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PredictionTaskForm.cs
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PredictionTaskForm.cs
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using Accord.MachineLearning;
using Accord.MachineLearning.Bayes;
using Accord.MachineLearning.DecisionTrees;
using Accord.MachineLearning.DecisionTrees.Learning;
using Accord.MachineLearning.VectorMachines;
using Accord.MachineLearning.VectorMachines.Learning;
using Accord.Math;
using Accord.Math.Optimization;
using Accord.IO;
using Accord.Statistics.Distributions.Univariate;
using Accord.Statistics.Kernels;
using Accord.Statistics.Models.Regression;
using Accord.Statistics.Models.Regression.Fitting;
using Accord.Statistics.Models.Regression.Linear;
using JadeChem.CustomControls.EvaluationControls;
using JadeChem.CustomControls.ModelControls;
using JadeChem.CustomEventArgs;
using JadeChem.Dialogs;
using JadeChem.Models;
using JadeChem.Utils;
using TorchSharp;
using System.Data;
namespace JadeChem
{
public partial class PredictionTaskForm : Form
{
#region Fields
private DataTable? inputData = null;
private string[]? classLabels; // only for classification
private PredictionType predictionType = PredictionType.Regression;
private Dictionary<string, Dictionary<string, (double, double)>> inputScalersDictionary = new();
private Dictionary<string, Dictionary<string, (double, double)>> outputScalersDictionary = new();
private readonly Dictionary<string, MinMaxScaler> minMaxScalers = new();
private readonly Dictionary<string, StandardScaler> standardScalers = new();
private Dictionary<string, Dictionary<string, double>> dimensionalityReductionStepsDictionary = new();
private string[]? inputColumnNames;
private string[]? processedInputColumnNames;
private string? outputColumnName;
private VarianceThresholdFilter? varianceThresholdFilter;
private PCAFilter? pcaFilter;
private DataTable? processedDataset;
private double[][]? processedInputColumns;
private double[]? processedOutputColumnForRegression;
private string[]? processedOutputColumnForClassification;
private int[]? processedClassIndices;
private DataTable? trainDataset;
private double[][]? trainInputColumns;
private double[]? trainOutputColumnForRegression;
private string[]? trainOutputColumnForClassification;
private int[]? trainClassIndices;
private DataTable? testDataset;
private double[][]? testInputColumns;
private double[]? testOutputColumnForRegression;
private string[]? testOutputColumnForClassification;
private int[]? testClassIndices;
private string[]? predictedOutputColumnForClassification;
private double[]? predictedOutputColumnForRegression;
private DataTable? predictionDataset;
private double[][]? predictionInputColumns;
private double[]? predictionOutputColumnForRegression;
private string[]? predictionOutputColumnForClassification;
private object? model;
private DeviceType deviceType;
private torch.ScalarType dataType;
// Dialogs
private DataProcessingDialog? dataProcessingDialog;
// Flags
private bool isDataProcessingDialogResetNeeded = true;
#endregion
#region Enumeration
public enum PredictionType
{
BinaryClassification,
MulticlassClassification,
Regression
}
#endregion
#region Events
public delegate void InputDataLoadedEventHandler(DataTableEventArgs e);
public event InputDataLoadedEventHandler? InputDataLoaded;
public delegate void ProcessedDataLoadedEventHandler(DataTableEventArgs e);
public event ProcessedDataLoadedEventHandler? ProcessedDataLoaded;
public delegate void TrainDatasetLoadedEventHandler(DataTableEventArgs e);
public event TrainDatasetLoadedEventHandler? TrainDatasetLoaded;
public delegate void TestDatasetLoadedEventHandler(DataTableEventArgs e);
public event TestDatasetLoadedEventHandler? TestDatasetLoaded;
public delegate void ModelLoadedEventHandler(EventArgs e);
public event ModelLoadedEventHandler? ModelLoaded;
public delegate void ModelTrainedEventHandler(ModelEventArgs e);
public event ModelTrainedEventHandler? ModelTrained;
public delegate void ModelEvaluatedEventHandler(EventArgs e);
public event ModelEvaluatedEventHandler? ModelEvaluated;
public delegate void PredictionDataLoadedEventHandler(DataTableEventArgs e);
public event PredictionDataLoadedEventHandler? PredictionDataLoaded;
#endregion
#region Constructor
public PredictionTaskForm()
{
InitializeComponent();
// Register event listeners
InputDataLoaded += OnInputDataLoaded;
ProcessedDataLoaded += OnProcessedDataLoaded;
