private static void Predict(MLContext mlContext, ITransformer model)
{
var predictionFunction = model.MakePredictionFunction <SentimentData, SentimentPrediction>(mlContext);
SentimentData sampleStatement = new SentimentData
{
SentimentText = "This is a very rude movie"
};
var resultprediction = predictionFunction.Predict(sampleStatement);
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of model with a single sample and test dataset ===============");
Console.WriteLine();
Console.WriteLine($"Sentiment: {sampleStatement.SentimentText} | Prediction: {(Convert.ToBoolean(resultprediction.Prediction) ? "Toxic" : "Not Toxic")} | Probability: {resultprediction.Probability} ");
Console.WriteLine("=============== End of Predictions ===============");
Console.WriteLine();
}
private static void UseModelWithSingleItem(MLContext mlContext, ITransformer model, string sentimentText)
{
PredictionEngine <SentimentData, SentimentPrediction> predictionFunction = model.CreatePredictionEngine <SentimentData, SentimentPrediction>(mlContext);
SentimentData sampleStatement = new SentimentData
{
SentimentText = sentimentText
};
var resultprediction = predictionFunction.Predict(sampleStatement);
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of model with a single sample and test dataset ===============");
Console.WriteLine($"Sentiment: {sampleStatement.SentimentText} | Prediction: {(Convert.ToBoolean(resultprediction.Prediction) ? "Positive" : "Negative")} | Probability: {resultprediction.Probability} ");
Console.WriteLine("=============== End of Predictions ===============");
Console.WriteLine();
}
private static void UseModelWithSingleItemMulti(MLContext mlContext, ITransformer model)
{
PredictionEngine <SentimentData, SentimentPredictionMulti> predictionFunction = mlContext.Model.CreatePredictionEngine <SentimentData, SentimentPredictionMulti>(model);
SentimentData sampleStatement = new SentimentData
{
SentimentText = "This show is great"
};
var resultPrediction = predictionFunction.Predict(sampleStatement);
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of model with a single sample and test dataset ===============");
Console.WriteLine();
Console.WriteLine($"Sentiment: {resultPrediction.SentimentText} | Prediction: {(Convert.ToBoolean(resultPrediction.Prediction) ? "Positive" : "Negative")} ");
Console.WriteLine("=============== End of Predictions ===============");
Console.WriteLine();
}
public static void UseModelWithSingleItem(MLContext mlContext, ITransformer model)
{
PredictionEngine <SentimentData, SentimentPrediction> predictionFunction = mlContext.Model.CreatePredictionEngine <SentimentData, SentimentPrediction>(model);
SentimentData sampleStatement = new SentimentData {
SentimentText = "This is a very bad steak"
};
var resultPrediction = predictionFunction.Predict(sampleStatement);
Console.WriteLine();
Console.WriteLine("========== Prediction test of model with a single sample and test dataset ==========");
Console.WriteLine();
Console.WriteLine($"Sentiment: {resultPrediction.SentimentText} | " +
$"Prediction: {(Convert.ToBoolean(resultPrediction.Prediction) ? "Positive" : "Negative")} | " +
$"Probability: {resultPrediction.Probability}");
Console.WriteLine("========== End of predictions ==========");
Console.WriteLine();
}
private static void UseLoadedModelWithUserInput(MLContext mlContext)
{
//Load the saved (already trained) model
ITransformer loadedModel;
using (var stream = new FileStream(_modelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
loadedModel = mlContext.Model.Load(stream);
}
PredictionEngine <SentimentData, SentimentPrediction> predictionFunction = loadedModel.CreatePredictionEngine <SentimentData, SentimentPrediction>(mlContext);
Console.WriteLine();
Console.WriteLine("=== Prediction test of model with user input ===");
Console.WriteLine();
Console.WriteLine("Please enter your comment here:");
string comment = Console.ReadLine();
SentimentData sentimentStatement = new SentimentData
{
