def _tdivdemo(file='data/nasa93dem.csv'):
#==============================================================================
# We start by recursively clustering the model.
#==============================================================================
makeaModel = makeAModel()
m = makeaModel.csv2py(file)
rseed(1)
#alias = dict (zip(makeaModel.translate.values(),makeaModel.translate.keys()))
#print alias
#def num2str(lst):
# return [alias[z] for z in lst]
prepare(m) # Initialize all parameters for where2 to run
tree = where2(m, m._rows) # Decision tree using where2
tbl = table(file)
headerLabel = '=klass'
Rows = []
for k, _ in leaves(tree):
for j in k.val:
tmp = (j.cells)
tmp.append('_' + str(id(k) % 1000))
j.__dict__.update({'cells': tmp})
Rows.append(j.cells)
tbl2 = makeMeATable(tbl, headerLabel, Rows)
#print
testCase = [tbl2._rows.pop(randi(0, len(tbl2._rows))) for k in xrange(500)]
t = discreteNums(tbl2, map(lambda x: x.cells, tbl2._rows))
myTree = tdiv(t)
showTdiv(myTree)
loc = leaveOneOut(testCase[randi(0, len(testCase))], myTree)
def _tdivdemo(file='data/nasa93dem.csv'):
#==============================================================================
# We start by recursively clustering the model.
#==============================================================================
makeaModel=makeAModel()
m=makeaModel.csv2py(file)
#alias = dict (zip(makeaModel.translate.values(),makeaModel.translate.keys()))
#print alias
#def num2str(lst):
# return [alias[z] for z in lst]
prepare(m) # Initialize all parameters for where2 to run
tree=where2(m, m._rows) # Decision tree using where2
tbl = table(file)
headerLabel='=klass'
Rows=[]
for k,_ in leaves(tree):
for j in k.val:
tmp=(j.cells)
tmp.append('_'+str(id(k) % 1000))
j.__dict__.update({'cells': tmp})
Rows.append(j.cells)
tbl2=makeMeATable(tbl, headerLabel, Rows)
print
testCase=[tbl2._rows.pop(randi(0, len(tbl2._rows))) for k in xrange(500)]
t=discreteNums(tbl2, map(lambda x: x.cells, tbl2._rows))
myTree=tdiv(t)
showTdiv(myTree)
loc = leaveOneOut(testCase[randi(0, len(testCase))], myTree)
contrastSet = getContrastSet(loc, myTree)
print 'Contrast Set:', contrastSet
def _tdivdemo(file='data/nasa93dem.csv'):
#==============================================================================
# We start by recursively clustering the model.
#==============================================================================
makeaModel=makeAModel()
m=makeaModel.csv2py(file)
prepare(m) # Initialize all parameters for where2 to run
tree=where2(m, m._rows) # Decision tree using where2
tbl = table(file)
headerLabel='=klass'
Rows=[]
for k,_ in leaves(tree):
for j in k.val:
tmp=j.cells
tmp.append(id(k) % 1000)
j.__dict__.update({'cells': tmp})
Rows.append(j.cells)
tbl2=makeMeATable(tbl, headerLabel, Rows)
testCase=tbl2._rows.pop(randi(0,len(Rows)-1))
t=discreteNums(tbl2, map(lambda x: x.cells, tbl2._rows))
myTree=tdiv(t)
loc=apex(testCase, myTree)
print loc.__dict__
print 'Id: ',loc.mode, ' Level: ', loc.lvl, ' Variable: ', loc.f.name
showTdiv(myTree)
#==============================================================================
for node, lvl in dtnodes(myTree):
rows=map(lambda x:x.cells,node.rows)
def _tdivPrec(dir='camel/'):
#==============================================================================
# Recursively clustering the model.
#==============================================================================
train = ['camel-1.0.csv', 'camel-1.2.csv', 'camel-1.2.csv']
test = ['camel-1.6.csv']
rseed(1)
makeaModel = makeAModel()
_rows = []
# Concatenate training cases
for t in train:
file = dir + t
m = makeaModel.csv2py(file)
prepare(m) # Initialize all parameters for where2 to run
tree = where2(m, m._rows) # Decision tree using where2
tbl = table(file)
headerLabel = '=klass'
Rows = []
for k, _ in leaves(tree):
for j in k.val:
tmp = (j.cells)
tmp.append('_' + str(id(k) % 1000))
j.__dict__.update({'cells': tmp})
Rows.append(j.cells)
_rows += Rows
tbl2 = makeMeATable(tbl, headerLabel, _rows)
