def _testChain():
from rdkit.ML.DecTree import ID3
oPts= [ \
[1,0,0,0,1],
[1,0,0,0,1],
[1,0,0,0,1],
[1,0,0,0,1],
[1,0,0,0,1],
[1,0,0,0,1],
[1,0,0,0,1],
[0,0,1,1,0],
[0,0,1,1,0],
[0,0,1,1,1],
[0,1,0,1,0],
[0,1,0,1,0],
[0,1,0,0,1],
]
tPts = oPts
tree = ID3.ID3Boot(oPts,
attrs=range(len(oPts[0]) - 1),
nPossibleVals=[2] * len(oPts[0]))
tree.Print()
err, badEx = CrossValidate.CrossValidate(tree, oPts)
print('original error:', err)
err, badEx = CrossValidate.CrossValidate(tree, tPts)
print('original holdout error:', err)
newTree, frac2 = PruneTree(tree, oPts, tPts)
newTree.Print()
err, badEx = CrossValidate.CrossValidate(newTree, tPts)
print('pruned holdout error is:', err)
print(badEx)
def _testSpecific():
from rdkit.ML.DecTree import ID3
oPts= [ \
[0,0,1,0],
[0,1,1,1],
[1,0,1,1],
[1,1,0,0],
[1,1,1,1],
]
tPts = oPts + [[0, 1, 1, 0], [0, 1, 1, 0]]
tree = ID3.ID3Boot(oPts, attrs=range(3), nPossibleVals=[2] * 4)
tree.Print()
err, badEx = CrossValidate.CrossValidate(tree, oPts)
print('original error:', err)
err, badEx = CrossValidate.CrossValidate(tree, tPts)
print('original holdout error:', err)
newTree, frac2 = PruneTree(tree, oPts, tPts)
newTree.Print()
err, badEx = CrossValidate.CrossValidate(newTree, tPts)
print('pruned holdout error is:', err)
print(badEx)
print(len(tree), len(newTree))
def PruneTree(tree, trainExamples, testExamples, minimizeTestErrorOnly=1):
""" implements a reduced-error pruning of decision trees
This algorithm is described on page 69 of Mitchell's book.
Pruning can be done using just the set of testExamples (the validation set)
or both the testExamples and the trainExamples by setting minimizeTestErrorOnly
to 0.
**Arguments**
- tree: the initial tree to be pruned
- trainExamples: the examples used to train the tree
- testExamples: the examples held out for testing the tree
- minimizeTestErrorOnly: if this toggle is zero, all examples (i.e.
_trainExamples_ + _testExamples_ will be used to evaluate the error.
**Returns**
a 2-tuple containing:
1) the best tree
2) the best error (the one which corresponds to that tree)
"""
if minimizeTestErrorOnly:
testSet = testExamples
else:
testSet = trainExamples + testExamples
# remove any stored examples the tree may have
tree.ClearExamples()
#
# screen the test data through the tree so that we end up with the
# appropriate points stored at each node of the tree
#
totErr, badEx = CrossValidate.CrossValidate(tree,
testSet,
appendExamples=1)
#
# Prune
#
newTree = _Pruner(tree)
#
# And recalculate the errors
#
totErr, badEx = CrossValidate.CrossValidate(newTree, testSet)
newTree.SetBadExamples(badEx)
return newTree, totErr
def test1(self):
# " testing pruning with known results "
oPts = [
[0, 0, 1, 0],
[0, 1, 1, 1],
[1, 0, 1, 1],
[1, 1, 0, 0],
[1, 1, 1, 1],
]
tPts = oPts + [[0, 1, 1, 0], [0, 1, 1, 0]]
tree = ID3.ID3Boot(oPts, attrs=range(3), nPossibleVals=[2] * 4)
err, badEx = CrossValidate.CrossValidate(tree, oPts)
assert err == 0.0, 'bad initial error'
assert len(badEx) == 0, 'bad initial error'
# prune with original data, shouldn't do anything
f = StringIO()
with redirect_stdout(f):
PruneTree._verbose = True
newTree, err = PruneTree.PruneTree(tree, [], oPts)
PruneTree._verbose = False
self.assertIn('Pruner', f.getvalue())
assert newTree == tree, 'improper pruning'
# prune with train data
newTree, err = PruneTree.PruneTree(tree, [], tPts)
assert newTree != tree, 'bad pruning'
assert feq(err, 0.14286), 'bad error result'
def test1(self):
" testing pruning with known results "
oPts= [ \
[0,0,1,0],
[0,1,1,1],
[1,0,1,1],
[1,1,0,0],
[1,1,1,1],
]
tPts = oPts+[[0,1,1,0],[0,1,1,0]]
tree = ID3.ID3Boot(oPts,attrs=range(3),nPossibleVals=[2]*4)
err,badEx = CrossValidate.CrossValidate(tree,oPts)
assert err==0.0,'bad initial error'
assert len(badEx)==0,'bad initial error'
# prune with original data, shouldn't do anything
newTree,err = PruneTree.PruneTree(tree,[],oPts)
assert newTree==tree,'improper pruning'
# prune with train data
newTree,err = PruneTree.PruneTree(tree,[],tPts)
assert newTree!=tree,'bad pruning'
assert feq(err,0.14286),'bad error result'