def retag_files(self):
mp3_files = self.folders.retrieve_mp3_files(self.SOURCE_PATH)
for mp3_file in mp3_files:
complete_file_path = self.folders.join(self.SOURCE_PATH, mp3_file.name)
mp3 = self.tags.open_mp3_file(complete_file_path)
tags = self.tags.read_tags(mp3)
if len(tags) != 2 or not tags.get('song', False):
tags = self.tags.get_artist_and_song_from_file_name(mp3_file)
tags = self.utils.capitalize(tags)
source_file_path = self.folders.join(
self.SOURCE_PATH,
mp3_file.name,
)
new_path = self.folders.copy_file(
source_file_path,
self.DESTINATION_PATH,
'{}.mp3'.format(tags.get('song', mp3_file.name)),
)
new_mp3_file = self.tags.open_mp3_file(new_path)
self.tags.write_artist_and_song_tags(new_mp3_file, tags)
def get_t(object_id, question_id, number_of_objects):
"""
Returns the number of an object's descriptions that contain a specific tag
"""
global _descriptions
if not _descriptions:
_descriptions = [{} for _ in range(number_of_objects)]
all_tags = tags.get_all()
db.cursor.execute(
'SELECT description, objectID, descNum FROM Descriptions')
for row in db.cursor.fetchall():
for tag in all_tags:
if tag in row[0]:
if not tag in _descriptions[row[1] - 1]:
_descriptions[row[1] - 1][tag] = 1
else:
_descriptions[row[1] - 1][tag] += 1
tag = tags.get(question_id)
object_id = int(object_id)
o = _descriptions[object_id - 1]
if not tag in o:
return 0
return o[tag]
def get_t(object_id, question_id, number_of_objects):
"""
Returns the number of an object's descriptions that contain a specific tag
"""
global _descriptions
if not _descriptions:
_descriptions = [{} for _ in range(number_of_objects)]
all_tags = tags.get_all()
db.cursor.execute('SELECT description, objectID, descNum FROM Descriptions')
for row in db.cursor.fetchall():
for tag in all_tags:
if tag in row[0]:
if not tag in _descriptions[row[1]-1]:
_descriptions[row[1]-1][tag] = 1
else:
_descriptions[row[1]-1][tag] += 1
tag = tags.get(question_id)
object_id = int(object_id)
o = _descriptions[object_id-1]
if not tag in o:
return 0
return o[tag]
def get_t(object_id, question_id):
"""
Returns the number of descriptions that an object has that contains a specific tag
"""
tag = tags.get(question_id)
db.cursor.execute('SELECT COUNT(*) \
FROM Descriptions \
WHERE description LIKE %s \
AND objectID = %s', ('%{0}%'.format(tag), str(object_id)))
return db.cursor.fetchone()[0]
def ask(question_id, object, game, answer_data, answers, pO, Pi, p_tags, objects):
"""
Ask a question
"""
# Takes best question and updates all object probabilies based on the answer
probabilityD = get_tval()
question_tag = tags.get(question_id)
#answer = raw_input("Does it have " + tags[question_id-1] + "? (yes/no) ")
#answer = answer.lower()
answer = answer_data[object.id-1][question_id-1]
#print game_folder, object.id,objectlist[object.id-1][0],'qt->'+question_tag+' ' ,'ans->'+answer
for objectID in range(0, 17):
T = get_t(objectID+1, question_id)
N = objects[objectID][question_id][0]
D = objects[objectID][question_id][1]
if answer == 'yes':
answers.append(True)
K = probabilityD[T] + (N + 1)/(D + 2.0)
if Pi[0][question_id-1] == -1:
multiplier = K / 2
else:
multiplier = (K + Pi[objectID][question_id-1]) / 3
else:
