def check_speed(decoder, max_count=float('inf')):
"""
Test decoding speed accross several classifications.
Parameters
----------
decoder : BCIDecoder or BCIDecoderDaemon class
The decoder to assess its performance
max_count : int
The number of classification for averaging
"""
tm = Timer()
count = 0
mslist = []
while count < max_count:
while decoder.get_prob_unread() is None:
pass
count += 1
if tm.sec() > 1:
t = tm.sec()
ms = 1000.0 * t / count
# show time per classification and its reciprocal
print('%.0f ms/c %.1f Hz' % (ms, count / t))
mslist.append(ms)
count = 0
tm.reset()
print('mean = %.1f ms' % np.mean(mslist))
def fit_predict_thres(cls,
X_train,
Y_train,
X_test,
Y_test,
cnum,
label_list,
ignore_thres=None,
decision_thres=None):
"""
Any likelihood lower than a threshold is not counted as classification score
Confusion matrix, accuracy and F1 score (macro average) are computed.
Params
======
ignore_thres:
if not None or larger than 0, likelihood values lower than ignore_thres will be ignored
while computing confusion matrix.
"""
timer = Timer()
cls.fit(X_train, Y_train)
assert ignore_thres is None or ignore_thres >= 0
if ignore_thres is None or ignore_thres == 0:
Y_pred = cls.predict(X_test)
score = skmetrics.accuracy_score(Y_test, Y_pred)
cm = skmetrics.confusion_matrix(Y_test, Y_pred, label_list)
f1 = skmetrics.f1_score(Y_test, Y_pred, average='macro')
else:
if decision_thres is not None:
logger.error(
'decision threshold and ignore_thres cannot be set at the same time.'
)
raise ValueError
Y_pred = cls.predict_proba(X_test)
Y_pred_labels = np.argmax(Y_pred, axis=1)
Y_pred_maxes = np.array([x[i] for i, x in zip(Y_pred_labels, Y_pred)])
Y_index_overthres = np.where(Y_pred_maxes >= ignore_thres)[0]
Y_index_underthres = np.where(Y_pred_maxes < ignore_thres)[0]
Y_pred_overthres = np.array(
[cls.classes_[x] for x in Y_pred_labels[Y_index_overthres]])
Y_pred_underthres = np.array(
[cls.classes_[x] for x in Y_pred_labels[Y_index_underthres]])
Y_pred_underthres_count = np.array(
[np.count_nonzero(Y_pred_underthres == c) for c in label_list])
Y_test_overthres = Y_test[Y_index_overthres]
score = skmetrics.accuracy_score(Y_test_overthres, Y_pred_overthres)
cm = skmetrics.confusion_matrix(Y_test_overthres, Y_pred_overthres,
label_list)
cm = np.concatenate((cm, Y_pred_underthres_count[:, np.newaxis]),
axis=1)
f1 = skmetrics.f1_score(Y_test_overthres,
Y_pred_overthres,
average='macro')
logger.info('Cross-validation %d (%.3f) - %.1f sec' %
(cnum, score, timer.sec()))
return score, cm, f1
def get_predict_proba(cls, X_train, Y_train, X_test, Y_test, cnum):
"""
All likelihoods will be collected from every fold of a cross-validaiton. Based on these likelihoods,
a threshold will be computed that will balance the true positive rate of each class.
Available with binary classification scenario only.
"""
timer = Timer()
cls.fit(X_train, Y_train)
Y_pred = cls.predict_proba(X_test)
logger.info('Cross-validation %d (%d tests) - %.1f sec' %
(cnum, Y_pred.shape[0], timer.sec()))
return Y_pred[:, 0]
def sample_decoding(decoder):
"""
Decoding example
Parameters
----------
decoder : The decoder to use
"""
def get_index_max(seq):
if type(seq) == list:
return max(range(len(seq)), key=seq.__getitem__)
elif type(seq) == dict:
return max(seq, key=seq.__getitem__)
else:
logger.error('Unsupported input %s' % type(seq))
return None
# load trigger definitions for labeling
labels = decoder.get_label_names()
tm_watchdog = Timer(autoreset=True)
tm_cls = Timer()
while True:
praw = decoder.get_prob_unread()
psmooth = decoder.get_prob_smooth()
if praw is None:
# watch dog
if tm_cls.sec() > 5:
logger.warning(
'No classification was done in the last 5 seconds. Are you receiving data streams?'
)
tm_cls.reset()
tm_watchdog.sleep_atleast(0.001)
continue
txt = '[%8.1f msec]' % (tm_cls.msec())
for i, label in enumerate(labels):
txt += ' %s %.3f (raw %.3f)' % (label, psmooth[i], praw[i])
maxi = get_index_max(psmooth)
txt += ' %s' % labels[maxi]
print(txt)
tm_cls.reset()
def run(cfg, state=mp.Value('i', 1), queue=None):
redirect_stdout_to_queue(logger, queue, 'INFO')
# Wait the recording to start (GUI)
while state.value == 2: # 0: stop, 1:start, 2:wait
pass
# Protocol start if equals to 1
if not state.value:
sys.exit()
refresh_delay = 1.0 / cfg.REFRESH_RATE
cfg.tdef = TriggerDef(cfg.TRIGGER_FILE)
# visualizer
keys = {'left':81, 'right':83, 'up':82, 'down':84, 'pgup':85, 'pgdn':86,
'home':80, 'end':87, 'space':32, 'esc':27, ',':44, '.':46, 's':115, 'c':99,
'[':91, ']':93, '1':49, '!':33, '2':50, '@':64, '3':51, '#':35}
color = dict(G=(20, 140, 0), B=(210, 0, 0), R=(0, 50, 200), Y=(0, 215, 235),
K=(0, 0, 0), w=(200, 200, 200))
dir_sequence = []
for x in range(cfg.TRIALS_EACH):
dir_sequence.extend(cfg.DIRECTIONS)
random.shuffle(dir_sequence)
num_trials = len(cfg.DIRECTIONS) * cfg.TRIALS_EACH
event = 'start'
trial = 1
# Hardware trigger
if cfg.TRIGGER_DEVICE is None:
logger.warning('No trigger device set. Press Ctrl+C to stop or Enter to continue.')
