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ItemRecommendation.cs
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ItemRecommendation.cs
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// Copyright (C) 2010, 2011, 2012 Zeno Gantner
//
// This file is part of MyMediaLite.
//
// MyMediaLite is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// MyMediaLite is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with MyMediaLite. If not, see <http://www.gnu.org/licenses/>.
using System;
using System.Collections.Generic;
using System.Globalization;
using System.IO;
using System.Linq;
using System.Reflection;
using System.Text;
using Mono.Options;
using MyMediaLite;
using MyMediaLite.Data;
using MyMediaLite.DataType;
using MyMediaLite.Eval;
using MyMediaLite.GroupRecommendation;
using MyMediaLite.IO;
using MyMediaLite.ItemRecommendation;
using MyMediaLite.Util;
/// <summary>Item prediction program, see Usage() method for more information</summary>
class ItemRecommendation : CommandLineProgram<IRecommender>
{
// data
IPosOnlyFeedback training_data;
IPosOnlyFeedback test_data;
IList<int> test_users;
IList<int> candidate_items;
SparseBooleanMatrix group_to_user; // rows: groups, columns: users
ICollection<int> user_groups;
CandidateItems eval_item_mode = CandidateItems.UNION;
// command-line parameters (data)
ItemDataFileFormat file_format = ItemDataFileFormat.DEFAULT;
string test_users_file;
string candidate_items_file;
string user_groups_file;
// command-line parameters (other)
float rating_threshold = float.NaN;
int num_test_users = -1;
int predict_items_number = -1;
bool online_eval;
bool repeat_eval;
string group_method;
bool overlap_items;
bool in_training_items;
bool in_test_items;
bool all_items;
bool user_prediction;
public ItemRecommendation()
{
measure = "AUC";
}
protected override void ShowVersion()
{
var version = Assembly.GetEntryAssembly().GetName().Version;
Console.WriteLine("MyMediaLite Item Prediction from Positive-Only Feedback {0}.{1:00}", version.Major, version.Minor);
Console.WriteLine("Copyright (C) 2011, 2012 Zeno Gantner");
Console.WriteLine("Copyright (C) 2010 Zeno Gantner, Steffen Rendle, Christoph Freudenthaler");
Console.WriteLine("This is free software; see the source for copying conditions. There is NO");
Console.WriteLine("warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.");
Environment.Exit(0);
}
protected override void Usage(int exit_code)
{
var version = Assembly.GetEntryAssembly().GetName().Version;
Console.WriteLine("MyMediaLite item recommendation from positive-only feedback {0}.{1:00}", version.Major, version.Minor);
Console.WriteLine(@"
usage: item_recommendation --training-file=FILE --recommender=METHOD [OPTIONS]
methods (plus arguments and their defaults):");
Console.Write(" - ");
Console.WriteLine(string.Join("\n - ", Recommender.List("MyMediaLite.ItemRecommendation")));
Console.WriteLine(@" method ARGUMENTS have the form name=value
general OPTIONS:
--recommender=METHOD use METHOD for recommendations (default: MostPopular)
--group-recommender=METHOD use METHOD to combine the predictions for several users
--recommender-options=OPTIONS use OPTIONS as recommender options
--help display this usage information and exit
--version display version information and exit
--random-seed=N initialize random number generator with N
files:
--training-file=FILE read training data from FILE
--test-file=FILE read test data from FILE
--file-format=ignore_first_line|default
--no-id-mapping do not map user and item IDs to internal IDs, keep the original IDs
--data-dir=DIR load all files from DIR
--user-attributes=FILE file with user attribute information, 1 tuple per line
--item-attributes=FILE file with item attribute information, 1 tuple per line
--user-relations=FILE file with user relation information, 1 tuple per line
--item-relations=FILE file with item relation information, 1 tuple per line
--user-groups=FILE file with group-to-user mappings, 1 tuple per line
--save-model=FILE save computed model to FILE
--load-model=FILE load model from FILE
data interpretation:
--user-prediction transpose the user-item matrix and perform user prediction instead of item prediction
--rating-threshold=NUM (for rating data) interpret rating >= NUM as positive feedback
choosing the items for evaluation/prediction (mutually exclusive):
--candidate-items=FILE use items in FILE (one per line) as candidate items
--overlap-items use only items that are both in the training and the test set as candidate items
--in-training-items use only items in the training set as candidate items
--in-test-items use only items in the test set as candidate items
--all-items use all known items as candidate items
The default is to use both the items in the training and the test set as candidate items.
