/Prod_1_1/SVMPluginViewer/SVMViewerUserControl.cs

/**             © 2008 Avanade Inc. All Rights Reserved.

 * 

 * Authors:     Joris Valkonet joris.valkonet@avanade.com  Avanade Netherlands

 *              Thanh Luc      thanh.luc@avanade.com       Avanade Netherlands

 *              Mark Beerens   mark.beerens@avanade.com    Avanade Netherlands

 * 

 * Content:     This is the base class of the viewer and therefore implements the 

 *              IMiningModelViewerControl for the basic functionality and the 

 *              IMiningModelViewerControl2 interface for Excel functionality

 *                 

 * */



#region [Using]

using System;

using System.Collections.Generic;

using System.ComponentModel;

using System.Drawing;

using System.Data;

using System.Text;

using System.Windows.Forms;



using System.Collections;

using System.Data.OleDb;

using Microsoft.DataWarehouse.Interfaces;

//using System.Diagnostics;

using System.Drawing.Drawing2D;

#endregion

namespace Avanade.Datamining.SVMPluginViewer

{

    public partial class SVMViewerUserControl : UserControl, IMiningModelViewerControl, IMiningModelViewerControl2

    {



        #region [Parameters]

        //the colors of the predict values

        private SortedDictionary<String, Color> ValueColors = new SortedDictionary<String, Color>();

        //the color of a misclassification

        private Color misclassifiedColor = Color.Red;

        //did the user click on load already?

        private bool loaded = false;

        //do we need to show the misclassifications

        private bool showClassifier = true;

        //the selected data for visualization: [Trainingdata, TestData];

        private String data = "Trainingdata";

        //the selected column on the x-axis

        public string x_name;

        //the selected column on the y-axis

        public string y_name;

        //do we need to draw the axes

        private Boolean drawAxes = true;



        #endregion



        #region [Loading]

        /// <summary>

        /// Constructor.. This does nothing special, but initialization

        /// </summary>

        public SVMViewerUserControl()

        {

            InitializeComponent();

            BackColor = Color.White;

            // hide the following text labels

            this.label_X_Axis.Hide();

            this.label_Y_Axis.Hide();



            //set default values for display and grid options

            comboBox4.Text = "Yes";

            checkBox1.Checked = showClassifier;

            comboBox6.Text = "Trainingdata";

            //make sure that 2005 won't get into trouble.

            if (version != "2008")

            {

                comboBox6.Enabled = false;

            }

        }



        /// <summary>

        /// /...

        /// </summary>

        /// <param name="isActivated"></param>

        public void ViewerActivated(bool isActivated)

        {



        }



        /// <summary>

        /// This method is automatically executed by the host

        /// it loads the basic information into the model

        /// </summary>

        /// <param name="obj"></param>

        /// <returns></returns>

        public bool LoadViewerData(object obj)

        {

            //get the min and max values

            getMinMaxValues();

            //get the input attributes

            getAttributes();

            //set the predict classes



            setClasses();

            BindingSource source1 = new BindingSource();

            source1.DataSource = inputAttributes.Keys;

            BindingSource source2 = new BindingSource();

            source2.DataSource = inputAttributes.Keys;



            comboBox1.DataSource = source1;

            comboBox2.DataSource = source2;

            if (comboBox2.Items.Count > 1)

            {

                comboBox2.SelectedIndex = 1;

            }





            comboBox1.Refresh();

            comboBox2.Refresh();

            this.predict_name = getPredictAttribute();

            textBox1.Text = predict_name;

            textBox1.Refresh();

            button2.BackColor = misclassifiedColor;

            button2.Refresh();

            return true;

        }



        /// <summary>

        /// This method loads the data into the viewer. We have added a button for this, because

        /// loading the data can take a lot of time...

