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tSNE.py
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Fri, Oct 18, 09:28

tSNE.py
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"""
Created on Fri Jan 5 10:50:03 2024
@author: srpv
contact: vigneashwara.solairajapandiyan@empa.ch
The codes in this following script will be used for the topics on domain adaptation
--> Monitoring Of Laser Powder Bed FusionProcess By Bridging Dissimilar Process MapsUsingDeep Learning-based Domain Adaptation onAcoustic Emissions
@any reuse of this code should be authorized by the first owner, code author
"""
# %% Libraries to import
from Visualization_Utils import *
from matplotlib.pyplot import specgram
from matplotlib import animation
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
np.random.seed(1974)
# %%
def plot_embeddings(folder, tsne_fit, group, graph_name1, graph_title, limits, xlim=None, ylim=None):
x1 = tsne_fit[:, 0]
x2 = tsne_fit[:, 1]
group = np.ravel(group)
df = pd.DataFrame(dict(x=x1, y=x2, label=group))
groups = df.groupby('label')
uniq = list(set(df['label']))
uniq = np.sort(uniq)
print(uniq)
marker = ["o", "*", ">", "o", "*", ">"]
color = ['#dd221c', '#16e30b', 'blue', '#fab6b6', '#a6ffaa', '#b6befa']
plt.rcParams.update(plt.rcParamsDefault)
fig = plt.figure(figsize=(12, 9), dpi=100)
fig.set_facecolor('white')
plt.rcParams["legend.markerscale"] = 3
plt.rc("font", size=23)
a1, a2 = detect_limits(x1, limits)
b1, b2 = detect_limits(x2, limits)
plt.ylim(b1, b2)
plt.xlim(a1, a2)
# for i in uniq:
for i in range(len(uniq)):
indx = (df['label']) == uniq[i]
a = x1[indx]
b = x2[indx]
plt.plot(a, b, color=color[i], label=uniq[i], marker=marker[i], linestyle='', ms=10)
plt.xlabel('Dimension 1', labelpad=20, fontsize=25)
plt.ylabel('Dimension 2', labelpad=20, fontsize=25)
plt.title(graph_title, fontsize=30)
plt.legend()
plt.locator_params(nbins=6)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.legend(bbox_to_anchor=(1, 1), frameon=False)
plt.savefig(os.path.join(folder, graph_name1), bbox_inches='tight', dpi=100)
plt.show()
def TSNEplot(folder, output, target, graph_name1, graph_name2, graph_title, limits, perplexity):
# array of latent space, features fed rowise
output = np.array(output)
target = np.array(target)
print('target shape: ', target.shape)
print('output shape: ', output.shape)
print('perplexity: ', perplexity)
group = target
group = np.ravel(group)
RS = np.random.seed(1974)
tsne = TSNE(n_components=3, random_state=RS, perplexity=perplexity)
tsne_fit = tsne.fit_transform(output)
x1 = tsne_fit[:, 0]
x2 = tsne_fit[:, 1]
x3 = tsne_fit[:, 2]
df = pd.DataFrame(dict(x=x1, y=x2, z=x3, label=group))
groups = df.groupby('label')
uniq = list(set(df['label']))
print(uniq)
uniq = np.sort(uniq)
# uniq=["0","1","2","3"]
plot_embeddings(folder, tsne_fit, target, graph_name1,
graph_title, limits, xlim=None, ylim=None)
marker = ["o", "*", ">", "o", "*", ">"]
color = ['#dd221c', '#16e30b', 'blue', '#fab6b6', '#a6ffaa', '#b6befa']
fig = plt.figure(figsize=(20, 8), dpi=100)
fig.set_facecolor('white')
plt.rcParams["legend.markerscale"] = 2
plt.rc("font", size=20)
ax = plt.axes(projection='3d')
ax.grid(False)
ax.view_init(elev=15, azim=110) # 115
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))
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)
a1, a2 = detect_limits(x1, limits)
b1, b2 = detect_limits(x2, limits)
c1, c2 = detect_limits(x3, limits)
ax.set_ylim(b1, b2)
ax.set_zlim(c1, c2)
ax.set_xlim(a1, a2)
for i in range(len(uniq)):
print(i)
indx = (df['label']) == uniq[i]
a = x1[indx]
b = x2[indx]
c = x3[indx]
ax.plot(a, b, c, color=color[i], label=uniq[i], marker=marker[i], linestyle='', ms=10)
plt.xlabel('Dimension 1', labelpad=20, fontsize=15)
plt.ylabel('Dimension 2', labelpad=20, fontsize=15)