choosing the users for evaluation/prediction
--test-users=FILE predict items for users specified in FILE (one user per line)
prediction and evaluation:
--predict-items-number=N predict N items per user
--repeat-evaluation items accessed by a user before may be in the recommendations (and are not ignored in the evaluation)
prediction:
--prediction-file=FILE write ranked predictions to FILE, one user per line
evaluation:
--cross-validation=K perform k-fold cross-validation on the training data
--test-ratio=NUM evaluate by splitting of a NUM part of the feedback
--num-test-users=N evaluate on only N randomly picked users (to save time)
--online-evaluation perform online evaluation (use every tested user-item combination for incremental training)
--compute-fit display fit on training data
finding the right number of iterations (iterative methods)
--find-iter=N give out statistics every N iterations
--max-iter=N perform at most N iterations
--measure=MEASURE the evaluation measure to use for the abort conditions below (default is AUC)
--epsilon=NUM abort iterations if MEASURE is less than best result plus NUM
--cutoff=NUM abort if MEASURE is below NUM
");
Environment.Exit(exit_code);
}
static void Main(string[] args)
{
var program = new ItemRecommendation();
program.Run(args);
}
protected override void SetupOptions()
{
options
.Add("group-recommender=", v => group_method = v)
.Add("candidate-items=", v => candidate_items_file = v)
.Add("user-groups=", v => user_groups_file = v)
.Add("predict-items-number=", (int v) => predict_items_number = v)
.Add("num-test-users=", (int v) => num_test_users = v)
.Add("rating-threshold=", (float v) => rating_threshold = v)
.Add("file-format=", (ItemDataFileFormat v) => file_format = v)
.Add("user-prediction", v => user_prediction = v != null)
.Add("online-evaluation", v => online_eval = v != null)
.Add("repeat-evaluation", v => repeat_eval = v != null)
.Add("overlap-items", v => overlap_items = v != null)
.Add("all-items", v => all_items = v != null)
.Add("in-training-items", v => in_training_items = v != null)
.Add("in-test-items", v => in_test_items = v != null);
}
protected override void Run(string[] args)
{
base.Run(args);
bool no_eval = true;
if (test_ratio > 0 || test_file != null)
no_eval = false;
// set up recommender -- TODO generalize
if (load_model_file != null)
recommender = Model.Load(load_model_file);
else if (method != null)
recommender = Recommender.CreateItemRecommender(method);
else
recommender = Recommender.CreateItemRecommender("MostPopular");
// in case something went wrong ...
