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feat: implement func for session data prep

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Skudalen 2021-07-12 13:16:07 +02:00
parent 3c213cce7c
commit c009b0cdb2
1 changed files with 31 additions and 25 deletions
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Neural_Network_Analysis.py
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@ -90,12 +90,14 @@ def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_su
end_test_index = start_test_index + session_lengths[0][test_session_index-1] end_test_index = start_test_index + session_lengths[0][test_session_index-1]
end_subject_index = subject_starting_index + sum(session_lengths[0]) end_subject_index = subject_starting_index + sum(session_lengths[0])
print(session_lengths[0]) # Testing to check correctly slicing
'''
print(session_lengths[0], 'Sum:', sum(session_lengths[0]))
print('Subject start:', subject_starting_index) print('Subject start:', subject_starting_index)
print('Test start:', start_test_index) print('Test start:', start_test_index)
print('Test end:', end_test_index) print('Test end:', end_test_index)
print('Subject end:', end_subject_index, '\n -------') print('Subject end:', end_subject_index, '\n -------')
'''
if start_test_index == subject_starting_index: if start_test_index == subject_starting_index:
X_test = X[start_test_index:end_test_index] X_test = X[start_test_index:end_test_index]
y_test = y[start_test_index:end_test_index] y_test = y[start_test_index:end_test_index]
@ -115,9 +117,9 @@ def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_su
y_train = y[subject_starting_index:start_test_index] y_train = y[subject_starting_index:start_test_index]
X_test = X[start_test_index:end_test_index] X_test = X[start_test_index:end_test_index]
y_test = y[start_test_index:end_test_index] y_test = y[start_test_index:end_test_index]
X_train = X[end_test_index:end_subject_index] X_train = np.concatenate((X_train, X[end_test_index:end_subject_index]))
y_train = y[end_test_index:end_subject_index] y_train = np.concatenate((y_train, y[end_test_index:end_subject_index]))
#print(X_train.shape, '\n -------')
subject_starting_index = max(end_subject_index, end_test_index) subject_starting_index = max(end_subject_index, end_test_index)
for i in range(1, nr_subjects): for i in range(1, nr_subjects):
@ -125,12 +127,14 @@ def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_su
end_test_index = start_test_index + session_lengths[i][test_session_index-1] end_test_index = start_test_index + session_lengths[i][test_session_index-1]
end_subject_index = subject_starting_index + sum(session_lengths[i]) end_subject_index = subject_starting_index + sum(session_lengths[i])
print(session_lengths[i]) # Testing to check correctly slicing
'''
print(session_lengths[i], 'Sum:', sum(session_lengths[i]))
print('Subject start:', subject_starting_index) print('Subject start:', subject_starting_index)
print('Test start:', start_test_index) print('Test start:', start_test_index)
print('Test end:', end_test_index) print('Test end:', end_test_index)
print('Subject end:', end_subject_index, '\n -------') print('Subject end:', end_subject_index, '\n -------')
'''
if start_test_index == subject_starting_index: if start_test_index == subject_starting_index:
X_test = np.concatenate((X_test, X[start_test_index:end_test_index])) X_test = np.concatenate((X_test, X[start_test_index:end_test_index]))
y_test = np.concatenate((y_test, y[start_test_index:end_test_index])) y_test = np.concatenate((y_test, y[start_test_index:end_test_index]))
@ -151,7 +155,7 @@ def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_su
y_test = np.concatenate((y_test, y[start_test_index:end_test_index])) y_test = np.concatenate((y_test, y[start_test_index:end_test_index]))
X_train = np.concatenate((X_train, X[end_test_index:end_subject_index])) X_train = np.concatenate((X_train, X[end_test_index:end_subject_index]))
y_train = np.concatenate((y_train, y[end_test_index:end_subject_index])) y_train = np.concatenate((y_train, y[end_test_index:end_subject_index]))
#print(X_train.shape, '\n -------')
subject_starting_index = max(end_subject_index, end_test_index) subject_starting_index = max(end_subject_index, end_test_index)
return X_train, X_test, y_train, y_test return X_train, X_test, y_train, y_test
@ -202,34 +206,36 @@ def train(model, X_train, X_validation, y_train, y_validation, batch_size=64, ep
# Gives nr of datapoints for chosen session # Gives nr of datapoints for chosen session
# Input: session_lengths 2d-list, session_nr, nr of subjects # Input: session_lengths 2d-list, session_nr, nr of subjects
# Ouput: int(datapoints) # Ouput: int(datapoints)
def get_nr_in_session(session_lengths:list, session_nr, nr_subjects): def get_nr_in_session(session_lengths:list, session_nr, nr_subjects=5):
summ = 0 summ = 0
for i in range(nr_subjects): for i in range(nr_subjects):
summ += session_lengths[i][session_nr-1] summ += session_lengths[i][session_nr-1]
return summ return summ
# Prints session and training data
# Input: None
# Ouput: None -> print
def print_session_train_data(X_train, X_test, y_train, y_test, session_lengths, session_nr):
print(X_train.size)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
print('Datapoints in session ' + str(session_nr) + ':', get_nr_in_session(session_lengths, session_nr))
print('Should be remaining:', 2806 - get_nr_in_session(session_lengths, session_nr))
if __name__ == "__main__": if __name__ == "__main__":
# Load data # Load data
X, y, session_lengths = load_data_from_json(DATA_PATH_MFCC) X, y, session_lengths = load_data_from_json(DATA_PATH_MFCC)
print(X.shape)
print(y.shape)
print(session_lengths.shape)
# Get prepared data: train, validation, and test # Get prepared data: train, validation, and test
session_nr = 4
X_train, X_test, y_train, y_test = prepare_datasets_sessions(X, y, session_lengths, session_nr)
#print_session_train_data(X_train, X_test, y_train, y_test, session_lengths, session_nr)
#X_train, X_validation, X_test, y_train, y_validation, y_test = prepare_datasets_percentsplit(X, y, shuffle_vars=True, validation_size=0.2, test_size=0.25)
X_train, X_test, y_train, y_test = prepare_datasets_sessions(X, y, session_lengths, 3)
print(X_train.size)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
'''
# Make model # Make model
model = RNN_LSTM(input_shape=(1, 208)) model = RNN_LSTM(input_shape=(1, 208))
model.summary() model.summary()
@ -243,7 +249,7 @@ if __name__ == "__main__":
# evaluate model on test set # evaluate model on test set
test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2) test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
print('\nTest accuracy:', test_acc) print('\nTest accuracy:', test_acc)
'''