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Added OneVersusAll and PairwiseCoupling samples. - #3159
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Codecov Report
@@ Coverage Diff @@## master #3159 +/- ##
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+ Coverage 72.53% 72.64% +0.1%
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Files 808 807 -1 Lines 144740 145080 +340 Branches 16202 16213 +11 =========================================
+ Hits 104986 105391 +405 + Misses 35343 35271 -72 - Partials 4411 4418 +7
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| { | ||
| public static class OneVersusAll | ||
| { | ||
| public static void Example() |
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Example [](start = 27, length = 7)
you probably want to link this file to extension method.
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
``` #Resolved
| // Convert the string labels into key types. | ||
| mlContext.Transforms.Conversion.MapValueToKey("Label") | ||
| // Apply OneVersusAll multiclass trainer on top of SDCA Logistic Regression binary trainer. | ||
| .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression())); |
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usually multiclass pipelines add a MapKeyToValue at the end. #ByDesign
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| // Train the model. | ||
| var model = pipeline.Fit(split.TrainSet); | ||
| // Do prediction on the test set. |
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Do [](start = 15, length = 2)
Generate #Resolved
| // Micro Accuracy: 0.77 | ||
| // Macro Accuracy: 0.75 | ||
| // Log Loss: 0.69 | ||
| // Log Loss Reduction: 0.49 |
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// Log Loss Reduction: 0.49 [](start = 12, length = 29)
add reading the PredictedLabel column. #Resolved
| // Micro Accuracy: 0.75 | ||
| // Macro Accuracy: 0.73 | ||
| // Log Loss: 0.70 | ||
| // Log Loss Reduction: 0.49 |
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same here, add reading the PredictedLabel. #Resolved
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| var mlContext = new MLContext(seed: 0); | ||
| // Create a list of data examples. | ||
| var examples = DatasetUtils.GenerateRandomMulticlassClassificationExamples(1000); |
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GenerateRandomMulticlassClassificationExamples [](start = 40, length = 46)
is it possible to use something like GenerateRandomDataPoints that's used in binary classification?
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This method generates data points for multi class, with 4 labels. Except for this it does do it similarly to the GenerateRandomDataPoints. Not sure what other similarity you would want?
In reply to: 271461899 [](ancestors = 271461899)
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consider using T4 templates if you see a lot of duplicate code across multi-class samples. Below is a T4 we used for regression. All *.cs files will be autogenerated from the .tt template file. Refers to: docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.cs:1 in 01dccde. [](commit_id = 01dccde, deletion_comment = False) |
ganik
commented
Apr 2, 2019
Add NaiveBayes
| private class DataPoint | ||
| { | ||
| public uint Label { get; set; } | ||
| [VectorType(20)] |
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[VectorType(20)] [](start = 11, length = 17)
is the annotation necessary? #ByDesign
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| var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData); | ||
| SamplesUtils.ConsoleUtils.PrintMetrics(metrics); | ||
| // Expected output: |
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// Expected output: [](start = 12, length = 19)
how come this line is repeated? #Resolved
| // Look at 5 predictions | ||
| foreach (var p in predictions.Take(5)) | ||
| Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}"); | ||
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please add ExpectedOutputPerInstance after this #Resolved
| string Comments= ""; | ||
| string ExpectedOutputPerInstance= @"// Expected output: | ||
| // Label: 1, Prediction: 2 |
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Label: 1, Prediction: 2 [](start = 17, length = 23)
how come generated labels are 0,1,2 but here I see 1,2,3. how did they get changed? #Resolved
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| string ExpectedOutput = @"// Expected output: | ||
| // Expected output: | ||
| // Micro Accuracy: 0.35 |
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0.35 [](start = 30, length = 5)
can we get something above 60%? this is much worse that the other two. #ByDesign
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| // Micro Accuracy: 0.35 | ||
| // Macro Accuracy: 0.33 | ||
| // Log Loss: 34.54 | ||
| // Log Loss Reduction: -30.47 |
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we usually indent the lines below Expected output with an extra space. #Resolved
| <#=OptionsInclude#> | ||
| <# } #> | ||
| namespace Microsoft.ML.Samples.Dynamic.Trainers.MulticlassClassification |
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Microsoft.ML [](start = 10, length = 12)
please drop Microsoft.ML prefix as per #3205 #Resolved
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Part of #2522.
Adds a sample for OneVersusAll classification.
Adds a sample for PairwiseCoupling classification.