@Override
public double computeLoss() {
return Ratings.evaluate(this, ratings).get("RMSE")
+ regU * Math.pow(org.mymedialite.datatype.VectorExtensions.euclideanNorm(userBiases), 2)
+ regI * Math.pow(org.mymedialite.datatype.VectorExtensions.euclideanNorm(itemBiases), 2);
}
public static void main(String[] args) throws Exception {
// Handlers for uncaught exceptions and interrupts
Thread.setDefaultUncaughtExceptionHandler(new Handlers());
Runtime.getRuntime()
.addShutdownHook(
new Thread() {
@Override
public void run() {
displayStats();
}
});
// Recommender arguments
String method = null;
String recommender_options = "";
// Help/version
boolean show_help = false;
boolean show_version = false;
// Arguments for iteration search
int max_iter = 100;
double epsilon = 0;
double rmse_cutoff = Double.MAX_VALUE;
double mae_cutoff = Double.MAX_VALUE;
// Data arguments
String data_dir = "";
// Other arguments
boolean search_hp = false;
int random_seed = -1;
String prediction_line = "{0}\t{1}\t{2}";
for (String arg : args) {
int div = arg.indexOf("=") + 1;
String name;
String value;
if (div > 0) {
name = arg.substring(0, div);
value = arg.substring(div);
} else {
name = arg;
value = null;
}
// String-valued options
if (name.equals("--training-file=")) training_file = value;
else if (name.equals("--test-file=")) test_file = value;
else if (name.equals("--recommender=")) method = value;
else if (name.equals("--recommender-options=")) recommender_options += " " + value;
else if (name.equals("--data-dir=")) data_dir = value;
else if (name.equals("--user-attributes=")) user_attributes_file = value;
else if (name.equals("--item-attributes=")) item_attributes_file = value;
else if (name.equals("--user-relations=")) user_relations_file = value;
else if (name.equals("--item-relations=")) item_relations_file = value;
else if (name.equals("--save-model=")) save_model_file = value;
else if (name.equals("--load-model=")) load_model_file = value;
else if (name.equals("--prediction-file=")) prediction_file = value;
else if (name.equals("--prediction-line=")) prediction_line = value;
else if (name.equals("--chronological-split=")) chronological_split = value;
// Integer-valued options
else if (name.equals("--find-iter=")) find_iter = Integer.parseInt(value);
else if (name.equals("--max-iter=")) max_iter = Integer.parseInt(value);
else if (name.equals("--random-seed=")) random_seed = Integer.parseInt(value);
else if (name.equals("--cross-validation=")) cross_validation = Integer.parseInt(value);
// Double-valued options
else if (name.equals("--epsilon=")) epsilon = Double.parseDouble(value);
else if (name.equals("--rmse-cutoff=")) rmse_cutoff = Double.parseDouble(value);
else if (name.equals("--mae-cutoff=")) mae_cutoff = Double.parseDouble(value);
else if (name.equals("--test-ratio=")) test_ratio = Double.parseDouble(value);
// Enum options
else if (name.equals("--rating-type=")) rating_type = RatingType.valueOf(value);
else if (name.equals("--file-format=")) file_format = RatingFileFormat.valueOf(value);
// Boolean options
else if (name.equals("--compute-fit")) compute_fit = true;
else if (name.equals("--online-evaluation")) online_eval = true;
else if (name.equals("--show-fold-results")) show_fold_results = true;
else if (name.equals("--search-hp")) search_hp = true;
else if (name.equals("--help")) show_help = true;
else if (name.equals("--version")) show_version = true;
}
// ... some more command line parameter actions ...
boolean no_eval = true;
if (test_ratio > 0 || test_file != null || chronological_split != null) no_eval = false;
if (show_version) showVersion();
if (show_help) usage(0);
if (random_seed != -1) org.mymedialite.util.Random.initInstance(random_seed);
// Set up recommender
if (load_model_file != null) recommender = (RatingPredictor) Model.load(load_model_file);
else if (method != null) recommender = Recommender.createRatingPredictor(method);
else recommender = Recommender.createRatingPredictor("BiasedMatrixFactorization");
// In case something went wrong ...
