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November 14, 2017 08:22
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Neural Network Regression Problem.
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# exercise 8.2.6 | |
from matplotlib.pyplot import figure, plot, subplot, title, show, bar, legend, scatter | |
import numpy as np | |
from scipy.io import loadmat | |
import matplotlib.pyplot as plt | |
import neurolab as nl | |
from sklearn import model_selection | |
from scipy import stats | |
from Project_Clean_data import raw, header, is_binary | |
X_chest = np.loadtxt('chest.txt', dtype=int) | |
final_cand = np.loadtxt('final_cand.txt', dtype=int) | |
# select attribute to predict | |
target_attribute_name = 'Number of sexual partners' | |
target_index = list(header).index(target_attribute_name) | |
# prepare data | |
X = raw | |
y = X[:, target_index] | |
y = np.delete(y, final_cand) | |
X = np.delete(raw, target_index, 1) | |
X = np.delete(X, final_cand, 0) | |
X = X[:, 0:10] | |
attributeNames = np.delete(header, target_index) | |
N, M = X.shape | |
C = 2 | |
# Normalize data | |
X = stats.zscore(X); | |
## Normalize and compute PCA (UNCOMMENT to experiment with PCA preprocessing) | |
# Y = stats.zscore(X,0); | |
# U,S,V = np.linalg.svd(Y,full_matrices=False) | |
# V = V.T | |
##Components to be included as features | |
# k_pca = 3 | |
# X = X @ V[:,0:k_pca] | |
# N, M = X.shape | |
# Parameters for neural network classifier | |
n_hidden_units = 5 # number of hidden units | |
n_train = 2 # number of networks trained in each k-fold | |
learning_goal = 100 # stop criterion 1 (train mse to be reached) | |
max_epochs = 64 # stop criterion 2 (max epochs in training) | |
show_error_freq = 5 # frequency of training status updates | |
# K-fold crossvalidation | |
K = 5 # only five folds to speed up this example | |
CV = model_selection.KFold(K, shuffle=True) | |
# Variable for classification error | |
errors = np.zeros(K) | |
error_hist = np.zeros((max_epochs, K)) | |
bestnet = list() | |
k = 0 | |
for train_index, test_index in CV.split(X, y): | |
print('\nCrossvalidation fold: {0}/{1}'.format(k + 1, K)) | |
# extract training and test set for current CV fold | |
X_train = X[train_index, :] | |
y_train = y[train_index] | |
X_test = X[test_index, :] | |
y_test = y[test_index] | |
best_train_error = 1e100 | |
for i in range(n_train): | |
print('Training network {0}/{1}...'.format(i + 1, n_train)) | |
# Create randomly initialized network with 2 layers | |
ann = nl.net.newff([[-3, 3]] * M, [n_hidden_units, 1], [nl.trans.TanSig(), nl.trans.PureLin()]) | |
if i == 0: | |
bestnet.append(ann) | |
# train network | |
train_error = ann.train(X_train, y_train.reshape(-1, 1), goal=learning_goal, epochs=max_epochs, | |
show=show_error_freq) | |
if train_error[-1] < best_train_error: | |
bestnet[k] = ann | |
best_train_error = train_error[-1] | |
error_hist[range(len(train_error)), k] = train_error | |
print('Best train error: {0}...'.format(best_train_error)) | |
y_est = bestnet[k].sim(X_test).squeeze() | |
errors[k] = np.power(y_est - y_test, 2).sum().astype(float) / y_test.shape[0] | |
k += 1 | |
# Print the average least squares error | |
print('Mean-square error: {0}'.format(np.mean(errors))) | |
figure(figsize=(6, 7)); | |
subplot(2, 1, 1); | |
bar(range(0, K), errors); | |
title('Mean-square errors'); | |
subplot(2, 1, 2); | |
plot(error_hist); | |
title('Training error as function of BP iterations'); | |
figure(figsize=(6, 7)); | |
subplot(2, 1, 1); | |
plot(y_est); | |
plot(y_test); | |
title('Last CV-fold: est_y vs. test_y'); | |
subplot(2, 1, 2); | |
plot((y_est - y_test)); | |
title('Last CV-fold: prediction error (est_y-test_y)'); | |
show() | |
index = np.argmin(errors) | |
best_net = bestnet[index] | |
y_chest = X_chest[:, target_index] | |
X_chest = np.delete(X_chest, target_index, 1) | |
X_chest = X_chest[:, 0:10] | |
y_est = best_net.sim(X_chest).squeeze() | |
final_error = abs(y_chest - y_est) | |
x_axis = np.arange(0, np.size(y_chest)) | |
plt.clf() | |
plt.scatter(x_axis, final_error) | |
plt.xlabel('Subjects') | |
plt.ylabel('Estimation Error') | |
plt.title('Error Quantity for final test subjects') | |
show() | |
print('\nOn average, the prediction fails by {} {}'.format(final_error.mean(), target_attribute_name)) |
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