private static void RunPerformanceTest()
{
int iterations = 25000;
Console.OutputEncoding = Encoding.UTF8;
Stopwatch stopwatch = new Stopwatch();
long total = 0;
for (int i = 0; i < iterations; i++)
{
Console.SetCursorPosition(0, 0);
stopwatch.Restart();
Table table = new Table("One", "Two", "Three");
table.Config = TableConfiguration.Unicode();
table.AddRow("1", "2", "3");
table.AddRow("Short", "item", "Here");
table.AddRow("Longer items go here", "stuff", "stuff");
string tableString = table.ToString();
total += stopwatch.ElapsedTicks;
Console.WriteLine(i);
}
Console.WriteLine(total / iterations);
}
private static void ShowExampleTables()
{
Table table = new Table("One", "Two", "Three");
table.AddRow("1", "2", "3");
table.AddRow("Short", "item", "Here");
table.AddRow("Longer items go here", "stuff stuff", "stuff");
table.Config = TableConfiguration.Default();
string test = String.Empty.PadRight(Console.WindowWidth, '-');
Console.Write(test);
Console.SetCursorPosition(0, 0);
Console.Write(table.ToString());
Console.WriteLine();
table.Config = TableConfiguration.Markdown();
Console.Write(table.ToString());
Console.WriteLine();
table.Config = TableConfiguration.MySql();
Console.Write(table.ToString());
Console.WriteLine();
table.Config = TableConfiguration.MySqlSimple();
Console.Write(table.ToString());
Console.WriteLine();
table.Config = TableConfiguration.Unicode();
Console.Write(table.ToString());
Console.WriteLine();
table.Config = TableConfiguration.UnicodeAlt();
Console.Write(table.ToString());
Console.WriteLine();
}
private static void ShowExampleTables()
{
ShowAlignedTables();
Console.WriteLine();
Table table = new Table("One", "Two", "Three");
table.AddRow("1", "2", "3");
table.AddRow("Short", "item", "Here");
table.AddRow("Longer items go here", "stuff stuff", "stuff");
table.Config = TableConfiguration.Default();
Console.Write(table.ToString());
Console.WriteLine();
table.Config = TableConfiguration.Markdown();
Console.Write(table.ToString());
Console.WriteLine();
table.Config = TableConfiguration.MySql();
Console.Write(table.ToString());
Console.WriteLine();
table.Config = TableConfiguration.MySqlSimple();
Console.Write(table.ToString());
Console.WriteLine();
table.Config = TableConfiguration.Unicode();
Console.Write(table.ToString());
Console.WriteLine();
table.Config = TableConfiguration.UnicodeAlt();
Console.Write(table.ToString());
Console.WriteLine();
}
/// <summary>
/// Create table
/// </summary>
/// <param name="columns"></param>
/// <param name="rows"></param>
/// <param name="outputType"></param>
/// <param name="hasInnerRows"></param>
/// <returns></returns>
private static string Create(IEnumerable <ColumnHeader> columns,
IEnumerable <object[]> rows,
TableOutputType outputType,
bool hasInnerRows)
{
var table = new Table(columns.ToArray());
switch (outputType)
{
case TableOutputType.Text: table.Config = TableConfiguration.Default(); break;
case TableOutputType.Unicode: table.Config = TableConfiguration.Unicode(); break;
case TableOutputType.UnicodeAlt: table.Config = TableConfiguration.UnicodeAlt(); break;
case TableOutputType.Markdown: table.Config = TableConfiguration.Markdown(); break;
case TableOutputType.Html: table.Config = TableConfiguration.Unicode(); break;
default: break;
}
table.Config.hasInnerRows = hasInnerRows;
table.AddRows(rows);
var ret = table.ToString();
if (outputType == TableOutputType.Html)
{
ret = ToHtml(ret);
}
return(ret);
}
private static void RenderingTables()
{
var rand = new Random();
int num1 = rand.Next(), num2 = rand.Next(), num3 = rand.Next();
Table table = new Table("One", "Two", "Three")
.AddRow(num1++, num2++, num3++)
.AddRow("Short", "item", "Here")
.AddRow("Longer items go here", "stuff stuff", "stuff");
table.Config = TableConfiguration.Unicode();
var table2 = new Table("Log")
.AddRow($"{DateTime.Now} log something 1")
.AddRow($"{DateTime.Now} log something 2")
.AddRow($"{DateTime.Now} log something 3")
.AddRow($"{DateTime.Now} log something 4")
.AddRow($"{DateTime.Now} log something 5 ver long ........");
table2.Config = TableConfiguration.Unicode();
var tables = new ConsoleTables(table, table2);
System.Console.WriteLine(tables.ToString());
System.Console.WriteLine("Press CTRL+C to terminate watch log.");
}
/// <summary>
/// Helper method to write the machine learning pipeline to the console.
