Source code for rankeval.metrics.mrr

# Copyright (c) 2017, All Contributors (see CONTRIBUTORS file)
# Authors: Cristina Muntean <cristina.muntean@isti.cnr.it>
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.

"""

"""
import numpy as np
from rankeval.metrics import Metric


[docs]class MRR(Metric): """ This class implements Mean Reciprocal Rank. """ _threshold = 1 def __init__(self, name='MRR', cutoff=None, threshold=_threshold): """ This is the constructor of MRR, an object of type Metric, with the name MRR. The constructor also allows setting custom values in the following parameters. Parameters ---------- name: string MRR cutoff: int The top k results to be considered at per query level (e.g. 10) threshold: float This parameter considers relevant results all instances with labels different from 0, thus with a minimum label value of 1. It can be set to other values as well (e.g. 3), in the range of possible labels. """ super(MRR, self).__init__(name) self.cutoff = cutoff self.threshold = threshold
[docs] def eval(self, dataset, y_pred): """ The method computes MRR by taking as input the dataset and the predicted document scores. It returns the averaged MRR score over the entire dataset and the detailed MRR scores per query. The mean reciprocal rank is the average of the reciprocal ranks of results for a sample of queries. Parameters ---------- dataset : Dataset Represents the Dataset object on which to apply MRR. y_pred : numpy 1d array of float Represents the predicted document scores for each instance in the dataset. Returns ------- avg_score: float Represents the average MRR over all MRR scores per query. detailed_scores: numpy 1d array of floats Represents the detailed MRR scores for each query. It has the length of n_queries. """ return super(MRR, self).eval(dataset, y_pred)
[docs] def eval_per_query(self, y, y_pred): """ This method helps compute the MRR score per query. It is called by the eval function which averages and aggregates the scores for each query. We compute the reciprocal rank. The reciprocal rank of a query response is the multiplicative inverse of the rank of the first correct answer. Parameters ---------- y: numpy array Represents the labels of instances corresponding to one query in the dataset (ground truth). y_pred: numpy array. Represents the predicted document scores obtained during the model scoring phase for that query. Returns ------- mrr: float Represents the MRR score for one query. """ idx_y_pred_sorted = np.argsort(y_pred)[::-1] if self.cutoff is not None: idx_y_pred_sorted = idx_y_pred_sorted[:self.cutoff] # rank of max predicted score rank_max = None for i, idx in enumerate(idx_y_pred_sorted): if y[idx] >= self.threshold: rank_max = i break if rank_max is not None: return 1./(rank_max+1) else: return 0.
def __str__(self): s = self.name if self.cutoff is not None: s += "@{}".format(self.cutoff) if self.threshold != self._threshold: s += "[>{}]".format(self.threshold) return s