public static void AddRegionsToAreaSenti()
{
Console.WriteLine($"Analysing AreaSenti \n");
const string xmlTemplate = @"medians-{0}p02.xml";
var cfg = new[] { StatArea.SA4, StatArea.SA3, StatArea.SA2, StatArea.SA1 };
// location feature sets
var saLoader = new LoadStatisticalAreas();
var featureSets = new Dictionary <StatArea, Features>();
foreach (var area in cfg)
{
var xmlFile = Path.Combine(@"..\..", string.Format(xmlTemplate, area.ToString().ToLower()));
var features = saLoader.GetFeatures(xmlFile);
featureSets.Add(area, features);
}
// summarise
foreach (var area in cfg)
{
Console.WriteLine(
$"{area}\tregions:{featureSets[area].Count,6:N0}\tploygons: {featureSets[area].Sum(x => x.Locations.Count),8:N0}");
}
var sad = new SADictionary(featureSets);
var src = @"E:\uni\Cluster and Cloud Computing\extracted\newActivity";
var jr = new JsonRead <AreaSentiExtract>(new[] { src });
jr.DoLoad();
var requiredUsers = new Dictionary <long, string>();
using (var ifs = new StreamReader(@"..\..\userHomeCity.csv"))
{
var ln = ifs.ReadLine(); // skip header
while ((ln = ifs.ReadLine()) != null)
{
var arr = ln.Split(',');
requiredUsers.Add(long.Parse(arr[0]), arr[1]);
}
}
var filtered = jr.Records
.Where(x => requiredUsers.ContainsKey(x.User)).ToList();
// extract unique locations
var locs = filtered
.GroupBy(x => new { Y = x.Yloc, X = x.Xloc })
.ToDictionary(x => x.Key, x => x.Count());
using (var ofs = new StreamWriter($@"..\..\recentLocations.csv"))
{
ofs.WriteLine("Yloc,Xloc,Count");
foreach (var kvp in locs.OrderByDescending(x => x.Value))
{
ofs.WriteLine($"{kvp.Key.Y},{kvp.Key.X},{kvp.Value}");
}
}
var cls = new ClassifyArea(filtered, sad); //{SingleThreaded = true};
cls.DoClassification();
foreach (var sa in cfg)
{
var clusteredBySa = cls.Scores
.Where(x => x.Area.Regions.ContainsKey(sa))
.Select(x => new KeyValuePair <long, double>(x.Area.Regions[sa].Id, x.Parameters.Sentiment))
.ToLookup(x => x.Key);
using (var of = new StreamWriter($@"..\..\SentimentRecentWithRegion-{sa}.csv"))
{
of.WriteLine("RegionId,Name,Count,SumSentiment,CountExc,SumNeutralExc,Sentiment");
// collate regional averages
foreach (var rec in clusteredBySa)
{
var count = rec.Count();
var sm = rec.Sum(x => x.Value) * 100;
var avg = rec.Average(x => x.Value) * 100;
var counte = rec.Count(x => x.Value < -0.5 || 0.5 < x.Value);
var sme = rec.Where(x => x.Value < -0.5 || 0.5 < x.Value).Sum(x => x.Value) * 100;
of.WriteLine(
$"{rec.Key},\"{sad.SANames[sa][rec.Key]}\",{count},{sm:F2},{counte},{sme:F2},{avg:F2}");
}
}
}
}
private ClassifyArea(ClassifyArea src)
{
Sad = new SADictionary(src.Sad.SASets);
}
