From ef751e62c484badf66a142053e7c7b55eb5e38f2 Mon Sep 17 00:00:00 2001
From: Ayush <ayushgoel2004@gmail.com>
Date: Mon, 27 Jul 2020 17:08:44 +0530
Subject: [PATCH] Adding cart_test.go

---
 trees/cart_test.go | 109 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 109 insertions(+)
 create mode 100644 trees/cart_test.go

diff --git a/trees/cart_test.go b/trees/cart_test.go
new file mode 100644
index 0000000..047392a
--- /dev/null
+++ b/trees/cart_test.go
@@ -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)
+
+	})
+
+}