BinaryClassificationCatalog.CalibratorsCatalog.Platt 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
| Platt(Double, Double, String) |
Aggiunge la colonna di probabilità specificando il calibratore platt. |
| Platt(String, String, String) |
Aggiunge la colonna probabilità eseguendo il training del calibratore platt. |
Platt(Double, Double, String)
Aggiunge la colonna di probabilità specificando il calibratore platt.
public Microsoft.ML.Calibrators.FixedPlattCalibratorEstimator Platt (double slope, double offset, string scoreColumnName = "Score");member this.Platt : double * double * string -> Microsoft.ML.Calibrators.FixedPlattCalibratorEstimatorPublic Function Platt (slope As Double, offset As Double, Optional scoreColumnName As String = "Score") As FixedPlattCalibratorEstimatorParametri
- slope
- Double
Pendenza nella funzione dell'esponente del sigmoid.
- offset
- Double
Offset nella funzione dell'esponente del sigmoid.
- scoreColumnName
- String
Nome della colonna punteggio.
Restituisce
Esempio
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.BinaryClassification.Calibrators
{
public static class FixedPlatt
{
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);
// Download and featurize the dataset.
var data = Microsoft.ML.SamplesUtils.DatasetUtils
.LoadFeaturizedAdultDataset(mlContext);
// Leave out 10% of data for testing.
var trainTestData = mlContext.Data
.TrainTestSplit(data, testFraction: 0.3);
// Create data training pipeline for non calibrated trainer and train
// Naive calibrator on top of it.
var pipeline = mlContext.BinaryClassification.Trainers
.AveragedPerceptron();
// Fit the pipeline, and get a transformer that knows how to score new
// data.
var transformer = pipeline.Fit(trainTestData.TrainSet);
// Fit this pipeline to the training data.
// Let's score the new data. The score will give us a numerical
// estimation of the chance that the particular sample bears positive
// sentiment. This estimate is relative to the numbers obtained.
var scoredData = transformer.Transform(trainTestData.TestSet);
var outScores = mlContext.Data
.CreateEnumerable<ScoreValue>(scoredData, reuseRowObject: false);
PrintScore(outScores, 5);
// Preview of scoredDataPreview.RowView
// Score -0.09044361
// Score -9.105377
// Score -11.049
// Score -3.061928
// Score -6.375817
// Let's train a calibrator estimator on this scored dataset. The
// trained calibrator estimator produces a transformer that can
// transform the scored data by adding a new column names "Probability".
var calibratorEstimator = mlContext.BinaryClassification.Calibrators
.Platt(slope: -1f, offset: -0.05f);
var calibratorTransformer = calibratorEstimator.Fit(scoredData);
// Transform the scored data with a calibrator transfomer by adding a
// new column names "Probability". This column is a calibrated version
// of the "Score" column, meaning its values are a valid probability
// value in the [0, 1] interval representing the chance that the
// respective sample bears positive sentiment.
var finalData = calibratorTransformer.Transform(scoredData);
var outScoresAndProbabilities = mlContext.Data
.CreateEnumerable<ScoreAndProbabilityValue>(finalData,
reuseRowObject: false);
PrintScoreAndProbability(outScoresAndProbabilities, 5);
// Score -0.09044361 Probability 0.4898905
// Score -9.105377 Probability 0.0001167479
// Score -11.049 Probability 1.671815E-05
// Score -3.061928 Probability 0.04688989
// Score -6.375817 Probability 0.001786307
}
private static void PrintScore(IEnumerable<ScoreValue> values, int numRows)
{
foreach (var value in values.Take(numRows))
Console.WriteLine("{0, -10} {1, -10}", "Score", value.Score);
}
private static void PrintScoreAndProbability(
IEnumerable<ScoreAndProbabilityValue> values, int numRows)
{
foreach (var value in values.Take(numRows))
Console.WriteLine("{0, -10} {1, -10} {2, -10} {3, -10}", "Score",
value.Score, "Probability", value.Probability);
}
private class ScoreValue
{
public float Score { get; set; }
}
private class ScoreAndProbabilityValue
{
public float Score { get; set; }
public float Probability { get; set; }
}
}
}
Si applica a
Platt(String, String, String)
Aggiunge la colonna probabilità eseguendo il training del calibratore platt.