ax.set_zlabel('Dimension 3', labelpad=20, fontsize=15)
plt.title(graph_title, fontsize=20)
plt.locator_params(nbins=6)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(loc='upper left', frameon=False)
plt.savefig(os.path.join(folder, graph_name2), bbox_inches='tight', dpi=200)
plt.show()
return ax, fig
def plot_embeddings_1(folder, tsne_fit, group, graph_name1, graph_title, limits, xlim=None, ylim=None):
x1 = tsne_fit[:, 0]
x2 = tsne_fit[:, 1]
group = np.ravel(group)
df = pd.DataFrame(dict(x=x1, y=x2, label=group))
groups = df.groupby('label')
uniq = list(set(df['label']))
uniq = np.sort(uniq)
print(uniq)
marker = ["o", "*", ">", "o", "*", ">"]
color = ['#dd221c', '#16e30b', 'blue', '#fab6b6', '#a6ffaa', '#b6befa']
plt.rcParams.update(plt.rcParamsDefault)
fig = plt.figure(figsize=(12, 9), dpi=100)
fig.set_facecolor('white')
plt.rcParams["legend.markerscale"] = 3
plt.rc("font", size=23)
a1, a2 = detect_limits(x1, limits)
b1, b2 = detect_limits(x2, limits)
plt.ylim(b1, b2)
plt.xlim(a1, a2)
# for i in uniq:
for i in range(len(uniq)):
indx = (df['label']) == uniq[i]
a = x1[indx]
b = x2[indx]
plt.plot(a, b, color=color[i], label=uniq[i], marker=marker[i], linestyle='', ms=10)
plt.xlabel('Dimension 1', labelpad=20, fontsize=25)
plt.ylabel('Dimension 2', labelpad=20, fontsize=25)
plt.title(graph_title, fontsize=30)
plt.legend()
plt.locator_params(nbins=6)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.legend(bbox_to_anchor=(1, 1), frameon=False)
plt.savefig(os.path.join(folder, graph_name1), bbox_inches='tight', dpi=100)
plt.show()
def TSNEplot_1(folder, output, target, graph_name1, graph_name2, graph_title, limits, perplexity):
# array of latent space, features fed rowise
output = np.array(output)
target = np.array(target)
print('output shape: ', output.shape)
print('target shape: ', target.shape)
print('perplexity: ', perplexity)
group = target
group = np.ravel(group)
RS = np.random.seed(123)
tsne = TSNE(n_components=3, random_state=RS, perplexity=perplexity)
tsne_fit = tsne.fit_transform(output)
x1 = tsne_fit[:, 0]
x2 = tsne_fit[:, 1]
x3 = tsne_fit[:, 2]
marker = ["o", "*", ">", "o", "*", ">"]
color = ['#dd221c', '#16e30b', 'blue', '#fab6b6', '#a6ffaa', '#b6befa']
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)
plot_embeddings_1(folder, tsne_fit, target, graph_name1,
graph_title, limits, xlim=None, ylim=None)
plt.rcParams.update(plt.rcParamsDefault)
fig = plt.figure(figsize=(20, 8), dpi=100)
fig.set_facecolor('white')
plt.rcParams["legend.markerscale"] = 2
plt.rc("font", size=20)
ax = plt.axes(projection='3d')
ax.grid(False)
ax.view_init(elev=15, azim=110) # 115
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)
a1, a2 = detect_limits(x1, limits)
b1, b2 = detect_limits(x2, limits)
c1, c2 = detect_limits(x3, limits)
ax.set_ylim(b1, b2)
ax.set_zlim(c1, c2)
ax.set_xlim(a1, a2)
for i in range(len(uniq)):
print(i)
indx = (df['label']) == uniq[i]
a = x1[indx]
b = x2[indx]
c = x3[indx]
ax.plot(a, b, c, color=color[i], label=uniq[i], marker=marker[i], linestyle='', ms=10)
plt.xlabel('Dimension 1', labelpad=20, fontsize=15)
plt.ylabel('Dimension 2', labelpad=20, fontsize=15)
ax.set_zlabel('Dimension 3', labelpad=20, fontsize=15)
plt.title(graph_title, fontsize=20)
plt.locator_params(nbins=6)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(loc='upper left', frameon=False)
plt.savefig(os.path.join(folder, graph_name2), bbox_inches='tight', dpi=200)
plt.show()
return ax, fig
# %%
def detect_limits(scores_normal, limits):
# find q1 and q3 values
normal_avg, normal_std = np.average(scores_normal), np.std(scores_normal)
Threshold0 = normal_avg - (normal_std * limits)
Threshold1 = normal_avg + (normal_std * limits)
return Threshold0, Threshold1

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