#input()
trigger = Trigger(lpttype=cfg.TRIGGER_DEVICE, state=state)
if trigger.init(50) == False:
logger.error('\n** Error connecting to USB2LPT device. Use a mock trigger instead?')
input('Press Ctrl+C to stop or Enter to continue.')
trigger = Trigger(lpttype='FAKE')
trigger.init(50)
# timers
timer_trigger = Timer()
timer_dir = Timer()
timer_refresh = Timer()
t_dir = cfg.TIMINGS['DIR'] + random.uniform(-cfg.TIMINGS['DIR_RANDOMIZE'], cfg.TIMINGS['DIR_RANDOMIZE'])
t_dir_ready = cfg.TIMINGS['READY'] + random.uniform(-cfg.TIMINGS['READY_RANDOMIZE'], cfg.TIMINGS['READY_RANDOMIZE'])
bar = BarVisual(cfg.GLASS_USE, screen_pos=cfg.SCREEN_POS, screen_size=cfg.SCREEN_SIZE)
bar.fill()
bar.glass_draw_cue()
# start
while trial <= num_trials:
timer_refresh.sleep_atleast(refresh_delay)
timer_refresh.reset()
# segment= { 'cue':(s,e), 'dir':(s,e), 'label':0-4 } (zero-based)
if event == 'start' and timer_trigger.sec() > cfg.TIMINGS['INIT']:
event = 'gap_s'
bar.fill()
timer_trigger.reset()
trigger.signal(cfg.tdef.INIT)
elif event == 'gap_s':
if cfg.TRIAL_PAUSE:
bar.put_text('Press any key')
bar.update()
key = cv2.waitKey()
if key == keys['esc'] or not state.value:
break
bar.fill()
bar.put_text('Trial %d / %d' % (trial, num_trials))
event = 'gap'
timer_trigger.reset()
elif event == 'gap' and timer_trigger.sec() > cfg.TIMINGS['GAP']:
event = 'cue'
bar.fill()
bar.draw_cue()
trigger.signal(cfg.tdef.CUE)
timer_trigger.reset()
elif event == 'cue' and timer_trigger.sec() > cfg.TIMINGS['CUE']:
event = 'dir_r'
dir = dir_sequence[trial - 1]
if dir == 'L': # left
bar.move('L', 100, overlay=True)
trigger.signal(cfg.tdef.LEFT_READY)
elif dir == 'R': # right
bar.move('R', 100, overlay=True)
trigger.signal(cfg.tdef.RIGHT_READY)
elif dir == 'U': # up
bar.move('U', 100, overlay=True)
trigger.signal(cfg.tdef.UP_READY)
elif dir == 'D': # down
bar.move('D', 100, overlay=True)
trigger.signal(cfg.tdef.DOWN_READY)
elif dir == 'B': # both hands
bar.move('L', 100, overlay=True)
bar.move('R', 100, overlay=True)
trigger.signal(cfg.tdef.BOTH_READY)
else:
raise RuntimeError('Unknown direction %d' % dir)
timer_trigger.reset()
elif event == 'dir_r' and timer_trigger.sec() > t_dir_ready:
bar.fill()
bar.draw_cue()
event = 'dir'
timer_trigger.reset()
timer_dir.reset()
if dir == 'L': # left
trigger.signal(cfg.tdef.LEFT_GO)
elif dir == 'R': # right
trigger.signal(cfg.tdef.RIGHT_GO)
elif dir == 'U': # up
trigger.signal(cfg.tdef.UP_GO)
elif dir == 'D': # down
trigger.signal(cfg.tdef.DOWN_GO)
elif dir == 'B': # both
trigger.signal(cfg.tdef.BOTH_GO)
else:
raise RuntimeError('Unknown direction %d' % dir)
elif event == 'dir' and timer_trigger.sec() > t_dir:
event = 'gap_s'
bar.fill()
trial += 1
logger.info('trial ' + str(trial - 1) + ' done')
trigger.signal(cfg.tdef.BLANK)
timer_trigger.reset()
t_dir = cfg.TIMINGS['DIR'] + random.uniform(-cfg.TIMINGS['DIR_RANDOMIZE'], cfg.TIMINGS['DIR_RANDOMIZE'])
t_dir_ready = cfg.TIMINGS['READY'] + random.uniform(-cfg.TIMINGS['READY_RANDOMIZE'], cfg.TIMINGS['READY_RANDOMIZE'])
# protocol
if event == 'dir':
dx = min(100, int(100.0 * timer_dir.sec() / t_dir) + 1)
if dir == 'L': # L
bar.move('L', dx, overlay=True)
elif dir == 'R': # R
bar.move('R', dx, overlay=True)
elif dir == 'U': # U
bar.move('U', dx, overlay=True)
elif dir == 'D': # D
bar.move('D', dx, overlay=True)
elif dir == 'B': # Both
bar.move('L', dx, overlay=True)
bar.move('R', dx, overlay=True)
# wait for start
if event == 'start':
bar.put_text('Waiting to start')
bar.update()
key = 0xFF & cv2.waitKey(1)
if key == keys['esc'] or not state.value:
break
bar.finish()
with state.get_lock():
state.value = 0
def __init__(self,
image_path,
use_glass=False,
glass_feedback=True,
pc_feedback=True,
screen_pos=None,
screen_size=None):
"""
Input:
use_glass: if False, mock Glass will be used
glass_feedback: show feedback to the user?
pc_feedback: show feedback on the pc screen?
