- Implement CS2 min-cost-flow adaptor and generalize common min-cost-flow stuff

[match/match.git] / program / ProposalMatcherConfig.hs

module ProposalMatcherConfig
	(module ProposalMatcherConfig, minCostFlow) where

-- 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).
import NaiveMinCostFlow

-- 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.
--import CS2MinCostFlow

type Wt = Double -- Can be any RealFrac.

type Pref = Int

numAsWt x = fromInteger (toInteger x) :: Wt

reviewsEachProposal = 3 :: Int

prefIsExpert p = p <= 10
prefIsKnowledgeable p = p <= 20

prefIsBoring p = p > 15
prefIsVeryBoring p = p > 25

prefIsConflict p = p >= 40

-- For now this is absolute.  Later it might be proportional to a reviewer's
-- target load.
loadTolerance = 1 :: Int

-- Cost to overload by one review.
-- tx = 0 at target load, 1 at end of tolerance.
marginalLoadCost tx = 1000 + tx*1000 :: Wt

-- Cost to review a boring (or very boring) proposal.
-- lx = 0 at no load, 1 at target load.
marginalBoringCost lx = 1000 + lx*1000 :: Wt
-- Additional cost to review a very boring proposal.
marginalVeryBoringCost lx = 1000 + lx*1000 :: Wt

-- Cost to make a review.
-- I'm using quadratic cost functions as a first attempt.
assignmentCost pref = (numAsWt 10 + pref) ^ 2 :: Wt

-- Bonus for a first knowledgeable or expert review.
knowledgeableBonus = 1000 :: Wt

-- Bonus for an additional expert review.
expertBonus = 1000 :: Wt