if (recommender == null && method != null)
Usage(string.Format("Unknown recommendation method: '{0}'", method));
if (recommender == null && load_model_file != null)
Abort(string.Format("Could not load model from file {0}.", load_model_file));
recommender.Configure(recommender_options, (string m) => { Console.Error.WriteLine(m); Environment.Exit(-1); });
// TODO generalize
if (no_id_mapping)
{
user_mapping = new IdentityMapping();
item_mapping = new IdentityMapping();
}
if (load_user_mapping_file != null)
user_mapping = EntityMappingExtensions.LoadMapping(load_user_mapping_file);
if (load_item_mapping_file != null)
item_mapping = EntityMappingExtensions.LoadMapping(load_item_mapping_file);
// load all the data
LoadData();
Console.Write(training_data.Statistics(test_data, user_attributes, item_attributes));
// if requested, save ID mappings
if (save_user_mapping_file != null)
user_mapping.SaveMapping(save_user_mapping_file);
if (save_item_mapping_file != null)
item_mapping.SaveMapping(save_item_mapping_file);
TimeSpan time_span;
if (find_iter != 0)
{
if ( !(recommender is IIterativeModel) )
Abort("Only iterative recommenders (interface IIterativeModel) support --find-iter=N.");
var iterative_recommender = (IIterativeModel) recommender;
Console.WriteLine(recommender);
var eval_stats = new List<double>();
if (cross_validation > 1)
{
recommender.DoIterativeCrossValidation(cross_validation, test_users, candidate_items, eval_item_mode, repeat_eval, max_iter, find_iter);
}
else
{
if (load_model_file == null)
recommender.Train();
if (compute_fit)
Console.WriteLine("fit: {0} iteration {1} ", ComputeFit(), iterative_recommender.NumIter);
var results = Evaluate();
Console.WriteLine("{0} iteration {1}", results, iterative_recommender.NumIter);
for (int it = (int) iterative_recommender.NumIter + 1; it <= max_iter; it++)
{
TimeSpan t = Wrap.MeasureTime(delegate() {
iterative_recommender.Iterate();
});
training_time_stats.Add(t.TotalSeconds);
if (it % find_iter == 0)
{
if (compute_fit)
{
t = Wrap.MeasureTime(delegate() {
Console.WriteLine("fit: {0} iteration {1} ", ComputeFit(), it);
});
fit_time_stats.Add(t.TotalSeconds);
}
t = Wrap.MeasureTime(delegate() { results = Evaluate(); });
eval_time_stats.Add(t.TotalSeconds);
eval_stats.Add(results[measure]);
Console.WriteLine("{0} iteration {1}", results, it);
Model.Save(recommender, save_model_file, it);
Predict(prediction_file, test_users_file, it);
if (epsilon > 0.0 && eval_stats.Max() - results[measure] > epsilon)
{
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "{0} >> {1}", results["RMSE"], eval_stats.Min()));
Console.Error.WriteLine("Reached convergence on training/validation data after {0} iterations.", it);
break;
}
if (results[measure] < cutoff)
{
Console.Error.WriteLine("Reached cutoff after {0} iterations.", it);
Console.Error.WriteLine("DONE");
break;
}
}
} // for
}
}
else
{
Console.WriteLine(recommender + " ");
if (load_model_file == null)
{
if (cross_validation > 1)
{
var results = recommender.DoCrossValidation(cross_validation, test_users, candidate_items, eval_item_mode, compute_fit, true);
Console.Write(results);
no_eval = true;
}
else
{
time_span = Wrap.MeasureTime( delegate() { recommender.Train(); } );
Console.Write("training_time " + time_span + " ");
}
}
if (prediction_file != null)
{
Predict(prediction_file, test_users_file);
}
else if (!no_eval)
{
if (compute_fit)
Console.WriteLine("fit: {0}", ComputeFit());
if (online_eval)
time_span = Wrap.MeasureTime( delegate() {
var results = recommender.EvaluateOnline(test_data, training_data, test_users, candidate_items, eval_item_mode);
Console.Write(results);
});
else if (group_method != null)
{
GroupRecommender group_recommender = null;
Console.Write("group recommendation strategy: {0} ", group_method);
// TODO GroupUtils.CreateGroupRecommender(group_method, recommender);
if (group_method == "Average")
group_recommender = new Average(recommender);
else if (group_method == "Minimum")
group_recommender = new Minimum(recommender);
else if (group_method == "Maximum")
group_recommender = new Maximum(recommender);
else
Usage("Unknown group recommendation strategy in --group-recommender=METHOD");
