// GetSplitRuleFromSelection returns the DecisionTreeRule which maximises
// the information gain amongst consideredAttributes
//
// IMPORTANT: passing a zero-length consideredAttributes parameter will panic()
func (g *GiniCoefficientRuleGenerator) GetSplitRuleFromSelection(consideredAttributes []base.Attribute, f base.FixedDataGrid) *DecisionTreeRule {
var selectedAttribute base.Attribute
var selectedVal float64
// Parameter check
if len(consideredAttributes) == 0 {
panic("More Attributes should be considered")
}
// Minimize the averagge Gini index
minGini := math.Inf(1)
for _, s := range consideredAttributes {
var proposedDist map[string]map[string]int
var splitVal float64
if fAttr, ok := s.(*base.FloatAttribute); ok {
_, splitVal = getNumericAttributeEntropy(f, fAttr)
proposedDist = base.GetClassDistributionAfterThreshold(f, fAttr, splitVal)
} else {
proposedDist = base.GetClassDistributionAfterSplit(f, s)
}
avgGini := computeAverageGiniIndex(proposedDist)
if avgGini < minGini {
minGini = avgGini
selectedAttribute = s
selectedVal = splitVal
}
}
return &DecisionTreeRule{selectedAttribute, selectedVal}
}
// GetSplitAttributeFromSelection returns the class Attribute which maximises
// the information gain amongst consideredAttributes
//
// IMPORTANT: passing a zero-length consideredAttributes parameter will panic()
func (r *InformationGainRuleGenerator) GetSplitAttributeFromSelection(consideredAttributes []base.Attribute, f base.FixedDataGrid) base.Attribute {
var selectedAttribute base.Attribute
// Parameter check
if len(consideredAttributes) == 0 {
panic("More Attributes should be considered")
}
// Next step is to compute the information gain at this node
// for each randomly chosen attribute, and pick the one
// which maximises it
maxGain := math.Inf(-1)
// Compute the base entropy
classDist := base.GetClassDistribution(f)
baseEntropy := getBaseEntropy(classDist)
// Compute the information gain for each attribute
for _, s := range consideredAttributes {
proposedClassDist := base.GetClassDistributionAfterSplit(f, s)
localEntropy := getSplitEntropy(proposedClassDist)
informationGain := baseEntropy - localEntropy
if informationGain > maxGain {
maxGain = informationGain
selectedAttribute = s
}
}
// Pick the one which maximises IG
return selectedAttribute
}
// GetSplitRuleFromSelection returns a DecisionTreeRule which maximises
// the information gain amongst the considered Attributes.
//
// IMPORTANT: passing a zero-length consideredAttributes parameter will panic()
func (r *InformationGainRuleGenerator) GetSplitRuleFromSelection(consideredAttributes []base.Attribute, f base.FixedDataGrid) *DecisionTreeRule {
var selectedAttribute base.Attribute
// Parameter check
if len(consideredAttributes) == 0 {
panic("More Attributes should be considered")
}
// Next step is to compute the information gain at this node
// for each randomly chosen attribute, and pick the one
// which maximises it
maxGain := math.Inf(-1)
selectedVal := math.Inf(1)
// Compute the base entropy
classDist := base.GetClassDistribution(f)
baseEntropy := getBaseEntropy(classDist)
// Compute the information gain for each attribute
for _, s := range consideredAttributes {
var informationGain float64
var splitVal float64
if fAttr, ok := s.(*base.FloatAttribute); ok {
var attributeEntropy float64
attributeEntropy, splitVal = getNumericAttributeEntropy(f, fAttr)
informationGain = baseEntropy - attributeEntropy
} else {
proposedClassDist := base.GetClassDistributionAfterSplit(f, s)
localEntropy := getSplitEntropy(proposedClassDist)
informationGain = baseEntropy - localEntropy
}
if informationGain > maxGain {
maxGain = informationGain
selectedAttribute = s
selectedVal = splitVal
}
}
// Pick the one which maximises IG
return &DecisionTreeRule{selectedAttribute, selectedVal}
}
