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guess.py
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guess.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# GuessIt - A library for guessing information from filenames
# Copyright (c) 2011 Nicolas Wack <wackou@gmail.com>
#
# GuessIt is free software; you can redistribute it and/or modify it under
# the terms of the Lesser GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# GuessIt is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# Lesser GNU General Public License for more details.
#
# You should have received a copy of the Lesser GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
from __future__ import unicode_literals
from guessit import UnicodeMixin, s, u, base_text_type
from guessit.language import Language
from guessit.country import Country
import json
import datetime
import logging
log = logging.getLogger(__name__)
class Guess(UnicodeMixin, dict):
"""A Guess is a dictionary which has an associated confidence for each of
its values.
As it is a subclass of dict, you can use it everywhere you expect a
simple dict."""
def __init__(self, *args, **kwargs):
try:
confidence = kwargs.pop('confidence')
except KeyError:
confidence = 0
try:
raw = kwargs.pop('raw')
except KeyError:
raw = None
dict.__init__(self, *args, **kwargs)
self._confidence = {}
self._raw = {}
for prop in self:
self._confidence[prop] = confidence
self._raw[prop] = raw
def to_dict(self, advanced=False):
data = dict(self)
for prop, value in data.items():
if isinstance(value, datetime.date):
data[prop] = value.isoformat()
elif isinstance(value, (Language, Country, base_text_type)):
data[prop] = u(value)
elif isinstance(value, list):
data[prop] = [u(x) for x in value]
if advanced:
data[prop] = {"value": data[prop], "raw": self.raw(prop), "confidence": self.confidence(prop)}
return data
def nice_string(self, advanced=False):
if advanced:
data = self.to_dict(advanced)
return json.dumps(data, indent=4)
else:
data = self.to_dict()
parts = json.dumps(data, indent=4).split('\n')
for i, p in enumerate(parts):
if p[:5] != ' "':
continue
prop = p.split('"')[1]
parts[i] = (' [%.2f] "' % self.confidence(prop)) + p[5:]
return '\n'.join(parts)
def __unicode__(self):
return u(self.to_dict())
def confidence(self, prop):
return self._confidence.get(prop, -1)
def raw(self, prop):
return self._raw.get(prop, None)
def set(self, prop, value, confidence=None, raw=None):
self[prop] = value
if confidence is not None:
self._confidence[prop] = confidence
if raw is not None:
self._raw[prop] = raw
def set_confidence(self, prop, value):
self._confidence[prop] = value
def set_raw(self, prop, value):
self._raw[prop] = value
def update(self, other, confidence=None, raw=None):
dict.update(self, other)
if isinstance(other, Guess):
for prop in other:
self._confidence[prop] = other.confidence(prop)
self._raw[prop] = other.raw(prop)
if confidence is not None:
for prop in other:
self._confidence[prop] = confidence
if raw is not None:
for prop in other:
self._raw[prop] = raw
def update_highest_confidence(self, other):
"""Update this guess with the values from the given one. In case
there is property present in both, only the one with the highest one
is kept."""
if not isinstance(other, Guess):
raise ValueError('Can only call this function on Guess instances')
for prop in other:
if prop in self and self.confidence(prop) >= other.confidence(prop):
continue
self[prop] = other[prop]
self._confidence[prop] = other.confidence(prop)
self._raw[prop] = other.raw(prop)
def choose_int(g1, g2):
"""Function used by merge_similar_guesses to choose between 2 possible
properties when they are integers."""
v1, c1 = g1 # value, confidence
v2, c2 = g2
if (v1 == v2):
return (v1, 1 - (1 - c1) * (1 - c2))
else:
if c1 > c2:
return (v1, c1 - c2)
else:
return (v2, c2 - c1)
def choose_string(g1, g2):
"""Function used by merge_similar_guesses to choose between 2 possible
properties when they are strings.
If the 2 strings are similar, or one is contained in the other, the latter is returned
with an increased confidence.
If the 2 strings are dissimilar, the one with the higher confidence is returned, with
a weaker confidence.
Note that here, 'similar' means that 2 strings are either equal, or that they
differ very little, such as one string being the other one with the 'the' word
prepended to it.
