/// <summary>
/// Filters the news based on interest.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> connected to the database.
/// </param>
/// <param name="news">
/// The list of <c>NewsItem</c>s to filter.
/// </param>
/// <returns></returns>
public override List<NewsItem> Filter(Archivist archivist, List<NewsItem> news)
{
List<Tuple<NewsItem, float>> interestScores =
CalculateInterestScores(archivist, news);
// Sort by interest.
interestScores.Sort(delegate(Tuple<NewsItem, float> t1,
Tuple<NewsItem, float> t2)
{
return t2.Item2.CompareTo(t1.Item2);
}
);
// Convert Tuple to interest filtered List with item1.
List<NewsItem> result = new List<NewsItem>();
foreach (Tuple<NewsItem, float> item in interestScores)
{
// Is news item interesting?
if (item.Item2 >= THRESHOLD)
{
result.Add(item.Item1);
}
}
return result;
}
/// <summary>
/// Filters a given list of news for redundant <c>NewsItem</c>s. A
/// precondition to make this filter work is that the list of <c>NewsItem</c>s
/// given is ordered by interest.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> instance with database access.
/// </param>
/// <param name="news">
/// The list of <c>NewsItem</c>s to be filtered.
/// </param>
/// <returns>
/// A filtered list of <c>NewsItem</c>s in the same
/// order as in the input.
/// </returns>
public override List<NewsItem> Filter(Archivist archivist,
List<NewsItem> news)
{
// Filter in list.
List<NewsItem> result = FilterListRedundancy(archivist, news);
// Filter in database.
result = FilterDatabaseRedundancy(archivist, result);
return result;
}
/// <summary>
/// Seeds the data from JSON-files located in the given directory.
/// The directory must contain folders named as the category of its
/// contents.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> with database access
/// </param>
/// <param name="dataDir">
/// The directory the data is located in.
/// </param>
/// <param name="perCategory">
/// Number of <c>NewsItem</c>s per category to read.
/// </param>
/// <returns>
/// The number of <c>NewsItem</c>s added.
/// </returns>
public static int SeedDatabaseWithNews(Archivist archivist, string dataDir,
int perCategory = 3000)
{
int count = 0;
int errorCount = 0;
// Each category should be placed in its own directory.
string[] categoryDirs = Directory.GetDirectories(dataDir);
// Calculate number of files (3000 pr. category).
// The number of files is limited to ensure that no category is more
// likely than others.
int totalFilesCount = categoryDirs.Length * 3000;
foreach (string s in categoryDirs)
{
int categoryId = archivist.AddCategory(Path.GetFileName(s));
List<NewsMaterial> newsBuffer = new List<NewsMaterial>();
// Read 3000 files.
string[] files = Directory.GetFiles(s);
for (int i = 0; i < perCategory && i < files.Length; i++)
{
string file = files[i];
string json = File.ReadAllText(file, UTF8Encoding.UTF8);
NewsMaterial news = ParseJson(json);
if (news != null)
{
newsBuffer.Add(news);
count++;
}
else
{
Console.WriteLine("Error seeding item.");
errorCount++;
}
Console.WriteLine("Loaded {0}/{1}", count + errorCount,
totalFilesCount);
}
// Write the news to the archivist.
Stopwatch watch = new Stopwatch();
watch.Start();
archivist.AddNews(newsBuffer, categoryId, true);
watch.Stop();
Console.WriteLine("Time: {0}", watch.ElapsedMilliseconds);
Console.WriteLine("Seeded {0}.", newsBuffer.Count);
}
return count;
}
/// <summary>
/// Finds duplicates in a list of <c>NewsItem</c>s and the database.
/// </summary>
/// <param name="archivist">
/// An <c>Archivist</c> instance with database access.
/// </param>
/// <param name="news">
/// The list of news to compare with the matrix.
/// </param>
/// <returns>
/// Returns the filtered list.
/// </returns>
private List<NewsItem> FilterDatabaseRedundancy(Archivist archivist,
List<NewsItem> news)
{
// Create result list containing all news items. Redundant news
// will later be removed.
