Data file: yelp_labelled.txt
SentimentData.cs
using Microsoft.ML.Data;
// </SnippetAddUsings>
namespace SentimentAnalysis
{
// <SnippetDeclareTypes>
public class SentimentData
{
[LoadColumn(0)]
public string SentimentText;
[LoadColumn(1), ColumnName("Label")]
public bool Sentiment;
}
public class SentimentPrediction : SentimentData
{
[ColumnName("PredictedLabel")]
public bool Prediction { get; set; }
public float Probability { get; set; }
public float Score { get; set; }
}
// </SnippetDeclareTypes>
}
Program.cs
// <SnippetAddUsings>
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using static Microsoft.ML.DataOperationsCatalog;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms.Text;
// </SnippetAddUsings>
namespace SentimentAnalysis
{
class Program
{
// <SnippetDeclareGlobalVariables>
static readonly string _dataPath = Path.Combine(Environment.CurrentDirectory, "Data", "yelp_labelled.txt");
// </SnippetDeclareGlobalVariables>
static void Main(string[] args)
{
// Create ML.NET context/local environment - allows you to add steps in order to keep everything together
// as you discover the ML.NET trainers and transforms
// <SnippetCreateMLContext>
MLContext mlContext = new MLContext();
// </SnippetCreateMLContext>
// <SnippetCallLoadData>
TrainTestData splitDataView = LoadData(mlContext);
// </SnippetCallLoadData>
// <SnippetCallBuildAndTrainModel>
ITransformer model = BuildAndTrainModel(mlContext, splitDataView.TrainSet);
// </SnippetCallBuildAndTrainModel>
// <SnippetCallEvaluate>
Evaluate(mlContext, model, splitDataView.TestSet);
// </SnippetCallEvaluate>
// <SnippetCallUseModelWithSingleItem>
UseModelWithSingleItem(mlContext, model);
// </SnippetCallUseModelWithSingleItem>
// <SnippetCallUseModelWithBatchItems>
UseModelWithBatchItems(mlContext, model);
// </SnippetCallUseModelWithBatchItems>
Console.WriteLine();
Console.WriteLine("=============== End of process ===============");
}
public static TrainTestData LoadData(MLContext mlContext)
{
// Note that this case, loading your training data from a file,
// is the easiest way to get started, but ML.NET also allows you
// to load data from databases or in-memory collections.
// <SnippetLoadData>
IDataView dataView = mlContext.Data.LoadFromTextFile<SentimentData>(_dataPath, hasHeader: false);
// </SnippetLoadData>
// You need both a training dataset to train the model and a test dataset to evaluate the model.
// Split the loaded dataset into train and test datasets
// Specify test dataset percentage with the `testFraction`parameter
// <SnippetSplitData>
TrainTestData splitDataView = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.2);
// </SnippetSplitData>
// <SnippetReturnSplitData>
return splitDataView;
// </SnippetReturnSplitData>
}
public static ITransformer BuildAndTrainModel(MLContext mlContext, IDataView splitTrainSet)
{
// Create a flexible pipeline (composed by a chain of estimators) for creating/training the model.
// This is used to format and clean the data.
// Convert the text column to numeric vectors (Features column)
// <SnippetFeaturizeText>
var estimator = mlContext.Transforms.Text.FeaturizeText(outputColumnName: "Features", inputColumnName: nameof(SentimentData.SentimentText))
//</SnippetFeaturizeText>
// append the machine learning task to the estimator
// <SnippetAddTrainer>
.Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: "Label", featureColumnName: "Features"));
// </SnippetAddTrainer>
// Create and train the model based on the dataset that has been loaded, transformed.
// <SnippetTrainModel>
Console.WriteLine("=============== Create and Train the Model ===============");
var model = estimator.Fit(splitTrainSet);
Console.WriteLine("=============== End of training ===============");
Console.WriteLine();
// </SnippetTrainModel>
// Returns the model we trained to use for evaluation.
// <SnippetReturnModel>
return model;
// </SnippetReturnModel>
}
public static void Evaluate(MLContext mlContext, ITransformer model, IDataView splitTestSet)
{
// Evaluate the model and show accuracy stats
//Take the data in, make transformations, output the data.