time_span = Wrap.MeasureTime( delegate() {
var result = group_recommender.Evaluate(test_data, training_data, group_to_user, candidate_items);
Console.Write(result);
});
}
else
time_span = Wrap.MeasureTime( delegate() { Console.Write(Evaluate()); });
Console.Write(" testing_time " + time_span);
}
Console.WriteLine();
}
Model.Save(recommender, save_model_file);
DisplayStats();
}
protected override void CheckParameters(IList<string> extra_args)
{
base.CheckParameters(extra_args);
if (training_file == null)
Usage("Parameter --training-file=FILE is missing.");
if (online_eval && !(recommender is IIncrementalItemRecommender))
Abort(string.Format("Recommender {0} does not support incremental updates, which are necessary for an online experiment.", recommender.GetType().Name));
if (test_file == null && test_ratio == 0 && cross_validation == 0 && save_model_file == null && test_users_file == null)
Usage("Please provide either test-file=FILE, --test-ratio=NUM, --cross-validation=K, --save-model=FILE, or --test-users=FILE.");
if ((candidate_items_file != null ? 1 : 0) + (all_items ? 1 : 0) + (in_training_items ? 1 : 0) + (in_test_items ? 1 : 0) + (overlap_items ? 1 : 0) > 1)
Abort("--candidate-items=FILE, --all-items, --in-training-items, --in-test-items, and --overlap-items are mutually exclusive.");
if (test_file == null && test_ratio == 0 && cross_validation == 0 && overlap_items)
Abort("--overlap-items only makes sense with either --test-file=FILE, --test-ratio=NUM, or cross-validation=K.");
if (test_file == null && test_ratio == 0 && cross_validation == 0 && in_test_items)
Abort("--in-test-items only makes sense with either --test-file=FILE, --test-ratio=NUM, or cross-validation=K.");
if (test_file == null && test_ratio == 0 && cross_validation == 0 && in_training_items)
Abort("--in-training-items only makes sense with either --test-file=FILE, --test-ratio=NUM, or cross-validation=K.");
if (group_method != null && user_groups_file == null)
Abort("--group-recommender needs --user-groups=FILE.");
if (user_prediction)
{
if (recommender is IUserAttributeAwareRecommender || recommender is IItemAttributeAwareRecommender ||
recommender is IUserRelationAwareRecommender || recommender is IItemRelationAwareRecommender)
Abort("--user-prediction is not (yet) supported in combination with attribute- or relation-aware recommenders.");
if (user_groups_file != null)
Abort("--user-prediction is not (yet) supported in combination with --user-groups=FILE.");
}
}
protected override void LoadData()
{
TimeSpan loading_time = Wrap.MeasureTime(delegate() {
base.LoadData();
// training data
training_file = Path.Combine(data_dir, training_file);
training_data = double.IsNaN(rating_threshold)
? ItemData.Read(training_file, user_mapping, item_mapping, file_format == ItemDataFileFormat.IGNORE_FIRST_LINE)
: ItemDataRatingThreshold.Read(training_file, rating_threshold, user_mapping, item_mapping, file_format == ItemDataFileFormat.IGNORE_FIRST_LINE);
// user groups
if (user_groups_file != null)
{
group_to_user = RelationData.Read(Path.Combine(data_dir, user_groups_file), user_mapping); // assumption: user and user group IDs are disjoint
user_groups = group_to_user.NonEmptyRowIDs;
Console.WriteLine("{0} user groups", user_groups.Count);
}
// test data
if (test_ratio == 0)
{
if (test_file != null)
{
test_file = Path.Combine(data_dir, test_file);
test_data = double.IsNaN(rating_threshold)
? ItemData.Read(test_file, user_mapping, item_mapping, file_format == ItemDataFileFormat.IGNORE_FIRST_LINE)
: ItemDataRatingThreshold.Read(test_file, rating_threshold, user_mapping, item_mapping, file_format == ItemDataFileFormat.IGNORE_FIRST_LINE);
}
}
else
{
var split = new PosOnlyFeedbackSimpleSplit<PosOnlyFeedback<SparseBooleanMatrix>>(training_data, test_ratio);
training_data = split.Train[0];
test_data = split.Test[0];
}
if (group_method == "GroupsAsUsers")
{
Console.WriteLine("group recommendation strategy: {0}", group_method);
// TODO verify what is going on here
//var training_data_group = new PosOnlyFeedback<SparseBooleanMatrix>();
// transform groups to users
