def main(argv):
tools.defaultLog()
"""
packsize = 1024 --> about 42 sample packs per second
"""
packsize = 1024
dataset = []
dbControl = database.Controller()
required_features = [
"Feature_Centroid_Avg",
"Feature_Centroid_SD",
"Feature_Rolloff_Avg",
"Feature_Rolloff_SD",
"Feature_Flux",
"Feature_Spec_Flux_Avg",
"Feature_Spec_Flux_SD"
]
genres = [
'dubstep',
'house',
'trance'
]
training_data = dbControl.getTrainingSet(genres, 40)
ids_and_genres = []
print training_data
for genre in genres:
for song_id in training_data[genre]:
ids_and_genres.append((song_id, genre))
samples = []
classes = []
# iterating over all pulled songs
for id_and_genre in ids_and_genres:
log.debug("Fetching " + str(id_and_genre))
features = []
for feat in required_features:
features.append(dbControl.pullFeatureForSong(feat, id_and_genre[0],packsize)[0].value)
samples.append(np.array(features))
classes.append(id_and_genre[1])
machine = svm.SVC(C=1.0, kernel='poly', degree=2, shrinking=True, verbose=False)
# machine = mlp.Classifier(
# layers=(mlp.Layer("Sigmoid", units=4),
# mlp.Layer("Linear", units=len(genres))),
# learning_rate=0.02,
# n_stable=200)
training_indexes = []
hitRates = {}
def onTrain(index):
training_indexes.append(index)
def onValidate(index):
predicted = machine.predict(np.array([samples[index]]))[0]
expected = classes[index]
is_valid = (predicted == expected)
if not hitRates.has_key(expected):
hitRates[expected] = {}
hitRates[expected]["total"] = 0
hitRates[expected]["correct"] = 0
hitRates[expected]["total"] += 1
if predicted == expected:
hitRates[expected]["correct"] += 1
else:
if not hitRates[expected].has_key(predicted):
hitRates[expected][predicted] = 0
hitRates[expected][predicted] += 1
log.debug(str(is_valid) + " \t got " + str(predicted) + " expected " + str(expected))
return is_valid
def onDoneTraining():
chosen_samples = []
chosen_classes = []
for i in training_indexes:
chosen_samples.append(samples[i])
chosen_classes.append(classes[i])
X = np.array(chosen_samples)
Y = np.array(chosen_classes)
log.debug("Fitting ...")
machine.fit(X, Y)
holdOut = validation.HoldOutValidation(len(samples), onTrain, onValidate, onDoneTraining, validationPercent=0.2)
# the divide should be the number of genres. An equal amount of samples must be trained from each genre
hitRate = holdOut.performValidation(shuffle=False, divide=len(genres))
log.debug("hitRate = " + str(hitRate))
for genre, v in hitRates.iteritems():
log.debug(str(genre).upper() + ": " + str(v["correct"]) + "/" + str(v["total"]))
for hitg, amount in v.iteritems():
if hitg not in ["correct", "total"]:
log.debug("\t\t" + str(hitg) + "= " + str(amount))
pickled_save_path = "/home/damian/Music-Genre-Classification/Classifiers/SVM_Latest.pickled"
# set some meta paramaters
machine.required_features = required_features
machine.pack_size = packsize
machine.genres = genres
def main(argv):
tools.defaultLog()
packsize = 512
dataset = []
dbControl = database.Controller()
genres = ["house", "dubstep", "trance"]
training_data = dbControl.getTrainingSet(genres, 10)
ids_and_genres = []
print training_data
for genre in genres:
for song_id in training_data[genre]:
ids_and_genres.append((song_id, genre))
samples = []
classes = []
# iterating over all pulled songs
for id_and_genre in ids_and_genres:
log.debug("Fetching " + str(id_and_genre))
# featureDatas will contain an array of feature objects
feature_data_centroid = dbControl.pullFeatureForSong("Feature_Centroid", id_and_genre[0], packsize)
feature_data_rolloff = dbControl.pullFeatureForSong("Feature_Rolloff", id_and_genre[0], packsize)
# log.debug("feature_datas.length = " + str(len(feature_datas)))
# take 200 samples from the middle-ish
for i in range(2000, 2200):
centroid = float(feature_data_centroid[i].value)
rolloff = int(feature_data_rolloff[i].value)
samples.append((centroid, rolloff))
classes.append(id_and_genre[1])
machine = svm.SVC(C=1.0)
training_indexes = []
def onTrain(index):
training_indexes.append(index)
def onValidate(index):
predicted = machine.predict(samples[index])[0]
expected = classes[index]
is_valid = predicted == expected
log.debug(str(is_valid) + " \t got " + predicted + " expected " + expected)
return is_valid
def onDoneTraining():
chosen_samples = []
chosen_classes = []
for i in training_indexes:
chosen_samples.append(samples[i])
chosen_classes.append(classes[i])
machine.fit(chosen_samples, chosen_classes)
log.debug("Fitting.")
eightFold = validation.CrossValidation(len(samples), onTrain, onValidate, onDoneTraining, folds=8)
hitRate = eightFold.performValidation(shuffle=True)
log.debug("hitRate = " + str(hitRate))
# machine = svm.SVC()
# machine.fit(desc, classes)
# desc = [ [1, 1], [11,11]]
# classes = ['b', 'a']
# machine.fit(desc, classes)
# print machine.predict([3, 3])
# classifier = SupportVectorCLS()
# assign descriptors and classifications
pass
