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
