feat: complete problem 10~12
This commit is contained in:
parent
e6219a0c81
commit
a73d25b509
GPG Key ID:
48D331A3D6160354
174
hw6/p10.py
Normal file
174
hw6/p10.py
Normal file
@ -0,0 +1,174 @@
|
||||
import random
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
def load_data(path):
|
||||
datas = []
|
||||
with open(path) as fp:
|
||||
lines = fp.readlines()
|
||||
|
||||
for line in lines:
|
||||
tmp_data = [ 0 for _ in range(8) ]
|
||||
numbers = line.split()
|
||||
y = int(numbers[0])
|
||||
for i in numbers[1:]:
|
||||
index, value = i.split(':')
|
||||
index = int(index)
|
||||
value = float(value)
|
||||
tmp_data[index-1] = value
|
||||
x = tmp_data
|
||||
datas.append({
|
||||
'x': x,
|
||||
'y': y,
|
||||
})
|
||||
return datas
|
||||
|
||||
class Node():
|
||||
def __init__(self, datas):
|
||||
self.datas = datas
|
||||
self.theta = None
|
||||
self.feature_i = None
|
||||
|
||||
self.value = None
|
||||
|
||||
self.right = None
|
||||
self.left = None
|
||||
|
||||
def predict(self, data):
|
||||
if self.value != None:
|
||||
return self.value
|
||||
else:
|
||||
if data['x'][self.feature_i] <= self.theta:
|
||||
return self.left.predict(data)
|
||||
else:
|
||||
return self.right.predict(data)
|
||||
|
||||
def decision_stump(self):
|
||||
def get_impurity(features_i_y_pair):
|
||||
if len(features_i_y_pair) == 0:
|
||||
return 0
|
||||
y_bar = sum([ data['y'] for data in features_i_y_pair ]) / len(features_i_y_pair)
|
||||
|
||||
impurity = 0
|
||||
for data in features_i_y_pair:
|
||||
impurity += (data['y']-y_bar) ** 2
|
||||
impurity = impurity / len(features_i_y_pair)
|
||||
return impurity
|
||||
|
||||
different_y = set()
|
||||
for data in self.datas:
|
||||
different_y.add(data['y'])
|
||||
if len(different_y) == 1:
|
||||
self.value = list(different_y)[0]
|
||||
return
|
||||
|
||||
min_impurity = 1e9
|
||||
best_feature_i, best_theta = -100, -100
|
||||
|
||||
# find the best feature_i
|
||||
for feature_i in range(8):
|
||||
# only get feature_i
|
||||
# and sorted by feature_i, we want to get the best theta
|
||||
features_i_y_pair = [ {'feature_i': data['x'][feature_i], 'y': data['y'] } for data in self.datas ]
|
||||
features_i_y_pair = sorted(features_i_y_pair, key=lambda x: x['feature_i'])
|
||||
|
||||
# if all x are same
|
||||
if features_i_y_pair[0] == features_i_y_pair[-1]:
|
||||
continue
|
||||
thetas = [ (features_i_y_pair[index]['feature_i']+features_i_y_pair[index-1]['feature_i'])/2 for index in range(1, len(features_i_y_pair)) ]
|
||||
|
||||
# find the best theta
|
||||
for index, theta in enumerate(thetas):
|
||||
front_data = []
|
||||
back_data = []
|
||||
|
||||
for data in features_i_y_pair:
|
||||
if data['feature_i'] <= theta:
|
||||
front_data.append(data)
|
||||
else:
|
||||
back_data.append(data)
|
||||
|
||||
# get the impurity when (feature_i, theta)
|
||||
impurity = len(front_data) * get_impurity(front_data) + len(back_data) * get_impurity(back_data)
|
||||
if impurity < min_impurity:
|
||||
min_impurity = impurity
|
||||
best_feature_i = feature_i
|
||||
best_theta = theta
|
||||
|
||||
|
||||
if best_feature_i != -100:
|
||||
self.theta = best_theta
|
||||
self.feature_i = best_feature_i
|
||||
else:
|
||||
# print("NO DECISION")
|
||||
self.value = sum([data['y'] for data in self.datas]) / len(self.datas)
|
||||
|
||||
'''
|
||||
print("feature_i: ", self.feature_i)
|
||||
print("Theta: ", self.theta)
|
||||
print("Value: ", self.value)
|
