//Start the selection attributes process
func (as *AttributeSelection) StartSelection(instances data.Instances) {
as.input = data.NewInstancesWithInst(instances, len(instances.Attributes()))
as.input = instances
as.hasClass = as.input.ClassIndex() >= 0
as.selectedAttributes = as.SelectAttributes(as.input)
if len(as.selectedAttributes) == 0 {
panic("No selected attributes")
}
//Set output
fmt.Println(as.selectedAttributes, "as.selectedAttributes")
as.output = data.NewInstances()
attributes := make([]data.Attribute, 0)
for i := range as.selectedAttributes {
attributes = append(attributes, *as.input.Attribute(as.selectedAttributes[i]))
}
fmt.Println(attributes, "attributes")
as.output.SetDatasetName(as.input.DatasetName())
as.output.SetAttributes(attributes)
if as.hasClass {
as.output.SetClassIndex(len(as.selectedAttributes) - 1)
}
// Convert pending input instances
tmpInst := make([]data.Instance, 0)
for _, in := range as.input.Instances() {
tmpInst = append(tmpInst, as.convertInstance(in))
}
as.output.SetInstances(tmpInst)
}
func (as *AttributeSelection) SelectAttributes(data_ data.Instances) []int {
//***********attributeSet := make([]int, 0)
as.trainInstances = data_
as.doRank = as.search.GenerateRanking()
// check that a class index has been set
if as.trainInstances.ClassIndex() < 0 {
as.trainInstances.SetClassIndex(len(as.trainInstances.Attributes()) - 1)
}
// Initialize the attribute evaluator
as.evaluator.BuildEvaluator(as.trainInstances)
//fieldWith := int(math.Log(float64(len(as.trainInstances.Attributes()) + 1)))
// Do the search
//***********attributeSet =
as.search.Search(as.evaluator, as.trainInstances)
// InfoGain do not implements postprocessing in weka
//I won't use this check because in this implementation it will always be true
//due that search method always is going to be Ranker
if as.doRank {
}
as.attributeRanking = as.search.rankedAttributes()
// retrieve the number of attributes to retain
as.numToSelect = as.search.GetCalculatedNumToSelect()
fmt.Println(as.numToSelect, "as.numToSelect")
// determine fieldwidth for merit
f_p, w_p := 0, 0
for i := 0; i < as.numToSelect; i++ {
precision := math.Abs(as.attributeRanking[i][1]) - math.Abs(as.attributeRanking[i][1])
intPart := int(math.Abs(as.attributeRanking[i][1]))
if precision > 0 {
precision = math.Abs((math.Log(math.Abs(precision)) / math.Log(10))) + 3
}
if precision > float64(f_p) {
f_p = int(precision)
}
if intPart == 0 {
if w_p < 2 {
w_p = 2
}
} else if (math.Abs((math.Log(math.Abs(as.attributeRanking[i][1])) / math.Log(10))) + 1) > float64(w_p) {
if as.attributeRanking[i][1] > 0 {
w_p = int(math.Abs((math.Log(math.Abs(as.attributeRanking[i][1])) / math.Log(10))) + 1)
}
}
}
// set up the selected attributes array - usable by a filter or
// whatever
if as.trainInstances.ClassIndex() >= 0 {
as.selectedAttributeSet = make([]int, as.numToSelect+1)
as.selectedAttributeSet[as.numToSelect] = as.trainInstances.ClassIndex()
} else {
as.selectedAttributeSet = make([]int, as.numToSelect)
}
for i := 0; i < as.numToSelect; i++ {
as.selectedAttributeSet[i] = int(as.attributeRanking[i][0])
}
fmt.Println(as.selectedAttributeSet, "as.selectedAttributeSet")
if as.doXval {
as.CrossValidateAttribute()
}
if as.selectedAttributeSet != nil && !as.doXval {
as.attributeFilter = NewRemove()
as.attributeFilter.SetSelectedColumns(as.selectedAttributeSet)
as.attributeFilter.SetInvertSelection(true)
as.attributeFilter.SetInputFormat(as.trainInstances)
}
as.trainInstances = data.NewInstancesWithInst(as.trainInstances, 0)
return as.selectedAttributeSet
}
