/// <summary>
///
/// </summary>
/// <param name="dtree"></param>
private void PrintVariableImportance(CvDTree dtree )
{
Mat varImportance0 = dtree.GetVarImportance();
CvMat varImportance = varImportance0.ToCvMat();
if( varImportance == null )
{
Console.WriteLine( "Error: Variable importance can not be retrieved" );
return;
}
Console.Write( "Print variable importance information? (y/n) " );
string input = Console.ReadLine();
if( input[0] != 'y' && input[0] != 'Y' )
{
return;
}
for (int i = 0; i < varImportance.Cols * varImportance.Rows; i++)
{
double val = varImportance.DataArrayDouble[i];
int len = VarDesc[i].IndexOf('(');
Console.Write("{0}", VarDesc[i].Substring(0, len));
Console.WriteLine(": {0}%", val * 100.0);
}
}
/// <summary>
///
/// </summary>
/// <param name="dtree"></param>
private void InteractiveClassification(CvDTree dtree)
{
if (dtree == null)
{
return;
}
CvDTreeNode root = dtree.GetRoot();
CvDTreeTrainData data = dtree.GetData();
string input;
for (; ; )
{
CvDTreeNode node;
Console.Write("Start/Proceed with interactive mushroom classification (y/n): ");
input = Console.ReadLine();
if (input[0] != 'y' && input[0] != 'Y')
{
break;
}
Console.WriteLine("Enter 1-letter answers, '?' for missing/unknown value...");
// custom version of predict
node = root;
for (; ; )
{
CvDTreeSplit split = node.Split;
int dir = 0;
if (node.Left == null || node.Tn <= dtree.GetPrunedTreeIdx() || node.Split == null)
{
break;
}
for (; split != null; )
{
int j;
int vi = split.VarIdx;
int count = data.CatCount.DataArrayInt32[vi];
Console.Write("{0}: ", VarDesc[vi]);
input = Console.ReadLine();
if (input[0] == '?')
{
split = split.Next;
continue;
}
// convert the input character to the normalized value of the variable
unsafe
{
int* map = data.CatMap.DataInt32 + data.CatOfs.DataInt32[vi];
for (j = 0; j < count; j++)
{
if (map[j] == input[0])
{
break;
}
}
}
if (j < count)
{
dir = (split.Subset[j >> 5] & (1 << (j & 31))) != 0 ? -1 : 1;
if (split.Inversed)
{
dir = -dir;
}
break;
}
else
{
Console.WriteLine("Error: unrecognized value");
}
}
if (dir == 0)
{
Console.WriteLine("Impossible to classify the sample");
node = null;
break;
}
node = dir < 0 ? node.Left : node.Right;
}
if (node != null)
{
Console.Write("Prediction result: the mushroom is {0}\n", node.ClassIdx == 0 ? "EDIBLE" : "POISONOUS");
}
Console.Write("\n-----------------------------\n");
}
}
/// <summary>
///
/// </summary>
/// <param name="data"></param>
/// <param name="missing"></param>
/// <param name="responses"></param>
/// <param name="pWeight"></param>
/// <returns></returns>
private CvDTree MushroomCreateDTree(CvMat data, CvMat missing, CvMat responses, float pWeight)
{
float[] priors = { 1, pWeight };
CvMat varType = new CvMat(data.Cols + 1, 1, MatrixType.U8C1);
Cv.Set(varType, CvScalar.ScalarAll(CvStatModel.CV_VAR_CATEGORICAL)); // all the variables are categorical
CvDTree dtree = new CvDTree();
CvDTreeParams p = new CvDTreeParams(8, // max depth
10, // min sample count
0, // regression accuracy: N/A here
true, // compute surrogate split, as we have missing data
15, // max number of categories (use sub-optimal algorithm for larger numbers)
10, // the number of cross-validation folds
true, // use 1SE rule => smaller tree
true, // throw away the pruned tree branches
priors // the array of priors, the bigger p_weight, the more attention
// to the poisonous mushrooms
// (a mushroom will be judjed to be poisonous with bigger chance)
);
dtree.Train(data, DTreeDataLayout.RowSample, responses, null, null, varType, missing, p);
// compute hit-rate on the training database, demonstrates predict usage.
int hr1 = 0, hr2 = 0, pTotal = 0;
for (int i = 0; i < data.Rows; i++)
{
CvMat sample, mask;
Cv.GetRow(data, out sample, i);
Cv.GetRow(missing, out mask, i);
double r = dtree.Predict(sample, mask).Value;
bool d = Math.Abs(r - responses.DataArraySingle[i]) >= float.Epsilon;
if (d)
{
if (r != 'p')
hr1++;
else
hr2++;
}
//Console.WriteLine(responses.DataArraySingle[i]);
pTotal += (responses.DataArraySingle[i] == (float)'p') ? 1 : 0;
}
Console.WriteLine("Results on the training database");
Console.WriteLine("\tPoisonous mushrooms mis-predicted: {0} ({1}%)", hr1, (double)hr1 * 100 / pTotal);
Console.WriteLine("\tFalse-alarms: {0} ({1}%)", hr2, (double)hr2 * 100 / (data.Rows - pTotal));
varType.Dispose();
return dtree;
}
