Source code for rankeval.metrics.pfound

# 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 Pfound(Metric): """ This class implements Pfound with several parameters. The ERR metric is very similar to the pFound metric used by Yandex (Segalovich, 2010). [http://proceedings.mlr.press/v14/chapelle11a/chapelle11a.pdf]. In fact pFound is identical to the ERR variant described in (Chapelle et al., 2009, Section 7.2). We implemented pFound similar to ERR in section 7.2 of http://olivier.chapelle.cc/pub/err.pdf. """ def __init__(self, name='Pf', cutoff=None, p_abandonment=0.15): """ This is the constructor of Pfound, an object of type Metric, with the name Pf. The constructor also allows setting custom values in the following parameters. Parameters ---------- name: string Pf cutoff: int The top k results to be considered at per query level (e.g. 10), otherwise the default value is None and is computed on all the instances of a query. p_abandonment: float This parameter indicates the probability of abandonment, i.e. the user stops looking a the ranked list due to an external reason. The original cascade model of ERR has later been extended to include an abandonment probability: if the user is not satisfied at a given position, he will examine the next url with probability y, but has a probability 1-y of abandoning. """ super(Pfound, self).__init__(name) self.cutoff = cutoff self.p_abandonment = p_abandonment
[docs] def eval(self, dataset, y_pred): """ The method computes Pfound by taking as input the dataset and the predicted document scores. It returns the averaged Pfound score over the entire dataset and the detailed Pfound scores per query. Parameters ---------- dataset : Dataset Represents the Dataset object on which to apply Pfound. 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 Pfound over all Pfound scores per query. detailed_scores: numpy 1d array of floats Represents the detailed Pfound scores for each query. It has the length of n_queries. """ return super(Pfound, self).eval(dataset, y_pred)
[docs] def eval_per_query(self, y, y_pred): """ This method helps compute the Pfound score per query. It is called by the eval function which averages and aggregates the scores for each query. 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 ------- pfound: float Represents the Pfound 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] max_grade = y.max() # max relevance score prob_step_down = 1.0 pfound = 0.0 for i, idx in enumerate(idx_y_pred_sorted): utility = (pow(2., y[idx]) - 1.) / pow(2., max_grade) pfound += prob_step_down * utility * pow(self.p_abandonment, i) prob_step_down *= (1. - utility) return pfound
def __str__(self): s = self.name if self.cutoff is not None: s += "@{}".format(self.cutoff) return s