TrainDatasetLoaded += OnTrainDatasetLoaded;
TestDatasetLoaded += OnTestDatasetLoaded;
ModelLoaded += OnModelLoaded;
ModelTrained += OnModelTrained;
ModelEvaluated += OnModelEvaluated;
PredictionDataLoaded += OnPredictionDataLoaded;
}
#endregion
#region Methods
// Diagram
private void DiagramControl_InputDataBlockClicked(object sender, EventArgs e)
{
taskTabControl.SelectTab(inputDataTabPage);
}
private void DiagramControl_ProcessingArrowBlockClicked(object sender, EventArgs e)
{
taskTabControl.SelectTab(dataProcessingTabPage);
}
private void DiagramControl_ProcessedDataBlockClicked(object sender, EventArgs e)
{
taskTabControl.SelectTab(processedDataTabPage);
}
private void DiagramControl_TrainDatasetBlockClicked(object sender, EventArgs e)
{
taskTabControl.SelectTab(trainDatasetTabPage);
}
private void DiagramControl_TestDatasetBlockClicked(object sender, EventArgs e)
{
taskTabControl.SelectTab(testDatasetTabPage);
}
private void DiagramControl_ModelBlockClicked(object sender, EventArgs e)
{
taskTabControl.SelectTab(modelTabPage);
}
private void DiagramControl_EvaluationBlockClicked(object sender, EventArgs e)
{
taskTabControl.SelectTab(evaluationTabPage);
}
private void DiagramControl_PredictionBlockClicked(object sender, EventArgs e)
{
taskTabControl.SelectTab(predictionTabPage);
}
// inputDataTabPage
private void LoadDataButton_Click(object sender, EventArgs e)
{
if (this.inputData != null)
if (MessageBox.Show(this, "Do you want to override the current data?", "Override data", MessageBoxButtons.YesNoCancel, MessageBoxIcon.Question) != DialogResult.Yes)
return;
if (openDataFileFileDialog.ShowDialog(this) != DialogResult.OK)
return;
DataTable inputData;
try
{
inputData = DataFileLoader.LoadCsvFile(openDataFileFileDialog.FileName, inputDataHasHeadersCheckBox.Checked);
}
catch (Exception ex)
{
MessageBox.Show(this, ex.Message, "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
if (inputData != null)
{
// Raise the event
InputDataLoaded?.Invoke(new DataTableEventArgs() { Dataset = inputData });
}
}
private void OnInputDataLoaded(DataTableEventArgs e)
{
inputData = e.Dataset;
// Draw the outlines of inputDataBlock in the diagram
workflowDiagramControl.DrawInputDataBlockOutlines(true);
workflowDiagramControl.Refresh();
// Show the data in DataGridView
inputDataDataGridView.DataSource = null;
inputDataDataGridView.DataSource = inputData;
// Guess the prediction type
string[] classLabels = inputData.Columns[inputData.Columns.Count - 1].ToArray<string>().Distinct().OrderBy(x => x).ToArray();
if (classLabels.Length == 2) // Binary classification
{
predictionType = PredictionType.BinaryClassification;
binaryClassificationRadioButton.Checked = true;
}
else if (classLabels.Length <= 10) // Multiclass classification
{
predictionType = PredictionType.MulticlassClassification;
multiclassClassificationRadioButton.Checked = true;
}
else // Regression
{
predictionType = PredictionType.Regression;
regressionRadioButton.Checked = true;
}
// Get the columns for inputs/outputs selection
columnsDataGridView.Rows.Clear();
for (int columnIndex = 0; columnIndex < inputData.Columns.Count; columnIndex++)
{
if (columnIndex < inputData.Columns.Count - 1)
columnsDataGridView.Rows.Add(new object[] { inputData.Columns[columnIndex].ColumnName, true, false });
else
columnsDataGridView.Rows.Add(new object[] { inputData.Columns[columnIndex].ColumnName, false, true });
}
// Reset the dialog, dictionaries, and listboxes
isDataProcessingDialogResetNeeded = true;
inputScalersDictionary.Clear();
outputScalersDictionary.Clear();
dimensionalityReductionStepsDictionary.Clear();
processingStepsListBox.Items.Clear();
// Enable the next tab page
dataProcessingTableLayoutPanel.Enabled = true;
}
// dataProcessingTabpage
private void PredictionTypeRadioButton_CheckedChanged(object sender, EventArgs e)
{
if (binaryClassificationRadioButton.Checked)
predictionType = PredictionType.BinaryClassification;
else if (multiclassClassificationRadioButton.Checked)
predictionType = PredictionType.MulticlassClassification;
else
predictionType = PredictionType.Regression;
}
private void EditProcessingStepsButton_Click(object sender, EventArgs e)
{
if (inputData == null)
return;
// Get the input columns for feature extraction and input columns for model
string[] inputColumnNames;
string outputColumnName;
try
{
inputColumnNames = GetInputColumnNames();
outputColumnName = GetOutputColumnName();
if (inputColumnNames.Length == 0 && predictionType != PredictionType.Regression)