||||
for i in self.datas:
|
||||
print(" ", i)
|
||||
'''
|
||||
|
||||
|
||||
def expand(self):
|
||||
left_data = []
|
||||
for data in self.datas:
|
||||
if data['x'][self.feature_i] <= self.theta:
|
||||
left_data.append(data)
|
||||
|
||||
right_data = []
|
||||
for data in self.datas:
|
||||
if data['x'][self.feature_i] > self.theta:
|
||||
right_data.append(data)
|
||||
|
||||
|
||||
if len(right_data) == 0 or len(left_data) == 0:
|
||||
self.value = sum([data['y'] for data in self.datas]) / len(self.datas)
|
||||
else:
|
||||
self.left = Node(left_data)
|
||||
self.right = Node(right_data)
|
||||
|
||||
self.left.decision_stump()
|
||||
self.right.decision_stump()
|
||||
|
||||
if self.left.theta != None:
|
||||
self.left.expand()
|
||||
|
||||
if self.right.theta != None:
|
||||
self.right.expand()
|
||||
|
||||
# print("theta: {}, feature_i: {}, value:{}".format(self.theta, self.feature_i, self.value))
|
||||
|
||||
|
||||
def square_error(target, predict):
|
||||
return (predict-target) ** 2
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
train = load_data('hw6_train.dat')
|
||||
test = load_data('hw6_test.dat')
|
||||
|
||||
error_logs = []
|
||||
for i in range(2000):
|
||||
sample_size = int(len(train) / 2)
|
||||
bagging_train = random.sample(train, k=sample_size)
|
||||
|
||||
root = Node(bagging_train)
|
||||
root.decision_stump()
|
||||
root.expand()
|
||||
|
||||
errors = 0
|
||||
for data in test:
|
||||
predict_y = root.predict(data)
|
||||
error = square_error(data['y'], predict_y)
|
||||
errors += error
|
||||
print(i)
|
||||
print(" ANS:", errors/len(test))
|
||||
error_logs.append(errors/len(test))
|
||||
|
||||
plt.hist(error_logs)
|
||||
plt.savefig('p10.png')
|
||||
|
||||
|
||||
|
||||
219
hw6/p11.py
Normal file
219
hw6/p11.py
Normal file
@ -0,0 +1,219 @@
|
||||
import random
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
def load_data(path):
|
||||
datas = []
|
||||
with open(path) as fp:
|
||||
lines = fp.readlines()
|
||||
|
||||
for line in lines:
|
||||
tmp_data = [ 0 for _ in range(8) ]
|
||||
numbers = line.split()
|
||||
y = int(numbers[0])
|
||||
for i in numbers[1:]:
|
||||
index, value = i.split(':')
|
||||
index = int(index)
|
||||
value = float(value)
|
||||
tmp_data[index-1] = value
|
||||
x = tmp_data
|
||||
datas.append({
|
||||
'x': x,
|
||||
'y': y,
|
||||
})
|
||||
return datas
|
||||
|
||||
class Node():
|
||||
def __init__(self, datas):
|
||||
self.datas = datas
|
||||
self.theta = None
|
||||
self.feature_i = None
|
||||
|
||||
self.value = None
|
||||
|
||||
self.right = None
|
||||
self.left = None
|
||||
|
||||
def predict(self, data):
|
||||
if self.value != None:
|
||||
return self.value
|
||||
else:
|
||||
if data['x'][self.feature_i] <= self.theta:
|
||||
return self.left.predict(data)
|
||||
else:
|
||||
return self.right.predict(data)
|
||||
|
||||
def decision_stump(self):
|
||||
def get_impurity(features_i_y_pair):
|
||||
if len(features_i_y_pair) == 0:
|
||||
return 0
|
||||
y_bar = sum([ data['y'] for data in features_i_y_pair ]) / len(features_i_y_pair)
|
||||
|
||||
impurity = 0
|
||||
for data in features_i_y_pair:
|
||||
impurity += (data['y']-y_bar) ** 2
|
||||
impurity = impurity / len(features_i_y_pair)
|
||||
return impurity
|
||||
|
||||
different_y = set()
|
||||
for data in self.datas:
|
||||
different_y.add(data['y'])
|
||||
if len(different_y) == 1:
|
||||
self.value = list(different_y)[0]
|
||||
return
|
||||
|
||||
