# 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 scipy.stats as stats
from rankeval.metrics.metric import Metric
[docs]class SpearmanRho(Metric):
"""
This class implements Spearman's Rho.
We use the Spearman Rho coefficient implementation from scipy.
"""
def __init__(self, name='Rho'):
"""
This is the constructor of Spearman Rho, an object of type Metric, with
the name Rho. The constructor also allows setting custom values in the
following parameters.
Parameters
----------
name: string
Rho
"""
super(SpearmanRho, self).__init__(name)
[docs] def eval(self, dataset, y_pred):
"""
This method computes the Spearman Rho tau score over the entire dataset
and the detailed scores per query. It calls the eval_per query method
for each query in order to get the detailed Spearman Rho score.
Parameters
----------
dataset : Dataset
Represents the Dataset object on which to apply Spearman Rho.
y_pred : numpy 1d array of float
Represents the predicted document scores for each instance in the
dataset.
Returns
-------
avg_score: float
The overall Spearman Rho score (averages over the detailed scores).
detailed_scores: numpy 1d array of floats
The detailed Spearman Rho scores for each query, an array of length
of the number of queries.
"""
return super(SpearmanRho, self).eval(dataset, y_pred)
[docs] def eval_per_query(self, y, y_pred):
"""
This methods computes Spearman Rho at per query level (on the instances
belonging to a specific 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
-------
rho: float
The Spearman Rho per query.
"""
spearman_rho = stats.spearmanr(y, y_pred)
return spearman_rho.correlation
def __str__(self):
s = self.name
return s