/// <summary>
/// This is called before the start method as a way to pre-check that all of the parameters that are selected
/// are in fact valid for this module.
/// </summary>
/// <param name="error">A string that should be assigned a detailed error</param>
/// <returns>If the validation was successful or if there was a problem</returns>
public bool RuntimeValidation(ref string error)
{
this.Loaded = false;
this.IterativeRoot = this.Root as IIterativeModel;
return true;
}
/// <summary>Evaluate an iterative recommender on the folds of a dataset split, display results on STDOUT</summary>
/// <param name="recommender">an item recommender</param>
/// <param name="split">a positive-only feedback dataset split</param>
/// <param name="test_users">a collection of integers with all test users</param>
/// <param name="candidate_items">a collection of integers with all candidate items</param>
/// <param name="candidate_item_mode">the mode used to determine the candidate items</param>
/// <param name="repeated_events">allow repeated events in the evaluation (i.e. items accessed by a user before may be in the recommended list)</param>
/// <param name="max_iter">the maximum number of iterations</param>
/// <param name="find_iter">the report interval</param>
/// <param name="show_fold_results">if set to true to print per-fold results to STDERR</param>
public static void DoIterativeCrossValidation(
this IRecommender recommender,
ISplit<IPosOnlyFeedback> split,
IList<int> test_users,
IList<int> candidate_items,
CandidateItems candidate_item_mode,
RepeatedEvents repeated_events,
uint max_iter,
uint find_iter = 1,
bool show_fold_results = false)
{
if (!(recommender is IIterativeModel))
throw new ArgumentException("recommender must be of type IIterativeModel");
if (!(recommender is ItemRecommender))
throw new ArgumentException("recommender must be of type ItemRecommender");
var split_recommenders = new ItemRecommender[split.NumberOfFolds];
var iterative_recommenders = new IIterativeModel[split.NumberOfFolds];
var fold_results = new ItemRecommendationEvaluationResults[split.NumberOfFolds];
// initial training and evaluation
Parallel.For(0, (int) split.NumberOfFolds, i =>
{
try
{
split_recommenders[i] = (ItemRecommender) recommender.Clone(); // to avoid changes in recommender
split_recommenders[i].Feedback = split.Train[i];
split_recommenders[i].Train();
iterative_recommenders[i] = (IIterativeModel) split_recommenders[i];
fold_results[i] = Items.Evaluate(split_recommenders[i], split.Test[i], split.Train[i], test_users, candidate_items, candidate_item_mode, repeated_events);
if (show_fold_results)
Console.WriteLine("fold {0} {1} iteration {2}", i, fold_results, iterative_recommenders[i].NumIter);
}
catch (Exception e)
{
Console.Error.WriteLine("===> ERROR: " + e.Message + e.StackTrace);
throw;
}
});
Console.WriteLine("{0} iteration {1}", new ItemRecommendationEvaluationResults(fold_results), iterative_recommenders[0].NumIter);
// iterative training and evaluation
for (int it = (int) iterative_recommenders[0].NumIter + 1; it <= max_iter; it++)
{
Parallel.For(0, (int) split.NumberOfFolds, i =>
{
try
{
iterative_recommenders[i].Iterate();
if (it % find_iter == 0)
{
fold_results[i] = Items.Evaluate(split_recommenders[i], split.Test[i], split.Train[i], test_users, candidate_items, candidate_item_mode, repeated_events);
if (show_fold_results)
Console.WriteLine("fold {0} {1} iteration {2}", i, fold_results, it);
}
}
catch (Exception e)
{
Console.Error.WriteLine("===> ERROR: " + e.Message + e.StackTrace);
throw;
}
});
Console.WriteLine("{0} iteration {1}", new ItemRecommendationEvaluationResults(fold_results), it);
}
}
/// <summary>Evaluate an iterative recommender on the folds of a dataset split, display results on STDOUT</summary>
/// <param name="recommender">a rating predictor</param>
/// <param name="split">a rating dataset split</param>
