def evaluate_model(self, k, max_warping_window, train_data, train_label, test_data, test_label):
print '--------------------------'
print '--------------------------\n'
print 'Running for k = ', k
print 'Running for w = ', max_warping_window
model = KnnDtw(k_neighbours = k, max_warping_window = max_warping_window)
model.fit(train_data, train_label)
predicted_label, probability = model.predict(test_data)
print '\nPredicted : ', predicted_label
print 'Actual : ', test_label
accuracy, precision, recall, f1score = evaluate(self.labels, predicted_label, test_label)
print 'Avg/Total Accuracy :', accuracy
print 'Avg/Total Precision :', precision
print 'Avg/Total Recall :', recall
print 'Avg/Total F1 Score :', f1score
def predict(k, w, train, test):
click.echo('--- Predicting a label ---')
#click.echo('Predicting with k=%d and w=%d.' % (k,w))
train_data, train_label = load_labelled(train)
test_data = load_test(test)
click.echo(' - k : %d ' % k)
click.echo(' - w : %d ' % w)
click.echo(' - train : %s ' % train)
click.echo(' - test : %s ' % test)
click.echo('\nRunning...')
model = KnnDtw(k_neighbours=k, max_warping_window=w)
model.fit(train_data, train_label)
predicted_label, probability = model.predict(test_data)
click.echo('\nPredicted label : %s ' % str(predicted_label))
click.echo('\nDone.')
def evaluate_model(self, k, max_warping_window, train_data, train_label,
test_data, test_label):
print '--------------------------'
print '--------------------------\n'
print 'Running for k = ', k
print 'Running for w = ', max_warping_window
model = KnnDtw(k_neighbours=k, max_warping_window=max_warping_window)
model.fit(train_data, train_label)
predicted_label, probability = model.predict(test_data)
print '\nPredicted : ', predicted_label
print 'Actual : ', test_label
accuracy, precision, recall, f1score = evaluate(
self.labels, predicted_label, test_label)
print 'Avg/Total Accuracy :', accuracy
print 'Avg/Total Precision :', precision
print 'Avg/Total Recall :', recall
print 'Avg/Total F1 Score :', f1score
def online_identification(k, w, window_size, train, test):
click.echo('\n--- Online flight degradation identification ---')
train_csv = load_csvs(train, should_preprocess=False)
test_csv = load_csvs(test, should_preprocess=False)
click.echo(' - k : %d ' % k)
click.echo(' - w : %d ' % w)
click.echo(' - train : %s ' % train)
click.echo(' - test : %s ' % test)
click.echo('\nRunning...\n')
# For Test
last_second = test_csv[0]['data']['seconds'].iloc[-1]
#print('[TEST] Last second: {}'.format(last_second))
for time_window in range(0, last_second, window_size):
click.echo('Time window is: %d ' % time_window)
train_window = get_windows(train_csv, time_window, time_window + window_size - 1)
test_window = get_windows(test_csv, time_window, time_window + window_size - 1)
train_data, train_label = window_to_lists(train_window, 'pose_position_z')
test_data, test_label = window_to_lists(test_window, 'pose_position_z')
model = KnnDtw(k_neighbours = k, max_warping_window = w)
model.fit(np.array(train_data), np.array(train_label))
#click.echo(train_label)
predicted_label, probability = model.predict(test_data, parallel=True)
click.echo('Predicted label : %s ' % str(predicted_label))
click.echo('\n')
click.echo('\nDone.')
def predict(k, w, train, test):
click.echo('--- Predicting a label ---')
#click.echo('Predicting with k=%d and w=%d.' % (k,w))
train_data, train_label = load_labelled(train)
test_data = load_test(test)
click.echo(' - k : %d ' % k)
click.echo(' - w : %d ' % w)
click.echo(' - train : %s ' % train)
click.echo(' - test : %s ' % test)
click.echo('\nRunning...')
model = KnnDtw(k_neighbours = k, max_warping_window = w)
model.fit(train_data, train_label)
predicted_label, probability = model.predict(test_data)
click.echo('\nPredicted label : %s ' % str(predicted_label))
click.echo('\nDone.')
