y_score = clf.predict_proba(X_test)
y_hat = clf.predict(X_test)
ys = [y_s[y_h] for y_s, y_h in zip(y_score, y_hat)]
tmp = np.append(X_test,
np.reshape(y_test, (1, y_test.shape[0])).T,
axis=1)
tmp = np.append(tmp,
np.reshape(y_hat, (1, y_hat.shape[0])).T,
axis=1)
tmp = np.append(tmp, y_score, axis=1)
tmp = np.append(tmp, np.asarray(ys), axis=1)
output['data'] = [['X_' + str(i) for i in range(1, X_test.shape[1] + 1)] +
['y_label', 'y_hat', 'y_score', 'y_score', 'ys']] + \
tmp.tolist()
acc = accuracy_score(y_hat, y_test)
fpr, tpr, threshold = getScore('ROC', y_test, y_hat)
score_auc = getScore('AUC', y_test, y_hat)
output['score'].append([
lrt, lr, hu, ni, ii, acc, fpr[1], tpr[1],
metrics.auc(fpr, tpr), score_auc,
(endTime - startTime).seconds +
(endTime - startTime).microseconds / 10**6
])
saveXLSX(output, 'mlp_2classes_bcw.xlsx')
ys = [y_s[y_h-1] for y_s, y_h in zip(y_score, y_hat)]
tmp = np.append(X_test, np.reshape(y_test, (1,y_test.shape[0])).T, axis=1)
tmp = np.append(tmp, np.reshape(y_hat, (1,y_hat.shape[0])).T, axis=1)
tmp = np.append(tmp, y_score, axis=1)
tmp = np.append(tmp, np.asarray(ys), axis=1)
output['data'] = [['X_' + str(i) for i in range(1, X_test.shape[1] + 1)] +
['y_label', 'y_hat', 'y_score_1', 'y_score_2', 'y_score_3', 'ys']] + \
tmp.tolist()
acc = accuracy_score(y_hat, y_test)
confMatrix = confusion_matrix(y_test, y_hat).tolist()
# fpr, tpr, threshold = getScore('ROC', y_test, y_hat)
# score_auc = getScore('AUC', y_test, y_hat)
score_u = getScore('U', y_test, y_score)
# score_u_s = getScore('U_S', y_test, ys)
# score_u_m = getScore('U_MannWhitneyu', y_test, np.asarray(ys).T[0])
score_VUS_1 = getScore('VUS_1', y_test, y_hat)
score_VUS_2 = getScore('VUS_2', y_test, y_score)
output['score'].append(
[lrt, lr, hu, ni, ii, acc, score_u, score_VUS_1, score_VUS_2]
+ confMatrix[0] + confMatrix[1] + confMatrix[2] +
[(endTime - startTime).seconds + (endTime - startTime).microseconds / 10 ** 6])
saveXLSX(output, 'results/mlp_3classes_w.xlsx')
startTime = datetime.now()
for i in range(ni):
# print(i)
batch_xs, batch_ys = X_train, y_train
sess.run(train_step, feed_dict={x: batch_xs, yy_: batch_ys})
endTime = datetime.now()
correct_prediction = tf.equal(tf.argmax(yy,1), tf.argmax(yy_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
y_score = sess.run(yy, feed_dict={x: X_test, yy_: y_test})
acc = sess.run(accuracy, feed_dict={x: X_test, yy_: y_test})
y_hat = np.argmax(y_score, axis=1)
y_test = np.argmax(y_test, axis=1)
fpr, tpr, threshold = getScore('ROC', y_test, y_hat)
score_auc = getScore('AUC', y_test, y_hat)
tmp = np.append(X_test, np.reshape(y_test, (1, y_test.shape[0])).T, axis=1)
tmp = np.append(tmp, np.reshape(y_hat, (1, y_hat.shape[0])).T, axis=1)
tmp = np.append(tmp, y_score, axis=1)
# tmp = np.append(tmp, np.asarray(ys), axis=1)
output['data'] = [['X_' + str(i) for i in range(1, X_test.shape[1] + 1)] +
['y_label', 'y_hat', 'y_score', 'y_score']] + \
tmp.tolist()
output['score'].append(
[lrt, lr, hu, ni, ii, acc, fpr[1], tpr[1], metrics.auc(fpr, tpr), score_auc,
(endTime - startTime).seconds + (
endTime - startTime).microseconds / 10 ** 6])
axis=1)
tmp = np.append(tmp, y_score, axis=1)
tmp = np.append(tmp, np.asarray(ys), axis=1)
output['data'] = [['X_' + str(i) for i in range(1, X_test.shape[1] + 1)] +
['y_label', 'y_hat', 'y_score_1', 'y_score_2', 'y_score_3', 'ys']] + \
tmp.tolist()
acc = accuracy_score(y_hat, y_test)
confMatrix = confusion_matrix(y_test, y_hat).tolist()
# fpr, tpr, threshold = getScore('ROC', y_test, y_hat)
# score_auc = getScore('AUC', y_test, y_hat)
score_u = getScore('U', y_test, y_score)
# score_u_s = getScore('U_S', y_test, ys)
# score_u_m = getScore('U_MannWhitneyu', y_test, np.asarray(ys).T[0])
score_VUS_1 = getScore('VUS_1', y_test, y_hat)
score_VUS_2 = getScore('VUS_2', y_test, y_score)
output['score'].append(
[
lrt, lr, hu, ni, ii, acc, score_u, score_VUS_1,
score_VUS_2
] + confMatrix[0] + confMatrix[1] + confMatrix[2] +
[(endTime - startTime).seconds +
(endTime - startTime).microseconds / 10**6])
saveXLSX(output, 'results/mlp_3classes_bs.xlsx')