/// <param name="max_iter">the maximum number of iterations</param>
/// <param name="find_iter">the report interval</param>
/// <param name="show_fold_results">if set to true to print per-fold results to STDERR</param>
public static void DoIterativeCrossValidation(
this RatingPredictor recommender,
ISplit<IRatings> split,
int max_iter,
int find_iter = 1,
bool show_fold_results = false)
{
if (!(recommender is IIterativeModel))
throw new ArgumentException("recommender must be of type IIterativeModel");
var split_recommenders = new RatingPredictor[split.NumberOfFolds];
var iterative_recommenders = new IIterativeModel[split.NumberOfFolds];
var fold_results = new RatingPredictionEvaluationResults[split.NumberOfFolds];
// initial training and evaluation
Parallel.For(0, (int) split.NumberOfFolds, i =>
{
try
{
split_recommenders[i] = (RatingPredictor) recommender.Clone(); // to avoid changes in recommender
split_recommenders[i].Ratings = split.Train[i];
if (recommender is ITransductiveRatingPredictor)
((ITransductiveRatingPredictor) split_recommenders[i]).AdditionalFeedback = split.Test[i];
split_recommenders[i].Train();
iterative_recommenders[i] = (IIterativeModel) split_recommenders[i];
fold_results[i] = Ratings.Evaluate(split_recommenders[i], split.Test[i]);
if (show_fold_results)
Console.Error.WriteLine("fold {0} {1} iteration {2}", i, fold_results[i], iterative_recommenders[i].NumIter);
}
catch (Exception e)
{
Console.Error.WriteLine("===> ERROR: " + e.Message + e.StackTrace);
throw e;
}
});
Console.WriteLine("{0} iteration {1}", new RatingPredictionEvaluationResults(fold_results), iterative_recommenders[0].NumIter);
// iterative training and evaluation
for (int it = (int) iterative_recommenders[0].NumIter + 1; it <= max_iter; it++)
{
Parallel.For(0, (int) split.NumberOfFolds, i =>
{
try
{
iterative_recommenders[i].Iterate();
if (it % find_iter == 0)
{
fold_results[i] = Ratings.Evaluate(split_recommenders[i], split.Test[i]);
if (show_fold_results)
Console.Error.WriteLine("fold {0} {1} iteration {2}", i, fold_results[i], it);
}
}
catch (Exception e)
{
Console.Error.WriteLine("===> ERROR: " + e.Message + e.StackTrace);
throw e;
}
});
Console.WriteLine("{0} iteration {1}", new RatingPredictionEvaluationResults(fold_results), it);
}
}
/// <summary>
/// This is called before the start method as a way to pre-check that all of the parameters that are selected
/// are in fact valid for this module.
/// </summary>
/// <param name="error">A string that should be assigned a detailed error</param>
/// <returns>If the validation was successful or if there was a problem</returns>
public bool RuntimeValidation(ref string error)
{
IterativeRoot = Root as IIterativeModel;
if(ApplyTimeBlending && IterativeRoot == null)
{
error = "In '' the option Apply Time Blending is selected however the model system is not an compatible with IIterativeModel!";
return false;
}
return true;
}
/// <summary>
/// This is called before the start method as a way to pre-check that all of the parameters that are selected
/// are in fact valid for this module.
/// </summary>
/// <param name="error">A string that should be assigned a detailed error</param>
/// <returns>If the validation was successful or if there was a problem</returns>
public bool RuntimeValidation(ref string error)
{
// if we are attached to an iterative model load it in
this.IterativeRoot = this.Root as IIterativeModel;
return true;
}
/// <summary>
/// This is called before the start method as a way to pre-check that all of the parameters that are selected
/// are in fact valid for this module.
/// </summary>
/// <param name="error">A string that should be assigned a detailed error</param>
/// <returns>If the validation was successful or if there was a problem</returns>
public bool RuntimeValidation(ref string error)
{
IterativeRoot = Root as IIterativeModel;
return true;
}
