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using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class ProduceHashedNgrams
{
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();
// Create a small dataset as an IEnumerable.
var samples = new List<TextData>()
{
new TextData(){ Text = "This is an example to compute n-grams " +
"using hashing." },
new TextData(){ Text = "N-gram is a sequence of 'N' consecutive" +
" words/tokens." },
new TextData(){ Text = "ML.NET's ProduceHashedNgrams API " +
"produces count of n-grams and hashes it as an index into a " +
"vector of given bit length." },
new TextData(){ Text = "The hashing reduces the size of the " +
"output feature vector" },
new TextData(){ Text = "which is useful in case when number of " +
"n-grams is very large." },
};
// Convert training data to IDataView.
var dataview = mlContext.Data.LoadFromEnumerable(samples);
// A pipeline for converting text into numeric hashed n-gram features.
// The following call to 'ProduceHashedNgrams' requires the tokenized
// text /string as input. This is achieved by calling
// 'TokenizeIntoWords' first followed by 'ProduceHashedNgrams'.
// Please note that the length of the output feature vector depends on
// the 'numberOfBits' settings.
var textPipeline = mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
"Text")
.Append(mlContext.Transforms.Conversion.MapValueToKey("Tokens"))
.Append(mlContext.Transforms.Text.ProduceHashedNgrams(
"NgramFeatures", "Tokens",
numberOfBits: 5,
ngramLength: 3,
useAllLengths: false,
maximumNumberOfInverts: 1));
// Fit to data.
var textTransformer = textPipeline.Fit(dataview);
var transformedDataView = textTransformer.Transform(dataview);
// Create the prediction engine to get the features extracted from the
// text.
var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
TransformedTextData>(textTransformer);
// Convert the text into numeric features.
var prediction = predictionEngine.Predict(samples[0]);
// Print the length of the feature vector.
Console.WriteLine("Number of Features: " + prediction.NgramFeatures
.Length);
// Preview of the produced n-grams.
// Get the slot names from the column's metadata.
// The slot names for a vector column corresponds to the names
// associated with each position in the vector.
VBuffer<ReadOnlyMemory<char>> slotNames = default;
transformedDataView.Schema["NgramFeatures"].GetSlotNames(ref slotNames);
var NgramFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(
transformedDataView.Schema["NgramFeatures"]);
var slots = slotNames.GetValues();
Console.Write("N-grams: ");
foreach (var featureRow in NgramFeaturesColumn)
{
foreach (var item in featureRow.Items())
Console.Write($"{slots[item.Key]} ");
Console.WriteLine();
}
// Print the first 10 feature values.
Console.Write("Features: ");
for (int i = 0; i < 10; i++)
Console.Write($"{prediction.NgramFeatures[i]:F4} ");
// Expected output:
// Number of Features: 32
// N-grams: This|is|an example|to|compute compute|n-grams|using n-grams|using|hashing. an|example|to is|an|example a|sequence|of of|'N'|consecutive is|a|sequence N-gram|is|a ...
// Features: 0.0000 0.0000 2.0000 0.0000 0.0000 1.0000 0.0000 0.0000 1.0000 0.0000 ...
}
private class TextData
{
public string Text { get; set; }
}
private class TransformedTextData : TextData
{
public float[] NgramFeatures { get; set; }
}
}
}
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