StandardTrainersCatalog.SdcaNonCalibrated Método
Definición
Importante
Parte de la información hace referencia a la versión preliminar del producto, que puede haberse modificado sustancialmente antes de lanzar la versión definitiva. Microsoft no otorga ninguna garantía, explícita o implícita, con respecto a la información proporcionada aquí.
Sobrecargas
SdcaNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SdcaNonCalibratedBinaryTrainer+Options)
Cree SdcaNonCalibratedBinaryTrainer con opciones avanzadas, que predice un destino mediante un modelo de clasificación lineal entrenado sobre datos de etiquetas booleanas.
public static Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer SdcaNonCalibrated (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer.Options options);static member SdcaNonCalibrated : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer.Options -> Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer<Extension()>
Public Function SdcaNonCalibrated (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As SdcaNonCalibratedBinaryTrainer.Options) As SdcaNonCalibratedBinaryTrainerParámetros
Objeto instructor del catálogo de clasificación binaria.
Opciones del instructor.
Devoluciones
Ejemplos
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class SdcaNonCalibratedWithOptions
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// ML.NET doesn't cache data set by default. Therefore, if one reads a
// data set from a file and accesses it many times, it can be slow due
// to expensive featurization and disk operations. When the considered
// data can fit into memory, a solution is to cache the data in memory.
// Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
trainingData = mlContext.Data.Cache(trainingData);
// Define trainer options.
var options = new SdcaNonCalibratedBinaryTrainer.Options()
{
// Specify loss function.
LossFunction = new HingeLoss(),
// Make the convergence tolerance tighter.
ConvergenceTolerance = 0.05f,
// Increase the maximum number of passes over training data.
MaximumNumberOfIterations = 30,
// Give the instances of the positive class slightly more weight.
PositiveInstanceWeight = 1.2f,
};
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.SdcaNonCalibrated(options);
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data
.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data
.CreateEnumerable<Prediction>(transformedTestData,
reuseRowObject: false).ToList();
// Print 5 predictions.
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, "
+ $"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: True, Prediction: False
// Label: False, Prediction: False
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: True
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.EvaluateNonCalibrated(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.61
// AUC: 0.67
// F1 Score: 0.65
// Negative Precision: 0.69
// Negative Recall: 0.45
// Positive Precision: 0.56
// Positive Recall: 0.77
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 178 | 60 | 0.7479
// negative || 134 | 128 | 0.4885
// ||======================
// Precision || 0.5705 | 0.6809 |
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = randomFloat() > 0.5f;
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
// For data points with false label, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50)
.Select(x => x ? randomFloat() : randomFloat() +
0.03f).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public bool Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public bool Label { get; set; }
// Predicted label from the trainer.
public bool PredictedLabel { get; set; }
}
// Pretty-print BinaryClassificationMetrics objects.
private static void PrintMetrics(BinaryClassificationMetrics metrics)
{
Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
Console.WriteLine($"Negative Precision: " +
$"{metrics.NegativePrecision:F2}");
Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
Console.WriteLine($"Positive Precision: " +
$"{metrics.PositivePrecision:F2}");
Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}
Se aplica a
SdcaNonCalibrated(MulticlassClassificationCatalog+MulticlassClassificationTrainers, SdcaNonCalibratedMulticlassTrainer+Options)
Cree SdcaNonCalibratedMulticlassTrainer con opciones avanzadas, que predice un destino mediante un modelo de clasificación multiclase lineal entrenado con un método de descenso de coordenadas.
public static Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer SdcaNonCalibrated (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer.Options options);static member SdcaNonCalibrated : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer.Options -> Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer<Extension()>
Public Function SdcaNonCalibrated (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, options As SdcaNonCalibratedMulticlassTrainer.Options) As SdcaNonCalibratedMulticlassTrainerParámetros
Objeto instructor del catálogo de clasificación multiclase.
Opciones del instructor.
Devoluciones
Ejemplos
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers;
namespace Samples.Dynamic.Trainers.MulticlassClassification
{
public static class SdcaNonCalibratedWithOptions
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// ML.NET doesn't cache data set by default. Therefore, if one reads a
// data set from a file and accesses it many times, it can be slow due
// to expensive featurization and disk operations. When the considered
// data can fit into memory, a solution is to cache the data in memory.
// Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
trainingData = mlContext.Data.Cache(trainingData);
// Define trainer options.
var options = new SdcaNonCalibratedMulticlassTrainer.Options
{
Loss = new HingeLoss(),
L1Regularization = 0.1f,
BiasLearningRate = 0.01f,
NumberOfThreads = 1
};
// Define the trainer.
var pipeline =
// Convert the string labels into key types.
mlContext.Transforms.Conversion.MapValueToKey("Label")
// Apply SdcaNonCalibrated multiclass trainer.
