public void TestClassification()
{
FileInfo rawFile = TEMP_DIR.CreateFile("simple.csv");
FileInfo egaFile = TEMP_DIR.CreateFile("simple.ega");
FileInfo outputFile = TEMP_DIR.CreateFile("simple_output.csv");
FileUtil.CopyResource("Encog.Resources.simple.csv", rawFile);
FileUtil.CopyResource("Encog.Resources.simple-c.ega", egaFile);
EncogAnalyst analyst = new EncogAnalyst();
analyst.AddAnalystListener(new ConsoleAnalystListener());
analyst.Load(egaFile);
analyst.ExecuteTask("task-full");
ReadCSV csv = new ReadCSV(outputFile.ToString(), true, CSVFormat.English);
while (csv.Next())
{
Assert.AreEqual(csv.Get(3), csv.Get(4));
}
Assert.AreEqual(4, analyst.Script.Fields.Length);
Assert.AreEqual(3, analyst.Script.Fields[3].ClassMembers.Count);
csv.Close();
}
public void TestClassification()
{
FileInfo rawFile = TEMP_DIR.CreateFile("simple.csv");
FileInfo egaFile = TEMP_DIR.CreateFile("simple.ega");
FileInfo outputFile = TEMP_DIR.CreateFile("simple_output.csv");
FileUtil.CopyResource("Encog.Resources.simple.csv", rawFile);
FileUtil.CopyResource("Encog.Resources.simple-c.ega", egaFile);
EncogAnalyst analyst = new EncogAnalyst();
analyst.AddAnalystListener(new ConsoleAnalystListener());
analyst.Load(egaFile);
analyst.ExecuteTask("task-full");
ReadCSV csv = new ReadCSV(outputFile.ToString(), true, CSVFormat.English);
while (csv.Next())
{
Assert.AreEqual(csv.Get(3), csv.Get(4));
}
Assert.AreEqual(4, analyst.Script.Fields.Length);
Assert.AreEqual(3, analyst.Script.Fields[3].ClassMembers.Count);
csv.Close();
}
/// <summary>
/// This method is called to determine the birth year for a person. It
/// obtains 100 web pages that Yahoo returns for that person. Each of these
/// pages is then searched for the birth year of that person. Which ever year
/// is selected the largest number of times is selected as the birth year.
/// </summary>
public void Process()
{
ReadCSV famous = new ReadCSV("famous.csv");
Report("Building training data from list of famous people.");
DateTime started = new DateTime();
this.totalTasks = 0;
while (famous.Next())
{
String name = famous.Get("Person");
int year = famous.GetInt("Year");
CollectionWorker worker = new CollectionWorker(this, name,
year);
worker.Call();
this.totalTasks++;
}
long length = (DateTime.Now - started).Ticks;
length /= 1000L;
length /= 60;
Console.WriteLine("Took " + length
+ " minutes to collect training data from the Internet.");
Console.WriteLine("Writing training file");
WriteTrainingFile();
}
/// <summary>
/// Construct a loaded row.
/// </summary>
///
/// <param name="csv">The CSV file to use.</param>
/// <param name="extra">The number of extra columns to add.</param>
public LoadedRow(ReadCSV csv, int extra)
{
int count = csv.GetCount();
_data = new String[count + extra];
for (int i = 0; i < count; i++)
{
_data[i] = csv.Get(i);
}
}
private async void BeginLoadAsync()
{
tokenSource = new CancellationTokenSource();
BaseClassList = await ReadCSV.Get(Path, _progress, tokenSource.Token);
if (BaseClassList.Count != 0)
{
ProgressMessage = "Loading completed";
}
await Task.Delay(1000);
ProgressMessage = "Ready";
}
/// <summary>
/// Get the data for a specific column.
