def loadSentimentValues(catalog, sentimentvaluesfile):
sentimentvaluesfile = cf.data_dir + sentimentvaluesfile
input_file = csv.DictReader(open(sentimentvaluesfile, encoding="utf-8"),
delimiter=",")
for hashtag in input_file:
model.addHashtag(catalog, hashtag)
return catalog
def loadHashtags(analyzer):
"""
Carga las etiquetas del archivo csv.
"""
tagsfile = cf.data_dir + 'subsamples-small/user_track_hashtag_timestamp-small.csv'
input_file = csv.DictReader(open(tagsfile, encoding='utf-8'))
for event in input_file:
model.addHashtag(analyzer, event)
def loadSentimentValues(catalog):
file = cf.data_dir + sentimentvalues_file
input_file = csv.DictReader(open(file, encoding='utf-8'))
for hashtag_leido in input_file:
hashtag_agregar = {'hashtag': hashtag_leido['hashtag'].lower()}
if hashtag_leido['vader_avg'] == '':
hashtag_agregar['vader'] = -1
else:
hashtag_agregar['vader'] = float(hashtag_leido['vader_avg'])
model.addHashtag(catalog, hashtag_agregar)
def loadSentimentValues(analyzer):
sentimentFile = cf.data_dir + 'sentiment_values.csv'
input_file = csv.DictReader(open(sentimentFile, encoding="utf-8"), delimiter=',')
for line in input_file:
filtered = {
'hashtag': (line['hashtag'].lower()).replace(' ', ''),
'vader': line['vader_avg']
}
model.addHashtag(analyzer, filtered)
return analyzer
def loadHashtags(catalog):
"""
Carga los hashtags del archivo. Por cada hashtag se toma el único dato necesario:
el vader promedio.
"""
videosfile = cf.data_dir + 'sentiment_values.csv'
input_file = csv.DictReader(open(videosfile, encoding='utf-8'))
for hashtag in input_file:
try:
cada_hashtag = {
"hashtag": hashtag["hashtag"].lower(),
"vader_avg": float(hashtag["vader_avg"])
}
model.addHashtag(catalog, cada_hashtag)
except:
pass
def loadData(catalog):
delta_time = -1.0
delta_memory = -1.0
tracemalloc.start()
start_time = getTime()
start_memory = getMemory()
songfile1 = cf.data_dir + 'Subsamples/user_track_hashtag_timestamp/user_track_hashtag_timestamp-small.csv'
input_file1 = csv.DictReader(open(songfile1, encoding='utf-8'))
songfile2 = cf.data_dir + 'Subsamples/context_content_features/context_content_features-small.csv'
input_file2 = csv.DictReader(open(songfile2, encoding='utf-8'))
songfile3 = cf.data_dir + 'Subsamples/sentiment_values/sentiment_values1.csv'
input_file3 = csv.DictReader(open(songfile3, encoding='utf-8'))
model.createCharact(catalog)
model.createCharactSong(catalog)
for dicc in input_file3:
model.addHashtag(catalog['hashtags'], dicc)
for song in input_file1:
model.addTrack(catalog['tracksong'], song)
lstevent = lt.newList('ARRAY_LIST')
pos = 0
for song in input_file2:
issong = model.songByUserId(catalog, song)
if issong is not None:
pos += 1
if (pos in range(1, 6)) or (pos in range(63229, 63234)):
lt.addLast(lstevent, model.printEvent(issong))
model.addSongbyCharact(catalog, issong)
model.addArtist(catalog['artists'], issong)
model.addTrackHashtag(catalog['trackhashtag'], issong)
model.newAddSong(catalog)
model.addSong(catalog)
addGenre(catalog, None)
stop_memory = getMemory()
stop_time = getTime()
tracemalloc.stop()
delta_time = stop_time - start_time
delta_memory = deltaMemory(start_memory, stop_memory)
return (lstevent, (delta_time, delta_memory))
def loadData(analyzer, hashtag, sentiments, context):
"""
Carga los datos de los archivos CSV en el modelo
"""
hashtagsfile = cf.data_dir + hashtag
hashtag_file = csv.DictReader(open(hashtagsfile, encoding="utf-8"),
delimiter=",")
contextfile = cf.data_dir + context
context_file = csv.DictReader(open(contextfile, encoding="utf-8"),
delimiter=",")
sentimentsfile = cf.data_dir + sentiments
sentiments_file = csv.DictReader(open(sentimentsfile, encoding="utf-8"),
delimiter=",")
for event in context_file:
model.addEvent(analyzer, event)
posEvent = 1
for hastag in hashtag_file:
posEvent = model.addHashtag(analyzer, hastag, posEvent)
model.crearArboles(analyzer)
for sentiment in sentiments_file:
model.addSentiment(analyzer, sentiment)
return analyzer
def loadHashtags(catalog):
hashtagsfile = cf.data_dir + 'sentiment_values.csv'
input_file = csv.DictReader(open(hashtagsfile, encoding='utf-8'))
for hashtag in input_file:
model.addHashtag(catalog, hashtag)
def loadHashtags(cat, hashtags):
hfile = cf.data_dir + "subsamples-small" + "\\" + hashtags
input_file = csv.DictReader(open(hfile, encoding="utf-8"), delimiter=",")
for rep in input_file:
model.addHashtag(cat, rep)
print("Se cargó el archivo de Track-Hashtags")