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Merge pull request #3882 from Dmitry-A/master
[AutoML] bring AutoML API library to master
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‎Microsoft.ML.sln

Lines changed: 89 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,6 @@
11
Microsoft Visual Studio Solution File, Format Version 12.00
2-
# Visual Studio 15
3-
VisualStudioVersion = 15.0.27130.2026
2+
# Visual Studio Version 16
3+
VisualStudioVersion = 16.0.29209.152
44
MinimumVisualStudioVersion = 10.0.40219.1
55
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Core", "src\Microsoft.ML.Core\Microsoft.ML.Core.csproj", "{A6CA6CC6-5D7C-4D7F-A0F5-35E14B383B0A}"
66
EndProject
@@ -264,6 +264,18 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.StableApi", "t
264264
EndProject
265265
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Dnn", "src\Microsoft.ML.Dnn\Microsoft.ML.Dnn.csproj", "{4C2D1A8F-7AC1-4036-B5E3-4B31769D73B8}"
266266
EndProject
267+
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.AutoML.Tests", "test\Microsoft.ML.AutoML.Tests\Microsoft.ML.AutoML.Tests.csproj", "{C2652287-CD6D-40FB-B042-95FB56D09DB8}"
268+
EndProject
269+
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.AutoML", "src\Microsoft.ML.AutoML\Microsoft.ML.AutoML.csproj", "{E48285BF-F49A-4EA3-AED0-1BDDBF77EB80}"
270+
EndProject
271+
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Microsoft.ML.AutoML", "Microsoft.ML.AutoML", "{F5D11F71-2D61-4AE9-99D7-0F0B54649B15}"
272+
ProjectSection(SolutionItems) = preProject
273+
pkg\Microsoft.ML.AutoML\Microsoft.ML.AutoML.nupkgproj = pkg\Microsoft.ML.AutoML\Microsoft.ML.AutoML.nupkgproj
274+
pkg\Microsoft.ML.AutoML\Microsoft.ML.AutoML.symbols.nupkgproj = pkg\Microsoft.ML.AutoML\Microsoft.ML.AutoML.symbols.nupkgproj
275+
EndProjectSection
276+
EndProject
277+
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.AutoML.Samples", "docs\samples\Microsoft.ML.AutoML.Samples\Microsoft.ML.AutoML.Samples.csproj", "{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}"
278+
EndProject
267279
Global
268280
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269281
Debug|Any CPU = Debug|Any CPU
@@ -1528,6 +1540,77 @@ Global
15281540
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15301542
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1543+
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{E48285BF-F49A-4EA3-AED0-1BDDBF77EB80}.Release-netfx|Any CPU.Build.0 = Release-netfx|Any CPU
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{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Debug|x64.ActiveCfg = Debug|Any CPU
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{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Debug|x64.Build.0 = Debug|Any CPU
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{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Debug-netcoreapp3_0|x64.ActiveCfg = Debug-netcoreapp3_0|Any CPU
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1599+
{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Debug-netfx|Any CPU.ActiveCfg = Debug-netfx|Any CPU
1600+
{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Debug-netfx|Any CPU.Build.0 = Debug-netfx|Any CPU
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{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Debug-netfx|x64.Build.0 = Debug-netfx|Any CPU
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{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Release|Any CPU.ActiveCfg = Release|Any CPU
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1606+
{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Release|x64.Build.0 = Release|Any CPU
1607+
{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Release-netcoreapp3_0|Any CPU.ActiveCfg = Release-netcoreapp3_0|Any CPU
1608+
{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Release-netcoreapp3_0|Any CPU.Build.0 = Release-netcoreapp3_0|Any CPU
1609+
{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Release-netcoreapp3_0|x64.ActiveCfg = Release-netcoreapp3_0|Any CPU
1610+
{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Release-netcoreapp3_0|x64.Build.0 = Release-netcoreapp3_0|Any CPU
1611+
{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Release-netfx|Any CPU.ActiveCfg = Release-netfx|Any CPU
1612+
{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Release-netfx|Any CPU.Build.0 = Release-netfx|Any CPU
1613+
{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}.Release-netfx|x64.ActiveCfg = Release-netfx|Any CPU
15311614
EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE
@@ -1610,6 +1693,10 @@ Global
16101693
{AE4F7569-26F3-4160-8A8B-7A57D0DA3350} = {D3D38B03-B557-484D-8348-8BADEE4DF592}
16111694
{F308DC6B-7E59-40D7-A581-834E8CD99CFE} = {7F13E156-3EBA-4021-84A5-CD56BA72F99E}
16121695
{4C2D1A8F-7AC1-4036-B5E3-4B31769D73B8} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
1696+
{C2652287-CD6D-40FB-B042-95FB56D09DB8} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4}
1697+
{E48285BF-F49A-4EA3-AED0-1BDDBF77EB80} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{F5D11F71-2D61-4AE9-99D7-0F0B54649B15} = {D3D38B03-B557-484D-8348-8BADEE4DF592}
1699+
{A6924919-9E37-4023-8B7F-E85C8E3CC9B3} = {DA452A53-2E94-4433-B08C-041EDEC729E6}
16131700
EndGlobalSection
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GlobalSection(ExtensibilityGlobals) = postSolution
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SolutionGuid = {41165AF1-35BB-4832-A189-73060F82B01D}