/// </summary>
/// <param name="name">The column to read.</param>
/// <param name="csv">The CSV file to read from.</param>
/// <returns>The column data.</returns>
public String GetColumnData(String name, ReadCSV csv)
{
if (!_columnMapping.ContainsKey(name))
{
return(null);
}
BaseCachedColumn column = _columnMapping[name];
if (!(column is FileData))
{
return(null);
}
var fd = (FileData)column;
return(csv.Get(fd.Index));
}
/// <inheritdoc />
public string[] ReadLine()
{
if (_reader == null)
{
throw new EncogError("Please call rewind before reading the file.");
}
if (_reader.Next())
{
int len = _reader.ColumnCount;
var result = new string[len];
for (int i = 0; i < result.Length; i++)
{
result[i] = _reader.Get(i);
}
return(result);
}
_reader.Close();
return(null);
}
private void DetermineInputFieldValue(IInputField field, int index, bool headers)
{
if (field is InputFieldCSV)
{
var fieldCSV = (InputFieldCSV)field;
ReadCSV csv = _csvMap[field];
field.CurrentValueRaw = csv.Get(fieldCSV.ColumnName);
try
{
field.CurrentValue = CSVFormatUsed.Parse((string)field.CurrentValueRaw);
}
catch (FormatException)
{
field.CurrentValue = double.NaN;
}
}
else if (field is InputFieldMLDataSet)
{
var mlField = (InputFieldMLDataSet)field;
MLDataFieldHolder holder = _dataSetFieldMap
[field];
IMLDataPair pair = holder.Pair;
int offset = mlField.Offset;
if (offset < pair.Input.Count)
{
field.CurrentValue = pair.Input[offset];
field.CurrentValueRaw = pair.Input[offset];
}
else
{
offset -= pair.Input.Count;
field.CurrentValue = pair.Ideal[offset];
field.CurrentValueRaw = pair.Ideal[offset];
}
}
else
{
field.CurrentValueRaw = field.GetValue(index);
field.CurrentValue = (double)field.CurrentValueRaw;
}
}
/// <summary>
/// Read the CSV file.
/// </summary>
private void ReadFile()
{
ReadCSV csv = null;
try
{
csv = new ReadCSV(InputFilename.ToString(),
ExpectInputHeaders, Format);
ResetStatus();
int row = 0;
while (csv.Next() && !ShouldStop())
{
UpdateStatus("Reading data");
foreach (BaseCachedColumn column in Columns)
{
if (column is FileData)
{
if (column.Input)
{
var fd = (FileData)column;
String str = csv.Get(fd.Index);
double d = Format.Parse(str);
fd.Data[row] = d;
}
}
}
row++;
}
}
finally
{
ReportDone("Reading data");
if (csv != null)
{
csv.Close();
}
}
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
ErrorCalculation.Mode = ErrorCalculationMode.RMS;
// Download the data that we will attempt to model.
string filename = DownloadData(app.Args);
// Define the format of the data file.
// This area will change, depending on the columns and
// format of the file that you are trying to model.
var format = new CSVFormat('.', ' '); // decimal point and
// space separated
IVersatileDataSource source = new CSVDataSource(filename, true,
format);
var data = new VersatileMLDataSet(source);
data.NormHelper.Format = format;
ColumnDefinition columnSSN = data.DefineSourceColumn("SSN",
ColumnType.Continuous);
ColumnDefinition columnDEV = data.DefineSourceColumn("DEV",
ColumnType.Continuous);
// Analyze the data, determine the min/max/mean/sd of every column.
data.Analyze();
// Use SSN & DEV to predict SSN. For time-series it is okay to have
// SSN both as
// an input and an output.
data.DefineInput(columnSSN);
data.DefineInput(columnDEV);
data.DefineOutput(columnSSN);
// Create feedforward neural network as the model type.
// MLMethodFactory.TYPE_FEEDFORWARD.
// You could also other model types, such as:
// MLMethodFactory.SVM: Support Vector Machine (SVM)
// MLMethodFactory.TYPE_RBFNETWORK: RBF Neural Network
// MLMethodFactor.TYPE_NEAT: NEAT Neural Network
// MLMethodFactor.TYPE_PNN: Probabilistic Neural Network
var model = new EncogModel(data);
model.SelectMethod(data, MLMethodFactory.TypeFeedforward);
// Send any output to the console.
model.Report = new ConsoleStatusReportable();
// Now normalize the data. Encog will automatically determine the
// correct normalization
// type based on the model you chose in the last step.
data.Normalize();
// Set time series.
data.LeadWindowSize = 1;
data.LagWindowSize = WindowSize;
// Hold back some data for a final validation.
// Do not shuffle the data into a random ordering. (never shuffle
// time series)
// Use a seed of 1001 so that we always use the same holdback and
// will get more consistent results.
model.HoldBackValidation(0.3, false, 1001);
// Choose whatever is the default training type for this model.
model.SelectTrainingType(data);
// Use a 5-fold cross-validated train. Return the best method found.
// (never shuffle time series)
var bestMethod = (IMLRegression)model.Crossvalidate(5,
false);
// Display the training and validation errors.
Console.WriteLine(@"Training error: "
+ model.CalculateError(bestMethod,
model.TrainingDataset));
Console.WriteLine(@"Validation error: "
+ model.CalculateError(bestMethod,
model.ValidationDataset));
// Display our normalization parameters.
