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decision-tree-test.rkt
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decision-tree-test.rkt
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#lang racket
(require rackunit)
(require "decision-tree.rkt"
"test-utils.rkt"
"data-representation-abstraction.rkt")
(define TEST-DATA (list #(2.771244718 1.784783929 0)
#(1.728571309 1.169761413 0)
#(3.678319846 2.81281357 0)
#(3.961043357 2.61995032 0)
#(2.999208922 2.209014212 0)
#(7.497545867 3.162953546 1)
#(9.00220326 3.339047188 1)
#(7.444542326 0.476683375 1)
#(10.12493903 3.234550982 1)
#(6.642287351 3.319983761 1)))
(define PRECISION (expt 10 -9))
(test-case "data-empty? test case"
(check-true (data-empty? empty)
"data-empty? is not correct")
(check-false (data-empty? (list 1 2 3))
"data-empty? is not correct"))
(test-case "data-first test case"
(check-equal? (data-first (list #(0 0 0)
#(1 1 1)))
#(0 0 0)
"data-first is not correct"))
(test-case "data-rest test case"
(check-equal? (data-rest (list #(0 0 0)
#(1 1 1)))
(list #(1 1 1))
"data-rest is not correct")
(check-equal? (data-rest (list #(0 0 0)
#(1 1 1)
#(2 2 2)
#(3 3 3)))
(list #(1 1 1)
#(2 2 2)
#(3 3 3))
"data-rest is not correct"))
(test-case "data-length test case"
(check-equal? (data-length (list #(0)
#(1)
#(2)))
3
"data-length is not correct")
(check-equal? (data-length (list #(0)
#(1)
#(2 3)
#(3 4 5)))
4
"data-length is not correct"))
(test-case "data-point-length test case"
(check-equal? (data-point-length #(0.1 0.2 0.3245 123.213432 'a))
5
"data-point-length is not correct"))
(test-case "data-filter test-case"
(check-equal? (data-filter (lambda (data-point)
(> (data-point-get-col data-point 1) 3))
TEST-DATA)
(list #(7.497545867 3.162953546 1)
#(9.00220326 3.339047188 1)
#(10.12493903 3.234550982 1)
#(6.642287351 3.319983761 1)))
(check-equal? (data-filter (lambda (data-point)
(< (data-point-get-col data-point 0) 2))
TEST-DATA)
(list #(1.728571309 1.169761413 0))))
(test-case "data-map test case"
(check-equal?
(lists-approximately-equal?
(data-map (lambda (data-point)
(vector-set data-point 0
(+ (vector-ref data-point 0) 1.0)))
TEST-DATA)
(list #(3.771244718 1.784783929 0)
#(2.728571309 1.169761413 0)
#(4.678319846 2.81281357 0)
#(4.961043357 2.61995032 0)
#(3.999208922 2.209014212 0)
#(8.497545867 3.162953546 1)
#(10.00220326 3.339047188 1)
#(8.444542326 0.476683375 1)
#(11.12493903 3.234550982 1)
#(7.642287351 3.319983761 1))
PRECISION)
true))
(test-case "data-get-col test case"
(check-equal? (data-get-col (list #(123 43 1)
#(2132435 23 2)
#(1244 769 3))
2)
(list 1 2 3)))
(test-case "data-point-get-col"
(check-equal? (data-point-get-col #(0 32 478 282 1) 1)
32)
(check-equal? (data-point-get-col #(0 32 478 282 1) 0)
0))
(test-case "make-leaf-node test case"
(check-equal? (make-leaf-node (list #(3.771244718 1.784783929 0)
#(2.728571309 1.169761413 0)
#(4.678319846 2.81281357 0)))
(Node (list #(3.771244718 1.784783929 0)
