def constOptimize(net, base_img, guide_img, objective, iter_n, max_thres,
end, factr=factr, pgtol=pgtol, verbose=True):
proc_base = utils.preprocess(net, base_img)
proc_guide = utils.preprocess(net, guide_img)
src = net.blobs['data']
ch, h, w = proc_base.shape
src.reshape(1, ch, h, w)
# allocate image for network-produced details
src, dst = net.blobs['data'], net.blobs[end]
src.data[0] = proc_guide
net.forward(end='prob')
guide_features = dst.data[0].copy()
up_bnd = proc_base + max_thres
lw_bnd = proc_base - max_thres
mean_arr = net.transformer.mean['data']
if mean_arr.ndim == 1:
mean_arr = mean_arr.reshape((3, 1, 1))
up_bnd = np.minimum(up_bnd, 255 - mean_arr)
lw_bnd = np.maximum(lw_bnd, 0 - mean_arr)
bound = zip(lw_bnd.flatten(), up_bnd.flatten())
src.data[0] = proc_base
x, f, d = cnstOpt(calc_gstep, proc_base.flatten().astype(float),
args=(net, guide_features, end, objective, verbose),
bounds=bound, maxiter=iter_n, iprint=0, factr=factr,
pgtol=pgtol)
return x.reshape(proc_base.shape), f, d
def generate(sentence):
with open('train.txt') as fin:
train = fin.read()
train += ' ' + preprocess(sentence)
morse_codes = translateToMorseCode(train)
huffman_tree = HuffmanTree()
huffman_tree.train(morse_codes)
print huffman_tree.translate(preprocess(sentence))
def predict(clf, file_path, scaler=None):
data = preprocess(file_path)
X = extract_all_features(data, 44100)
X = np.asmatrix(X)
if scaler:
X = scaler.transform(X)
res = clf.predict(X[:,imporved_features()])[0]
return res
def submission():
"""
Generate submission file for the trained models.
"""
logging.info('Loading and compiling models...')
model_systole = get_model()
model_diastole = get_model()
logging.info('Loading models weights...')
model_systole.load_weights('../models/weights/weights_systole_best.hdf5')
model_diastole.load_weights('../models/weights/weights_diastole_best.hdf5')
# load val losses to use as sigmas for CDF
with open('./logs/val_loss.txt', mode='r') as f:
val_loss_systole = float(f.readline())
val_loss_diastole = float(f.readline())
logging.info('Loading validation data...')
X, ids = load_validation_data()
logging.info('Pre-processing images...')
X = preprocess(X)
batch_size = 32
logging.info('Predicting on validation data...')
pred_systole = model_systole.predict(X, batch_size=batch_size, verbose=1)
pred_diastole = model_diastole.predict(X, batch_size=batch_size, verbose=1)
# real predictions to CDF
cdf_pred_systole = real_to_cdf(pred_systole, val_loss_systole)
cdf_pred_diastole = real_to_cdf(pred_diastole, val_loss_diastole)
logging.info('Accumulating results...')
sub_systole = accumulate_study_results(ids, cdf_pred_systole)
sub_diastole = accumulate_study_results(ids, cdf_pred_diastole)
# write to submission file
logging.info('Writing submission to file...')
fi = csv.reader(open('../input/sample_submission_validate.csv'))
f = open('../submissions/submission_17.csv', 'w')
fo = csv.writer(f, lineterminator='\n')
fo.writerow(next(fi))
for line in fi:
idx = line[0]
key, target = idx.split('_')
key = int(key)
out = [idx]
if key in sub_systole:
if target == 'Diastole':
out.extend(list(sub_diastole[key][0]))
else:
out.extend(list(sub_systole[key][0]))
else:
logging.info('Miss {0}'.format(idx))
fo.writerow(out)
f.close()
logging.info('Done.')
def tokenize(text):
replacements = [("---", " "), ("--", " "), ("-", "")] # trying to capture multi-word keywords
for (src, tgt) in replacements:
text = text.replace(src, tgt)
words = utils.preprocess(text)
return filter(lambda w: w not in stops, words)
def trainAPI():
global vw, sequenceLabeler
model = request.args.get("model", "tagger1.bin")
N = request.args.get("iter", 10)
try:
data = request.args.get("data")
data = data.strip()
print "Traning:", data
sequenceLabeler.learn(preprocess([data]))
return "model trained"
except:
return "'data' field not present OR trainning error!!!"
def submission():
"""
Generate submission file for the trained models.
"""
print('Loading and compiling models...')
model_systole = get_vgg_model()
model_diastole = get_vgg_model()
print('Loading models weights...')
model_systole.load_weights('weights_systole_best.hdf5')
model_diastole.load_weights('weights_diastole_best.hdf5')
print('Loading validation data...')
X, ids = load_validation_data()
print('Pre-processing images...')
X = preprocess(X)
batch_size = 32
print('Predicting on validation data...')
pred_systole = model_systole.predict(X, batch_size=batch_size, verbose=1)
pred_diastole = model_diastole.predict(X, batch_size=batch_size, verbose=1)
# real predictions to CDF
cdf_pred_systole = pred_systole.cumsum(axis=-1)
cdf_pred_diastole = pred_diastole.cumsum(axis=-1)
print('Accumulating results...')
sub_systole = accumulate_study_results(ids, cdf_pred_systole)
sub_diastole = accumulate_study_results(ids, cdf_pred_diastole)
# write to submission file
print('Writing submission to file...')
fi = csv.reader(open('/data/heart/sample_submission_test.csv'))
f = open('submission.csv', 'w')
fo = csv.writer(f, lineterminator='\n')
fo.writerow(fi.next())
for line in fi:
idx = line[0]
key, target = idx.split('_')
key = int(key)
out = [idx]
if key in sub_systole:
if target == 'Diastole':
out.extend(list(sub_diastole[key][0]))
else:
out.extend(list(sub_systole[key][0]))
else:
print('Miss {0}'.format(idx))
fo.writerow(out)
f.close()
print('Done.')
