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Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v4.0.1

Package:

Microsoft.ML v1.0.0

Package:

Microsoft.ML v1.1.0

Package:

Microsoft.ML v1.2.0

Package:

Microsoft.ML v1.3.1

Package:

Microsoft.ML v1.4.0

Package:

Microsoft.ML v1.5.5

Package:

Microsoft.ML v1.6.0

Package:

Microsoft.ML v1.7.0

Package:

Microsoft.ML v2.0.1

Package:

Microsoft.ML v3.0.1

Package:

Microsoft.ML v5.0.0-preview.1.25125.4

Source:

TrainCatalog.cs

Source:

TrainCatalog.cs

Source:

TrainCatalog.cs

Important

Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.

public Microsoft.ML.Calibrators.NaiveCalibratorEstimator Naive(string labelColumnName = "Label", string scoreColumnName = "Score");

member this.Naive : string * string -> Microsoft.ML.Calibrators.NaiveCalibratorEstimator

Public Function Naive (Optional labelColumnName As String = "Label", Optional scoreColumnName As String = "Score") As NaiveCalibratorEstimator

Parameters

Returns

Examples

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;

namespace Samples.Dynamic.Trainers.BinaryClassification.Calibrators
{
 public static class Naive
 {
 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
 .Naive();

 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 0.4705882
 // Score -9.105377 Probability 0.01574803
 // Score -11.049 Probability 0
 // Score -3.061928 Probability 0.2539683
 // Score -6.375817 Probability 0.06766918
 }

 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; }
 }
 }
}