1. 加載測試數(shù)據(jù)（csv）

2.加載模型

3.訓(xùn)練數(shù)據(jù)

4.預(yù)測一句話的情緒

實(shí)現(xiàn)：

using System; using System.Collections.Generic; using System.IO; using System.Text; using System.Threading.Tasks; using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Models; using Microsoft.ML.Runtime.Api; using Microsoft.ML.Trainers; using Microsoft.ML.Transforms;namespace _02_SentimentAnalysis {public static class SentimentAnalysisExecutor{static readonly string _dataPath = Path.Combine(Environment.CurrentDirectory, "wikipedia-detox-250-line-data.tsv");static readonly string _testDataPath = Path.Combine(Environment.CurrentDirectory, "wikipedia-detox-250-line-test.tsv");static readonly string _modelpath = Path.Combine(Environment.CurrentDirectory, "Model.zip");public class SentimentData{[Column(ordinal: "0", name: "Label")]public float Sentiment;[Column(ordinal: "1")]public string SentimentText;}public class SentimentPrediction{[ColumnName("PredictedLabel")]public bool Sentiment;public override string ToString(){return Sentiment ? "Positive" : "Negtive";}}public static async Task<IEnumerable<SentimentPrediction>> Run(IEnumerable<SentimentData> inputs){var model = await Train();Evoluate(model);var results = model.Predict(inputs);return results;}private static void Evoluate(PredictionModel<SentimentData, SentimentPrediction> model){var testData = new TextLoader(_testDataPath).CreateFrom<SentimentData>();var evaluator = new BinaryClassificationEvaluator();BinaryClassificationMetrics metrics = evaluator.Evaluate(model, testData);Console.WriteLine();Console.WriteLine("PredictionModel quality metrics evaluation");Console.WriteLine("------------------------------------------");Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");Console.WriteLine($"Auc: {metrics.Auc:P2}");Console.WriteLine($"F1Score: {metrics.F1Score:P2}");}private static async Task<PredictionModel<SentimentData, SentimentPrediction>> Train(){var pipeline = new LearningPipeline();pipeline.Add(new TextLoader(_dataPath).CreateFrom<SentimentData>());pipeline.Add(new TextFeaturizer("Features", "SentimentText"));pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2 });PredictionModel<SentimentData, SentimentPrediction> model =pipeline.Train<SentimentData, SentimentPrediction>();await model.WriteAsync(_modelpath);return model;}} }

2. 調(diào)用：

using System;namespace _02_SentimentAnalysis {class Program{static void Main(string[] args){var ret = SentimentAnalysisExecutor.Run(new[]{new SentimentAnalysisExecutor.SentimentData{SentimentText = "Please refrain from adding nonsense to Wikipedia."},new SentimentAnalysisExecutor.SentimentData{SentimentText = "He is the best, and the article should say that."},new SentimentAnalysisExecutor.SentimentData{SentimentText = "I'm not sure If that is correct."},}).Result;foreach (var sentimentPrediction in ret){Console.WriteLine(sentimentPrediction);}Console.ReadLine();}} }

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