hirt 0.0.1.0 → 0.0.1.1

raw patch · 7 files changed

+110/−9 lines, 7 filesdep ~containersdep ~hmatrix

Dependency ranges changed: containers, hmatrix

Files

• ChangeLog.md +12/−0
• Driver.hs +2/−1
• Engine.hs +1/−1
• Statistics.hs +2/−2
• Types.hs +1/−1
• demo.sh +85/−0
• hirt.cabal +7/−4

@@ -0,0 +1,12 @@+0.0.1.1+-------++- added simple demo+- renamed statistic TaskCount to Count+- fixed nondescript error message on empty input+- relaxed version bounds on dependencies++0.0.1.0+-------++- initial version

@@ -86,6 +86,7 @@         istate = Engine.init responses         engine = setupEngine args in do     printStats resp responses+    when (length resp > 0) $ do     maybeWriteFile oResponses (formatResp oRespFormat responses)     putStrLn "Reading initial task parameters..."     istate <- maybe return readParams iTaskParams $ istate@@ -96,7 +97,7 @@     thetas results `seq` params results `seq` return ()     putStrLn "Calculating bayes probabilities..."     let (bayesBounds, bayesValues) = calcBayes results in do-    putStrLn "Writing bayes probability values..."+    putStrLn "Saving bayes probability values..."     maybeWriteFile oBayesPlot . buildTable $ bayesValues     putStrLn "Writing task parameters..."     let taskBase = tableTaskParams . getTaskParamsList $ results in do

@@ -35,7 +35,7 @@   where     [dt] = diff (thetas old) (thetas new)     dp = diff (params old) (params new)-    diff xs ys = (max' . trans . sub xs) ys+    diff xs ys = (max' . trans) (sub xs ys)       where         max' :: [[Double]] -> [Double]         max' = map (maximum . map abs)

@@ -20,7 +20,7 @@ import System.Random.MWC  statTheta :: StatisticType -> ContestantsData -> IO Statistic-statTheta TaskCount xs = return . SingleStatistic . map (fromIntegral . V.length . snd) $ xs+statTheta Count xs = return . SingleStatistic . map (fromIntegral . V.length . snd) $ xs statTheta SolvedProp xs = return . SingleStatistic . map (prop . snd) $ xs   where     prop = mean . V.map (ok . snd)@@ -75,7 +75,7 @@   statTask :: StatisticType -> TasksData -> IO Statistic-statTask TaskCount xs = return . SingleStatistic . map (fromIntegral . V.length . snd) $ xs+statTask Count xs = return . SingleStatistic . map (fromIntegral . V.length . snd) $ xs statTask SolvedProp xs = return . SingleStatistic . map (prop . snd) $ xs   where     prop = mean . V.map (ok . snd)

@@ -69,7 +69,7 @@ type TaskParams = UV.Vector TaskParam type Thetas = UV.Vector Theta -data StatisticType = TaskCount | SolvedProp | LogLikelihood+data StatisticType = Count | SolvedProp | LogLikelihood                    | DLogLikelihood | FisherSEM | Bootstrap     deriving (Eq, Show, Data, Typeable) 

@@ -0,0 +1,85 @@+#!/bin/sh -v++function find_exe() {+    HIRT=+    [ -z "$HIRT" ] && type hirt 2>/dev/null && HIRT=hirt+    [ -z "$HIRT" ] && [ -x hirt ] && HIRT=./hirt+    [ -z "$HIRT" ] && [ -x dist/build/hirt/hirt ] && HIRT=dist/build/hirt/hirt+}++find_exe++if [ -z "$HIRT" ]; then+    echo "Could not find executable..."+    echo "Perhaps build it with:"+    echo "	cabal configure && cabal build"+    echo " or   cabal install hirt"+    exit 1+fi >&2+++# a very simple sample+RESPONSES=/tmp/responses+TASKPARAM=/tmp/params+BAYES=/tmp/bayes++cat > "$RESPONSES" << EOF+contestant task result+c1 t1 0+c1 t2 0+c2 t1 0+c2 t2 1+c3 t2 1+c3 t1 1+EOF++# fit model with default options+"$HIRT" "$RESPONSES"++# the differences in loglikehood on this very small example are tiny,+# throwing off the JML estimate, let's try BFGS++"$HIRT" "$RESPONSES" --algo lbfgsb++# Better, but not quite there yet.+# We will increase the precision.++"$HIRT" "$RESPONSES" --algo lbfgsb --prec 1e-30++# Let's save task parameters for later use+# and show contestant ability estimates.++"$HIRT" "$RESPONSES" --algo lbfgsb --prec 1e-30 --otaskparam "$TASKPARAM" --otheta /dev/stdout++# Now, let's use the task parameter estimates+# to estimate contestant abilities.+# We will use one round of JML to leave task parameters fixed.++"$HIRT" "$RESPONSES" --algo jml -n 1 --itaskparam "$TASKPARAM" --otheta /dev/stdout++# Now we will show some statistics++"$HIRT" "$RESPONSES" --algo lbfgsb --prec 1e-30 --otaskparam /dev/stdout --otheta /dev/stdout \+    --taskstats count --taskstats solvedprop --taskstats loglikelihood --taskstats dloglikelihood \+    --thetastats count --thetastats solvedprop --thetastats loglikelihood --thetastats dloglikelihood \+    --thetastats fishersem --thetastats bootstrap++# Finally, we will plot a graph of the bayes expected a posteriori probability++"$HIRT" "$RESPONSES" --algo lbfgsb --prec 1e-30 --otaskparam /dev/stdout --otheta /dev/stdout \+    --obayesplot /tmp/bayes++cat > /tmp/plot.R << EOF+library(ggplot2)++x <- read.table(file="$BAYES", header=T)+p <- ggplot(data=x, aes(x=x, y=p, color=contestant)) ++     geom_line(size=1) ++     scale_color_brewer(palette="Set1",name="") ++     scale_x_continuous(expression(theta)) ++     scale_y_continuous("y")++ggsave(file="/tmp/plot.pdf", w=10, h=7, plot=p)+EOF++Rscript /tmp/plot.R

@@ -1,5 +1,5 @@ Name:                hirt-Version:             0.0.1.0+Version:             0.0.1.1 Synopsis:            Calculates IRT 2PL and 3PL models Description:      Program for fitting Item Response Theory (IRT) two (2PL) and@@ -30,12 +30,15 @@ Build-type:          Simple Cabal-version:       >=1.6 +Extra-source-files:+    ChangeLog.md+    demo.sh+ Flag PL3     Description: Compile for 3PL model, doesn't support JML yet.                  Model needs to be selected at compile time.     Default: False --- Extra-source-files:  Executable hirt      Main-is: Main.hs@@ -45,12 +48,12 @@       Build-depends: base >= 4 && < 5,                     vector >= 0.9 && < 0.10,-                    containers >= 0.4 && < 0.5,+                    containers >= 0.4 && < 0.6,                     text >= 0.11.1.13 && < 0.12,                     attoparsec >= 0.10.1 && < 0.11,                     text-format >= 0.3.0.7 && < 0.4,                     csv >= 0.1.2 && < 0.2,-                    hmatrix >= 0.13.1.0 && < 0.14,+                    hmatrix >= 0.13.1.0 && < 0.15,                     numeric-extras >= 0.0.2.2 && < 0.1,                     cmdargs >= 0.9.3 && < 0.10,                     random >= 1.0.1.1 && < 1.1,