/// </summary>
/// <param name="mlContext">The machine learning context.</param>
/// <param name="dataView">The data view object holding the data.</param>
/// <param name="pipeline">The machine learning pipeline.</param>
/// <param name="numberOfRows">The maximum number of rows to write.</param>
public static void WritePipeline(MLContext mlContext, IDataView dataView, IEstimator <ITransformer> pipeline, int numberOfRows = 4)
{
// helper method to write a value to the console table
object WriteValue(object value)
{
if (value is VBuffer <float> )
{
return("<vector>");
}
else
{
return(value);
}
}
// get a preview of the transformed data
var transformer = pipeline.Fit(dataView);
var transformedData = transformer.Transform(dataView);
var preview = transformedData.Preview(maxRows: numberOfRows);
// set up a console table
var table = new Table(
TableConfiguration.Unicode(),
(from c in preview.ColumnView select c.Column.Name).ToArray());
// fill the table with results
foreach (var row in preview.RowView)
{
table.AddRow((from c in row.Values select WriteValue(c.Value)).ToArray());
}
// write the table
Console.WriteLine(table.ToString());
}
public static void WriteTable(this ICollection <Claim> claims, TextWriter writer = null)
{
var table = new Table("id", "add player", "drop player")
{
Config = TableConfiguration.Unicode()
};
if (claims.Any())
{
table.AddRows(claims
.OrderBy(c => c.Priority)
.ThenBy(c => c.Id)
.Select(c => new[]
{
c.Id.ToString(),
c.Add.Description,
c.Drop.Description
}));
}
else
{
table.AddRow("", "", "");
}
(writer ?? Console.Out).Write(new ConsoleTables(table));
}
public static void Print(Competion competion)
{
if (competion == null)
{
throw new ArgumentNullException(nameof(competion));
}
var printPlayers = new Table("Player");
var printWinners = new Table("Comp ID", "Game ID", "Winner");
var printCompetitors = new Table("Game ID", "Name", "Score", "Winner");
// printWinners.Config = TableConfiguration.Markdown();
printPlayers.Config = TableConfiguration.Unicode();
printWinners.Config = TableConfiguration.Unicode();
printCompetitors.Config = TableConfiguration.Unicode();
foreach (var name in competion.Players.Select(p => p.FullName))
{
printPlayers.AddRow(name);
}
foreach (var game in competion.Games)
{
printWinners.AddRow(competion.Id, game.Id, game.Winner.Player.FullName);
foreach (var competitor in game.Competitors)
{
printCompetitors.AddRow(game.Id, competitor.Player.FullName, competitor.Score, competitor.Id == game.Winner.Id ? ":) - Winner" : ":(");
}
}
var tables = new ConsoleTables(printPlayers, printWinners, printCompetitors);
Console.Write(tables.ToString());
}
public static void Run()
{
Console.OutputEncoding = Encoding.UTF8;
Clock.BenchmarkTime(() =>
{
Table table = new Table("One", "Two", "Three");
table.Config = TableConfiguration.Unicode();
table.AddRow("1", "2", "3");
table.AddRow("Short", "item", "Here");
table.AddRow("Longer items go here", "stuff", "stuff");
string tableString = table.ToString();
}, 100, 500);
}
/// <summary>
/// Create table
/// </summary>
/// <param name="columns"></param>
/// <param name="rows"></param>
