Make the evaluator's review weights configurable.

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

diff --git a/program/PMDefaults.hs b/program/PMDefaults.hs
index b5ef0c7..2f4a687 100644 (file)
--- a/program/PMDefaults.hs
+++ b/program/PMDefaults.hs
@@ -1,13 +1,14 @@
 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).
@@ -15,11 +16,15 @@ 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.
+-- 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,
 
+-- The number of reviews each proposal should get.
 reviewsEachProposal = 3,
 
+-- === Interpretation of the preference values ===
+
 prefIsExpert = \p -> p <= 10,
 prefIsKnowledgeable = \p -> p <= 20,
 
@@ -28,8 +33,12 @@ prefIsVeryBoring = \p -> p > 25,
 
 prefIsConflict = \p -> p >= 40,
 
--- For now this is absolute.  Later it might be proportional to a reviewer's
--- target load.
+-- === 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 = 1,
 
 -- Cost to overload by one review.
@@ -42,7 +51,7 @@ marginalBoringCost = \lx -> 1000 + lx*1000,
 -- 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,
 
@@ -50,6 +59,22 @@ assignmentCost = \pref -> (numAsWt 10 + pref) ^ 2,
 knowledgeableBonus = 1000,
 
 -- Bonus for an additional expert review.
-expertBonus = 1000
+expertBonus = 1000,
+
+-- === 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 numAsWt [rep, rep-1 .. 1]
 
 }