SentimentText = comment
};
//Predict results for user input
var resultprediction = predictionFunction.Predict(sentimentStatement);
Console.WriteLine($"Sentiment: {sentimentStatement.SentimentText} | Prediction: {(Convert.ToBoolean(resultprediction.Prediction) ? "Positive" : "Negative")} | Probability: {resultprediction.Probability} ");
Console.WriteLine();
Console.WriteLine();
Console.WriteLine("Please enter another comment here:");
string comment2 = Console.ReadLine();
SentimentData sentimentStatement2 = new SentimentData
{
SentimentText = comment2
};
//Just another example
var resultprediction2 = predictionFunction.Predict(sentimentStatement2);
Console.WriteLine($"Sentiment: {sentimentStatement2.SentimentText} | Prediction: {(Convert.ToBoolean(resultprediction2.Prediction) ? "Positive" : "Negative")} | Probability: {resultprediction2.Probability} ");
Console.WriteLine("=== End of predictions with user data ===");
Console.WriteLine();
}
/// <summary>
///
/// </summary>
/// <param name="mlContext"></param>
/// <param name="model"></param>
private static void UseModelWithSingleItem(MLContext mlContext, ITransformer model, string opinion)
{
PredictionEngine <SentimentData, SentimentPrediction> predictionFunction = null;
try
{
predictionFunction = mlContext.Model.CreatePredictionEngine <SentimentData, SentimentPrediction>(model);
SentimentData sampleStatement = new SentimentData {
SentimentText = opinion
};
var resultPrediction = predictionFunction.Predict(sampleStatement);
Console.WriteLine($"Sentiment: {resultPrediction.SentimentText} | Prediction: {(Convert.ToBoolean(resultPrediction.Prediction) ? "Positive" : "Negative")} | Probability: {resultPrediction.Probability} ");
}
catch (Exception ex)
{
Console.WriteLine(ex.StackTrace);
}
}
private static void UseModelWithSingleItem(MLContext mlContext, ITransformer model)
{
PredictionEngine <SentimentData, SentimentPrediction> predictionFunction = mlContext.Model.CreatePredictionEngine <SentimentData, SentimentPrediction>(model);
//The PredictionEngine allows you to perform a prediction on a single instance of data
SentimentData sampleStatement = new SentimentData
{
SentimentText = "This place is very good"
};
var resultPrediction = predictionFunction.Predict(sampleStatement);
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of model with a single sample and test dataset ===============");
Console.WriteLine();
Console.WriteLine($"Sentiment: {resultPrediction.SentimentText} | Prediction: {(Convert.ToBoolean(resultPrediction.Prediction) ? "Positive" : "Negative")} | Probability: {resultPrediction.Probability} ");
Console.WriteLine("=============== End of Predictions ===============");
Console.WriteLine();
}
private static void UseModelWithSingleItem(MLContext mlContext, ITransformer model)
{
PredictionEngine <SentimentData, SentimentPrediction> predictionFunction = mlContext.Model.CreatePredictionEngine <SentimentData, SentimentPrediction>(model);
SentimentData sampleStatement = new SentimentData
{
SentimentText = "This show is great"
};
var resultPrediction = predictionFunction.Predict(sampleStatement);
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of model with a single sample and test dataset ===============");
Console.WriteLine();
Console.WriteLine($"Sentiment: {resultPrediction.SentimentText} | Prediction: {(Convert.ToBoolean(resultPrediction.Prediction) ? "Positive" : "Negative")} ");
Console.WriteLine("=============== End of Predictions ===============");
Console.WriteLine();
using (System.IO.StreamWriter file =
new System.IO.StreamWriter(@"C:\Users\siust\OneDrive\Desktop\test.txt", true))
{
file.WriteLine($"Sentiment: {resultPrediction.SentimentText} | Prediction: {(Convert.ToBoolean(resultPrediction.Prediction) ? "Positive" : "Negative")} ");;
}
}
/// <summary>
/// 创建测试数据的单个注释。
/// 根据测试数据预测情绪。
/// 结合测试数据和预测进行报告。
/// 显示预测结果。
/// </summary>
/// <param name="mlContext">环境</param>
/// <param name="model">模型</param>
private static void UseModelWithSingleItem(MLContext mlContext, ITransformer model)
