def compute_dtw(data, dataarray, w):
click.echo('--- Compute DTW ---')
timeseries, timeseries_label = load_labelled(dataarray)
timeserie_1 = load_test(data)
click.echo(' - data : %s ' % data)
click.echo(' - dataarray : %s ' % dataarray)
click.echo(' - w : %d ' % w)
click.echo('\nRunning...')
unsorted_dtws = get_distances(timeserie_1[0], data_array=timeseries, max_warping_window=w)
# Save plots
dtw_plots(unsorted_dtws)
click.echo('Done. Plots have been saved.')
click.echo('Choose a maximum number for labelling good and bad data based on DTW values.')
click.echo('Check the plots to take better decsision.')
click.echo(' Example: If value for Good is 150, all data with DTW 0-150 will be labelled "Good".')
# Enter limit for 'Good'
good_value = raw_input(' > Enter a value for "Good" (Ex: 150) : ')
# Enter limit for 'Bad'
bad_value = raw_input(' > Enter a value for "Bad" (Ex: 350) : ')
# Print and save results to CSV
fileName = raw_input(' > Enter a file name (add .csv at the end) : ')
label_dtws(unsorted_dtws, int(good_value), int(bad_value), fileName)
click.echo('\nDone.')
def compute_dtw(data, dataarray, w):
click.echo('--- Compute DTW ---')
timeseries, timeseries_label = load_labelled(dataarray)
timeserie_1 = load_test(data)
click.echo(' - data : %s ' % data)
click.echo(' - dataarray : %s ' % dataarray)
click.echo(' - w : %d ' % w)
click.echo('\nRunning...')
unsorted_dtws = get_distances(timeserie_1[0], data_array=timeseries, max_warping_window=w)
# Save plots
dtw_plots(unsorted_dtws)
click.echo('Done. Plots have been saved.')
click.echo('Choose a maximum number for labelling good and bad data based on DTW values.')
click.echo('Check the plots to take better decsision.')
click.echo(' Example: If value for Good is 150, all data with DTW 0-150 will be labelled "Good".')
# Enter limit for 'Good'
good_value = raw_input(' > Enter a value for "Good" (Ex: 150) : ')
# Enter limit for 'Bad'
bad_value = raw_input(' > Enter a value for "Bad" (Ex: 350) : ')
# Print and save results to CSV
fileName = raw_input(' > Enter a file name (add .csv at the end) : ')
label_dtws(unsorted_dtws, int(good_value), int(bad_value), fileName)
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.')
def find_best_w(wmin, wmax, step, k, seed, train):
click.echo('--- Find best w ---')
wmin = int(min(wmin, wmax))
wmax = int(max(wmin, wmax))
ws = range(wmin, wmax, step)
train_data, train_label = load_labelled(train)
click.echo(' - ws : %s ' % str(ws))
click.echo(' - k : %d ' % k)
click.echo(' - seed : %d ' % seed)
click.echo(' - train : %s ' % train)
click.echo(' - Training data size: %d' % len(train_data))
click.echo('\nRunning...')
trainer = Trainer(seed=seed, data=train_data, data_labels=train_label)
trainer.find_best_w(k, ws)
click.echo('\nDone.')
def find_best_k(kmin, kmax, w, seed, train):
click.echo('--- Find best k ---')
kmin = int(min(kmin, kmax))
kmax = int(max(kmin, kmax))
ks = range(kmin, kmax + 1)
train_data, train_label = load_labelled(train)
click.echo(' - ks : %s ' % str(ks))
click.echo(' - w : %d ' % w)
click.echo(' - seed : %d ' % seed)
click.echo(' - train : %s ' % train)
click.echo(' - Training data size: %d' % len(train_data))
click.echo('\nRunning...')
trainer = Trainer(seed=seed, data=train_data, data_labels=train_label)
trainer.find_best_k(ks, w)
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.')
def find_best_w(wmin, wmax, step, k, seed, train):
click.echo('--- Find best w ---')
wmin = int(min(wmin, wmax))
wmax = int(max(wmin, wmax))
ws = range(wmin, wmax, step)
train_data, train_label = load_labelled(train)
click.echo(' - ws : %s ' % str(ws))
click.echo(' - k : %d ' % k)
click.echo(' - seed : %d ' % seed)
click.echo(' - train : %s ' % train)
click.echo(' - Training data size: %d' % len(train_data))
click.echo('\nRunning...')
trainer = Trainer(seed=seed, data=train_data, data_labels=train_label)
trainer.find_best_w(k, ws)
click.echo('\nDone.')
def find_best_k(kmin, kmax, w, seed, train):
click.echo('--- Find best k ---')
kmin = int(min(kmin, kmax))
kmax = int(max(kmin, kmax))
ks = range(kmin, kmax + 1)
train_data, train_label = load_labelled(train)
click.echo(' - ks : %s ' % str(ks))
click.echo(' - w : %d ' % w)
click.echo(' - seed : %d ' % seed)
click.echo(' - train : %s ' % train)
click.echo(' - Training data size: %d' % len(train_data))
click.echo('\nRunning...')
trainer = Trainer(seed=seed, data=train_data, data_labels=train_label)
trainer.find_best_k(ks, w)
click.echo('\nDone.')
