print('****Minibatch Gradient Descent****')
print('\n--Training--\n')
hyperparam = {'eta': 0.3,
'epochs': 300,
'minibatches': 1,
'adaptive': 0.99}
print('\nHyperparamters\n')
for k, v in hyperparam.items():
print(k, '\t', v)
print('\nNumber of Training Examples: ', X_train.shape[0], '\n')
h_thetaf, cost = glm.fit(lr.J,
lr.gradJ,
hyperparam,
scaledtrain_data)
plot_cost(cost)
h_thetad = scale.denormalize(h_thetaf)
print('Coefficients\t', h_thetaf)
for i, h_theta in enumerate(h_thetad):
print('h_theta' + str(i), '\t', h_theta)
yp_train = glm.predict(identity, X_train, h_thetaf)
plot_errors(y_train, yp_train)
corr_train = metrics.r2(X_train, y_train, h_thetaf)
print('R**2\t', corr_train)
print('\n--Testing--')
yp_test = glm.predict(identity, X_test, h_thetaf)
plot_errors(y_test, yp_test)
corr_test = metrics.r2(X_test, y_test, h_thetaf)
print('R**2\t', corr_test)
scaledtrain_data = zip(X_train, y_train)
# Scale the testing data using the same scaling parameters
# used for the training data
Z_test, y_test = zip(*test_data)
X_test = transform(Z_test)
h_theta0 = [0., 0., 0., 0., 0.]
print('****Gradient Descent****')
h_thetaf, cost = glm.fit(lr.J,
lr.gradJ,
h_theta0,
eta=0.3,
it_max=5000,
gf='gd')(scaledtrain_data)
lr.plot_cost(cost)
h_thetad = scale.denormalize(h_thetaf)
yp_train = glm.predict(identity, X_train, h_thetaf)
print('\n--Training--')
print('Coefficients\t', h_thetaf)
print(
tabulate(list(zip(yp_train, y_train)),
headers=['yp', 'yi'],
tablefmt='fancy_grid'))
print('Coefficients\t', h_thetad)
for i, h_theta in enumerate(h_thetad):
print('h_theta' + unicode(i), '\t', h_theta)
corr_train = metrics.r2(X_train, y_train, h_thetaf)
print('R**2\t', corr_train)