/// <param name="outputType"></param>
/// <param name="hasInnerRows"></param>
/// <returns></returns>
private static string Create(IEnumerable <ColumnHeader> columns,
IEnumerable <object[]> rows,
TableOutputType outputType,
bool hasInnerRows)
{
string ret;
if (rows.Count() == 0)
{
ret = "";
}
else if (outputType == TableOutputType.Html)
{
ret = ToHtml(columns, rows);
}
else if (outputType == TableOutputType.Json)
{
ret = ToJson(columns.ToArray(), rows, false);
}
else if (outputType == TableOutputType.JsonPretty)
{
ret = ToJson(columns.ToArray(), rows, true);
}
else
{
var table = new Table(columns.ToArray());
switch (outputType)
{
case TableOutputType.Text: table.Config = TableConfiguration.Default(); break;
case TableOutputType.Unicode: table.Config = TableConfiguration.Unicode(); break;
case TableOutputType.UnicodeAlt: table.Config = TableConfiguration.UnicodeAlt(); break;
case TableOutputType.Markdown: table.Config = TableConfiguration.Markdown(); break;
default: break;
}
table.Config.hasInnerRows = hasInnerRows;
table.AddRows(rows);
ret = table.ToString();
}
return(ret);
}
/// <summary>
/// Helper method to write the machine learning pipeline to the console.
/// </summary>
/// <param name="preview">The data preview to write.</param>
/// <param name="numberOfRows">The maximum number of rows to write.</param>
public static void WritePreview(DataDebuggerPreview preview, int numberOfRows = 10)
{
// set up a console table
var table = new Table(
TableConfiguration.Unicode(),
(from c in preview.ColumnView select c.Column.Name).ToArray());
// fill the table with results
foreach (var row in preview.RowView)
{
table.AddRow((from c in row.Values
select c.Value is VBuffer <float>? "<vector>" : c.Value
).ToArray());
}
// write the table
Console.WriteLine(table.ToString());
}
/// <summary>
/// Helper method to write a single column preview to the console.
/// </summary>
/// <param name="preview">The data preview to write.</param>
/// <param name="column">The name of the column to write.</param>
public static void WritePreviewColumn(DataDebuggerPreview preview, string column)
{
// set up a console table
var table = new Table(TableConfiguration.Unicode(), new ColumnHeader(column));
// fill the table with results
foreach (var row in preview.RowView)
{
foreach (var col in row.Values)
{
if (col.Key == column)
{
var vector = (VBuffer <float>)col.Value;
table.AddRow(string.Concat(vector.DenseValues()));
}
}
}
// write the table
Console.WriteLine(table.ToString());
}
public static void WriteTable(this ICollection <PlayerResponse> players, TextWriter writer = null)
{
var table = new Table("id", "name", "pos", "team")
{
Config = TableConfiguration.Unicode()
};
if (players.Any())
{
table.AddRows(players
.OrderBy(p => p.Name.Last, StringComparer.OrdinalIgnoreCase)
.ThenBy(p => p.Name.First, StringComparer.OrdinalIgnoreCase)
.Select(p => new[] { p.PlayerId.ToString(), p.Name.Full, p.Position, p.TeamAbbr }));
}
else
{
table.AddRow("", "", "", "");
}
(writer ?? Console.Out).Write(new ConsoleTables(table));
}
/// <summary>
/// Helper method to write the machine learning pipeline to the console.
