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Classifier.py
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# -*- 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
"Acoustic emission signature of martensitic transformation in Laser Powder Bed Fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction"
@any reuse of this code should be authorized by the first owner, code author
"""
# libraries to import
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report, confusion_matrix
import itertools
import os
from sklearn import metrics
# import pydot
import collections
# import pydotplus
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import RandomizedSearchCV
from sklearn.feature_selection import SelectFromModel
import joblib
from sklearn.model_selection import cross_val_score
from IPython.display import Image
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split # implementing train-test-split
from sklearn.neural_network import MLPClassifier
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import os
# from Plots import *
# %%
def classifier_linear(Exptype, folder_created):
"""
This function performs linear classification using logistic regression.
Parameters:
Exptype (str): The type of experiment.
folder_created (str): The path to the folder where the embeddings and labels are stored.
Returns:
None
"""
train_embeddings = Exptype+'_train_embeddings'+'_' + '.npy'
train_embeddings = os.path.join(folder_created, train_embeddings)
train_labelsname = Exptype+'_train_labels'+'_'+'.npy'
train_labelsname = os.path.join(folder_created, train_labelsname)
test_embeddings = Exptype+'_test_embeddings'+'_' + '.npy'
test_embeddings = os.path.join(folder_created, test_embeddings)
test_labelsname = Exptype+'_test_labels'+'_'+'.npy'
test_labelsname = os.path.join(folder_created, test_labelsname)
X_train = np.load(train_embeddings).astype(np.float64)
y_train = np.load(train_labelsname).astype(np.float64)
X_test = np.load(test_embeddings).astype(np.float64)
y_test = np.load(test_labelsname).astype(np.float64)
y_pred_prob, pred_prob = LR(X_train, X_test, y_train, y_test, folder_created)
def LR(X_train, X_test, y_train, y_test, folder_created):
"""
Logistic Regression classifier.
Args:
X_train (array-like): Training data features.
X_test (array-like): Test data features.
y_train (array-like): Training data labels.
y_test (array-like): Test data labels.
folder_created (str): Path to the folder where the output files will be saved.
Returns:
tuple: A tuple containing the predicted probabilities and predicted labels.
"""
model = LogisticRegression(max_iter=1000, random_state=123)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
pred_prob = model.predict_proba(X_test)
y_pred_prob = np.vstack((y_test, predictions)).transpose()
y_pred_prob = np.hstack((y_pred_prob, pred_prob))
print("LogisticRegression Accuracy:", metrics.accuracy_score(y_test, predictions))
print(classification_report(y_test, predictions))
print(confusion_matrix(y_test, predictions))
graph_name1 = 'LR'+'_without normalization w/o Opt'
graph_name2 = 'Logistic Regression'
graph_1 = 'LR_Confusion_Matrix_No_Opt.png'
graph_2 = 'LR_Confusion_Matrix_Opt.png'
titles_options = [(graph_name1, None, graph_1),
(graph_name2, 'true', graph_2)]
for title, normalize, graphname in titles_options:
plt.figure(figsize=(20, 10), dpi=400)
disp = ConfusionMatrixDisplay.from_estimator(model, X_test, y_test,
display_labels=[
'Ti64', 'Ti64_3Fe', 'Ti64_6Fe'],
cmap=plt.cm.Reds, xticks_rotation='vertical',
normalize=normalize)
plt.title(title, size=12)
plt.savefig(os.path.join(folder_created, graphname), bbox_inches='tight', dpi=400)
savemodel = os.path.join(folder_created, 'LR_model.sav')
joblib.dump(model, savemodel)
return y_pred_prob, pred_prob

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