CW_task4_kernel_SVM_with_Gridsearch

# Importing Libraries:
from sklearn import svm
import numpy as np
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import make_scorer, f1_score
#The script imports necessary libraries, including scikit-learn for the SVM model, NumPy for numerical operations, and matplotlib for plotting.
​
# Loading Training Data:
data = np.load('C:/Users/print15207/MATLAB Drive/Print HVDC/Smartgrid CW/train_dataset.npy',allow_pickle=True)
#The training dataset is loaded from the specified NumPy file.
​
# Extracting Features and Labels:
x = data.item()['feature']
y = data.item()['label']
#The features (x) and labels (y) are extracted from the loaded data.
​
# Splitting the Data into Training and Testing Sets:
x1=x[:4800] #Only classify between class 0 (normal measurement) and class 1 (FDI attack measurement)
y1=y[:4800]
x_train, x_test, y_train, y_test = train_test_split(x1, y1, test_size=0.319, random_state=42)
#The data is split into training and testing sets using train_test_split from scikit-learn.
print("Training set size: ",x_train.shape)
print("Testing set size: ",x_test.shape)
​
# Define the SVM model
svm_model = svm.SVC()
​
# Define the hyperparameter grid to search
param_grid = {
    'C': [0.1, 1, 10, 100],
    'gamma': [0.01, 0.1, 1, 10]
}
​
# Define F1 score as the evaluation metric for hyperparameter tuning
scorer = make_scorer(f1_score)
​
# Perform Grid Search with Cross Validation
grid_search = GridSearchCV(estimator=svm_model, param_grid=param_grid, scoring=scorer, cv=5)
grid_search.fit(x_train, y_train)
​
# Print the best hyperparameters
print("Best Hyperparameters:", grid_search.best_params_)
​
# Evaluate the model with the best hyperparameters on the test set
best_svm_model = grid_search.best_estimator_
test_predictions = best_svm_model.predict(x_test)
test_f1_score = f1_score(y_test, test_predictions)
​
print("F1 Score on Test Set with Best Hyperparameters:", test_f1_score)

#results
#Training set size:  (3268, 34)
#Testing set size:  (1532, 34)
#Best Hyperparameters: {'C': 10, 'gamma': 10}
#F1 Score on Test Set with Best Hyperparameters: 0.999330207635633