Page MenuHomec4science
Files F90716979

Generalization.py
No OneTemporary
Actions

File Metadata

Created
Mon, Nov 4, 03:22

Generalization.py
View Options

# -*- coding: utf-8 -*-
"""
@author: srpv
contact: vigneashwara.solairajapandiyan@empa.ch, vigneashpandiyan@gmail.com
The codes in this following script will be used for the publication of the following work
"Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process"
@any reuse of this code should be authorized by the first owner, code author
"""
# libraries to import
from Utils import *
from Network import *
from Dataloader import *
from Loss import *
from Visualization import *
from matplotlib import animation
import os
def generalization(Material_1, Material_2, windowsize, total_path, model, device):
"""
Perform generalization analysis on two materials.
Args:
Material_1 (str): Name of the first material.
Material_2 (str): Name of the second material.
total_path (str): Path to the data.
model: The trained model.
device: The device to run the model on.
Returns:
tuple: A tuple containing the training results, test results, training labels, and test labels.
"""
folder_created = os.path.join('Figures/', (str(Material_1) + str(Material_2)))
print(folder_created)
try:
os.makedirs(folder_created, exist_ok=True)
print("Directory created....")
except OSError as error:
print("Directory already exists....")
rawspace_1, classspace = data_pipeline(Material_1, total_path, windowsize)
rawspace_2, classspace = data_pipeline(Material_2, total_path, windowsize)
rawspace = np.stack((rawspace_1, rawspace_2), axis=2)
X_train, X_test, y_train, y_test = train_test_split(
rawspace, classspace, test_size=0.30, random_state=123)
index_train = pd.DataFrame(y_train)
index_test = pd.DataFrame(y_test)
train_results, train_labels = compute_embeddings(model, X_train, y_train, index_train, device,
(str(Material_1) + str(Material_2)) + '_train', folder_created)
test_results, test_labels = compute_embeddings(model, X_test, y_test, index_test, device,
(str(Material_1) + str(Material_2)) + '_test', folder_created)
graph_name_2D = folder_created + '/' + \
(str(Material_1) + str(Material_2)) + '_Feature_2D' + '.png'
plot_embeddings(test_results, test_labels, graph_name_2D)
graph_name_2D = folder_created + '/' + \
(str(Material_1) + str(Material_2)) + '_Feature_2D_reduced' + '.png'
plot_embeddings_reduced(test_results, test_labels, graph_name_2D, test_size=0.60)
graph_name_3D = folder_created + '/' + \
(str(Material_1) + str(Material_2)) + '_Feature_3D' + '.png'
ax, fig = TSNEplot(test_results, test_labels, graph_name_3D,
test_size=0.60, ang=115, perplexity=10)
graph_name = folder_created + '/' + (str(Material_1) + str(Material_2)) + '_Tsne_3D' + '.gif'
def rotate(angle):
ax.view_init(azim=angle)
angle = 3
ani = animation.FuncAnimation(fig, rotate, frames=np.arange(0, 360, angle), interval=50)
ani.save(graph_name, writer=animation.PillowWriter(fps=20))
graph_name_3D = folder_created + '/' + \
(str(Material_1) + str(Material_2)) + '_Feature_3D_reduced' + '.png'
ax, fig = TSNEplot(test_results, test_labels, graph_name_3D,
test_size=0.60, ang=115, perplexity=10)
graph_name = folder_created + '/' + \
(str(Material_1) + str(Material_2)) + '_Tsne_3D_reduced' + '.gif'
def rotate(angle):
ax.view_init(azim=angle)
angle = 3
ani = animation.FuncAnimation(fig, rotate, frames=np.arange(0, 360, angle), interval=50)
ani.save(graph_name, writer=animation.PillowWriter(fps=20))
return train_results, test_results, train_labels, test_labels

Event Timeline

c4science · Help