import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt

from scipy.optimize import minimize
from sklearn.preprocessing import PolynomialFeatures

pd.set_option('display.notebook_repr_html', False)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 150)
pd.set_option('display.max_seq_items', None)

sns.set_context('notebook')
sns.set_style('white')

%matplotlib inline

def load_data(file, delimeter=','):
    data = np.loadtxt(file, delimiter=delimeter)
    print('load_data: dimensions: ',data.shape)
    print(data[1:6,:])
    return data

def plot_data(data, label_x, label_y, label_pos, label_neg, axes=None):
    if axes == None: axes = plt.gca()
    # 获得正负样本的下标(即哪些是正样本，哪些是负样本)
    neg = data[:,2] == 0
    pos = data[:,2] == 1
    axes.scatter(data[pos][:,0], data[pos][:,1], marker='+', c='k', 
                 s=60, linewidth=2, label=label_pos)
    axes.scatter(data[neg][:,0], data[neg][:,1], marker='o', c='y', 
                 s=60, label=label_neg)
    axes.set_xlabel(label_x)
    axes.set_ylabel(label_y)
    axes.legend(frameon= True, fancybox = True);

"""
Explanation: 逻辑斯特回归示例

逻辑斯特回归
正则化后的逻辑斯特回归
End of explanation
"""


data = load_data('input/data1.txt', ',')

# X = np.c_[np.ones((data.shape[0],1)), data[:,0:2]]
# y = np.c_[data[:,2]]

plot_data(data, 'Exam 1 score', 'Exam 2 score', 'Pass', 'Fail')

"""
Explanation: 逻辑斯特回归
End of explanation
"""


#定义sigmoid函数
def sigmoid(z):
    return(1 / (1 + np.exp(-z)))

"""
Explanation: 逻辑斯特回归假设
$$ h_{\theta}(x) = g(\theta^{T}x)$$
$$ g(z)=\frac{1}{1+e^{−z}} $$
End of explanation
"""


#定义损失函数
def costFunction(theta, X, y):
    m = y.size
    h = sigmoid(X.dot(theta))
    
    J = -1.0*(1.0/m)*(np.log(h).T.dot(y)+np.log(1-h).T.dot(1-y))
               
    if np.isnan(J[0]):
        return(np.inf)
    return J[0]

"""
Explanation: 其实scipy包里有一个函数可以完成一样的功能:<BR>
http://docs.scipy.org/doc/scipy/reference/generated/scipy.special.expit.html#scipy.special.expit
损失函数
$$ J(\theta) = \frac{1}{m}\sum_{i=1}^{m}\big[-y^{(i)}\, log\,( h_\theta\,(x^{(i)}))-(1-y^{(i)})\,log\,(1-h_\theta(x^{(i)}))\big]$$
向量化的损失函数(矩阵形式)
$$ J(\theta) = \frac{1}{m}\big((\,log\,(g(X\theta))^Ty+(\,log\,(1-g(X\theta))^T(1-y)\big)$$
End of explanation
"""


#求解梯度
def gradient(theta, X, y):
    m = y.size
    h = sigmoid(X.dot(theta.reshape(-1,1)))
    
    grad =(1.0/m)*X.T.dot(h-y)

    return(grad.flatten())

initial_theta = np.zeros(X.shape[1])
cost = costFunction(initial_theta, X, y)
grad = gradient(initial_theta, X, y)
print('Cost: \n', cost)
print('Grad: \n', grad)

"""
Explanation: 求偏导(梯度)
$$ \frac{\delta J(\theta)}{\delta\theta_{j}} = \frac{1}{m}\sum_{i=1}^{m} ( h_\theta (x^{(i)})-y^{(i)})x^{(i)}_{j} $$
向量化的偏导(梯度)
$$ \frac{\delta J(\theta)}{\delta\theta_{j}} = \frac{1}{m} X^T(g(X\theta)-y)$$
End of explanation
"""


res = minimize(costFunction, initial_theta, args=(X,y), jac=gradient, options={'maxiter':400})
res

"""
Explanation: 最小化损失函数
End of explanation
"""


def predict(theta, X, threshold=0.5):
    p = sigmoid(X.dot(theta.T)) >= threshold
    return(p.astype('int'))

"""
Explanation: 做一下预测吧
End of explanation
"""


sigmoid(np.array([1, 45, 85]).dot(res.x.T))

"""
Explanation: 咱们来看看考试1得分45，考试2得分85的同学通过概率有多高
End of explanation
"""


plt.scatter(45, 85, s=60, c='r', marker='v', label='(45, 85)')
plotData(data, 'Exam 1 score', 'Exam 2 score', 'Admitted', 'Not admitted')
x1_min, x1_max = X[:,1].min(), X[:,1].max(),
x2_min, x2_max = X[:,2].min(), X[:,2].max(),
xx1, xx2 = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))
h = sigmoid(np.c_[np.ones((xx1.ravel().shape[0],1)), xx1.ravel(), xx2.ravel()].dot(res.x))
h = h.reshape(xx1.shape)
plt.contour(xx1, xx2, h, [0.5], linewidths=1, colors='b');

