Page MenuHomec4science
Files F73600353

RF.py
No OneTemporary
Actions

File Metadata

Created
Tue, Jul 23, 01:22

RF.py
View Options

"""
@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 required:
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.model_selection import RepeatedStratifiedKFold
import joblib
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import numpy as np
from sklearn import metrics
import pandas as pd
import os
import numpy
#%%
def RF(X_train, X_test, y_train, y_test, n_estimators, classes, total_path):
"""
Trains a Random Forest classifier on the given training data and evaluates its performance on the test data.
Parameters:
X_train (array-like): The training data features.
X_test (array-like): The test data features.
y_train (array-like): The training data labels.
y_test (array-like): The test data labels.
n_estimators (int): The number of trees in the random forest.
classes (array-like): The class labels.
total_path (str): The path to save the generated graphs and model.
Returns:
None
"""
print('Model to be trained is RF')
model = RandomForestClassifier(n_estimators=n_estimators, oob_score=True)
model.fit(X_train, y_train)
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
scores = cross_val_score(model, X_test, y_test, scoring='accuracy', cv=cv, n_jobs=-1)
print('Accuracy: %.3f (%.3f)' % (np.mean(scores), np.std(scores)))
# Accuracy of the model
predictions = model.predict(X_test)
print("RF Accuracy:", metrics.accuracy_score(y_test, predictions))
print(classification_report(y_test, predictions))
print(confusion_matrix(y_test, predictions))
# Plotting of the model
graph_name1 = 'Random Forest'+'_without normalization w/o Opt'
graph_name2 = 'Random Forest'
graph_1 = 'Random Forest'+'_Confusion_Matrix'+'_'+'No_Opt'+'.png'
graph_2 = 'Random Forest'+'_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=200)
disp = ConfusionMatrixDisplay.from_estimator(model, X_test, y_test,
display_labels=classes,
cmap=plt.cm.Blues, xticks_rotation='vertical',
normalize=normalize, values_format='0.2f')
# disp.ax_.set_title(title)
plt.title(title, size=12)
plt.savefig(os.path.join(total_path, graphname), bbox_inches='tight', dpi=400)
plt.show()
savemodel = 'Random Forest'+'_model'+'.sav'
joblib.dump(model, savemodel)

Event Timeline

c4science · Help