Integrating MongoDB with TensorFlow and C#

1
using MongoDB.Bson; 
2
using System.Collections.Generic; 
3
using Microsoft.ML.Data;
4
5
    public class SampleData
6
    {
7
        public ObjectId Id { get; set; } // This corresponds to the MongoDB _id field
8
        public List<double> X { get; set; } = new List<double>();
9
        public List<double> Y { get; set; } = new List<double>();
10
    }
11
12
    public class DataPoint
13
    {
14
        public float X { get; set; }
15
        public float Y { get; set; }
16
    }
17
18
    public class Prediction
19
    {
20
        [ColumnName("Score")]
21
        public float PredictedY { get; set; }
22
    }

1
// MongoDB connection string
2
var client = new MongoClient("mongodb://localhost:27017");
3
var database = client.GetDatabase("linear-data");
4
var collection = database.GetCollection<SampleData>("sampleData");

1
using MongoDB.Bson;
2
using MongoDB.Driver;
3
using System.Collections.Generic;

1
// Define the data to insert
2
        var sampleData = new SampleData
3
        {
4
            X = new List<double> { 3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1 },
5
            Y = new List<double> { 1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3 }
6
        };
7
8
// Insert the data into MongoDB 
9
collection.InsertOne(sampleData);

1
// Fetch the data from MongoDB as SampleData
2
var fetchedSampleData = collection.Find(new BsonDocument()).FirstOrDefault();
3
4
// Assuming you have only one document, use the fetchedSampleData directly
5
var xArray = fetchedSampleData.X.Select(value => (float)value).ToArray();
6
var yArray = fetchedSampleData.Y.Select(value => (float)value).ToArray();

1
// Create a new ML context
2
        var mlContext = new MLContext();
3
4
        // Create the ML.NET data structures
5
        var data = xArray.Zip(yArray, (x, y) => new DataPoint { X = x, Y = y }).ToList();
6
        var dataView = mlContext.Data.LoadFromEnumerable(data);

1
// Define the trainer
2
var pipeline = mlContext.Transforms.Concatenate("Features", new[] { "X" })
3
    .Append(mlContext.Transforms.NormalizeMinMax("Features"))
4
    .Append(mlContext.Regression.Trainers.Sdca(labelColumnName: "Y", featureColumnName: "Features"));
5
6
// Train the model
7
var model = pipeline.Fit(dataView);

1
// Use the model to make predictions
2
var predictions = model.Transform(dataView);
3
var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: "Y");
4
5
Console.WriteLine($"R^2: {metrics.RSquared}");
6
Console.WriteLine($"RMSE: {metrics.RootMeanSquaredError}");

1
// Display the predictions
2
 var predictionFunction = mlContext.Model.CreatePredictionEngine<DataPoint, Prediction>(model);
3
        foreach (var point in data)
4
        {
5
            var prediction = predictionFunction.Predict(point);
6
            Console.WriteLine($"X: {point.X}, Y: {point.Y}, Predicted: {prediction.PredictedY}");
7
        }

1
dotnet run

1
Data seeded successfully!

1
R^2: -131160.77737920854

1
RMSE: 256.3340977791911

1
X: 3.3, Y: 1.7, Predicted: 84.26584
2
X: 4.4, Y: 2.76, Predicted: -8.049469
3
X: 5.5, Y: 2.09, Predicted: -100.36478
4
X: 6.71, Y: 3.19, Predicted: -201.91168
5
X: 6.93, Y: 1.694, Predicted: -220.3747
6
X: 4.168, Y: 1.573, Predicted: 11.420654
7
X: 9.779, Y: 3.366, Predicted: -459.47144
8
X: 6.182, Y: 2.596, Predicted: -157.60028
9
X: 7.59, Y: 2.53, Predicted: -275.76392
10
X: 2.167, Y: 1.221, Predicted: 179.35062
11
X: 7.042, Y: 2.827, Predicted: -229.77405
12
X: 10.791, Y: 3.465, Predicted: -544.4015
13
X: 5.313, Y: 1.65, Predicted: -84.6712
14
X: 7.997, Y: 2.904, Predicted: -309.9206
15
X: 5.654, Y: 2.42, Predicted: -113.28891
16
X: 9.27, Y: 2.94, Predicted: -416.75458
17
X: 3.1, Y: 1.3, Predicted: 101.050446

1
Data seeded successfully!
2
R^2: -131160.77737920854
3
RMSE: 256.3340977791911
4
X: 3.3, Y: 1.7, Predicted: 84.26584
5
X: 4.4, Y: 2.76, Predicted: -8.049469
6
X: 5.5, Y: 2.09, Predicted: -100.36478
7
X: 6.71, Y: 3.19, Predicted: -201.91168
8
X: 6.93, Y: 1.694, Predicted: -220.3747
9
X: 4.168, Y: 1.573, Predicted: 11.420654
10
X: 9.779, Y: 3.366, Predicted: -459.47144
11
X: 6.182, Y: 2.596, Predicted: -157.60028
12
X: 7.59, Y: 2.53, Predicted: -275.76392
13
X: 2.167, Y: 1.221, Predicted: 179.35062
14
X: 7.042, Y: 2.827, Predicted: -229.77405
15
X: 10.791, Y: 3.465, Predicted: -544.4015
16
X: 5.313, Y: 1.65, Predicted: -84.6712
17
X: 7.997, Y: 2.904, Predicted: -309.9206
18
X: 5.654, Y: 2.42, Predicted: -113.28891
19
X: 9.27, Y: 2.94, Predicted: -416.75458
20
X: 3.1, Y: 1.3, Predicted: 101.050446