def testGithubIssue18(self):
d = [0, 1, 2, 3, 4]
a = [0, 0, 1, 1, 1]
tpl = Quantize.FindVarMultQuantBounds(d, 1, a, 2)
d2 = [(x, ) for x in d]
self.assertRaises(ValueError, lambda: Quantize.FindVarMultQuantBounds(d2, 1, a, 2))
self.assertRaises(ValueError, lambda: Quantize._FindStartPoints(d2, a, len(d2)))
def _computeQuantBounds(self):
neg = len(self._trainingExamples)
natr = len(self._attrs)
# make a list of results and values
allVals = numpy.zeros((neg, natr), 'd')
res = [] # list of y values
i = 0
for eg in self._trainingExamples:
res.append(eg[-1])
j = 0
for ai in self._attrs:
val = eg[ai]
allVals[i, j] = val
j += 1
i += 1
# now loop over each of the columns and compute the bounds
# the number of bounds is determined by the maximum info gain
i = 0
for ai in self._attrs:
nbnds = self._qBounds[ai]
if nbnds > 0:
mbnds = []
mgain = -1.0
for j in range(1, nbnds + 1):
bnds, igain = Quantize.FindVarMultQuantBounds(
allVals[:, i], j, res, self._nClasses)
if (igain > mgain):
mbnds = bnds
mgain = igain
self._QBoundVals[ai] = mbnds
i += 1
def testMultSplit3(self):
""" 4 possible results
"""
d = [(1.,0),
(1.1,0),
(1.2,0),
(1.4,2),
(1.4,2),
(1.6,2),
(2.,2),
(2.1,1),
(2.1,1),
(2.1,1),
(2.2,1),
(2.3,1),
(3.0,3),
(3.1,3),
(3.2,3),
(3.3,3)]
varValues = map(lambda x:x[0],d)
resCodes = map(lambda x:x[1],d)
nPossibleRes =4
res = Quantize.FindVarMultQuantBounds(varValues,3,resCodes,nPossibleRes)
target = ([1.30, 2.05, 2.65],1.97722)
assert Quantize.feq(res[1],target[1],1e-4),\
'InfoGain comparison failed: %s != %s'%(res[1],target[1])
assert min(map(lambda x,y:Quantize.feq(x,y,1e-4),res[0],target[0]))==1,\
'split bound comparison failed: %s != %s'%(res[0],target[0])
def runIt(namesAndTypes,dbConnect,nBounds,resCol,typesToDo=['float']):
results = map(lambda x:x[0],dbConnect.GetColumns(namesAndTypes[resCol][0]))
nPossibleRes = max(results)+1
for cName,cType in namesAndTypes:
if cType in typesToDo:
dList = map(lambda x:x[0],dbConnect.GetColumns(cName))
qDat = Quantize.FindVarMultQuantBounds(dList,nBounds,results,nPossibleRes)
print cName, qDat
def testMultSplit2(self):
""" same test as testMultSplit1, but out of order
"""
d = [(1., 0), (2.1, 1), (1.1, 0), (1.2, 0), (1.4, 2), (1.6, 2), (2., 2), (1.4, 2), (2.1, 1),
(2.2, 1), (2.1, 1), (2.3, 1)]
varValues, resCodes = zip(*d)
nPossibleRes = 3
res = Quantize.FindVarMultQuantBounds(varValues, 2, resCodes, nPossibleRes)
target = ([1.3, 2.05], 1.55458)
assert Quantize.feq(res[1],target[1],1e-4),\
'InfoGain comparison failed: %s != %s'%(res[1],target[1])
assert min(map(lambda x,y:Quantize.feq(x,y,1e-4),res[0],target[0]))==1,\
'split bound comparison failed: %s != %s'%(res[0],target[0])
def testMultSplit5(self):
""" dual valued, with an island, a bit noisy
"""
d = [(1., 0), (1.1, 0), (1.2, 0), (1.4, 1), (1.4, 0), (1.6, 1), (2., 1), (2.1, 0), (2.1, 0),
(2.1, 0), (2.2, 1), (2.3, 0)]
varValues, resCodes = zip(*d)
nPossibleRes = 2
res = Quantize.FindVarMultQuantBounds(varValues, 2, resCodes, nPossibleRes)
target = ([1.3, 2.05], .34707)
assert Quantize.feq(res[1],target[1],1e-4),\
'InfoGain comparison failed: %s != %s'%(res[1],target[1])
assert min(map(lambda x,y:Quantize.feq(x,y,1e-4),res[0],target[0]))==1,\
'split bound comparison failed: %s != %s'%(res[0],target[0])
def testMultSplit1_simple_dual(self):
# """ simple dual split """
d = [(1., 0), (1.1, 0), (1.2, 0), (1.4, 2), (1.4, 2), (1.6, 2),