def main():
vw = []
sl = []
while True:
inp = raw_input("> ")
inp = inp.strip()
words = inp.split()
cmd = words[0]
if cmd == "/save":
for temp in vw:
temp.finish()
sys.exit(1)
if cmd == "/train":
data = " ".join(words[1:]).strip()
for i in range(10):
for temp in sl:
temp.learn(preprocess([data]))
elif cmd == "/query":
data = " ".join(words[1:]).strip()
output = set()
for s in sl:
output.add(postprocess(query(s, data)))
for out in output:
print "\t", out
elif cmd == "/start":
data = " ".join(words[1:]).strip()
if os.path.isfile(data + ".1") and os.path.isfile(data + ".2") and os.path.isfile(
data + ".3") and os.path.isfile(data + ".4"):
vw = [
pyvw.vw("--quiet -i " + data + ".1 -f "+data + ".1"),
pyvw.vw("--quiet -i " + data + ".2 -f "+data + ".2"),
pyvw.vw("--quiet -i " + data + ".3 -f "+data + ".3"),
pyvw.vw("--quiet -i " + data + ".4 -f "+data + ".4")
]
else:
vw = [
pyvw.vw("--search 3 --quiet --search_task hook --ring_size 2048 -f " + data + ".1"),
pyvw.vw("--search 3 --quiet --search_task hook --ring_size 2048 -f " + data + ".2"),
pyvw.vw("--search 3 --quiet --search_task hook --ring_size 2048 -f " + data + ".3"),
pyvw.vw("--search 3 --quiet --search_task hook --ring_size 2048 -f " + data + ".4")
]
sl = [
vw[0].init_search_task(SequenceLabeler),
vw[1].init_search_task(SequenceLabeler2),
vw[2].init_search_task(SequenceLabeler3),
vw[3].init_search_task(SequenceLabeler4)
]
def create_bb_pip(tfr, nepoch, sbatch, mean, shuffle=True):
tf_mean = tf.constant(mean, dtype=tf.float32)
tf_mean = tf.reshape(tf_mean, [1, 1, 1, 3])
fqueue = tf.train.string_input_producer([tfr], num_epochs=nepoch * 10)
image, idx, bbx = read_single_image(fqueue, 64)
data = tf.train.batch([image, idx, bbx],
batch_size=sbatch,
num_threads=1,
capacity=sbatch * 3)
# preprocess input images
data[0] = preprocess(data[0], tf_mean)
return data
def predict(model, img_path, device):
from utils import preprocess, transform
model.eval()
with torch.no_grad():
in_shape = np.asarray(cv2.imread(img_path)).shape
img = preprocess(img_path)
fin_shape = np.asarray(img).shape
img = transform(img)
img = Variable(img).to(device)
img = img.view(1, 1, fin_shape[0],fin_shape[1])
output = model(img)
img = ((255*output.cpu().clone().detach().numpy()).squeeze().squeeze())
img = cv2.resize(img, (in_shape[1],in_shape[0]),interpolation = cv2.INTER_AREA)
return img
def extract_feature(self, images, batch_size, preprocess=False, config=None, is_training=False):
num_images = images.shape[0] if type(images)==np.ndarray else len(images)
num_features = self.outputs.shape[1]
result = np.ndarray((num_images, num_features), dtype=np.float32)
for start_idx in range(0, num_images, batch_size):
end_idx = min(num_images, start_idx + batch_size)
inputs = images[start_idx:end_idx]
if preprocess:
assert config is not None
inputs = utils.preprocess(inputs, config, is_training)
feed_dict = {self.inputs: inputs,
self.phase_train_placeholder: False,
self.keep_prob_placeholder: 1.0}
result[start_idx:end_idx] = self.sess.run(self.outputs, feed_dict=feed_dict)
return result
def embed(self, X0, X1, X2, X3, X4):
X0 = preprocess(X0)
X1 = preprocess(X1)
X2 = preprocess(X2)
X3 = preprocess(X3)
X4 = preprocess(X4)
X0_latent, X1_latent, X2_latent, X3_latent, X4_latent = self.sess.run(
[
self.c0_test, self.c1_test, self.c2_test, self.c3_test,
self.c4_test
],
feed_dict={
self.x0: X0,
self.x1: X1,
self.x2: X2,
self.x3: X3,
self.x4: X4
})
return X0_latent[:,
0, :], X1_latent[:,
0, :], X2_latent[:,
0, :], X3_latent[:,
0, :], X4_latent[:,
0, :]
def decode(self, words, lower=False):
""" Return the words with tags of the given words.
args:
- words (list): Input words.
- lower (bool, optional): If lower is True, all uppercase characters in a list \
of the words are converted into lowercase characters.
return:
- object : The object of the words with tags.
"""
if not type(words) == list:
raise AssertionError("Please input a list of words.")
words = [utils.preprocess(w) for w in words]
postags = self._postagging(words, lower)
return postags
def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = float(data["steering_angle"])
# The current throttle of the car, how hard to push peddle
throttle = float(data["throttle"])
# The current speed of the car
speed = float(data["speed"])
# The current image from the center camera of the car
image = Image.open(BytesIO(base64.b64decode(data["image"])))
try:
image = np.asarray(image) # from PIL image to numpy array
image = utils.preprocess(image) # apply the preprocessing
image = np.array([image]) # the model expects 4D array
# predict the steering angle for the image
steering_angle = float(model.predict(image, batch_size=1))
