evaluate module

The evaluate module defines the evaluate() function and GridSearch class

class surprise.evaluate.GridSearch(algo_class, param_grid, measures=['rmse', 'mae'], verbose=1)

The GridSearch class, used to evaluate the performance of an algorithm on various combinations of parameters, and extract the best combination. It is analogous to GridSearchCV from scikit-learn.

See User Guide for usage.

Parameters:

• algo_class (AlgoBase) – A class object of of the algorithm to evaluate.
• param_grid (dict) – The dictionary has algo_class parameters as keys (string) and list of parameters as the desired values to try. All combinations will be evaluated with desired algorithm.
• measures (list of string) – The performance measures to compute. Allowed names are function names as defined in the accuracy module. Default is ['rmse', 'mae'].
• verbose (int) – Level of verbosity. If 0, nothing is printed. If 1, accuracy measures for each parameters combination are printed, with combination values. If 2, folds accuracy values are also printed. Default is 1.

cv_results

dict of arrays – A dict that contains all parameters and accuracy information for each combination. Can be imported into a pandas DataFrame.

best_estimator

dict of AlgoBase – Using an accuracy measure as key, get the estimator that gave the best accuracy results for the chosen measure.

best_score

dict of floats – Using an accuracy measure as key, get the best score achieved for that measure.

best_params

dict of dicts – Using an accuracy measure as key, get the parameters combination that gave the best accuracy results for the chosen measure.

best_index

dict of ints – Using an accuracy measure as key, get the index that can be used with cv_results_ that achieved the highest accuracy for that measure.

evaluate(data)

Runs the grid search on dataset.

Class instance attributes can be accessed after the evaluate is done.

Parameters:data (Dataset) – The dataset on which to evaluate the algorithm.

surprise.evaluate.evaluate(algo, data, measures=['rmse', 'mae'], with_dump=False, dump_dir=None, verbose=1)

Evaluate the performance of the algorithm on given data.

Depending on the nature of the data parameter, it may or may not perform cross validation.

Parameters:

• algo (AlgoBase) – The algorithm to evaluate.
• data (Dataset) – The dataset on which to evaluate the algorithm.
• measures (list of string) – The performance measures to compute. Allowed names are function names as defined in the accuracy module. Default is ['rmse', 'mae'].
• with_dump (bool) – If True, the predictions and the algorithm will be dumped for later further analysis at each fold (see FAQ). The file names will be set as: '<date>-<algorithm name>-<fold number>'. Default is False.
• dump_dir (str) – The directory where to dump to files. Default is '~/.surprise_data/dumps/'.
• verbose (int) – Level of verbosity. If 0, nothing is printed. If 1 (default), accuracy measures for each folds are printed, with a final summary. If 2, every prediction is printed.

Returns:

A dictionary containing measures as keys and lists as values. Each list contains one entry per fold.