TreeExtensions.FastTree Metodo
Definizione
Importante
Alcune informazioni sono relative alla release non definitiva del prodotto, che potrebbe subire modifiche significative prima della release definitiva. Microsoft non riconosce alcuna garanzia, espressa o implicita, in merito alle informazioni qui fornite.
Overload
| FastTree(BinaryClassificationCatalog+BinaryClassificationTrainers, FastTreeBinaryTrainer+Options) |
Creare FastTreeBinaryTrainer con opzioni avanzate, che stimano una destinazione usando un modello di classificazione binaria dell'albero delle decisioni. |
| FastTree(RankingCatalog+RankingTrainers, FastTreeRankingTrainer+Options) |
Creare un FastTreeRankingTrainer oggetto con opzioni avanzate, che classifica una serie di input in base alla pertinenza, usando un modello di classificazione dell'albero delle decisioni. |
| FastTree(RegressionCatalog+RegressionTrainers, FastTreeRegressionTrainer+Options) |
Creare FastTreeRegressionTrainer con opzioni avanzate, che stimano una destinazione usando un modello di regressione dell'albero delle decisioni. |
| FastTree(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Int32, Double) |
Creare FastTreeBinaryTrainer, che stima una destinazione usando un modello di classificazione binaria dell'albero delle decisioni. |
| FastTree(RegressionCatalog+RegressionTrainers, String, String, String, Int32, Int32, Int32, Double) |
Creare FastTreeRegressionTrainer, che stima una destinazione usando un modello di regressione dell'albero delle decisioni. |
| FastTree(RankingCatalog+RankingTrainers, String, String, String, String, Int32, Int32, Int32, Double) |
Creare un FastTreeRankingTraineroggetto , che classifica una serie di input in base alla pertinenza, usando un modello di classificazione dell'albero delle decisioni. |
FastTree(BinaryClassificationCatalog+BinaryClassificationTrainers, FastTreeBinaryTrainer+Options)
Creare FastTreeBinaryTrainer con opzioni avanzate, che stimano una destinazione usando un modello di classificazione binaria dell'albero delle decisioni.
public static Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer FastTree (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer.Options options);static member FastTree : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer.Options -> Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer<Extension()>
Public Function FastTree (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As FastTreeBinaryTrainer.Options) As FastTreeBinaryTrainerParametri
Oggetto BinaryClassificationCatalog.
- options
- FastTreeBinaryTrainer.Options
Opzioni del formatore.
Restituisce
Esempio
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class FastTreeWithOptions
{
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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);
// Define trainer options.
var options = new FastTreeBinaryTrainer.Options
{
// Use L2Norm for early stopping.
EarlyStoppingMetric = EarlyStoppingMetric.L2Norm,
// Create a simpler model by penalizing usage of new features.
FeatureFirstUsePenalty = 0.1,
// Reduce the number of trees to 50.
NumberOfTrees = 50
};
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.FastTree(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: True
// Label: False, Prediction: False
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: False
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.78
// AUC: 0.88
// F1 Score: 0.79
// Negative Precision: 0.83
// Negative Recall: 0.74
// Positive Precision: 0.74
// Positive Recall: 0.84
// Log Loss: 0.62
// Log Loss Reduction: 37.77
// Entropy: 1.00
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 185 | 53 | 0.7773
// negative || 83 | 179 | 0.6832
// ||======================
// Precision || 0.6903 | 0.7716 |
}
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());
}
}
}
Si applica a
FastTree(RankingCatalog+RankingTrainers, FastTreeRankingTrainer+Options)
Creare un FastTreeRankingTrainer oggetto con opzioni avanzate, che classifica una serie di input in base alla pertinenza, usando un modello di classificazione dell'albero delle decisioni.
public static Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer FastTree (this Microsoft.ML.RankingCatalog.RankingTrainers catalog, Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer.Options options);static member FastTree : Microsoft.ML.RankingCatalog.RankingTrainers * Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer.Options -> Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer<Extension()>
Public Function FastTree (catalog As RankingCatalog.RankingTrainers, options As FastTreeRankingTrainer.Options) As FastTreeRankingTrainerParametri
- catalog
- RankingCatalog.RankingTrainers
Oggetto RankingCatalog.