throw new Exception("No column to be processed!");
}
catch (Exception ex)
{
MessageBox.Show(this, ex.Message, "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
// Check if the column selection was changed
string[] selectedInputColumnNames = inputScalersDictionary.Keys.ToArray();
if (!selectedInputColumnNames.SequenceEqual(inputColumnNames))
isDataProcessingDialogResetNeeded = true;
// Reset the dialogs if the column selection was changed or this is the first time the dialog is shown
if (isDataProcessingDialogResetNeeded)
{
// Add column names to the dictionaries
Dictionary<string, Dictionary<string, (double, double)>> inputScalersDictionary = new();
for (int columnIndex = 0; columnIndex < inputColumnNames.Length; columnIndex++)
inputScalersDictionary[inputColumnNames[columnIndex]] = new Dictionary<string, (double, double)>();
Dictionary<string, Dictionary<string, (double, double)>> outputScalersDictionary = new();
if (predictionType == PredictionType.Regression)
outputScalersDictionary[outputColumnName] = new Dictionary<string, (double, double)>();
Dictionary<string, Dictionary<string, double>> dimensionalityReductionStepsDictionary = new();
// Reset the dialog
dataProcessingDialog = new DataProcessingDialog(inputScalersDictionary, outputScalersDictionary, dimensionalityReductionStepsDictionary);
}
else
{
dataProcessingDialog = new DataProcessingDialog(inputScalersDictionary.DeepClone(), outputScalersDictionary.DeepClone(), dimensionalityReductionStepsDictionary.DeepClone());
}
// Show the dialog
if (dataProcessingDialog.ShowDialog(this) == DialogResult.OK)
{
// Turn off the flag
isDataProcessingDialogResetNeeded = false;
// Save the dictionaries
inputScalersDictionary = dataProcessingDialog.InputScalersDictionary.DeepClone();
outputScalersDictionary = dataProcessingDialog.OutputScalersDictionary.DeepClone();
dimensionalityReductionStepsDictionary = dataProcessingDialog.DimensionalityReductionStepsDictionary.DeepClone();
// Create the scalers for each column
minMaxScalers.Clear();
standardScalers.Clear();
foreach (KeyValuePair<string, Dictionary<string, (double, double)>> keyValuePair in inputScalersDictionary)
{
string columnName = keyValuePair.Key;
if (keyValuePair.Value.ContainsKey("Min-max scaling"))
{
(double minOutput, double maxOutput) = keyValuePair.Value["Min-max scaling"];
minMaxScalers[columnName] = new MinMaxScaler(minOutput, maxOutput);
}
else if (keyValuePair.Value.ContainsKey("Standardization"))
{
standardScalers[columnName] = new StandardScaler();
}
}
foreach (KeyValuePair<string, Dictionary<string, (double, double)>> keyValuePair in outputScalersDictionary)
{
string columnName = keyValuePair.Key;
if (keyValuePair.Value.ContainsKey("Min-max scaling"))
{
(double minOutput, double maxOutput) = keyValuePair.Value["Min-max scaling"];
minMaxScalers[columnName] = new MinMaxScaler(minOutput, maxOutput);
}
else if (keyValuePair.Value.ContainsKey("Standardization"))
{
standardScalers[columnName] = new StandardScaler();
}
}
// Add processing steps to the list box
processingStepsListBox.Items.Clear();
for (int columnIndex = 0; columnIndex < inputScalersDictionary.Keys.Count; columnIndex++)
{
string columnName = inputScalersDictionary.Keys.ElementAt(columnIndex);
foreach (KeyValuePair<string, (double, double)> keyValuePair in inputScalersDictionary[columnName])
{
if (keyValuePair.Key == "Standardization")
processingStepsListBox.Items.Add(columnName + " → Standardization");
if (keyValuePair.Key == "Min-max scaling")
{
(double minOutput, double maxOutput) = keyValuePair.Value;
processingStepsListBox.Items.Add(columnName + " → Min-max scaling { range=(" + minOutput.ToString() + ", " + maxOutput.ToString() + ") }");
}
}
}
for (int columnIndex = 0; columnIndex < outputScalersDictionary.Keys.Count; columnIndex++)
{
string columnName = outputScalersDictionary.Keys.ElementAt(columnIndex);
foreach (KeyValuePair<string, (double, double)> keyValuePair in outputScalersDictionary[columnName])
{
if (keyValuePair.Key == "Standardization")
processingStepsListBox.Items.Add(columnName + " → Standardization");
if (keyValuePair.Key == "Min-max scaling")
{
(double minOutput, double maxOutput) = keyValuePair.Value;
processingStepsListBox.Items.Add(columnName + " → Min-max scaling { range=(" + minOutput.ToString() + ", " + maxOutput.ToString() + ") }");
}
}
}
foreach (KeyValuePair<string, Dictionary<string, double>> keyValuePair in dimensionalityReductionStepsDictionary)
