def Experiment_2(skfold_data):
print("Running experiment 2...")
Accuracy = []
m1 = list(np.arange(0, 1, 0.1))
m2 = list(np.arange(1, 11, 1))
m = m1 + m2
for sm in m:
sm = round(sm, 2)
check_acc = []
for i in range(len(skfold_data)):
t_train, t_test = train_test.kfold_train_test(skfold_data, i)
train_x, train_y, test_x, test_y = train_test.train_test_split(
t_train, t_test)
accu = NBC.predictMAP(train_x, train_y, test_x, test_y, sm)
check_acc.append(accu)
Accuracy.append(check_acc)
#calculating average accuracy
avgAccuracy2 = []
for i in range(len(Accuracy)):
x = np.average(Accuracy[i])
avgAccuracy2.append(x)
print("list of accuracies:")
print(Accuracy)
print("list of average accuracies")
print(avgAccuracy2)
#calculating standard deviation
std = []
for i in range(len(Accuracy)):
x = np.std(Accuracy[i])
std.append(x)
print("Standard Deviation: ")
print(std)
#plotting
plt.errorbar(m, avgAccuracy2, std)
plt.xlabel('smoothing factor')
plt.ylabel('Average Accuracies')
plt.show()
def Experiment_1(skfold_data):
print("Running experiment 1...")
accuracy_m0 = []
accuracy_m1 = []
#smoothing factor
m = [0, 1]
#for each smoothing factor
for sm in m:
for i in range(len(skfold_data)):
#this loop considers ith fold for test dataset
#get train(900) and test(100)
t_train, t_test = train_test.kfold_train_test(skfold_data, i)
#generate subsample factors
sampling_factor = np.arange(0.1, 1.1, 0.1)
check_acc = []
size_of_train = []
for n in sampling_factor:
#loop for subsamples
n = round(n, 2)
#n = 0.2
sample_size_for_train = int(len(t_train) * n)
size_of_train.append(sample_size_for_train)
#randomly select datapoints
#sample_train = random.sample(t_train,sample_size_for_train)
sample_train = t_train[0:sample_size_for_train]
train_x, train_y, test_x, test_y = train_test.train_test_split(
sample_train, t_test)
accu = NBC.predictMAP(train_x, train_y, test_x, test_y, sm)
#append all the accuracies of subsamples of kth fold
check_acc.append(accu)
if (sm == 0):
accuracy_m0.append(check_acc)
if (sm == 1):
accuracy_m1.append(check_acc)
avgAccu0, avgAccu1 = calAccuracy(accuracy_m0, accuracy_m1)
print("Average accuracies when m=0: ")
print(avgAccu0)
print("Average accuracies when m=1: ")
print(avgAccu1)
#calculate standard deviation
sd_0 = []
sd_1 = []
for i in range(len(accuracy_m0)):
x1 = np.std(accuracy_m0[i])
sd_0.append(x1)
x2 = np.std(accuracy_m1[i])
sd_1.append(x2)
print("standard deviation for m=0: ", sd_0)
print("standard deviation for m=1: ", sd_1)
"""Refered some online material to know about how to plot error bar graphs"""
"""https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.errorbar.html"""
"""https://pythonforundergradengineers.com/python-matplotlib-error-bars.html"""
plt.errorbar(size_of_train, avgAccu0, sd_0, label='m=0')
plt.errorbar(size_of_train, avgAccu1, sd_1, label='m=1')
plt.legend(loc='lower right')
plt.xlabel('train set size')
plt.ylabel('Average Accuracies')
plt.show()