        /// </summary>

        /// <param name="sender"></param>

        /// <param name="e"></param>

        private void button4_Click(object sender, EventArgs e)

        {

            loaded = true;

            button4.Hide();

            RedrawScatterplot();

        }





        /// <summary>

        /// THis method assigns the colors to the predict values and loads the

        /// predict values in the combobox

        /// </summary>

        private void setClasses()

        {

            //get the classes and assign a color to each class

            String [] classes = getClasses();

            int i = 0;

            foreach (string value in classes)

            {

                switch (i)

                {

                    case 0: ValueColors.Add(value, Color.ForestGreen); break;

                    case 1: ValueColors.Add(value, Color.Blue); break;

                    case 2: ValueColors.Add(value, Color.Green); break;

                    case 3: ValueColors.Add(value, Color.Black); break;

                    case 4: ValueColors.Add(value, Color.Yellow); break;

                    case 5: ValueColors.Add(value, Color.Purple); break;

                    case 6: ValueColors.Add(value, Color.Chocolate); break;

                    case 7: ValueColors.Add(value, Color.DarkOliveGreen); break;

                    case 8: ValueColors.Add(value, Color.FloralWhite); break;

                    default:

                        {

                            Random rnd = new Random();

                            ValueColors.Add(value, Color.FromArgb(rnd.Next(0, 255), rnd.Next(0, 255), rnd.Next(0, 255))); 

                            break;

                        }

                }

                i++;

                

            }



            //load the classes in the combobox

            comboBox3.DataSource = classes;

            comboBox3.Refresh();

            Color tmpColor;

            ValueColors.TryGetValue(comboBox3.Text, out tmpColor);

            button1.BackColor = tmpColor;

        }

        #endregion



        #region [Redraw]

        /// <summary>

        /// This method redraw the scatter plot, after a value has changed somewhere

        /// but.. only when the user has clicked on the load button...

        /// </summary>

        private void RedrawScatterplot()

        {

            if (loaded)

            {

                if (this.comboBox1.Text == this.comboBox2.Text)

                {   // Check whether the X and Y comboboxes contain the same value



                    MessageBox.Show("The X and Y should not be assigned the same attribute.");

                }

                else

                {

                    // Turn mouse pointer into hourglass

                    Cursor.Current = Cursors.WaitCursor;



                    this.x_name = this.comboBox1.Text;

                    this.y_name = this.comboBox2.Text;

                    this.predict_name = this.textBox1.Text;

                    String x_type;

                    String y_type;

                    inputAttributes.TryGetValue(x_name, out x_type);

                    inputAttributes.TryGetValue(y_name, out y_type);

                    this.GetTrainingData();



                    //determine the types of the selected columns and

                    //the viewer shown depends on the types of the 

                    //attributes

                    if (x_type == "Continuous" && y_type == "Continuous")

                    {

                        //get the min and max values

                        maxValues.TryGetValue(x_name, out maxX);

                        minValues.TryGetValue(x_name, out minX);

                        maxValues.TryGetValue(y_name, out maxY);

                        minValues.TryGetValue(y_name, out minY);

                        

                        // Execute Paint 

                        Paint += new PaintEventHandler(OnPaint);



                        // Refresh user control to redraw itself

                        this.Refresh();



                    }

                    else if (x_type == "Discrete" && y_type == "Discrete")

                    {

                        //draw the bars

                        Paint += new PaintEventHandler(OnDiscretePaint);

                        this.Refresh();

                    }

                    else if (x_type == "Discrete") //and y continuous

                    {

                        MessageBox.Show("In this version of the viewer the combination of a discrete and continuous attribute is not supported by a visualization", "Sorry");

                    }

                    else

                    {

                        //continuous versus discrete

                        MessageBox.Show("In this version of the viewer the combination of a discrete and continuous attribute is not supported by a visualization", "Sorry");

                    }



                    // Set names for Scatterplot Axes

                    this.label_X_Axis.Text = String.Format("X:  {0}", this.comboBox1.Text);

                    this.label_Y_Axis.Text = String.Format("Y:  {0}", this.comboBox2.Text);

                    this.label_X_Axis.Visible = true;

                    this.label_Y_Axis.Visible = true;

                    // Reset mouse pointer to normal

                    Cursor.Current = Cursors.Default;

                }

            }

        }



        #endregion



        #region [Control]



        /// <summary>

        /// Update de button when a different predict value is selected

        /// </summary>

        /// <param name="sender"></param>

        /// <param name="e"></param>

        private void comboBox3_SelectedIndexChanged(object sender, EventArgs e)