{
if (keyValuePair.Key == "Variance threshold")
processingStepsListBox.Items.Add("Variance threshold { threshold = " + keyValuePair.Value["threshold"] + " }");
else if (keyValuePair.Key == "Principle component analysis")
processingStepsListBox.Items.Add("Principle component analysis { nComponents = " + keyValuePair.Value["nComponents"] + " }");
}
}
}
private void ProcessButton_Click(object sender, EventArgs e)
{
if (inputData == null)
return;
Cursor = Cursors.WaitCursor;
try
{
// Get the input and output columns
inputColumnNames = GetInputColumnNames();
outputColumnName = GetOutputColumnName();
// Check if no input column for model
if (inputColumnNames.Length == 0)
throw new Exception("No input column was selected!");
// Check if a column was selected for multiple roles
for (int columnIndex = 0; columnIndex < columnsDataGridView.Rows.Count; columnIndex++)
{
int numberOfRoles = 0;
for (int roleIndex = 1; roleIndex < columnsDataGridView.ColumnCount; roleIndex++)
if ((bool)columnsDataGridView.Rows[columnIndex].Cells[roleIndex].Value == true)
numberOfRoles++;
if (numberOfRoles >= 2)
throw new Exception("You cannot choose multiple roles for 1 column!");
}
// Check output column
string[] outputColumn = inputData.Columns[outputColumnName].ToArray<string>();
string[] classLabel = outputColumn.Distinct().OrderBy(x => x).ToArray();
if (predictionType == PredictionType.BinaryClassification && classLabel.Length != 2)
throw new Exception("For binary classification, output column must contain 2 classes!");
if (predictionType == PredictionType.MulticlassClassification && classLabel.Length < 2)
throw new Exception("For multiclass classification, output column must contain 2 or more classes!");
if (predictionType == PredictionType.MulticlassClassification && classLabel.Length > 10)
throw new Exception("The output column contains too many classes!");
this.classLabels = classLabel;
// Reset the dictionaries if the data processing dialog needs to be reset
if (isDataProcessingDialogResetNeeded)
{
// Add column names to the dictionaries
Dictionary<string, Dictionary<string, (double, double)>> inputScalersDictionary = new();
for (int columnIndex = 0; columnIndex < inputColumnNames.Length; columnIndex++)
inputScalersDictionary[inputColumnNames[columnIndex]] = new Dictionary<string, (double, double)>();
Dictionary<string, Dictionary<string, (double, double)>> outputScalersDictionary = new();
if (predictionType == PredictionType.Regression)
outputScalersDictionary[outputColumnName] = new Dictionary<string, (double, double)>();
Dictionary<string, Dictionary<string, double>> dimensionalityReductionStepsDictionary = new();
// Reset the dialog
dataProcessingDialog = new DataProcessingDialog(inputScalersDictionary, outputScalersDictionary, dimensionalityReductionStepsDictionary);
// Turn off the flag
isDataProcessingDialogResetNeeded = false;
// Save the dictionaries
this.inputScalersDictionary = dataProcessingDialog.InputScalersDictionary.DeepClone();
this.outputScalersDictionary = dataProcessingDialog.OutputScalersDictionary.DeepClone();
this.dimensionalityReductionStepsDictionary = dataProcessingDialog.DimensionalityReductionStepsDictionary.DeepClone();
// Create the scalers for each column
minMaxScalers.Clear();
standardScalers.Clear();
foreach (KeyValuePair<string, Dictionary<string, (double, double)>> keyValuePair in inputScalersDictionary)
{
string columnName = keyValuePair.Key;
if (keyValuePair.Value.ContainsKey("Min-max scaling"))
{
(double minOutput, double maxOutput) = keyValuePair.Value["Min-max scaling"];
minMaxScalers[columnName] = new MinMaxScaler(minOutput, maxOutput);
}
else if (keyValuePair.Value.ContainsKey("Standardization"))
{
standardScalers[columnName] = new StandardScaler();
}
}
foreach (KeyValuePair<string, Dictionary<string, (double, double)>> keyValuePair in outputScalersDictionary)
{
string columnName = keyValuePair.Key;
if (keyValuePair.Value.ContainsKey("Min-max scaling"))
{
(double minOutput, double maxOutput) = keyValuePair.Value["Min-max scaling"];
minMaxScalers[columnName] = new MinMaxScaler(minOutput, maxOutput);
}
else if (keyValuePair.Value.ContainsKey("Standardization"))
{
standardScalers[columnName] = new StandardScaler();
}
}
}
else
{
dataProcessingDialog = new DataProcessingDialog(inputScalersDictionary.DeepClone(), outputScalersDictionary.DeepClone(), dimensionalityReductionStepsDictionary.DeepClone());
}
// Get input columns
double[][] inputColumns = new double[inputData.Rows.Count][];