.Append(mlContext.MulticlassClassification.Trainers
.SdcaNonCalibrated(options));
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data
.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data
.CreateEnumerable<Prediction>(transformedTestData,
reuseRowObject: false).ToList();
// Look at 5 predictions
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, " +
$"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: 1, Prediction: 1
// Label: 2, Prediction: 2
// Label: 3, Prediction: 2
// Label: 2, Prediction: 2
// Label: 3, Prediction: 3
// Evaluate the overall metrics
var metrics = mlContext.MulticlassClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Micro Accuracy: 0.91
// Macro Accuracy: 0.91
// Log Loss: 0.22
// Log Loss Reduction: 0.80
// Confusion table
// ||========================
// PREDICTED || 0 | 1 | 2 | Recall
// TRUTH ||========================
// 0 || 145 | 0 | 15 | 0.9063
// 1 || 0 | 164 | 13 | 0.9266
// 2 || 12 | 7 | 144 | 0.8834
// ||========================
// Precision ||0.9236 |0.9591 |0.8372 |
}
// Generates random uniform doubles in [-0.5, 0.5)
// range with labels 1, 2 or 3.
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)(random.NextDouble() - 0.5);
for (int i = 0; i < count; i++)
{
// Generate Labels that are integers 1, 2 or 3
var label = random.Next(1, 4);
yield return new DataPoint
{
Label = (uint)label,
// Create random features that are correlated with the label.
// The feature values are slightly increased by adding a
// constant multiple of label.
Features = Enumerable.Repeat(label, 20)
.Select(x => randomFloat() + label * 0.2f).ToArray()
};
}
}
// Example with label and 20 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public uint Label { get; set; }
[VectorType(20)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public uint Label { get; set; }
// Predicted label from the trainer.
public uint PredictedLabel { get; set; }
}
// Pretty-print MulticlassClassificationMetrics objects.
public static void PrintMetrics(MulticlassClassificationMetrics metrics)
{
Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
Console.WriteLine(
$"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}
Se aplica a
SdcaNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, ISupportSdcaClassificationLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>)
Cree SdcaNonCalibratedBinaryTrainer, que predice un destino mediante un modelo de clasificación lineal.
public static Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer SdcaNonCalibrated (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, Microsoft.ML.Trainers.ISupportSdcaClassificationLoss lossFunction = default, float? l2Regularization = default, float? l1Regularization = default, int? maximumNumberOfIterations = default);static member SdcaNonCalibrated : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * Microsoft.ML.Trainers.ISupportSdcaClassificationLoss * Nullable<single> * Nullable<single> * Nullable<int> -> Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer<Extension()>
Public Function SdcaNonCalibrated (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional lossFunction As ISupportSdcaClassificationLoss = Nothing, Optional l2Regularization As Nullable(Of Single) = Nothing, Optional l1Regularization As Nullable(Of Single) = Nothing, Optional maximumNumberOfIterations As Nullable(Of Integer) = Nothing) As SdcaNonCalibratedBinaryTrainerParámetros
Objeto instructor del catálogo de clasificación binaria.
- featureColumnName
- String
Nombre de la columna de característica. Los datos de columna deben ser un vector de tamaño conocido de Single.
- exampleWeightColumnName
- String
Nombre de la columna de peso de ejemplo (opcional).
- lossFunction
- ISupportSdcaClassificationLoss
La función de pérdida minimizada en el proceso de entrenamiento. El valor predeterminado es LogLoss si no se especifica.
Peso L2 para regularización.
Hiperparámetros de regularización L1. Los valores más altos tienden a dar lugar a un modelo más disperso.
Número máximo de pases que se van a realizar a través de los datos.
Devoluciones
Ejemplos
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class SdcaNonCalibrated
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// ML.NET doesn't cache data set by default. Therefore, if one reads a
// data set from a file and accesses it many times, it can be slow due
// to expensive featurization and disk operations. When the considered
// data can fit into memory, a solution is to cache the data in memory.
// Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
trainingData = mlContext.Data.Cache(trainingData);
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.SdcaNonCalibrated();
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data
.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data
.CreateEnumerable<Prediction>(transformedTestData,
reuseRowObject: false).ToList();
// Print 5 predictions.
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, "
+ $"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: True, Prediction: True
// Label: False, Prediction: True
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: True
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.EvaluateNonCalibrated(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.65
// AUC: 0.69
// F1 Score: 0.64
// Negative Precision: 0.68
// Negative Recall: 0.65
// Positive Precision: 0.63
// Positive Recall: 0.66
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 154 | 84 | 0.6471
// negative || 95 | 167 | 0.6374
// ||======================
// Precision || 0.6185 | 0.6653 |
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = randomFloat() > 0.5f;
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
// For data points with false label, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50)
.Select(x => x ? randomFloat() : randomFloat() +
0.03f).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public bool Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public bool Label { get; set; }
// Predicted label from the trainer.
public bool PredictedLabel { get; set; }
}
// Pretty-print BinaryClassificationMetrics objects.