min_impurity = 1e9
|
||||
best_feature_i, best_theta = -100, -100
|
||||
|
||||
# find the best feature_i
|
||||
for feature_i in range(8):
|
||||
# only get feature_i
|
||||
# and sorted by feature_i, we want to get the best theta
|
||||
features_i_y_pair = [ {'feature_i': data['x'][feature_i], 'y': data['y'] } for data in self.datas ]
|
||||
features_i_y_pair = sorted(features_i_y_pair, key=lambda x: x['feature_i'])
|
||||
|
||||
# if all x are same
|
||||
if features_i_y_pair[0] == features_i_y_pair[-1]:
|
||||
continue
|
||||
thetas = [ (features_i_y_pair[index]['feature_i']+features_i_y_pair[index-1]['feature_i'])/2 for index in range(1, len(features_i_y_pair)) ]
|
||||
|
||||
# find the best theta
|
||||
for index, theta in enumerate(thetas):
|
||||
front_data = []
|
||||
back_data = []
|
||||
|
||||
for data in features_i_y_pair:
|
||||
if data['feature_i'] <= theta:
|
||||
front_data.append(data)
|
||||
else:
|
||||
back_data.append(data)
|
||||
|
||||
# get the impurity when (feature_i, theta)
|
||||
impurity = len(front_data) * get_impurity(front_data) + len(back_data) * get_impurity(back_data)
|
||||
if impurity < min_impurity:
|
||||
min_impurity = impurity
|
||||
best_feature_i = feature_i
|
||||
best_theta = theta
|
||||
|
||||
|
||||
if best_feature_i != -100:
|
||||
self.theta = best_theta
|
||||
self.feature_i = best_feature_i
|
||||
else:
|
||||
# print("NO DECISION")
|
||||
self.value = sum([data['y'] for data in self.datas]) / len(self.datas)
|
||||
|
||||
'''
|
||||
print("feature_i: ", self.feature_i)
|
||||
print("Theta: ", self.theta)
|
||||
print("Value: ", self.value)
|
||||
for i in self.datas:
|
||||
print(" ", i)
|
||||
'''
|
||||
|
||||
|
||||
def expand(self):
|
||||
left_data = []
|
||||
for data in self.datas:
|
||||
if data['x'][self.feature_i] <= self.theta:
|
||||
left_data.append(data)
|
||||
|
||||
right_data = []
|
||||
for data in self.datas:
|
||||
if data['x'][self.feature_i] > self.theta:
|
||||
right_data.append(data)
|
||||
|
||||
|
||||
if len(right_data) == 0 or len(left_data) == 0:
|
||||
self.value = sum([data['y'] for data in self.datas]) / len(self.datas)
|
||||
else:
|
||||
self.left = Node(left_data)
|
||||
self.right = Node(right_data)
|
||||
|
||||
self.left.decision_stump()
|
||||
self.right.decision_stump()
|
||||
|
||||
if self.left.theta != None:
|
||||
self.left.expand()
|
||||
|
||||
if self.right.theta != None:
|
||||
self.right.expand()
|
||||
|
||||
# print("theta: {}, feature_i: {}, value:{}".format(self.theta, self.feature_i, self.value))
|
||||
|
||||
|
||||
def square_error(target, predict):
|
||||
return (predict-target) ** 2
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
train = load_data('hw6_train.dat')
|
||||
test = load_data('hw6_test.dat')
|
||||
|
||||
e_in_logs = []
|
||||
e_out_logs = []
|
||||
roots = []
|
||||
FOREST_SIZE = 2000
|
||||
for i in range(FOREST_SIZE):
|
||||
sample_size = int(len(train) / 2)
|
||||
bagging_train = random.sample(train, k=sample_size)
|
||||
|
||||
root = Node(bagging_train)
|
||||
root.decision_stump()
|
||||
root.expand()
|
||||
roots.append(root)
|
||||
|
||||
errors = 0
|
||||
for data in train:
|
||||
predict_y = root.predict(data)
|
||||
error = square_error(data['y'], predict_y)
|
||||
errors += error
|
||||
print(i)
|
||||
print(" E_in:", errors/len(test))
|
||||
e_in_logs.append(errors/len(test))
|
||||
|
||||
errors = 0
|
||||
for data in test:
|
||||
predict_y = root.predict(data)