#(2.728571309 1.169761413 0)
#(4.678319846 2.81281357 0))
'none
'none
empty
empty)))
(test-case "last-split-node? test case"
(check-false (last-split-node? (make-leaf-node (list #(3.771244718 1.784783929 0)
#(2.728571309 1.169761413 0)
#(4.678319846 2.81281357 1)))))
(check-true (last-split-node? (Node (list #(3.771244718 1.784783929 0)
#(2.728571309 1.169761413 0)
#(4.678319846 2.81281357 1))
0
4.678319846
(make-leaf-node (list #(3.771244718 1.784783929 0)
#(2.728571309 1.169761413 0)))
(make-leaf-node (list #(4.678319846 2.81281357 1)))))))
(test-case "gini-index test case"
(check-equal? (gini-index (list
(list #(1.1 2.2 3.3 4.4 0)
#(1.1 2.2 3.3 4.4 0))
(list #(1.1 2.2 3.3 4.4 1)
#(1.1 2.2 3.3 4.4 1)))
4)
0.0
"gini index of perfect split is not 0.0")
(check-equal? (gini-index (list
(list #(1.1 2.2 3.3 4.4 0)
#(1.1 2.2 3.3 4.4 1))
(list #(1.1 2.2 3.3 4.4 0)
#(1.1 2.2 3.3 4.4 1)))
4)
1.0
"gini index of worst split is not 1.0")
(check-= (gini-index (list
(list #(1.1 2.2 3.3 4.4 0)
#(1.1 2.2 3.3 4.4 1)
#(1.1 2.2 3.3 4.4 1))
(list #(1.1 2.2 3.3 4.4 0)
#(1.1 2.2 3.3 4.4 0)
#(1.1 2.2 3.3 4.4 1)))
4)
0.888888888
PRECISION
(string-append "gini index of split is not "
(number->string 0.888888888))))
(test-case
"split-data test case"
(check-equal? (split-data (list #(1.0 1.0 1.0 1.0 0)
#(1.2 1.0 1.0 1.0 0)
#(1.4 1.0 1.0 1.0 0)
#(1.6 1.0 1.0 1.0 0)
#(1.8 1.0 1.0 1.0 0)
#(2.0 1.0 1.0 1.0 0))
0
1.5)
(list (list #(1.0 1.0 1.0 1.0 0)
#(1.2 1.0 1.0 1.0 0)
#(1.4 1.0 1.0 1.0 0))
(list #(1.6 1.0 1.0 1.0 0)
#(1.8 1.0 1.0 1.0 0)
#(2.0 1.0 1.0 1.0 0)))
"split-data does not split correctly")
(check-equal? (split-data (list #(1.0 1.0 1.0 1.0 0)
#(1.2 4.0 1.0 1.0 0)
#(1.4 1.0 1.0 1.0 0)
#(1.6 3.0 1.0 1.0 0)
#(1.8 1.0 1.0 1.0 0)
#(2.0 2.0 1.0 1.0 0))
1
2.5)
(list (list #(1.0 1.0 1.0 1.0 0)
#(1.4 1.0 1.0 1.0 0)
#(1.8 1.0 1.0 1.0 0)
#(2.0 2.0 1.0 1.0 0))
(list #(1.2 4.0 1.0 1.0 0)
#(1.6 3.0 1.0 1.0 0)))
"split-data does not split correctly"))
(test-case "get-best-split test case"
(let ([test-data (list #(2.771244718 1.784783929 0)
#(1.728571309 1.169761413 0)
#(3.678319846 2.81281357 0)
#(3.961043357 2.61995032 0)
#(2.999208922 2.209014212 0)
#(7.497545867 3.162953546 1)
#(9.00220326 3.339047188 1)
#(7.444542326 0.476683375 1)
#(10.12493903 3.234550982 1)
#(6.642287351 3.319983761 1))]
[feature-column-indices (list 0 1)]
[label-column-index 2])
(check-equal? (get-best-split test-data
feature-column-indices
label-column-index)
(Split 0
6.642287351
(list (list #(2.771244718 1.784783929 0)
#(1.728571309 1.169761413 0)
#(3.678319846 2.81281357 0)
#(3.961043357 2.61995032 0)
#(2.999208922 2.209014212 0))
(list #(7.497545867 3.162953546 1)
#(9.00220326 3.339047188 1)
#(7.444542326 0.476683375 1)
#(10.12493903 3.234550982 1)
#(6.642287351 3.319983761 1)))
0.0)
"get-best-split does not give the best split")))
(test-case "predict-at-leaf-node test case"
(check-equal? (predict-at-leaf-node (make-leaf-node (list #(1.0 2.0 0)
#(3.0 4.0 0)
#(5.0 6.0 1)