# lower the throttle as the speed increases
# if the speed is above the current speed limit, we are on a downhill.
# make sure we slow down first and then go back to the original max speed.
global speed_limit
if speed > speed_limit:
speed_limit = MIN_SPEED # slow down
else:
speed_limit = MAX_SPEED
throttle = 1.0 - steering_angle**2 - (speed/speed_limit)**2
#steering_angle_list.append(steering_angle)
#i_list.append(i+1)
#df = DataFrame({'time_values': i_list, 'Steering angle': steering_angle_list})
#df.to_excel('steering_angle_data.xlsx', sheet_name='sheet1', index=False)
file_object.write(str(steering_angle))
file_object.write('\n')
print('{} {} {}'.format(steering_angle, throttle, speed))
send_control(steering_angle, throttle)
except Exception as e:
file_object.close()
print(e)
# save frame
if args.image_folder != '':
#df = DataFrame({'time_values': i_list, 'Steering angle': steering_angle_list})
#df.to_excel('steering_angle_data.xlsx', sheet_name='sheet1', index=False)
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
else:
sio.emit('manual', data={}, skip_sid=True)
def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = float(data["steering_angle"])
# The current throttle of the car, how hard to push peddle
throttle = float(data["throttle"])
# The current speed of the car
speed = float(data["speed"])
# The current image from the center camera of the car
original_image = Image.open(BytesIO(base64.b64decode(data["image"])))
try:
image = np.asarray(original_image) # from PIL image to numpy array
image = utils.preprocess(image) # apply the preprocessing
image = transformations(image)
image = torch.Tensor(image)
#image = np.array([image]) # the model expects 4D array
image = image.view(1, 3, 75, 320)
image = Variable(image)
# predict the steering angle for the image
steering_angle = model(image).view(-1).data.numpy()[0]
# lower the throttle as the speed increases
# if the speed is above the current speed limit, we are on a downhill.
# make sure we slow down first and then go back to the original max speed.
global speed_limit
if speed > speed_limit:
speed_limit = MIN_SPEED # slow down
else:
speed_limit = MAX_SPEED
throttle = 1.0 - steering_angle**2 - (speed / speed_limit)**2
#throttle = controller.update(float(speed)) - 0.1
print('{} {} {}'.format(steering_angle, throttle, speed))
send_control(steering_angle, throttle)
except Exception as e:
print("Exception")
print(e)
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
original_image.save('{}.jpg'.format(image_filename))
else:
sio.emit('manual', data={}, skip_sid=True)
def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = float(data["steering_angle"])
# The current throttle of the car
throttle = float(data["throttle"])
# The current speed of the car
speed = float(data["speed"])
# The current image from the center camera of the car
image = Image.open(BytesIO(base64.b64decode(data["image"])))
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
try:
image = np.asarray(image) # from PIL image to numpy array
image_copy = np.copy(image)
image_copy = autoenconder_model.normalize_and_reshape(image_copy)
loss = anomaly_detection.test_on_batch(image_copy, image_copy)
image = utils.preprocess(image) # apply the preprocessing
image = np.array([image]) # the model expects 4D array
# predict the steering angle for the image
steering_angle = float(model.predict(image, batch_size=1))
# lower the throttle as the speed increases
# if the speed is above the current speed limit, we are on a downhill.
# make sure we slow down first and then go back to the original max speed.
global speed_limit
if speed > speed_limit:
speed_limit = MIN_SPEED # slow down
else:
speed_limit = MAX_SPEED
throttle = 1.0 - steering_angle**2 - (speed/speed_limit)**2
if loss > 0.035:
print('{} {} {} {} WARNING'.format(steering_angle, throttle, speed, loss))
else:
print('{} {} {} {} OK'.format(steering_angle, throttle, speed, loss))
send_control(steering_angle, throttle)
except Exception as e:
print(e)
else:
# NOTE: DON'T EDIT THIS.
sio.emit('manual', data={}, skip_sid=True)
def classify(v):
####### training part ###############
samples = np.loadtxt('generalsamples.data', np.float32)
responses = np.loadtxt('generalresponses.data', np.float32)
responses = responses.reshape((responses.size, 1))
model = cv2.ml.KNearest_create()
model.train(samples, cv2.ml.ROW_SAMPLE, responses)
############################# testing part #########################
cap = cv2.VideoCapture(1)
labels = ['S', 'U', 'H']
while (True):
ret, im = cap.read()
#print(str('train' + str(i)+'.jpg'))
#im = cv2.imread(str('train/' + str(i)+'.jpg'))
img = utils.preprocess(im)
im2 = img.copy()
image, contours, hierarchy = cv2.findContours(im2, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
############################# classification #########################
for cnt in contours:
area = cv2.contourArea(cnt)
if utils.check_rectangle(area):
[x, y, w, h] = cv2.boundingRect(cnt)
if utils.check_ratio(x, y, w, h):
cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 2)
retval, results, neigh_resp, dists = model.findNearest(
utils.roismall(img, x, y, w, h), k=3)
string = labels[int(results[0][0])]
cv2.putText(im,
string, (x + 3, y + h + 3),
0,
2, (255, 0, 0),
thickness=3)
cv2.imshow('im', im)
#cv2.imshow('processed',img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = float(data["steering_angle"])
# The current throttle of the car, how hard to push peddle
throttle = float(data["throttle"])
# The current speed of the car
speed = float(data["speed"])
# The current image from the center camera of the car
image = Image.open(BytesIO(base64.b64decode(data["image"])))
img = np.asarray(image)
img = utils.preprocess(img)
try:
# from PIL image to numpy array
#image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# predict the steering angle for the image
img = Variable(torch.cuda.FloatTensor([img], device=device)).permute(0,3,1,2)
steering_angle_throttle = model(img)
#steering_angle = steering_angle_throttle[0].item()
#throttle = steering_angle_throttle[1].item()
steering_angle = steering_angle_throttle.item()
#print(f'steering angle {steering_angle}')
# lower the throttle as the speed increases
# if the speed is above the current speed limit, we are on a downhill.
# make sure we slow down first and then go back to the original max speed.
global speed_limit
if speed > speed_limit:
speed_limit = MIN_SPEED # slow down
else:
speed_limit = MAX_SPEED
throttle = 1.0 - steering_angle**2 - (speed/speed_limit)**2
print('sterring_angle: {} throttle: {} spped: {}'.format(steering_angle, throttle, speed))
send_control(steering_angle, throttle)
except Exception as e:
print(e)
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
else:
sio.emit('manual', data={}, skip_sid=True)
def main(args):
# I/O
config_file = args.config_file
config = utils.import_file(config_file, 'config')
#trainset = utils.Dataset(config.train_dataset_path)
testset = utils.Dataset(config.test_dataset_path)
network = BaseNetwork()
network.initialize(config, 0 ) #trainset.num_classes
# Initalization for running
log_dir = utils.create_log_dir(config, config_file)
summary_writer = tf.summary.FileWriter(log_dir, network.graph)
if config.restore_model is not None:
network.restore_model(config.restore_model, config.restore_scopes)
# Set up LFW test protocol and load images
print('Loading images...')