|
||||
error = square_error(data['y'], predict_y)
|
||||
errors += error
|
||||
print(i)
|
||||
print(" E_out:", errors/len(test))
|
||||
e_out_logs.append(errors/len(test))
|
||||
|
||||
plt.scatter(e_in_logs, e_out_logs, c='b')
|
||||
|
||||
e_in_logs = []
|
||||
e_out_logs = []
|
||||
|
||||
errors = 0
|
||||
for data in train:
|
||||
# predict with aggregation
|
||||
ans_sum = 0
|
||||
for i in range(FOREST_SIZE):
|
||||
predict_y = roots[i].predict(data)
|
||||
ans_sum += predict_y
|
||||
predict_y = ans_sum / FOREST_SIZE
|
||||
error = square_error(data['y'], predict_y)
|
||||
errors += error
|
||||
e_in_logs.append(errors/len(train))
|
||||
|
||||
errors = 0
|
||||
for data in test:
|
||||
# predict with aggregation
|
||||
ans_sum = 0
|
||||
for i in range(FOREST_SIZE):
|
||||
predict_y = roots[i].predict(data)
|
||||
ans_sum += predict_y
|
||||
predict_y = ans_sum / FOREST_SIZE
|
||||
error = square_error(data['y'], predict_y)
|
||||
errors += error
|
||||
e_out_logs.append(errors/len(test))
|
||||
|
||||
plt.scatter(e_in_logs, e_out_logs, c='r', s=100)
|
||||
|
||||
plt.savefig('p11.png')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
198
hw6/p12.py
Normal file
198
hw6/p12.py
Normal file
@ -0,0 +1,198 @@
|
||||
import random
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
def load_data(path):
|
||||
datas = []
|
||||
with open(path) as fp:
|
||||
lines = fp.readlines()
|
||||
|
||||
for line in lines:
|
||||
tmp_data = [ 0 for _ in range(8) ]
|
||||
numbers = line.split()
|
||||
y = int(numbers[0])
|
||||
for i in numbers[1:]:
|
||||
index, value = i.split(':')
|
||||
index = int(index)
|
||||
value = float(value)
|
||||
tmp_data[index-1] = value
|
||||
x = tmp_data
|
||||
datas.append({
|
||||
'x': x,
|
||||
'y': y,
|
||||
})
|
||||
return datas
|
||||
|
||||
class Node():
|
||||
def __init__(self, datas):
|
||||
self.datas = datas
|
||||
self.theta = None
|
||||
self.feature_i = None
|
||||
|
||||
self.value = None
|
||||
|
||||
self.right = None
|
||||
self.left = None
|
||||
|
||||
def predict(self, data):
|
||||
if self.value != None:
|
||||
return self.value
|
||||
else:
|
||||
if data['x'][self.feature_i] <= self.theta:
|
||||
return self.left.predict(data)
|
||||
else:
|
||||
return self.right.predict(data)
|
||||
|
||||
def decision_stump(self):
|
||||
def get_impurity(features_i_y_pair):
|
||||
if len(features_i_y_pair) == 0:
|
||||
return 0
|
||||
y_bar = sum([ data['y'] for data in features_i_y_pair ]) / len(features_i_y_pair)
|
||||
|
||||
impurity = 0
|
||||
for data in features_i_y_pair:
|
||||
impurity += (data['y']-y_bar) ** 2
|
||||
impurity = impurity / len(features_i_y_pair)
|
||||
return impurity
|
||||
|
||||
different_y = set()
|
||||
for data in self.datas:
|
||||
different_y.add(data['y'])
|
||||
if len(different_y) == 1:
|
||||
self.value = list(different_y)[0]
|
||||
return
|
||||
|
||||
min_impurity = 1e9
|
||||
best_feature_i, best_theta = -100, -100
|
||||
|
||||
# find the best feature_i
|
||||
for feature_i in range(8):
|
||||
# only get feature_i
|
||||
# and sorted by feature_i, we want to get the best theta
|
||||
features_i_y_pair = [ {'feature_i': data['x'][feature_i], 'y': data['y'] } for data in self.datas ]
|
||||
features_i_y_pair = sorted(features_i_y_pair, key=lambda x: x['feature_i'])
|
||||
|
||||
# if all x are same
|
||||
if features_i_y_pair[0] == features_i_y_pair[-1]:
|
||||
continue
|
||||