screen_pos: screen position in (x,y)
screen_size: screen size in (x,y)
"""
# screen size and message setting
if screen_size is None:
if sys.platform.startswith('win'):
from win32api import GetSystemMetrics
screen_width = GetSystemMetrics(0)
screen_height = GetSystemMetrics(1)
else:
screen_width = 1024
screen_height = 768
screen_size = (screen_width, screen_height)
else:
screen_width, screen_height = screen_size
if screen_pos is None:
screen_x, screen_y = (0, 0)
else:
screen_x, screen_y = screen_pos
self.text_size = 2
self.img = np.zeros((screen_height, screen_width, 3), np.uint8)
self.glass = bgi_client.GlassControl(mock=not use_glass)
self.glass.connect('127.0.0.1', 59900)
self.set_glass_feedback(glass_feedback)
self.set_pc_feedback(pc_feedback)
self.set_cue_color(boxcol='B', crosscol='W')
self.width = self.img.shape[1]
self.height = self.img.shape[0]
hw = int(self.barwidth / 2)
self.cx = int(self.width / 2)
self.cy = int(self.height / 2)
self.xl1 = self.cx - hw
self.xl2 = self.xl1 - self.barwidth
self.xr1 = self.cx + hw
self.xr2 = self.xr1 + self.barwidth
self.yl1 = self.cy - hw
self.yl2 = self.yl1 - self.barwidth
self.yr1 = self.cy + hw
self.yr2 = self.yr1 + self.barwidth
if os.path.isdir(image_path):
# load images
left_image_path = '%s/left' % image_path
right_image_path = '%s/right' % image_path
tm = Timer()
logger.info('Reading images from %s' % left_image_path)
self.left_images = read_images(left_image_path, screen_size)
logger.info('Reading images from %s' % right_image_path)
self.right_images = read_images(right_image_path, screen_size)
logger.info('Took %.1f s' % tm.sec())
else:
# load pickled images
# note: this is painfully slow in Pytohn 2 even with cPickle (3s vs 27s)
assert image_path[-4:] == '.pkl', 'The file must be of .pkl format'
logger.info('Loading image binary file %s ...' % image_path)
tm = Timer()
with gzip.open(image_path, 'rb') as fp:
image_data = pickle.load(fp)
self.left_images = image_data['left_images']
self.right_images = image_data['right_images']
feedback_w = self.left_images[0].shape[1] / 2
feedback_h = self.left_images[0].shape[0] / 2
loc_x = [int(self.cx - feedback_w), int(self.cx + feedback_w)]
loc_y = [int(self.cy - feedback_h), int(self.cy + feedback_h)]
img_fit = np.zeros((screen_height, screen_width, 3), np.uint8)
# adjust to the current screen size
logger.info('Fitting images into the current screen size')
for i, img in enumerate(self.left_images):
img_fit = np.zeros((screen_height, screen_width, 3), np.uint8)
img_fit[loc_y[0]:loc_y[1], loc_x[0]:loc_x[1]] = img
self.left_images[i] = img_fit
for i, img in enumerate(self.right_images):
img_fit = np.zeros((screen_height, screen_width, 3), np.uint8)
img_fit[loc_y[0]:loc_y[1], loc_x[0]:loc_x[1]] = img
self.right_images[i] = img_fit
logger.info('Took %.1f s' % tm.sec())
logger.info('Done.')
cv2.namedWindow("Protocol", cv2.WND_PROP_FULLSCREEN)
cv2.moveWindow("Protocol", screen_x, screen_y)
cv2.setWindowProperty("Protocol", cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
def log_decoding(decoder,
logfile,
amp_name=None,
pklfile=True,
matfile=False,
autostop=False,
prob_smooth=False):
"""
Decode online and write results with event timestamps
Parameters
----------
decoder : BCIDecoder or BCIDecoderDaemon class
The decoder to use
logfile : str
The file path to contain the result in Python pickle format
amp_name : str
The stream name to connect to
pklfile : bool
If True, export the results to Python pickle format
matfile : bool
If True, export the results to .mat file
autostop : bool
If True, automatically finish when no more data is received.
prob_smooth : bool
If True, use smoothed probability values according to decoder's smoothing parameter.
"""
import cv2
import scipy
# run event acquisition process in the background
state = mp.Value('i', 1)
event_queue = mp.Queue()
proc = mp.Process(target=_log_decoding_helper,
args=[state, event_queue, amp_name, autostop])
proc.start()
logger.info('Spawned event acquisition process.')
# init variables and choose decoding function
labels = decoder.get_label_names()
probs = []
prob_times = []
if prob_smooth:
decode_fn = decoder.get_prob_smooth_unread
else:
decode_fn = decoder.get_prob_unread
# simple controller UI
cv2.namedWindow("Decoding", cv2.WINDOW_AUTOSIZE)
cv2.moveWindow("Decoding", 1400, 50)
img = np.zeros([100, 400, 3], np.uint8)
cv2.putText(img, 'Press any key to start', (20, 60),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
cv2.imshow("Decoding", img)
cv2.waitKeyEx()
img *= 0
cv2.putText(img, 'Press ESC to stop', (40, 60), cv2.FONT_HERSHEY_SIMPLEX,
1, (255, 255, 255), 2, cv2.LINE_AA)
cv2.imshow("Decoding", img)
key = 0
started = False
tm_watchdog = Timer(autoreset=True)
tm_cls = Timer()
while key != 27:
prob, prob_time = decode_fn(True)
t_lsl = pylsl.local_clock()
key = cv2.waitKeyEx(1)
if prob is None:
# watch dog
if tm_cls.sec() > 5:
if autostop and started:
logger.info('No more streaming data. Finishing.')
break
tm_cls.reset()
tm_watchdog.sleep_atleast(0.001)
continue
probs.append(prob)
prob_times.append(prob_time)
txt = '[%.3f] ' % prob_time
txt += ', '.join(['%s: %.2f' % (l, p) for l, p in zip(labels, prob)])
txt += ' (%d ms, LSL Diff = %.3f)' % (tm_cls.msec(),
(t_lsl - prob_time))
logger.info(txt)
if not started:
started = True
tm_cls.reset()
# finish up processes
cv2.destroyAllWindows()
logger.info('Cleaning up event acquisition process.')
state.value = 0
decoder.stop()
event_times, event_values = event_queue.get()
proc.join()
# save values
if len(prob_times) == 0:
logger.error('No decoding result. Please debug.')
import pdb
pdb.set_trace()
t_start = prob_times[0]
probs = np.vstack(probs)
event_times = np.array(event_times)
event_times = event_times[np.where(event_times >= t_start)[0]] - t_start
prob_times = np.array(prob_times) - t_start
event_values = np.array(event_values)
data = dict(probs=probs,
prob_times=prob_times,
event_times=event_times,
event_values=event_values,
labels=labels)
if pklfile:
with open(logfile, 'wb') as f:
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
logger.info('Saved to %s' % logfile)
if matfile:
pp = io.parse_path(logfile)
pp = Path(logfile)
matout = '%s/%s.mat' % (pp.parent, pp.stem)
scipy.io.savemat(matout, data)
logger.info('Saved to %s' % matout)
class Feedback:
"""
Perform a classification with visual feedback
"""
def __init__(self, cfg, viz, tdef, trigger, logfile=None):
self.cfg = cfg
self.tdef = tdef
self.trigger = trigger
self.viz = viz
self.viz.fill()
self.refresh_delay = 1.0 / self.cfg.REFRESH_RATE
self.bar_step_left = self.cfg.BAR_STEP['left']
self.bar_step_right = self.cfg.BAR_STEP['right']
self.bar_step_up = self.cfg.BAR_STEP['up']
self.bar_step_down = self.cfg.BAR_STEP['down']
self.bar_step_both = self.cfg.BAR_STEP['both']
if type(self.cfg.BAR_BIAS) is tuple:
self.bar_bias = list(self.cfg.BAR_BIAS)
else:
