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feat: add logging function to multiple plots

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Skudalen 2021-07-21 13:29:37 +02:00
parent 177ee63302
commit 978b1a2ccd
2 changed files with 170 additions and 22 deletions
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Neural_Network_Analysis.py
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@ -367,10 +367,10 @@ def plot_train_history(history, val_data=False):
plt.show()
# Plots the training history of four networks inverse cross-validated
# Plots the training history of four networks inverse cross-validated (single trained)
# Input: data, nr of sessions in total, batch_size and epochs
# Ouput: None -> plot
def plot_comp_session_spread(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
def plot_comp_spread_single(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
history_dict = {'GRU': [],
'LSTM': [],
@ -407,6 +407,26 @@ def plot_comp_session_spread(X, y, session_lengths, nr_sessions, batch_size=64,
del model_CNN_1D
K.clear_session()
# Logging data to CSV. Just copy, not implemented
'''
# Log data stream to CSV
csv_path = str(Path.cwd()) + '/logs/Network_acc_comparison_single/comparison_acc_data.csv'
with open(csv_path, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(['GRU_train_acc', 'LSTM_train_acc', 'FFN_train_acc', 'CNN_1D_train_acc', 'GRU_val_acc', 'LSTM_val_acc', 'FFN_val_acc', 'CNN_1D_val_acc'])
data = zip(*history_dict.values(), *history_dict_val.values())
writer.writerows(data)
csv_file.close()
# Log best results to CSV
csv_path = str(Path.cwd()) + '/logs/Network_acc_comparison_single/comparison_best.csv'
with open(csv_path, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(['GRU_train_acc', 'LSTM_train_acc', 'FFN_train_acc', 'CNN_1D_train_acc', 'GRU_val_acc', 'LSTM_val_acc', 'FFN_val_acc', 'CNN_1D_val_acc'])
writer.writerow( [np.max(history_dict.get('GRU_train')), np.max(history_dict.get('LSTM_train')), np.max(history_dict.get('FFN_train')), np.max(history_dict.get('CNN_1D_train')),
np.max(history_dict_val.get('GRU_val')), np.max(history_dict_val.get('LSTM_val')), np.max(history_dict_val.get('FFN_val')), np.max(history_dict_val.get('CNN_1D_val'))] )
csv_file.close()
'''
fig, axs = plt.subplots(2, 2, sharey=True)
plt.ylim(0, 1)
@ -446,10 +466,10 @@ def plot_comp_session_spread(X, y, session_lengths, nr_sessions, batch_size=64,
plt.show()
# Plots the average training history of four networks inverse cross-validated
# Plots the average training history of four networks inverse cross-validated (single trained)
# Input: data, nr of sessions in total, batch_size and epochs
# Ouput: None -> plot
def plot_comp_accuracy(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
def plot_comp_accuracy_single(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
#'''
history_dict = {'GRU_train': [],
'LSTM_train': [],
@ -517,7 +537,25 @@ def plot_comp_accuracy(X, y, session_lengths, nr_sessions, batch_size=64, epochs
'LSTM_val': [0.5, 0.9, 0.4, 0.8],
'FFN_val': [0.75, 0.8, 0.4, 0.8],
'CNN_1D_val': [0.8, 0.95, 0.4, 0.8]}
'''
#'''
# Log data stream to CSV
csv_path = str(Path.cwd()) + '/logs/Network_acc_comparison_single/comparison_acc_data.csv'
with open(csv_path, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(['GRU_train_acc', 'LSTM_train_acc', 'FFN_train_acc', 'CNN_1D_train_acc', 'GRU_val_acc', 'LSTM_val_acc', 'FFN_val_acc', 'CNN_1D_val_acc'])
data = zip(*history_dict.values(), *history_dict_val.values())
writer.writerows(data)
csv_file.close()
# Log best results to CSV
csv_path = str(Path.cwd()) + '/logs/Network_acc_comparison_single/comparison_best.csv'
with open(csv_path, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(['GRU_train_acc', 'LSTM_train_acc', 'FFN_train_acc', 'CNN_1D_train_acc', 'GRU_val_acc', 'LSTM_val_acc', 'FFN_val_acc', 'CNN_1D_val_acc'])
writer.writerow( [np.max(history_dict.get('GRU_train')), np.max(history_dict.get('LSTM_train')), np.max(history_dict.get('FFN_train')), np.max(history_dict.get('CNN_1D_train')),