List<NewsItem> result = new List<NewsItem>(news);
DateTime dateLimit = DateTime.Now.Subtract(
new TimeSpan(24 * 7 * 2, 0, 0));
SparseMatrix matrix = GetTermDocumentMatrix(archivist, news, dateLimit);
// If matrix == null, there are no news items in the db, and thus
// no redundant news.
if (matrix == null)
{
return news;
}
// Transpose matrix and thus prepare for multiplication.
SparseMatrix mTransposed = matrix.Transpose();
// Normalize all rows to make cosine similarity calculation faster.
mTransposed = mTransposed.NormalizeRows();
// Find redundancy value for each news item.
foreach (NewsItem item in news)
{
// Get tf-idf vector representing this news item.
SparseVector newsVector = archivist.GetTfIdfVector(item);
newsVector = newsVector.Normalize();
// Calculate product.
SparseVector product = mTransposed.VectorProduct(newsVector);
// Remove item from result list if product contains element
// with value >= threshold.
foreach (int entry in product.NonZeroIndices)
{
if (product[entry] >= THRESHOLD)
{
result.Remove(item);
break;
}
}
}
return result;
}
/// <summary>
/// Curates undread news from the given <c>Archivist</c> instance based
/// on a maximum reading time, a maximum individual item reading time
/// and a limit.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> instance giving access to the news that
/// should be curated.
/// </param>
/// <param name="limit">
/// The maximum number of news to return.
/// </param>
/// <param name="maxTime">
/// The maximum reading time for the curated news.
/// </param>
/// <param name="maxItemTime">
/// The maximum reading time per news item.
/// </param>
/// <returns>
/// A list of unread, curated <c>NewsItem</c>s received from the
/// <c>Archivist</c> instance.
/// </returns>
public static List<NewsItem> GetCuratedNews(Archivist archivist,
int limit, int maxTime = -1, int maxItemTime = - 1)
{
// Get cached data and update if null.
List<NewsItem> curatedNews = CachedNews;
if (curatedNews == null)
{
// Update needed.
NewsQuery query = new NewsQuery();
// Only fetch unread news.
query.Read = ReadStatus.Unread;
query.OrderDateDesc = true;
query.Limit = 100;
curatedNews = archivist.GetNews(query);
// If no news was found then there's no need to filter them.
if (curatedNews.Count == 0)
{
return curatedNews;
}
// Filter for interesting news.
InterestFilter interestFilter = new InterestFilter();
curatedNews = interestFilter.Filter(archivist, curatedNews);
// Filter redundancy.
RedundancyFilter redundancyFilter = new RedundancyFilter();
curatedNews = redundancyFilter.Filter(archivist, curatedNews);
// Update cache.
CachedNews = curatedNews;
}
// Filter quantity.
QuantityFilter quantityFilter = new QuantityFilter(limit,
maxTime, maxItemTime);
curatedNews = quantityFilter.Filter(archivist, curatedNews);
// Return curated list.
return curatedNews;
}
/// <summary>
/// Filters a list of news according to the parameters set
/// by the constructor, a precondition to this method is that the list of
/// <c>NewsItems</c>s is sorted by descending interesting score.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c>
/// </param>
/// <param name="news">
/// A list of <c>News</c> to filter.
/// </param>
/// <returns>
/// A list of <c>NewsItem</c> filtered as specified by the constructor.
/// </returns>
public override List<NewsItem> Filter(Archivist archivist,
List<NewsItem> news)
{
List<NewsItem> listCopy = news.ToList();
// Only filter for time if MaxTime has been set.
if (MaxTime > -1)
{
int readingSpeed = archivist.GetReadingSpeed();
int remainingTime = MaxTime;
int articleLength = 0;
if (readingSpeed <= 0)
{
// Not possible to filter for. Return an empty list.
return new List<NewsItem>();
}
foreach (NewsItem item in news)
{
articleLength = CalculateNewsReadTime(item, readingSpeed);
if (MaxItemTime > 0 && articleLength > MaxItemTime)
{
listCopy.Remove(item);
}
else if (articleLength <= remainingTime)
{
remainingTime -= articleLength;
}
else
{
listCopy.Remove(item);
}
}
}
// Trim list to max count.