self.bar_bias = self.cfg.BAR_BIAS
# New decoder: already smoothed by the decoder so bias after.
#self.alpha_old = self.cfg.PROB_ACC_ALPHA
#self.alpha_new = 1.0 - self.cfg.PROB_ACC_ALPHA
if hasattr(self.cfg,
'BAR_REACH_FINISH') and self.cfg.BAR_REACH_FINISH == True:
self.premature_end = True
else:
self.premature_end = False
self.tm_trigger = Timer()
self.tm_display = Timer()
self.tm_watchdog = Timer()
if logfile is not None:
self.logf = open(logfile, 'w')
else:
self.logf = None
# STIMO only
if self.cfg.WITH_STIMO is True:
if self.cfg.STIMO_COMPORT is None:
atens = [x for x in serial.tools.list_ports.grep('ATEN')]
if len(atens) == 0:
raise RuntimeError('No ATEN device found. Stop.')
try:
self.stimo_port = atens[0].device
except AttributeError: # depends on Python distribution
self.stimo_port = atens[0][0]
else:
self.stimo_port = self.cfg.STIMO_COMPORT
self.ser = serial.Serial(self.stimo_port, self.cfg.STIMO_BAUDRATE)
logger.info('STIMO serial port %s is_open = %s' %
(self.stimo_port, self.ser.is_open))
# FES only
if self.cfg.WITH_FES is True:
self.stim = fes.Motionstim8()
self.stim.OpenSerialPort(self.cfg.FES_COMPORT)
self.stim.InitializeChannelListMode()
logger.info('Opened FES serial port')
def __del__(self):
# STIMO only
if self.cfg.WITH_STIMO is True:
self.ser.close()
logger.info('Closed STIMO serial port %s' % self.stimo_port)
# FES only
if self.cfg.WITH_FES is True:
stim_code = [0, 0, 0, 0, 0, 0, 0, 0]
self.stim.UpdateChannelSettings(stim_code)
self.stim.CloseSerialPort()
logger.info('Closed FES serial port')
def classify(self,
decoder,
true_label,
title_text,
bar_dirs,
state='start',
prob_history=None):
"""
Run a single trial
"""
def list2string(vec, fmt, sep=' '):
return sep.join(fmt % x for x in vec)
true_label_index = bar_dirs.index(true_label)
self.tm_trigger.reset()
if self.bar_bias is not None:
bias_idx = bar_dirs.index(self.bar_bias[0])
if self.logf is not None:
self.logf.write('True label: %s\n' % true_label)
tm_classify = Timer(autoreset=True)
self.stimo_timer = Timer()
while True:
self.tm_display.sleep_atleast(self.refresh_delay)
self.tm_display.reset()
if state == 'start' and self.tm_trigger.sec(
) > self.cfg.TIMINGS['INIT']:
state = 'gap_s'
if self.cfg.TRIALS_PAUSE:
self.viz.put_text('Press any key')
self.viz.update()
key = cv2.waitKeyEx()
if key == KEYS['esc']:
return
self.viz.fill()
self.tm_trigger.reset()
self.trigger.signal(self.tdef.INIT)
elif state == 'gap_s':
if self.cfg.TIMINGS['GAP'] > 0:
self.viz.put_text(title_text)
state = 'gap'
self.tm_trigger.reset()
elif state == 'gap' and self.tm_trigger.sec(
) > self.cfg.TIMINGS['GAP']:
state = 'cue'
self.viz.fill()
self.viz.draw_cue()
self.viz.glass_draw_cue()
self.trigger.signal(self.tdef.CUE)
self.tm_trigger.reset()
elif state == 'cue' and self.tm_trigger.sec(
) > self.cfg.TIMINGS['READY']:
state = 'dir_r'
if self.cfg.SHOW_CUE is True:
if self.cfg.FEEDBACK_TYPE == 'BAR':
self.viz.move(true_label,
100,
overlay=False,
barcolor='G')
elif self.cfg.FEEDBACK_TYPE == 'BODY':
self.viz.put_text(DIRS[true_label], 'R')
if true_label == 'L': # left
self.trigger.signal(self.tdef.LEFT_READY)
elif true_label == 'R': # right
self.trigger.signal(self.tdef.RIGHT_READY)
elif true_label == 'U': # up
self.trigger.signal(self.tdef.UP_READY)
elif true_label == 'D': # down
self.trigger.signal(self.tdef.DOWN_READY)
elif true_label == 'B': # both hands
self.trigger.signal(self.tdef.BOTH_READY)
else:
raise RuntimeError('Unknown direction %s' % true_label)
self.tm_trigger.reset()
'''
if self.cfg.FEEDBACK_TYPE == 'BODY':
self.viz.set_pc_feedback(False)
self.viz.move(true_label, 100, overlay=False, barcolor='G')
if self.cfg.FEEDBACK_TYPE == 'BODY':
self.viz.set_pc_feedback(True)
if self.cfg.SHOW_CUE is True:
self.viz.put_text(dirs[true_label], 'R')
if true_label == 'L': # left
self.trigger.signal(self.tdef.LEFREADY)
elif true_label == 'R': # right
self.trigger.signal(self.tdef.RIGHT_READY)
elif true_label == 'U': # up
self.trigger.signal(self.tdef.UP_READY)
elif true_label == 'D': # down
self.trigger.signal(self.tdef.DOWN_READY)
elif true_label == 'B': # both hands
self.trigger.signal(self.tdef.BOTH_READY)
else:
raise RuntimeError('Unknown direction %s' % true_label)
self.tm_trigger.reset()
'''
elif state == 'dir_r' and self.tm_trigger.sec(
) > self.cfg.TIMINGS['DIR_CUE']:
self.viz.fill()
self.viz.draw_cue()
self.viz.glass_draw_cue()
state = 'dir'
# initialize bar scores
bar_label = bar_dirs[0]
bar_score = 0
probs = [1.0 / len(bar_dirs)] * len(bar_dirs)
self.viz.move(bar_label, bar_score, overlay=False)
probs_acc = np.zeros(len(probs))
if true_label == 'L': # left
self.trigger.signal(self.tdef.LEFT_GO)
elif true_label == 'R': # right
self.trigger.signal(self.tdef.RIGHT_GO)
elif true_label == 'U': # up
self.trigger.signal(self.tdef.UP_GO)
elif true_label == 'D': # down
self.trigger.signal(self.tdef.DOWN_GO)
elif true_label == 'B': # both
self.trigger.signal(self.tdef.BOTH_GO)
else:
raise RuntimeError('Unknown truedirection %s' % true_label)
self.tm_watchdog.reset()
self.tm_trigger.reset()
elif state == 'dir':
if self.tm_trigger.sec() > self.cfg.TIMINGS['CLASSIFY'] or (
