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Code Issues Releases Wiki Activity

Renaming Impurity Functions

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This commit is contained in:
Ayush 2020-07-30 11:21:06 +05:30
parent 1954aae7a6
commit d587340e4a
2 changed files with 8 additions and 8 deletions
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8
trees/cart_classifier.go
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@ -39,7 +39,7 @@ type CARTDecisionTreeClassifier struct {
} }
// Calculate Gini Impurity of Target Labels // Calculate Gini Impurity of Target Labels
func giniImpurity(y []int64, labels []int64) (float64, int64) { func computeGiniImpurityAndModeLabel(y []int64, labels []int64) (float64, int64) {
nInstances := len(y) nInstances := len(y)
gini := 0.0 gini := 0.0
maxLabelCount := 0 maxLabelCount := 0
@ -62,7 +62,7 @@ func giniImpurity(y []int64, labels []int64) (float64, int64) {
} }
// Calculate Entropy loss of Target Labels // Calculate Entropy loss of Target Labels
func entropy(y []int64, labels []int64) (float64, int64) { func computeEntropyAndModeLabel(y []int64, labels []int64) (float64, int64) {
nInstances := len(y) nInstances := len(y)
entropy := 0.0 entropy := 0.0
maxLabelCount := 0 maxLabelCount := 0
@ -91,9 +91,9 @@ func entropy(y []int64, labels []int64) (float64, int64) {
func calculateClassificationLoss(y []int64, labels []int64, criterion string) (float64, int64) { func calculateClassificationLoss(y []int64, labels []int64, criterion string) (float64, int64) {
if criterion == GINI { if criterion == GINI {
return giniImpurity(y, labels) return computeGiniImpurityAndModeLabel(y, labels)
} else if criterion == ENTROPY { } else if criterion == ENTROPY {
return entropy(y, labels) return computeEntropyAndModeLabel(y, labels)
} else { } else {
panic("Invalid impurity function, choose from GINI or ENTROPY") panic("Invalid impurity function, choose from GINI or ENTROPY")
} }

8
trees/cart_regressor.go
View File

@ -59,7 +59,7 @@ func meanAbsoluteError(y []float64, yBar float64) float64 {
} }
// Turn Mean Absolute Error into impurity function for decision trees. // Turn Mean Absolute Error into impurity function for decision trees.
func maeImpurity(y []float64) (float64, float64) { func computeMaeImpurityAndAverage(y []float64) (float64, float64) {
yHat := average(y) yHat := average(y)
return meanAbsoluteError(y, yHat), yHat return meanAbsoluteError(y, yHat), yHat
} }
@ -76,16 +76,16 @@ func meanSquaredError(y []float64, yBar float64) float64 {
} }
// Convert mean squared error into impurity function for decision trees // Convert mean squared error into impurity function for decision trees
func mseImpurity(y []float64) (float64, float64) { func computeMseImpurityAndAverage(y []float64) (float64, float64) {
yHat := average(y) yHat := average(y)
return meanSquaredError(y, yHat), yHat return meanSquaredError(y, yHat), yHat
} }
func calculateRegressionLoss(y []float64, criterion string) (float64, float64) { func calculateRegressionLoss(y []float64, criterion string) (float64, float64) {
if criterion == MAE { if criterion == MAE {
return maeImpurity(y) return computeMaeImpurityAndAverage(y)
} else if criterion == MSE { } else if criterion == MSE {
return mseImpurity(y) return computeMseImpurityAndAverage(y)
} else { } else {
panic("Invalid impurity function, choose from MAE or MSE") panic("Invalid impurity function, choose from MAE or MSE")
} }