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Code Issues Releases Wiki Activity

Adding cart_test.go

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This commit is contained in:
Ayush 2020-07-27 17:08:44 +05:30
parent 91a27e3ca0
commit ef751e62c4
1 changed files with 109 additions and 0 deletions
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109
trees/cart_test.go Normal file
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@ -0,0 +1,109 @@
package trees
import (
"fmt"
"testing"
. "github.com/smartystreets/goconvey/convey"
)
func TestRegressor(t *testing.T) {
Convey("Doing a CART Test", t, func() {
// For Classification Trees:
// Is Gini being calculated correctly
gini, giniMaxLabel := giniImpurity([]int64{1, 0, 0, 1}, []int64{0, 1})
So(gini, ShouldEqual, 0.5)
So(giniMaxLabel, ShouldNotBeNil)
// Is Entropy being calculated correctly
entropy, entropyMaxLabel := entropy([]int64{1, 0, 0, 1}, []int64{0, 1})
So(entropy, ShouldEqual, 1.0)
So(entropyMaxLabel, ShouldNotBeNil)
// Is Data being split into left and right properly
classifierData := [][]float64{[]float64{1, 3, 6},
[]float64{1, 2, 3},
[]float64{1, 9, 6},
[]float64{1, 11, 1}}
classifiery := []int64{0, 1, 0, 0}
leftdata, rightdata, lefty, righty := classifierCreateSplit(classifierData, 1, classifiery, 5.0)
So(len(leftdata), ShouldEqual, 2)
So(len(lefty), ShouldEqual, 2)
So(len(rightdata), ShouldEqual, 2)
So(len(righty), ShouldEqual, 2)
// Is isolating unique values working properly
So(len(classifierFindUnique([]float64{10, 1, 1})), ShouldEqual, 2)
// is data reordered correctly
orderedData, orderedY := classifierReOrderData(classifierGetFeature(classifierData, 1), classifierData, classifiery)
fmt.Println(orderedData)
fmt.Println(orderedY)
So(orderedData[1][1], ShouldEqual, 3.0)
So(orderedY[0], ShouldEqual, 1)
// Is split being updated properly based on threshold
leftdata, lefty, rightdata, righty = classifierUpdateSplit(leftdata, lefty, rightdata, righty, 1, 9.5)
So(len(leftdata), ShouldEqual, 3)
So(len(rightdata), ShouldEqual, 1)
// Is the root Node null when tree is not trained?
tree := NewDecisionTreeClassifier("gini", -1, []int64{0, 1})
So(tree.RootNode, ShouldBeNil)
So(tree.triedSplits, ShouldBeEmpty)
// ------------------------------------------
// For Regression Trees
// Is MAE being calculated correctly
mae, maeMaxLabel := maeImpurity([]float64{1, 3, 5})
So(mae, ShouldEqual, (4.0 / 3.0))
So(maeMaxLabel, ShouldNotBeNil)
// Is Entropy being calculated correctly
mse, mseMaxLabel := mseImpurity([]float64{1, 3, 5})
So(mse, ShouldEqual, (8.0 / 3.0))
So(mseMaxLabel, ShouldNotBeNil)
// Is Data being split into left and right properly
data := [][]float64{[]float64{1, 3, 6},
[]float64{1, 2, 3},
[]float64{1, 9, 6},
[]float64{1, 11, 1}}
y := []float64{1, 2, 3, 4}
leftData, rightData, leftY, rightY := regressorCreateSplit(data, 1, y, 5.0)
So(len(leftData), ShouldEqual, 2)
So(len(lefty), ShouldEqual, 2)
So(len(rightData), ShouldEqual, 2)
So(len(righty), ShouldEqual, 2)
// Is isolating unique values working properly
So(len(regressorFindUnique([]float64{10, 1, 1})), ShouldEqual, 2)
// is data reordered correctly
regressorOrderedData, regressorOrderedY := regressorReOrderData(regressorGetFeature(data, 1), data, y)
So(regressorOrderedData[1][1], ShouldEqual, 3.0)
So(regressorOrderedY[0], ShouldEqual, 2)
// Is split being updated properly based on threshold
leftData, leftY, rightData, rightY = regressorUpdateSplit(leftData, leftY, rightData, rightY, 1, 9.5)
So(len(leftData), ShouldEqual, 3)
So(len(rightData), ShouldEqual, 1)
// Is the root Node null when tree is not trained?
regressorTreetree := NewDecisionTreeRegressor("mae", -1)
So(regressorTreetree.RootNode, ShouldBeNil)
So(regressorTreetree.triedSplits, ShouldBeEmpty)
})
}