answers.append(False)
K = (1 - probabilityD[T]) + (D - N + 1)/(D + 2.0)
if Pi[0][question_id-1] == -1:
multiplier = K / 2
else:
multiplier = (K + 1 - Pi[objectID][question_id-1]) / 3
pO[objectID] = pO[objectID] * multiplier
# Normalize the probabilities so that all object probabilities will sum to 1
pO = pO / np.sum(pO)
# Save the qustions to each answer and the updated probabilities
with open("example.txt", "a") as myfile:
myfile.write(question_tag + " -> " + answer+ " \n")
myfile.write(str(pO) + "\n")
return pO, answers
def decode(self, filename):
ext = filename.split('.')[-1].lower()
result = {
'wav': os.path.join(self.tmpdir, os.path.splitext(filename)[0]) + u'.wav',
'lossless': False,
'tags': tags.get(filename),
}
if ext == 'flac':
self.pipe([[ 'flac', '-sd', '-o', result['wav'], filename ]])
result['lossless'] = True
result['target'] = 'flac'
elif ext == 'mp3':
self.pipe([[ 'lame', '--decode', '--quiet', filename, result['wav'] ]])
result['target'] = 'mp3_dl'
else:
return None
for tag in sorted(result['tags']):
logger.info('Tag %s: %s' % (tag, '; '.join(result['tags'][tag])))
return result
def _record_results(self, game, game_answers, game_questions, guess, result, number_of_objects):
"""
Puts results into the DB as well as writing them to file for examination
"""
# TODO: make sure this can be used for new objects and when playing with the human's choice of object
log.info('Recording object results to the database')
for i in range(0, len(game_questions)):
T = questions.get_t(self.id, game_questions[i], number_of_objects)
if game_answers[i] == True:
db.cursor.execute("SELECT yes_answers FROM Pqd where t_value = %s", (T,))
yes_count = db.cursor.fetchone()[0]
#print yes_count, 'yes'
db.cursor.execute("UPDATE Pqd SET yes_answers = %s WHERE t_value = %s", (yes_count + 1, T))
db.cursor.execute("SELECT total_answers FROM Pqd where t_value = %s", (T,))
total_count = db.cursor.fetchone()[0]
#print total_count
db.cursor.execute("UPDATE Pqd SET total_answers = %s WHERE t_value = %s", (total_count + 1, T))
db.cursor.execute("INSERT INTO answers (oid, qid, answer) VALUES (%s, %s, %s)", (str(self.id), game_questions[i], game_answers[i]))
db.connection.commit()
if result == 0:
result = 'lose'
else:
result = 'win'
# TODO: clean up all the text files because this is kind of ridiculous
with open("game.txt", "a") as myfile:
myfile.write(str(game.id)+','+ str(self.id) +','+ str(guess.name)+"," + str(self.name) + "," + str(len(game_questions)) + "," + result + "\n")
myfile.close()
with open("answers.txt", "a") as answerfile:
answerfile.write("\n" + str(game.id) + " " + str(self.id) + " " + result + "\n")
for i in range(0, len(game_questions)):
answerfile.write(tags.get(game_questions[i]) + " -> " + str(game_answers[i]) + "\n")
answerfile.close()
def _record_results(self, game, game_answers, game_questions, guess, result, number_of_objects):
"""
Puts results into the DB as well as writing them to file for examination
"""
# TODO: make sure this can be used for new objects and when playing with the human's choice of object
log.info('Recording object results to the database')
for i in range(0, len(game_questions)):