lfwtest = LFWTest(testset.images)
lfwtest.init_standard_proto(config.lfw_pairs_file)
lfwtest.images = utils.preprocess(lfwtest.image_paths, config, is_training=False)
#trainset.start_batch_queue(config, True)
#
# Main Loop
#
print('\nStart Training\nname: %s\n# epochs: %d\nepoch_size: %d\nbatch_size: %d\n'\
% (config.name, config.num_epochs, config.epoch_size, config.batch_size))
global_step = 0
# Testing on LFW
print('Testing on Neetis LFW protocol...')
embeddings = network.extract_feature(lfwtest.images, config.batch_size)
print(type(embeddings))
accuracy_embeddings, threshold_embeddings = lfwtest.test_standard_proto(embeddings)
print('Embeddings Accuracy: %2.4f Threshold %2.3f' % (accuracy_embeddings, threshold_embeddings))
with open(os.path.join(log_dir,'lfw_result.txt'),'at') as f:
f.write('%d\t%.5f\n' % (global_step,accuracy_embeddings))
summary = tf.Summary()
summary.value.add(tag='lfw/accuracy', simple_value=accuracy_embeddings)
summary_writer.add_summary(summary, global_step)
def read_data_from_file(data_path):
maybe_download()
with open(data_path) as f:
text = f.read()
###########################################################
# ------------------- Preprocessing -----------------------
# 1. Tokenize punctuations e.g. period -> <PERIOD>
# 2. Remove words that show up five times or fewer
words = utils.preprocess(text)
# Hmm, let's take a look at the processed data
print('First 30 words:', words[:30])
print('Total words:', len(words))
print('Total unique words:', len(set(words)))
# Create two dictionaries to convert words to integers
vocab_to_int, int_to_vocab = utils.create_lookup_tables(words)
n_vocab = len(int_to_vocab)
# Convert words into integers
int_words = [vocab_to_int[w] for w in words]
###########################################################
# ------------------- Subsampling -------------------------
# Some words like "the", "a", "of" etc don't provide much
# information. So we might want to remove some of them.
# This results in faster and better result.
# The probability that a word is discarded is
# P(w) = 1 - sqrt(1 / frequency(w))
each_word_count = Counter(int_words)
total_count = len(int_words)
threshold = 1e-5 # FLAGS.drop_word_threshold
freqs = {word: count/total_count for word,
count in each_word_count.items()}
probs = {word: 1 - np.sqrt(threshold/freqs[word])
for word in each_word_count}
train_words = [word for word in int_words if random.random() <
(1 - probs[word])]
print('After subsampling, first 30 words:', train_words[:30])
print('After subsampling, total words:', len(train_words))
# Subsampling makes it worse for eliminating contextual info
# return train_words, int_to_vocab, vocab_to_int, n_vocab
return int_words, int_to_vocab, vocab_to_int, n_vocab
def get_tweets():
global batch_start_time
processed_tweet = []
try:
for line in api.GetStreamSample():
if 'text' in line and line['lang'] == u'en':
text = line['text'].encode('utf-8').replace('\n', ' ')
p_t = preprocess(text) # process tweets
if p_t:
processed_tweet += p_t,
if time.time(
) - batch_start_time >= tw * 60: # time is over for this batch
return processed_tweet
return processed_tweet # server-side interruption
except:
pass
def preprocess_clustering(text: str):
text = preprocess(text)
tokens = text.split(' ')
doc = []
for token in tokens:
if token in string.punctuation:
continue
if token.isnumeric():
continue
if len(token) < 2:
continue
# lemmatize the words
token = LEMMATIZER.lemmatize(token)
doc.append(token)
return doc
def __getitem__(self, index):
_img = Image.open(self.images[index]).convert('RGB')
_target = Image.open(self.masks[index])
_img, _target = preprocess(_img, _target,
flip=True if self.train else False,
scale=(0.5, 2.0) if self.train else None,
crop=(self.crop_size, self.crop_size) if self.train else (1025, 2049))
if self.transform is not None:
_img = self.transform(_img)
if self.target_transform is not None:
_target = self.target_transform(_target)
# print(_img.shape)
return _img, _target
def __init__(self, base_dir, batch_size, rst, max_size=500,
normalize=True, preprocessing=True):
BATCH_FILES = 4
self.base_dir = base_dir
self.batch_size = batch_size
self.rst = rst
self.normalize = normalize
self.max_size = max_size
self.preprocessing = preprocessing
self.x = self.get_content_images()
if self.preprocessing:
self.x = utils.preprocess(self.x)
if normalize:
self.x = utils.norm(self.x)
def read_phone_data(f_name):
with open(f_name, 'r', encoding='utf-8') as f_phone:
phone_data = []
phone = f_phone.readlines()
phone.insert(0, '\n')
for idx, line in tqdm(enumerate(phone)):
if idx + 1 < len(phone) and phone[idx + 1] == '\n': continue
if line == '\n':
i, text = 0, ''
i += 1
if i > 3:
text += line
if idx + 2 < len(phone) and phone[idx + 2] == '\n':
label = int(phone[idx + 1].replace('\n', ''))
phone_data.append({label: preprocess(text)})
return phone_data
def preprocess(self, stop_filter=True, pos_filter=True):
"""Preprocess document text
This method can filter out basic stopwords listed in the utils file
and parts of speech.