#(7.0 8.0 1)
#(9.0 0.0 1)))
2)
1
"predict-at-leaf-node does not give the correct label")
(check-equal? (predict-at-leaf-node (make-leaf-node (list #(1.0 2.0 0)
#(3.0 4.0 0)
#(5.0 6.0 0)
#(7.0 8.0 1)
#(9.0 0.0 1)))
2)
0
"predict-at-leaf-node does not give the correct label"))
(test-case "leaf-node? test case"
(check-true (leaf-node? (make-leaf-node (list #(5.0 6.0 0)
#(7.0 8.0 1))))
"leaf-node? is not correct")
(check-false (let* ([subset (list #(5.0 6.0 0)
#(7.0 8.0 1))]
[best-split (get-best-split subset (list 0 1) 2)])
(leaf-node? (Node subset
(Split-index best-split)
(Split-value best-split)
(make-leaf-node (list #(5.0 6.0 0)))
(make-leaf-node (list #(7.0 8.0 1))))))
"leaf-node? is not correct")
(let ([node (Node (list #(3.771244718 1.784783929 0)
#(2.728571309 1.169761413 0)
#(4.678319846 2.81281357 1))
0
4.678319846
(make-leaf-node (list #(3.771244718 1.784783929 0)
#(2.728571309 1.169761413 0)))
(make-leaf-node (list #(4.678319846 2.81281357 1))))])
(check-true (and (leaf-node? (Node-left node))
(leaf-node? (Node-right node))))))
(test-case "fit test case"
(let ([test-data (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0) ;;
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))]
[feature-column-indices (list 0 1)]
[label-column-index 2])
(check-equal? (fit #:train-data test-data
#:feature-column-indices (list 0 1)
#:label-column-index 2
#:max-depth 2
#:min-data-points 4
#:min-data-points-ratio 0.02)
(let ([best-split (get-best-split test-data(list 0 1) 2)])
(Node test-data
(Split-index best-split)
(Split-value best-split)
(make-leaf-node (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0)))
(make-leaf-node (list #(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1)))))
"split is not correct"))
(let* ([test-data (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0) ;;
#(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))]
[best-split (get-best-split test-data (list 0 1) 2)])
(check-equal? (fit #:train-data test-data
#:feature-column-indices (list 0 1)
#:label-column-index 2
#:max-depth 3
#:min-data-points 2
#:min-data-points-ratio 0.02)
(Node test-data
(Split-index best-split)
(Split-value best-split)
(make-leaf-node (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0)))
(let* ([subset (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))]
[best-split (get-best-split subset (list 0 1) 2)])
(Node subset
(Split-index best-split)
(Split-value best-split)
(make-leaf-node (list #(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1)))
(make-leaf-node (list #(2.3 1.1 0)
#(2.0 1.1 0))))))
"split is not correct"))
(let* ([test-data (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))]
[best-split (get-best-split test-data (list 0 1) 2)])
(check-equal? (fit #:train-data test-data
#:feature-column-indices (list 0 1)
#:label-column-index 2
#:max-depth 3
#:min-data-points 2
#:min-data-points-ratio 0.02)
(Node test-data
(Split-index best-split)
(Split-value best-split)
