def run_test(trX, trY,res_file):
desired_dt20 = 0.78
desired_dt50 = 0.78
desired_knn1 = 0.70
desired_knn3 = 0.73
print '\n\nFirst, we run DT and KNN on the training/development data to '
print 'ensure that we are getting roughly the right accuracies.'
print 'We use the first 80% of the data as training, and the last'
print '20% as test.'
decTree = DT.DT()
res = 1
print '\nDT (cutoff=20)...'
sizeX = trX.shape
end = int(np.round(sizeX[0]*0.80,decimals=0))
testRun = tt.TrainTest(decTree, trX[:end, :], trY[:end], trX[end:, :], trY[end:], 20)
acc = testRun.run_tt()
res += testRun.verifyAcc(acc['acc'], desired_dt20)
print'\nTrainTime, TestTime', acc['trainTime'], acc['testTime']
res_file.write('\nDT (cutoff=20)')
res_file.write('\nTrainTime, TestTime ' + str(acc['trainTime']) + ', ' + str(acc['testTime']))
print '\nDT (cutoff=50)...'
testRun = tt.TrainTest(decTree, trX[:end, :], trY[:end], trX[end:sizeX[0], :], trY[end:sizeX[0]], 50)
acc = testRun.run_tt()
res += testRun.verifyAcc(acc['acc'], desired_dt50)
print'\nTrainTime, TestTime', acc['trainTime'], acc['testTime']
res_file.write('\nDT (cutoff=50)')
res_file.write('\nTrainTime, TestTime ' + str(acc['trainTime']) + ', ' + str(acc['testTime']))
knnModel = KNN.KNN()
print '\nKNN (K=1)'
max_size = sizeX[0] if sizeX[0] < 10001 else 10000
end = int(np.round(max_size*0.80,decimals=0))
testRun = tt.TrainTest(knnModel, trX[:end, :], trY[:end], trX[end:sizeX[0], :], trY[end:sizeX[0]], 1)
acc = testRun.run_tt()
res += testRun.verifyAcc(acc['acc'], desired_knn1)
print'\nTrainTime, TestTime', acc['trainTime'], acc['testTime']
res_file.write('\nKNN (K=1)')
res_file.write('\nTrainTime, TestTime ' + str(acc['trainTime']) + ', ' + str(acc['testTime']))
print '\nKNN (K=3)'
testRun = tt.TrainTest(knnModel, trX[:end, :], trY[:end], trX[end:sizeX[0], :], trY[end:sizeX[0]], 3)
acc = testRun.run_tt()
res += testRun.verifyAcc(acc['acc'], desired_knn3)
print'\nTrainTime, TestTime', acc['trainTime'], acc['testTime']
res_file.write('\nKNN (K=3)')
res_file.write('\nTrainTime, TestTime ' + str(acc['trainTime']) + ', ' + str(acc['testTime']))
raw_input('\nPress enter to continue...')
return
#CopaLeche.updateCopa()
#MENUUU
opcion = int(
input(
"MENU\n 1-ORGANIZACIONES \n 2-COPAS \n 3-PAISES \n 4-LIGAS \n 5-EQUIPOS \n 6-JUGADOR \n 7- DTs \n 8- Salir \n OPCION: "
))
ORG = Organizacion()
COPA = Copa()
PAIS = Pais()
Ligue = Liga()
Team = Equipo()
Player = Jugador()
DeTe = DT()
while (opcion != 8):
#opcion = int(input("\n MENU\n 1-ORGANIZACIONES \n 2-COPAS \n 3-PAISES \n 4-LIGAS \n 5-EQUIPOS \n 6-JUGADOR \n 7- DTs \n 8- Salir \n OPCION: "))
if (opcion == 1):
opcionOrg = int(
input(
"\n 1-CREAR ORGANIZACION \n 2-INSERTAR ORG EN BASE \n 3-VER ORGANIZACIONES DE LA BASE \n 4-MODIFICAR UNA ORGANIZACION \n 5-ELIMINAR UNA ORGANIZACION \n 6-VOLVER AL 1ER MENU \n OPCION: "
))
if (opcionOrg == 1):
nombre_org = input("Escriba el nombre de la organizacion: ")
import sys
import time
import marshal
import stat
def phfunc(name, obj):
marshal.dump(obj, open(name,'w'))
if __name__=='__main__':
bt = time.time()
fname=sys.argv[1]
mtime=os.stat(fname)[stat.ST_MTIME]
cform=sys.argv[1]+'.dtcc'
try:
cmtime=os.stat(cform)[stat.ST_MTIME]
comp_form=marshal.load(open(cform))
except:
comp_form=None
cmtime=-1
d=DT.DT(open(fname).read(), fname, comp_form, mtime, cmtime,
lambda x, y=cform: phfunc(y, x))
class dumb: pass
ns=dumb()
text = d(ns)
et = time.time()
print text
print 'elapsed time:', et - bt
'''
import numpy as np
import DT as dt
import KNN as knn
if __name__ == '__main__':
print 'running tests on DT and KNN'
#This is the class example [mathy, test >= 80, project >= 80, early]
#with a slight change so that non-mathy first splits on early.