T = questions.get_t(self.id, game_questions[i], number_of_objects)
if game_answers[i] == True:
db.cursor.execute("SELECT yes_answers FROM Pqd where t_value = %s", (T,))
yes_count = db.cursor.fetchone()[0]
#print yes_count, 'yes'
db.cursor.execute("UPDATE Pqd SET yes_answers = %s WHERE t_value = %s", (yes_count + 1, T))
db.cursor.execute("SELECT total_answers FROM Pqd where t_value = %s", (T,))
total_count = db.cursor.fetchone()[0]
#print total_count
db.cursor.execute("UPDATE Pqd SET total_answers = %s WHERE t_value = %s", (total_count + 1, T))
db.cursor.execute("INSERT INTO answers (oid, qid, answer) VALUES (%s, %s, %s)", (str(self.id), game_questions[i], game_answers[i]))
db.connection.commit()
if result == 0:
result = 'lose'
else:
result = 'win'
# TODO: clean up all the text files because this is kind of ridiculous
with open("game.txt", "a") as myfile:
myfile.write(str(game.id)+','+ str(self.id) +','+ str(guess.name)+"," + str(self.name) + "," + str(len(game_questions)) + "," + result + "\n")
myfile.close()
with open("answers.txt", "a") as answerfile:
answerfile.write("\n" + str(game.id) + " " + str(self.id) + " " + result + "\n")
for i in range(0, len(game_questions)):
answerfile.write(tags.get(game_questions[i]) + " -> " + str(game_answers[i]) + "\n")
answerfile.close()
def _record_results(self, game, game_answers, game_questions, guess, result):
"""
Puts results into the DB as well as writing them to file for examination
"""
log.info('Recording object results')
for i in range(0, len(game_questions)):
T = questions.get_t(self.id, game_questions[i])
if game_answers[i] == True:
db.cursor.execute("SELECT yes_answers FROM Pqd where t_value = %s", (T,))
yes_count = db.cursor.fetchone()[0]
#print yes_count, 'yes'
db.cursor.execute("UPDATE Pqd SET yes_answers = %s WHERE t_value = %s", (yes_count + 1, T))
db.cursor.execute("SELECT total_answers FROM Pqd where t_value = %s", (T,))
total_count = db.cursor.fetchone()[0]
#print total_count
db.cursor.execute("UPDATE Pqd SET total_answers = %s WHERE t_value = %s", (total_count + 1, T))
db.cursor.execute("INSERT INTO answers (oid, qid, answer) VALUES (%s, %s, %s)", (str(self.id), game_questions[i], game_answers[i]))
db.connection.commit()
if result == 0:
result = 'lose'
else:
result = 'win'
with open("game.txt", "a") as myfile:
myfile.write(str(game.id)+','+ str(self.id) +','+ str(guess.name)+"," + str(len(game_questions)) + "," + result + "\n")
myfile.close()
with open("answers.txt", "a") as answerfile:
answerfile.write("\n" + str(game.id) + " " + str(self.id) + " " + result + "\n")
for i in range(0, len(game_questions)):
answerfile.write(tags.get(game_questions[i]) + " -> " + str(game_answers[i]) + "\n")
answerfile.close()
def ask(question_id, object_we_play, game, answers, pO, Pi, objects,
number_of_objects, answer_data):
"""
Asks a question and updates probabilities based on the answer
"""
# gets probability that an answer will be yes for each of the 7 possible t-values of question/answer combos
probabilityD = get_tval()
# gets the correct question for the given tag
# This is where we'll update the code to include newly-generated questions; right now the code just pulls pre-generated questions from a database.