private static void PrintMetrics(BinaryClassificationMetrics metrics)
{
Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
Console.WriteLine($"Negative Precision: " +
$"{metrics.NegativePrecision:F2}");
Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
Console.WriteLine($"Positive Precision: " +
$"{metrics.PositivePrecision:F2}");
Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}
Se aplica a
SdcaNonCalibrated(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, ISupportSdcaClassificationLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>)
Cree SdcaNonCalibratedMulticlassTrainer, que predice un destino mediante un modelo de clasificación multiclase lineal entrenado con un método de descenso de coordenadas.
public static Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer SdcaNonCalibrated (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, Microsoft.ML.Trainers.ISupportSdcaClassificationLoss lossFunction = default, float? l2Regularization = default, float? l1Regularization = default, int? maximumNumberOfIterations = default);static member SdcaNonCalibrated : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * string * string * string * Microsoft.ML.Trainers.ISupportSdcaClassificationLoss * Nullable<single> * Nullable<single> * Nullable<int> -> Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer<Extension()>
Public Function SdcaNonCalibrated (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional lossFunction As ISupportSdcaClassificationLoss = Nothing, Optional l2Regularization As Nullable(Of Single) = Nothing, Optional l1Regularization As Nullable(Of Single) = Nothing, Optional maximumNumberOfIterations As Nullable(Of Integer) = Nothing) As SdcaNonCalibratedMulticlassTrainerParámetros
Objeto instructor del catálogo de clasificación multiclase.
- labelColumnName
- String
Nombre de la columna de etiquetas. Los datos de columna deben ser KeyDataViewType.
- featureColumnName
- String
Nombre de la columna de característica. Los datos de columna deben ser un vector de tamaño conocido de Single.
- exampleWeightColumnName
- String
Nombre de la columna de peso de ejemplo (opcional).
- lossFunction
- ISupportSdcaClassificationLoss
Función de pérdida que se va a minimizar. El valor predeterminado es LogLoss si no se especifica.
Peso L2 para regularización.
Hiperparámetros de regularización L1. Los valores más altos tienden a dar lugar a un modelo más disperso.
Número máximo de pases que se van a realizar a través de los datos.
Devoluciones
Ejemplos
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.MulticlassClassification
{
public static class SdcaNonCalibrated
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// ML.NET doesn't cache data set by default. Therefore, if one reads a
// data set from a file and accesses it many times, it can be slow due
// to expensive featurization and disk operations. When the considered
// data can fit into memory, a solution is to cache the data in memory.
// Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
trainingData = mlContext.Data.Cache(trainingData);
// Define the trainer.
var pipeline =
// Convert the string labels into key types.
mlContext.Transforms.Conversion
.MapValueToKey(nameof(DataPoint.Label))
// Apply SdcaNonCalibrated multiclass trainer.
.Append(mlContext.MulticlassClassification.Trainers
.SdcaNonCalibrated());
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data
.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data
.CreateEnumerable<Prediction>(transformedTestData,
reuseRowObject: false).ToList();
// Look at 5 predictions
foreach (var p in predictions.Take(5))
Console.WriteLine($"Label: {p.Label}, " +
$"Prediction: {p.PredictedLabel}");
// Expected output:
// Label: 1, Prediction: 1
// Label: 2, Prediction: 2
// Label: 3, Prediction: 2
// Label: 2, Prediction: 2
// Label: 3, Prediction: 3
// Evaluate the overall metrics
var metrics = mlContext.MulticlassClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Micro Accuracy: 0.91
// Macro Accuracy: 0.91
// Log Loss: 0.57
// Log Loss Reduction: 0.48
// Confusion table
// ||========================
// PREDICTED || 0 | 1 | 2 | Recall
// TRUTH ||========================
// 0 || 147 | 0 | 13 | 0.9188
// 1 || 0 | 165 | 12 | 0.9322
// 2 || 11 | 8 | 144 | 0.8834
// ||========================
// Precision ||0.9304 |0.9538 |0.8521 |
}
// Generates random uniform doubles in [-0.5, 0.5)
// range with labels 1, 2 or 3.
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
float randomFloat() => (float)(random.NextDouble() - 0.5);
for (int i = 0; i < count; i++)
{
// Generate Labels that are integers 1, 2 or 3
var label = random.Next(1, 4);
yield return new DataPoint
{
Label = (uint)label,
// Create random features that are correlated with the label.
// The feature values are slightly increased by adding a
// constant multiple of label.
Features = Enumerable.Repeat(label, 20)
.Select(x => randomFloat() + label * 0.2f).ToArray()
};
}
}
// Example with label and 20 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public uint Label { get; set; }
[VectorType(20)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public uint Label { get; set; }
// Predicted label from the trainer.
public uint PredictedLabel { get; set; }
}
// Pretty-print MulticlassClassificationMetrics objects.
public static void PrintMetrics(MulticlassClassificationMetrics metrics)
{
Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
Console.WriteLine(
$"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
}
}
}
Se aplica a
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