NormalizationHelper helper = data.NormHelper;
Console.WriteLine(helper.ToString());
// Display the final model.
Console.WriteLine(@"Final model: " + bestMethod);
// Loop over the entire, original, dataset and feed it through the
// model. This also shows how you would process new data, that was
// not part of your training set. You do not need to retrain, simply
// use the NormalizationHelper class. After you train, you can save
// the NormalizationHelper to later normalize and denormalize your
// data.
source.Close();
var csv = new ReadCSV(filename, true, format);
var line = new String[2];
// Create a vector to hold each time-slice, as we build them.
// These will be grouped together into windows.
var slice = new double[2];
var window = new VectorWindow(WindowSize + 1);
IMLData input = helper.AllocateInputVector(WindowSize + 1);
// Only display the first 100
int stopAfter = 100;
while (csv.Next() && stopAfter > 0)
{
var result = new StringBuilder();
line[0] = csv.Get(2); // ssn
line[1] = csv.Get(3); // dev
helper.NormalizeInputVector(line, slice, false);
// enough data to build a full window?
if (window.IsReady())
{
window.CopyWindow(((BasicMLData)input).Data, 0);
String correct = csv.Get(2); // trying to predict SSN.
IMLData output = bestMethod.Compute(input);
String predicted = helper
.DenormalizeOutputVectorToString(output)[0];
result.Append(line);
result.Append(" -> predicted: ");
result.Append(predicted);
result.Append("(correct: ");
result.Append(correct);
result.Append(")");
Console.WriteLine(result.ToString());
}
// Add the normalized slice to the window. We do this just after
// the after checking to see if the window is ready so that the
// window is always one behind the current row. This is because
// we are trying to predict next row.
window.Add(slice);
stopAfter--;
}
csv.Close();
// Delete data file and shut down.
File.Delete(filename);
EncogFramework.Instance.Shutdown();
}
/// <summary>
/// Extract fields from a file into a numeric array for machine learning.
/// </summary>
///
/// <param name="analyst">The analyst to use.</param>
/// <param name="headers">The headers for the input data.</param>
/// <param name="csv">The CSV that holds the input data.</param>
/// <param name="outputLength">The length of the returned array.</param>
/// <param name="skipOutput">True if the output should be skipped.</param>
/// <returns>The encoded data.</returns>
public static double[] ExtractFields(EncogAnalyst analyst,
CSVHeaders headers, ReadCSV csv,
int outputLength, bool skipOutput)
{
var output = new double[outputLength];
int outputIndex = 0;
foreach (AnalystField stat in analyst.Script.Normalize.NormalizedFields)
{
if (stat.Action == NormalizationAction.Ignore)
{
continue;
}
if (stat.Output && skipOutput)
{
continue;
}
int index = headers.Find(stat.Name);
String str = csv.Get(index);
// is this an unknown value?
if (str.Equals("?") || str.Length == 0)
{
IHandleMissingValues handler = analyst.Script.Normalize.MissingValues;
double[] d = handler.HandleMissing(analyst, stat);
// should we skip the entire row
if (d == null)
{
return(null);
}
// copy the returned values in place of the missing values
for (int i = 0; i < d.Length; i++)
{
output[outputIndex++] = d[i];
}
}
else
{
// known value
if (stat.Action == NormalizationAction.Normalize)
{
double d = csv.Format.Parse(str.Trim());
d = stat.Normalize(d);
output[outputIndex++] = d;
}
else
{
double[] d = stat.Encode(str.Trim());
foreach (double element in d)
{
output[outputIndex++] = element;
}
}
}
}
return(output);
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
// Download the data that we will attempt to model.
string irisFile = DownloadData(app.Args);
// Define the format of the data file.
// This area will change, depending on the columns and
// format of the file that you are trying to model.
IVersatileDataSource source = new CSVDataSource(irisFile, false,
CSVFormat.DecimalPoint);
var data = new VersatileMLDataSet(source);
data.DefineSourceColumn("sepal-length", 0, ColumnType.Continuous);
data.DefineSourceColumn("sepal-width", 1, ColumnType.Continuous);
data.DefineSourceColumn("petal-length", 2, ColumnType.Continuous);
data.DefineSourceColumn("petal-width", 3, ColumnType.Continuous);
// Define the column that we are trying to predict.
