/// <summary>
/// Splits the observations and targets into a training and a test set.
/// </summary>
/// <param name="splitter">The type of splitter used for determining the distribution of observations</param>
/// <param name="observations">The observations for the problem</param>
/// <param name="targets">The targets for the problem</param>
/// <returns></returns>
public static TrainingTestSetSplit SplitSet(this ITrainingTestIndexSplitter <double> splitter,
F64Matrix observations, double[] targets)
{
if (observations.RowCount != targets.Length)
{
throw new ArgumentException("Observations and targets has different number of rows");
}
var indexSplit = splitter.Split(targets);
var trainingSet = new ObservationTargetSet((F64Matrix)observations.Rows(indexSplit.TrainingIndices),
targets.GetIndices(indexSplit.TrainingIndices));
var testSet = new ObservationTargetSet((F64Matrix)observations.Rows(indexSplit.TestIndices),
targets.GetIndices(indexSplit.TestIndices));
return(new TrainingTestSetSplit(trainingSet, testSet));
}
public void TrainingTestIndexSplitterExtensions_SplitSet()
{
var observations = new F64Matrix(new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }, 10, 1);
var targets = new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
var splitter = new NoShuffleTrainingTestIndexSplitter <double>(0.6);
var actual = splitter.SplitSet(observations, targets);
var trainingIndices = Enumerable.Range(0, 6).ToArray();
var testIndices = Enumerable.Range(6, 4).ToArray();
var expected = new TrainingTestSetSplit(
new ObservationTargetSet((F64Matrix)observations.Rows(trainingIndices),
targets.GetIndices(trainingIndices)),
new ObservationTargetSet((F64Matrix)observations.Rows(testIndices),
targets.GetIndices(testIndices)));
Assert.AreEqual(expected, actual);
}
public void F64Matrix_GetRows()
{
// matrix is created row wise
var matrix = new F64Matrix(new double[] { 1, 2, 3,
4, 5, 6,
7, 8, 9 }, 3, 3);
// returns selected rows a a new matrix
var rows = matrix.Rows(new int[] { 0, 2 }); // [1, 2, 3,
// 7, 8, 9]
}
private void UpdateClassificationResults()
{
float[,] meanSubVals = new float[numGroups, subVals.ColumnCount];
for (int group = 0; group < numGroups; group++)
{
for (int sub = 0; sub < subVals.ColumnCount; sub++)
{
int[] correctRows = Global.IntArray(group * numPerGroup, (group + 1) * numPerGroup);
IMatrix <double> membersOfGroup = subVals.Rows(correctRows);
double[] substrateFromGroup = membersOfGroup.Column(sub);
float meanVal = (float)Global.Sum(substrateFromGroup) / substrateFromGroup.Length;
meanSubVals[group, sub] = meanVal;
}
}
Global.LastClassificationResults.meanSubValues.Add(meanSubVals);
}
public void Predict(int iterations = DefaultNNIterations, int targetOffset = 1, string targetName = DefaultTargetName, bool pauseAtEnd = false)
{
_iterations = iterations;
_targetName = targetName;
_targetOffset = targetOffset;
Program.StatusLogger.Info($"Iterations: {_iterations}");
Program.StatusLogger.Info($"Target: {_targetName}");
Program.StatusLogger.Info($"Offset: {_targetOffset}");
var data = new ConcurrentDictionary <int, ModelData>();
if (File.Exists(Path()))
{
data = JsonConvert.DeserializeObject <ConcurrentDictionary <int, ModelData> >(File.ReadAllText(Path()));
//data = TypeSerializer.DeserializeFromReader<ConcurrentDictionary<int, ModelData>>(new StreamReader(Path()));
Program.StatusLogger.Info("Cached data was loaded.");
}
else
{
//http://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/UK-HPI-full-file-2019-07.csv
var header = File.ReadLines("UK-HPI-full-file-2019-07.csv").First();
var columnNames = header.Split(",");
var parser = new CsvParser(() => new StringReader(File.ReadAllText("UK-HPI-full-file-2019-07.csv")), ',', false, true);
var creditData = _creditDataExtractor.Extract();
var populationData = _populationDataExtractor.Extract();
var otherPopulationData = _otherPopulationDataExtractor.Extract();
var densityData = _londonDensityDataExtractor.Extract();
var gvaData = _gvaDataExtractor.Extract();
var featureRows = parser.EnumerateRows().ToArray();
var targets = parser.EnumerateRows(_targetName).ToArray();
string previousKey = null;
for (int i = 0; i < featureRows.Length; i++)
{
var item = featureRows[i];
var key = item.GetValue("RegionName");
var date = DateTime.ParseExact(item.GetValue("Date"), "dd/MM/yyyy", new CultureInfo("en-GB"), DateTimeStyles.AssumeLocal);
if (key != previousKey)
{
Program.StatusLogger.Info($"Processing {key}");
}
previousKey = key;
var regionFeatures = item.GetValues(columnNames.Except(excludeColumns).ToArray()).Select(s => ParseRowValue(s));
