mirror of
https://github.com/SickGear/SickGear.git
synced 2024-12-24 03:33:38 +00:00
318 lines
9.8 KiB
Python
318 lines
9.8 KiB
Python
#!/usr/bin/env python2
|
|
# -*- 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
|
|
|
|
dict.__init__(self, *args, **kwargs)
|
|
|
|
self._confidence = {}
|
|
for prop in self:
|
|
self._confidence[prop] = confidence
|
|
|
|
|
|
def to_dict(self):
|
|
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]
|
|
|
|
return data
|
|
|
|
def nice_string(self):
|
|
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 set(self, prop, value, confidence=None):
|
|
self[prop] = value
|
|
if confidence is not None:
|
|
self._confidence[prop] = confidence
|
|
|
|
def set_confidence(self, prop, value):
|
|
self._confidence[prop] = value
|
|
|
|
def update(self, other, confidence=None):
|
|
dict.update(self, other)
|
|
if isinstance(other, Guess):
|
|
for prop in other:
|
|
self._confidence[prop] = other.confidence(prop)
|
|
|
|
if confidence is not None:
|
|
for prop in other:
|
|
self._confidence[prop] = confidence
|
|
|
|
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)
|
|
|
|
|
|
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))
|
|
|
|
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
|