AMES - Model induction

AMES - House Prices/Model induction

AMES_model1.json is induced by the following script -

from AMESDev import *

(uub,aab,aatrb,aateb) = amesBucketedIO(20)
vvb = uvars(uub) - sset([VarStr("Id")])
vvbl = sset([VarStr("SalePriceB")])
vvbk = vvb - vvbl

hhb = hrhrred(aahr(uub,aab),vvb)

model = "AMES_model1"
(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed) = (2919, 8, 2919, 20, (20*3), 3, 1459, 1, 15, 7, 5)

(uu1,df1) = decomperIO(uub,vvbk,hhb,wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed)

open(model+".json","w").write(decompFudsPersistentsEncode(decompFudsPersistent(df1)))

(a,ad) = summation(mult,seed,uu1,df1,hhb)
print("alignment: %.2f" % a)
print("alignment density: %.2f" % ad)

The first section loads the sample,

from AMESDev import *

(uub,aab,aatrb,aateb) = amesBucketedIO(20)
vvb = uvars(uub) - sset([VarStr("Id")])
vvbl = sset([VarStr("SalePriceB")])
vvbk = vvb - vvbl

hhb = hrhrred(aahr(uub,aab),vvb)

Then the parameters are defined,

model = "AMES_model1"
(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed) = (2919, 8, 2919, 20, (20*3), 3, 1459, 1, 15, 7, 5)

Here the limit of the underlying volume, xmax, is set to the histogram size, 2919,

size(aab)
# 2919 % 1

In general, the maximum-roll-by-derived-dimension decomper is such that increasing any of the parameters generally increases the summed alignment valency-density at the cost of computation time and space. In this case the parameters are chosen such that AMES_engine1 runs on a Windows 7 Xeon CPU 5150 @ 2.66GHz in less than 1GMB memory in 5017 seconds.

Then the decomper (defined in AMESDev) is run,

def decomperIO(uu,vv,hr,wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed):
    return parametersSystemsHistoryRepasDecomperMaxRollByMExcludedSelfHighestFmaxIORepa(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed,uu,vv,hr)

(uu1,df1) = decomperIO(uu,vvk,hr,wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed)

Then the model is is written to AMES_model1.json,

open(model+".json","w").write(decompFudsPersistentsEncode(decompFudsPersistent(df1)))

Finally, the summed alignment and the summed alignment valency-density are calculated,

(a,ad) = summation(mult,seed,uu1,df1,hhb)
print("alignment: %.2f" % a)
print("alignment density: %.2f" % ad)

The summed alignment is,

alignment: 23920.39
alignment density: 10661.49

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