thetas = [ (features_i_y_pair[index]['feature_i']+features_i_y_pair[index-1]['feature_i'])/2 for index in range(1, len(features_i_y_pair)) ]
|
||||
|
||||
# find the best theta
|
||||
for index, theta in enumerate(thetas):
|
||||
front_data = []
|
||||
back_data = []
|
||||
|
||||
for data in features_i_y_pair:
|
||||
if data['feature_i'] <= theta:
|
||||
front_data.append(data)
|
||||
else:
|
||||
back_data.append(data)
|
||||
|
||||
# get the impurity when (feature_i, theta)
|
||||
impurity = len(front_data) * get_impurity(front_data) + len(back_data) * get_impurity(back_data)
|
||||
if impurity < min_impurity:
|
||||
min_impurity = impurity
|
||||
best_feature_i = feature_i
|
||||
best_theta = theta
|
||||
|
||||
|
||||
if best_feature_i != -100:
|
||||
self.theta = best_theta
|
||||
self.feature_i = best_feature_i
|
||||
else:
|
||||
# print("NO DECISION")
|
||||
self.value = sum([data['y'] for data in self.datas]) / len(self.datas)
|
||||
|
||||
'''
|
||||
print("feature_i: ", self.feature_i)
|
||||
print("Theta: ", self.theta)
|
||||
print("Value: ", self.value)
|
||||
for i in self.datas:
|
||||
print(" ", i)
|
||||
'''
|
||||
|
||||
|
||||
def expand(self):
|
||||
left_data = []
|
||||
for data in self.datas:
|
||||
if data['x'][self.feature_i] <= self.theta:
|
||||
left_data.append(data)
|
||||
|
||||
right_data = []
|
||||
for data in self.datas:
|
||||
if data['x'][self.feature_i] > self.theta:
|
||||
right_data.append(data)
|
||||
|
||||
|
||||
if len(right_data) == 0 or len(left_data) == 0:
|
||||
self.value = sum([data['y'] for data in self.datas]) / len(self.datas)
|
||||
else:
|
||||
self.left = Node(left_data)
|
||||
self.right = Node(right_data)
|
||||
|
||||
self.left.decision_stump()
|
||||
self.right.decision_stump()
|
||||
|
||||
if self.left.theta != None:
|
||||
self.left.expand()
|
||||
|
||||
if self.right.theta != None:
|
||||
self.right.expand()
|
||||
|
||||
# print("theta: {}, feature_i: {}, value:{}".format(self.theta, self.feature_i, self.value))
|
||||
|
||||
|
||||
def square_error(target, predict):
|
||||
return (predict-target) ** 2
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
train = load_data('hw6_train.dat')
|
||||
test = load_data('hw6_test.dat')
|
||||
|
||||
e_in_logs = []
|
||||
e_out_logs = []
|
||||
roots = []
|
||||
FOREST_SIZE = 2000
|
||||
for i in range(FOREST_SIZE):
|
||||
sample_size = int(len(train) / 2)
|
||||
bagging_train = random.sample(train, k=sample_size)
|
||||
|
||||
root = Node(bagging_train)
|
||||
root.decision_stump()
|
||||
root.expand()
|
||||
roots.append(root)
|
||||
|
||||
errors = 0
|
||||
for data in test:
|
||||
predict_y = root.predict(data)
|
||||
error = square_error(data['y'], predict_y)
|
||||
errors += error
|
||||
print(i)
|
||||
print(" E_out:", errors/len(test))
|
||||
e_out_logs.append(errors/len(test))
|
||||
|
||||
plt.plot(list(range(FOREST_SIZE)), e_out_logs, c='b')
|
||||
|
||||
e_in_logs = []
|
||||
e_out_logs = []
|
||||
|
||||
for T in range(1, FOREST_SIZE+1):
|
||||
errors = 0
|
||||
for data in test:
|
||||
# predict with aggregation
|
||||
ans_sum = 0
|
||||
for i in range(T):
|
||||
predict_y = roots[i].predict(data)
|
||||
ans_sum += predict_y
|
||||
predict_y = ans_sum / T
|
||||
error = square_error(data['y'], predict_y)
|
||||
errors += error
|
||||
e_out_logs.append(errors/len(test))
|
||||
|
||||
plt.plot(list(range(FOREST_SIZE)), e_out_logs, c='r')
|
||||
plt.savefig('p12.png')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user