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Sample for ReplaceMissingValues. #2773
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using Microsoft.ML.Data; | ||
using Microsoft.ML.SamplesUtils; | ||
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namespace Microsoft.ML.Samples.Dynamic | ||
{ | ||
public static class ReplaceMissingValues | ||
{ | ||
public static void Example() | ||
{ | ||
// Creating the ML.Net IHostEnvironment object, needed for the pipeline. | ||
var mlContext = new MLContext(); | ||
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// Download the training and validation files. | ||
string dataFile = DatasetUtils.DownloadMslrWeb10k(); | ||
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You can use the There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. My feeling is that we should use in-memory data when possible. In, #2726, we discuss some of in-memory data's advantages. #Resolved |
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// Create the loader to load the data. | ||
var loader = mlContext.Data.CreateTextLoader( | ||
columns: new[] | ||
{ | ||
new TextLoader.Column("Label", DataKind.Single, 0), | ||
new TextLoader.Column("GroupId", DataKind.String, 1), | ||
new TextLoader.Column("Features", DataKind.Single, new[] { new TextLoader.Range(2, 138) }) | ||
} | ||
); | ||
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// Load the raw dataset. | ||
var data = loader.Load(dataFile); | ||
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show a preview of before the transformation too. |
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// Create the featurization pipeline. First, hash the GroupId column. | ||
var pipeline = mlContext.Transforms.Conversion.Hash("GroupId") | ||
// Replace missing values in Features column with the default replacement value for its type. | ||
.Append(mlContext.Transforms.ReplaceMissingValues("Features")); | ||
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// Fit the pipeline and transform the dataset. | ||
var transformedData = pipeline.Fit(data).Transform(data); | ||
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Can you show some output after this line? e.g. the data preview or any metrics etc. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. +1. Please also show how to inspect the prediction result example-by-example. |
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} | ||
} | ||
} |
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using Microsoft.ML.Data; | ||
using Microsoft.ML.SamplesUtils; | ||
using Microsoft.ML.Transforms; | ||
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namespace Microsoft.ML.Samples.Dynamic | ||
{ | ||
public static class ReplaceMissingValuesColumnOptions | ||
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Please rename file and class to ReplaceMissingValuesWithOptions to match other samples (although this particular one doesn't sound ideal) #Resolved |
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{ | ||
public static void Example() | ||
{ | ||
// Creating the ML.Net IHostEnvironment object, needed for the pipeline. | ||
var mlContext = new MLContext(); | ||
|
||
// Download the training and validation files. | ||
string dataFile = DatasetUtils.DownloadMslrWeb10k(); | ||
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||
// Create the loader to load the data. | ||
var loader = mlContext.Data.CreateTextLoader( | ||
columns: new[] | ||
{ | ||
new TextLoader.Column("Label", DataKind.Single, 0), | ||
new TextLoader.Column("GroupId", DataKind.String, 1), | ||
new TextLoader.Column("Features", DataKind.Single, new[] { new TextLoader.Range(2, 138) }) | ||
} | ||
); | ||
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// Load the raw dataset. | ||
var data = loader.Load(dataFile); | ||
// Create the featurization pipeline. First, hash the GroupId column. | ||
var pipeline = mlContext.Transforms.Conversion.Hash("GroupId") | ||
// Replace missing values in Features column with the default replacement value for its type. | ||
.Append(mlContext.Transforms.ReplaceMissingValues(new MissingValueReplacingEstimator.ColumnOptions("Features", "Features", MissingValueReplacingEstimator.ColumnOptions.ReplacementMode.Mean))); | ||
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please showcase that with column option two or more columns can be processed at the same time. you can have two columns and process on with Mean and the other with Minimum. also update the comment line above this line. |
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// Fit the pipeline and transform the dataset. | ||
var transformedData = pipeline.Fit(data).Transform(data); | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Please at least print out the columns affected before/after this transformation. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. |
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} | ||
} | ||
} |
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Following is the standard comments for the
MLContext
in the samples.// 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.
Please see other samples in the folder.
#Resolved