logistic_train.py

import numpy as np
import matplotlib.pyplot as plt

from sklearn.utils import shuffle
from process import get_binary_data

X, Y = get_binary_data()
X, Y = shuffle(X, Y)

# create train and test sets
Xtrain = X[:-100]
Ytrain = Y[:-100]
Xtest = X[-100:]
Ytest = Y[-100:]

# randomly initialize weights
D = X.shape[1]
W = np.random.randn(D)
b = 0 # bias term

# make predictions
def sigmoid(a):
    return 1 / (1 + np.exp(-a))

def forward(X, W, b):
    return sigmoid(X.dot(W) + b)

# calculate the accuracy
def classification_rate(Y, P):
    return np.mean(Y == P)

# cross entropy
def cross_entropy(T, pY):
    return -np.mean(T*np.log(pY) + (1 - T)*np.log(1 - pY))

# train loop
train_costs = []
test_costs = []
learning_rate = 0.001
for i in xrange(10000):
    pYtrain = forward(Xtrain, W, b)
    pYtest = forward(Xtest, W, b)

    ctrain = cross_entropy(Ytrain, pYtrain)
    ctest = cross_entropy(Ytest, pYtest)
    train_costs.append(ctrain)
    test_costs.append(ctest)

    # gradient descent
    W -= learning_rate*Xtrain.T.dot(pYtrain - Ytrain)
    b -= learning_rate*(pYtrain - Ytrain).sum()
    if i % 1000 == 0:
        print i, ctrain, ctest

print "Final train classification_rate:", classification_rate(Ytrain, np.round(pYtrain))
print "Final test classification_rate:", classification_rate(Ytest, np.round(pYtest))

legend1, = plt.plot(train_costs, label='train cost')
legend2, = plt.plot(test_costs, label='test cost')
plt.legend([legend1, legend2])
plt.show()