- options
- FastTreeRankingTrainer.Options
Opzioni del formatore.
Restituisce
Esempio
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;
namespace Samples.Dynamic.Trainers.Ranking
{
public static class FastTreeWithOptions
{
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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);
// Define trainer options.
var options = new FastTreeRankingTrainer.Options
{
// Use NdcgAt3 for early stopping.
EarlyStoppingMetric = EarlyStoppingRankingMetric.NdcgAt3,
// Create a simpler model by penalizing usage of new features.
FeatureFirstUsePenalty = 0.1,
// Reduce the number of trees to 50.
NumberOfTrees = 50,
// Specify the row group column name.
RowGroupColumnName = "GroupId"
};
// Define the trainer.
var pipeline = mlContext.Ranking.Trainers.FastTree(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);
// Take the top 5 rows.
var topTransformedTestData = mlContext.Data.TakeRows(
transformedTestData, 5);
// Convert IDataView object to a list.
var predictions = mlContext.Data.CreateEnumerable<Prediction>(
topTransformedTestData, reuseRowObject: false).ToList();
// Print 5 predictions.
foreach (var p in predictions)
Console.WriteLine($"Label: {p.Label}, Score: {p.Score}");
// Expected output:
// Label: 5, Score: 8.807633
// Label: 1, Score: -10.71331
// Label: 3, Score: -8.134147
// Label: 3, Score: -6.545538
// Label: 1, Score: -10.27982
// Evaluate the overall metrics.
var metrics = mlContext.Ranking.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// DCG: @1:40.57, @2:61.21, @3:74.11
// NDCG: @1:0.96, @2:0.95, @3:0.97
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0, int groupSize = 10)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = random.Next(0, 5);
yield return new DataPoint
{
Label = (uint)label,
GroupId = (uint)(i / groupSize),
// Create random features that are correlated with the label.
// For data points with larger labels, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50).Select(
x => randomFloat() + x * 0.1f).ToArray()
};
}
}
// Example with label, groupId, and 50 feature values. A data set is a
// collection of such examples.
private class DataPoint
{
[KeyType(5)]
public uint Label { get; set; }
[KeyType(100)]
public uint GroupId { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public uint Label { get; set; }
// Score produced from the trainer.
public float Score { get; set; }
}
// Pretty-print RankerMetrics objects.
public static void PrintMetrics(RankingMetrics metrics)
{
Console.WriteLine("DCG: " + string.Join(", ",
metrics.DiscountedCumulativeGains.Select(
(d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
Console.WriteLine("NDCG: " + string.Join(", ",
metrics.NormalizedDiscountedCumulativeGains.Select(
(d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
}
}
}
Si applica a
FastTree(RegressionCatalog+RegressionTrainers, FastTreeRegressionTrainer+Options)
Creare FastTreeRegressionTrainer con opzioni avanzate, che stimano una destinazione usando un modello di regressione dell'albero delle decisioni.
public static Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer FastTree (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer.Options options);static member FastTree : Microsoft.ML.RegressionCatalog.RegressionTrainers * Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer.Options -> Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer<Extension()>
Public Function FastTree (catalog As RegressionCatalog.RegressionTrainers, options As FastTreeRegressionTrainer.Options) As FastTreeRegressionTrainerParametri
Oggetto RegressionCatalog.
Opzioni del formatore.
Restituisce
Esempio
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;
namespace Samples.Dynamic.Trainers.Regression
{
public static class FastTreeWithOptionsRegression
{
// This example requires installation of additional NuGet
// package for Microsoft.ML.FastTree found at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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);
// Define trainer options.
var options = new FastTreeRegressionTrainer.Options
{
LabelColumnName = nameof(DataPoint.Label),
FeatureColumnName = nameof(DataPoint.Features),
// Use L2-norm for early stopping. If the gradient's L2-norm is
// smaller than an auto-computed value, training process will stop.