self.premature_end and bar_score >= 100):
if not hasattr(
self.cfg,
'SHOW_RESULT') or self.cfg.SHOW_RESULT is True:
# show classfication result
if self.cfg.WITH_STIMO is True:
if self.cfg.STIMO_FULLGAIT_CYCLE is not None and bar_label == 'U':
res_color = 'G'
elif self.cfg.TRIALS_RETRY is False or bar_label == true_label:
res_color = 'G'
else:
res_color = 'Y'
else:
res_color = 'Y'
if self.cfg.FEEDBACK_TYPE == 'BODY':
self.viz.move(bar_label,
bar_score,
overlay=False,
barcolor=res_color,
caption=DIRS[bar_label],
caption_color=res_color)
else:
self.viz.move(bar_label,
100,
overlay=False,
barcolor=res_color)
else:
if self.cfg.FEEDBACK_TYPE == 'BODY':
self.viz.move(bar_label,
bar_score,
overlay=False,
barcolor=res_color,
caption='TRIAL END',
caption_color=res_color)
else:
self.viz.move(bar_label,
0,
overlay=False,
barcolor=res_color)
self.trigger.signal(self.tdef.FEEDBACK)
# STIMO
if self.cfg.WITH_STIMO is True and self.cfg.STIMO_CONTINUOUS is False:
if self.cfg.STIMO_FULLGAIT_CYCLE is not None:
if bar_label == 'U':
self.ser.write(
self.cfg.STIMO_FULLGAIT_PATTERN[0])
logger.info('STIMO: Sent 1')
time.sleep(self.cfg.STIMO_FULLGAIT_CYCLE)
self.ser.write(
self.cfg.STIMO_FULLGAIT_PATTERN[1])
logger.info('STIMO: Sent 2')
time.sleep(self.cfg.STIMO_FULLGAIT_CYCLE)
elif self.cfg.TRIALS_RETRY is False or bar_label == true_label:
if bar_label == 'L':
self.ser.write(b'1')
logger.info('STIMO: Sent 1')
elif bar_label == 'R':
self.ser.write(b'2')
logger.info('STIMO: Sent 2')
# FES event mode mode
if self.cfg.WITH_FES is True and self.cfg.FES_CONTINUOUS is False:
if bar_label == 'L':
stim_code = [0, 30, 0, 0, 0, 0, 0, 0]
self.stim.UpdateChannelSettings(stim_code)
logger.info('FES: Sent Left')
time.sleep(0.5)
stim_code = [0, 0, 0, 0, 0, 0, 0, 0]
self.stim.UpdateChannelSettings(stim_code)
elif bar_label == 'R':
stim_code = [30, 0, 0, 0, 0, 0, 0, 0]
self.stim.UpdateChannelSettings(stim_code)
time.sleep(0.5)
logger.info('FES: Sent Right')
stim_code = [0, 0, 0, 0, 0, 0, 0, 0]
self.stim.UpdateChannelSettings(stim_code)
if self.cfg.DEBUG_PROBS:
msg = 'DEBUG: Accumulated probabilities = %s' % list2string(
probs_acc, '%.3f')
logger.info(msg)
if self.logf is not None:
self.logf.write(msg + '\n')
if self.logf is not None:
self.logf.write('%s detected as %s (%d)\n\n' %
(true_label, bar_label, bar_score))
self.logf.flush()
# end of trial
state = 'feedback'
self.tm_trigger.reset()
else:
# classify
probs_new = decoder.get_prob_smooth_unread()
if probs_new is None:
if self.tm_watchdog.sec() > 3:
logger.warning(
'No classification being done. Are you receiving data streams?'
)
self.tm_watchdog.reset()
else:
self.tm_watchdog.reset()
if prob_history is not None:
prob_history[true_label].append(
probs_new[true_label_index])
probs_acc += np.array(probs_new)
'''
New decoder: already smoothed by the decoder so bias after.
'''
probs = list(probs_new)
if self.bar_bias is not None:
probs[bias_idx] += self.bar_bias[1]
newsum = sum(probs)
probs = [p / newsum for p in probs]
'''
# Method 2: bias and smoothen
if self.bar_bias is not None:
# print('BEFORE: %.3f %.3f'% (probs_new[0], probs_new[1]) )
probs_new[bias_idx] += self.bar_bias[1]
newsum = sum(probs_new)
probs_new = [p / newsum for p in probs_new]
# print('AFTER: %.3f %.3f'% (probs_new[0], probs_new[1]) )
for i in range(len(probs_new)):
probs[i] = probs[i] * self.alpha_old + probs_new[i] * self.alpha_new
'''
''' Original method
# Method 1: smoothen and bias
for i in range( len(probs_new) ):
probs[i] = probs[i] * self.alpha_old + probs_new[i] * self.alpha_new
# bias bar
if self.bar_bias is not None:
probs[bias_idx] += self.bar_bias[1]
newsum = sum(probs)
probs = [p/newsum for p in probs]
'''
# determine the direction
# TODO: np.argmax(probs)
max_pidx = np.argmax(probs)
max_label = bar_dirs[max_pidx]
if self.cfg.POSITIVE_FEEDBACK is False or \
(self.cfg.POSITIVE_FEEDBACK and true_label == max_label):
dx = probs[max_pidx]
if max_label == 'R':
dx *= self.bar_step_right
elif max_label == 'L':
dx *= self.bar_step_left
elif max_label == 'U':
dx *= self.bar_step_up
elif max_label == 'D':
dx *= self.bar_step_down
elif max_label == 'B':
dx *= self.bar_step_both
else:
logger.debug('Direction %s using bar step %d' %
(max_label, self.bar_step_left))
dx *= self.bar_step_left
# slow start
selected = self.cfg.BAR_SLOW_START['selected']
if self.cfg.BAR_SLOW_START[
selected] and self.tm_trigger.sec(
) < self.cfg.BAR_SLOW_START[selected]:
dx *= self.tm_trigger.sec(
) / self.cfg.BAR_SLOW_START[selected][0]
# add likelihoods
if max_label == bar_label:
bar_score += dx
else:
bar_score -= dx
# change of direction
if bar_score < 0:
bar_score = -bar_score
bar_label = max_label
bar_score = int(bar_score)
if bar_score > 100:
bar_score = 100
if self.cfg.FEEDBACK_TYPE == 'BODY':
if self.cfg.SHOW_CUE:
self.viz.move(bar_label,
bar_score,
overlay=False,
caption=DIRS[true_label],
caption_color='G')
else:
self.viz.move(bar_label,
bar_score,
overlay=False)
else:
self.viz.move(bar_label,
bar_score,
overlay=False)
# send the confidence value continuously
if self.cfg.WITH_STIMO and self.cfg.STIMO_CONTINUOUS:
