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Standard_Functions.py
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Standard_Functions.py
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import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
def plot_history(history):
"""
Code adapted from: https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/
by Jason Brownlee
Function that displays the accuracy and validation loss graphs for a trained model.
"""
SMALL_SIZE = 16
MEDIUM_SIZE = 20
BIGGER_SIZE = 50
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
#plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
x = range(1, len(acc) + 1)
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.plot(x, acc, 'b', label='Training accuracy')
plt.plot(x, val_acc, 'r', label='Validation accuracy')
plt.title('Training and Validation Accuracy', fontsize=22)
plt.legend()
plt.subplot(1, 2, 2)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.plot(x, loss, 'b', label='Training loss')
plt.plot(x, val_loss, 'r', label='Validation loss')
plt.title('Training and Validation Loss', fontsize=22)
plt.legend()
''' Many of these functions are extraneous and have already been created by libraries such as
sklearn. I highly suggest that you use the perfomance metrics functions created by sklearn.
The reason I hardcoded many of these functions was so that I would have more flexibility in
outputting my results. However, in most cases it is much easier to simply use output processing functions
created by other people. '''
def accuracy_per_class(y_pred, y_test, columns_list):
""" Assumes y_pred and y_test are identical shape and are both one hot encoded. Both y_pred
and y_test should be numpy arrays. Columns_list should just be a list of columns.
Dict format: {class: [correct, incorrect]} """
accuracy_dict = {}
correct_index = 0
incorrect_index = 1
for row in range(y_pred.shape[0]):
curr_correct_index = np.argmax(y_test[row])
curr_col_name = columns_list[curr_correct_index]
if np.argmax(y_pred[row]) == curr_correct_index:
if curr_col_name in accuracy_dict:
accuracy_dict[curr_col_name][correct_index] += 1
else:
accuracy_dict[curr_col_name] = [1, 0]
elif np.argmax(y_pred[row]) != curr_correct_index:
if curr_col_name in accuracy_dict:
accuracy_dict[curr_col_name][incorrect_index] += 1
else:
accuracy_dict[curr_col_name] = [0, 1]
for class_name in accuracy_dict:
lst = accuracy_dict[class_name]
percentage = lst[correct_index] / (lst[incorrect_index]+lst[correct_index])
accuracy_dict[class_name].insert(0, percentage)
return accuracy_dict
def accuracy_per_class_binary(y_pred, y_test):
""" [correct, incorrect] """
accuracy_dict = {'normal': [0,0], 'anomaly': [0,0]}
for row in range(y_pred.shape[0]):
target_output = y_test[row]
predicted_output = np.around(y_pred[row], decimals=0)
if target_output == 0:
if predicted_output != target_output:
accuracy_dict['normal'][1] += 1
elif predicted_output == target_output:
accuracy_dict['normal'][0] += 1
if target_output == 1:
if predicted_output != target_output:
accuracy_dict['anomaly'][1] += 1
elif predicted_output == target_output:
accuracy_dict['anomaly'][0] += 1
for class_name in accuracy_dict:
lst = accuracy_dict[class_name]
percentage = lst[0]/(lst[1]+lst[0])
accuracy_dict[class_name].insert(0, percentage)
return accuracy_dict
def tp_tn_fp_fn(accuracy_foreach_class_dict):
""" The dictionary I defined previously. """
tp = accuracy_foreach_class_dict["anomaly"][1]
tn = accuracy_foreach_class_dict["normal"][1]
fp = accuracy_foreach_class_dict["normal"][2]
fn = accuracy_foreach_class_dict["anomaly"][2]
return tp, tn, fp, fn
''' Functions to calculate and display a confusion matrix. You can find other ways to
present your results if needed. '''
def confusion_matrix_type(y_pred, y_test):
confusion_matrix_dict = {
'normal': [0, 0, 0, 0, 0],
'dos': [0, 0, 0, 0, 0],
'r2l': [0, 0, 0, 0, 0],
'u2r': [0, 0, 0, 0, 0],
'probe': [0, 0, 0, 0, 0]
}
index_to_type_dict = {
0: 'normal',
1: 'dos',
2: 'r2l',
3: 'u2r',
4: 'probe'
}
for row in range(y_pred.shape[0]):
target_output = y_test[row]
target_index = np.argmax(target_output)
predicted_output = y_pred[row]
predicted_index = np.argmax(predicted_output)
target_type = index_to_type_dict[target_index]
predicted_type = index_to_type_dict[predicted_index]
confusion_matrix_dict[target_type][predicted_index] += 1
return confusion_matrix_dict
def show_confusion_matrix(confusion_matrix_dict):
SMALL_SIZE = 14
MEDIUM_SIZE = 17
BIGGER_SIZE = 50
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
index_to_type_dict = {
0: 'normal',
1: 'dos',
2: 'r2l',
3: 'u2r',
4: 'probe'
}
ax = plt.subplot()
confusion_matrix_array = []
for i in range(5):
the_type = index_to_type_dict[i]
confusion_matrix_array.append(confusion_matrix_dict[the_type])
confusion_matrix_array = np.asarray(confusion_matrix_array)
confusion_matrix_array = normalize(confusion_matrix_array, axis=1, norm='l2')
sns.heatmap(confusion_matrix_array, annot=True, ax=ax, cmap='Reds')
ax.set_xlabel('Predicted Labels')
ax.set_ylabel('True Labels')
ax.set_title('Confusion Matrix', fontsize=23)
ax.xaxis.set_ticklabels(['normal', 'dos', 'r2l', 'u2r', 'probe'])
ax.yaxis.set_ticklabels(['probe', 'u2r', 'r2l', 'dos', 'normal'])
# plt.matshow(confusion_matrix_array)
# plt.colorbar()
plt.show()