np.max(history_dict_val.get('GRU_val')), np.max(history_dict_val.get('LSTM_val')), np.max(history_dict_val.get('FFN_val')), np.max(history_dict_val.get('CNN_1D_val'))] )
csv_file.close()
# Plot:
fig, axs = plt.subplots(2, sharey=True)
@ -544,10 +582,10 @@ def plot_comp_accuracy(X, y, session_lengths, nr_sessions, batch_size=64, epochs
plt.style.use('seaborn-dark-palette')
plt.show()
# Plots training and validation history for CNN_1D network with SOFT and HARD data
# Plots training and validation history for CNN_1D network with SOFT and HARD data (single trained)
# Input: SOFT and HARD raw data, respective session_lengths, *details
# Output: None -> plot
def plot_comp_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, nr_sessions, batch_size=64, epochs=30):
def plot_comp_SoftHard_single(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, nr_sessions, batch_size=64, epochs=30):
#'''
train_dict = {'SOFT':[], 'HARD':[]}
val_dict = {'SOFT':[], 'HARD':[]}
@ -565,8 +603,8 @@ def plot_comp_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, ses
model_CNN_1D = CNN_1D(input_shape=(208, 1))
CNN_1D_h = train(model_CNN_1D, X_train_soft, y_train_soft, 1, batch_size=batch_size, epochs=epochs,
X_validation=X_val_soft, y_validation=y_val_soft)
train_dict['SOFT'] = list(CNN_1D_h.history['accuracy'])
val_dict['SOFT'] = list(CNN_1D_h.history['val_accuracy'])
train_dict['SOFT'].append(list(CNN_1D_h.history['accuracy']))
val_dict['SOFT'].append(list(CNN_1D_h.history['val_accuracy']))
del model_CNN_1D
K.clear_session()
@ -574,22 +612,42 @@ def plot_comp_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, ses
model_CNN_1D = CNN_1D(input_shape=(208, 1))
CNN_1D_h = train(model_CNN_1D, X_train_hard, y_train_hard, 1, batch_size=batch_size, epochs=epochs,
X_validation=X_val_hard, y_validation=y_val_hard)
train_dict['HARD'] = list(CNN_1D_h.history['accuracy'])
val_dict['HARD'] = list(CNN_1D_h.history['val_accuracy'])
train_dict['HARD'].append(list(CNN_1D_h.history['accuracy']))
val_dict['HARD'].append(list(CNN_1D_h.history['val_accuracy']))
del model_CNN_1D
K.clear_session()
# Averaging out session training for each network
for key in train_dict:
train_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*train_dict[key])))
for key in val_dict:
val_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*val_dict[key])))
'''
history_dict = {'GRU_train': [0.5, 0.8, 0.4, 0.8],
'LSTM_train': [0.5, 0.9, 0.3, 0.9],
'FFN_train': [0.75, 0.8, 0.2, 0.7],
'CNN_1D_train': [0.8, 0.95, 0.1, 0.6]}
history_dict_val = {'GRU_val': [0.5, 0.8, 0.4, 0.8],
'LSTM_val': [0.5, 0.9, 0.4, 0.8],
'FFN_val': [0.75, 0.8, 0.4, 0.8],
'CNN_1D_val': [0.8, 0.95, 0.4, 0.8]}
train_dict = {'SOFT': [0.1, 0.7, 0.5, 0.69],
'HARD': [0.55, 0.9, 0.3, 0.92]}
val_dict = {'SOFT': [0.34, 0.85, 0.41, 0.74],
'HARD': [0.63, 0.99, 0.49, 0.88]}
'''
# Log data stream to CSV
csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_single/soft_hard_comparison_acc_data.csv'
with open(csv_path, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(['soft_train_acc', 'hard_train_acc', 'soft_val_acc', 'hard_val_acc'])
data = zip(*train_dict.values(), *val_dict.values())
writer.writerows(data)
csv_file.close()
# Log best results to CSV
csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_single/soft_hard_comparison_best.csv'
with open(csv_path, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(['soft_train_best', 'hard_train_best', 'soft_val_best', 'hard_val_best'])
writer.writerow( [np.max(train_dict.get('SOFT')), np.max(train_dict.get('HARD')), np.max(val_dict.get('SOFT')), np.max(val_dict.get('HARD'))] )
csv_file.close()
# Plot:
fig, axs = plt.subplots(2, sharey=True)
plt.ylim(0, 1)
@ -612,6 +670,92 @@ def plot_comp_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, ses
plt.show()
# Plots training and validation history for CNN_1D network with SOFT and HARD data (three-session-trained)
# Input: SOFT and HARD raw data, respective session_lengths, *details
# Output: None -> plot
def plot_comp_SoftHard_3(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, nr_sessions, batch_size=64, epochs=30):
