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utils.py
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utils.py
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import torch
import random
import numpy as np
from itertools import cycle
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from sklearn.preprocessing import label_binarize
from sklearn.metrics import confusion_matrix
def plot_confusion_matrix(y_true, y_pred, savename, title, classes):
plt.figure(figsize=(12, 8), dpi=100)
np.set_printoptions(precision=2)
cm = confusion_matrix(y_true, y_pred)
# Calculation of probability value of confusion matrix
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
ind_array = np.arange(len(classes))
x, y = np.meshgrid(ind_array, ind_array)
for x_val, y_val in zip(x.flatten(), y.flatten()):
c = cm[y_val][x_val]
plt.text(x_val, y_val, "%0.3f" % (c,), color='red', fontsize=15, va='center', ha='center')
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.binary)
plt.title(title)
plt.colorbar()
xlocations = np.array(range(len(classes)))
plt.xticks(xlocations, classes, rotation=90)
plt.yticks(xlocations, classes)
plt.ylabel('Actual label')
plt.xlabel('Predict label')
# offset the tick
tick_marks = np.array(range(len(classes))) + 0.5
plt.gca().set_xticks(tick_marks, minor=True)
plt.gca().set_yticks(tick_marks, minor=True)
plt.gca().xaxis.set_ticks_position('none')
plt.gca().yaxis.set_ticks_position('none')
plt.grid(True, which='minor', linestyle='-')
plt.gcf().subplots_adjust(bottom=0.15)
# show confusion matrix
plt.savefig(savename)
plt.cla()
plt.close()
def plot_curve(epoch_list, train_loss, train_acc, test_acc, savename, title):
epoch = epoch_list
plt.subplot(2, 1, 1)
plt.plot(epoch, train_acc, label="train_acc")
plt.plot(epoch, test_acc, label="test_acc")
plt.title('{}'.format(title))
plt.ylabel('accuracy')
plt.legend(loc='best')
plt.subplot(2, 1, 2)
plt.plot(epoch, train_loss, label="train_loss")
plt.xlabel('times')
plt.ylabel('loss')
plt.legend(loc='best')
plt.savefig('{}.png'.format(savename))
class EarlyStopping(): # Early stop
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience=12, verbose=False, delta=0):
self.patience = patience
self.verbose = verbose
self.counter = 0
self.best_score = None
self.early_stop = False
self.test_acc_min = np.Inf
self.delta = delta
def __call__(self, test_acc):
score = test_acc
if self.best_score is None:
self.best_score = score
self.save_checkpoint(test_acc)
elif score <= self.best_score + self.delta:
self.counter += 1
print(f'EarlyStopping counter ----- > : {self.counter} out of {self.patience}')
if self.counter >= self.patience:
self.early_stop = True
else:
self.best_score = score
self.save_checkpoint(test_acc)
self.counter = 0
def save_checkpoint(self, test_acc):
'''Saves model when validation loss decrease.'''
if self.verbose:
print(f'test acc ----> ({self.test_acc_min:.6f} --> {test_acc:.6f}). Saving model ...')
self.test_acc_min = test_acc