‎docs/samples/Microsoft.ML.AutoML.Samples/BinaryClassificationExperiment.cs

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using System;
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using System.IO;
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using System.Linq;
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using Microsoft.ML.AutoML;
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using Microsoft.ML.Data;
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namespace Microsoft.ML.AutoML.Samples
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{
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public static class BinaryClassificationExperiment
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{
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private static string TrainDataPath = "<Path to your train dataset goes here>";
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private static string TestDataPath = "<Path to your test dataset goes here>";
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private static string ModelPath = @"<Desired model output directory goes here>\SentimentModel.zip";
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private static uint ExperimentTime = 60;
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public static void Run()
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{
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MLContext mlContext = new MLContext();
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// STEP 1: Load data
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IDataView trainDataView = mlContext.Data.LoadFromTextFile<SentimentIssue>(TrainDataPath, hasHeader: true);
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IDataView testDataView = mlContext.Data.LoadFromTextFile<SentimentIssue>(TestDataPath, hasHeader: true);
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// STEP 2: Run AutoML experiment
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Console.WriteLine($"Running AutoML binary classification experiment for {ExperimentTime} seconds...");
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ExperimentResult<BinaryClassificationMetrics> experimentResult = mlContext.Auto()
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.CreateBinaryClassificationExperiment(ExperimentTime)
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.Execute(trainDataView);
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// STEP 3: Print metric from the best model
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RunDetail<BinaryClassificationMetrics> bestRun = experimentResult.BestRun;
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Console.WriteLine($"Total models produced: {experimentResult.RunDetails.Count()}");
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Console.WriteLine($"Best model's trainer: {bestRun.TrainerName}");
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Console.WriteLine($"Metrics of best model from validation data --");
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PrintMetrics(bestRun.ValidationMetrics);
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// STEP 4: Evaluate test data
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IDataView testDataViewWithBestScore = bestRun.Model.Transform(testDataView);
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BinaryClassificationMetrics testMetrics = mlContext.BinaryClassification.EvaluateNonCalibrated(testDataViewWithBestScore);
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Console.WriteLine($"Metrics of best model on test data --");
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PrintMetrics(testMetrics);
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// STEP 5: Save the best model for later deployment and inferencing
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using (FileStream fs = File.Create(ModelPath))
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mlContext.Model.Save(bestRun.Model, trainDataView.Schema, fs);
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// STEP 6: Create prediction engine from the best trained model
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var predictionEngine = mlContext.Model.CreatePredictionEngine<SentimentIssue, SentimentPrediction>(bestRun.Model);
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// STEP 7: Initialize a new sentiment issue, and get the predicted sentiment
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var testSentimentIssue = new SentimentIssue
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{
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Text = "I hope this helps."
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};
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var prediction = predictionEngine.Predict(testSentimentIssue);
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Console.WriteLine($"Predicted sentiment for test issue: {prediction.Prediction}");
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Console.WriteLine("Press any key to continue...");
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Console.ReadKey();
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}
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private static void PrintMetrics(BinaryClassificationMetrics metrics)
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{
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Console.WriteLine($"Accuracy: {metrics.Accuracy}");
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Console.WriteLine($"AreaUnderPrecisionRecallCurve: {metrics.AreaUnderPrecisionRecallCurve}");
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Console.WriteLine($"AreaUnderRocCurve: {metrics.AreaUnderRocCurve}");
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Console.WriteLine($"F1Score: {metrics.F1Score}");
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Console.WriteLine($"NegativePrecision: {metrics.NegativePrecision}");
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Console.WriteLine($"NegativeRecall: {metrics.NegativeRecall}");
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Console.WriteLine($"PositivePrecision: {metrics.PositivePrecision}");
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Console.WriteLine($"PositiveRecall: {metrics.PositiveRecall}");
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}
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}
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}

