y_test = test_data["ytest"].flatten() yy_test = np.ones(y_test.shape) yy_test[y_test == 0] = -1 print "Done!" sys.stdout.flush() ############################################################################# # your code for setting up the best SVM classifier for this dataset # # Design the training parameters for the SVM. # # What should the learning_rate be? What should C be? # # What should num_iters be? Should X be scaled? Should X be kernelized? # ############################################################################# # your experiments below svm = LinearSVM_twoclass() svm.theta = np.zeros((X.shape[1],)) it = 2000 lr = 1e-4 print it, " iters" print "learning rate ", lr Cvals = [0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100] # [0.01] #[0.01,0.03,0.1,0.3,1,3,10,30] sigma_vals = [0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100] # [0.01] #[0.01,0.03,0.1,0.3,1,3,10,30] # best_C = 0.05 best_C = 0.05 # best_sigma = 1 best_sigma = 1 best_acc = 0
# Part 2: Training linear SVM #
# We train a linear SVM on the data set and the plot the learned #
# decision boundary #
############################################################################
############################################################################
# TODO #
# You will change this line to vary C. #
############################################################################
C = 1
############################################################################
svm = LinearSVM_twoclass()
svm.theta = np.zeros((XX.shape[1], ))
svm.train(XX, yy, learning_rate=1e-4, C=C, num_iters=50000, verbose=True)
# classify the training data
y_pred = svm.predict(XX)
print "Accuracy on training data = ", metrics.accuracy_score(yy, y_pred)
# visualize the decision boundary
utils.plot_decision_boundary(scaleX, y, svm, 'x1', 'x2', ['neg', 'pos'])
plt.savefig('fig2.pdf')
############################################################################
# Part 3: Training SVM with a kernel #
# Design the training parameters for the SVM. #
# What should the learning_rate be? What should C be? #
# What should num_iters be? Should X be scaled? Should X be kernelized? #
#############################################################################
# your experiments below
X_train, X_val, y_train, y_val = train_test_split(X,
y,
test_size=0.8,
random_state=42)
print X_train.shape
print X_val.shape
svm = LinearSVM_twoclass()
svm.theta = np.zeros((X.shape[1], ))
'''
first select the best gaussian kernelized svm
'''
Cvals = [0.01, 0.03, 0.1, 0.3, 10, 30]
sigma_vals = [0.01, 0.03, 0.1, 0.3, 10, 30]
learning_rates = [1e-4, 1e-3, 1e-2, 1e-1, 1]
iterations = [100, 1000, 10000]
best_acc = 0
for sigma_val in sigma_vals:
K = np.array([
utils.gaussian_kernel(x1, x2, sigma_val) for x1 in X_train
for x2 in X_train
]).reshape(X_train.shape[0], X_train.shape[0])