// <SnippetTransformData>
Console.WriteLine("=============== Evaluating Model accuracy with Test data===============");
IDataView predictions = model.Transform(splitTestSet);
// </SnippetTransformData>
// BinaryClassificationContext.Evaluate returns a BinaryClassificationEvaluator.CalibratedResult
// that contains the computed overall metrics.
// <SnippetEvaluate>
CalibratedBinaryClassificationMetrics metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label");
// </SnippetEvaluate>
// The Accuracy metric gets the accuracy of a model, which is the proportion
// of correct predictions in the test set.
// The AreaUnderROCCurve metric is equal to the probability that the algorithm ranks
// a randomly chosen positive instance higher than a randomly chosen negative one
// (assuming 'positive' ranks higher than 'negative').
// The F1Score metric gets the model's F1 score.
// The F1 score is the harmonic mean of precision and recall:
// 2 * precision * recall / (precision + recall).
// <SnippetDisplayMetrics>
Console.WriteLine();
Console.WriteLine("Model quality metrics evaluation");
Console.WriteLine("--------------------------------");
Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($"Auc: {metrics.AreaUnderRocCurve:P2}");
Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
Console.WriteLine("=============== End of model evaluation ===============");
//</SnippetDisplayMetrics>
}
private static void UseModelWithSingleItem(MLContext mlContext, ITransformer model)
{
// <SnippetCreatePredictionEngine1>
PredictionEngine<SentimentData, SentimentPrediction> predictionFunction = mlContext.Model.CreatePredictionEngine<SentimentData, SentimentPrediction>(model);
// </SnippetCreatePredictionEngine1>
// <SnippetCreateTestIssue1>
SentimentData sampleStatement = new SentimentData
{
SentimentText = "This was a very bad steak"
};
// </SnippetCreateTestIssue1>
// <SnippetPredict>
var resultPrediction = predictionFunction.Predict(sampleStatement);
// </SnippetPredict>
// <SnippetOutputPrediction>
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of model with a single sample and test dataset ===============");
Console.WriteLine();
Console.WriteLine($"Sentiment: {resultPrediction.SentimentText} | Prediction: {(Convert.ToBoolean(resultPrediction.Prediction) ? "Positive" : "Negative")} | Probability: {resultPrediction.Probability} ");
Console.WriteLine("=============== End of Predictions ===============");
Console.WriteLine();
// </SnippetOutputPrediction>
}
public static void UseModelWithBatchItems(MLContext mlContext, ITransformer model)
{
// Adds some comments to test the trained model's data points.
// <SnippetCreateTestIssues>
IEnumerable<SentimentData> sentiments = new[]
{
new SentimentData
{
SentimentText = "This was a horrible meal"
},
new SentimentData
{
SentimentText = "I love this spaghetti."
}
};
// </SnippetCreateTestIssues>
// Load batch comments just created
// <SnippetPrediction>
IDataView batchComments = mlContext.Data.LoadFromEnumerable(sentiments);
IDataView predictions = model.Transform(batchComments);
// Use model to predict whether comment data is Positive (1) or Negative (0).
IEnumerable<SentimentPrediction> predictedResults = mlContext.Data.CreateEnumerable<SentimentPrediction>(predictions, reuseRowObject: false);
// </SnippetPrediction>
// <SnippetAddInfoMessage>
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of loaded model with multiple samples ===============");
// </SnippetAddInfoMessage>
Console.WriteLine();
// <SnippetDisplayResults>
foreach (SentimentPrediction prediction in predictedResults)
{
Console.WriteLine($"Sentiment: {prediction.SentimentText} | Prediction: {(Convert.ToBoolean(prediction.Prediction) ? "Positive" : "Negative")} | Probability: {prediction.Probability} ");
}
Console.WriteLine("=============== End of predictions ===============");
// </SnippetDisplayResults>
}
}
}
Output:
Model quality metrics evaluation -------------------------------- Accuracy: 83.96% Auc: 89.88% F1Score: 84.38% =============== End of model evaluation =============== =============== Prediction Test of model with a single sample and test dataset =============== Sentiment: This was a very bad steak | Prediction: Negative | Probability: 0.026840538 =============== End of Predictions =============== =============== Prediction Test of loaded model with multiple samples =============== Sentiment: This was a horrible meal | Prediction: Negative | Probability: 0.041398816 Sentiment: I love this spaghetti. | Prediction: Positive | Probability: 0.99719304
https://github.com/gantovnik/wordpress_examples/tree/main/ex306
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