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README.md

AMES - House Prices: Advanced Regression Techniques

This repository contains tests of the AlignmentRepaPy repository using data from the Ames Housing dataset compiled by Dean De Cock. Full details of the dataset are in Kaggle Data Set - House Prices: Advanced Regression Techniques. The AlignmentRepaPy repository is a fast Haskell implementation of some of the practicable inducers described in the paper The Theory and Practice of Induction by Alignment at https://greenlake.co.uk/.

Documentation

There is an analysis of this dataset here, with sections (a) predicting sale price without modelling and (b) induced modelling of sale price.

Installation

The AMES executables require the AlignmentRepaPy module which is in the AlignmentRepaPy repository. See the AlignmentRepaPy repository for installation instructions of the Python compiler and libraries.

Then download the zip files or use git to get the MUSHPy repository and the underlying AlignmentPy and AlignmentRepaPy repositories -

cd
git clone https://github.com/caiks/AlignmentPy.git
git clone https://github.com/caiks/AlignmentRepaPy.git
git clone https://github.com/caiks/AMESPy.git

Usage

To experiment with the dataset in the interpreter,

cd AMESPy
export PYTHONPATH=../AlignmentPy:../AlignmentRepaPy
python3
from AMESDev import *

(uu,aa) = amesIO()
vv = uvars(uu)
vvl = sset([VarStr("edible")])
vvk = vv - vvl

hr = aahr(uu,aa)

(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed) = ((9*9*10), 8, (9*9*10), 10, (10*3), 3, (9*9*10), 1, 3, 3, 5)

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

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

summation(mult,seed,uu1,df1,hr)
(61302.44944051167, 26729.333546815153)

The practicable model induction is described here.

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