"""
Explanation: 画决策边界
End of explanation
"""


data2 = loaddata('input/data2.txt', ',')

# 拿到X和y
y = np.c_[data2[:,2]]
X = data2[:,0:2]

# 画个图
plotData(data2, 'Microchip Test 1', 'Microchip Test 2', 'y = 1', 'y = 0')

"""
Explanation: 加正则化项的逻辑斯特回归
End of explanation
"""


poly = PolynomialFeatures(6)
XX = poly.fit_transform(data2[:,0:2])
# 看看形状(特征映射后x有多少维了)
XX.shape

"""
Explanation: 咱们整一点多项式特征出来(最高6阶)
End of explanation
"""


# 定义损失函数
def costFunctionReg(theta, reg, *args):
    m = y.size
    h = sigmoid(XX.dot(theta))
    
    J = -1.0*(1.0/m)*(np.log(h).T.dot(y)+np.log(1-h).T.dot(1-y)) + (reg/(2.0*m))*np.sum(np.square(theta[1:]))
    
    if np.isnan(J[0]):
        return(np.inf)
    return(J[0])

"""
Explanation: 正则化后损失函数
$$ J(\theta) = \frac{1}{m}\sum_{i=1}^{m}\big[-y^{(i)}\, log\,( h_\theta\,(x^{(i)}))-(1-y^{(i)})\,log\,(1-h_\theta(x^{(i)}))\big] + \frac{\lambda}{2m}\sum_{j=1}^{n}\theta_{j}^{2}$$
向量化的损失函数(矩阵形式)
$$ J(\theta) = \frac{1}{m}\big((\,log\,(g(X\theta))^Ty+(\,log\,(1-g(X\theta))^T(1-y)\big) + \frac{\lambda}{2m}\sum_{j=1}^{n}\theta_{j}^{2}$$
End of explanation
"""


def gradientReg(theta, reg, *args):
    m = y.size
    h = sigmoid(XX.dot(theta.reshape(-1,1)))
      
    grad = (1.0/m)*XX.T.dot(h-y) + (reg/m)*np.r_[[[0]],theta[1:].reshape(-1,1)]
        
    return(grad.flatten())

initial_theta = np.zeros(XX.shape[1])
costFunctionReg(initial_theta, 1, XX, y)

fig, axes = plt.subplots(1,3, sharey = True, figsize=(17,5))

# 决策边界，咱们分别来看看正则化系数lambda太大太小分别会出现什么情况
# Lambda = 0 : 就是没有正则化，这样的话，就过拟合咯
# Lambda = 1 : 这才是正确的打开方式
# Lambda = 100 : 卧槽，正则化项太激进，导致基本就没拟合出决策边界

for i, C in enumerate([0.0, 1.0, 100.0]):
    # 最优化 costFunctionReg
    res2 = minimize(costFunctionReg, initial_theta, args=(C, XX, y), jac=gradientReg, options={'maxiter':3000})
    
    # 准确率
    accuracy = 100.0*sum(predict(res2.x, XX) == y.ravel())/y.size    

    # 对X,y的散列绘图
    plotData(data2, 'Microchip Test 1', 'Microchip Test 2', 'y = 1', 'y = 0', axes.flatten()[i])
    
    # 画出决策边界
    x1_min, x1_max = X[:,0].min(), X[:,0].max(),
    x2_min, x2_max = X[:,1].min(), X[:,1].max(),
    xx1, xx2 = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))
    h = sigmoid(poly.fit_transform(np.c_[xx1.ravel(), xx2.ravel()]).dot(res2.x))
    h = h.reshape(xx1.shape)
    axes.flatten()[i].contour(xx1, xx2, h, [0.5], linewidths=1, colors='g');       
    axes.flatten()[i].set_title('Train accuracy {}% with Lambda = {}'.format(np.round(accuracy, decimals=2), C))

"""
Explanation: 偏导(梯度)
$$ \frac{\delta J(\theta)}{\delta\theta_{j}} = \frac{1}{m}\sum_{i=1}^{m} ( h_\theta (x^{(i)})-y^{(i)})x^{(i)}{j} + \frac{\lambda}{m}\theta{j}$$
向量化的偏导(梯度)
$$ \frac{\delta J(\theta)}{\delta\theta_{j}} = \frac{1}{m} X^T(g(X\theta)-y) + \frac{\lambda}{m}\theta_{j}$$
$$\text{注意，我们另外自己加的参数 } \theta_{0} \text{ 不需要被正则化}$$
End of explanation
"""