foreach (int group_id in group_to_user.NonEmptyRowIDs)
foreach (int user_id in group_to_user[group_id])
foreach (int item_id in training_data.UserMatrix.GetEntriesByRow(user_id))
training_data.Add(group_id, item_id);
// add the users that do not belong to groups
//training_data = training_data_group;
// transform groups to users
var test_data_group = new PosOnlyFeedback<SparseBooleanMatrix>();
foreach (int group_id in group_to_user.NonEmptyRowIDs)
foreach (int user_id in group_to_user[group_id])
foreach (int item_id in test_data.UserMatrix.GetEntriesByRow(user_id))
test_data_group.Add(group_id, item_id);
test_data = test_data_group;
group_method = null; // deactivate s.t. the normal eval routines are used
}
if (user_prediction)
{
// swap file names for test users and candidate items
var ruf = test_users_file;
var rif = candidate_items_file;
test_users_file = rif;
candidate_items_file = ruf;
// swap user and item mappings
var um = user_mapping;
var im = item_mapping;
user_mapping = im;
item_mapping = um;
// transpose training and test data
training_data = training_data.Transpose();
// transpose test data
if (test_data != null)
test_data = test_data.Transpose();
}
if (recommender is MyMediaLite.ItemRecommendation.ItemRecommender)
((ItemRecommender)recommender).Feedback = training_data;
// test users
if (test_users_file != null)
test_users = user_mapping.ToInternalID( File.ReadLines(Path.Combine(data_dir, test_users_file)).ToArray() );
else
test_users = test_data != null ? test_data.AllUsers : training_data.AllUsers;
// if necessary, perform user sampling
if (num_test_users > 0 && num_test_users < test_users.Count)
{
var old_test_users = new HashSet<int>(test_users);
var new_test_users = new int[num_test_users];
for (int i = 0; i < num_test_users; i++)
{
int random_index = MyMediaLite.Util.Random.GetInstance().Next(old_test_users.Count - 1);
new_test_users[i] = old_test_users.ElementAt(random_index);
old_test_users.Remove(new_test_users[i]);
}
test_users = new_test_users;
}
// candidate items
if (candidate_items_file != null)
candidate_items = item_mapping.ToInternalID( File.ReadLines(Path.Combine(data_dir, candidate_items_file)).ToArray() );
else if (all_items)
candidate_items = Enumerable.Range(0, item_mapping.InternalIDs.Max() + 1).ToArray();
if (candidate_items != null)
eval_item_mode = CandidateItems.EXPLICIT;
else if (in_training_items)
eval_item_mode = CandidateItems.TRAINING;
else if (in_test_items)
eval_item_mode = CandidateItems.TEST;
else if (overlap_items)
eval_item_mode = CandidateItems.OVERLAP;
else
eval_item_mode = CandidateItems.UNION;
});
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "loading_time {0,0:0.##}", loading_time.TotalSeconds));
Console.Error.WriteLine("memory {0}", Memory.Usage);
}
ItemRecommendationEvaluationResults ComputeFit()
{
return recommender.Evaluate(training_data, training_data, test_users, candidate_items, eval_item_mode, true, predict_items_number);
}
ItemRecommendationEvaluationResults Evaluate()
{
return recommender.Evaluate(test_data, training_data, test_users, candidate_items, eval_item_mode, repeat_eval, predict_items_number);
}
void Predict(string prediction_file, string predict_for_users_file, int iteration)
{
if (prediction_file == null)
return;
Predict(prediction_file + "-it-" + iteration, predict_for_users_file);
}
void Predict(string prediction_file, string predict_for_users_file)
{
if (candidate_items == null)
candidate_items = training_data.AllItems;
IList<int> user_list = null;
if (predict_for_users_file != null)
user_list = user_mapping.ToInternalID( File.ReadLines(Path.Combine(data_dir, predict_for_users_file)).ToArray() );
TimeSpan time_span = Wrap.MeasureTime( delegate() {
recommender.WritePredictions(
training_data,
candidate_items, predict_items_number, // TODO move this argument to the end of the list
prediction_file, user_list,
user_mapping, item_mapping);
if (user_list != null)
Console.Error.WriteLine("Wrote predictions for {0} users to file {1}.", user_list.Count, prediction_file);
else
Console.Error.WriteLine("Wrote predictions to file {0}.", prediction_file);
});
Console.Write(" prediction_time " + time_span);
}
}