(make-leaf-node (list #(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1)))
(make-leaf-node (list #(2.3 1.1 0)
#(2.0 1.1 0))))
"split is not correct")))
(test-case "labels-elements-equal? test case"
(check-true (labels-elements-equal? empty)
"labels-elements-equal? is not correct")
(check-true (labels-elements-equal? (list 1 1 1))
"labels-elements-equal? is not correct")
(check-true (labels-elements-equal? (data-get-col (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0))
2))
"labels-elements-equal? is not correct")
(check-false (labels-elements-equal? (list 1 2 3))
"labels-elements-equal? is not correct"))
(test-case "data-majority-prediction test case"
(check-equal? (data-majority-prediction (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
2)
1)
(check-equal? (data-majority-prediction (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 0)
#(2.0 1.0 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
2)
0)
(check-equal? (data-majority-prediction (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 0)
#(2.0 1.0 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1)
#(2.4 1.0 1))
2)
0))
#;(struct Node
(data
predicted-label
split-feature-index
split-value
left right)
#:transparent)
(test-case "node-majority-prediction test case"
(check-equal? (node-majority-prediction
(Node (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
1
1.1
(list #(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
(list #(2.3 1.1 0)
#(2.0 1.1 0)))
2)
1)
(check-equal? (node-majority-prediction
(Node (list #(2.3 1.1 1)
#(2.0 1.1 1)
#(2.3 1.0 0)
#(2.0 1.0 0)
#(2.3 1.0 0)
#(2.0 1.0 0)
#(2.4 1.0 0))
1
1.1
(list #(2.3 1.0 0)
#(2.0 1.0 0)
#(2.3 1.0 0)
#(2.0 1.0 0)
#(2.4 1.0 0))
(list #(2.3 1.1 1)
#(2.0 1.1 1)))
2)
0))
(test-case "data-partition test case"
(check-values-equal? (data-partition (lambda (data-point)
(= (data-point-get-col data-point 2) 0))
(list #(2.3 1.1 1)
#(2.0 1.1 1)
#(2.3 1.0 0)
#(2.0 1.0 0)
#(2.3 1.0 0)
#(2.0 1.0 0)
#(2.4 1.0 0)))
(list (list #(2.3 1.0 0)
#(2.0 1.0 0)
#(2.3 1.0 0)
#(2.0 1.0 0)
#(2.4 1.0 0))
(list #(2.3 1.1 1)
#(2.0 1.1 1)))))
(test-case "predict test case"
(let ([tree (Node (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0) ;
#(2.3 1.1 0)
#(2.0 1.1 0) ;;
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
0 ;; split index
2.0 ;; split value
(make-leaf-node (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0)))
(Node (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
1
1.1
(make-leaf-node (list #(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1)))
(make-leaf-node (list #(2.3 1.1 0)
#(2.0 1.1 0)))))])
(check-equal? (predict tree #(2.3 1.1 0) 2) 0)
(check-equal? (predict tree #(2.3 1.0 0) 2) 1)))
(test-case "data-predict test case"
(let ([tree (Node (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0) ;
#(2.3 1.1 0)
#(2.0 1.1 0) ;;
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
0
2.0
(make-leaf-node (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0)))
