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using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;
namespace Samples.Dynamic
{
class ReplaceMissingValues
{
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var mlContext = new MLContext();
// Get a small dataset as an IEnumerable and convert it to an IDataView.
var samples = new List<DataPoint>()
{
new DataPoint(){ Features = new float[3] {float.PositiveInfinity, 1,
0 } },
new DataPoint(){ Features = new float[3] {0, float.NaN, 1} },
new DataPoint(){ Features = new float[3] {-1, 2, -3} },
new DataPoint(){ Features = new float[3] {-1, float.NaN, -3} },
};
var data = mlContext.Data.LoadFromEnumerable(samples);
// Here we use the default replacement mode, which replaces the value
// with the default value for its type.
var defaultPipeline = mlContext.Transforms.ReplaceMissingValues(
"MissingReplaced", "Features", MissingValueReplacingEstimator
.ReplacementMode.DefaultValue);
// Now we can transform the data and look at the output to confirm the
// behavior of the estimator. This operation doesn't actually evaluate
// data until we read the data below.
var defaultTransformer = defaultPipeline.Fit(data);
var defaultTransformedData = defaultTransformer.Transform(data);
// We can extract the newly created column as an IEnumerable of
// SampleDataTransformed, the class we define below.
var defaultRowEnumerable = mlContext.Data.CreateEnumerable<
SampleDataTransformed>(defaultTransformedData, reuseRowObject:
false);
// And finally, we can write out the rows of the dataset, looking at the
// columns of interest.
foreach (var row in defaultRowEnumerable)
Console.WriteLine("Features: [" + string.Join(", ", row.Features) +
"]\t MissingReplaced: [" + string.Join(", ", row
.MissingReplaced) + "]");
// Expected output:
// Features: [∞, 1, 0] MissingReplaced: [∞, 1, 0]
// Features: [0, NaN, 1] MissingReplaced: [0, 0, 1]
// Features: [-1, 2, -3] MissingReplaced: [-1, 2, -3]
// Features: [-1, NaN, -3] MissingReplaced: [-1, 0, -3]
// Here we use the mean replacement mode, which replaces the value with
// the mean of the non values that were not missing.
var meanPipeline = mlContext.Transforms.ReplaceMissingValues(
"MissingReplaced", "Features", MissingValueReplacingEstimator
.ReplacementMode.Mean);
// Now we can transform the data and look at the output to confirm the
// behavior of the estimator. This operation doesn't actually evaluate
// data until we read the data below.
var meanTransformer = meanPipeline.Fit(data);
var meanTransformedData = meanTransformer.Transform(data);
// We can extract the newly created column as an IEnumerable of
// SampleDataTransformed, the class we define below.
var meanRowEnumerable = mlContext.Data.CreateEnumerable<
SampleDataTransformed>(meanTransformedData, reuseRowObject: false);
// And finally, we can write out the rows of the dataset, looking at the
// columns of interest.
foreach (var row in meanRowEnumerable)
Console.WriteLine("Features: [" + string.Join(", ", row.Features) +
"]\t MissingReplaced: [" + string.Join(", ", row
.MissingReplaced) + "]");
// Expected output:
// Features: [∞, 1, 0] MissingReplaced: [∞, 1, 0]
// Features: [0, NaN, 1] MissingReplaced: [0, 1.5, 1]
// Features: [-1, 2, -3] MissingReplaced: [-1, 2, -3]
// Features: [-1, NaN, -3] MissingReplaced: [-1, 1.5, -3]
}
private class DataPoint
{
[VectorType(3)]
public float[] Features { get; set; }
}
private sealed class SampleDataTransformed : DataPoint
{
[VectorType(3)]
public float[] MissingReplaced { get; set; }
}
}
}
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