var creditDataKey = _creditDataExtractor.GetKey(date, creditData.Keys.ToArray());
if (!creditData.ContainsKey(creditDataKey))
{
regionFeatures = regionFeatures.Concat(Enumerable.Repeat(-1d, creditData.Values.First().Length));
Trace.WriteLine($"Credit data not found: {creditDataKey}");
}
else
{
regionFeatures = regionFeatures.Concat(creditData[creditDataKey]);
}
var modelData = new ModelData
{
Name = key,
Code = item.GetValue("AreaCode"),
Date = date,
Observations = regionFeatures.ToArray(),
OriginalTarget = ParseTarget(item.GetValue(_targetName))
};
modelData.Observations = modelData.Observations
.Concat(_populationDataExtractor.Get(populationData, modelData))
.Concat(_londonDensityDataExtractor.Get(densityData, modelData))
.Concat(_otherPopulationDataExtractor.Get(otherPopulationData, modelData))
.Concat(_gvaDataExtractor.Get(gvaData, modelData))
.ToArray();
data.TryAdd(i, modelData);
}
_targetCalculator.Calculate(data, _targetOffset);
//TypeSerializer.SerializeToWriter<ConcurrentDictionary<int, ModelData>>(data, new StreamWriter(Path()));
var json = JsonConvert.SerializeObject(data, Formatting.Indented);
File.WriteAllText(Path(), json);
}
var itemCount = 0;
Parallel.ForEach(data.OrderBy(o => o.Value.Date).GroupBy(g => g.Value.Name).AsParallel(), new ParallelOptions {
MaxDegreeOfParallelism = -1
}, (grouping) =>
{
var lastDate = grouping.Last().Value.Date;
var dataWithTarget = grouping.Where(s => s.Value.OriginalTarget.HasValue && s.Value.Target != -1);
if (dataWithTarget.Any())
{
var allObservations = dataWithTarget.Select(s => s.Value.Observations).ToArray();
var allTargets = dataWithTarget.Select(s => s.Value.Target).ToArray();
//var validation = new TimeSeriesCrossValidation<double>((int)(allObservationsExceptLast.RowCount * 0.8), 0, 1);
//var validationPredictions = validation.Validate((IIndexedLearner<double>)learner, allObservationsExceptLast, allTargetsExceptLast);
//var crossMetric = new MeanSquaredErrorRegressionMetric();
//var crossError = crossMetric.Error(validation.GetValidationTargets(allTargetsExceptLast), validationPredictions);
//_totalCrossError += crossError;
var meanZeroTransformer = new MeanZeroFeatureTransformer();
var minMaxTransformer = new MinMaxTransformer(0d, 1d);
var lastObservations = grouping.Last().Value.Observations;
F64Matrix allTransformed = minMaxTransformer.Transform(meanZeroTransformer.Transform(allObservations.Append(lastObservations).ToArray()));
var transformed = new F64Matrix(allTransformed.Rows(Enumerable.Range(0, allTransformed.RowCount - 1).ToArray()).Data(), allTransformed.RowCount - 1, allTransformed.ColumnCount);
var splitter = new RandomTrainingTestIndexSplitter <double>(trainingPercentage: 0.7, seed: 24);
var trainingTestSplit = splitter.SplitSet(transformed, allTargets);
transformed = trainingTestSplit.TrainingSet.Observations;
var testSet = trainingTestSplit.TestSet;
//var learner = GetRandomForest();
//var learner = GetAda();
//var learner = GetNeuralNet(grouping.First().Value.Observations.Length, transformed.RowCount);
var learner = GetEnsemble(grouping.First().Value.Observations.Length, transformed.RowCount);
Program.StatusLogger.Info("Learning commenced " + grouping.First().Value.Name);
var model = learner.Learn(transformed, trainingTestSplit.TrainingSet.Targets);
Program.StatusLogger.Info("Learning completed " + grouping.First().Value.Name);
if (model.GetRawVariableImportance().Any(a => a > 0))
{
var importanceSummary = string.Join(",\r\n", model.GetRawVariableImportance().Select((d, i) => i.ToString() + ":" + d.ToString()));
Program.StatusLogger.Info("Raw variable importance:\r\n" + importanceSummary);
}
var lastTransformed = allTransformed.Row(transformed.RowCount);
var prediction = model.Predict(lastTransformed);
//var before = item.Value.Item2[transformed.RowCount - _targetOffset - 1];
var change = -1; //Math.Round(prediction / before, 2);
var testPrediction = model.Predict(testSet.Observations);
var metric = new MeanSquaredErrorRegressionMetric();
var error = metric.Error(testSet.Targets, testPrediction);
var averageError = 0d;
lock (Locker)
{
_totalError += error;
itemCount++;
averageError = Math.Round(_totalError / itemCount, 3);
}
var isLondon = London.Contains(grouping.First().Value.Name);
var message = $"TotalError: {Math.Round(_totalError, 3)}, AverageError: {averageError}, Target: {_targetName}, Offset: {_targetOffset}, Region: {grouping.First().Value.Name}, London: {isLondon}, Error: {Math.Round(error, 3)}, Next: {Math.Round(prediction, 3)}, Change: {change}";
Program.Logger.Info(message);
}
});
if (pauseAtEnd)
{
Console.WriteLine("Press any key to continue");
Console.ReadKey();
}
}