(Node (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
1
1.1
(make-leaf-node (list #(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1)))
(make-leaf-node (list #(2.3 1.1 0)
#(2.0 1.1 0)))))])
(check-equal? (data-predict tree (list #(2.3 1.1 0)
#(2.3 1.0 0))
2)
(list 0 1))))
(test-case "cross-validation-split"
(check-equal? (cross-validation-split '(0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19)
4
#:random-state 12345)
(list (list 12 3 10 0 13)
(list 6 9 7 19 8)
(list 18 15 14 5 16)
(list 1 4 2 11 17))))
(test-case "accuracy-metric test case"
(check-equal? (accuracy-metric (list 0 0 0 0)
(list 1 0 0 0))
3/4)
(check-equal? (accuracy-metric (list 0 0 0 0)
(list 1 1 0 0))
1/2)
(check-equal? (accuracy-metric (list 1 1 1 1)
(list 1 0 0 0))
1/4)
(check-equal? (accuracy-metric (list 0 0 0 0)
(list 0 0 0 0))
1)
(check-equal? (accuracy-metric (list 0 0 0)
(list 1 0 0))
2/3))
(test-case "leave-one-out-k-folds"
(check-equal? (leave-one-out-k-folds (list (list #(1 1)
#(1 1)
#(1 1)
#(1 1))
(list #(2 2)
#(2 2)
#(2 2)
#(2 2))
(list #(3 3)
#(3 3)
#(3 3)
#(3 3))
(list #(4 4)
#(4 4)
#(4 4)
#(4 4)))
(list #(3 3)
#(3 3)
#(3 3)
#(3 3)))
(list (list #(1 1)
#(1 1)
#(1 1)
#(1 1))
(list #(2 2)
#(2 2)
#(2 2)
#(2 2))
(list #(4 4)
#(4 4)
#(4 4)
#(4 4)))))
(test-case "data-point-take-features"
(check-equal? (data-point-take-features #(0 1 2 3 4) 4)
#(0 1 2 3))
(check-equal? (data-point-take-features #(10 9 8 4) 3)
#(10 9 8)))
(test-case "get-predictions"
(let ([tree (Node (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
1
1.1
(make-leaf-node (list #(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1)))
(make-leaf-node (list #(2.3 1.1 0)
#(2.0 1.1 0))))])
(check-equal? (get-predictions tree (list #(2.4 1.2)
#(1.9 0.9)
#(3.0 3.0)
#(0.0 0.5))
2)
(list 0 1 0 1))))
(test-case "evaluate-algorithm"
(check-equal? (length (evaluate-algorithm #:data-set (shuffle TEST-DATA)
#:n-folds 4
#:feature-column-indices (list 0 1 2 3)
#:label-column-index 2
#:max-depth 3
#:min-data-points 4
#:min-data-points-ratio 0.02
#:min-impurity-split (expt 10 -7)
#:stop-at-no-impurity-improvement true
#:random-state 0))
4)
;; TODO: real test cose
)
(test-case "count-leaves"
(check-equal?
(count-leaves (Node (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
1
1.1
(make-leaf-node (list #(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1)))
(make-leaf-node (list #(2.3 1.1 0)
#(2.0 1.1 0)))))
2)
(check-equal?
(count-leaves (Node (list #(2.3 1.1 3.0 0)
#(2.0 1.1 3.0 0)
#(2.3 1.0 4.0 0)
#(2.0 1.0 3.0 1)
#(2.3 1.0 3.0 1)
#(2.0 1.0 3.0 1)
#(2.4 1.0 3.0 1))
1
1.1
(Node (list #(2.3 1.0 4.0 0)
#(2.0 1.0 3.0 1)
#(2.3 1.0 3.0 1)
#(2.0 1.0 3.0 1)
#(2.4 1.0 3.0 1))
2
4.0
(make-leaf-node (list #(2.0 1.0 3.0 1)
#(2.3 1.0 3.0 1)
#(2.0 1.0 3.0 1)
#(2.4 1.0 3.0 1)))
(make-leaf-node (list #(2.3 1.0 4.0 0))))
(make-leaf-node (list #(2.3 1.1 3.0 0)
#(2.0 1.1 3.0 0)))))
3))
(test-case "get-last-split-nodes test case"
(check-equal?
