BinaryClassificationCatalog.CalibratorsCatalog.Platt Yöntem
Tanım
Önemli
Bazı bilgiler ürünün ön sürümüyle ilgilidir ve sürüm öncesinde önemli değişiklikler yapılmış olabilir. Burada verilen bilgilerle ilgili olarak Microsoft açık veya zımni hiçbir garanti vermez.
Aşırı Yüklemeler
| Platt(Double, Double, String) |
Platt calibrator belirterek olasılık sütunu ekler. |
| Platt(String, String, String) |
Platt calibrator eğiterek olasılık sütunu ekler. |
Platt(Double, Double, String)
Platt calibrator belirterek olasılık sütunu ekler.
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 FixedPlattCalibratorEstimatorParametreler
- slope
- Double
Sigmoid üssü işlevinin eğimi.
- offset
- Double
Sigmoid üssü işlevinin uzaklığı.
- scoreColumnName
- String
Puan sütununun adı.
Döndürülenler
Örnekler
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; }
}
}
}
Şunlara uygulanır
Platt(String, String, String)
Platt calibrator eğiterek olasılık sütunu ekler.
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 PlattCalibratorEstimatorParametreler
- labelColumnName
- String
Etiket sütununun adı.
- scoreColumnName
- String
Puan sütununun adı.
- exampleWeightColumnName
- String
Örnek ağırlık sütununun adı (isteğe bağlı).
Döndürülenler
Örnekler
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; }
}
}
}