ColumnDefinition outputColumn = data.DefineSourceColumn("species", 4,
ColumnType.Nominal);
// Analyze the data, determine the min/max/mean/sd of every column.
data.Analyze();
// Map the prediction column to the output of the model, and all
// other columns to the input.
data.DefineSingleOutputOthersInput(outputColumn);
// Create feedforward neural network as the model type. MLMethodFactory.TYPE_FEEDFORWARD.
// You could also other model types, such as:
// MLMethodFactory.SVM: Support Vector Machine (SVM)
// MLMethodFactory.TYPE_RBFNETWORK: RBF Neural Network
// MLMethodFactor.TYPE_NEAT: NEAT Neural Network
// MLMethodFactor.TYPE_PNN: Probabilistic Neural Network
var model = new EncogModel(data);
model.SelectMethod(data, MLMethodFactory.TypeFeedforward);
// Send any output to the console.
model.Report = new ConsoleStatusReportable();
// Now normalize the data. Encog will automatically determine the correct normalization
// type based on the model you chose in the last step.
data.Normalize();
// Hold back some data for a final validation.
// Shuffle the data into a random ordering.
// Use a seed of 1001 so that we always use the same holdback and will get more consistent results.
model.HoldBackValidation(0.3, true, 1001);
// Choose whatever is the default training type for this model.
model.SelectTrainingType(data);
// Use a 5-fold cross-validated train. Return the best method found.
var bestMethod = (IMLRegression)model.Crossvalidate(5, true);
// Display the training and validation errors.
Console.WriteLine(@"Training error: " + model.CalculateError(bestMethod, model.TrainingDataset));
Console.WriteLine(@"Validation error: " + model.CalculateError(bestMethod, model.ValidationDataset));
// Display our normalization parameters.
NormalizationHelper helper = data.NormHelper;
Console.WriteLine(helper.ToString());
// Display the final model.
Console.WriteLine(@"Final model: " + bestMethod);
// Loop over the entire, original, dataset and feed it through the model.
// This also shows how you would process new data, that was not part of your
// training set. You do not need to retrain, simply use the NormalizationHelper
// class. After you train, you can save the NormalizationHelper to later
// normalize and denormalize your data.
source.Close();
var csv = new ReadCSV(irisFile, false, CSVFormat.DecimalPoint);
var line = new String[4];
IMLData input = helper.AllocateInputVector();
while (csv.Next())
{
var result = new StringBuilder();
line[0] = csv.Get(0);
line[1] = csv.Get(1);
line[2] = csv.Get(2);
line[3] = csv.Get(3);
String correct = csv.Get(4);
helper.NormalizeInputVector(line, ((BasicMLData)input).Data, false);
IMLData output = bestMethod.Compute(input);
String irisChosen = helper.DenormalizeOutputVectorToString(output)[0];
result.Append(line);
result.Append(" -> predicted: ");
result.Append(irisChosen);
result.Append("(correct: ");
result.Append(correct);
result.Append(")");
Console.WriteLine(result.ToString());
}
csv.Close();
// Delete data file ande shut down.
File.Delete(irisFile);
EncogFramework.Instance.Shutdown();
}
/// <summary>
/// Perform the analysis.
/// </summary>
/// <param name="target">The Encog analyst object to analyze.</param>
public void Process(EncogAnalyst target)
{
int count = 0;
CSVFormat csvFormat = ConvertStringConst
.ConvertToCSVFormat(_format);
var csv = new ReadCSV(_filename, _headers, csvFormat);
// pass one, calculate the min/max
while (csv.Next())
{
if (_fields == null)
{
GenerateFields(csv);
}
for (int i = 0; i < csv.ColumnCount; i++)
{
if (_fields != null)
{
_fields[i].Analyze1(csv.Get(i));
}
}
count++;
}
if (count == 0)
{
throw new AnalystError("Can't analyze file, it is empty.");
}
if (_fields != null)
{
foreach (AnalyzedField field in _fields)
{
field.CompletePass1();
}
}
csv.Close();
// pass two, standard deviation
csv = new ReadCSV(_filename, _headers, csvFormat);
while (csv.Next())
{
for (int i = 0; i < csv.ColumnCount; i++)
{
if (_fields != null)
{
_fields[i].Analyze2(csv.Get(i));
}
}
}
if (_fields != null)
{
foreach (AnalyzedField field in _fields)
{
field.CompletePass2();
}
}
csv.Close();
String str = _script.Properties.GetPropertyString(