(get-last-split-nodes
(Node (list #(2.3 1.1 3.0 0)
#(2.0 1.1 3.0 0)
#(2.3 1.0 4.0 0)
#(2.0 1.0 3.0 1)
#(2.3 1.0 3.0 1)
#(2.0 1.0 3.0 1)
#(2.4 1.0 3.0 1))
1
1.1
(Node (list #(2.3 1.0 4.0 0)
#(2.0 1.0 3.0 1)
#(2.3 1.0 3.0 1)
#(2.0 1.0 3.0 1)
#(2.4 1.0 3.0 1))
2
4.0
(make-leaf-node (list #(2.0 1.0 3.0 1)
#(2.3 1.0 3.0 1)
#(2.0 1.0 3.0 1)
#(2.4 1.0 3.0 1)))
(make-leaf-node (list #(2.3 1.0 4.0 0))))
(make-leaf-node (list #(2.3 1.1 3.0 0)
#(2.0 1.1 3.0 0)))))
(list (Node (list #(2.3 1.0 4.0 0)
#(2.0 1.0 3.0 1)
#(2.3 1.0 3.0 1)
#(2.0 1.0 3.0 1)
#(2.4 1.0 3.0 1))
2
4.0
(make-leaf-node (list #(2.0 1.0 3.0 1)
#(2.3 1.0 3.0 1)
#(2.0 1.0 3.0 1)
#(2.4 1.0 3.0 1)))
(make-leaf-node (list #(2.3 1.0 4.0 0)))))))
(test-case "make-leaf-node-from-split-node test case"
(check-equal?
(make-leaf-node-from-split-node
(Node (list #(2.3 1.0 4.0 0)
#(2.0 1.0 3.0 1)
#(2.3 1.0 3.0 1)
#(2.0 1.0 3.0 1)
#(2.4 1.0 3.0 1))
2
4.0
(make-leaf-node (list #(2.0 1.0 3.0 1)
#(2.3 1.0 3.0 1)
#(2.0 1.0 3.0 1)
#(2.4 1.0 3.0 1)))
(make-leaf-node (list #(2.3 1.0 4.0 0)))))
(make-leaf-node (list #(2.3 1.0 4.0 0)
#(2.0 1.0 3.0 1)
#(2.3 1.0 3.0 1)
#(2.0 1.0 3.0 1)
#(2.4 1.0 3.0 1)))))
(test-case "prune-tree test case"
(let* ([tree (Node (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0) ;
#(2.3 1.1 0)
#(2.0 1.1 0) ;;
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
0
2.0
(make-leaf-node (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0)))
(Node (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
1
1.1
(make-leaf-node (list #(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1)))
(make-leaf-node (list #(2.3 1.1 0)
#(2.0 1.1 0)))))]
[split-node (Node (list #(2.3 1.1 0)
#(2.0 1.1 0)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
1
1.1
(make-leaf-node (list #(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1)))
(make-leaf-node (list #(2.3 1.1 0)
#(2.0 1.1 0))))]
[tranformed-node (make-leaf-node-from-split-node split-node)])
(check-equal? (prune-node-from-tree tree split-node)
(Node (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0) ;
#(2.3 1.1 0)
#(2.0 1.1 0) ;;
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.3 1.0 1)
#(2.0 1.0 1)
#(2.4 1.0 1))
0
2.0
(make-leaf-node (list #(1.0 1.0 0)
#(1.2 1.0 0)
#(1.1 1.0 0)
#(1.4 1.0 0)
#(1.2 1.0 0)
#(1.2 1.0 0)))
tranformed-node))))
(test-case "select-better-tree test case"
(let ([tree (Node (list #(1.0 0.10 0)
#(1.1 0.11 0)