(2., 2), (2.1, 1), (2.1, 1), (2.1, 1), (2.2, 1), (2.3, 1)]
varValues, resCodes = zip(*d)
nPossibleRes = 3
res = Quantize.FindVarMultQuantBounds(varValues, 2, resCodes,
nPossibleRes)
target = ([1.3, 2.05], 1.55458)
self.assertEqual(
min(map(lambda x, y: Quantize.feq(x, y, 1e-4), res[0], target[0])),
1, 'split bound comparison failed: %s != %s' % (res[0], target[0]))
self.assertTrue(
Quantize.feq(res[1], target[1], 1e-4),
'InfoGain comparison failed: %s != %s' % (res[1], target[1]))
def testMultSplit4_dualValued_island(self):
# """ dual valued, with an island """
d = [(1., 0), (1.1, 0), (1.2, 0), (1.4, 1), (1.4, 1), (1.6, 1),
(2., 1), (2.1, 0), (2.1, 0), (2.1, 0), (2.2, 0), (2.3, 0)]
varValues, resCodes = zip(*d)
nPossibleRes = 2
res = Quantize.FindVarMultQuantBounds(varValues, 2, resCodes,
nPossibleRes)
target = ([1.3, 2.05], .91830)
self.assertTrue(
Quantize.feq(res[1], target[1], 1e-4),
'InfoGain comparison failed: %s != %s' % (res[1], target[1]))
self.assertEqual(
min(map(lambda x, y: Quantize.feq(x, y, 1e-4), res[0], target[0])),
1, 'split bound comparison failed: %s != %s' % (res[0], target[0]))
def testMultSplit2_outOfOrder(self):
# """ same test as testMultSplit1, but out of order """
d = [(1., 0), (2.1, 1), (1.1, 0), (1.2, 0), (1.4, 2), (1.6, 2),
(2., 2), (1.4, 2), (2.1, 1), (2.2, 1), (2.1, 1), (2.3, 1)]
varValues, resCodes = zip(*d)
nPossibleRes = 3
res = Quantize.FindVarMultQuantBounds(varValues, 2, resCodes,
nPossibleRes)
target = ([1.3, 2.05], 1.55458)
self.assertTrue(
Quantize.feq(res[1], target[1], 1e-4),
'InfoGain comparison failed: %s != %s' % (res[1], target[1]))
self.assertEqual(
min([Quantize.feq(x, y, 1e-4) for x, y in zip(res[0], target[0])]),
1, 'split bound comparison failed: %s != %s' % (res[0], target[0]))
def testMultSplit5_dualValued_island_noisy(self):
# """ dual valued, with an island, a bit noisy """
d = [(1., 0), (1.1, 0), (1.2, 0), (1.4, 1), (1.4, 0), (1.6, 1),
(2., 1), (2.1, 0), (2.1, 0), (2.1, 0), (2.2, 1), (2.3, 0)]
varValues, resCodes = zip(*d)
nPossibleRes = 2
res = Quantize.FindVarMultQuantBounds(varValues, 2, resCodes,
nPossibleRes)
target = ([1.3, 2.05], .34707)
self.assertTrue(
Quantize.feq(res[1], target[1], 1e-4),
'InfoGain comparison failed: %s != %s' % (res[1], target[1]))
self.assertEqual(
min([Quantize.feq(x, y, 1e-4) for x, y in zip(res[0], target[0])]),
1, 'split bound comparison failed: %s != %s' % (res[0], target[0]))
def testMultSplit3_4results(self):
# """ 4 possible results """
d = [(1., 0), (1.1, 0), (1.2, 0), (1.4, 2), (1.4, 2), (1.6, 2),
(2., 2), (2.1, 1), (2.1, 1), (2.1, 1), (2.2, 1), (2.3, 1),
(3.0, 3), (3.1, 3), (3.2, 3), (3.3, 3)]
varValues, resCodes = zip(*d)
nPossibleRes = 4
res = Quantize.FindVarMultQuantBounds(varValues, 3, resCodes,
nPossibleRes)
target = ([1.30, 2.05, 2.65], 1.97722)
self.assertTrue(
Quantize.feq(res[1], target[1], 1e-4),
'InfoGain comparison failed: %s != %s' % (res[1], target[1]))
self.assertEqual(
min(map(lambda x, y: Quantize.feq(x, y, 1e-4), res[0], target[0])),
1, 'split bound comparison failed: %s != %s' % (res[0], target[0]))
def QuantTreeBoot(examples,
attrs,
nPossibleVals,
nBoundsPerVar,
initialVar=None,
maxDepth=-1,
**kwargs):
""" Bootstrapping code for the QuantTree
If _initialVar_ is not set, the algorithm will automatically
choose the first variable in the tree (the standard greedy
approach). Otherwise, _initialVar_ will be used as the first
split.
