Source code for surprise.prediction_algorithms.random_pred

""" Algorithm predicting a random rating.

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 fit(self, trainset):, 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) return self def estimate(self, *_): return np.random.normal(self.trainset.global_mean, self.sigma)