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Plots.py
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Wed, Jan 15, 16:10

Plots.py
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import pandas as pd
import numpy as np
import torch
from torch.nn import functional as F
from torch import nn
import matplotlib.pyplot as plt
import seaborn as sns
plt.rc("font", size=15)
def Three_embeddings(embeddings, targets,graph_name,ang, xlim=None, ylim=None):
group=targets
df2 = pd.DataFrame(group)
df2.columns = ['Categorical']
df2=df2['Categorical'].replace(0,'P1')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(1,'P2')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(2,'P3')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(3,'P4')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(4,'P5')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(5,'P6')
group = pd.DataFrame(df2)
group=group.to_numpy()
group = np.ravel(group)
x1=embeddings[:, 0]
x2=embeddings[:, 1]
x3=embeddings[:, 2]
df = pd.DataFrame(dict(x=x1, y=x2,z=x3, label=group))
groups = df.groupby('label')
uniq = list(set(df['label']))
uniq=np.sort(uniq)
#uniq=["0","1","2","3"]
fig = plt.figure(figsize=(12,6), dpi=100)
fig.set_facecolor('white')
plt.rcParams["legend.markerscale"] = 2
ax = plt.axes(projection='3d')
ax.grid(False)
ax.view_init(azim=ang)#115
marker= ["*",">","X","o","s","d"]
color = [ 'g', 'r', 'blue', 'cyan','orange','purple']
ax.set_facecolor('white')
ax.w_xaxis.pane.fill = False
ax.w_yaxis.pane.fill = False
ax.w_zaxis.pane.fill = False
ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
# make the grid lines transparent
ax.xaxis._axinfo["grid"]['color'] = (1,1,1,0)
ax.yaxis._axinfo["grid"]['color'] = (1,1,1,0)
ax.zaxis._axinfo["grid"]['color'] = (1,1,1,0)
graph_title = "Feature space distribution"
j=0
for i in uniq:
print(i)
indx = group == i
a=x1[indx]
b=x2[indx]
c=x3[indx]
ax.plot(a, b, c ,color=color[j],label=uniq[j],marker=marker[j],linestyle='',ms=7)
j=j+1
plt.xlabel ('Weights_1', labelpad=10)
plt.ylabel ('Weights_2', labelpad=10)
ax.set_zlabel('Weights_3',labelpad=10)
plt.title(str(graph_title),fontsize = 15)
plt.legend(markerscale=20)
plt.locator_params(nbins=6)
plt.xticks(fontsize = 14)
plt.yticks(fontsize = 14)
#plt.zticks(fontsize = 25)
plt.legend(loc='upper left',frameon=False)
plt.savefig(graph_name, bbox_inches='tight',dpi=400)
plt.show()
return ax,fig
def Dataframe_Manipulation(Distance,target):
df1 = pd.DataFrame(Distance)
df1.columns = ['Distance']
df2 = pd.DataFrame(target)
df2.columns = ['Categorical']
df2=df2['Categorical'].replace(0,'P1')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(1,'P2')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(2,'P3')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(3,'P4')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(4,'P5')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(5,'P6')
df2 = pd.DataFrame(df2)
df=pd.concat([df1,df2], axis=1)
new_columns = list(df.columns)
new_columns[-1] = 'Target'
df.columns = new_columns
df.Target.value_counts()
df = df.sample(frac=1.0)
print(df.shape)
return df
def Dataframe_Manipulation_Classifier(target):
df2 = pd.DataFrame(target)
df2.columns = ['Categorical']
df2=df2['Categorical'].replace(0,'P1')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(1,'P2')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(2,'P3')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(3,'P4')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(4,'P5')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(5,'P6')
df2 = pd.DataFrame(df2)
return df2
def Semisupervised_prediction(df,class_name,Threshold,color):
losses = df[df.Target == str(class_name)].drop(labels='Target', axis=1)
losses = np.asarray(losses)
correct = sum(l > Threshold for l in losses)
print(f'Correct {str(class_name)} predictions: {correct}/{len(losses)}')
plt.figure()
sns.distplot(losses, bins=50,rug_kws={"color": "w"}, kde=True,color=color);
plt.axvline(x=Threshold, c='r', linestyle='--',linewidth=4)
graphname=str(class_name)+'_distribution'+'.png'
plt.title(f'{correct}/{len(losses)} ' + 'Reconstruction_'+str(class_name))
# plt.title('Reconstruction loss_'+str(class_name))
plt.savefig(graphname,dpi=800)
plt.show()
plt.clf()
return losses
def Threshold_calculation(df,class_name):
losses = df[df.Target == str(class_name)].drop(labels='Target', axis=1)
scores_normal = np.asarray(losses)
normal_avg, normal_std = np.average(scores_normal), np.std(scores_normal)
Threshold = normal_avg + (normal_std * 3)
print('Threshold:',Threshold)
return Threshold
from prettytable import PrettyTable
def count_parameters(model):
table = PrettyTable(["Modules", "Parameters"])
total_params = 0
for name, parameter in model.named_parameters():
if not parameter.requires_grad: continue
param = parameter.numel()
table.add_row([name, param])
total_params+=param
print(table)
print(f"Total Trainable Params: {total_params}")
return total_params

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