def create_trained_model():
genres = [
'classical',
'jazz',
# 'opera',
# 'reggae',
'rock',
'house',
'hip hop'
]
required_features = [
'Centroid_AVG',
'Centroid_SD',
'RollOff_AVG',
'RollOff_SD',
'Flux_AVG',
'Flux_SD',
'BPM',
'Noise',
'NoiseTwo'
]
db_service = DatabaseService()
training_set = db_service.get_training_set(genres, required_features, 100, verbose=True)
learner = LearnerSVC()
learner.fit(training_set)
save_model(learner, features=required_features, genres=genres)
def main(argv):
####################################################
# Constants
####################################################
songs_per_genre = 200
# songs_per_genre -100
# c, r, h, hh = %83.59
# c, j, r, h, hh = %77.83
genres = [
'classical',
'jazz',
# 'opera',
# 'reggae',
'rock',
'house',
'hip hop'
]
required_features = [
'Centroid_AVG',
'Centroid_SD',
'RollOff_AVG',
'RollOff_SD',
'Flux_AVG',
'Flux_SD',
'BPM',
'Noise',
# 'NoiseTwo'
]
####################################################
# Training Set Fetch
####################################################
db_service = DatabaseService()
training_set = db_service.get_training_set(genres, required_features, songs_per_genre, verbose=True)
cross_val = CrossValidation(training_set, k_folds=6)
average_hit_rate = 0
count = 0
validator = Validator()
for ts, vs in cross_val:
learner = LearnerSVC()
learner.fit(ts)
results = validator.validate_next(learner, vs)
average_hit_rate += results.hit_rate()
count += 1.0
print results
print "Total samples: " + str(training_set.total_samples())
print "Training/Validation: " + str(cross_val.training_size()) + " / " + str(cross_val.validation_size())
print "Average hit rate: %" + "%.2f" % (100 * average_hit_rate / count)
