ML.net error: Could not find input column 'MonthNr,DayNr,roc_1,roc_2

Hello,

This is my first time I am trying to put down code for ML.net and train a model and have encountered a problem that I don't understand.

I have created a SQL database table like below:

CREATE TABLE AAPL1day
(
    [INDEX] INT IDENTITY(1,1) PRIMARY KEY,
    [DateTime] SMALLDATETIME NOT NULL,
    [MonthNr] FLOAT NOT NULL,
    [DayNr] FLOAT NOT NULL
    [roc_1] FLOAT
    [roc_2] FLOAT
    [hypo_upordown7days] FLOAT
);

This is the first rows in the database:

• The Feature columns that I am trying to use for training are:

MonthNr, DayNr, roc_1, roc_2

• The Hypothes column or Target column is the below for which I am trying to make predictions:, hypo_upordown7days

I have now for my first time trying to put down the code in order to train and predict with below code.

But I do get this error on the line: var model = pipeline.Fit(split.TrainSet);

"System.ArgumentOutOfRangeException: 'Could not find input column 'MonthNr,DayNr,roc_1,roc_2'

Parameter name: inputSchema'"

Below is my complete code for what I don't understand where the problem is?

[code]

        void trainModel()
        {
            // Set up MLContext
            var mlContext = new MLContext();

            // Define database connection parameters
            string databaseName = "AAPL";
            string connectionString = "Server=localhost;Database=" + databaseName + ";Trusted_Connection=True;";
            var tableName = "AAPL_1day";

            // Get the column names from the database table
            var columnNames = GetColumnNamesFromDatabaseTable(connectionString, tableName);

            // Generate the list of column conditions for IS NOT NULL
            var columnConditions = columnNames.Select(column => $"{column} IS NOT NULL");

            // Create the SELECT query with the column conditions
            var query = $"SELECT * FROM {tableName} WHERE {string.Join(" AND ", columnConditions)}";

            // Create the database source with the dynamically generated query
            var databaseSource = new DatabaseSource(SqlClientFactory.Instance, connectionString, query);

            // Load the data from the database
            var loader = mlContext.Data.CreateDatabaseLoader<AAPLData>();
            var dataView = loader.Load(databaseSource);

            // Split the data into train and test sets
            var split = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.35);

            
            // Exclude columns with specific names and include columns with "hypo" substring
            var includedColumns = columnNames
                .Where(columnName => (!columnName.Equals("INDEX", StringComparison.OrdinalIgnoreCase) &&
                                     !columnName.Equals("DateTime", StringComparison.OrdinalIgnoreCase)) ||
                                     columnName == "hypo_upordown7days");


            // Define the data pipeline
            var pipeline = mlContext.Transforms.Conversion.MapValueToKey("hypo_upordown7days")
                .Append(mlContext.Transforms.Concatenate("Features", string.Join(",", includedColumns.Where(columnName => columnName != "hypo_upordown7days"))))
                .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                .Append(mlContext.Transforms.DropColumns("Features"))
                .Append(mlContext.Transforms.Conversion.MapKeyToValue("hypo_upordown7days"));



            // Train the model
            var model = pipeline.Fit(split.TrainSet);

            // Make predictions
            var predictions = model.Transform(split.TestSet);

            // Evaluate the model
            var metrics = mlContext.BinaryClassification.Evaluate(predictions, labelColumnName: "hypo_upordown7days");

            // Retrieve the evaluation metrics
            var accuracy = metrics.Accuracy;
            var auc = metrics.AreaUnderRocCurve;

            MessageBox.Show($"Accuracy: {accuracy}\n" + $"AUC: {auc}");
        }

        // Define a class to hold your data
        public class AAPLData
        {
            [LoadColumn(2)] public float MonthNr { get; set; }
            [LoadColumn(3)] public float DayNr { get; set; }
            [LoadColumn(4)] public float roc_1 { get; set; }
            [LoadColumn(5)] public float roc_2 { get; set; }
            [LoadColumn(6)] public float hypo_upordown7days { get; set; }
        }

        public class AAPLPrediction
        {
            [ColumnName("PredictedLabel")]
            public byte Prediction { get; set; }
        }

        public static List<string> GetColumnNamesFromDatabaseTable(string connectionString, string tableName)
        {
            var columnNames = new List<string>();
            using (var connection = new SqlConnection(connectionString))
            {
                connection.Open();

                // Get the schema information for the table
                var schemaTable = connection.GetSchema("Columns", new[] { null, null, tableName });
                foreach (DataRow row in schemaTable.Rows)
                {
                    // Extract the column name from the schema table
                    var columnName = row["COLUMN_NAME"].ToString();
                    columnNames.Add(columnName);
                }
            }
            return columnNames;
        }

[/code]