/// </summary>
/// <param name="preview">The data preview to write.</param>
public static void WritePreview(DataDebuggerPreview preview)
{
// set up a console table
var table = new Table(
TableConfiguration.Unicode(),
preview.ColumnView.Select(c => new ColumnHeader(c.Column.Name)).ToArray());
// fill the table with results
foreach (var row in preview.RowView)
{
table.AddRow((from c in row.Values
select c.Value is VBuffer <float>? "<vector>" : c.Value
).ToArray());
}
// write the table
Console.WriteLine(table.ToString());
// set up a console table
table = new Table(TableConfiguration.Unicode(), new ColumnHeader("Location"));
// fill the table with results
foreach (var row in preview.RowView)
{
foreach (var col in row.Values)
{
if (col.Key == "Location")
{
var vector = (VBuffer <float>)col.Value;
table.AddRow(string.Concat(vector.DenseValues()));
}
}
}
// write the table
Console.WriteLine(table.ToString());
}
/// <summary>
/// The main program entry point.
/// </summary>
/// <param name="args">The command line arguments.</param>
static void Main(string[] args)
{
// create a machine learning context
var context = new MLContext();
// load data
Console.WriteLine("Loading data....");
var columnDef = new TextLoader.Column[]
{
new TextLoader.Column(nameof(Digit.PixelValues), DataKind.Single, 1, 784),
new TextLoader.Column("Number", DataKind.Single, 0)
};
var trainDataView = context.Data.LoadFromTextFile(
path: trainDataPath,
columns: columnDef,
hasHeader: true,
separatorChar: ',');
var testDataView = context.Data.LoadFromTextFile(
path: testDataPath,
columns: columnDef,
hasHeader: true,
separatorChar: ',');
// build a training pipeline
// step 1: map the number column to a key value and store in the label column
var pipeline = context.Transforms.Conversion.MapValueToKey(
outputColumnName: "Label",
inputColumnName: "Number",
keyOrdinality: ValueToKeyMappingEstimator.KeyOrdinality.ByValue)
// step 2: concatenate all feature columns
.Append(context.Transforms.Concatenate(
"Features",
nameof(Digit.PixelValues)))
// step 3: cache data to speed up training
.AppendCacheCheckpoint(context)
// step 4: train the model with SDCA
.Append(context.MulticlassClassification.Trainers.SdcaMaximumEntropy(
labelColumnName: "Label",
featureColumnName: "Features"))
// step 5: map the label key value back to a number
.Append(context.Transforms.Conversion.MapKeyToValue(
outputColumnName: "Number",
inputColumnName: "Label"));
// train the model
Console.WriteLine("Training model....");
var model = pipeline.Fit(trainDataView);
// use the model to make predictions on the test data
Console.WriteLine("Evaluating model....");
var predictions = model.Transform(testDataView);
// evaluate the predictions
var metrics = context.MulticlassClassification.Evaluate(
data: predictions,
labelColumnName: "Number",
scoreColumnName: "Score");
// show evaluation metrics
Console.WriteLine($"Evaluation metrics");
Console.WriteLine($" MicroAccuracy: {metrics.MicroAccuracy:0.###}");
Console.WriteLine($" MacroAccuracy: {metrics.MacroAccuracy:0.###}");
Console.WriteLine($" LogLoss: {metrics.LogLoss:#.###}");
Console.WriteLine($" LogLossReduction: {metrics.LogLossReduction:#.###}");
Console.WriteLine();
// grab three digits from the test data
var digits = context.Data.CreateEnumerable <Digit>(testDataView, reuseRowObject: false).ToArray();
var testDigits = new Digit[] { digits[5], digits[16], digits[28], digits[63], digits[129] };
// create a prediction engine
var engine = context.Model.CreatePredictionEngine <Digit, DigitPrediction>(model);
