def search_word(self, word):
word_stemmed = lyrics_to_bow.lyrics_to_bow(word).iterkeys()[0]
result = self.lyrics.execute("SELECT count FROM lyrics WHERE word='%s';" % word_stemmed)
total = 0
for count in result:
total += count[0]
return total
def predict_lyrics(self, lyrics,user_pred=None):
lyrics_stemmed = lyrics_to_bow.lyrics_to_bow(lyrics)
print "LYRICS: %s" %lyrics
print "STEMMED: %s" %lyrics_stemmed
indexed_dict={}
for key in lyrics_stemmed.iterkeys():
try:
indexed_dict[self.word_indices.index(key)]=lyrics_stemmed[key]
#if the word isnt among the 5000
except ValueError, err:
pass
def readLyrics(dir):
mxmBow = fileManager(BOW, 'r').split(',')
files = sorted(listdir(dir))
newSet = []
songLyrics = []
bow = '{word}:{freq}'
for lyrics in files:
bow = lyrics_to_bow(fileManager(RANGE1 + lyrics, 'r'))
try:
for word, freq in bow.iteritems():
try:
songLyrics.append(
bow.format(
word=str(mxmBow.index(word) + 1),
freq=str(freq)
)
)
except ValueError:
continue
newSet.append(
[
sep.join(
lyrics[:-4].split('-lyrics-')[::-1]
).replace('-', '')
] +
sorted(
songLyrics, key=lambda s: int(s.partition(':')[0])
)
)
songLyrics = []
except AttributeError:
print "Failed to index", lyrics
continue
return '\n'.join(sorted([','.join(line) for line in newSet]))
def predict_genre(song_title, artist=None):
# check if we already have the lryics
#dfLyricBow=
#if
lyrics, title, url = gsl.get_song_lyricsProx(song_title, artist)
if isinstance(lyrics, basestring):
bow = lb.lyrics_to_bow(lyrics)
#return title + lyrics
features = create_features(bow)
tfidfv = joblib.load('pkldir/tfidf_txf.pkl')
tfFeatures = tfidfv.transform(features)
furrest = joblib.load('pkldir/forest785.pkl')
prediction = furrest.predict(tfFeatures)
return prediction + title + lyrics
elif np.isnan(lyrics):
return 'Couldn\'t find the lyrics.'
#print 'Are these the right lyrics? \n %s' % (lyrics)
else:
return '...nothing'
def predict_genre(song_title,artist=None):
# check if we already have the lryics
#dfLyricBow=
#if
lyrics, title, url = gsl.get_song_lyricsProx(song_title,artist)
if isinstance(lyrics,basestring):
bow = lb.lyrics_to_bow(lyrics)
#return title + lyrics
features = create_features(bow)
tfidfv = joblib.load('pkldir/tfidf_txf.pkl')
tfFeatures = tfidfv.transform(features)
furrest = joblib.load('pkldir/forest785.pkl')
prediction = furrest.predict(tfFeatures)
return prediction + title + lyrics
elif np.isnan(lyrics):
return 'Couldn\'t find the lyrics.'
#print 'Are these the right lyrics? \n %s' % (lyrics)
else:
return '...nothing'
def stem_word(word):
"""Use modified Porter-Stemmer to stem a given word."""
stemmed_word = list(ltb.lyrics_to_bow(word).keys())
stemmed_word = stemmed_word[0]
return stemmed_word
def stem_word(word):
"""Use modified Porter-Stemmer to stem a given word."""
stemmed_word = list(ltb.lyrics_to_bow(word).keys())
stemmed_word = stemmed_word[0]
return stemmed_word
