CW_task4_Random Forest

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.metrics import make_scorer, f1_score
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# Load the dataset
data = np.load('C:/Users/print15207/MATLAB Drive/Print HVDC/Smartgrid CW/train_dataset.npy', allow_pickle=True)
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# Extract features and labels
x = data.item()['feature']
y = data.item()['label']
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x1=x[:4800] #Only classify between class 0 (normal measurement) and class 1 (FDI attack measurement)
y1=y[:4800]
# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(x1, y1, test_size=0.315, random_state=42)
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# Print the shape of training and testing sets
print("Training set sizee:", x_train.shape)
print("Testing set size:", x_test.shape)
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from sklearn.ensemble import RandomForestClassifier
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# Initialize a Random Forest model
rf_model = RandomForestClassifier(n_estimators=77, random_state=42)
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# Train the Random Forest model
rf_model.fit(x_train, y_train)
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# Make predictions
rf_test_predict = rf_model.predict(x_test)
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# Make predictions on the test set
test_predict = rf_model.predict(x_test)
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# Evaluate the model
accuracy = accuracy_score(y_test, test_predict)
# y_test set is the true value and test_predict set is the predicted value
print("accuracy on test set: ", accuracy)
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# Evaluate the model using TPR and FPR
conf_matrix = confusion_matrix(y_test, test_predict)
print("Confusion Matrix:")
print(conf_matrix)
TN, FP, FN, TP = conf_matrix.ravel()
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# Calculate TPR and FPR
TPR = TP / (TP + FN)
FPR = FP / (FP + TN)
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# Print or use the metrics
print("True Positive Rate (TPR):", TPR)
print("False Positive Rate (FPR):", FPR)
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# Define F1 score as the evaluation metric for hyperparameter tuning
scorer = make_scorer(f1_score)
test_f1_score = f1_score(y_test, test_predict)
print("F1 score on test set: ",test_f1_score)
​
#Result with elapsed time: 5 seconds:
#Training set sizee: (3288, 34)
#Testing set size: (1512, 34)
#accuracy on test set:  0.998015873015873
#Confusion Matrix:
[[772   3]
 [  0 737]]
#True Positive Rate (TPR): 1.0
#False Positive Rate (FPR): 0.003870967741935484
#F1 score on test set:  0.997968855788761