if self.stimo_timer.sec(
) >= self.cfg.STIMO_COOLOFF:
if bar_label == 'U':
stimo_code = bar_score
else:
stimo_code = 0
self.ser.write(bytes([stimo_code]))
logger.info('Sent STIMO code %d' %
stimo_code)
self.stimo_timer.reset()
# with FES
if self.cfg.WITH_FES is True and self.cfg.FES_CONTINUOUS is True:
if self.stimo_timer.sec(
) >= self.cfg.STIMO_COOLOFF:
if bar_label == 'L':
stim_code = [
bar_score, 0, 0, 0, 0, 0, 0, 0
]
else:
stim_code = [
0, bar_score, 0, 0, 0, 0, 0, 0
]
self.stim.UpdateChannelSettings(stim_code)
logger.info('Sent FES code %d' % bar_score)
self.stimo_timer.reset()
if self.cfg.DEBUG_PROBS:
if self.bar_bias is not None:
biastxt = '[Bias=%s%.3f] ' % (
self.bar_bias[0], self.bar_bias[1])
else:
biastxt = ''
msg = '%s%s prob %s acc %s bar %s%d (%.1f ms)' % \
(biastxt, bar_dirs, list2string(probs_new, '%.2f'), list2string(probs, '%.2f'),
bar_label, bar_score, tm_classify.msec())
logger.info(msg)
if self.logf is not None:
self.logf.write(msg + '\n')
elif state == 'feedback' and self.tm_trigger.sec(
) > self.cfg.TIMINGS['FEEDBACK']:
self.trigger.signal(self.tdef.BLANK)
if self.cfg.FEEDBACK_TYPE == 'BODY':
state = 'return'
self.tm_trigger.reset()
else:
state = 'gap_s'
self.viz.fill()
self.viz.update()
return bar_label
elif state == 'return':
self.viz.set_glass_feedback(False)
if self.cfg.WITH_STIMO:
self.viz.move(bar_label,
bar_score,
overlay=False,
barcolor='B')
else:
self.viz.move(bar_label,
bar_score,
overlay=False,
barcolor='Y')
self.viz.set_glass_feedback(True)
bar_score -= 5
if bar_score <= 0:
state = 'gap_s'
self.viz.fill()
self.viz.update()
return bar_label
self.viz.update()
key = cv2.waitKeyEx(1)
if key == KEYS['esc']:
return
elif key == KEYS['space']:
dx = 0
bar_score = 0
probs = [1.0 / len(bar_dirs)] * len(bar_dirs)
self.viz.move(bar_dirs[0], bar_score, overlay=False)
self.viz.update()
logger.info('probs and dx reset.')
self.tm_trigger.reset()
elif key in ARROW_KEYS and ARROW_KEYS[key] in bar_dirs:
# change bias on the fly
if self.bar_bias is None:
self.bar_bias = [ARROW_KEYS[key], BIAS_INCREMENT]
else:
if ARROW_KEYS[key] == self.bar_bias[0]:
self.bar_bias[1] += BIAS_INCREMENT
elif self.bar_bias[1] >= BIAS_INCREMENT:
self.bar_bias[1] -= BIAS_INCREMENT
else:
self.bar_bias = [ARROW_KEYS[key], BIAS_INCREMENT]
if self.bar_bias[1] == 0:
self.bar_bias = None
else:
bias_idx = bar_dirs.index(self.bar_bias[0])
def train_decoder(cfg, featdata, feat_file=None):
"""
Train the final decoder using all data
"""
def sort_by_value(s, reverse=False):
assert type(s) == dict or type(
s) == list, 'Input must be a dictionary or list.'
if type(s) == list:
s = dict(enumerate(s))
s_rev = dict((v, k) for k, v in s.items())
if not len(s_rev) == len(s):
logger.warning('sort_by_value(): %d identical values' %
(len(s.values()) - len(set(s.values())) + 1))
values = sorted(s_rev, reverse=reverse)
keys = [s_rev[x] for x in values]
return keys, values
# Init a classifier
selected_classifier = cfg.CLASSIFIER['selected']
cls_params = cfg.CLASSIFIER[selected_classifier]
if selected_classifier == 'GB':
cls = GradientBoostingClassifier(
loss='deviance',
learning_rate=cls_params['learning_rate'],
n_estimators=cls_params['trees'],
subsample=1.0,
max_depth=cls_params['depth'],
random_state=cls_params['seed'],
max_features='sqrt',
verbose=0,
warm_start=False,
presort='auto')
elif selected_classifier == 'XGB':
cls = XGBClassifier(loss='deviance',
learning_rate=cls_params['learning_rate'],
n_estimators=cls_params['trees'],
subsample=1.0,
max_depth=cls_params['depth'],
random_state=cfg.GB['seed'],
max_features='sqrt',
verbose=0,
warm_start=False,
presort='auto')
elif selected_classifier == 'RF':
cls = RandomForestClassifier(n_estimators=cls_params['trees'],
max_features='auto',
max_depth=cls_params['depth'],
n_jobs=cfg.N_JOBS,
random_state=cls_params['seed'],
oob_score=False,
class_weight='balanced_subsample')
elif selected_classifier == 'LDA':
cls = LDA(solver=cls_params['solver'],
shrinkage=cls_params['shrinkage'])
elif selected_classifier == 'rLDA':
cls = rLDA(cls_params["r_coeff"])
else:
logger.error('Unknown classifier %s' % selected_classifier)
raise ValueError
# Setup features
X_data = featdata['X_data']
Y_data = featdata['Y_data']
wlen = featdata['wlen']
if cfg.FEATURES['PSD']['wlen'] is None:
cfg.FEATURES['PSD']['wlen'] = wlen
w_frames = featdata['w_frames']
ch_names = featdata['ch_names']
X_data_merged = np.concatenate(X_data)
Y_data_merged = np.concatenate(Y_data)
if cfg.CV['BALANCE_SAMPLES']:
X_data_merged, Y_data_merged = balance_samples(
X_data_merged,
Y_data_merged,
cfg.CV['BALANCE_SAMPLES'],
verbose=True)
# Start training the decoder
logger.info('Training the decoder')
timer = Timer()
cls.n_jobs = cfg.N_JOBS
cls.fit(X_data_merged, Y_data_merged)
logger.info('Trained %d samples x %d dimension in %.1f sec' %\
(X_data_merged.shape[0], X_data_merged.shape[1], timer.sec()))
cls.n_jobs = 1 # always set n_jobs=1 for testing
# Export the decoder
classes = {c: cfg.tdef.by_value[c] for c in np.unique(Y_data)}
if cfg.FEATURES['selected'] == 'PSD':
data = dict(cls=cls,
ch_names=ch_names,
psde=featdata['psde'],
sfreq=featdata['sfreq'],