TrimNewsList(listCopy, MaxCount);
return listCopy;
}
/// <summary>
/// Aggregates news from <c>NewsSource</c>s found in the given
/// <c>Archivist</c>.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> to get <c>NewsSource</c>s from and
/// store <c>NewsItem</c>s in.
/// </param>
public static void Aggregate(Archivist archivist)
{
// Collect news sources from the archivist.
List<NewsSource> sources = CollectNewsSources(archivist);
List<NewsSource> sourcesCopy = sources.ToList();
// Run through all sources.
foreach (NewsSource source in sourcesCopy)
{
if (source != null &&
source.ContentExpirationTime != null &&
DateTime.Now.CompareTo(source.ContentExpirationTime) < 0)
{
sources.Remove(source);
}
}
// Aggregate news from the filtered sources.
AggregateNewsFromSources(archivist, sources.FindAll(n => !n.IsBlocked));
}
/// <summary>
/// Aggregates news from the given list of <c>NewsSource</c>s.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> of which to save aggregated news.
/// </param>
/// <param name="sources">
/// The <c>NewsSource</c>s of which to load news from.
/// </param>
private static void AggregateNewsFromSources(Archivist archivist,
List<NewsSource> sources)
{
List<NewsMaterial> newNewsMaterial = new List<NewsMaterial>();
// Run ParseNewsItems on each news source.
// In paralell
Parallel.ForEach(sources, (source, state) =>
{
if (source != null)
{
newNewsMaterial.AddRange(ParseNewsItems(source));
}
});
foreach (NewsSource source in sources)
{
archivist.UpdateNewsSource(source);
}
archivist.AddNews(newNewsMaterial);
}
/// <summary> /// Filters the list of <c>NewsItem</c>.s /// </summary> /// <param name="archivist"> /// The <c>Archivist</c> instance for database access. /// </param> /// <param name="news"> /// The list of <c>NewsItem</c>s to sort. /// </param> /// <returns> /// The filtered list. /// </returns> public abstract List<NewsItem> Filter(Archivist archivist, List<NewsItem> news);
/// <summary>
/// Reads a JSON file with news sources and saves in archivist.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> instance with database access.
/// </param>
/// <param name="file">
/// The path to the JSON-file containing <c>NewsSource</c>s.
/// </param>
public static void SeedNewsSources(Archivist archivist, string file)
{
List<NewsSource> newsSources = new List<NewsSource>();
string json = File.ReadAllText(file, UTF8Encoding.UTF8);
try
{
// Parse JSON and assign various values.
var values = JArray.Parse(json);
for (int i = 0; i < values.Count; i++)
{
// Parse and add news source to list.
string name = values[i]["name"].ToString();
string url = values[i]["url"].ToString();
try
{
Uri uri = new Uri(url);
NewsSource newsSource = new NewsSource(name, uri);
newsSources.Add(newsSource);
}
catch (UriFormatException)
{
Console.WriteLine("Could not parse news item URL.");
}
}
}
catch (Newtonsoft.Json.JsonReaderException)
{
Console.WriteLine("Error parsing news source");
}
// Add to database.
archivist.AddNewsSources(newsSources);
}
/// <summary>
/// Write the cosine similarity of the given <c>NewsItem</c>s to
/// the file cosine_similarity_log.txt.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> for getting database information.
/// </param>
/// <param name="news">
/// The <c>NewsItem</c>s to print the cosine similarity of.
/// </param>
private static void WriteCosineSimilarity(Archivist archivist, List<NewsItem> news)
{
// Sort the news according to title.
news.Sort((n1, n2) => int.Parse(n1.Title).CompareTo(int.Parse(n2.Title)));
// Get the vector of all news and add them to a matrix.
List<SparseVector> vectors = new List<SparseVector>();
foreach (NewsItem n in news)
{
vectors.Add(archivist.GetTfIdfVector(n));
}
// Create a matrix.
SparseMatrix matrix =
new SparseMatrix(vectors.First().Dimension, vectors.Count);
// Add entries to the matrix.
for (int i = 0; i < vectors.Count; i++)
{
for (int j = 0; j < vectors.First().Dimension; j++)
{
matrix[j, i] = vectors[i][j];
}
}
// Transpose matrix and thus prepare for multiplication.