for (int columnIndex = 0; columnIndex < inputColumnNames.Length; columnIndex++)
{
double[] column = inputData.Columns[inputColumnNames[columnIndex]].ToArray();
if (inputColumns[0] == null)
inputColumns = column.ToJagged();
else
inputColumns = inputColumns.Concatenate(column.ToJagged());
}
if (inputColumns[0] == null)
throw new Exception("No input column was selected or extracted!");
// Scaling
processingProgressLabel.Text = "Scaling...";
processingProgressLabel.Update();
Thread.Sleep(500);
double[][] scaledInputColumns = new double[inputData.Rows.Count][];
// Scale input and feature columns
for (int columnIndex = 0; columnIndex < inputColumnNames.Length; columnIndex++)
{
string columnName = inputColumnNames[columnIndex];
double[] column = inputColumns.GetColumn(columnIndex);
// Scale input columns
if (inputScalersDictionary.ContainsKey(columnName))
{
double[] scaledColumn = ScaleColumn(column, columnName);
// Add scaledColumn to the matrix
if (scaledInputColumns[0] == null)
scaledInputColumns = scaledColumn.ToJagged();
else
scaledInputColumns = scaledInputColumns.Concatenate(scaledColumn.ToJagged());
}
}
// Dimensionality reduction
processingProgressLabel.Text = "Dimensionality reduction...";
processingProgressLabel.Update();
Thread.Sleep(500);
processedInputColumns = new double[inputData.Rows.Count][];
if (dimensionalityReductionStepsDictionary.ContainsKey("Variance threshold") && dimensionalityReductionStepsDictionary.ContainsKey("Principle component analysis"))
{
// Apply variance threshold and PCA filters
double threshold = dimensionalityReductionStepsDictionary["Variance threshold"]["threshold"];
varianceThresholdFilter = new VarianceThresholdFilter();
(processedInputColumnNames, processedInputColumns) = varianceThresholdFilter.FitTransform(inputColumnNames, scaledInputColumns, threshold);
int nComponents = (int)dimensionalityReductionStepsDictionary["Principle component analysis"]["nComponents"];
pcaFilter = new PCAFilter();
(processedInputColumnNames, processedInputColumns) = pcaFilter.FitTransform(processedInputColumns, nComponents);
}
else if (dimensionalityReductionStepsDictionary.ContainsKey("Variance threshold"))
{
// Apply variance threshold filter only
double threshold = dimensionalityReductionStepsDictionary["Variance threshold"]["threshold"];
varianceThresholdFilter = new VarianceThresholdFilter();
(processedInputColumnNames, processedInputColumns) = varianceThresholdFilter.FitTransform(inputColumnNames, scaledInputColumns, threshold);
}
else if (dimensionalityReductionStepsDictionary.ContainsKey("Principle component analysis"))
{
// Apply PCA filter only
int nComponents = (int)dimensionalityReductionStepsDictionary["Principle component analysis"]["nComponents"];
pcaFilter = new PCAFilter();
(processedInputColumnNames, processedInputColumns) = pcaFilter.FitTransform(scaledInputColumns, nComponents);
}
else
{
// No filter
processedInputColumnNames = inputColumnNames;
processedInputColumns = scaledInputColumns;
}
processingProgressLabel.Text = "";
processingProgressLabel.Update();
// Process output columns (only for regression)
if (predictionType == PredictionType.Regression)
{
// Scale output columns
double[] scaledOutputColumn;
double[] column = inputData.Columns[outputColumnName].ToArray();
if (outputScalersDictionary.ContainsKey(outputColumnName))
scaledOutputColumn = ScaleColumn(column, outputColumnName);
else
scaledOutputColumn = column;
processedOutputColumnForRegression = scaledOutputColumn;
}
else
{
processedOutputColumnForClassification = inputData.Columns[outputColumnName].ToArray<string>();
}
// Combine the input and output column into a table
if (predictionType == PredictionType.Regression)
{
double[][] processedColumns = processedInputColumns.Concatenate(processedOutputColumnForRegression.ToJagged());
string[] processedColumnNames = processedInputColumnNames.Concatenate(outputColumnName);
processedDataset = processedColumns.ToTable(processedColumnNames);
}
else
{
processedDataset = processedInputColumns.ToTable(processedInputColumnNames);
processedDataset.Columns.Add(outputColumnName, typeof(string));
for (int rowIndex = 0; rowIndex < inputData.Rows.Count; rowIndex++)
if (processedOutputColumnForClassification != null)
processedDataset.Rows[rowIndex][outputColumnName] = processedOutputColumnForClassification[rowIndex];
}
}
catch (Exception ex)
{
MessageBox.Show(this, ex.Message, "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
