def grad_check(x,beta,delta):
"""
Takes a formatted classifier-trait pair example (x), and the current
parameter vector (beta) along with the learning rate and a perturbation
scale (delta). It first calculates the gradient for this example. A
perturbation vector is constructed composed of normally distributed values
with standard deviation delta. It then applies the perturbation to beta and calculates
the difference between LCL(beta,x) and LCL(beta + delta,x). If the gradient
is accurate, then LCL(beta + delta,x) - LCL(beta,x) should roughly equal
delta * grad.
"""
grad = np.zeros(np.size(beta))
y = x[0] #classifier value
traits = x[1:] #trait vector
p = sgd.logistic(beta,traits)
x_send = x.reshape(1,np.size(x)) #formatting for LCL function
grad = (y - p)*traits #calculate gradient
LCL_prior = sgd.LCL(beta,x_send) #initial LCL
delta_vec = delta * randn(np.size(beta)) #create perturbation vector
beta_prime = beta + delta_vec #perturbed beta vector
LCL_prime = sgd.LCL(beta_prime,x_send) #LCL(beta + delta,x)
diff = np.dot(delta_vec,grad)
del_LCL = LCL_prime - LCL_prior #calculated del:LCL(x,beta)
return diff, del_LCL, diff/del_LCL
def test_logistic(self):
self.assertAlmostEqual(logistic(1), 0.7310585786300049)
self.assertAlmostEqual(logistic(2), 0.8807970779778823)
self.assertAlmostEqual(logistic(-1), 0.2689414213699951)
def test_logistic(self):
self.assertAlmostEqual(logistic(1), 0.7310585786300049)
self.assertAlmostEqual(logistic(2), 0.8807970779778823)
self.assertAlmostEqual(logistic(-1), 0.2689414213699951)
def test_logistic(self):
input = logistic(np.matrix([1,2,-1]))
output = np.matrix([0.7310585786300049, 0.8807970779778823, 0.2689414213699951])
for i in range(len(input)):
self.assertAlmostEqual(input[0, i], output[0, i])
def run_sgd():
# load the data and preprocess it
train_data = np.loadtxt('../dataset/1571/train_npcomp.dat', dtype='float')
test_data = np.loadtxt('../dataset/1571/test_npcomp.dat', dtype='float')
# save a copy of the original data set, before shuffling and dividing
train_data_orig = np.copy(train_data)
# save first half as training data, second half as validation data
D = train_data.shape[1] - 1
N_trainex = train_data_orig.shape[0]
# shuffle the training set before splitting it
randstate1 = np.random.get_state()
np.random.shuffle(train_data_orig)
if N_trainex%2 == 0:
N_trainex = int(N_trainex/2)
else:
N_trainex = int(N_trainex/2)+1
valid_data = train_data_orig[N_trainex:]
train_data = train_data_orig[:N_trainex]
N_trainex = train_data.shape[0]
N_validex = valid_data.shape[0]
N_testex = test_data.shape[0]
# preprocess the data
train_data, tdmean, tdstd = dr.preprocess(train_data, full_output=True)
valid_data = dr.preprocess(valid_data, rescale=False)
test_data = dr.preprocess(test_data, rescale=False)
# rescale validation and test sets same as training data
valid_data[:,1:-1] -= np.resize(tdmean, (N_validex,D))
valid_data[:,1:-1] /= tdstd
test_data[:,1:-1] -= np.resize(tdmean, (N_testex,D))
test_data[:,1:-1] /= tdstd
# initiate the grid search over mu
trained_pars, mu_max, lr_max, LCL_max = sgd.mu_gridsearch(train_data, valid_data)
# calculate error rate on validation data set
errors = 0
for ind_ex in range(N_validex):
if sgd.logistic(trained_pars, valid_data[ind_ex,1:]) >= 0.5:
errors += 1 - valid_data[ind_ex,0]
else:
errors += valid_data[ind_ex,0]
error_rate_valid = errors/N_validex
print 'Error rate on validation data = ' + str(error_rate_valid*100) + '%\n'
# calculate LCL on test data
LCL_test = sgd.LCL(trained_pars, test_data)
# calculate error rate on test data set
errors = 0
for ind_ex in range(N_testex):
if sgd.logistic(trained_pars, test_data[ind_ex,1:]) >= 0.5:
errors += 1 - test_data[ind_ex,0]
else:
errors += test_data[ind_ex,0]
error_rate_test = errors/N_testex
print 'Error rate on test data = ' + str(error_rate_test*100) + '%\n'
return mu_max, lr_max, LCL_max, error_rate_valid, LCL_test, error_rate_test
def test_logistic(self):
"""Tests the logistic function.
"""
self.assertAlmostEqual(logistic(1), 0.7310585786300049)
self.assertAlmostEqual(logistic(2), 0.8807970779778823)
self.assertAlmostEqual(logistic(-1), 0.2689414213699951)
def test_logistic(self):
input = logistic(np.matrix([1,2,-1]))
output = np.matrix([0.7310585786300049, 0.8807970779778823, 0.2689414213699951])
for i in range(len(input)):
self.assertAlmostEqual(input[0, i], output[0, i])
