Revize 3c4b53fe

Re #9049 Implemented Rollback method

Server/ServerApp/Predictor/PredictionController.cs

@@ -77 +77 @@
                 string json = File.ReadAllText(pathToConfig);
                 this.Configuration = JsonConvert.DeserializeObject<PredictorConfiguration>(json);
             }
-            catch (System.IO.IOException e)
+            catch (System.IO.FileNotFoundException e)
             {
                 Console.WriteLine("Warning: could not find a configuration file, creating a new one:");
                 Console.WriteLine(e.Message.PadLeft(4));
@@ -87 +87 @@
             this.DataParser = dataParser;
             this.Predictors = new IPredictor[this.Configuration.PredictorCount];
             this.FeatureExtractor = new FeatureExtractor(this.DataParser, this.Configuration);
-            this.DataFilenames = dataParser.WeatherDataUsed.Concat(dataParser.ActivityDataUsed);
-            this.PredictorID = DateTime.Now.ToString();
 
-            for (int i = 0; i < this.Configuration.PredictorCount; i++)
+            DirectoryInfo di = new DirectoryInfo(Configuration.ModelDataPath);
+            FileInfo[] files = di.GetFiles();
+            if (Array.FindAll(files, f => Regex.IsMatch(f.Name, @"[-]?\d+_\d+.zip")).GroupBy(f => f.Name.Split("_.".ToCharArray())[0]).OrderBy(f => DateTime.FromBinary(Convert.ToInt64(f.Key))).Any())
             {
-                Predictors[i] = new NaiveBayesClassifier();
+                _log.Info("Found existing predictors, loading the newest.");
+                this.Load(Array.FindAll(files, f => Regex.IsMatch(f.Name, @"[-]?\d+_\d+.zip")).GroupBy(f => f.Name.Split("_.".ToCharArray())[0]).OrderBy(f => DateTime.FromBinary(Convert.ToInt64(f.Key))).Last().Select(f => f.Name.Split("_".ToCharArray())[0]).First());
+            }
+            else
+            {
+                _log.Info("No predictors found, creating new ones");
+                for (int i = 0; i < this.Configuration.PredictorCount; i++)
+                {
+                    Predictors[i] = new NaiveBayesClassifier();
+                }
             }
             PredictorConfiguration.SaveConfig(PredictorConfiguration.DEFAULT_CONFIG_PATH, Configuration);
         }
@@ -101 +110 @@
             return new List<string>(this.Configuration.BuildingsToAreas.Keys);
         }
 
-        public void Load(string predictorID)
+        public int Load(string predictorID)
         {
             DirectoryInfo di = new DirectoryInfo(Configuration.ModelDataPath);
             FileInfo[] files = di.GetFiles($"{predictorID}_*.zip");
-            IPredictor[] newPredictors = new IPredictor[this.Configuration.PredictorCount];
+            if (Array.FindAll(files, f => Regex.IsMatch(f.Name, $@"{predictorID}_\d+.zip")).Any()){
+                IPredictor[] newPredictors = new IPredictor[this.Configuration.PredictorCount];
+                try
+                {
+                    for (int i = 0; i < this.Configuration.PredictorCount; i++)
+                    {
+                        newPredictors[i] = new NaiveBayesClassifier(Array.Find(files, f => Regex.IsMatch(f.Name, $"{predictorID}_{i}.zip")).FullName);
+                    }
+                    this.Predictors = newPredictors;
+                    files = di.GetFiles($"{predictorID}.txt");
+                    this.DataFilenames = File.ReadLines(files[0].FullName);
+                    this.PredictorID = predictorID;
+                } catch (FileNotFoundException e)
+                {
+                    _log.Error(e.ToString());
+                    return 2;
+                }
+            } else
+            {
+                // TODO indicate exception
+                _log.Debug("Could not find predictor with given predictorID");
+                return 1;
+            }
+            return 0;
+        }
+
+        public void Save()
+        {
+            DirectoryInfo di = new DirectoryInfo(Configuration.ModelDataPath);
+
             for (int i = 0; i < this.Configuration.PredictorCount; i++)
             {
-                newPredictors[i] = new NaiveBayesClassifier(Array.Find(files, f => Regex.IsMatch(f.Name, $"{predictorID}_{i}.zip")).FullName);
+                Predictors[i].Save(Path.Combine(di.FullName, $"{PredictorID}_{i}.zip"));
             }
-            this.Predictors = newPredictors;
-            files = di.GetFiles($"{predictorID}.txt");
-            this.DataFilenames = File.ReadLines(files[0].FullName);
-            this.PredictorID = predictorID;
+            File.WriteAllLinesAsync(Path.Combine(di.FullName, $"{PredictorID}.txt"), this.DataFilenames);
         }
 
-        public void Rollback()
+        public int Rollback()
         {
+            DirectoryInfo di = new DirectoryInfo(Configuration.ModelDataPath);
+            FileInfo[] files = di.GetFiles();
+            if (Array.FindAll(files, f => Regex.IsMatch(f.Name, @"[-]?\d+_\d+.zip")).GroupBy(f => f.Name.Split("_.".ToCharArray())[0]).OrderBy(f => DateTime.FromBinary(Convert.ToInt64(f.Key))).Any())
+            {
+                string RollbackedPredictorID = Array.FindAll(files, f => Regex.IsMatch(f.Name, @"[-]?\d+_\d+.zip")).GroupBy(f => f.Name.Split("_.".ToCharArray())[0]).OrderBy(f => DateTime.FromBinary(Convert.ToInt64(f.Key))).Last().Select(f => f.Name.Split("_".ToCharArray())[0]).First();
+                this.Delete(this.PredictorID);
+                return this.Load(RollbackedPredictorID);
+            } else
+            {
+                // indicate that older model does not exist
+                return 1;
+            }
+        }
+
+        private void Delete(string predictorID)
+        {
+            DirectoryInfo di = new DirectoryInfo(Configuration.ModelDataPath);
 
+            for (int i = 0; i < this.Configuration.PredictorCount; i++)
+            {
+                File.Delete(Path.Combine(di.FullName, $"{PredictorID}_{i}.zip"));
+            }
+            File.Delete(Path.Combine(di.FullName, $"{PredictorID}.txt"));
         }
 
         public Response Predict(Request request)
@@ -216 +273 @@
         public void Train()
         {
             DataParser.Parse(DateTime.MinValue, DateTime.MaxValue, this.Configuration.TimeResolution, wholeDay: false);
-                for (int i = 0; i < this.Predictors.Length; i++)
-                {
-                    // train on all available data
-                    List<ModelInput> data = FeatureExtractor.PrepareTrainingInput(i);
-                    Console.WriteLine("Training predictor with {0} samples.", data.Count);
-                    this.Predictors[i].Fit(data);
-                }
+            for (int i = 0; i < this.Predictors.Length; i++)
+            {
+                // train on all available data
+                List<ModelInput> data = FeatureExtractor.PrepareTrainingInput(i);
+                Console.WriteLine("Training predictor with {0} samples.", data.Count);
+                this.Predictors[i].Fit(data);
+            }
+            this.DataFilenames = this.DataParser.WeatherDataUsed.Concat(this.DataParser.ActivityDataUsed);
+            this.PredictorID = DateTime.Now.ToBinary().ToString();
+            this.Save();
         }
 
         public IEnumerable<string> GetDataFileNames()