#(1.2 0.12 0)
#(1.3 0.13 0)
#(1.4 0.14 0)
#(1.5 0.15 0)
#(1.6 0.16 0)
#(1.7 0.17 0)
#(1.8 0.18 0)
#(1.9 0.19 0)
#(2.0 0.20 0)
#(2.1 0.21 0)
#(2.2 0.22 0)
#(2.3 0.23 0)
#(2.4 0.24 0)
#(2.5 0.25 0)
#(2.6 0.26 0)
#(2.7 0.27 0)
#(3.0 0.10 0)
#(3.0 0.20 1))
0
3.0
(make-leaf-node (list #(1.0 0.10 0)
#(1.1 0.11 0)
#(1.2 0.12 0)
#(1.3 0.13 0)
#(1.4 0.14 0)
#(1.5 0.15 0)
#(1.6 0.16 0)
#(1.7 0.17 0)
#(1.8 0.18 0)
#(1.9 0.19 0)
#(2.0 0.20 0)
#(2.1 0.21 0)
#(2.2 0.22 0)
#(2.3 0.23 0)
#(2.4 0.24 0)
#(2.5 0.25 0)
#(2.6 0.26 0)
#(2.7 0.27 0)))
(Node (list #(3.0 0.10 0) ; the node, which will be pruned away
#(3.0 0.20 1))
1
0.2
(make-leaf-node (list #(3.0 0.10 0)))
(make-leaf-node (list #(3.0 0.20 1)))))]
[pruned-tree (Node (list #(1.0 0.10 0)
#(1.1 0.11 0)
#(1.2 0.12 0)
#(1.3 0.13 0)
#(1.4 0.14 0)
#(1.5 0.15 0)
#(1.6 0.16 0)
#(1.7 0.17 0)
#(1.8 0.18 0)
#(1.9 0.19 0)
#(2.0 0.20 0)
#(2.1 0.21 0)
#(2.2 0.22 0)
#(2.3 0.23 0)
#(2.4 0.24 0)
#(2.5 0.25 0)
#(2.6 0.26 0)
#(2.7 0.27 0)
#(3.0 0.10 0)
#(3.0 0.20 1))
0
3.0
(make-leaf-node (list #(1.0 0.10 0)
#(1.1 0.11 0)
#(1.2 0.12 0)
#(1.3 0.13 0)
#(1.4 0.14 0)
#(1.5 0.15 0)
#(1.6 0.16 0)
#(1.7 0.17 0)
#(1.8 0.18 0)
#(1.9 0.19 0)
#(2.0 0.20 0)
#(2.1 0.21 0)
#(2.2 0.22 0)
#(2.3 0.23 0)
#(2.4 0.24 0)
#(2.5 0.25 0)
#(2.6 0.26 0)
#(2.7 0.27 0)))
(make-leaf-node (list #(3.0 0.10 0) ; the pruned node
#(3.0 0.20 1))))]
;; the pruning set is only +0.01 and +0.001 in all rows
;; so that the values do not pass decision boundaries
[pruning-set (list #(1.01 0.101 0)
#(1.11 0.111 0)
#(1.21 0.121 0)
#(1.31 0.131 0)
#(1.41 0.141 0)
#(1.51 0.151 0)
#(1.61 0.161 0)
#(1.71 0.171 0)
#(1.81 0.181 0)
#(1.91 0.191 0)
#(2.01 0.201 0)
#(2.11 0.211 0)
#(2.21 0.221 0)
#(2.31 0.231 0)
#(2.41 0.241 0)
#(2.51 0.251 0)
#(2.61 0.261 0)
#(2.71 0.271 0)
#(3.01 0.101 0)
#(3.01 0.201 1))]
[feature-column-indices (list 0 1)]
[label-column-index 2]
;; 6% classification error tolerance,
;; so that 1 of 20 data points misclassification does not matter
[accuracy-tolerance 0.06])
;; since the 5% improvement are below the tolerance for lost accuracy
;; when pruning, the tree should indeed be pruned.
(check-equal? (select-better-tree tree
pruned-tree
pruning-set
feature-column-indices
label-column-index
accuracy-tolerance)
pruned-tree)
;; now try with a lower tolerance, which should not allow pruning
(check-equal? (select-better-tree tree
pruned-tree
pruning-set
feature-column-indices