#!/usr/bin/env python
# coding: utf-8

# ## Programming Exercise 2 - Logistic Regression
# 
# - [Logistic regression](#Logistic-regression)
# - [Regularized logistic regression](#Regularized-logistic-regression)

# In[2]:


# %load ../../standard_import.txt
import pandas as pd
import numpy as np
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)
 
#%config InlineBackend.figure_formats = {'pdf',}
get_ipython().run_line_magic('matplotlib', 'inline')

import seaborn as sns
sns.set_context('notebook')
sns.set_style('white')


# In[3]:


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


# In[4]:


def plotData(data, label_x, label_y, label_pos, label_neg, axes=None):
    # Get indexes for class 0 and class 1
    neg = data[:,2] == 0
    pos = data[:,2] == 1
    
    # If no specific axes object has been passed, get the current axes.
    if axes == None:
        axes = plt.gca()
    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], c='y', s=60, label=label_neg)
    axes.set_xlabel(label_x)
    axes.set_ylabel(label_y)
    axes.legend(frameon= True, fancybox = True);


# ### Logistic regression

# In[5]:


data = loaddata('data/ex2data1.txt', ',')


# In[6]:


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


# In[7]:


plotData(data, 'Exam 1 score', 'Exam 2 score', 'Admitted', 'Not admitted')


# #### Logistic regression hypothesis
# #### $$ h_{\theta}(x) = g(\theta^{T}x)$$
# #### $$ g(z)=\frac{1}{1+e^{−z}} $$

# In[8]:


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


# Scipy actually has a convenience function which does exactly the same:<BR>
# http://docs.scipy.org/doc/scipy/reference/generated/scipy.special.expit.html#scipy.special.expit

# #### Cost Function
# #### $$ J(\theta) = \frac{1}{m}\sum_{i=1}^{m}\big[-y^{(i)}\, log\,( h_\theta\,(x^{(i)}))-(1-y^{(i)})\,log\,(1-h_\theta(x^{(i)}))\big]$$
# #### Vectorized Cost Function
# #### $$ J(\theta) = \frac{1}{m}\big((\,log\,(g(X\theta))^Ty+(\,log\,(1-g(X\theta))^T(1-y)\big)$$

# In[9]:


def costFunction(theta, X, y):
    m = y.size
    h = sigmoid(X.dot(theta))
    
    J = -1*(1/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])


# #### Partial derivative
# 
# #### $$ \frac{\delta J(\theta)}{\delta\theta_{j}} = \frac{1}{m}\sum_{i=1}^{m} ( h_\theta (x^{(i)})-y^{(i)})x^{(i)}_{j} $$ 
# #### Vectorized
# #### $$ \frac{\delta J(\theta)}{\delta\theta_{j}} = \frac{1}{m} X^T(g(X\theta)-y)$$
# 

# In[10]:


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

    return(grad.flatten())


# In[11]:


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)


# #### Optimize cost function

# In[12]:


res = minimize(costFunction, initial_theta, args=(X,y), method=None, jac=gradient, options={'maxiter':400})
res


# #### Predict

# In[13]:


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


# In[14]:


# Student with Exam 1 score 45 and Exam 2 score 85
# Predict using the optimized Theta values from above (res.x)
sigmoid(np.array([1, 45, 85]).dot(res.x.T))


# In[15]:


p = predict(res.x, X) 
print('Train accuracy {}%'.format(100*sum(p == y.ravel())/p.size))


# #### Decision boundary

# In[16]:


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');


# ### Regularized logistic regression

# In[17]:


data2 = loaddata('data/ex2data2.txt', ',')


# In[18]:


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


# In[19]:


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


# #### Polynomials

# In[20]:


# Note that this function inserts a column with 'ones' in the design matrix for the intercept.
poly = PolynomialFeatures(6)
XX = poly.fit_transform(data2[:,0:2])
XX.shape


# #### Regularized Cost Function
# #### $$ J(\theta) = \frac{1}{m}\sum_{i=1}^{m}\big[-y^{(i)}\, log\,( h_\theta\,(x^{(i)}))-(1-y^{(i)})\,log\,(1-h_\theta(x^{(i)}))\big] + \frac{\lambda}{2m}\sum_{j=1}^{n}\theta_{j}^{2}$$
# #### Vectorized Cost Function
# #### $$ J(\theta) = \frac{1}{m}\big((\,log\,(g(X\theta))^Ty+(\,log\,(1-g(X\theta))^T(1-y)\big) + \frac{\lambda}{2m}\sum_{j=1}^{n}\theta_{j}^{2}$$

# In[21]:


def costFunctionReg(theta, reg, *args):
    m = y.size
    h = sigmoid(XX.dot(theta))
    
    J = -1*(1/m)*(np.log(h).T.dot(y)+np.log(1-h).T.dot(1-y)) + (reg/(2*m))*np.sum(np.square(theta[1:]))
    
    if np.isnan(J[0]):
        return(np.inf)
    return(J[0])


# #### Partial derivative
# 
# #### $$ \frac{\delta J(\theta)}{\delta\theta_{j}} = \frac{1}{m}\sum_{i=1}^{m} ( h_\theta (x^{(i)})-y^{(i)})x^{(i)}_{j} + \frac{\lambda}{m}\theta_{j}$$ 
# #### Vectorized
# #### $$ \frac{\delta J(\theta)}{\delta\theta_{j}} = \frac{1}{m} X^T(g(X\theta)-y) + \frac{\lambda}{m}\theta_{j}$$
# ##### $$\text{Note: intercept parameter } \theta_{0} \text{ is not to be regularized}$$

# In[22]:


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


# In[23]:


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


# In[34]:


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

# Decision boundaries
# Lambda = 0 : No regularization --> too flexible, overfitting the training data
# Lambda = 1 : Looks about right
# Lambda = 100 : Too much regularization --> high bias

for i, C in enumerate([0, 1, 100]):
    # Optimize costFunctionReg
    res2 = minimize(costFunctionReg, initial_theta, args=(C, XX, y), method=None, jac=gradientReg, options={'maxiter':3000})
    
    # Accuracy
    accuracy = 100*sum(predict(res2.x, XX) == y.ravel())/y.size    

    # Scatter plot of X,y
    plotData(data2, 'Microchip Test 1', 'Microchip Test 2', 'y = 1', 'y = 0', axes.flatten()[i])
    
    # Plot decisionboundary
    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))