docs/machine-learning/tutorials/snippets/text-classification-tf/csharp/Program.cs - main - aprogrammer/dotnet-docs

docs/machine-learning/tutorials/snippets/text-classification-tf/csharp/Program.cs
185 строк6 KB
Genevieve Warren
Fix nullability warnings (/machine-learning and /standard) (#35124)
01 май 2023, 23:59
01 май 2023, 23:5920f4c58
// <SnippetAddUsings>
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;
// </SnippetAddUsings>

// <SnippetDeclareGlobalVariables>
string _modelPath = Path.Combine(Environment.CurrentDirectory, "sentiment_model");
// </SnippetDeclareGlobalVariables>

// Create MLContext to be shared across the model creation workflow objects
// <SnippetCreateMLContext>
MLContext mlContext = new MLContext();
// </SnippetCreateMLContext>

// Dictionary to encode words as integers.
// <SnippetCreateLookupMap>
var lookupMap = mlContext.Data.LoadFromTextFile(Path.Combine(_modelPath, "imdb_word_index.csv"),
    columns: new[]
        {
            new TextLoader.Column("Words", DataKind.String, 0),
            new TextLoader.Column("Ids", DataKind.Int32, 1),
        },
    separatorChar: ','
    );
// </SnippetCreateLookupMap>

// The model expects the input feature vector to be a fixed length vector.
// This action resizes the variable length array generated by the lookup map
// to a fixed length vector. If there are less than 600 words in the sentence,
// the remaining indices will be filled with zeros. If there are more than
// 600 words in the sentence, then the array is truncated at 600.
// <SnippetResizeFeatures>
Action<VariableLength, FixedLength> ResizeFeaturesAction = (s, f) =>
{
    var features = s.VariableLengthFeatures;
    Array.Resize(ref features, Config.FeatureLength);
    f.Features = features;
};
// </SnippetResizeFeatures>

// Load the TensorFlow model.
// <SnippetLoadTensorFlowModel>
TensorFlowModel tensorFlowModel = mlContext.Model.LoadTensorFlowModel(_modelPath);
// </SnippetLoadTensorFlowModel>

// <SnippetGetModelSchema>
DataViewSchema schema = tensorFlowModel.GetModelSchema();
Console.WriteLine(" =============== TensorFlow Model Schema =============== ");
var featuresType = (VectorDataViewType)schema["Features"].Type;
Console.WriteLine($"Name: Features, Type: {featuresType.ItemType.RawType}, Size: ({featuresType.Dimensions[0]})");
var predictionType = (VectorDataViewType)schema["Prediction/Softmax"].Type;
Console.WriteLine($"Name: Prediction/Softmax, Type: {predictionType.ItemType.RawType}, Size: ({predictionType.Dimensions[0]})");

// </SnippetGetModelSchema>

// <SnippetTokenizeIntoWords>
IEstimator<ITransformer> pipeline =
    // Split the text into individual words
    mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "ReviewText")
    // </SnippetTokenizeIntoWords>

    // <SnippetMapValue>
    // Map each word to an integer value. The array of integer makes up the input features.
    .Append(mlContext.Transforms.Conversion.MapValue("VariableLengthFeatures", lookupMap,
        lookupMap.Schema["Words"], lookupMap.Schema["Ids"], "TokenizedWords"))
    // </SnippetMapValue>

    // <SnippetCustomMapping>
    // Resize variable length vector to fixed length vector.
    .Append(mlContext.Transforms.CustomMapping(ResizeFeaturesAction, "Resize"))
    // </SnippetCustomMapping>

    // <SnippetScoreTensorFlowModel>
    // Passes the data to TensorFlow for scoring
    .Append(tensorFlowModel.ScoreTensorFlowModel("Prediction/Softmax", "Features"))
    // </SnippetScoreTensorFlowModel>

    // <SnippetCopyColumns>
    // Retrieves the 'Prediction' from TensorFlow and copies to a column
    .Append(mlContext.Transforms.CopyColumns("Prediction", "Prediction/Softmax"));
    // </SnippetCopyColumns>

// <SnippetCreateModel>
// Create an executable model from the estimator pipeline
IDataView dataView = mlContext.Data.LoadFromEnumerable(new List<MovieReview>());
ITransformer model = pipeline.Fit(dataView);
// </SnippetCreateModel>

// <SnippetCallPredictSentiment>
PredictSentiment(mlContext, model);
// </SnippetCallPredictSentiment>

void PredictSentiment(MLContext mlContext, ITransformer model)
{
    // <SnippetCreatePredictionEngine>
    var engine = mlContext.Model.CreatePredictionEngine<MovieReview, MovieReviewSentimentPrediction>(model);
    // </SnippetCreatePredictionEngine>

    // <SnippetCreateTestData>
    var review = new MovieReview()
    {
        ReviewText = "this film is really good"
    };
    // </SnippetCreateTestData>

    // Predict with TensorFlow pipeline.
    // <SnippetPredict>
    var sentimentPrediction = engine.Predict(review);
    // </SnippetPredict>

    // <SnippetDisplayPredictions>
    Console.WriteLine($"Number of classes: {sentimentPrediction.Prediction?.Length}");
    Console.WriteLine($"Is sentiment/review positive? {(sentimentPrediction.Prediction?[1] > 0.5 ? "Yes." : "No.")}");
    // </SnippetDisplayPredictions>

    /////////////////////////////////// Expected output ///////////////////////////////////
    //
    // Name: Features, Type: System.Int32, Size: 600
    // Name: Prediction/Softmax, Type: System.Single, Size: 2
    //
    // Number of classes: 2
    // Is sentiment/review positive ? Yes
    // Prediction Confidence: 0.65
}

// <SnippetMovieReviewClass>
/// <summary>
/// Class to hold original sentiment data.
/// </summary>
public class MovieReview
{
    public string? ReviewText { get; set; }
}
//</SnippetMovieReviewClass>

//<SnippetPrediction>
/// <summary>
/// Class to contain the output values from the transformation.
/// </summary>
public class MovieReviewSentimentPrediction
{
    [VectorType(2)]
    public float[]? Prediction { get; set; }
}
// </SnippetPrediction>

// <SnippetVariableLengthFeatures>
/// <summary>
/// Class to hold the variable length feature vector. Used to define the
/// column names used as input to the custom mapping action.
/// </summary>
public class VariableLength
{
    /// <summary>
    /// This is a variable length vector designated by VectorType attribute.
    /// Variable length vectors are produced by applying operations such as 'TokenizeWords' on strings
    /// resulting in vectors of tokens of variable lengths.
    /// </summary>
    [VectorType]
    public int[]? VariableLengthFeatures { get; set; }
}
// </SnippetVariableLengthFeatures>

// <SnippetFeatureConfig>
static class Config
{
    public const int FeatureLength = 600;
}
// </SnippetFeatureConfig>

// <SnippetFixedLengthFeatures>
/// <summary>
/// Class to hold the fixed length feature vector. Used to define the
/// column names used as output from the custom mapping action,
/// </summary>
public class FixedLength
{
    /// <summary>
    /// This is a fixed length vector designated by VectorType attribute.
    /// </summary>
    [VectorType(Config.FeatureLength)]
    public int[]? Features { get; set; }
}
// </SnippetFixedLengthFeatures>