"""
attrs = list(attrs)
for i in range(len(nBoundsPerVar)):
if nBoundsPerVar[i] == -1 and i in attrs:
attrs.remove(i)
tree = QuantTree.QuantTreeNode(None, 'node')
nPossibleRes = nPossibleVals[-1]
tree._nResultCodes = nPossibleRes
resCodes = [int(x[-1]) for x in examples]
counts = [0] * nPossibleRes
for res in resCodes:
counts[res] += 1
if initialVar is None:
best, gainHere, qBounds = FindBest(resCodes, examples, nBoundsPerVar,
nPossibleRes, nPossibleVals, attrs,
**kwargs)
else:
best = initialVar
if nBoundsPerVar[best] > 0:
vTable = map(lambda x, z=best: x[z], examples)
qBounds, gainHere = Quantize.FindVarMultQuantBounds(
vTable, nBoundsPerVar[best], resCodes, nPossibleRes)
elif nBoundsPerVar[best] == 0:
vTable = ID3.GenVarTable(examples, nPossibleVals, [best])[0]
gainHere = entropy.InfoGain(vTable)
qBounds = []
else:
gainHere = -1e6
qBounds = []
tree.SetName('Var: %d' % (best))
tree.SetData(gainHere)
tree.SetLabel(best)
tree.SetTerminal(0)
tree.SetQuantBounds(qBounds)
nextAttrs = list(attrs)
if not kwargs.get('recycleVars', 0):
nextAttrs.remove(best)
indices = list(range(len(examples)))
if len(qBounds) > 0:
for bound in qBounds:
nextExamples = []
for index in list(indices):
ex = examples[index]
if ex[best] < bound:
nextExamples.append(ex)
indices.remove(index)
if len(nextExamples):
tree.AddChildNode(
BuildQuantTree(nextExamples,
best,
nextAttrs,
nPossibleVals,
nBoundsPerVar,
depth=1,
maxDepth=maxDepth,
**kwargs))
else:
v = numpy.argmax(counts)
tree.AddChild('%d??' % (v), label=v, data=0.0, isTerminal=1)
# add the last points remaining
nextExamples = []
for index in indices:
nextExamples.append(examples[index])
if len(nextExamples) != 0:
tree.AddChildNode(
BuildQuantTree(nextExamples,
best,
nextAttrs,
nPossibleVals,
nBoundsPerVar,
depth=1,
maxDepth=maxDepth,
**kwargs))
else:
v = numpy.argmax(counts)
tree.AddChild('%d??' % (v), label=v, data=0.0, isTerminal=1)
else:
for val in range(nPossibleVals[best]):
nextExamples = []
for example in examples:
if example[best] == val:
nextExamples.append(example)
if len(nextExamples) != 0:
tree.AddChildNode(
BuildQuantTree(nextExamples,
best,
nextAttrs,
nPossibleVals,
nBoundsPerVar,
depth=1,
maxDepth=maxDepth,
**kwargs))
else:
v = numpy.argmax(counts)
tree.AddChild('%d??' % (v), label=v, data=0.0, isTerminal=1)
return tree
def FindBest(resCodes,
examples,
nBoundsPerVar,
nPossibleRes,
nPossibleVals,
attrs,
exIndices=None,
**kwargs):
bestGain = -1e6
best = -1
bestBounds = []
if exIndices is None:
exIndices = list(range(len(examples)))
if not len(exIndices):
return best, bestGain, bestBounds
nToTake = kwargs.get('randomDescriptors', 0)
if nToTake > 0:
nAttrs = len(attrs)
if nToTake < nAttrs:
ids = list(range(nAttrs))
random.shuffle(ids, random=random.random)
tmp = [attrs[x] for x in ids[:nToTake]]
attrs = tmp
for var in attrs:
nBounds = nBoundsPerVar[var]
if nBounds > 0:
# vTable = map(lambda x,z=var:x[z],examples)
try:
vTable = [examples[x][var] for x in exIndices]
except IndexError:
print('index error retrieving variable: %d' % var)
raise
qBounds, gainHere = Quantize.FindVarMultQuantBounds(
vTable, nBounds, resCodes, nPossibleRes)
# print('\tvar:',var,qBounds,gainHere)
elif nBounds == 0:
vTable = ID3.GenVarTable((examples[x] for x in exIndices),
nPossibleVals, [var])[0]
gainHere = entropy.InfoGain(vTable)
qBounds = []
else:
gainHere = -1e6
qBounds = []
if gainHere > bestGain:
bestGain = gainHere
bestBounds = qBounds
best = var
elif bestGain == gainHere:
if len(qBounds) < len(bestBounds):
best = var
bestBounds = qBounds
if best == -1:
print('best unaltered')
print('\tattrs:', attrs)
print('\tnBounds:', numpy.take(nBoundsPerVar, attrs))
print('\texamples:')
for example in (examples[x] for x in exIndices):
print('\t\t', example)
if 0:
print('BEST:', len(exIndices), best, bestGain, bestBounds)
if (len(exIndices) < 10):
print(len(exIndices), len(resCodes), len(examples))
exs = [examples[x] for x in exIndices]
vals = [x[best] for x in exs]
sortIdx = numpy.argsort(vals)
sortVals = [exs[x] for x in sortIdx]
sortResults = [resCodes[x] for x in sortIdx]
for i in range(len(vals)):
print(' ', i, ['%.4f' % x for x in sortVals[i][1:-1]],
sortResults[i])
return best, bestGain, bestBounds