Compute RMSE between two parameter vectors.

Add a small utility function that enables to compute the root mean
squared error between two parameter vectors (i.e., the L2 norm of the
difference vector).

choix/__init__.py

@@ -23,6 +23,7 @@
 from .utils import (
  footrule_dist,
  kendalltau_dist,
+ rmse,
  log_likelihood_pairwise,
  log_likelihood_rankings,
  log_likelihood_top1,

choix/utils.py

@@ -143,6 +143,28 @@ def kendalltau_dist(params1, params2=None):
  return round((n_pairs - n_pairs * tau) / 2)
 
 
+def rmse(params1, params2):
+ r"""Compute the root-mean-squared error between two models.
+
+ Parameters
+ ----------
+ params1 : array_like
+ Parameters of the first model.
+ params2 : array_like
+ Parameters of the second model.
+
+ Returns
+ -------
+ error : float
+ Root-mean-squared error.
+ """
+ assert len(params1) == len(params2)
+ params1 = np.asarray(params1) - np.mean(params1)
+ params2 = np.asarray(params2) - np.mean(params2)
+ sqrt_n = math.sqrt(len(params1))
+ return np.linalg.norm(params1 - params2, ord=2) / sqrt_n
+
+
 def log_likelihood_pairwise(data, params):
  """Compute the log-likelihood of model parameters."""
  loglik = 0

docs/conf.py

@@ -66,7 +66,7 @@
 # The short X.Y version.
 version = '0.3'
 # The full version, including alpha/beta/rc tags.
-release = '0.3.1'
+release = '0.3.2'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

setup.py

@@ -12,7 +12,7 @@ def readme():
 
 setup(
  name='choix',
- version='0.3.1',
+ version='0.3.2',
  author='Lucas Maystre',
  author_email='[email protected]',
  description="Inference algorithms for models based on Luce's choice axiom.",