EarlyStoppingMetric =
Microsoft.ML.Trainers.FastTree.EarlyStoppingMetric.L2Norm,
// Create a simpler model by penalizing usage of new features.
FeatureFirstUsePenalty = 0.1,
// Reduce the number of trees to 50.
NumberOfTrees = 50
};
// Define the trainer.
var pipeline =
mlContext.Regression.Trainers.FastTree(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(5, 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 for the Label, side by side with the actual
// Label for comparison.
foreach (var p in predictions)
Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
// Expected output:
// Label: 0.985, Prediction: 0.950
// Label: 0.155, Prediction: 0.111
// Label: 0.515, Prediction: 0.475
// Label: 0.566, Prediction: 0.575
// Label: 0.096, Prediction: 0.093
// Evaluate the overall metrics
var metrics = mlContext.Regression.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Mean Absolute Error: 0.03
// Mean Squared Error: 0.00
// Root Mean Squared Error: 0.03
// RSquared: 0.99 (closer to 1 is better. The worst case is 0)
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
for (int i = 0; i < count; i++)
{
float label = (float)random.NextDouble();
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
Features = Enumerable.Repeat(label, 50).Select(
x => x + (float)random.NextDouble()).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public float Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public float Label { get; set; }
// Predicted score from the trainer.
public float Score { get; set; }
}
// Print some evaluation metrics to regression problems.
private static void PrintMetrics(RegressionMetrics metrics)
{
Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
Console.WriteLine(
"Root Mean Squared Error: " + metrics.RootMeanSquaredError);
Console.WriteLine("RSquared: " + metrics.RSquared);
}
}
}
Si applica a
FastTree(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Int32, Double)
Creare FastTreeBinaryTrainer, che stima una destinazione usando un modello di classificazione binaria dell'albero delle decisioni.
public static Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer FastTree (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, int numberOfLeaves = 20, int numberOfTrees = 100, int minimumExampleCountPerLeaf = 10, double learningRate = 0.2);static member FastTree : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * int * int * int * double -> Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer<Extension()>
Public Function FastTree (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfLeaves As Integer = 20, Optional numberOfTrees As Integer = 100, Optional minimumExampleCountPerLeaf As Integer = 10, Optional learningRate As Double = 0.2) As FastTreeBinaryTrainerParametri
Oggetto BinaryClassificationCatalog.
- labelColumnName
- String
Nome della colonna dell'etichetta. I dati della colonna devono essere Boolean.
- featureColumnName
- String
Nome della colonna di funzionalità. I dati della colonna devono essere un vettore di dimensioni note di Single.
- exampleWeightColumnName
- String
Nome della colonna peso di esempio (facoltativo).
- numberOfLeaves
- Int32
Numero massimo di foglie per albero delle decisioni.
- numberOfTrees
- Int32
Numero totale di alberi delle decisioni da creare nell'ensemble.
- minimumExampleCountPerLeaf
- Int32
Numero minimo di punti dati necessari per formare una nuova foglia dell'albero.
- learningRate
- Double
Frequenza di apprendimento.
Restituisce
Esempio
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class FastTree
{
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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);
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.FastTree();
// 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: False
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: False
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.81
// AUC: 0.91
// F1 Score: 0.80
// Negative Precision: 0.82
// Negative Recall: 0.80
// Positive Precision: 0.79
// Positive Recall: 0.81
// Log Loss: 0.59
// Log Loss Reduction: 41.04
// Entropy: 1.00
//
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 185 | 53 | 0.7773
// negative || 83 | 179 | 0.6832
// ||======================
// Precision || 0.6903 | 0.7716 |
}
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());
}
}
}
Si applica a
FastTree(RegressionCatalog+RegressionTrainers, String, String, String, Int32, Int32, Int32, Double)
Creare FastTreeRegressionTrainer, che stima una destinazione usando un modello di regressione dell'albero delle decisioni.