processingProgressBar.Visible = false;
processingProgressLabel.Text = "";
Cursor = Cursors.Default;
return;
}
processingProgressLabel.Text = "Processing done.";
Cursor = Cursors.Default;
// Show the processed dataset
if (processedDataset.Columns.Count > 600)
{
MessageBox.Show(this, "The processed dataset contains too many columns and cannot be showed. You should reduce the number of features.", "Warning", MessageBoxButtons.OK, MessageBoxIcon.Warning);
processedDataDataGridView.DataSource = null;
}
else
{
processedDataDataGridView.DataSource = null;
processedDataDataGridView.DataSource = processedDataset;
processedDataDataGridView.Refresh();
}
// Raise the event
ProcessedDataLoaded?.Invoke(new DataTableEventArgs() { Dataset = processedDataset });
}
private void OnProcessedDataLoaded(DataTableEventArgs e)
{
// Draw the outlines of processedData in the diagram
workflowDiagramControl.DrawProcessDataBlockOutlines(true);
workflowDiagramControl.Refresh();
// Disable changing prediction type after the data is processed
binaryClassificationRadioButton.Enabled = false;
multiclassClassificationRadioButton.Enabled = false;
regressionRadioButton.Enabled = false;
// Get the column of class indices
processedClassIndices = new int[processedInputColumns.Rows()];
if (processedOutputColumnForClassification != null)
for (int rowIndex = 0; rowIndex < processedOutputColumnForClassification.Rows(); rowIndex++)
processedClassIndices[rowIndex] = classLabels.IndexOf(processedOutputColumnForClassification[rowIndex]);
// Enable the next tab pages
processedDataTableLayoutPanel.Enabled = true;
modelTableLayoutPanel.Enabled = true;
}
// processedDataTabPage
private void ProcessedDataDataGridView_CellFormatting(object sender, DataGridViewCellFormattingEventArgs e)
{
if (e.ColumnIndex == processedDataDataGridView.ColumnCount - 1)
{
e.CellStyle ??= new DataGridViewCellStyle();
e.CellStyle.BackColor = Color.Cyan;
}
}
private void SplitDataButton_Click(object sender, EventArgs e)
{
if (inputData == null ||
processedInputColumns == null ||
processedInputColumnNames == null ||
outputColumnName == null)
return;
EditSplitRatioDialog editSplitRatioDialog = new(inputData.Rows.Count, EditSplitRatioDialog.SplitType.TrainTestSplit);
if (editSplitRatioDialog.ShowDialog(this) != DialogResult.OK)
return;
double splitRatio = editSplitRatioDialog.SplitRatio;
int randomSeed = editSplitRatioDialog.RandomSeed;
TrainTestSpliter trainTestSpliter = new();
if (predictionType == PredictionType.Regression)
{
double[][] trainOutputColumnsForRegression;
double[][] testOutputColumnsForRegression;
(trainInputColumns, trainOutputColumnsForRegression, testInputColumns, testOutputColumnsForRegression) = trainTestSpliter.Split(processedInputColumns, processedOutputColumnForRegression.ToJagged(), 1 - splitRatio, randomSeed);
trainOutputColumnForRegression = trainOutputColumnsForRegression.GetColumn(0);
testOutputColumnForRegression = testOutputColumnsForRegression.GetColumn(0);
// Combine the input and output column into a tables
string[] processedInputColumnNames = this.processedInputColumnNames;
double[][] trainValues = trainInputColumns.Concatenate(trainOutputColumnForRegression.ToJagged());
string[] trainColumnNames = processedInputColumnNames.Concatenate(outputColumnName);
trainDataset = trainValues.ToTable(trainColumnNames);
double[][] testValues = testInputColumns.Concatenate(testOutputColumnForRegression.ToJagged());
string[] testColumnNames = processedInputColumnNames.Concatenate(outputColumnName);
testDataset = testValues.ToTable(testColumnNames);
}
else // Classification
{
string[][] trainOutputColumnsForClassification;
string[][] testOutputColumnsForClassification;
(trainInputColumns, trainOutputColumnsForClassification, testInputColumns, testOutputColumnsForClassification) = trainTestSpliter.Split(processedInputColumns, processedOutputColumnForClassification.ToJagged(), 1 - splitRatio, randomSeed);
trainOutputColumnForClassification = trainOutputColumnsForClassification.GetColumn(0);
testOutputColumnForClassification = testOutputColumnsForClassification.GetColumn(0);
trainClassIndices = processedClassIndices.Get(trainTestSpliter.TrainIndices);
testClassIndices = processedClassIndices.Get(trainTestSpliter.TestIndices);
// Combine the input and output column into a table
trainDataset = trainInputColumns.ToTable(processedInputColumnNames);
testDataset = testInputColumns.ToTable(processedInputColumnNames);