        {

            Color color = Color.Brown;

            if(ValueColors.TryGetValue(comboBox3.Text, out color)){

                button1.BackColor = color;

            }

            else

            {

                button1.BackColor = color;

            }

        }



        /// <summary>

        /// This method enables you to select a color for a predict value

        /// </summary>

        /// <param name="sender"></param>

        /// <param name="e"></param>

        private void button1_Click(object sender, EventArgs e)

        {

            //set the color of the selected class.. if it is predefined and show the color selection dialog

            if (ValueColors.ContainsKey(comboBox3.Text))

            {

                Color tmpColor = Color.Brown;

                ValueColors.TryGetValue(comboBox3.Text, out tmpColor);

                colorDialog1.Color = tmpColor;

            }

            colorDialog1.ShowDialog(this);



            //obtain the color, update the data structure and repaint the button

            button1.BackColor = colorDialog1.Color;

            if (ValueColors.ContainsKey(comboBox3.Text))

            {

                ValueColors.Remove(comboBox3.Text );

            }

            ValueColors.Add(comboBox3.Text, colorDialog1.Color);



            //repaint

            RedrawScatterplot();

        }



        /// <summary>

        /// This method can change the color of the misclassifications

        /// </summary>

        /// <param name="sender"></param>

        /// <param name="e"></param>

        private void button2_Click(object sender, EventArgs e)

        {

            //set de misclassification color in the colordialog and show the diaglog

            colorDialog1.Color = misclassifiedColor;

            colorDialog1.ShowDialog(this);

            //ask the misclassification color from the dialog and repaint the button

            misclassifiedColor = colorDialog1.Color;

            button2.BackColor = misclassifiedColor;



            //repaint

            RedrawScatterplot();

        }



        /// <summary>

        /// This method redraws the scatter plot when different attributes are chosen

        /// </summary>

        /// <param name="sender"></param>

        /// <param name="e"></param>

        private void comboBox1_SelectedIndexChanged(object sender, EventArgs e)

        {

            RedrawScatterplot();

        }



        /// <summary>

        /// This methode redraws the scatter plot when different attribtues are chosen

        /// </summary>

        /// <param name="sender"></param>

        /// <param name="e"></param>

        private void comboBox2_SelectedIndexChanged(object sender, EventArgs e)

        {

            RedrawScatterplot();

        }



        /// <summary>

        /// The method redraws the scatter plot when different display is chosen

        /// </summary>

        /// <param name="sender"></param>

        /// <param name="e"></param>

        private void comboBox5_SelectedIndexChanged(object sender, EventArgs e)

        {

            RedrawScatterplot();

        }



        /// <summary>

        /// this method redraws the scatter plot when a different grid option is chosen

        /// </summary>

        /// <param name="sender"></param>

        /// <param name="e"></param>

        private void comboBox4_SelectedIndexChanged(object sender, EventArgs e)

        {

            RedrawScatterplot();

        }



        /// <summary>

        /// Show classifier is changes

        /// </summary>

        /// <param name="sender"></param>

        /// <param name="e"></param>

        private void checkBox1_CheckedChanged(object sender, EventArgs e)

        {

            showClassifier = checkBox1.Checked;

            RedrawScatterplot();

        }



        /// <summary>

        /// The type of data is changed

        /// </summary>

        /// <param name="sender"></param>

        /// <param name="e"></param>

        private void comboBox6_SelectedIndexChanged(object sender, EventArgs e)

        {

            if (!comboBox6.Text.Equals(data))

            {

                if (comboBox6.Text.Equals("Testingdata"))

                {

                    data = "Testingdata";

                }

                else

                {

                    data = "Trainingdata";

                }

                RedrawScatterplot();

            }

        }



        /// <summary>

        /// Displays the accuracy of the selected data

        /// </summary>

        /// <param name="sender"></param>

        /// <param name="e"></param>

        private void button3_Click(object sender, EventArgs e)

        {

            MessageBox.Show("Trainingset accuracy: " + getTrainingAccuracy() + "\n Testset accuracy: " + getTestAccuracy(), "Dataset details");

        }

        #endregion



        private void SVMViewerUserControl_Load(object sender, EventArgs e)

        {



        }

    } 

}