accuracy module¶

The surprise.accuracy module provides with tools for computing accuracy metrics on a set of predictions.

Available accuracy metrics:

 rmse Compute RMSE (Root Mean Squared Error). mae Compute MAE (Mean Absolute Error). fcp Compute FCP (Fraction of Concordant Pairs).
surprise.accuracy.fcp(predictions, verbose=True)[source]

Compute FCP (Fraction of Concordant Pairs).

Computed as described in paper Collaborative Filtering on Ordinal User Feedback by Koren and Sill, section 5.2.

Parameters: predictions (list of Prediction) – A list of predictions, as returned by the test method. verbose – If True, will print computed value. Default is True. The Fraction of Concordant Pairs. ValueError – When predictions is empty.
surprise.accuracy.mae(predictions, verbose=True)[source]

Compute MAE (Mean Absolute Error).

$\text{MAE} = \frac{1}{|\hat{R}|} \sum_{\hat{r}_{ui} \in \hat{R}}|r_{ui} - \hat{r}_{ui}|$
Parameters: predictions (list of Prediction) – A list of predictions, as returned by the test method. verbose – If True, will print computed value. Default is True. The Mean Absolute Error of predictions. ValueError – When predictions is empty.
surprise.accuracy.rmse(predictions, verbose=True)[source]

Compute RMSE (Root Mean Squared Error).

$\text{RMSE} = \sqrt{\frac{1}{|\hat{R}|} \sum_{\hat{r}_{ui} \in \hat{R}}(r_{ui} - \hat{r}_{ui})^2}.$
Parameters: predictions (list of Prediction) – A list of predictions, as returned by the test method. verbose – If True, will print computed value. Default is True. The Root Mean Squared Error of predictions. ValueError – When predictions is empty.