""" fontTools.misc.classifyTools.py -- tools for classifying things.
"""
[docs]
class Classifier(object):
"""
Main Classifier object, used to classify things into similar sets.
"""
def __init__(self, sort=True):
self._things = set() # set of all things known so far
self._sets = [] # list of class sets produced so far
self._mapping = {} # map from things to their class set
self._dirty = False
self._sort = sort
[docs]
def add(self, set_of_things):
"""
Add a set to the classifier. Any iterable is accepted.
"""
if not set_of_things:
return
self._dirty = True
things, sets, mapping = self._things, self._sets, self._mapping
s = set(set_of_things)
intersection = s.intersection(things) # existing things
s.difference_update(intersection) # new things
difference = s
del s
# Add new class for new things
if difference:
things.update(difference)
sets.append(difference)
for thing in difference:
mapping[thing] = difference
del difference
while intersection:
# Take one item and process the old class it belongs to
old_class = mapping[next(iter(intersection))]
old_class_intersection = old_class.intersection(intersection)
# Update old class to remove items from new set
old_class.difference_update(old_class_intersection)
# Remove processed items from todo list
intersection.difference_update(old_class_intersection)
# Add new class for the intersection with old class
sets.append(old_class_intersection)
for thing in old_class_intersection:
mapping[thing] = old_class_intersection
del old_class_intersection
[docs]
def update(self, list_of_sets):
"""
Add a a list of sets to the classifier. Any iterable of iterables is accepted.
"""
for s in list_of_sets:
self.add(s)
def _process(self):
if not self._dirty:
return
# Do any deferred processing
sets = self._sets
self._sets = [s for s in sets if s]
if self._sort:
self._sets = sorted(self._sets, key=lambda s: (-len(s), sorted(s)))
self._dirty = False
# Output methods
[docs]
def getThings(self):
"""Returns the set of all things known so far.
The return value belongs to the Classifier object and should NOT
be modified while the classifier is still in use.
"""
self._process()
return self._things
[docs]
def getMapping(self):
"""Returns the mapping from things to their class set.
The return value belongs to the Classifier object and should NOT
be modified while the classifier is still in use.
"""
self._process()
return self._mapping
[docs]
def getClasses(self):
"""Returns the list of class sets.
The return value belongs to the Classifier object and should NOT
be modified while the classifier is still in use.
"""
self._process()
return self._sets
[docs]
def classify(list_of_sets, sort=True):
"""
Takes a iterable of iterables (list of sets from here on; but any
iterable works.), and returns the smallest list of sets such that
each set, is either a subset, or is disjoint from, each of the input
sets.
In other words, this function classifies all the things present in
any of the input sets, into similar classes, based on which sets
things are a member of.
If sort=True, return class sets are sorted by decreasing size and
their natural sort order within each class size. Otherwise, class
sets are returned in the order that they were identified, which is
generally not significant.
>>> classify([]) == ([], {})
True
>>> classify([[]]) == ([], {})
True
>>> classify([[], []]) == ([], {})
True
>>> classify([[1]]) == ([{1}], {1: {1}})
True
>>> classify([[1,2]]) == ([{1, 2}], {1: {1, 2}, 2: {1, 2}})
True
>>> classify([[1],[2]]) == ([{1}, {2}], {1: {1}, 2: {2}})
True
>>> classify([[1,2],[2]]) == ([{1}, {2}], {1: {1}, 2: {2}})
True
>>> classify([[1,2],[2,4]]) == ([{1}, {2}, {4}], {1: {1}, 2: {2}, 4: {4}})
True
>>> classify([[1,2],[2,4,5]]) == (
... [{4, 5}, {1}, {2}], {1: {1}, 2: {2}, 4: {4, 5}, 5: {4, 5}})
True
>>> classify([[1,2],[2,4,5]], sort=False) == (
... [{1}, {4, 5}, {2}], {1: {1}, 2: {2}, 4: {4, 5}, 5: {4, 5}})
True
>>> classify([[1,2,9],[2,4,5]], sort=False) == (
... [{1, 9}, {4, 5}, {2}], {1: {1, 9}, 2: {2}, 4: {4, 5}, 5: {4, 5},
... 9: {1, 9}})
True
>>> classify([[1,2,9,15],[2,4,5]], sort=False) == (
... [{1, 9, 15}, {4, 5}, {2}], {1: {1, 9, 15}, 2: {2}, 4: {4, 5},
... 5: {4, 5}, 9: {1, 9, 15}, 15: {1, 9, 15}})
True
>>> classes, mapping = classify([[1,2,9,15],[2,4,5],[15,5]], sort=False)
>>> set([frozenset(c) for c in classes]) == set(
... [frozenset(s) for s in ({1, 9}, {4}, {2}, {5}, {15})])
True
>>> mapping == {1: {1, 9}, 2: {2}, 4: {4}, 5: {5}, 9: {1, 9}, 15: {15}}
True
"""
classifier = Classifier(sort=sort)
classifier.update(list_of_sets)
return classifier.getClasses(), classifier.getMapping()
if __name__ == "__main__":
import sys, doctest
sys.exit(doctest.testmod(optionflags=doctest.ELLIPSIS).failed)