50 lines
1.8 KiB
C#
50 lines
1.8 KiB
C#
using BotSharp.Core.Abstractions;
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using BotSharp.Core.Agents;
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using DotNetToolkit;
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using Microsoft.Extensions.Configuration;
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using Newtonsoft.Json.Linq;
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using System;
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using System.Collections.Generic;
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using System.IO;
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using System.Text;
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using System.Threading.Tasks;
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namespace BotSharp.Core.Engines.Classifiers
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{
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public class FasttextClassifier : INlpPipeline
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{
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public IConfiguration Configuration { get; set; }
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public Task<bool> Predict(Agent agent, JObject data, PipeModel meta)
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{
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throw new NotImplementedException();
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}
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public async Task<bool> Train(Agent agent, JObject data, PipeModel meta)
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{
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meta.Model = "classification-fasttext.model";
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var algorithmDir = Path.Join(AppDomain.CurrentDomain.GetData("ContentRootPath").ToString(), "Algorithms");
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var dirTrain = Path.Join(AppDomain.CurrentDomain.GetData("DataPath").ToString(), "TrainingFiles", agent.Id);
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var dirModel = Path.Join(AppDomain.CurrentDomain.GetData("DataPath").ToString(), "ModelFiles", agent.Id);
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string parsedTrainingDataFileName = Path.Join(dirTrain, $"classification-fasttext.parsed.txt");
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string modelFileName = Path.Join(dirModel, meta.Model);
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// assemble corpus
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StringBuilder corpus = new StringBuilder();
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agent.Corpus.UserSays.ForEach(x => corpus.AppendLine($"__label__{x.Intent} {x.Text}"));
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File.WriteAllText(parsedTrainingDataFileName, corpus.ToString());
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var output = CmdHelper.Run(Path.Join(algorithmDir, "fasttext"), $"supervised -input {parsedTrainingDataFileName} -output {modelFileName}");
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Console.WriteLine($"Saved model to {modelFileName}");
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meta.Meta = new JObject();
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meta.Meta["compiled at"] = "Aug 3, 2018";
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return true;
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}
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}
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}
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