public static Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer FastTree (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, int numberOfLeaves = 20, int numberOfTrees = 100, int minimumExampleCountPerLeaf = 10, double learningRate = 0.2);static member FastTree : Microsoft.ML.RegressionCatalog.RegressionTrainers * string * string * string * int * int * int * double -> Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer<Extension()>
Public Function FastTree (catalog As RegressionCatalog.RegressionTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfLeaves As Integer = 20, Optional numberOfTrees As Integer = 100, Optional minimumExampleCountPerLeaf As Integer = 10, Optional learningRate As Double = 0.2) As FastTreeRegressionTrainerParametri
Oggetto RegressionCatalog.
- labelColumnName
- String
Nome della colonna dell'etichetta. I dati della colonna devono essere Single.
- featureColumnName
- String
Nome della colonna di funzionalità. I dati della colonna devono essere un vettore di dimensioni note di Single.
- exampleWeightColumnName
- String
Nome della colonna peso di esempio (facoltativo).
- numberOfLeaves
- Int32
Numero massimo di foglie per albero delle decisioni.
- numberOfTrees
- Int32
Numero totale di alberi delle decisioni da creare nell'ensemble.
- minimumExampleCountPerLeaf
- Int32
Numero minimo di punti dati necessari per formare una nuova foglia dell'albero.
- learningRate
- Double
Frequenza di apprendimento.
Restituisce
Esempio
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.Regression
{
public static class FastTreeRegression
{
// This example requires installation of additional NuGet
// package for Microsoft.ML.FastTree found at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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);
// Define the trainer.
var pipeline = mlContext.Regression.Trainers.FastTree(
labelColumnName: nameof(DataPoint.Label),
featureColumnName: nameof(DataPoint.Features));
// 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(5, 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 for the Label, side by side with the actual
// Label for comparison.
foreach (var p in predictions)
Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
// Expected output:
// Label: 0.985, Prediction: 0.938
// Label: 0.155, Prediction: 0.131
// Label: 0.515, Prediction: 0.517
// Label: 0.566, Prediction: 0.519
// Label: 0.096, Prediction: 0.089
// Evaluate the overall metrics
var metrics = mlContext.Regression.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Mean Absolute Error: 0.03
// Mean Squared Error: 0.00
// Root Mean Squared Error: 0.03
// RSquared: 0.99 (closer to 1 is better. The worst case is 0)
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0)
{
var random = new Random(seed);
for (int i = 0; i < count; i++)
{
float label = (float)random.NextDouble();
yield return new DataPoint
{
Label = label,
// Create random features that are correlated with the label.
Features = Enumerable.Repeat(label, 50).Select(
x => x + (float)random.NextDouble()).ToArray()
};
}
}
// Example with label and 50 feature values. A data set is a collection of
// such examples.
private class DataPoint
{
public float Label { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public float Label { get; set; }
// Predicted score from the trainer.
public float Score { get; set; }
}
// Print some evaluation metrics to regression problems.
private static void PrintMetrics(RegressionMetrics metrics)
{
Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
Console.WriteLine(
"Root Mean Squared Error: " + metrics.RootMeanSquaredError);
Console.WriteLine("RSquared: " + metrics.RSquared);
}
}
}
Si applica a
FastTree(RankingCatalog+RankingTrainers, String, String, String, String, Int32, Int32, Int32, Double)
Creare un FastTreeRankingTraineroggetto , che classifica una serie di input in base alla pertinenza, usando un modello di classificazione dell'albero delle decisioni.
public static Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer FastTree (this Microsoft.ML.RankingCatalog.RankingTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string rowGroupColumnName = "GroupId", string exampleWeightColumnName = default, int numberOfLeaves = 20, int numberOfTrees = 100, int minimumExampleCountPerLeaf = 10, double learningRate = 0.2);static member FastTree : Microsoft.ML.RankingCatalog.RankingTrainers * string * string * string * string * int * int * int * double -> Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer<Extension()>
Public Function FastTree (catalog As RankingCatalog.RankingTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional rowGroupColumnName As String = "GroupId", Optional exampleWeightColumnName As String = Nothing, Optional numberOfLeaves As Integer = 20, Optional numberOfTrees As Integer = 100, Optional minimumExampleCountPerLeaf As Integer = 10, Optional learningRate As Double = 0.2) As FastTreeRankingTrainerParametri
- catalog
- RankingCatalog.RankingTrainers
Oggetto RankingCatalog.