‎docs/samples/Microsoft.ML.AutoML.Samples/DataStructures/PixelData.cs

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using Microsoft.ML.Data;
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namespace Microsoft.ML.AutoML.Samples
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{
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public class PixelData
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{
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[LoadColumn(0, 63)]
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[VectorType(64)]
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public float[] PixelValues;
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[LoadColumn(64)]
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public float Number;
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}
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}

‎docs/samples/Microsoft.ML.AutoML.Samples/DataStructures/PixelPrediction.cs

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@@ -0,0 +1,10 @@
1+
using Microsoft.ML.Data;
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namespace Microsoft.ML.AutoML.Samples
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{
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public class PixelPrediction
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{
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[ColumnName("PredictedLabel")]
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public float Prediction;
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}
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}

‎docs/samples/Microsoft.ML.AutoML.Samples/DataStructures/SentimentIssue.cs

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1+
using Microsoft.ML.Data;
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namespace Microsoft.ML.AutoML.Samples
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{
5+
public class SentimentIssue
6+
{
7+
[LoadColumn(0)]
8+
public bool Label { get; set; }
9+
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[LoadColumn(1)]
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public string Text { get; set; }
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}
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}

‎docs/samples/Microsoft.ML.AutoML.Samples/DataStructures/SentimentPrediction.cs

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1+
using Microsoft.ML.Data;
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3+
namespace Microsoft.ML.AutoML.Samples
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{
5+
public class SentimentPrediction
6+
{
7+
// ColumnName attribute is used to change the column name from
8+
// its default value, which is the name of the field.
9+
[ColumnName("PredictedLabel")]
10+
public bool Prediction { get; set; }
11+
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public float Score { get; set; }
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}
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}

‎docs/samples/Microsoft.ML.AutoML.Samples/DataStructures/TaxiTrip.cs

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1+
using Microsoft.ML.Data;
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namespace Microsoft.ML.AutoML.Samples
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{
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public class TaxiTrip
6+
{
7+
[LoadColumn(0)]
8+
public string VendorId;
9+
10+
[LoadColumn(1)]
11+
public float RateCode;
12+
13+
[LoadColumn(2)]
14+
public float PassengerCount;
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[LoadColumn(3)]
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public float TripTimeInSeconds;
18+
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[LoadColumn(4)]
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public float TripDistance;
21+
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[LoadColumn(5)]
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public string PaymentType;
24+
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[LoadColumn(6)]
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public float FareAmount;
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}
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}

‎docs/samples/Microsoft.ML.AutoML.Samples/DataStructures/TaxiTripFarePrediction.cs

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using Microsoft.ML.Data;
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namespace Microsoft.ML.AutoML.Samples
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{
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public class TaxiTripFarePrediction
6+
{
7+
[ColumnName("Score")]
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public float FareAmount;
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}
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}

‎docs/samples/Microsoft.ML.AutoML.Samples/Microsoft.ML.AutoML.Samples.csproj

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>netcoreapp2.1</TargetFramework>
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</PropertyGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\..\src\Microsoft.ML.AutoML\Microsoft.ML.AutoML.csproj" />
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</ItemGroup>
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</Project>

