Source code for surprise.prediction_algorithms.random_pred

""" Algorithm predicting a random rating.
"""

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import numpy as np

from .algo_base import AlgoBase


[docs]class NormalPredictor(AlgoBase): """Algorithm predicting a random rating based on the distribution of the training set, which is assumed to be normal. The prediction :math:`\hat{r}_{ui}` is generated from a normal distribution :math:`\mathcal{N}(\hat{\mu}, \hat{\sigma}^2)` where :math:`\hat{\mu}` and :math:`\hat{\sigma}` are estimated from the training data using Maximum Likelihood Estimation: .. math:: \\hat{\mu} &= \\frac{1}{|R_{train}|} \\sum_{r_{ui} \\in R_{train}} r_{ui}\\\\\\\\\ \\hat{\sigma} &= \\sqrt{\\sum_{r_{ui} \\in R_{train}} \\frac{(r_{ui} - \\hat{\mu})^2}{|R_{train}|}} """ def __init__(self): AlgoBase.__init__(self) def train(self, trainset): AlgoBase.train(self, trainset) num = sum((r - self.trainset.global_mean)**2 for (_, _, r) in self.trainset.all_ratings()) denum = self.trainset.n_ratings self.sigma = np.sqrt(num / denum) def estimate(self, *_): return np.random.normal(self.trainset.global_mean, self.sigma)