# Test case!
_rows = []
for tt in test:
file = dir + tt
m = makeaModel.csv2py(file)
prepare(m) # Initialize all parameters for where2 to run
tree = where2(m, m._rows) # Decision tree using where2
tbl = table(file)
headerLabel = '=klass'
Rows = []
for k, _ in leaves(tree):
for j in k.val:
tmp = (j.cells)
tmp.append('_' + str(id(k) % 1000))
j.__dict__.update({'cells': tmp})
Rows.append(j.cells)
_rows += Rows
tbl3 = makeMeATable(tbl, headerLabel, _rows)
testCase = tbl3._rows
print testCase
t = discreteNums(tbl2, map(lambda x: x.cells, tbl2._rows))
myTree = tdiv(t)
showTdiv(myTree)
loc = leaveOneOut(testCase[randi(0, len(testCase))], myTree)
contrastSet = getContrastSet(loc, myTree)
print 'Contrast Set:', contrastSet
def _tdivPrec(dir='camel/'):
#==============================================================================
# Recursively clustering the model.
#==============================================================================
train=['camel-1.0.csv', 'camel-1.2.csv', 'camel-1.2.csv']
test=['camel-1.6.csv']
rseed(1)
makeaModel=makeAModel()
_rows=[]
# Concatenate training cases
for t in train:
file=dir+t
m=makeaModel.csv2py(file)
prepare(m) # Initialize all parameters for where2 to run
tree=where2(m, m._rows) # Decision tree using where2
tbl = table(file)
headerLabel='=klass'
Rows=[]
for k,_ in leaves(tree):
for j in k.val:
tmp=(j.cells)
tmp.append('_'+str(id(k) % 1000))
j.__dict__.update({'cells': tmp})
Rows.append(j.cells)
_rows+=Rows
tbl2=makeMeATable(tbl, headerLabel, _rows)
# Test case!
_rows=[]
for tt in test:
file=dir+tt
m=makeaModel.csv2py(file)
prepare(m) # Initialize all parameters for where2 to run
tree=where2(m, m._rows) # Decision tree using where2
tbl = table(file)
headerLabel='=klass'
Rows=[]
for k,_ in leaves(tree):
for j in k.val:
tmp=(j.cells)
tmp.append('_'+str(id(k) % 1000))
j.__dict__.update({'cells': tmp})
Rows.append(j.cells)
_rows+=Rows
tbl3=makeMeATable(tbl, headerLabel, _rows)
testCase=tbl3._rows
print testCase
t=discreteNums(tbl2, map(lambda x: x.cells, tbl2._rows))
myTree=tdiv(t)
showTdiv(myTree)
loc = leaveOneOut(testCase[randi(0, len(testCase))], myTree)
contrastSet = getContrastSet(loc, myTree)
print 'Contrast Set:', contrastSet
def _tdivdemo(file='data/nasa93dem.csv'):
#==============================================================================
# We start by recursively clustering the model.
#==============================================================================
makeaModel=makeAModel()
m=makeaModel.csv2py(file)
alias = dict (zip(makeaModel.translate.values(),makeaModel.translate.keys()))
def num2str(lst):
return [alias[z] for z in lst]
prepare(m) # Initialize all parameters for where2 to run
tree=where2(m, m._rows) # Decision tree using where2
tbl = table(file)
headerLabel='=klass'
Rows=[]
for k,_ in leaves(tree):
for j in k.val:
tmp=num2str(j.cells)
tmp.append('_'+str(id(k) % 1000))
j.__dict__.update({'cells': tmp})
Rows.append(j.cells)
tbl2=makeMeATable(tbl, headerLabel, Rows)
testCase=tbl2._rows.pop(1)
t=discreteNums(tbl2, map(lambda x: x.cells, tbl2._rows))
myTree=tdiv(t)
showTdiv(myTree)
loc = leaveOneOut(testCase, myTree)
print loc.__dict__
getContrastSet(loc, myTree)
#==============================================================================
#for node, lvl in dtnodes(myTree):
#rows=map(lambda x:x.cells,node.rows)
#pdb.set_trace()
#print lvl, len(rows), [ k._id for k in node.rows]
#==============================================================================
headerLabels={}
[headerLabels.update({k.name:indx}) for indx, k in enumerate(tbl2.headers)]
def _tdivdemo(file='data/nasa93dem.csv'):
#==============================================================================
# We start by recursively clustering the model.
#==============================================================================
makeaModel=makeAModel()
m=makeaModel.csv2py(file)
prepare(m) # Initialize all parameters for where2 to run
tree=where2(m, m._rows) # Decision tree using where2
tbl = table(file)
headerLabel='=klass'
Rows=[]
for k,_ in leaves(tree):
for j in k.val:
tmp=j.cells
tmp.append(id(k) % 1000)
j.__dict__.update({'cells': tmp})
Rows.append(j.cells)
tbl2=makeMeATable(tbl, headerLabel, Rows)
t=discreteNums(tbl2, Rows)
myTree=tdiv(t)
showTdiv(myTree)