:param stop_filter: stopword filter status
:type stop_filter: bool
:param pos_filter: parts of speech filter status
:type pos_filter: bool
"""
self.text = ". ".join(
preprocess(self.text,
stop_filter=stop_filter,
pos_filter=pos_filter))
def train_baseline():
train_df = pd.read_csv(train_df_path, index_col=0)
sentences_train, dictionary, y_train_encoded = preprocess(
train_df, processed_train_df_path, encoder)
y_train = train_df['relation_type'].values
vectorizer.fit(sentences_train)
X_train = vectorizer.transform(sentences_train)
print('training...')
classifier = RandomForestClassifier(n_estimators=700,
max_depth=60,
n_jobs=-1,
class_weight='balanced')
classifier.fit(X_train, y_train)
print('trained')
return classifier, dictionary
def train_baseline():
train_df = pd.read_csv(train_df_path, index_col=0)
sentences_train, dictionary, y_train_encoded = preprocess(
train_df, processed_train_df_path, encoder)
y_train = train_df['relation_type'].values
vectorizer.fit(sentences_train)
X_train = vectorizer.transform(sentences_train)
print('training...')
classifier = MLPClassifier(activation='tanh',
alpha=0.1,
hidden_layer_sizes=(30, 5),
learning_rate='constant')
classifier.fit(X_train, y_train)
print('trained')
return classifier, dictionary
def raw_utterance_with_keyword(self, vocab, train=True):
cache = cacher('dts_ConvAI2.raw_utterance_with_keyword', vocab, train)
if cache.cached:
return cache.data
examples, corpus, check_dict = self.raw_utterance(train)
field = Field(vocab)
kwext = KeywordExtractor(field)
for example in prolog(examples, name=' -extract keywords'):
kws = kwext.extract(example['uttr'].lst)
example['kwpos'] = kws['kwpos']
example['keywords'] = kws['keywords']
examples = preprocess(examples, field, log=' -process to_pack cls')
return cache.cache((examples, field, corpus, check_dict))
def synthesize_x1(self, X1_latent, parents=None):
if isinstance(X1_latent, int):
N = X1_latent
X1_latent = np.random.uniform(size=(N, self.latent_dim[1]))
X1_noise = np.random.normal(scale=0.5, size=(N, self.noise_dim[1]))
else:
N = X1_latent.shape[0]
X1_noise = np.zeros((N, self.noise_dim[1]))
if parents is None:
X0 = self.synthesize_x0(1)[0]
else:
X0 = parents[0]
X0 = preprocess(X0)
X0 = np.tile(X0, (N,1,1,1))
X1 = self.sess.run(self.x1_fake, feed_dict={self.c1: X1_latent, self.z1: X1_noise, self.x0_fake: X0})
return [postprocess(X1), postprocess(X0)]
def cell_transform(xs, indexes=None):
Fs = []
xs = [preprocess(x) for x in xs]
for xmb in tqdm(
iter_data(xs, size=hps.nbatch), ncols=80, leave=False,
total=len(xs)//hps.nbatch):
smb = np.zeros((2, hps.nbatch, hps.nhidden))
n = len(xmb)
xmb, mmb = batch_pad(xmb, hps.nbatch, hps.nsteps)
smb = sess.run(cells, {X: xmb, S: smb, M: mmb})
smb = smb[:, :n, :]
if indexes is not None:
smb = smb[:, :, indexes]
Fs.append(smb)
Fs = np.concatenate(Fs, axis=1).transpose(1, 0, 2)
return Fs
def live(state_widget, model, camera, prediction_widget):
global dataset
while state_widget.value == 'live':
image = camera.value
preprocessed = preprocess(image)
output = model(preprocessed).detach().cpu().numpy().flatten()
category_index = dataset.categories.index(category_widget.value)
x = output[2 * category_index]
y = output[2 * category_index + 1]
x = int(camera.width * (x / 2.0 + 0.5))
y = int(camera.height * (y / 2.0 + 0.5))
prediction = image.copy()
prediction = cv2.circle(prediction, (x, y), 8, (255, 0, 0), 3)
prediction_widget.value = bgr8_to_jpeg(prediction)
def read_data_from_file(data_path: str) -> tuple:
"""
生成训练的词列表，以及列表的长度。
:param data_path:
:return:
"""
maybe_download()
with open(data_path) as f:
text = f.read()
# 将文本中的特殊标点符号用指定的字符进行替换。
words = utils.preprocess(text)
print('First 30 words:', words[:30])
print('Total words:', len(words))
print('Total unique words:', len(set(words)))
# 根据文本生成的单词频率进行由高到低的排序，过滤掉低频词（词出现的次数<5），生成字典id2word以及word2id。
vocab_to_int, int_to_vocab = utils.create_lookup_tables(words)
n_vocab = len(int_to_vocab)
# 由原来的词频进而转化成词的序列，序列通过enumerate来实现的。
int_words = [vocab_to_int[w] for w in words]
