def getHistory(data_set_size):
# set your experiment seed for train test split
EXPERIMENT_SEED = 42
FEATURE_COUNT = 200
VALIDATION_PERCENT = 0.1
DEFAULT_LAYERS = 1
DEFAULT_NODES = len(classes) + 1
DEFAULT_H_ACTIVATION = 'relu'
DEFAULT_O_ACTIVATION = 'softmax'
DEFAULT_LOSS = 'categorical_crossentropy'
DEFAULT_BATCH = 200
DEFAULT_EPOCHS = 200
TEST_RATIO = 0.34
DATA_SET = data_set_size
# use the best model
MODEL_NAME = 'matt'
## Process Data
# Load the Data Management's interface
import sys
sys.path.append('../Back_End/')
sys.path.append('../Data_Management/')
import CSVInterface
import song_result_interface
import pandasDB
print('Initializing Data Management interface...')
# reads the data from the csv
reader = CSVInterface.featRead()
DB = pandasDB.DataBase()
D = {}
D['X'] = {
'small' : reader.getSubset(
reader.getFrame('features'),
sub='small'
),
'medium': reader.getSubset(
reader.getFrame('features'),
sub='medium'
),
'cleanLarge' : reader.getSubset(
reader.getFrame('features'),
sub='cleanLarge'
)
}
D['Y'] = {
'small' : reader.getSubset(
reader.getFrame('track')['genre_top'],
sub='small'
),
'medium': reader.getSubset(
reader.getFrame('track')['genre_top'],
sub='medium'
),
'cleanLarge': reader.getSubset(
reader.getFrame('track')['genre_top'],
sub='cleanLarge'
),
}
# Show all the weights prior to training
# net.show_weights(net.num_hidden_layers + 1)
# The data after removing outliers
# data = outlier_method(RawData)
#get the features
# indepent_features = reader.selectN(n=FEATURE_COUNT)
indepent_features = ['mfcc', 'spectral_contrast']
print('Constructing datasets')
print('X')
# the ind vars
# X = pd.DataFrame(D['X'][DATA_SET].iloc[:, indepent_features])
X = pd.DataFrame(D['X'][DATA_SET][indepent_features])
print('Y')
# the dependent var
Y = pd.DataFrame(D['Y'][DATA_SET], columns=['genre_top'])
print('train/validation split')
# Test and train split using encoded Y labels (vector of 0s with one 1)
trainx, testx, trainy, testy = train_test_split(
X.values,
encode(Y), # one hot encoder, see ANN_encode.py
test_size=VALIDATION_PERCENT, # validation size
random_state=EXPERIMENT_SEED
)
sample = trainx[0].copy()
print('Data done!\n\n********')
## Build the neural network
print('\nBuilding neural net')
print('input : {}'.format(len(sample)))
print('output: {}\n'.format(NUM_GENRES))
net = 0
history = 0
callback = 0
# Use this for pre trained models
net = ANN(p=Parameter(
num_input=len(sample),
num_hidden_layers=1,
nodes_per_hidden=len(sample) + 1,
num_output=NUM_GENRES,
hidden_activation=DEFAULT_H_ACTIVATION,
output_activation=DEFAULT_O_ACTIVATION,
initialize=False,
loss_function=DEFAULT_LOSS,
features = indepent_features
))
# Show the weights
# net.show_weights(net.num_hidden_layers + 1)
# Train the network
# returns history of training process, and a callback object that can
# extract information about the model at the end of events ANN_callbacks.py
h, callback = net.train(
trainx,
trainy,
num_iter=DEFAULT_EPOCHS,
testing=(testx, np.array(testy)),
batch=DEFAULT_BATCH,
interactive=False
)
return h
print('Plotting 1 model')
## Process Data
# Load the Data Management's interface
import sys
sys.path.append('../Back_End/')
sys.path.append('../Data_Management/')
import CSVInterface
import song_result_interface
import pandasDB
print('Initializing Data Management interface...')
# reads the data from the csv
reader = CSVInterface.featRead()
DB = pandasDB.DataBase()
# D = { X | Y }
# D[X][Y]
D = {}
# X
D['X'] = {
'small': reader.getSubset(reader.getFrame('features'), sub='small'),
'cleanLarge': reader.getSubset(reader.getFrame('features'),
sub='cleanLarge')
}
# Y
D['Y'] = {
'small':
reader.getSubset(reader.getFrame('track')['genre_top'], sub='small'),
actualGenre,
'actualScore':
str(
round(
result_list[0]['prediction']['genres'][actualGenre] * 100,
4)),
'songScore':
str(result_list[0]['prediction']['score']),
'modelScore':
str(round(neuralNet.get_mean_score(), 4)),
'error':
error,
'redirect_link':
redirect_link,
'predictionVector':
str(result_list[0]['prediction']['genres'])
})
else:
return jsonify({'error': error})
if __name__ == '__main__':
# create the database upon startup
database = pandasDB.DataBase()
# create the neural network upon startup
neuralNet = ANN_class.ANN(trained_model='matt')
app.run(debug=True, port=8080,
host='0.0.0.0') #run app on port 8080 in debug mode