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hard_and_soft_parzen.py
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hard_and_soft_parzen.py
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# Coded by Philippe Schoeb
# October 12th 2023
import numpy as np
winequality = np.genfromtxt("winequality.txt")
def draw_rand_label(x, label_list):
seed = abs(np.sum(x))
while seed < 1:
seed = 10 * seed
seed = int(1000000 * seed)
np.random.seed(seed)
return np.random.choice(label_list)
class HardParzen:
def __init__(self, h):
self.h = h
def train(self, train_inputs, train_labels):
self.label_list = np.unique(train_labels)
self.labels = train_labels
self.inputs = train_inputs
def compute_predictions(self, test_data):
number_random = 0
labels_predicted = []
for i in range(len(test_data)):
candidate = [0] * len(self.label_list) # Where we keep our scores
for j in range(len(self.labels)):
dist = np.linalg.norm(test_data[i] - self.inputs[j])
if dist <= self.h:
index = -1 # We search for the good index
label = self.labels[j]
for k in range(len(self.label_list)):
if self.label_list[k] == label:
index = k
break
candidate[index] += 1 # Add to the scores
if np.sum(candidate) == 0: # Random label
labels_predicted.append(int(draw_rand_label(test_data[i], self.label_list)))
number_random += 1
else:
labels_predicted.append(int(self.label_list[np.argmax(candidate)]))
print("Number of randomized choices : " + str(number_random))
labels_predicted = np.array(labels_predicted)
return labels_predicted
class SoftRBFParzen:
def __init__(self, sigma):
self.sigma = sigma
def train(self, train_inputs, train_labels):
self.label_list = np.unique(train_labels)
self.inputs = train_inputs
self.labels = train_labels
def compute_predictions(self, test_data):
normalisation = float(1)/((2*np.pi)**(11/2)*self.sigma**11)
labels_predicted = []
for i in range(len(test_data)):
candidate = [0] * len(self.label_list)
for j in range(len(self.labels)):
dist = np.linalg.norm(test_data[i] - self.inputs[j])
kernel = normalisation * np.exp(-(dist**2)/(2*(self.sigma**2)))
if kernel < 0:
print("Kernel < 0, problem. Kernel = " + str(kernel))
index = -1
label = self.labels[j]
for k in range(len(self.label_list)):
if self.label_list[k] == label:
index = k
break
candidate[index] += kernel
labels_predicted.append(int(self.label_list[np.argmax(candidate)]))
labels_predicted = np.array(labels_predicted)
return labels_predicted
def split_dataset(wineQuality):
train = []
valid = []
test = []
for i in range(len(wineQuality)):
if i % 5 == 0 or i % 5 == 1 or i % 5 == 2:
train.append(wineQuality[i])
elif i % 5 == 3:
valid.append(wineQuality[i])
else:
test.append(wineQuality[i])
train = np.array(train)
valid = np.array(valid)
test = np.array(test)
return train, valid, test
class ErrorRate:
def __init__(self, x_train, y_train, x_val, y_val):
self.x_train = x_train
self.y_train = y_train
self.x_val = x_val
self.y_val = y_val
def hard_parzen(self, h):
HardParzen.__init__(HardParzen, h)
HardParzen.train(HardParzen, self.x_train, self.y_train)
predictions = HardParzen.compute_predictions(HardParzen, self.x_val)
number_errors = 0
for i in range(len(predictions)):
if int(predictions[i]) != int(self.y_val[i]):
number_errors += 1
error = float(number_errors)/len(predictions)
return error
def soft_parzen(self, sigma):
SoftRBFParzen.__init__(SoftRBFParzen, sigma)
SoftRBFParzen.train(SoftRBFParzen, self.x_train, self.y_train)
predictions = SoftRBFParzen.compute_predictions(SoftRBFParzen, self.x_val)
number_errors = 0
for i in range(len(predictions)):
if int(predictions[i]) != int(self.y_val[i]):
number_errors += 1
error = float(number_errors) / len(predictions)
return error
def get_test_errors(wineQuality):
train, valid, test = split_dataset(wineQuality)
ErrorRate.__init__(ErrorRate, train[:, :11], train[:, 11], valid[:, :11], valid[:, 11])
valeurs = [0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 1, 3, 10, 20]
error1 = [0] * len(valeurs)
error2 = [0] * len(valeurs)
for i in range(len(valeurs)):
h = valeurs[i]
sigma = valeurs[i]
error1[i] = ErrorRate.hard_parzen(ErrorRate, h)
error2[i] = ErrorRate.soft_parzen(ErrorRate, sigma)
print("Error rate with h = " + str(h) + ", Hard : " + str(error1[i]))
print("Error rate with sigma = " + str(sigma) + ", Soft : " + str(error2[i]))
index1 = np.argmin(error1)
index2 = np.argmin(error2)
best_h = valeurs[index1]
best_sigma = valeurs[index2]
ErrorRate.__init__(ErrorRate, train[:, :11], train[:, 11], test[:, :11], test[:, 11])
error_hard = ErrorRate.hard_parzen(ErrorRate, best_h)
error_soft = ErrorRate.soft_parzen(ErrorRate, best_sigma)
print("For hard with test set, h = " + str(best_h) + ", error_rate = " + str(error_hard))
print("For soft with test set, sigma = " + str(best_sigma) + ", error_rate = " + str(error_soft))
errors = np.array([error_hard, error_soft])
return errors
get_test_errors(winequality)