SparseMatrix mTransposed = matrix.Transpose().NormalizeRows();
// Calculate the result.
SparseMatrix result = mTransposed.Product(matrix.Transpose().NormalizeRows().Transpose());
// Write to file.
using (StreamWriter file = new StreamWriter("cosine_similarity_log.txt"))
{
int rowLength = result.Rows;
int columnLength = result.Columns;
// Print header.
file.Write(" ");
for (int i = 0; i < result.Columns; i++)
{
file.Write("dok" + (i + 1).ToString("0#") + " ");
}
file.WriteLine();
// Print the matrix.
for (int i = 0; i < rowLength; i++)
{
// Print doc title.
file.Write("dok" + (i + 1).ToString("0#") + " ");
for (int j = 0; j < columnLength; j++)
{
file.Write(result[i, j].ToString("0.##0") + " ");
}
if (i != rowLength - 1)
{
file.WriteLine();
}
}
}
}
/// <summary>
/// Calculates and assigns a score for each <c>NewsItem</c> based
/// on interest and weighted by an age-factor.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> instance with database access.
/// </param>
/// <param name="news">
/// A list of <c>NewsItem</c> that should be given a score.
/// </param>
/// <returns>
/// A list of tuples containing a <c>NewsItem</c> and a score.
/// </returns>
private List<Tuple<NewsItem, float>> CalculateInterestScores(Archivist archivist,
List<NewsItem> news)
{
List<Tuple<NewsItem, float>> result = new List<Tuple<NewsItem, float>>();
// Get unique term count (size of the dictionary).
int uniqueTermCount = archivist.GetUniqueTermCount();
// Calculate score for each NewsItem.
foreach (NewsItem n in news)
{
// No need to calculate if term count = 0.
if (n.Terms.Count == 0)
{
continue;
}
// Calculate the number of interesting term occurrences
// belonging to n.category.
int interestingCategoryCount = CountTermsWithQuery(archivist, n.Category, true);
// Calculate the number of not-interesting term occurrences
// belonging to n.category.
int nInterestingCategoryCount = CountTermsWithQuery(archivist, n.Category, false);
Dictionary<string, int> interestTermsCount = CountTermsWithQuery(archivist,
n.Terms.Select(p => p.TermName).ToList<string>(),
n.Category,
true);
Dictionary<string, int> nInterestTermsCount = CountTermsWithQuery(archivist,
n.Terms.Select(p => p.TermName).ToList<string>(),
n.Category,
false);
// The first part of the naive bayes P(T|i).
double probFirstPart = 0;
// The first part of the naive bayes P(T|not-i).
double probSecondPart = 0;
// For each term in document.
foreach (Term term in n.Terms)
{
// Number of occurrences of the term in interesting news.
int interestCount = interestTermsCount.ContainsKey(term.TermName) ?
interestTermsCount[term.TermName] :
0;
// Number of occurrences of the term in non-interesting news.
int nInterestCount = nInterestTermsCount.ContainsKey(term.TermName) ?
nInterestTermsCount[term.TermName] :
0;
// Calculate location weight.
double indexWeight = 1 + Math.Log(
(n.WordCount * 2.0 - term.MedianIndex) / n.WordCount, 2);
// Calculate P(T|i).
probFirstPart += Math.Log10(indexWeight * (interestCount + 1.0) /
(double)(interestingCategoryCount
+ uniqueTermCount));
// Calculate P(T|not-i).
probSecondPart += Math.Log10(indexWeight * (nInterestCount + 1.0) /
(double)(nInterestingCategoryCount
+ uniqueTermCount));
}
// Calculate age.
TimeSpan age = DateTime.Now.Subtract(n.Date);
// Calculate age weight.
double ageWeight = Math.Pow(0.99, Math.Ceiling(age.TotalHours));
// Calculate probability.
float probability = (float)(ageWeight /
(1 + Math.Pow(10, probSecondPart - probFirstPart)));
// Add to result.
result.Add(new Tuple<NewsItem, float>(n, probability));
}
return result;
}
/// <summary>
/// Collects newssources from the given <c>Archivist</c>.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> of which to collect <c>NewsSource</c>s from.
/// </param>
/// <returns>
/// A list of <c>NewsSource</c>s.
