_precision_score([1, 1, 0], [1, 0, 0.5], k=2) == 0.5
    assert ranking_precision_score([1, 1, 0], [3, 2, 1], k=3) == \
            ranking_precision_score([1, 1, 0], [1, 0, 0.5], k=3)

    # Average precision
    from sklearn.metrics import average_precision_score as ap
    assert average_precision_score([1, 1, 0], [3, 2, 1]) == ap([1, 1, 0], [3, 2, 1])
    assert average_precision_score([1, 1, 0], [3, 1, 0]) == ap([1, 1, 0], [3, 1, 0])

👍 Nice implementation with a nice API! Are there plans to publish these snippets as a Python module?

The step gains = 2 ** rel - 1 in the method dcg_from_ranking is an issue as for even as small values as 70 since it overflows, e.g., ndcg_score([10,1,70], [2, 1, 0]) gives 1.59.

I am assuming the ndcg_score y_true should be binary (ground truth), but am unsure what format y_score should be in. When calculating auc for example the y_true would be binary and the y_score does not have to be any given range. I could feed a vector of any range. The y_score I have is the output from an ALS collaborative model.

For example: [ 1.09253478e-01 1.97033856e-01 5.51080274e-01 ..., 1.77992064e-03 1.90066773e-12 1.74711004e-04]

Should I scale this vector in some way before using it as an argument to the ndcg_score function?

Nice implementation! I'd love if they were part of sckit! perhaps I'd use pos_label as a parameter with default value 1, as in other metrics from sckit

I like your adaptive dominator in the precision@k code! Takes care of one of the precision@k weaknesses when the number of relevant documents are less than k.

hey! what do you mean by "true relevance labels"? is it the relevance score (unnormalized score showing the relevance of an item to a query, the higher the more relevant) or the position of the item in the result (zero being the highest shown) imagine an item is the most relevant does it have a high value or low value ?