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Utils.py
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Wed, Feb 5, 09:52

Utils.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Feb 8 22:10:18 2020
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-- Author: Vigneashwara Pandiyan
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Utils file for visualization/ Plots
"""
#%%
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import torch
from sklearn.metrics import confusion_matrix
import seaborn as sns
import pandas as pd
#%%
def plot_confusion_matrix(y_true, y_pred,classes,plotname):
# Build confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Normalise
cmn = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
cmn=cmn*100
fig, ax = plt.subplots(figsize=(12,9))
sns.set(font_scale=3)
b=sns.heatmap(cmn, annot=True, fmt='.1f', xticklabels=classes, yticklabels=classes,cmap="coolwarm",linewidths=0.1,annot_kws={"size": 25},cbar_kws={'label': 'Classification Accuracy %'})
for b in ax.texts: b.set_text(b.get_text() + " %")
plt.ylabel('Actual',fontsize=25)
plt.xlabel('Predicted',fontsize=25)
plt.margins(0.2)
ax.set_yticklabels(ax.get_yticklabels(), rotation=90, va="center", fontsize= 20)
ax.set_xticklabels(ax.get_xticklabels(), va="center",fontsize= 20)
# plt.setp(ax.get_yticklabels(), rotation='vertical')
plotname=str(plotname)
plt.savefig(plotname,bbox_inches='tight')
plt.show()
plt.clf()
#%%
def plots(iteration,Loss_value,Total_Epoch,Accuracy,Learning_rate,Training_loss_mean,Training_loss_std,class_names,Times):
iteration = np.array(iteration)
Loss_value = np.array(Loss_value)
Total_Epoch = np.array(Total_Epoch)
Accuracy = np.array(Accuracy)
Learning_rate = np.array(Learning_rate)
Training_loss_mean = np.array(Training_loss_mean)
Training_loss_std = np.array(Training_loss_std)
Times = np.array(Times)
Accuracyfile = 'Accuracy'+'.npy'
Lossfile = 'Loss_value'+'.npy'
Timesfile = 'Times'+'.npy'
np.save(Timesfile,Times,allow_pickle=True)
np.save(Accuracyfile,Accuracy,allow_pickle=True)
np.save(Lossfile,Loss_value, allow_pickle=True)
fig, ax = plt.subplots()
plt.plot(Loss_value,'r',linewidth =2.0)
# ax.fill_between(Loss_value, Training_loss_mean - Training_loss_std, Training_loss_mean + Training_loss_std, alpha=0.9)
plt.title('Iteration vs Loss_Value')
plt.xlabel('Iteration')
plt.ylabel('Loss_Value')
plot_1= 'Loss_value_'+ '.png'
plt.savefig(plot_1, dpi=600,bbox_inches='tight')
plt.show()
plt.clf()
plt.figure(2)
plt.plot(Total_Epoch,Accuracy,'g',linewidth =2.0)
plt.title('Total_Epoch vs Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plot_2= 'Accuracy_'+'.png'
plt.savefig(plot_2, dpi=600,bbox_inches='tight')
plt.show()
plt.figure(3)
plt.plot(Total_Epoch,Learning_rate,'b',linewidth =2.0)
plt.title('Total_Epoch vs Learning_Rate')
plt.xlabel('Epochs')
plt.ylabel('Learning_Rate')
plot_3= 'Learning_rate_'+ '.png'
plt.savefig(plot_3, dpi=600,bbox_inches='tight')
plt.show()
graphname='Iteration'+'_weightage'+'.png'
fig, ax = plt.subplots(figsize=(7,5), dpi=100)
ax = sns.countplot(Times,palette=["#fbab17", "#0515bf", "#10a310", "#e9150d"])
ax.set_xticklabels(class_names);
ax.xaxis.label.set_size(10)
plt.savefig(graphname,bbox_inches='tight',pad_inches=0.1,dpi=800)
plt.show()
plt.clf()
#%%
def classweight(values,counts):
class_weight=[]
tot=sum(counts)
for group in counts:
value=1-(group/tot)
print(value)
class_weight.append(value)
class_weight = np.array(class_weight)
return class_weight
#%%
def windowresults(testset,model,device,window):
y_pred = []
y_true = []
model.eval()
# iterate over test data
for batches in testset:
data,output = batches
data,output =data.to(device,dtype=torch.float),output.to(device,dtype=torch.long)
output=output.squeeze()
# print("output",output)
prediction = model(data)
prediction = torch.argmax(prediction, dim=1)
# print("prediction",prediction)
prediction=prediction.data.cpu().numpy()
output=output.data.cpu().numpy()
y_true.extend(output) # Save Truth
y_pred.extend(prediction) # Save Prediction
classes = ('LoF pores', 'Conduction mode', 'Keyhole pores')
plotname= 'CNN_LSTM_Multivariate_'+str(window)+'_confusion_matrix'+'.png'
plt.figure()
plot_confusion_matrix(y_true, y_pred,classes,plotname)
#%%
def distribution_plot(data,window_length):
data=data.cpu().detach().numpy()
df = pd.DataFrame(data, columns=['Back reflection', 'Infra red','Visible', 'Acoustic signal'])
# df=df.div(df.sum(axis=1), axis=0)
sns.set(style="white")
fig=plt.subplots(figsize=(5,3), dpi=800)
# sns.displot(data, kind="kde", multiple="stack",alpha=.5,)
fig = sns.kdeplot(df['Back reflection'], shade=True,alpha=.5, color="red")
fig = sns.kdeplot(df['Infra red'], shade=True,alpha=.5, color="green")
fig = sns.kdeplot(df['Visible'], shade=True,alpha=.5, color="#0000FF")
fig = sns.kdeplot(df['Acoustic signal'], shade=True, alpha=.5,color="#FFD700")
plt.title("Saliency distribution across sensors \n for a window length of "+str(window_length)+" ms")
plt.legend(labels=["Back reflection","Infra-red","Visible","Acoustic signal"])
title=str(window_length)+'_'+'.png'
plt.xlim([0.0, 0.35])
plt.ylim([0.0, 100])
plt.xlabel('Derivative relative amplitude (r.u)')
plt.savefig(title, bbox_inches='tight')
plt.show()

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