/// </returns>
private static List<NewsSource> CollectNewsSources(Archivist archivist)
{
return archivist.GetNewsSources(int.MaxValue);
}
/// <summary>
/// Counts the number of documents satisfying the parameter values.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> instance with database access.
/// </param>
/// <param name="terms">
/// A list of terms the documents should contain.
/// </param>
/// <param name="category">
/// A <c>Category</c> the documents should belong to.
/// </param>
/// <param name="interesting">
/// Whether the documents should be marked interesting or not.
/// </param>
/// <returns>
/// The number of documents satisfying the parameter values.
/// </returns>
private Dictionary<string, int> CountTermsWithQuery(Archivist archivist, List<string> terms,
Category category, bool interesting)
{
// Create query instance.
TermCountQuery termQuery = new TermCountQuery();
// Add term to query if not null.
if (terms != null)
{
termQuery.Terms = terms;
}
// Set category if not null.
if (category != null)
{
termQuery.CategoryIds = new List<int> { category.Id };
}
termQuery.Interest = interesting ?
Database.InterestStatus.Interesting :
Database.InterestStatus.Uninteresting;
// Send query and return result.
return archivist.GetIndividualTermCount(termQuery);
}
/// <summary>
/// Counts the number of documents satisfying the parameter values.
/// </summary>
/// <param name="archivist">
/// The <c>Archivist</c> instance with database access.
/// </param>
/// <param name="category">
/// A <c>Category</c> the documents should belong to.
/// </param>
/// <param name="interesting">
/// Whether the documents should be marked interesting or not.
/// </param>
/// <returns>
/// The number of documents satisfying the parameter values.
/// </returns>
private int CountTermsWithQuery(Archivist archivist, Category category,
bool interesting)
{
// Send query and return result.
return archivist.GetTermCountInCategory(category,
interesting ? InterestStatus.Interesting : InterestStatus.Uninteresting);
}
/// <summary>
/// Creates a term-document matrix from the database based on the given
/// parameters.
/// </summary>
/// <param name="archivist">
/// An <c>Archivist</c> instance with database access.
/// </param>
/// <param name="news">
/// A list of <c>NewsItem</c>s to be excluded from the matrix.
/// </param>
/// <param name="dateLimit">
/// A date limit indicating whether old news should be included in the
/// matrix.
/// </param>
/// <returns>
/// A term-document matrix with tf-idf values representing news in the
/// database.
/// </returns>
private SparseMatrix GetTermDocumentMatrix(Archivist archivist,
List<NewsItem> news, DateTime dateLimit)
{
// Create query which defines what the matrix should be made of.
NewsQuery query = new NewsQuery();
query.NewerThan = dateLimit;
query.ExcludedNews = news;
query.Read = ReadStatus.Read;
// Return result of request.
return archivist.GetTfIdfMatrix(query);
}
/// <summary>
/// Finds redundant <c>NewsItem</c>s in the given list,
/// and returns a new list without the <c>NewsItem</c>s.
/// </summary>
/// <param name="archivist">
/// An <c>Archivist</c> instance with database access.
/// </param>
/// <param name="news">
/// The list of <c>NewsItem</c>s to filter.
/// </param>
/// <returns>
/// A filtered list of <c>NewsItem</c>s.
/// </returns>
private List<NewsItem> FilterListRedundancy(Archivist archivist,
List<NewsItem> news)
{
List<NewsItem> result = new List<NewsItem>(news);
// Find redundancy for each news item.
for (int i = 0; i < news.Count - 1; i++)
{
// Get Tf-idf vector for current item.
SparseVector itemVector = archivist.GetTfIdfVector(news[i]);
itemVector = itemVector.Normalize();
// For all items with index j > i, calculate cosine similarity.
for (int j = i + 1; j < news.Count; j++)
{
// Get sparsevector from news item to compare.
SparseVector compareVector = archivist.GetTfIdfVector(news[j]);
compareVector = compareVector.Normalize();
// Remove from list if cosine similarity exeeds or equals
// threshold.
if (compareVector.DotProduct(itemVector) >= THRESHOLD)
{
result.Remove(news[i]);
break;
}
}
}
return result;
}