ScriptProperties.SetupConfigAllowedClasses) ?? "";
bool allowInt = str.Contains("int");
bool allowReal = str.Contains("real") ||
str.Contains("double");
bool allowString = str.Contains("string");
// remove any classes that did not qualify
foreach (AnalyzedField field in _fields)
{
if (field.Class)
{
if (!allowInt && field.Integer)
{
field.Class = false;
}
if (!allowString && (!field.Integer && !field.Real))
{
field.Class = false;
}
if (!allowReal && field.Real && !field.Integer)
{
field.Class = false;
}
}
}
// merge with existing
if ((target.Script.Fields != null) &&
(_fields.Length == target.Script.Fields.Length))
{
for (int i = 0; i < _fields.Length; i++)
{
// copy the old field name
_fields[i].Name = target.Script.Fields[i].Name;
if (_fields[i].Class)
{
IList <AnalystClassItem> t = _fields[i].AnalyzedClassMembers;
IList <AnalystClassItem> s = target.Script.Fields[i].ClassMembers;
if (s.Count == t.Count)
{
for (int j = 0; j < s.Count; j++)
{
if (t[j].Code.Equals(s[j].Code))
{
t[j].Name = s[j].Name;
}
}
}
}
}
}
// now copy the fields
var df = new DataField[_fields.Length];
for (int i_4 = 0; i_4 < df.Length; i_4++)
{
df[i_4] = _fields[i_4].FinalizeField();
}
target.Script.Fields = df;
}
/// <summary>
/// Private constructor.
/// </summary>
private PropertyConstraints()
{
_data = new Dictionary <String, List <PropertyEntry> >();
try
{
Stream mask0 = ResourceLoader.CreateStream("Encog.Resources.analyst.csv");
var csv = new ReadCSV(mask0, false, CSVFormat.EgFormat);
while (csv.Next())
{
String sectionStr = csv.Get(0);
String nameStr = csv.Get(1);
String typeStr = csv.Get(2);
// determine type
PropertyType t;
if ("boolean".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeBoolean;
}
else if ("real".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeDouble;
}
else if ("format".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeFormat;
}
else if ("int".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeInteger;
}
else if ("list-string".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeListString;
}
else if ("string".Equals(typeStr, StringComparison.InvariantCultureIgnoreCase))
{
t = PropertyType.TypeString;
}
else
{
throw new AnalystError("Unknown type constraint: "
+ typeStr);
}
var entry = new PropertyEntry(t, nameStr,
sectionStr);
List <PropertyEntry> list;
if (_data.ContainsKey(sectionStr))
{
list = _data[sectionStr];
}
else
{
list = new List <PropertyEntry>();
_data[sectionStr] = list;
}
list.Add(entry);
}
csv.Close();
mask0.Close();
}
catch (IOException e)
{
throw new EncogError(e);
}
}
/// <summary>
/// Analyze the input file.
/// </summary>
/// <param name="input">The input file.</param>
/// <param name="headers">True, if there are headers.</param>
/// <param name="format">The format of the CSV data.</param>
public virtual void Analyze(FileInfo input, bool headers,
CSVFormat format)
{
ResetStatus();
InputFilename = input;
ExpectInputHeaders = headers;
Format = format;
_columnMapping.Clear();
_columns.Clear();
// first count the rows
TextReader reader = null;
try
{
int recordCount = 0;
reader = new StreamReader(InputFilename.OpenRead());
while (reader.ReadLine() != null)
{
UpdateStatus(true);
recordCount++;
}
if (headers)
{
recordCount--;
}
RecordCount = recordCount;
}
catch (IOException ex)
{
throw new QuantError(ex);
}
finally
{
ReportDone(true);
if (reader != null)
{
try
{
reader.Close();
}
catch (IOException e)
{
throw new QuantError(e);
}
}
InputFilename = input;
ExpectInputHeaders = headers;
Format = format;
}
// now analyze columns
ReadCSV csv = null;
try
{
csv = new ReadCSV(input.ToString(), headers, format);
if (!csv.Next())
{
throw new QuantError("File is empty");
}
for (int i = 0; i < csv.ColumnCount; i++)
{
String name;
if (headers)
{
name = AttemptResolveName(csv.ColumnNames[i]);
}
else
{
name = "Column-" + (i + 1);
}
// determine if it should be an input/output field
String str = csv.Get(i);
bool io = false;
try
{
Format.Parse(str);
io = true;
}
catch (FormatException ex)
{
EncogLogging.Log(ex);
}
AddColumn(new FileData(name, i, io, io));
}
}
finally
{
if (csv != null)
{
csv.Close();
}
Analyzed = true;
}
}