// set up a table to show the predictions
var table = new Table(TableConfiguration.Unicode());
table.AddColumn("Digit");
for (var i = 0; i < 10; i++)
{
table.AddColumn($"P{i}");
}
// predict each test digit
for (var i = 0; i < testDigits.Length; i++)
{
var prediction = engine.Predict(testDigits[i]);
table.AddRow(
testDigits[i].Number,
prediction.Score[0].ToString("P2"),
prediction.Score[1].ToString("P2"),
prediction.Score[2].ToString("P2"),
prediction.Score[3].ToString("P2"),
prediction.Score[4].ToString("P2"),
prediction.Score[5].ToString("P2"),
prediction.Score[6].ToString("P2"),
prediction.Score[7].ToString("P2"),
prediction.Score[8].ToString("P2"),
prediction.Score[9].ToString("P2"));
}
// show results
Console.WriteLine(table.ToString());
Console.ReadKey();
}
static void Main(string[] args)
{
var context = new MLContext();
Console.WriteLine("Loading Data...");
var colDef = new TextLoader.Column[] {
new TextLoader.Column(nameof(Digit.PixelValues), DataKind.Single, 1, 784),
new TextLoader.Column("Number", DataKind.Single, 0)
};
var trainDataView = context.Data.LoadFromTextFile(trainDataPath, colDef, hasHeader: true, separatorChar: ',');
var testDataView = context.Data.LoadFromTextFile(testDataPath, colDef, hasHeader: true, separatorChar: ',');
var pipeline = context.Transforms.Conversion.MapValueToKey(outputColumnName: "Label", inputColumnName: "Number", keyOrdinality: ValueToKeyMappingEstimator.KeyOrdinality.ByValue)
.Append(context.Transforms.Concatenate("Features", nameof(Digit.PixelValues)))
.AppendCacheCheckpoint(context)
.Append(context.MulticlassClassification.Trainers.OneVersusAll(context.BinaryClassification.Trainers.FastForest(), "Label"))
.Append(context.Transforms.Conversion.MapKeyToValue(outputColumnName: "Number", inputColumnName: "Label"));
Console.WriteLine("Training the model...");
var model = pipeline.Fit(trainDataView);
Console.WriteLine("Evaluating model...");
var predictions = model.Transform(testDataView);
var metrics = context.MulticlassClassification.Evaluate(predictions, labelColumnName: "Number", scoreColumnName: "Score");
// show evaluation metrics
Console.WriteLine($"Evaluation metrics");
Console.WriteLine($" MicroAccuracy: {metrics.MicroAccuracy:0.###}");
Console.WriteLine($" MacroAccuracy: {metrics.MacroAccuracy:0.###}");
Console.WriteLine($" LogLoss: {metrics.LogLoss:#.###}");
Console.WriteLine($" LogLossReduction: {metrics.LogLossReduction:#.###}");
Console.WriteLine();
var digits = context.Data.CreateEnumerable <Digit>(testDataView, false).ToArray();
var testDigits = new Digit[] {
digits[215], // 0
digits[202], // 1
digits[199], // 2
digits[200], // 3
digits[198], // 4
digits[207], // 5
digits[201], // 6
digits[220], // 7
digits[226], // 8
digits[235] // 9
};
var engine = context.Model.CreatePredictionEngine <Digit, DigitPrediction>(model);
var table = new BetterConsoleTables.Table(TableConfiguration.Unicode());
table.AddColumn("Digits");
for (var i = 0; i < 10; i++)
{
table.AddColumn($"P{i}");
}
for (var i = 0; i < testDigits.Length; i++)
{
var prediction = engine.Predict(testDigits[i]);
table.AddRow(
testDigits[i].Number,
prediction.Score[0].ToString("P2"),
prediction.Score[1].ToString("P2"),
prediction.Score[2].ToString("P2"),
prediction.Score[3].ToString("P2"),
prediction.Score[4].ToString("P2"),
prediction.Score[5].ToString("P2"),
prediction.Score[6].ToString("P2"),
prediction.Score[7].ToString("P2"),
prediction.Score[8].ToString("P2"),
prediction.Score[9].ToString("P2"));
}
// show results
Console.WriteLine(table.ToString());
}