def set_position(self, x, y):
inp = [x, y]
position = self.rect[:2]
if self.grid():
inp = mul_lists(inp, self.config.grid)
position = mul_lists(position, self.config.grid)
self.config.delta = sub_lists(inp, position)
def call_rank(ranking_index, flattened, n_large, start_words=[], model=None):
"""ranking_index: 0 = TFIDF; 1 = C-value; 2 = C-value + Unigrams; 3 = TF"""
ranking_fn = ranking_fns[ranking_index]
ranking_fn_name = ranking_fn_names[ranking_index]
set_status('ranking with %s' % ranking_fn_name, model=model)
if debug:
print 'ranking with %s' % ranking_fn_name
scored_phrases, phrase_frequencies = ranking_fn(flattened)
set_status('ordering', model=model)
if debug:
print 'ordering'
ordered_phrases = sorted(scored_phrases.iteritems(),
key=lambda p: p[1],
reverse=True)
# ordered_fname ='../phrase_lists/%s.phrases' % ranking_index
# print 'writing ordered phrases to file %s' % ordered_fname
# with open(ordered_fname, 'w') as f:
# for o in ordered_phrases[:n_large]:
# f.write('%s\n' % str(o))
if debug:
print 'mapping'
ranked_phrases = [p[0] for p in ordered_phrases]
if debug:
print 'trimming large'
large_phrases = ranked_phrases[:n_large]
if start_words:
if debug:
print 'looking for start words', start_words
found_start_words = []
for start_word in start_words:
matches = (ranked_phrase for ranked_phrase in ranked_phrases
if start_word in sub_lists(ranked_phrase, proper=False))
try:
word = matches.next()
if word not in large_phrases:
found_start_words.append(word)
except StopIteration:
if debug:
print 'start word %s not found' % start_word
if debug:
print 'found start words', found_start_words
top_phrases = found_start_words + large_phrases
else:
top_phrases = large_phrases
filtered_frequencies = dict(
(phrase, freq) for (phrase, freq) in phrase_frequencies.items()
if phrase in top_phrases)
return top_phrases, filtered_frequencies, scored_phrases
def cnc(phrase_lists,
c_value_threshold=0,
include_unigrams=False,
weight_by_length=True):
"""given a list of phrases, run the cnc algorithm and return a dictionary of word, c-value (ranking) pairs"""
frequency_dists_by_length = {}
for phrase in phrase_lists:
l = len(phrase)
if l not in frequency_dists_by_length:
frequency_dists_by_length[l] = FreqDist()
frequency_dists_by_length[l].inc(tuple(phrase))
# word -> C-value(word)
phrase_scores = {}
# word -> num occurrences(word)
phrase_frequencies = FreqDist()
# word -> (t(word), c(word))
sub_phrase_scores = {}
# traverse from longest phrases to shortest
for length, frequency_dist in sorted(frequency_dists_by_length.items(), \
key=lambda pair: pair[0], reverse=True):
# update global frequency counts with all counts of this length
phrase_frequencies.update(frequency_dist)
# within each phrase length, traverse from most common phrases to least
for phrase, frequency in frequency_dist.iteritems():
if phrase in sub_phrase_scores:
t, c = sub_phrase_scores[phrase]
subtractive = 1.0 / c * t
else:
subtractive = 0
if weight_by_length:
if include_unigrams:
weight = log(length + 1, 2)
else:
weight = log(length, 2)
else:
weight = 1
c_value = weight * (frequency - subtractive)
if c_value >= c_value_threshold:
phrase_scores[phrase] = c_value
for sub_phrase in utils.sub_lists(phrase):
if sub_phrase in sub_phrase_scores:
t, c = sub_phrase_scores[sub_phrase]
else:
t, c = 0, 0
sub_phrase_scores[sub_phrase] = t + frequency, c + 1
return phrase_scores, phrase_frequencies
def cnc(phrase_lists, c_value_threshold=0, include_unigrams=False, weight_by_length=True):
"""given a list of phrases, run the cnc algorithm and return a dictionary of word, c-value (ranking) pairs"""
frequency_dists_by_length = {}
for phrase in phrase_lists:
l = len(phrase)
if l not in frequency_dists_by_length:
frequency_dists_by_length[l] = FreqDist()
frequency_dists_by_length[l].inc(tuple(phrase))
# word -> C-value(word)
phrase_scores = {}
# word -> num occurrences(word)
phrase_frequencies = FreqDist()
# word -> (t(word), c(word))
sub_phrase_scores = {}
# traverse from longest phrases to shortest
for length, frequency_dist in sorted(frequency_dists_by_length.items(), key=lambda pair: pair[0], reverse=True):
# update global frequency counts with all counts of this length
phrase_frequencies.update(frequency_dist)
# within each phrase length, traverse from most common phrases to least
for phrase, frequency in frequency_dist.iteritems():
if phrase in sub_phrase_scores:
t, c = sub_phrase_scores[phrase]
subtractive = 1.0 / c * t
else:
subtractive = 0
if weight_by_length:
if include_unigrams:
weight = log(length + 1, 2)
else:
weight = log(length, 2)
else:
weight = 1
c_value = weight * (frequency - subtractive)
if c_value >= c_value_threshold:
phrase_scores[phrase] = c_value
for sub_phrase in utils.sub_lists(phrase):
if sub_phrase in sub_phrase_scores:
t, c = sub_phrase_scores[sub_phrase]
else:
t, c = 0, 0
sub_phrase_scores[sub_phrase] = t + frequency, c + 1
return phrase_scores, phrase_frequencies
def call_rank(ranking_index, flattened, n_large, start_words=[], model=None):
"""ranking_index: 0 = TFIDF; 1 = C-value; 2 = C-value + Unigrams; 3 = TF"""
ranking_fn = ranking_fns[ranking_index]
ranking_fn_name = ranking_fn_names[ranking_index]
set_status('ranking with %s' % ranking_fn_name, model=model)
if debug:
print 'ranking with %s' % ranking_fn_name
scored_phrases, phrase_frequencies = ranking_fn(flattened)
set_status('ordering', model=model)
if debug:
print 'ordering'
ordered_phrases = sorted(scored_phrases.iteritems(),
key=lambda p: p[1], reverse=True)
# ordered_fname ='../phrase_lists/%s.phrases' % ranking_index
# print 'writing ordered phrases to file %s' % ordered_fname
# with open(ordered_fname, 'w') as f:
# for o in ordered_phrases[:n_large]:
# f.write('%s\n' % str(o))
if debug:
print 'mapping'
ranked_phrases = [p[0] for p in ordered_phrases]
if debug:
print 'trimming large'
large_phrases = ranked_phrases[:n_large]
if start_words:
if debug:
print 'looking for start words', start_words
found_start_words = []
for start_word in start_words:
matches = (ranked_phrase for ranked_phrase in ranked_phrases if start_word in sub_lists(ranked_phrase, proper=False))
try:
word = matches.next()
if word not in large_phrases:
found_start_words.append(word)
except StopIteration:
if debug:
print 'start word %s not found' % start_word
if debug:
print 'found start words', found_start_words
top_phrases = found_start_words + large_phrases
else:
top_phrases = large_phrases
filtered_frequencies = dict((phrase, freq) for (phrase, freq) in phrase_frequencies.items() if phrase in top_phrases)
return top_phrases, filtered_frequencies, scored_phrases