public Microsoft.ML.Calibrators.PlattCalibratorEstimator Platt (string labelColumnName = "Label", string scoreColumnName = "Score", string exampleWeightColumnName = default);member this.Platt : string * string * string -> Microsoft.ML.Calibrators.PlattCalibratorEstimatorPublic Function Platt (Optional labelColumnName As String = "Label", Optional scoreColumnName As String = "Score", Optional exampleWeightColumnName As String = Nothing) As PlattCalibratorEstimatorParametri
- labelColumnName
- String
Nome della colonna etichetta.
- scoreColumnName
- String
Nome della colonna punteggio.
- exampleWeightColumnName
- String
Nome della colonna di peso di esempio (facoltativo).
Restituisce
Esempio
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.BinaryClassification.Calibrators
{
public static class Platt
{
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);
// Download and featurize the dataset.
var data = Microsoft.ML.SamplesUtils.DatasetUtils
.LoadFeaturizedAdultDataset(mlContext);
// Leave out 10% of data for testing.
var trainTestData = mlContext.Data
.TrainTestSplit(data, testFraction: 0.3);
// Create data training pipeline for non calibrated trainer and train
// Naive calibrator on top of it.
var pipeline = mlContext.BinaryClassification.Trainers
.AveragedPerceptron();
// Fit the pipeline, and get a transformer that knows how to score new
// data.
var transformer = pipeline.Fit(trainTestData.TrainSet);
// Fit this pipeline to the training data.
// Let's score the new data. The score will give us a numerical
// estimation of the chance that the particular sample bears positive
// sentiment. This estimate is relative to the numbers obtained.
var scoredData = transformer.Transform(trainTestData.TestSet);
var outScores = mlContext.Data
.CreateEnumerable<ScoreValue>(scoredData, reuseRowObject: false);
PrintScore(outScores, 5);
// Preview of scoredDataPreview.RowView
// Score -0.09044361
// Score -9.105377
// Score -11.049
// Score -3.061928
// Score -6.375817
// Let's train a calibrator estimator on this scored dataset. The
// trained calibrator estimator produces a transformer that can
// transform the scored data by adding a new column names "Probability".
var calibratorEstimator = mlContext.BinaryClassification.Calibrators
.Platt();
var calibratorTransformer = calibratorEstimator.Fit(scoredData);
// Transform the scored data with a calibrator transfomer by adding a
// new column names "Probability". This column is a calibrated version
// of the "Score" column, meaning its values are a valid probability
// value in the [0, 1] interval representing the chance that the
// respective sample bears positive sentiment.
var finalData = calibratorTransformer.Transform(scoredData);
var outScoresAndProbabilities = mlContext.Data
.CreateEnumerable<ScoreAndProbabilityValue>(finalData,
reuseRowObject: false);
PrintScoreAndProbability(outScoresAndProbabilities, 5);
// Score -0.09044361 Probability 0.423026
// Score -9.105377 Probability 0.02139676
// Score -11.049 Probability 0.01014891
// Score -3.061928 Probability 0.1872233
// Score -6.375817 Probability 0.05956031
}
private static void PrintScore(IEnumerable<ScoreValue> values, int numRows)
{
foreach (var value in values.Take(numRows))
Console.WriteLine("{0, -10} {1, -10}", "Score", value.Score);
}
private static void PrintScoreAndProbability(
IEnumerable<ScoreAndProbabilityValue> values, int numRows)
{
foreach (var value in values.Take(numRows))
Console.WriteLine("{0, -10} {1, -10} {2, -10} {3, -10}", "Score",
value.Score, "Probability", value.Probability);
}
private class ScoreValue
{
public float Score { get; set; }
}
private class ScoreAndProbabilityValue
{
public float Score { get; set; }
public float Probability { get; set; }
}
}
}