# Natural Language Toolkit: Decision Tree Classifiers
#
# Copyright (C) 2001-2015 NLTK Project
# Author: Edward Loper <edloper@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
"""
A classifier model that decides which label to assign to a token on
the basis of a tree structure, where branches correspond to conditions
on feature values, and leaves correspond to label assignments.
"""
from __future__ import print_function, unicode_literals, division
from collections import defaultdict
from nltk.probability import FreqDist, MLEProbDist, entropy
from nltk.classify.api import ClassifierI
from nltk.compat import python_2_unicode_compatible
@python_2_unicode_compatible
[docs]class DecisionTreeClassifier(ClassifierI):
[docs] def __init__(self, label, feature_name=None, decisions=None, default=None):
"""
:param label: The most likely label for tokens that reach
this node in the decision tree. If this decision tree
has no children, then this label will be assigned to
any token that reaches this decision tree.
:param feature_name: The name of the feature that this
decision tree selects for.
:param decisions: A dictionary mapping from feature values
for the feature identified by ``feature_name`` to
child decision trees.
:param default: The child that will be used if the value of
feature ``feature_name`` does not match any of the keys in
``decisions``. This is used when constructing binary
decision trees.
"""
self._label = label
self._fname = feature_name
self._decisions = decisions
self._default = default
[docs] def labels(self):
labels = [self._label]
if self._decisions is not None:
for dt in self._decisions.values():
labels.extend(dt.labels())
if self._default is not None:
labels.extend(self._default.labels())
return list(set(labels))
[docs] def classify(self, featureset):
# Decision leaf:
if self._fname is None:
return self._label
# Decision tree:
fval = featureset.get(self._fname)
if fval in self._decisions:
return self._decisions[fval].classify(featureset)
elif self._default is not None:
return self._default.classify(featureset)
else:
return self._label
[docs] def error(self, labeled_featuresets):
errors = 0
for featureset, label in labeled_featuresets:
if self.classify(featureset) != label:
errors += 1
return errors/len(labeled_featuresets)
[docs] def pseudocode(self, prefix='', depth=4):
"""
Return a string representation of this decision tree that
expresses the decisions it makes as a nested set of pseudocode
if statements.
"""
if self._fname is None:
return "%sreturn %r\n" % (prefix, self._label)
s = ''
for (fval, result) in sorted(self._decisions.items()):
s += '%sif %s == %r: ' % (prefix, self._fname, fval)
if result._fname is not None and depth>1:
s += '\n'+result.pseudocode(prefix+' ', depth-1)
else:
s += 'return %r\n' % result._label
if self._default is not None:
if len(self._decisions) == 1:
s += '%sif %s != %r: '% (prefix, self._fname,
list(self._decisions.keys())[0])
else:
s += '%selse: ' % (prefix,)
if self._default._fname is not None and depth>1:
s += '\n'+self._default.pseudocode(prefix+' ', depth-1)
else:
s += 'return %r\n' % self._default._label
return s
def __str__(self):
return self.pretty_format()
@staticmethod
[docs] def train(labeled_featuresets, entropy_cutoff=0.05, depth_cutoff=100,
support_cutoff=10, binary=False, feature_values=None,
verbose=False):
"""
:param binary: If true, then treat all feature/value pairs as
individual binary features, rather than using a single n-way
branch for each feature.
"""
# Collect a list of all feature names.
feature_names = set()
for featureset, label in labeled_featuresets:
for fname in featureset:
feature_names.add(fname)
# Collect a list of the values each feature can take.
if feature_values is None and binary:
feature_values = defaultdict(set)
for featureset, label in labeled_featuresets:
for fname, fval in featureset.items():
feature_values[fname].add(fval)
# Start with a stump.
if not binary:
tree = DecisionTreeClassifier.best_stump(
feature_names, labeled_featuresets, verbose)
else:
tree = DecisionTreeClassifier.best_binary_stump(
feature_names, labeled_featuresets, feature_values, verbose)
# Refine the stump.
tree.refine(labeled_featuresets, entropy_cutoff, depth_cutoff-1,
support_cutoff, binary, feature_values, verbose)
# Return it
return tree
@staticmethod
[docs] def leaf(labeled_featuresets):
label = FreqDist(label for (featureset, label)
in labeled_featuresets).max()
return DecisionTreeClassifier(label)
@staticmethod
[docs] def stump(feature_name, labeled_featuresets):
label = FreqDist(label for (featureset, label)
in labeled_featuresets).max()