trX=np.array([[1,1,1,1],[1,1,1,0],[0,1,0,1],[0,0,1,1],[0,0,1,1],[0,0,0,0],[0,0,0,0],[1,0,1,1],[1,0,0,1],[0,0,1,1],[1,0,0,0],[0,0,1,1],[0,1,0,1],[0,0,1,0]])
trY=np.array([[1],[1],[0],[0],[0],[1],[0],[1],[0],[0],[0],[0],[0],[1]])
deX = np.array([[0,1,0,0],[0,0,1,0],[0,1,1,1]])
deY = np.array([[0],[1],[0]])
decTree = dt.DT()
print 'DT, cutoff=0'
trainModel = decTree.res('train',X=trX,Y=trY,h_param=0)
decTree.DTdraw(trainModel)
output = decTree.res('predict',model=trainModel,test_case=deX)
print output
knnMode = knn.KNN()
print 'KNN, k=1'
trainModel = knnMode.res('train',X=trX,Y=trY,h_param=1)
output = knnMode.res('predict',model=trainModel,test_case=deX)
print output
print 'Done'
res_file.write('\n' + disp + '\ndone')
base = 'baseline'+data_types[i]
if base not in results.keys():
print "Lets run some baseline measures..."
res = run_test(trX,trY,res_file)
res_file.write('\n' + base + '\n')
res_file.write(str(res))
raw_input('Press enter to continue...')
dec = 'dt'+data_types[i]
if dec not in results.keys():
print '\nNow we vary the cutoff for the decision tree and see how it affects accuracy...'
thresh = [5,10,20,40,80,160]
decTree = DT.DT()
res = run_comps(decTree, thresh, trX[0:4800, :], trY[0:4800], trX[4801:6000, :],
trY[4801:6000],"Figure 2: DT cutoff versus accuracy (MNIST)","DT cutoff","../figure2.png")
results[dec] = res
res_file.write('\n' + dec + '\n')
res_file.write(str(res))
raw_input('Press enter to continue...')
neigh = 'knn'+data_types[i]
if neigh not in results.keys():
print '\nNow we vary the k for the KNN classifier and see how it affects accuracy...'
allK = [1,8,16,32,64,128]
knnModel = KNN.KNN()
res = run_comps(knnModel, allK, trX[0:2000, :], trY[0:2000], trX[2001:2501, :],
trY[2001:2501],"Figure 3: KNN count versus accuracy (MNIST)","KNN count","../figure3.png")
results[neigh] = res
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 16 15:19:55 2017
@author: Thomas
"""
import DT
import pool
import row
import server
h = DT.DT('dc.in')
h.disp()
def pertest(name, item):
print name, item
print len(pfunc(item))
class nsc:
this = 'that'
num = 1
ns = nsc()
import DT
t = timer.Timer()
print t, 'start'
x = DT.DT(open('tests/test1.dtml').read())
print t, 'cooked'
output = x(ns)
print t, 'rendered'
#node=x.node
#for i in node.children:
# pertest('i',i)
# if hasattr(i, 'children'):
# for j in i.children:
# pertest('j',j)
# if hasattr(j,'children'):
# for k in j.children:
# pertest('k', k)
print 'serializing'
def TrainingOnDT():
global filenameForTrainingResult,filenameForPredictedResult,\
DTClass_weight,DTMax_features,\
DTCriterion,unknowRow,hamRow,spamRow,PercisionRow,recallRow
print DTClass_weight.get()
DTconfusion_matrix, percision, recall, combinedResultOnActualAndPred = DT.DT(
DTCriterion.get(), DTMax_features.get(), DTClass_weight.get())
print DTconfusion_matrix, percision, recall
strOnUnknowRow = "unknown: " + " ".join( str(x)
for x in \
DTconfusion_matrix[0])
strOnHamRow= "Ham: " + " ".join(str(x) for x in \
DTconfusion_matrix[1])
strOnSpamRow = "Spam: " + " ".join(str(x) for x in \
DTconfusion_matrix[2])
strOnPercisionRow = "precision On spam is : " + str(percision)
strOnRecallRow = "Recall on spam is : " + str(recall)
unknowRow.set(strOnUnknowRow)
hamRow.set(strOnHamRow)
spamRow.set(strOnSpamRow)
PercisionRow.set(strOnPercisionRow)
recallRow.set(strOnRecallRow)
# ####write Condussion result into file
print filenameForTrainingResult
if (filenameForTrainingResult == ''):
pass
else:
fileOnTrainingResult = open(filenameForTrainingResult, 'w')
try:
fileOnTrainingResult.write("The result of confusion matrix")
fileOnTrainingResult.write('\r\n')
fileOnTrainingResult.write(" predicted ")
fileOnTrainingResult.write('\r\n')
fileOnTrainingResult.write(
" unknown ham spam")
fileOnTrainingResult.write('\r\n')
fileOnTrainingResult.write(strOnUnknowRow)
fileOnTrainingResult.write('\r\n')
fileOnTrainingResult.write(strOnHamRow)
fileOnTrainingResult.write('\r\n')
fileOnTrainingResult.write(strOnSpamRow)
fileOnTrainingResult.write('\r\n')
fileOnTrainingResult.write(strOnPercisionRow)
fileOnTrainingResult.write('\r\n')
fileOnTrainingResult.write(strOnRecallRow)
finally:
fileOnTrainingResult.close()
# ###Write ID --Actual result --Predicted resutl into file
print filenameForPredictedResult
if (filenameForPredictedResult == ''):
pass
else:
fileOnIndividualResult = open(filenameForPredictedResult, 'w')
fileOnIndividualResult.write(" ID "
"\tActual\tPredicted\r\n")
try:
for i in combinedResultOnActualAndPred:
fileOnIndividualResult.write(i)
# fileOnIndividualResult.write('\r\n')
finally:
fileOnIndividualResult.close()
print strOnUnknowRow
