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visualizations.py
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visualizations.py
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from datetime import datetime
# import matplotlib.pyplot as plt
import numpy as np
import visdom
from matplotlib import rcParams
from sklearn.metrics import (auc, confusion_matrix,
multilabel_confusion_matrix, roc_auc_score,
roc_curve)
from sklearn.preprocessing import LabelBinarizer, label_binarize
from sklearn.utils.multiclass import unique_labels
rcParams.update({'figure.autolayout': True, 'figure.figsize': (6, 9)})
class Visualizations:
def __init__(self, env_name=None):
if env_name is None:
self.time_training_started = datetime.now()
self.env_name = str(self.time_training_started.strftime("%d-%m %Hh%M"))
self.vis = visdom.Visdom(env=self.env_name)
# windows
self.loss_win = None
self.loss_epoch_win = None
self.current_batch_win = None
self.initial_time_win = None
self.confusion_matrix_win = None
self.roc_score_win = None
# common attributes
self.epoch_current = 0
self.epoch_total = 0
self.operation = None
def plot_loss(self, loss, step, name):
self.loss_win = self.vis.line(
[loss], [step],
win=self.loss_win,
name=name,
update='append' if self.loss_win else None,
opts=dict(
xlabel='Step',
ylabel='Loss',
title='Loss (média dos últimos 10 lotes)',
))
def plot_epoch_loss(self, epoch_history):
'''Espera uma lista, onde cada elemento é uma lista
Params:
epoch_history: cada
'''
# mean_per_epoch = [(i, np.mean(loss_history[-10:])) for i, loss_history in enumerate(epoch_history)]
mean_per_epoch = [
np.mean(loss_history[-10:]) for loss_history in epoch_history
]
self.loss_epoch_win = self.vis.line(
mean_per_epoch, [x for x in range(len(mean_per_epoch))],
win=self.loss_epoch_win,
update='replace' if self.loss_epoch_win else None,
opts=dict(
xlabel='Epoch',
ylabel='Loss',
title='Epoch Loss(média dos últimos 10 lotes do Epoch)',
))
def plot_roc_auc_score(self, y_true, y_pred, epoch):
"""FIXME! briefly describe function
:param y_true:
:param y_pred:
:param epoch: int - current epoch
:returns:
:rtype:
"""
lb = LabelBinarizer()
lb.fit(y_true)
y_true_binarizado = lb.transform(y_true)
y_pred_binarizado = lb.transform(y_pred)
current_score = roc_auc_score(y_true_binarizado,
y_pred_binarizado,
average="macro")
self.roc_score_win = self.vis.line(
np.array([current_score]),
[epoch],
win=self.roc_score_win,
update='append' if self.roc_score_win else None,
opts=dict(xlabel='Epoch', ylabel='AUC', title='ROC Score'))
def plot_confusion_matrix(self, y_true, y_pred):
import matplotlib.pyplot as plt
ax, fig = plot_confusion_matrix(y_true,
y_pred,
['Alemão', 'Inglês', 'Espanhol'],
normalize=True)
# cm = multilabel_confusion_matrix(y_true, y_pred)
# fig.fig(figsize=(6, 9))
self.confusion_matrix_win = self.vis.matplot(
fig, win=self.confusion_matrix_win)
def plot_current_batch(self, current_batch, batch_size, dataset_length):
self.current_batch_win = self.vis.text(
F"Mode: {self.operation}<br><br>"
F"Epoch: {self.epoch_current} of {self.epoch_total}<br>"
F"Current Batch: {current_batch} of {dataset_length // batch_size}",
win=self.current_batch_win,
append=False)
def update_elapsed_time(self, isFinished=False):
current_elapsed_time_inSeconds = (
datetime.now() - self.time_training_started).total_seconds()
hours, remainder = divmod(current_elapsed_time_inSeconds, 3600)
minutes, seconds = divmod(remainder, 60)
self.initial_time_win = self.vis.text(
F"Time started: {self.env_name}<br>"
F"Elapsed Time: {hours:2.0f}h{minutes:2.0f}m{seconds:2.0f}s<br>",
win=self.initial_time_win,
append=False)
if isFinished:
self.initial_time_win = self.vis.text(F"Training Finished.",
win=self.initial_time_win,
append=True)
def plot_confusion_matrix(y_true,
y_pred,
classes,
normalize=False,
title=None,
cmap=None):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
import matplotlib.pyplot as plt
# default
cmap = plt.cm.Blues
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
# Compute confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Only use the labels that appear in the data
# classes = classes[unique_labels(y_true, y_pred)]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(
xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes,
yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(),
rotation=45,
ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j,
i,
format(cm[i, j], fmt),
ha="center",
va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
return ax, fig