picks=featdata['picks'],
classes=classes,
epochs=cfg.EPOCH,
w_frames=w_frames,
w_seconds=cfg.FEATURES['PSD']['wlen'],
wstep=cfg.FEATURES['PSD']['wstep'],
spatial=cfg.SP_FILTER,
spatial_ch=cfg.SP_CHANNELS,
spectral=cfg.TP_FILTER[cfg.TP_FILTER['selected']],
spectral_ch=cfg.TP_CHANNELS,
notch=cfg.NOTCH_FILTER[cfg.NOTCH_FILTER['selected']],
notch_ch=cfg.NOTCH_CHANNELS,
multiplier=cfg.MULTIPLIER,
ref_ch=cfg.REREFERENCE[cfg.REREFERENCE['selected']],
decim=cfg.FEATURES['PSD']['decim'])
if cfg.SAVE_FEATURES:
data["SAVED_FEAT"] = dict(X=X_data_merged, Y=Y_data_merged)
clsfile = '%s/classifier/classifier-%s.pkl' % (cfg.DATA_PATH,
platform.architecture()[0])
make_dirs('%s/classifier' % cfg.DATA_PATH)
with open(clsfile, 'wb') as f:
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
logger.info('Decoder saved to %s' % clsfile)
# Reverse-lookup frequency from FFT
fq = 0
if type(cfg.FEATURES['PSD']['wlen']) == list:
fq_res = 1.0 / cfg.FEATURES['PSD']['wlen'][0]
else:
fq_res = 1.0 / cfg.FEATURES['PSD']['wlen']
fqlist = []
while fq <= cfg.FEATURES['PSD']['fmax']:
if fq >= cfg.FEATURES['PSD']['fmin']:
fqlist.append(fq)
fq += fq_res
# Show top distinctive features
if cfg.FEATURES['selected'] == 'PSD':
logger.info('Good features ordered by importance')
if selected_classifier in ['RF', 'GB', 'XGB']:
keys, values = sort_by_value(list(cls.feature_importances_),
reverse=True)
elif selected_classifier in ['LDA', 'rLDA']:
keys, values = sort_by_value(cls.coef_.reshape(-1).tolist(),
reverse=True)
keys = np.array(keys)
values = np.array(values)
if cfg.EXPORT_GOOD_FEATURES:
if feat_file is None:
gfout = open('%s/classifier/good_features.txt' % cfg.DATA_PATH,
'w')
else:
gfout = open(feat_file, 'w')
if type(wlen) is not list:
ch_names = [ch_names[c] for c in featdata['picks']]
else:
ch_names = []
for w in range(len(wlen)):
for c in featdata['picks']:
ch_names.append('w%d-%s' % (w, ch_names[c]))
chlist, hzlist = features.feature2chz(keys, fqlist, ch_names=ch_names)
valnorm = values[:cfg.FEAT_TOPN].copy()
valsum = np.sum(valnorm)
if valsum == 0:
valsum = 1
valnorm = valnorm / valsum * 100.0
# show top-N features
for i, (ch, hz) in enumerate(zip(chlist, hzlist)):
if i >= cfg.FEAT_TOPN:
break
txt = '%-3s %5.1f Hz normalized importance %-6s raw importance %-6s feature %-5d' %\
(ch, hz, '%.2f%%' % valnorm[i], '%.2f%%' % (values[i] * 100.0), keys[i])
logger.info(txt)
if cfg.EXPORT_GOOD_FEATURES:
gfout.write('Importance(%) Channel Frequency Index\n')
for i, (ch, hz) in enumerate(zip(chlist, hzlist)):
gfout.write('%.3f\t%s\t%s\t%d\n' %
(values[i] * 100.0, ch, hz, keys[i]))
gfout.close()
def cross_validate(cfg, featdata, cv_file=None):
"""
Perform cross validation
"""
# Init a classifier
selected_classifier = cfg.CLASSIFIER['selected']
cls_params = cfg.CLASSIFIER[selected_classifier]
if selected_classifier == 'GB':
cls = GradientBoostingClassifier(
loss='deviance',
learning_rate=cls_params['learning_rate'],
presort='auto',
n_estimators=cls_params['trees'],
subsample=1.0,
max_depth=cls_params['depth'],
random_state=cls_params['seed'],
max_features='sqrt',
verbose=0,
warm_start=False)
elif selected_classifier == 'XGB':
cls = XGBClassifier(loss='deviance',
learning_rate=cls_params['learning_rate'],
presort='auto',
n_estimators=cls_params['trees'],
subsample=1.0,
max_depth=cls_params['depth'],
random_state=cls_params,
max_features='sqrt',
verbose=0,
warm_start=False)
elif selected_classifier == 'RF':
cls = RandomForestClassifier(n_estimators=cls_params['trees'],
max_features='auto',
max_depth=cls_params['depth'],
n_jobs=cfg.N_JOBS,
random_state=cls_params['seed'],
oob_score=False,
class_weight='balanced_subsample')
elif selected_classifier == 'LDA':
cls = LDA(solver=cls_params['solver'],
shrinkage=cls_params['shrinkage'])
elif selected_classifier == 'rLDA':
cls = rLDA(cls_params['r_coeff'])
else:
logger.error('Unknown classifier type %s' % selected_classifier)
raise ValueError
# Setup features
X_data = featdata['X_data']
Y_data = featdata['Y_data']
wlen = featdata['wlen']
# Choose CV type
ntrials, nsamples, fsize = X_data.shape
selected_cv = cfg.CV_PERFORM['selected']
if selected_cv == 'LeaveOneOut':
logger.info('%d-fold leave-one-out cross-validation' % ntrials)
if SKLEARN_OLD:
cv = LeaveOneOut(len(Y_data))
else:
cv = LeaveOneOut()
elif selected_cv == 'StratifiedShuffleSplit':
logger.info(
'%d-fold stratified cross-validation with test set ratio %.2f' %
(cfg.CV_PERFORM[selected_cv]['folds'],
cfg.CV_PERFORM[selected_cv]['test_ratio']))
if SKLEARN_OLD:
cv = StratifiedShuffleSplit(
Y_data[:, 0],
cfg.CV_PERFORM[selected_cv]['folds'],
test_size=cfg.CV_PERFORM[selected_cv]['test_ratio'],
random_state=cfg.CV_PERFORM[selected_cv]['seed'])
else:
cv = StratifiedShuffleSplit(
n_splits=cfg.CV_PERFORM[selected_cv]['folds'],
test_size=cfg.CV_PERFORM[selected_cv]['test_ratio'],
random_state=cfg.CV_PERFORM[selected_cv]['seed'])
else:
logger.error('%s is not supported yet. Sorry.' %
cfg.CV_PERFORM[cfg.CV_PERFORM['selected']])
raise NotImplementedError
logger.info('%d trials, %d samples per trial, %d feature dimension' %
(ntrials, nsamples, fsize))