#'''
train_dict = {'SOFT':[], 'HARD':[]}
val_dict = {'SOFT':[], 'HARD':[]}
for i in range(nr_sessions):
# Prepare data
X_train_soft, X_val_soft, y_train_soft, y_val_soft = prepare_datasets_sessions(X_soft, y_soft, session_lengths_soft, i)
X_train_hard, X_val_hard, y_train_hard, y_val_hard = prepare_datasets_sessions(X_hard, y_hard, session_lengths_hard, i)
X_train_soft = np.reshape(X_train_soft, (X_train_soft.shape[0], 208, 1))
X_val_soft = np.reshape(X_val_soft, (X_val_soft.shape[0], 208, 1))
X_train_hard = np.reshape(X_train_hard, (X_train_hard.shape[0], 208, 1))
X_val_hard = np.reshape(X_val_hard, (X_val_hard.shape[0], 208, 1))
# CNN_1D SOFT
model_CNN_1D = CNN_1D(input_shape=(208, 1))
CNN_1D_h = train(model_CNN_1D, X_train_soft, y_train_soft, 1, batch_size=batch_size, epochs=epochs,
X_validation=X_val_soft, y_validation=y_val_soft)
train_dict['SOFT'].append(list(CNN_1D_h.history['accuracy']))
val_dict['SOFT'].append(list(CNN_1D_h.history['val_accuracy']))
del model_CNN_1D
K.clear_session()
# CNN_1D HARD
model_CNN_1D = CNN_1D(input_shape=(208, 1))
CNN_1D_h = train(model_CNN_1D, X_train_hard, y_train_hard, 1, batch_size=batch_size, epochs=epochs,
X_validation=X_val_hard, y_validation=y_val_hard)
train_dict['HARD'].append(list(CNN_1D_h.history['accuracy']))
val_dict['HARD'].append(list(CNN_1D_h.history['val_accuracy']))
del model_CNN_1D
K.clear_session()
# Averaging out session training for each network
for key in train_dict:
train_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*train_dict[key])))
for key in val_dict:
val_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*val_dict[key])))
'''
train_dict = {'SOFT': [0.1, 0.7, 0.5, 0.69],
'HARD': [0.55, 0.9, 0.3, 0.92]}
val_dict = {'SOFT': [0.34, 0.85, 0.41, 0.74],
'HARD': [0.63, 0.99, 0.49, 0.88]}
'''
# Log data stream to CSV
csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_3/soft_hard_comparison_acc_data.csv'
with open(csv_path, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(['soft_train_acc', 'hard_train_acc', 'soft_val_acc', 'hard_val_acc'])
data = zip(*train_dict.values(), *val_dict.values())
writer.writerows(data)
csv_file.close()
# Log best results to CSV
csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_3/soft_hard_comparison_best.csv'
with open(csv_path, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(['soft_train_best', 'hard_train_best', 'soft_val_best', 'hard_val_best'])
writer.writerow( [np.max(train_dict.get('SOFT')), np.max(train_dict.get('HARD')), np.max(val_dict.get('SOFT')), np.max(val_dict.get('HARD'))] )
csv_file.close()
# Plot:
fig, axs = plt.subplots(2, sharey=True)
plt.ylim(0, 1)
plt.subplots_adjust(hspace=1.0, top=0.85, bottom=0.15, right=0.75)
fig.suptitle('Model training and validation with SOFT/HARD data', fontsize=16)
axs[0].plot(train_dict['SOFT'], ':', label='CNN_1D SOFT')
axs[0].plot(train_dict['HARD'], '--', label='CNN_1D HARD')
axs[0].set_title('Training accuracy')
axs[1].plot(val_dict['SOFT'], ':', label='CNN_1D SOFT')
axs[1].plot(val_dict['HARD'], '--', label='CNN_1D HARD')
axs[1].set_title('Validation accuracy')
for ax in axs.flat:
ax.set(xlabel='Epochs', ylabel='Accuracy')
plt.legend(bbox_to_anchor=(1.05, 1.5), title='Models used\n', loc='center left')
plt.style.use('seaborn-dark-palette')
plt.show()
# ----- MODELS ------
@ -801,8 +945,12 @@ if __name__ == "__main__":
# ----- PLOTTING ------
#plot_4xinverse_cross_val(X, y, session_lengths, NR_SESSIONS, epochs=30)
#plot_4_x_average_val(X_soft, y_soft, session_lengths, NR_SESSIONS, epochs=30)
#plot_comp_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
#plot_comp_spread_single(X, y, session_lengths, NR_SESSIONS, epochs=30)
#plot_comp_accuracy_single(X_soft, y_soft, session_lengths_soft, NR_SESSIONS, epochs=30)
#plot_comp_SoftHard_single(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
plot_comp_SoftHard_3(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)