{
// <SnippetCreatePredictionEngine1>
// PredictionEngine 是一个简便 API，可使用它对单个数据实例执行预测 PredictionEngine 不是线程安全型
// 生产环境请使用 PredictionEnginePool服务
// 1. 创建一次性预测引擎
// 2. 训练实体
// 3. 测试实体 必须包含同样的列
PredictionEngine <SentimentData, SentimentPrediction> predictionFunction = mlContext.Model.CreatePredictionEngine <SentimentData, SentimentPrediction>(model);
// </SnippetCreatePredictionEngine1>
// <SnippetCreateTestIssue1>
// 创建一个测试对象 写入说出的话
SentimentData sampleStatement = new SentimentData
{
SentimentText = "This was a very bad steak"
};
// </SnippetCreateTestIssue1>
// <SnippetPredict>
//将测试评论数据传递到 PredictionEngine
//Predict() 函数对单行数据进行预测
var resultPrediction = predictionFunction.Predict(sampleStatement);
// </SnippetPredict>
// <SnippetOutputPrediction>
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of model with a single sample and test dataset ===============");
Console.WriteLine();
Console.WriteLine($"Sentiment: {resultPrediction.SentimentText} | Prediction: {(Convert.ToBoolean(resultPrediction.Prediction) ? "Positive" : "Negative")} | Probability: {resultPrediction.Probability} ");
Console.WriteLine("=============== End of Predictions ===============");
Console.WriteLine();
// </SnippetOutputPrediction>
}
static void Main(string[] args)
{
MLContext mlContext = new MLContext();
// Load Data
IDataView dataView = mlContext.Data.LoadFromTextFile <SentimentData>(_dataPath, hasHeader: false);
TrainTestData splitDataView = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.2);
// BuildAndTrainModel
var estimator = mlContext.Transforms.Text
.FeaturizeText(outputColumnName: "Features", inputColumnName: nameof(SentimentData.SentimentText))
.Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: "Label", featureColumnName: "Features", maximumNumberOfIterations: 100));
Console.WriteLine("=============== Create and Train the Model ===============");
var model = estimator.Fit(splitDataView.TrainSet);
Console.WriteLine("=============== End of training ===============");
Console.WriteLine();
// Evaluate
Console.WriteLine("=============== Evaluating Model accuracy with Test data===============");
IDataView predictions = model.Transform(splitDataView.TestSet);
CalibratedBinaryClassificationMetrics metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label");
Console.WriteLine();
Console.WriteLine("Model quality metrics evaluation");
Console.WriteLine("--------------------------------");
Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($"Auc: {metrics.AreaUnderRocCurve:P2}");
Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
Console.WriteLine("=============== End of model evaluation ===============");
// UseModelWithSingleItem
PredictionEngine <SentimentData, SentimentPrediction> predictionFunction = mlContext.Model.CreatePredictionEngine <SentimentData, SentimentPrediction>(model);
SentimentData sampleStatement = new SentimentData
{
SentimentText = "이 영화 정말 재미없어요"
};
var resultPrediction = predictionFunction.Predict(sampleStatement);
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of model with a single sample and test dataset===============");
Console.WriteLine();
Console.WriteLine($"Sentiment: {resultPrediction.SentimentText} | Prediction: {(Convert.ToBoolean(resultPrediction.Prediction) ? "Positive" : "Negative")} | Probability: {resultPrediction.Probability} ");
Console.WriteLine("=============== End of Predictions ===============");
Console.WriteLine();
// UseModelWithBatchItems
IEnumerable <SentimentData> sentiments = new[]
{
new SentimentData {
SentimentText = "지루한 영화에요"
},
new SentimentData {
SentimentText = "이거 정말 최고에요!"
},
new SentimentData {
SentimentText = "올해의 영화로 손꼽고 싶군요"
}
};
IDataView batchComments = mlContext.Data.LoadFromEnumerable(sentiments);
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);
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of loaded model with multiple samples ===============");
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 ===============");
}