question_tag = tags.get(question_id)
questions = tags.get_questions()
question = questions[question_id - 1]
# in not simulated game, get answer from user
if config.args.notsimulated:
answer = interface.ask(question + " ")
# in simulated game, get answer from answer bank in database
else:
answer = answer_data[game.id - 1][object_we_play.id - 1][question_id -
1]
print question, answer
answers.append(answer)
multipliers = []
for objectID in range(17): # for all known objects
# count the number of that object's descriptions that contain the tag used to formulate that question
T = get_t(objectID + 1, question_id, number_of_objects)
# number of yes answers to this question/object combo
yes_answers = objects[objectID][question_id - 1][0]
# total number of answers given for this question/object combo
total_answers = objects[objectID][question_id - 1][1]
# TODO: see if Dr. Nielsen has any suggestions for improving the use of the
# image models since they reduce the accuracy at the moment
if answer:
K = probabilityD[T] + (yes_answers + 1) / (total_answers + 2.0)
# if Pi is -1 then it means we're skipping the image models for that tag
if Pi[0][question_id - 1] == -1:
multiplier = K / 2
multipliers.append(multiplier)
else:
multiplier = (K + Pi[objectID][question_id - 1]) / 3
multipliers.append(multiplier)
else:
K = (1 - probabilityD[T]) + (total_answers - yes_answers +
1) / (total_answers + 2.0)
if Pi[0][question_id - 1] == -1:
multiplier = K / 2
else:
multiplier = (K + 1 - Pi[objectID][question_id - 1]) / 3
pO[objectID] *= multiplier
for objectID in range(
17, number_of_objects
): # TODO: for all new objects/unknown tags for a new object
# this assigns a probability to unknown objects based on the idea that
# an object is about as likely as the rest of the objects to have the quality
# of a certain tag - i.e. many objects are balls, but not many objects are polka dotted
# TODO: make more sophisticated - some tags are opposites, so if we know an opposite then we
# can take that into consideration when finding the multiplier
multiplier = np.mean(multipliers)
pO[objectID] *= multiplier
# Normalize the probabilities so that all object probabilities will sum to 1
pO /= np.sum(pO)
# Save the questions to each answer and the updated probabilities
with open("example.txt", "a") as myfile:
myfile.write(str(question_tag) + " -> " + str(answer) + " \n")
myfile.write(str(pO) + "\n")
return pO, answers
def ask(question_id, object_we_play, game, answers, pO, Pi, objects, number_of_objects, answer_data):
"""
Asks a question and updates probabilities based on the answer
"""
# gets probability that an answer will be yes for each of the 7 possible t-values of question/answer combos
probabilityD = get_tval()
# gets the correct question for the given tag
question_tag = tags.get(question_id)
questions = tags.get_questions()
question = questions[question_id - 1]
# in not simulated game, get answer from user
if config.args.notsimulated:
answer = interface.ask(question + " ")
# in simulated game, get answer from answer bank in database
else:
answer = answer_data[game.id-1][object_we_play.id-1][question_id-1]
print question, answer
answers.append(answer)
multipliers = []
for objectID in range(17): # for all known objects
# count the number of that object's descriptions that contain the tag used to formulate that question
T = get_t(objectID+1, question_id, number_of_objects)
# number of yes answers to this question/object combo
yes_answers = objects[objectID][question_id-1][0]
# total number of answers given for this question/object combo
total_answers = objects[objectID][question_id-1][1]
# TODO: see if Dr. Nielsen has any suggestions for improving the use of the
# image models since they reduce the accuracy at the moment
if answer:
K = probabilityD[T] + (yes_answers + 1)/(total_answers + 2.0)
# if Pi is -1 then it means we're skipping the image models for that tag
if Pi[0][question_id-1] == -1:
multiplier = K / 2
multipliers.append(multiplier)
else:
multiplier = (K + Pi[objectID][question_id-1]) / 3
multipliers.append(multiplier)
else:
K = (1 - probabilityD[T]) + (total_answers - yes_answers + 1)/(total_answers + 2.0)
if Pi[0][question_id-1] == -1:
multiplier = K / 2
else:
multiplier = (K + 1 - Pi[objectID][question_id-1]) / 3
pO[objectID] *= multiplier
for objectID in range(17,number_of_objects): # TODO: for all new objects/unknown tags for a new object
# this assigns a probability to unknown objects based on the idea that
# an object is about as likely as the rest of the objects to have the quality
# of a certain tag - i.e. many objects are balls, but not many objects are polka dotted
# TODO: make more sophisticated - some tags are opposites, so if we know an opposite then we
# can take that into consideration when finding the multiplier
multiplier = np.mean(multipliers)
pO[objectID] *= multiplier
# Normalize the probabilities so that all object probabilities will sum to 1
pO /= np.sum(pO)
# Save the questions to each answer and the updated probabilities
with open("example.txt", "a") as myfile:
myfile.write(str(question_tag) + " -> " + str(answer) + " \n")
myfile.write(str(pO) + "\n")
return pO, answers