###########################################################
# ------------------- Subsampling -------------------------
# Some words like "the", "a", "of" etc don't provide much
# information. So we might want to remove some of them.
# This results in faster and better result.
# The probability that a word is discarded is
# P(w) = 1 - sqrt(1 / frequency(w))
each_word_count = Counter(int_words)
total_count = len(int_words)
threshold = FLAGS.drop_word_threshold
# 统计词频
freq_s = {
word: count / total_count
for word, count in each_word_count.items()
}
prob_s = {
word: 1 - np.sqrt(threshold / freq_s[word])
for word in each_word_count
}
train_words = [
word for word in int_words if random.random() < (1 - prob_s[word])
]
print('After subsampling, first 30 words:', train_words[:30])
print('After subsampling, total words:', len(train_words))
return train_words, int_to_vocab, vocab_to_int, n_vocab
def detect(sess, model, names, image, path):
preprocess = eval(args.preprocess)
_, height, width, _ = image.get_shape().as_list()
_image = read_image(path)
image_original = np.array(np.uint8(_image))
image_height, image_width, _ = image_original.shape
image_std = preprocess(
np.array(np.uint8(_image.resize((width, height)))).astype(np.float32))
feed_dict = {image: np.expand_dims(image_std, 0)}
tensors = [model.conf, model.xy_min, model.xy_max]
conf, xy_min, xy_max = sess.run(
[tf.check_numerics(t, t.op.name) for t in tensors],
feed_dict=feed_dict)
boxes = utils.postprocess.non_max_suppress(conf[0], xy_min[0], xy_max[0],
args.threshold,
args.threshold_iou)
scale = [image_width / model.cell_width, image_height / model.cell_height]
fig = plt.figure()
ax = fig.gca()
ax.imshow(image_original)
colors = [
prop['color'] for _, prop in zip(
names, itertools.cycle(plt.rcParams['axes.prop_cycle']))
]
cnt = 0
for _conf, _xy_min, _xy_max in boxes:
index = np.argmax(_conf)
if _conf[index] > args.threshold:
wh = _xy_max - _xy_min
_xy_min = _xy_min * scale
_wh = wh * scale
linewidth = min(_conf[index] * 10, 3)
ax.add_patch(
patches.Rectangle(_xy_min,
_wh[0],
_wh[1],
linewidth=linewidth,
edgecolor=colors[index],
facecolor='none'))
ax.annotate(names[index] + ' (%.1f%%)' % (_conf[index] * 100),
_xy_min,
color=colors[index])
cnt += 1
fig.canvas.set_window_title('%d objects detected' % cnt)
ax.set_xticks([])
ax.set_yticks([])
return fig
def create_bb_pip(tfr_pool, nepoch, sbatch, mean, shuffle=True):
if len(tfr_pool) == 3:
ebs = [int(sbatch * 0.5), int(sbatch * 0.3), sbatch - int(sbatch * 0.5) - int(sbatch * 0.3)]
elif len(tfr_pool) == 1:
ebs = [sbatch]
else:
print("Input Format is not recognized")
return
data_pool = []
for ix, tfr in enumerate(tfr_pool):
cur_ebs = ebs[ix]
tokens = tfr.split('/')[-1].split('_')
dim = int(tokens[-1].split('.')[0][1:])
tf_mean = tf.constant(mean, dtype=tf.float32)
tf_mean = tf.reshape(tf_mean, [1, 1, 1, 3])
fqueue = tf.train.string_input_producer([tfr], num_epochs=nepoch)
image, gt_key, gt_3d, gt_2d, occ = read_one_datum(fqueue, dim)
if shuffle:
data = tf.train.shuffle_batch([image, gt_key, gt_3d, gt_2d, occ], batch_size=cur_ebs,
num_threads=12, capacity=sbatch * 6, min_after_dequeue=cur_ebs * 3)
else:
data = tf.train.batch([image, gt_key, gt_3d, gt_2d, occ], batch_size=cur_ebs,
num_threads=12, capacity=cur_ebs * 5)
# preprocess input images
# print("data0]", data[0])
data[0] = preprocess(data[0], tf_mean) #
# data[0] = preprocess_norm(data[0]) #
if ix == 0:
for j in range(len(data)):
data_pool.append([data[j]])
else:
for j in range(len(data)):
data_pool[j].append(data[j])
combined_data = []
for dd in data_pool:
combined_data.append(tf.concat(dd, axis=0))
# print("sanity check : combined_data", combined_data)
return combined_data
def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = float(data["steering_angle"])
# The current throttle of the car, how hard to push peddle
throttle = float(data["throttle"])
# The current speed of the car
speed = float(data["speed"])
ensembling_weight = 0.66
# The current image from the center camera of the car
image = Image.open(BytesIO(base64.b64decode(data["image"])))
try:
image = np.asarray(image) # from PIL image to numpy array
image = utils.preprocess(image) # apply the preprocessing
image = np.array([image]) # the model expects 4D array
global smoothed_angle
# predict the steering angle for the image
steering_angle1 = float(model.predict(image, batch_size=1))
# smoothed_angle += 0.2 * pow(abs((steering_angle - smoothed_angle)), 2.0 / 3.0) * (steering_angle - smoothed_angle) / abs(steering_angle - smoothed_angle)
# lower the throttle as the speed increases
steering_angle2 = float(model2.predict(image, batch_size=1))
steering_angle = float(
(ensembling_weight * steering_angle1 + steering_angle2 *
(1.0 - ensembling_weight)))
# if the speed is above the current speed limit, we are on a downhill.
# make sure we slow down first and then go back to the original max speed.
global speed_limit
if speed > speed_limit:
speed_limit = MIN_SPEED # slow down
else:
speed_limit = MAX_SPEED
throttle = 1.0 - steering_angle**2 - (speed / speed_limit)**2
print('{} {} {}'.format(steering_angle, throttle, speed))
send_control(steering_angle, throttle)
except Exception as e:
print(e)
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
else:
sio.emit('manual', data={}, skip_sid=True)
def __getitem__(self, idx):
files = self.sample_files[idx]
pre_img = cv2.imread(files['pre_img'])
post_img = cv2.imread(files['post_img'])
if self.rgb:
pre_img = cv2.cvtColor(pre_img, cv2.COLOR_BGR2RGB)
post_img = cv2.cvtColor(post_img, cv2.COLOR_BGR2RGB)
if self.mode in [
'train', 'oodtrain', 'guptatrain', "ood2train", "ood3train",
"singletrain"
]:
sample = self.get_sample_with_mask(files, pre_img, post_img)
sample['image_id'] = files['img_id']
if self.preprocessing is not None:
transformed = preprocess(sample['pre_img'],
sample['post_img'],
sample['mask_img'],
flip=self.preprocessing['flip'],
scale=self.preprocessing['scale'],
crop=self.preprocessing['crop'])
sample['pre_img'] = transformed[0]
sample['post_img'] = transformed[1]
sample['mask_img'] = transformed[2]
elif self.mode in [
'oodtest', 'oodhold', 'guptatest', 'guptahold', "ood2test",
"ood2hold", "ood3test", "ood3hold", "singletest", "singlehold"
]:
pre_img = self.data_transforms(pre_img)
post_img = self.data_transforms(post_img)
sample = {
'pre_img': pre_img,
'post_img': post_img,
'image_id': files['img_id']
}
post_json = json.loads(open(files['post_json']).read())
buildings = self._get_building_from_json(post_json)
sample['mask_img'] = self.make_mask_img(**buildings)
else:
pre_img = self.data_transforms(pre_img)
post_img = self.data_transforms(post_img)
sample = {
'pre_img': pre_img,
'post_img': post_img,
'image_id': files['img_id']
}
return sample
def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = float(data["steering_angle"])
# The current throttle of the car, how hard to push peddle
throttle = float(data["throttle"])
# The current speed of the car
speed = float(data["speed"])
# The current image from the center camera of the car
image = Image.open(BytesIO(base64.b64decode(data["image"])))
try:
image = np.asarray(image)
image = utils.preprocess(image) # apply the preprocessing
image = np.array([image]) # the model expects 4D array
# predict the steering angle for the image
steering_angle = float(model.predict(image, batch_size=1))