‎docs/samples/Microsoft.ML.AutoML.Samples/MulticlassClassificationExperiment.cs

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using System;
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using System.IO;
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using System.Linq;
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using Microsoft.ML.AutoML;
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using Microsoft.ML.Data;
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namespace Microsoft.ML.AutoML.Samples
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{
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public static class MulticlassClassificationExperiment
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{
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private static string TrainDataPath = "<Path to your train dataset goes here>";
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private static string TestDataPath = "<Path to your test dataset goes here>";
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private static string ModelPath = @"<Desired model output directory goes here>\OptDigitsModel.zip";
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private static string LabelColumnName = "Number";
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private static uint ExperimentTime = 60;
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public static void Run()
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{
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MLContext mlContext = new MLContext();
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// STEP 1: Load data
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IDataView trainDataView = mlContext.Data.LoadFromTextFile<PixelData>(TrainDataPath, separatorChar: ',');
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IDataView testDataView = mlContext.Data.LoadFromTextFile<PixelData>(TestDataPath, separatorChar: ',');
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// STEP 2: Run AutoML experiment
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Console.WriteLine($"Running AutoML multiclass classification experiment for {ExperimentTime} seconds...");
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ExperimentResult<MulticlassClassificationMetrics> experimentResult = mlContext.Auto()
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.CreateMulticlassClassificationExperiment(ExperimentTime)
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.Execute(trainDataView, LabelColumnName);
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// STEP 3: Print metric from the best model
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RunDetail<MulticlassClassificationMetrics> bestRun = experimentResult.BestRun;
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Console.WriteLine($"Total models produced: {experimentResult.RunDetails.Count()}");
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Console.WriteLine($"Best model's trainer: {bestRun.TrainerName}");
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Console.WriteLine($"Metrics of best model from validation data --");
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PrintMetrics(bestRun.ValidationMetrics);
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// STEP 4: Evaluate test data
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IDataView testDataViewWithBestScore = bestRun.Model.Transform(testDataView);
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MulticlassClassificationMetrics testMetrics = mlContext.MulticlassClassification.Evaluate(testDataViewWithBestScore, labelColumnName: LabelColumnName);
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Console.WriteLine($"Metrics of best model on test data --");
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PrintMetrics(testMetrics);
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// STEP 5: Save the best model for later deployment and inferencing
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using (FileStream fs = File.Create(ModelPath))
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mlContext.Model.Save(bestRun.Model, trainDataView.Schema, fs);
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// STEP 6: Create prediction engine from the best trained model
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var predictionEngine = mlContext.Model.CreatePredictionEngine<PixelData, PixelPrediction>(bestRun.Model);
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// STEP 7: Initialize new pixel data, and get the predicted number
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var testPixelData = new PixelData
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{
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PixelValues = new float[] { 0, 0, 1, 8, 15, 10, 0, 0, 0, 3, 13, 15, 14, 14, 0, 0, 0, 5, 10, 0, 10, 12, 0, 0, 0, 0, 3, 5, 15, 10, 2, 0, 0, 0, 16, 16, 16, 16, 12, 0, 0, 1, 8, 12, 14, 8, 3, 0, 0, 0, 0, 10, 13, 0, 0, 0, 0, 0, 0, 11, 9, 0, 0, 0 }
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};
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var prediction = predictionEngine.Predict(testPixelData);
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Console.WriteLine($"Predicted number for test pixels: {prediction.Prediction}");
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Console.WriteLine("Press any key to continue...");
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Console.ReadKey();
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}
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private static void PrintMetrics(MulticlassClassificationMetrics metrics)
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{
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Console.WriteLine($"LogLoss: {metrics.LogLoss}");
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Console.WriteLine($"LogLossReduction: {metrics.LogLossReduction}");
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Console.WriteLine($"MacroAccuracy: {metrics.MacroAccuracy}");
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Console.WriteLine($"MicroAccuracy: {metrics.MicroAccuracy}");
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}
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}
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}

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