if (recommender == null && method != null) usage("Unknown rating prediction method: " + method);
if (recommender == null && load_model_file != null)
usage("Could not load model from file " + load_model_file);
checkParameters();
try {
recommender = Recommender.configure(recommender, recommender_options, new ErrorHandler());
} catch (IllegalAccessException e) {
System.err.println("Unable to instantiate recommender: " + recommender.toString());
System.exit(0);
}
// ID mapping objects
if (file_format == RatingFileFormat.KDDCUP_2011) {
user_mapping = new IdentityMapping();
item_mapping = new IdentityMapping();
}
// Load all the data
loadData(
data_dir,
user_attributes_file,
item_attributes_file,
user_relations_file,
item_relations_file,
!online_eval);
System.out.println(
"Ratings range: " + recommender.getMinRating() + ", " + recommender.getMaxRating());
if (test_ratio > 0) {
RatingsSimpleSplit split = new RatingsSimpleSplit(training_data, test_ratio);
// TODO check
training_data = split.train().get(0);
recommender.setRatings(training_data);
// TODO check
test_data = split.test().get(0);
System.out.println("Test ratio: " + test_ratio);
}
if (chronological_split != null) {
RatingsChronologicalSplit split =
chronological_split_ratio != -1
? new RatingsChronologicalSplit(
(ITimedRatings) training_data, chronological_split_ratio)
: new RatingsChronologicalSplit(
(ITimedRatings) training_data, chronological_split_time);
training_data = split.train().get(0);
recommender.setRatings(training_data);
test_data = split.test().get(0);
if (test_ratio != -1)
System.out.println("Test ratio (chronological): " + chronological_split_ratio);
else System.out.println("Split time:" + chronological_split_time);
}
System.out.print(
Extensions.statistics(training_data, test_data, user_attributes, item_attributes, false));
if (find_iter != 0) {
if (!(recommender instanceof IIterativeModel))
usage("Only iterative recommenders (interface IIterativeModel) support --find-iter=N.");
System.out.println("Recommender: " + recommender.toString());
if (cross_validation > 1) {
RatingsCrossValidation.doIterativeCrossValidation(
recommender, cross_validation, max_iter, find_iter);
} else {
IIterativeModel iterative_recommender = (IIterativeModel) recommender;
if (load_model_file == null) recommender.train();
if (compute_fit)
System.out.println(
"Fit "
+ Ratings.evaluate(recommender, training_data)
+ " iteration "
+ iterative_recommender.getNumIter());
System.out.println(
Ratings.evaluate(recommender, test_data)
+ " iteration "
+ iterative_recommender.getNumIter());
for (int it = iterative_recommender.getNumIter() + 1; it <= max_iter; it++) {
long start = Calendar.getInstance().getTimeInMillis();
iterative_recommender.iterate();
training_time_stats.add(
(double) (Calendar.getInstance().getTimeInMillis() - start) / 1000);
if (it % find_iter == 0) {
if (compute_fit) {
start = Calendar.getInstance().getTimeInMillis();
System.out.println(
"Fit " + Ratings.evaluate(recommender, training_data) + " iteration " + it);
fit_time_stats.add(
(double) (Calendar.getInstance().getTimeInMillis() - start) / 1000);
}
HashMap<String, Double> results = null;
start = Calendar.getInstance().getTimeInMillis();
results = Ratings.evaluate(recommender, test_data);
eval_time_stats.add((double) (Calendar.getInstance().getTimeInMillis() - start) / 1000);
rmse_eval_stats.add(results.get("RMSE"));
System.out.println(results + " iteration " + it);
Model.save(recommender, save_model_file, it);
if (prediction_file != null)
org.mymedialite.ratingprediction.Extensions.writePredictions(
recommender,
test_data,
prediction_file + "-it-" + it,
user_mapping,
item_mapping,
prediction_line);
if (epsilon > 0.0 && results.get("RMSE") - Collections.min(rmse_eval_stats) > epsilon) {
System.out.println(results.get("RMSE") + " >> " + Collections.min(rmse_eval_stats));
System.out.println(
"Reached convergence on training/validation data after " + it + " iterations.");
break;
}
if (results.get("RMSE") > rmse_cutoff || results.get("MAE") > mae_cutoff) {
System.out.println("Reached cutoff after " + it + " iterations.");
break;
}
}
} // for
}
} else {
long start = Calendar.getInstance().getTimeInMillis();
System.out.println("Recommender: " + recommender);
if (load_model_file == null) {
if (cross_validation > 1) {
RatingPredictionEvaluationResults results =
RatingsCrossValidation.doCrossValidation(
recommender, cross_validation, compute_fit, show_fold_results);
System.out.println(results);
no_eval = true;
} else {
if (search_hp) {
double result = NelderMead.findMinimum("RMSE", recommender);
System.out.println("Estimated quality (on split): " + result);
}
recommender.train();
System.out.println(
"Training time: "
+ (double) (Calendar.getInstance().getTimeInMillis() - start) / 1000
+ " seconds");
}
}
if (!no_eval) {
start = Calendar.getInstance().getTimeInMillis();
if (online_eval) System.out.println(RatingsOnline.evaluateOnline(recommender, test_data));
else System.out.println(Ratings.evaluate(recommender, test_data));
System.out.println(
"Testing time: "
+ (double) (Calendar.getInstance().getTimeInMillis() - start) / 1000
+ " seconds");
if (compute_fit) {
System.out.print("Fit:");
start = Calendar.getInstance().getTimeInMillis();
System.out.print(Ratings.evaluate(recommender, training_data));
System.out.println(
" fit time: " + (double) (Calendar.getInstance().getTimeInMillis() - start) / 1000);
}
if (prediction_file != null) {
System.out.print("Predict:");
start = Calendar.getInstance().getTimeInMillis();
org.mymedialite.ratingprediction.Extensions.writePredictions(
recommender, test_data, prediction_file, user_mapping, item_mapping, prediction_line);
System.out.println(
" prediction time "
+ (double) (Calendar.getInstance().getTimeInMillis() - start) / 1000);
}
}
// System.out.println();
}
Model.save(recommender, save_model_file);
// displayStats();
}