# lower the throttle as the speed increases
# if the speed is above the current speed limit, we are on a downhill.
global speed_limit
if speed > speed_limit:
speed_limit = MIN_SPEED # slow down
else:
speed_limit = MAX_SPEED
#Calculate throttle from spped and steering angle
throttle = 1.0 - steering_angle**2 - (speed/speed_limit)**2
print('{} {} {}'.format(steering_angle, throttle, speed))
send_control(steering_angle, throttle)
except Exception as e:
print(e)
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
else:
sio.emit('manual', data={}, skip_sid=True)
def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = float(data["steering_angle"])
# The current throttle of the car, how hard to push peddle
throttle = float(data["throttle"])
# The current speed of the car
speed = float(data["speed"])
# The current image from the center camera of the car
image = Image.open(BytesIO(base64.b64decode(data["image"])))
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
try:
image = np.asarray(image) # from PIL image to numpy array
image = utils.preprocess(image) # apply the preprocessing
image = np.array([image]) # the model expects 4D array
# predict the steering angle for the image
steering_angle = float(model.predict(image, batch_size=1))
# lower the throttle as the speed increases
# if the speed is above the current speed limit, we are on a downhill.
# make sure we slow down first and then go back to the original max speed.
global speed_limit
if speed > speed_limit:
speed_limit = MIN_SPEED # slow down
else:
speed_limit = MAX_SPEED
throttle = 1.0 - steering_angle**2 - (speed/speed_limit)**2
print('{} {} {}'.format(steering_angle, throttle, speed))
send_control(steering_angle, throttle)
except Exception as e:
print(e)
else:
sio.emit('manual', data={}, skip_sid=True)
def get_data():
"""
获取语料库
"""
texts = []
subfolds = os.listdir("../Sample")
for subfold in subfolds:
subdir = "../Sample/{}".format(subfold)
if os.path.isdir(subdir):
files = os.listdir(subdir)
for file in files:
text = open("{}/{}".format(subdir, file)).read()
text = utils.preprocess(text)
text = utils.getWordlist(text)
texts.append(text)
# 去除只出现一次的词
frequency = defaultdict(int)
for text in texts:
for token in text:
frequency[token] += 1
texts = [[token for token in text if frequency[token] > 1] for text in texts]
return texts
def train():
"""
Training systole and diastole models.
"""
print('Loading and compiling models...')
model_systole = get_model(img_size)
model_diastole = get_model(img_size)
print('Loading training data...')
X, y = load_train_data()
print('Pre-processing images...')
X = preprocess(X)
# split to training and test
X_train, y_train, X_test, y_test = split_data(X, y, split_ratio=0.2)
# define image generator for random rotations
datagen = ImageDataGenerator(featurewise_center=False,
featurewise_std_normalization=False,
rotation_range=15)
nb_iter = 300
epochs_per_iter = 1
batch_size = 64
calc_crps = 1 # calculate CRPS every n-th iteration (set to 0 if CRPS estimation is not needed)
# remember min val. losses (best iterations), used as sigmas for submission
min_val_loss_systole = sys.float_info.max
min_val_loss_diastole = sys.float_info.max
if not os.path.exists(STATS):
os.makedirs(STATS)
with open(STATS + 'RMSE_CRPS.txt', 'w') as f:
names = ['train_RMSE_d', 'train_RMSE_s', 'test_RMSE_d', 'test_RMSE_s', 'train_crps', 'test_crps']
f.write('\t'.join([str(name) for name in names]) + '\n')
print('-'*50)
print('Training...')
print('-'*50)
for i in range(nb_iter):
print('-'*50)
print('Iteration {0}/{1}'.format(i + 1, nb_iter))
print('-'*50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
print('Fitting systole model...')
hist_systole = model_systole.fit(X_train_aug, y_train[:, 0], shuffle=True, nb_epoch=epochs_per_iter, batch_size=batch_size, validation_data=(X_test, y_test[:, 0]))
print('Fitting diastole model...')
hist_diastole = model_diastole.fit(X_train_aug, y_train[:, 1], shuffle=True, nb_epoch=epochs_per_iter, batch_size=batch_size, validation_data=(X_test, y_test[:, 1]))
# sigmas for predicted data, actually loss function values (RMSE)
loss_systole = hist_systole.history['loss'][-1]
loss_diastole = hist_diastole.history['loss'][-1]
val_loss_systole = hist_systole.history['val_loss'][-1]
val_loss_diastole = hist_diastole.history['val_loss'][-1]
if calc_crps > 0 and i % calc_crps == 0:
print('Evaluating CRPS...')
pred_systole = model_systole.predict(X_train, batch_size=batch_size, verbose=1)
pred_diastole = model_diastole.predict(X_train, batch_size=batch_size, verbose=1)
val_pred_systole = model_systole.predict(X_test, batch_size=batch_size, verbose=1)
val_pred_diastole = model_diastole.predict(X_test, batch_size=batch_size, verbose=1)
# CDF for train and test data (actually a step function)
cdf_train = real_to_cdf(np.concatenate((y_train[:, 0], y_train[:, 1])))
cdf_test = real_to_cdf(np.concatenate((y_test[:, 0], y_test[:, 1])))
# CDF for predicted data
cdf_pred_systole = real_to_cdf(pred_systole, loss_systole)
cdf_pred_diastole = real_to_cdf(pred_diastole, loss_diastole)
cdf_val_pred_systole = real_to_cdf(val_pred_systole, val_loss_systole)
cdf_val_pred_diastole = real_to_cdf(val_pred_diastole, val_loss_diastole)
# evaluate CRPS on training data
crps_train = crps(cdf_train, np.concatenate((cdf_pred_systole, cdf_pred_diastole)))
print('CRPS(train) = {0}'.format(crps_train))
# evaluate CRPS on test data
crps_test = crps(cdf_test, np.concatenate((cdf_val_pred_systole, cdf_val_pred_diastole)))
print('CRPS(test) = {0}'.format(crps_test))
print('Saving weights...')
