Evaluation class for item recommendation. More...
Static Public Member Functions | |
static double | ComputeFit (this ItemRecommender recommender, IList< int > test_users=null, IList< int > candidate_items=null, CandidateItems candidate_item_mode=CandidateItems.OVERLAP) |
Computes the AUC fit of a recommender on the training data. | |
static ItemRecommendationEvaluationResults | Evaluate (this IRecommender recommender, IPosOnlyFeedback test, IPosOnlyFeedback training, IList< int > test_users=null, IList< int > candidate_items=null, CandidateItems candidate_item_mode=CandidateItems.OVERLAP, bool repeated_events=false) |
Evaluation for rankings of items. | |
Properties | |
static ICollection< string > | Measures [get] |
the evaluation measures for item prediction offered by the class |
Evaluation class for item recommendation.
static double ComputeFit | ( | this ItemRecommender | recommender, | |
IList< int > | test_users = null , |
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IList< int > | candidate_items = null , |
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CandidateItems | candidate_item_mode = CandidateItems.OVERLAP | |||
) | [inline, static] |
Computes the AUC fit of a recommender on the training data.
recommender | the item recommender to evaluate | |
test_users | a list of integers with all test users; if null, use all users in the test cases | |
candidate_items | a list of integers with all candidate items | |
candidate_item_mode | the mode used to determine the candidate items |
static ItemRecommendationEvaluationResults Evaluate | ( | this IRecommender | recommender, | |
IPosOnlyFeedback | test, | |||
IPosOnlyFeedback | training, | |||
IList< int > | test_users = null , |
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IList< int > | candidate_items = null , |
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CandidateItems | candidate_item_mode = CandidateItems.OVERLAP , |
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bool | repeated_events = false | |||
) | [inline, static] |
Evaluation for rankings of items.
User-item combinations that appear in both sets are ignored for the test set, and thus in the evaluation, except the boolean argument repeated_events is set.
The evaluation measures are listed in the Measures property. Additionally, 'num_users' and 'num_items' report the number of users that were used to compute the results and the number of items that were taken into account.
Literature:
On multi-core/multi-processor systems, the routine tries to use as many cores as possible, which should to an almost linear speed-up.
recommender | item recommender | |
test | test cases | |
training | training data | |
test_users | a list of integers with all test users; if null, use all users in the test cases | |
candidate_items | a list of integers with all candidate items | |
candidate_item_mode | the mode used to determine the candidate items | |
repeated_events | allow repeated events in the evaluation (i.e. items accessed by a user before may be in the recommended list) |
ICollection<string> Measures [static, get] |
the evaluation measures for item prediction offered by the class
The evaluation measures currently are:
An item recommender is better than another according to one of those measures its score is higher.