# Find the best label for each value.
freqs = defaultdict(FreqDist) # freq(label|value)
for featureset, label in labeled_featuresets:
feature_value = featureset.get(feature_name)
freqs[feature_value][label] += 1
decisions = dict((val, DecisionTreeClassifier(freqs[val].max()))
for val in freqs)
return DecisionTreeClassifier(label, feature_name, decisions)
[docs] def refine(self, labeled_featuresets, entropy_cutoff, depth_cutoff,
support_cutoff, binary=False, feature_values=None,
verbose=False):
if len(labeled_featuresets) <= support_cutoff: return
if self._fname is None: return
if depth_cutoff <= 0: return
for fval in self._decisions:
fval_featuresets = [(featureset, label) for (featureset, label)
in labeled_featuresets
if featureset.get(self._fname) == fval]
label_freqs = FreqDist(label for (featureset, label)
in fval_featuresets)
if entropy(MLEProbDist(label_freqs)) > entropy_cutoff:
self._decisions[fval] = DecisionTreeClassifier.train(
fval_featuresets, entropy_cutoff, depth_cutoff,
support_cutoff, binary, feature_values, verbose)
if self._default is not None:
default_featuresets = [(featureset, label) for (featureset, label)
in labeled_featuresets
if featureset.get(self._fname) not in
self._decisions]
label_freqs = FreqDist(label for (featureset, label)
in default_featuresets)
if entropy(MLEProbDist(label_freqs)) > entropy_cutoff:
self._default = DecisionTreeClassifier.train(
default_featuresets, entropy_cutoff, depth_cutoff,
support_cutoff, binary, feature_values, verbose)
@staticmethod
[docs] def best_stump(feature_names, labeled_featuresets, verbose=False):
best_stump = DecisionTreeClassifier.leaf(labeled_featuresets)
best_error = best_stump.error(labeled_featuresets)
for fname in feature_names:
stump = DecisionTreeClassifier.stump(fname, labeled_featuresets)
stump_error = stump.error(labeled_featuresets)
if stump_error < best_error:
best_error = stump_error
best_stump = stump
if verbose:
print(('best stump for %6d toks uses %-20s err=%6.4f' %
(len(labeled_featuresets), best_stump._fname, best_error)))
return best_stump
@staticmethod
[docs] def binary_stump(feature_name, feature_value, labeled_featuresets):
label = FreqDist(label for (featureset, label)
in labeled_featuresets).max()
# Find the best label for each value.
pos_fdist = FreqDist()
neg_fdist = FreqDist()
for featureset, label in labeled_featuresets:
if featureset.get(feature_name) == feature_value:
pos_fdist[label] += 1
else:
neg_fdist[label] += 1
decisions = {}
default = label
# But hopefully we have observations!
if pos_fdist.N() > 0:
decisions = {feature_value: DecisionTreeClassifier(pos_fdist.max())}
if neg_fdist.N() > 0:
default = DecisionTreeClassifier(neg_fdist.max())
return DecisionTreeClassifier(label, feature_name, decisions, default)
@staticmethod
[docs] def best_binary_stump(feature_names, labeled_featuresets, feature_values,
verbose=False):
best_stump = DecisionTreeClassifier.leaf(labeled_featuresets)
best_error = best_stump.error(labeled_featuresets)
for fname in feature_names:
for fval in feature_values[fname]:
stump = DecisionTreeClassifier.binary_stump(
fname, fval, labeled_featuresets)
stump_error = stump.error(labeled_featuresets)
if stump_error < best_error:
best_error = stump_error
best_stump = stump
if best_stump._decisions:
descr = '%s=%s' % (best_stump._fname,
list(best_stump._decisions.keys())[0])
else:
descr = '(default)'
if verbose:
print(('best stump for %6d toks uses %-20s err=%6.4f' %
(len(labeled_featuresets), descr, best_error)))
return best_stump
##//////////////////////////////////////////////////////
## Demo
##//////////////////////////////////////////////////////
def f(x):
return DecisionTreeClassifier.train(x, binary=True, verbose=True)
def demo():
from nltk.classify.util import names_demo, binary_names_demo_features
classifier = names_demo(f, #DecisionTreeClassifier.train,
binary_names_demo_features)
print(classifier.pp(depth=7))
print(classifier.pseudocode(depth=7))
if __name__ == '__main__':
demo()