public MovieTweetingLibSvmFeatureBuilder(MovieTweetingsDataContainer container)
: base()
{
_container = container;
_maxFollowerCount = _container.Tweets.Values.Max(t => t.FollowersCount);
_maxFriendCount = _container.Tweets.Values.Max(t => t.FriendsCount);
}
public MovieTweetingLibSvmFeatureBuilder(MovieTweetingsDataContainer container)
: base()
{
_container = container;
_maxFollowerCount = _container.Tweets.Values.Max(t => t.FollowersCount);
_maxFriendCount = _container.Tweets.Values.Max(t => t.FriendsCount);
}
static void Run(string trainingSet, string testSet)
{
// step 1: dataset
var container = new MovieTweetingsDataContainer();
var reader = new MovieTweetingsReader(trainingSet, testSet);
reader.LoadData(container);
Console.WriteLine("Data container statistics:\n {0}", container.ToString());
var dataset = new ItemRatingDataset(container);
var featureBuilder = new MovieTweetingLibSvmFeatureBuilder(container);
// svm parameters
var svmParameters = new SvmParameter
{
SvmType = SvmType.C_SVC,
KernelType = KernelType.Linear,
CacheSize = 128,
C = 1,
Eps = 1e-3,
Shrinking = true,
Probability = false
};
// step 2: recommender
var labelSelector = new Func <ItemRating, double>(ir =>
{
var t = container.Tweets[ir];
return(((t.RetweetCount + t.FavoriteCount) > 0) ? 1.0 : 0.0);
});
var recommender = new LibSvmClassifier(svmParameters, featureBuilder, labelSelector);
// step3: evaluation
var ep = new EvaluationPipeline <ItemRating>(new EvalutationContext <ItemRating>(recommender, dataset));
ep.Evaluators.Add(new WriteChallengeOutput(container, "test_output.dat"));
ep.Run();
}
static void Run(string trainingSet, string testSet)
{
// step 1: dataset
var container = new MovieTweetingsDataContainer();
var reader = new MovieTweetingsReader(trainingSet, testSet);
reader.LoadData(container);
Console.WriteLine("Data container statistics:\n {0}", container.ToString());
var dataset = new ItemRatingDataset(container);
var featureBuilder = new MovieTweetingLibSvmFeatureBuilder(container);
// svm parameters
var svmParameters = new SvmParameter
{
SvmType = SvmType.C_SVC,
KernelType = KernelType.Linear,
CacheSize = 128,
C = 1,
Eps = 1e-3,
Shrinking = true,
Probability = false
};
// step 2: recommender
var labelSelector = new Func<ItemRating, double>(ir =>
{
var t = container.Tweets[ir];
return ((t.RetweetCount + t.FavoriteCount) > 0) ? 1.0 : 0.0;
});
var recommender = new LibSvmClassifier(svmParameters, featureBuilder, labelSelector);
// step3: evaluation
var ep = new EvaluationPipeline<ItemRating>(new EvalutationContext<ItemRating>(recommender, dataset));
ep.Evaluators.Add(new WriteChallengeOutput(container, "test_output.dat"));
ep.Run();
}
public WriteChallengeOutput(MovieTweetingsDataContainer container, string outputPath)
{
_container = container;
_outputFile = outputPath;
}