# Do it!
timer_cv = Timer()
scores, cm_txt = crossval_epochs(cv,
X_data,
Y_data,
cls,
cfg.tdef.by_value,
cfg.CV['BALANCE_SAMPLES'],
n_jobs=cfg.N_JOBS,
ignore_thres=cfg.CV['IGNORE_THRES'],
decision_thres=cfg.CV['DECISION_THRES'])
t_cv = timer_cv.sec()
# Export results
txt = 'Cross validation took %d seconds.\n' % t_cv
txt += '\n- Class information\n'
txt += '%d epochs, %d samples per epoch, %d feature dimension (total %d samples)\n' %\
(ntrials, nsamples, fsize, ntrials * nsamples)
for ev in np.unique(Y_data):
txt += '%s: %d trials\n' % (cfg.tdef.by_value[ev],
len(np.where(Y_data[:, 0] == ev)[0]))
if cfg.CV['BALANCE_SAMPLES']:
txt += 'The number of samples was balanced using %ssampling.\n' % cfg.BALANCE_SAMPLES.lower(
)
txt += '\n- Experiment condition\n'
txt += 'Sampling frequency: %.3f Hz\n' % featdata['sfreq']
txt += 'Spatial filter: %s (channels: %s)\n' % (cfg.SP_FILTER,
cfg.SP_CHANNELS)
txt += 'Spectral filter: %s (channels: %s)\n' % (
cfg.TP_FILTER[cfg.TP_FILTER['selected']], cfg.TP_CHANNELS)
txt += 'Notch filter: %s (channels: %s)\n' % (
cfg.NOTCH_FILTER[cfg.NOTCH_FILTER['selected']], cfg.NOTCH_CHANNELS)
txt += 'PSD Channels: ' + ','.join(
[str(featdata['ch_names'][p]) for p in featdata['picks']]) + '\n'
txt += 'PSD range: %.1f - %.1f Hz\n' % (cfg.FEATURES['PSD']['fmin'],
cfg.FEATURES['PSD']['fmax'])
txt += 'Window step: %.2f msec\n' % (
1000.0 * cfg.FEATURES['PSD']['wstep'] / featdata['sfreq'])
if type(wlen) is list:
for i, w in enumerate(wlen):
txt += 'Window size: %.1f msec\n' % (w * 1000.0)
txt += 'Epoch range: %s sec\n' % (cfg.EPOCH[i])
else:
txt += 'Window size: %.1f msec\n' % (cfg.FEATURES['PSD']['wlen'] *
1000.0)
txt += 'Epoch range: %s sec\n' % (cfg.EPOCH)
txt += 'Decimation factor: %d\n' % cfg.FEATURES['PSD']['decim']
# Compute stats
cv_mean, cv_std = np.mean(scores), np.std(scores)
txt += '\n- Average CV accuracy over %d epochs (random seed=%s)\n' % (
ntrials, cfg.CV_PERFORM[cfg.CV_PERFORM['selected']]['seed'])
if cfg.CV_PERFORM[cfg.CV_PERFORM['selected']] in [
'LeaveOneOut', 'StratifiedShuffleSplit'
]:
txt += "mean %.3f, std: %.3f\n" % (cv_mean, cv_std)
txt += 'Classifier: %s, ' % selected_classifier
if selected_classifier == 'RF':
txt += '%d trees, %s max depth, random state %s\n' % (
cfg.CLASSIFIER['RF']['trees'], cfg.CLASSIFIER['RF']['depth'],
cfg.CLASSIFIER['RF']['seed'])
elif selected_classifier == 'GB' or selected_classifier == 'XGB':
txt += '%d trees, %s max depth, %s learing_rate, random state %s\n' % (
cfg.CLASSIFIER['GB']['trees'], cfg.CLASSIFIER['GB']['depth'],
cfg.CLASSIFIER['GB']['learning_rate'],
cfg.CLASSIFIER['GB']['seed'])
elif selected_classifier == 'rLDA':
txt += 'regularization coefficient %.2f\n' % cfg.CLASSIFIER['rLDA'][
'r_coeff']
if cfg.CV['IGNORE_THRES'] is not None:
txt += 'Decision threshold: %.2f\n' % cfg.CV['IGNORE_THRES']
txt += '\n- Confusion Matrix\n' + cm_txt
logger.info(txt)
# Export to a file
if 'export_result' in cfg.CV_PERFORM[selected_cv] and cfg.CV_PERFORM[
selected_cv]['export_result'] is True:
if cv_file is None:
if cfg.EXPORT_CLS is True:
make_dirs('%s/classifier' % cfg.DATA_PATH)
fout = open('%s/classifier/cv_result.txt' % cfg.DATA_PATH, 'w')
else:
fout = open('%s/cv_result.txt' % cfg.DATA_PATH, 'w')
else:
fout = open(cv_file, 'w')
fout.write(txt)
fout.close()