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feat: make func to get nr of datapoints

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in a given session
This commit is contained in:
Skudalen 2021-07-12 11:49:28 +02:00
parent ec6c2c9dcc
commit 3c213cce7c
1 changed files with 76 additions and 37 deletions
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113
Neural_Network_Analysis.py
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@ -83,44 +83,78 @@ def prepare_datasets_percentsplit(X, y, shuffle_vars, validation_size=0.2, test_
# Ouput: X_train, X_test, y_train, y_test
def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_subjects=5):
#X_train = np.empty((1, 1, 208))
#y_train = np.empty((1, 208))
#X_test = np.empty((1, 1, 208))
#y_test = np.empty((1, 208))
X = X.tolist()
y = y.tolist()
session_lengths = session_lengths.tolist()
X_train = y_train = X_test = y_test = []
subject_starting_index = 0
start_test_index = subject_starting_index + sum(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])
for i in range(nr_subjects):
start_test_index = sum(session_lengths[i][:test_session_index])
print(session_lengths[0])
print('Subject start:', subject_starting_index)
print('Test start:', start_test_index)
print('Test end:', end_test_index)
print('Subject end:', end_subject_index, '\n -------')
if start_test_index == subject_starting_index:
X_test = X[start_test_index:end_test_index]
y_test = y[start_test_index:end_test_index]
X_train = X[end_test_index:end_subject_index]
y_train = y[end_test_index:end_subject_index]
elif end_test_index == end_subject_index:
#print(X[subject_starting_index:start_test_index].shape)
X_train = X[subject_starting_index:start_test_index]
y_train = y[subject_starting_index:start_test_index]
X_test = X[start_test_index:end_test_index]
#print(X[start_test_index:end_test_index].shape, '\n ---')
y_test = y[start_test_index:end_test_index]
else:
X_train = X[subject_starting_index:start_test_index]
y_train = y[subject_starting_index:start_test_index]
X_test = X[start_test_index:end_test_index]
y_test = y[start_test_index:end_test_index]
X_train = X[end_test_index:end_subject_index]
y_train = y[end_test_index:end_subject_index]
subject_starting_index = max(end_subject_index, end_test_index)
for i in range(1, nr_subjects):
start_test_index = subject_starting_index + sum(session_lengths[i][:test_session_index-1])
end_test_index = start_test_index + session_lengths[i][test_session_index-1]
end_subject_index = sum(session_lengths[i])
end_subject_index = subject_starting_index + sum(session_lengths[i])
print(session_lengths[i])
print('Subject start:', subject_starting_index)
print('Test start:', start_test_index)
print('Test end:', end_test_index)
print('Subject end:', end_subject_index, '\n -------')
if start_test_index == subject_starting_index:
X_test.append(X[start_test_index:end_test_index])
y_test.append(y[start_test_index:end_test_index])
X_train.append(X[end_test_index:end_subject_index])
y_train.append(y[end_test_index:end_subject_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]))
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]))
elif end_test_index == end_subject_index:
X_train.append(X[subject_starting_index:start_test_index])
y_train.append(y[subject_starting_index:start_test_index])
X_test.append(X[start_test_index:end_test_index])
y_test.append(y[start_test_index:end_test_index])
#print(X[subject_starting_index:start_test_index].shape)
X_train = np.concatenate((X_train, X[subject_starting_index:start_test_index]))
y_train = np.concatenate((y_train, y[subject_starting_index:start_test_index]))
#print(X[start_test_index:end_test_index].shape, '\n ---')
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]))
else:
X_train.append(X[subject_starting_index:start_test_index])
y_train.append(y[subject_starting_index:start_test_index])
X_test.append(X[start_test_index:end_test_index])
y_test.append(y[start_test_index:end_test_index])
X_train.append(X[end_test_index:end_subject_index])
y_train.append(y[end_test_index:end_subject_index])
subject_starting_index = end_subject_index
X_train = np.concatenate((X_train, X[subject_starting_index:start_test_index]))
y_train = np.concatenate((y_train, y[subject_starting_index:start_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]))
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]))
subject_starting_index = max(end_subject_index, end_test_index)
return np.array(X_train), np.array(X_test), np.array(y_train), np.array(y_test)
return X_train, X_test, y_train, y_test
# Creates a RNN_LSTM neural network model
# Input: input shape, classes of classification
@ -150,7 +184,7 @@ def RNN_LSTM(input_shape, nr_classes=5):
# Trains the model
# Input: Keras.model, batch_size, nr epochs, training, and validation data
# Ouput: History
def train(model, batch_size, epochs, X_train, X_validation, y_train, y_validation):
def train(model, X_train, X_validation, y_train, y_validation, batch_size=64, epochs=30):
optimiser = keras.optimizers.Adam(learning_rate=0.0001)
model.compile(optimizer=optimiser,
@ -165,6 +199,16 @@ def train(model, batch_size, epochs, X_train, X_validation, y_train, y_validatio
return history
# Gives nr of datapoints for chosen session
# Input: session_lengths 2d-list, session_nr, nr of subjects
# Ouput: int(datapoints)
def get_nr_in_session(session_lengths:list, session_nr, nr_subjects):
summ = 0
for i in range(nr_subjects):
summ += session_lengths[i][session_nr-1]
return summ
if __name__ == "__main__":
# Load data
@ -175,14 +219,9 @@ if __name__ == "__main__":
print(session_lengths.shape)
# Get prepared data: train, validation, and test
'''
(X_train, X_validation,
X_test, y_train,
y_validation,
y_test) = prepare_datasets_percentsplit(X, y, validation_size=0.2, test_size=0.25, shuffle_vars=True)
'''
(X_train, X_test,
y_train, y_test) = prepare_datasets_sessions(X, y, session_lengths)
#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)