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| import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')
data_path = '../data/german.data-numeric'
def load_data(tsize=0.8, shuffle=True):
data_list = pd.read_csv(data_path, header=None, sep='\s+')
data_array = data_list.values
height, width = data_array.shape[:2]
data_x = data_array[:, :(width - 1)]
data_y = data_array[:, (width - 1)]
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, train_size=tsize, test_size=(1 - tsize),
shuffle=shuffle)
y_train = np.atleast_2d(np.array(list(map(lambda x: 1 if x == 2 else 0, y_train)))).T
y_test = np.atleast_2d(np.array(list(map(lambda x: 1 if x == 2 else 0, y_test)))).T
return x_train, y_train, x_test, y_test
def init_weights(inputs):
"""
初始化权重，符合标准正态分布
"""
return np.atleast_2d(np.random.uniform(size=inputs)).T
def sigmoid(x):
return 1 / (1 + np.exp(-1 * x))
def logistic_regression(w, x):
"""
w大小为(n+1)x1
x大小为mx(n+1)
"""
z = x.dot(w)
return sigmoid(z)
def compute_loss(w, x, y, isBatch=True):
"""
w大小为(n+1)x1
x大小为mx(n+1)
y大小为mx1
"""
lr_value = logistic_regression(w, x)
if isBatch:
n = y.shape[0]
res = -1.0 / n * (y.T.dot(np.log(lr_value)) + (1 - y.T).dot(np.log(1 - lr_value)))
return res[0][0]
else:
res = -1.0 * (y * (np.log(lr_value)) + (1 - y) * (np.log(1 - lr_value)))
return res[0]
def compute_gradient(w, x, y, isBatch=True):
"""
梯度计算
"""
lr_value = logistic_regression(w, x)
if isBatch:
n = y.shape[0]
return 1.0 / n * x.T.dot(lr_value - y)
else:
return np.atleast_2d(1.0 * x.T * (lr_value - y)).T
def compute_predict_accuracy(predictions, y):
results = predictions > 0.5
res = len(list(filter(lambda x: x[0] == x[1], np.dstack((results, y))[:, 0]))) / len(results)
return res
def draw(res_list, title=None, xlabel=None):
if title is not None:
plt.title(title)
if xlabel is not None:
plt.xlabel(xlabel)
plt.plot(res_list)
plt.show()
if __name__ == '__main__':
train_data, train_label, test_data, test_label = load_data()
mu = np.mean(train_data, axis=0)
std = np.std(train_data, axis=0)
train_data = (train_data - mu) / std
test_data = (test_data - mu) / std
train_data = np.insert(train_data, 0, np.ones(train_data.shape[0]), axis=1)
test_data = np.insert(test_data, 0, np.ones(test_data.shape[0]), axis=1)
lr = 0.0001
w = init_weights(train_data.shape[1])
epoches = 50000
batch_size = 128
num = train_label.shape[0]
loss_list = []
accuracy_list = []
loss = 0
best_accuracy = 0
best_w = None
for i in range(epoches):
loss = 0
train_num = 0
for j in range(0, num, batch_size):
loss += compute_loss(w, train_data[j:j + batch_size], train_label[j:j + batch_size], isBatch=True)
train_num += 1
gradient = compute_gradient(w, train_data[j:j + batch_size], train_label[j:j + batch_size], isBatch=True)
tempW = w - lr * gradient
w = tempW
loss_list.append(loss / train_num)
accuracy = compute_predict_accuracy(logistic_regression(w, train_data), train_label)
accuracy_list.append(accuracy)
if accuracy > best_accuracy:
best_accuracy = accuracy
best_w = w.copy()
draw(loss_list, title='损失值')
draw(accuracy_list, title='训练集检测精度')
print('train accuracy: %.3f' % (max(accuracy_list)))
test_accuracy = compute_predict_accuracy(logistic_regression(best_w, test_data), test_label)
print('test accuracy: %.3f' % (test_accuracy))
|
