module PMDefaults where
import PMInstance
-import ProposalMatcher
+import PMConfig
import qualified NaiveMinCostFlow
import qualified CS2MinCostFlow
+-- A default set of configuration values; see module PMConfig.
pmDefaults = PMConfig {
--- Choose a min-cost flow implementation (timings on mattlaptop2):
+-- === Choose a min-cost flow implementation (timings on mattlaptop2) ===
-- A naive implementation that is slow for all but the smallest instances
-- (30s on a 20x50 example).
-minCostFlow = NaiveMinCostFlow.minCostFlow,
+--minCostFlow = NaiveMinCostFlow.minCostFlow,
-- Uses CS2 (http://www.igsystems.com/cs2/), which requires a license for
-- non-research use but is faster (<1s on a 20x50 example, 64s on a 60x500
--- example). Configure the path to cs2.exe in CS2MinCostFlow.hs.
---minCostFlow = CS2MinCostFlow.minCostFlow,
+-- example). Configure the path to cs2.exe in CS2MinCostFlow.hs. Remember to
+-- compile CS2 with -DPRINT_ANS, or this won't work!
+minCostFlow = CS2MinCostFlow.minCostFlow,
-reviewsEachProposal = 3,
+-- The number of reviews each proposal should get.
+reviewsEachProposal = 4,
-prefIsExpert = \p -> p <= 10,
-prefIsKnowledgeable = \p -> p <= 20,
+-- Applies to non-PC papers
+pcReviewsEachProposal = 3,
-prefIsBoring = \p -> p > 15,
-prefIsVeryBoring = \p -> p > 25,
+-- === Interpretation of the preference values ===
-prefIsConflict = \p -> p >= 40,
+expIsExpert = \x -> x >= 3,
+expIsKnowledgeable = \x -> x >= 2,
--- For now this is absolute. Later it might be proportional to a reviewer's
--- target load.
-loadTolerance = 1,
+prefIsBoring = \p -> p < 50,
+prefIsVeryBoring = \p -> p < 0,
+
+prefIsConflict = \p -> p <= -100,
+
+-- === Tuning parameters for the matcher ===
+
+-- The number of reviews by which a reviewer's load may exceed his/her target
+-- load of (relativeLoad * ceiling(numProps * reviewsEachProposal /
+-- totalRelativeLoad)). For now this is an additive constant; perhaps it should
+-- be proportional to the target load.
+loadTolerance = 2,
+
+ercLoadTolerance = 3,
-- Cost to overload by one review.
-- tx = 0 at target load, 1 at end of tolerance.
-- Additional cost to review a very boring proposal.
marginalVeryBoringCost = \lx -> 1000 + lx*1000,
--- Cost to make a review.
+-- Cost to make a review. Used by the evaluator too.
-- I'm using quadratic cost functions as a first attempt.
-assignmentCost = \pref -> (numAsWt 10 + pref) ^ 2,
+assignmentCost = \pref -> (widenInteger 10 + prefNewToOld pref) ^ 2,
-- Bonus for a first knowledgeable or expert review.
-knowledgeableBonus = 1000,
+knowledgeableBonus = 3000,
-- Bonus for an additional expert review.
-expertBonus = 1000
+expertBonus = 3000,
+
+-- === Parameters for the random-instance generator ===
+
+-- Number of topics.
+numTopics = 20,
+
+-- Exponent of the Zipf distribution used to choose topics for each proposal.
+topicZipfExponent = -0.5,
+
+-- === Parameters for the matching evaluator ===
+
+-- The weights given to the best, ..., worst review of a proposal in evaluating
+-- its "unhappiness". Default is [reviewsEachProposal, ..., 1] since it's most
+-- important for the best review to be good.
+reviewEvalWeights = let rep = reviewsEachProposal pmDefaults in
+ map widenInteger [rep, rep-1 .. 1]
}