>>> s(choose_string(('Hello', 0.75), ('World', 0.5)))
('Hello', 0.25)
>>> s(choose_string(('Hello', 0.5), ('hello', 0.5)))
('Hello', 0.75)
>>> s(choose_string(('Hello', 0.4), ('Hello World', 0.4)))
('Hello', 0.64)
>>> s(choose_string(('simpsons', 0.5), ('The Simpsons', 0.5)))
('The Simpsons', 0.75)
"""
v1, c1 = g1 # value, confidence
v2, c2 = g2
if not v1:
return g2
elif not v2:
return g1
v1, v2 = v1.strip(), v2.strip()
v1l, v2l = v1.lower(), v2.lower()
combined_prob = 1 - (1 - c1) * (1 - c2)
if v1l == v2l:
return (v1, combined_prob)
# check for common patterns
elif v1l == 'the ' + v2l:
return (v1, combined_prob)
elif v2l == 'the ' + v1l:
return (v2, combined_prob)
# if one string is contained in the other, return the shortest one
elif v2l in v1l:
return (v2, combined_prob)
elif v1l in v2l:
return (v1, combined_prob)
# in case of conflict, return the one with highest confidence
else:
if c1 > c2:
return (v1, c1 - c2)
else:
return (v2, c2 - c1)
def _merge_similar_guesses_nocheck(guesses, prop, choose):
"""Take a list of guesses and merge those which have the same properties,
increasing or decreasing the confidence depending on whether their values
are similar.
This function assumes there are at least 2 valid guesses."""
similar = [guess for guess in guesses if prop in guess]
g1, g2 = similar[0], similar[1]
other_props = set(g1) & set(g2) - set([prop])
if other_props:
log.debug('guess 1: %s' % g1)
log.debug('guess 2: %s' % g2)
for prop in other_props:
if g1[prop] != g2[prop]:
log.warning('both guesses to be merged have more than one '
'different property in common, bailing out...')
return
# merge all props of s2 into s1, updating the confidence for the
# considered property
v1, v2 = g1[prop], g2[prop]
c1, c2 = g1.confidence(prop), g2.confidence(prop)
new_value, new_confidence = choose((v1, c1), (v2, c2))
if new_confidence >= c1:
msg = "Updating matching property '%s' with confidence %.2f"
else:
msg = "Updating non-matching property '%s' with confidence %.2f"
log.debug(msg % (prop, new_confidence))
g2[prop] = new_value
g2.set_confidence(prop, new_confidence)
g1.update(g2)
guesses.remove(g2)
def merge_similar_guesses(guesses, prop, choose):
"""Take a list of guesses and merge those which have the same properties,
increasing or decreasing the confidence depending on whether their values
are similar."""
similar = [guess for guess in guesses if prop in guess]
if len(similar) < 2:
# nothing to merge
return
if len(similar) == 2:
_merge_similar_guesses_nocheck(guesses, prop, choose)
if len(similar) > 2:
log.debug('complex merge, trying our best...')
before = len(guesses)
_merge_similar_guesses_nocheck(guesses, prop, choose)
after = len(guesses)
if after < before:
# recurse only when the previous call actually did something,
# otherwise we end up in an infinite loop
merge_similar_guesses(guesses, prop, choose)
def merge_all(guesses, append=None):
"""Merge all the guesses in a single result, remove very unlikely values,
and return it.
You can specify a list of properties that should be appended into a list
instead of being merged.
>>> s(merge_all([ Guess({'season': 2}, confidence=0.6),
... Guess({'episodeNumber': 13}, confidence=0.8) ]))
{'season': 2, 'episodeNumber': 13}
>>> s(merge_all([ Guess({'episodeNumber': 27}, confidence=0.02),
... Guess({'season': 1}, confidence=0.2) ]))
{'season': 1}
>>> s(merge_all([ Guess({'other': 'PROPER'}, confidence=0.8),
... Guess({'releaseGroup': '2HD'}, confidence=0.8) ],
... append=['other']))
{'releaseGroup': '2HD', 'other': ['PROPER']}
"""
if not guesses:
return Guess()
result = guesses[0]
if append is None:
append = []
for g in guesses[1:]:
# first append our appendable properties
for prop in append:
if prop in g:
result.set(prop, result.get(prop, []) + [g[prop]],
# TODO: what to do with confidence here? maybe an
# arithmetic mean...
confidence=g.confidence(prop),
raw=g.raw(prop))
del g[prop]
# then merge the remaining ones
dups = set(result) & set(g)
if dups:
log.warning('duplicate properties %s in merged result...' % [ (result[p], g[p]) for p in dups] )
result.update_highest_confidence(g)
# delete very unlikely values
for p in list(result.keys()):
if result.confidence(p) < 0.05:
del result[p]
# make sure our appendable properties contain unique values
for prop in append:
try:
value = result[prop]
if isinstance(value, list):
result[prop] = list(set(value))
else:
result[prop] = [ value ]
except KeyError:
pass
return result