Implemented max-min, which takes the maximum minimum distance into account instead of average distances
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@ -20,7 +20,7 @@
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[training-data {:keys [downsample-rate]}]
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(take (int (* downsample-rate (count training-data))) (shuffle training-data)))
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(defn select-downsample-tournament
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(defn select-downsample-avg
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"uses case-tournament selection to select a downsample that is biased to being spread out"
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[training-data {:keys [downsample-rate case-t-size]}]
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(let [shuffled-cases (shuffle training-data)
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@ -42,6 +42,27 @@
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(shuffle (concat (utils/drop-nth selected-case-index tournament)
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rest-of-cases))))))))
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(defn select-downsample-maxmin
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"uses tournament selection to select a downsample that has it's cases maximally far away"
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[training-data {:keys [downsample-rate case-t-size]}]
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(let [shuffled-cases (shuffle training-data)
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goal-size (int (* downsample-rate (count training-data)))]
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(loop [new-downsample (conj [] (first shuffled-cases))
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cases-to-pick-from (rest shuffled-cases)]
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(if (>= (count new-downsample) goal-size)
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new-downsample
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(let [tournament (take case-t-size cases-to-pick-from)
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rest-of-cases (drop case-t-size cases-to-pick-from)
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min-case-distances (metrics/min-of-colls
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(map (fn [distance-list]
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(utils/filter-by-index distance-list (map #(:index %) tournament)))
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(map #(:distances %) new-downsample)))
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selected-case-index (metrics/argmax min-case-distances)]
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(prn {:min-case-distances min-case-distances :selected-case-index selected-case-index})
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(recur (conj new-downsample (nth tournament selected-case-index))
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(shuffle (concat (utils/drop-nth selected-case-index tournament)
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rest-of-cases))))))))
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(defn select-downsample-metalex
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"uses meta-lexicase selection to select a downsample that is biased to being spread out"
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[training-data {:keys [downsample-rate]}])
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@ -52,7 +52,8 @@
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(prn {:data (some #(when (zero? (:index %)) %) indexed-training-data)})
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(let [training-data (if (= (:parent-selection argmap) :ds-lexicase)
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(case (:ds-function argmap)
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:case-tournament (downsample/select-downsample-tournament indexed-training-data argmap)
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:case-avg (downsample/select-downsample-avg indexed-training-data argmap)
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:case-maxmin (downsample/select-downsample-maxmin indexed-training-data argmap)
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(downsample/select-downsample-random indexed-training-data argmap))
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indexed-training-data) ;defaults to random
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full-evaluated-pop (sort-by :total-error
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@ -65,13 +66,8 @@
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population))
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best-individual (first ds-evaluated-pop)
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best-individual-passes-ds (and (= (:parent-selection argmap) :ds-lexicase) (<= (:total-error best-individual) solution-error-threshold))
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tot-evaluated-pop (when best-individual-passes-ds ;evaluate the whole pop on all training data
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(sort-by :total-error
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(mapper
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(partial error-function argmap (:training-data argmap))
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population)))
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;;best individual on all training-cases
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tot-best-individual (if best-individual-passes-ds (first tot-evaluated-pop) best-individual)]
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tot-best-individual (if best-individual-passes-ds (first full-evaluated-pop) best-individual)]
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(prn (first training-data))
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(if (:custom-report argmap)
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((:custom-report argmap) ds-evaluated-pop generation argmap)
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@ -18,9 +18,13 @@
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(defn mean-of-colls
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"returns the mean of multiple colls"
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[coll]
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;(prn {:func :mean-of-colls :coll coll})
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(map mean (math/transpose coll)))
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(defn min-of-colls
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"returns the smallest value of multiple colls"
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[coll]
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(map #(apply min %) (math/transpose coll)))
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(defn median
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"Returns the median of a collection."
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[coll]
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@ -14,6 +14,11 @@
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(t/is (= (m/mean-of-colls '((1 2 3) (4 3 2 1))) '(2.5 2.5 2.5)))
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(t/is (= (m/mean-of-colls '((1))) '(1.0))))
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(t/deftest min-of-colls-test
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(t/is (= (m/min-of-colls '((1 2 3 4) (4 3 2 1))) '(1 2 2 1)))
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(t/is (= (m/min-of-colls '((1 2 3) (4 3 2 1))) '(1 2 2)))
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(t/is (= (m/min-of-colls '((1))) '(1))))
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(t/deftest mean-test
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(t/is (= (m/mean '(1 2 3 4)) 2.5))
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(t/is (= (m/mean '()) 0)))
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