trainDataset.Columns.Add(outputColumnName, typeof(string));
testDataset.Columns.Add(outputColumnName, typeof(string));
for (int rowIndex = 0; rowIndex < trainDataset.Rows.Count; rowIndex++)
trainDataset.Rows[rowIndex][outputColumnName] = trainOutputColumnForClassification[rowIndex];
for (int rowIndex = 0; rowIndex < testDataset.Rows.Count; rowIndex++)
testDataset.Rows[rowIndex][outputColumnName] = testOutputColumnForClassification[rowIndex];
}
// Show trainDataset and testDataset
trainDatasetDataGridView.DataSource = null;
trainDatasetDataGridView.DataSource = trainDataset;
testDatasetDataGridView.DataSource = null;
testDatasetDataGridView.DataSource = testDataset;
// Raise the event
TrainDatasetLoaded?.Invoke(new DataTableEventArgs() { Dataset = trainDataset });
TestDatasetLoaded?.Invoke(new DataTableEventArgs() { Dataset = testDataset });
}
private void ProcessedDataVisualizeButton_Click(object sender, EventArgs e)
{
if (predictionType == PredictionType.Regression)
{
if (processedInputColumnNames == null ||
outputColumnName == null ||
processedInputColumns == null ||
processedOutputColumnForRegression == null)
return;
VisualizeRegressionDataDialog visualizeRegressionDataDialog = new(processedInputColumnNames, outputColumnName, processedInputColumns, processedOutputColumnForRegression);
visualizeRegressionDataDialog.Show(this);
}
else // Classification
{
if (processedInputColumnNames == null ||
outputColumnName == null ||
processedInputColumns == null ||
processedOutputColumnForClassification == null)
return;
VisualizeClassificationDataDialog visualizeClassificationDataDialog = new(processedInputColumnNames, outputColumnName, processedInputColumns, processedOutputColumnForClassification);
visualizeClassificationDataDialog.Show(this);
}
}
private void OnTrainDatasetLoaded(DataTableEventArgs e)
{
// Draw the outlines of trainDatasetBlock in the diagram
workflowDiagramControl.DrawTrainDatasetBlockOutlines(true);
workflowDiagramControl.Refresh();
// Enable the next tab page
trainDatasetTableLayoutPanel.Enabled = true;
}
private void OnTestDatasetLoaded(DataTableEventArgs e)
{
// Draw the outlines of testDatasetBlock in the diagram
workflowDiagramControl.DrawTestDatasetBlockOutlines(true);
workflowDiagramControl.Refresh();
// Enable the next tab page
testDatasetTableLayoutPanel.Enabled = true;
}
// trainDatasetTabPage
private void TrainDatasetDataGridView_CellFormatting(object sender, DataGridViewCellFormattingEventArgs e)
{
if (e.ColumnIndex == trainDatasetDataGridView.ColumnCount - 1)
{
e.CellStyle ??= new DataGridViewCellStyle();
e.CellStyle.BackColor = Color.Cyan;
}
}
private void TrainDatasetVisualizeButton_Click(object sender, EventArgs e)
{
if (predictionType == PredictionType.Regression)
{
if (processedInputColumnNames == null ||
outputColumnName == null ||
trainInputColumns == null ||
trainOutputColumnForRegression == null)
return;
VisualizeRegressionDataDialog visualizeRegressionDataDialog = new(processedInputColumnNames, outputColumnName, trainInputColumns, trainOutputColumnForRegression);
visualizeRegressionDataDialog.Show(this);
}
else // Classification
{
if (processedInputColumnNames == null ||
outputColumnName == null ||
trainInputColumns == null ||
trainOutputColumnForClassification == null)
return;
VisualizeClassificationDataDialog visualizeClassificationDataDialog = new(processedInputColumnNames, outputColumnName, trainInputColumns, trainOutputColumnForClassification);
visualizeClassificationDataDialog.Show(this);
}
}
// testDatasetTabPage
private void TestDatasetDataGridView_CellFormatting(object sender, DataGridViewCellFormattingEventArgs e)
{
if (e.ColumnIndex == testDatasetDataGridView.ColumnCount - 1)
{
e.CellStyle ??= new DataGridViewCellStyle();
e.CellStyle.BackColor = Color.Cyan;
}
}
private void TestDatasetVisualizeButton_Click(object sender, EventArgs e)
{
if (predictionType == PredictionType.Regression)
{
if (processedInputColumnNames == null ||
outputColumnName == null ||
testInputColumns == null ||
testOutputColumnForRegression == null)
return;
VisualizeRegressionDataDialog visualizeRegressionDataDialog = new(processedInputColumnNames, outputColumnName, testInputColumns, testOutputColumnForRegression);
visualizeRegressionDataDialog.Show(this);
}
else // Classification
{
if (processedInputColumnNames == null ||
outputColumnName == null ||
testInputColumns == null ||
testOutputColumnForClassification == null)
return;