- labelColumnName
- String
Nome della colonna dell'etichetta. I dati della colonna devono essere Single o KeyDataViewType.
- featureColumnName
- String
Nome della colonna di funzionalità. I dati della colonna devono essere un vettore di dimensioni note di Single.
- rowGroupColumnName
- String
Nome della colonna del gruppo.
- exampleWeightColumnName
- String
Nome della colonna peso di esempio (facoltativo).
- numberOfLeaves
- Int32
Numero massimo di foglie per albero delle decisioni.
- numberOfTrees
- Int32
Numero totale di alberi delle decisioni da creare nell'ensemble.
- minimumExampleCountPerLeaf
- Int32
Numero minimo di punti dati necessari per formare una nuova foglia dell'albero.
- learningRate
- Double
Frequenza di apprendimento.
Restituisce
Esempio
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic.Trainers.Ranking
{
public static class FastTree
{
// This example requires installation of additional NuGet package for
// Microsoft.ML.FastTree at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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);
// Define the trainer.
var pipeline = mlContext.Ranking.Trainers.FastTree();
// 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);
// Take the top 5 rows.
var topTransformedTestData = mlContext.Data.TakeRows(
transformedTestData, 5);
// Convert IDataView object to a list.
var predictions = mlContext.Data.CreateEnumerable<Prediction>(
topTransformedTestData, reuseRowObject: false).ToList();
// Print 5 predictions.
foreach (var p in predictions)
Console.WriteLine($"Label: {p.Label}, Score: {p.Score}");
// Expected output:
// Label: 5, Score: 13.0154
// Label: 1, Score: -19.27798
// Label: 3, Score: -12.43686
// Label: 3, Score: -8.178633
// Label: 1, Score: -17.09313
// Evaluate the overall metrics.
var metrics = mlContext.Ranking.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// DCG: @1:41.95, @2:63.33, @3:75.65
// NDCG: @1:0.99, @2:0.98, @3:0.99
}
private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
int seed = 0, int groupSize = 10)
{
var random = new Random(seed);
float randomFloat() => (float)random.NextDouble();
for (int i = 0; i < count; i++)
{
var label = random.Next(0, 5);
yield return new DataPoint
{
Label = (uint)label,
GroupId = (uint)(i / groupSize),
// Create random features that are correlated with the label.
// For data points with larger labels, the feature values are
// slightly increased by adding a constant.
Features = Enumerable.Repeat(label, 50).Select(
x => randomFloat() + x * 0.1f).ToArray()
};
}
}
// Example with label, groupId, and 50 feature values. A data set is a
// collection of such examples.
private class DataPoint
{
[KeyType(5)]
public uint Label { get; set; }
[KeyType(100)]
public uint GroupId { get; set; }
[VectorType(50)]
public float[] Features { get; set; }
}
// Class used to capture predictions.
private class Prediction
{
// Original label.
public uint Label { get; set; }
// Score produced from the trainer.
public float Score { get; set; }
}
// Pretty-print RankerMetrics objects.
public static void PrintMetrics(RankingMetrics metrics)
{
Console.WriteLine("DCG: " + string.Join(", ",
metrics.DiscountedCumulativeGains.Select(
(d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
Console.WriteLine("NDCG: " + string.Join(", ",
metrics.NormalizedDiscountedCumulativeGains.Select(
(d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
}
}
}
Si applica a
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