VisualizeClassificationDataDialog visualizeClassificationDataDialog = new(processedInputColumnNames, outputColumnName, testInputColumns, testOutputColumnForClassification);
visualizeClassificationDataDialog.Show(this);
}
}
// modelTabPage
private void LoadModelButton_Click(object sender, EventArgs e)
{
string modelName = "";
if (modelPanel.Controls.Count > 0)
{
if (MessageBox.Show(this, "Do you want to override the current model?", "Override model", MessageBoxButtons.YesNoCancel, MessageBoxIcon.Question) != DialogResult.Yes)
return;
}
switch (predictionType)
{
case PredictionType.BinaryClassification:
BinaryClassificationModelSelectionDialog binaryClassificationModelSelectionDialog = new();
if (binaryClassificationModelSelectionDialog.ShowDialog(this) == DialogResult.OK)
modelName = binaryClassificationModelSelectionDialog.ModelName;
else
return;
break;
case PredictionType.MulticlassClassification:
MulticlassClassificationModelSelectionDialog multiclassClassificationModelSelectionDialog = new();
if (multiclassClassificationModelSelectionDialog.ShowDialog(this) == DialogResult.OK)
modelName = multiclassClassificationModelSelectionDialog.ModelName;
else
return;
break;
case PredictionType.Regression:
RegressionModelSelectionDialog regressionModelSelectionDialog = new();
if (regressionModelSelectionDialog.ShowDialog(this) == DialogResult.OK)
modelName = regressionModelSelectionDialog.ModelName;
else
return;
break;
}
switch (modelName)
{
case "K-Nearest Neighbors":
modelPanel.Controls.Clear();
KNNModelControl knnModelControl = new();
modelPanel.Controls.Add(knnModelControl);
knnModelControl.Dock = DockStyle.Fill;
knnModelControl.TrainButtonClicked += TrainModel;
// Raise the event
ModelLoaded?.Invoke(EventArgs.Empty);
break;
case "Naïve Bayes":
modelPanel.Controls.Clear();
NaiveBayesModelControl naiveBayesModelControl = new();
modelPanel.Controls.Add(naiveBayesModelControl);
naiveBayesModelControl.Dock = DockStyle.Fill;
naiveBayesModelControl.TrainButtonClicked += TrainModel;
// Raise the event
ModelLoaded?.Invoke(EventArgs.Empty);
break;
case "Logistic Regression":
modelPanel.Controls.Clear();
LogisticRegressionModelControl logisticRegressionModelControl = new();
modelPanel.Controls.Add(logisticRegressionModelControl);
logisticRegressionModelControl.Dock = DockStyle.Fill;
logisticRegressionModelControl.TrainButtonClicked += TrainModel;
// Raise the event
ModelLoaded?.Invoke(EventArgs.Empty);
break;
case "Decision Tree":
modelPanel.Controls.Clear();
DecisionTreeModelControl decisionTreeModelControl = new();
modelPanel.Controls.Add(decisionTreeModelControl);
decisionTreeModelControl.Dock = DockStyle.Fill;
decisionTreeModelControl.TrainButtonClicked += TrainModel;
// Raise the event
ModelLoaded?.Invoke(EventArgs.Empty);
break;
case "Multinomial Logistic Regression":
modelPanel.Controls.Clear();
MultinomialLogisticRegressionModelControl multinomialLogisticRegressionModelControl = new();
modelPanel.Controls.Add(multinomialLogisticRegressionModelControl);
multinomialLogisticRegressionModelControl.Dock = DockStyle.Fill;
multinomialLogisticRegressionModelControl.TrainButtonClicked += TrainModel;
// Raise the event
ModelLoaded?.Invoke(EventArgs.Empty);
break;
case "Random Forest":
modelPanel.Controls.Clear();
RandomForestModelControl randomForestModelControl = new();
modelPanel.Controls.Add(randomForestModelControl);
randomForestModelControl.Dock = DockStyle.Fill;
randomForestModelControl.TrainButtonClicked += TrainModel;
// Raise the event
ModelLoaded?.Invoke(EventArgs.Empty);
break;
case "Linear Regression":
modelPanel.Controls.Clear();
LinearRegressionModelControl linearRegressionModelControl = new();
modelPanel.Controls.Add(linearRegressionModelControl);
linearRegressionModelControl.Dock = DockStyle.Fill;
linearRegressionModelControl.TrainButtonClicked += TrainModel;
// Raise the event
ModelLoaded?.Invoke(EventArgs.Empty);
break;
case "Ridge Regression":
modelPanel.Controls.Clear();
RidgeRegressionModelControl ridgeRegressionModelControl = new();
modelPanel.Controls.Add(ridgeRegressionModelControl);
ridgeRegressionModelControl.Dock = DockStyle.Fill;
ridgeRegressionModelControl.TrainButtonClicked += TrainModel;
// Raise the event
ModelLoaded?.Invoke(EventArgs.Empty);
break;
case "Lasso Regression":
modelPanel.Controls.Clear();
LassoRegressionModelControl lassoRegressionModelControl = new();
modelPanel.Controls.Add(lassoRegressionModelControl);
lassoRegressionModelControl.Dock = DockStyle.Fill;
lassoRegressionModelControl.TrainButtonClicked += TrainModel;