# save weights so they can be loaded later
model_systole.save_weights(MODELS + 'weights_systole.hdf5', overwrite=True)
model_diastole.save_weights(MODELS + 'weights_diastole.hdf5', overwrite=True)
# for best (lowest) val losses, save weights
if val_loss_systole < min_val_loss_systole:
min_val_loss_systole = val_loss_systole
model_systole.save_weights(MODELS + 'weights_systole_best.hdf5', overwrite=True)
if val_loss_diastole < min_val_loss_diastole:
min_val_loss_diastole = val_loss_diastole
model_diastole.save_weights(MODELS + 'weights_diastole_best.hdf5', overwrite=True)
# save best (lowest) val losses in file (to be later used for generating submission)
with open(MODELS + 'val_loss.txt', mode='w+') as f:
f.write(str(min_val_loss_systole))
f.write('\n')
f.write(str(min_val_loss_diastole))
with open(STATS + 'RMSE_CRPS.txt', 'a') as f:
# train_RMSE_d train_RMSE_s test_RMSE_d test_RMSE_s train_crps test_crps
rmse_values = [loss_diastole, loss_systole, val_loss_diastole, val_loss_systole]
crps_values = [crps_train, crps_test]
f.write('\t'.join([str(val) for val in rmse_values + crps_values]) + '\n')
print('Saving stats images...')
write_images(STATS)
if (i != 0) & ((i + 1) % 100 == 0):
print('Submitting learned model....')
SUBMISSION_FOLDER = SUBMISSION + preproc_type + "/" + model_name + "/" + get_name() + "_ITERS" + str(i + 1) + "/"
if not os.path.exists(SUBMISSION_FOLDER):
os.makedirs(SUBMISSION_FOLDER)
copyfile(MODELS + 'weights_systole_best.hdf5', SUBMISSION_FOLDER + 'weights_systole_best.hdf5')
copyfile(MODELS + 'weights_diastole_best.hdf5', SUBMISSION_FOLDER + 'weights_diastole_best.hdf5')
copyfile(MODELS + 'val_loss.txt', SUBMISSION_FOLDER + 'val_loss.txt')
os.system('python submission.py %s %s %s' % (preproc_type, model_name, SUBMISSION_FOLDER))
def main(num_epochs=500):
# Load the dataset
print 'Loading dataset ...'
eng_para = pd.read_csv('data/2g_gongcan.csv')
#eng_para = eng_para.loc[:, ['LAC', 'CI', 'Angle', 'Longitude', 'Latitude', 'Power', 'GSM Neighbor Count', 'TD Neighbor Count']]
tr_feature, tr_label, tr_ids = load_dataset('data/forward_recovered.csv', eng_para, True)
te_feature, te_label, te_ids = load_dataset('data/backward_recovered.csv', eng_para, False)
## !!! maybe here need to ensure train data are the same shape as test data
train_size, n_con = tr_feature.shape
test_size, n_con = te_feature.shape
n_dis = len(tr_ids)
# Create neural network model
print 'Preprocessing data ...'
# Standardize continous input
tr_feature, te_feature = preprocess(tr_feature, te_feature)
tr_input = {'con_input' : tr_feature}
te_input = {'con_input' : te_feature}
# Prepare embedding input
dis_dims, vocab_sizes = [], []
for ii, tr_ids_, te_ids_ in zip(range(n_dis), tr_ids, te_ids): # make sure tr_ids contain several different discrete features
vocab_size, vocab_dict = make_vocab(tr_ids_, te_ids_)
tr_id_idx_, te_id_idx_ = [], []
dis_dim = len(tr_ids_)
for i in range(dis_dim):
tr_id_idx_ += map(lambda x: vocab_dict[x], tr_ids_[i])
te_id_idx_ += map(lambda x: vocab_dict[x], te_ids_[i])
tr_ids = np.array(tr_id_idx_, dtype=np.int32).reshape(dis_dim, train_size).transpose()
te_ids = np.array(te_id_idx_, dtype=np.int32).reshape(dis_dim, test_size).transpose()
## Add discrete feature to dict
tr_input['emb_input%d' % ii] = tr_ids
te_input['emb_input%d' % ii] = te_ids
dis_dims.append(dis_dim)
vocab_sizes.append(vocab_size)
print 'Building model and compiling functions ...'
# Define network structure
l_output = build_mlp(n_con, n_dis, dis_dims, vocab_sizes)
# Set batch size
bi = BatchIterator(batch_size=10)
# Build network
network = NeuralNet(l_output,
regression=True,
update_learning_rate=1e-5,
update=nesterov_momentum,
update_momentum=0.9,
train_split=TrainSplit(eval_size=0.05),
verbose=1,
batch_iterator_train=bi,
objective_loss_function=lasagne.objectives.squared_error,
max_epochs=5000)
pickle_name = 'MLP-0.10.pickle'
mul_val = 10000.
lon_offset = np.mean(tr_label[:, 0])
lon_std = np.mean(tr_label[:, 0])
lat_offset = np.mean(tr_label[:, 1])
lat_std = np.mean(tr_label[:, 1])
######## Change Target
tr_label[:, 0] = (tr_label[:, 0] - lon_offset) * mul_val
tr_label[:, 1] = (tr_label[:, 1] - lat_offset) * mul_val
tr_label = tr_label.astype(np.float32)
print tr_label
is_train = True
if is_train:
network.fit(tr_input, tr_label)
# Dump Network
with open('model/'+pickle_name, 'wb') as f:
pickle.dump(network, f, -1)
else:
# Load Network
f = open('model/'+pickle_name)
network = pickle.load(f)
# Make prediction
te_pred = network.predict(te_input)
te_pred[:, 0] = te_pred[:, 0] / mul_val + lon_offset
te_pred[:, 1] = te_pred[:, 1] / mul_val + lat_offset
f_out = open('pred.csv', 'w')
for pred_pt, true_pt in zip(te_pred, te_label):
f_out.write('%f,%f,%f,%f\n' % (pred_pt[0], pred_pt[1], true_pt[0], true_pt[1]))
# Generate report
gen_report(te_label, te_pred, pickle_name)
def mlp(tr_data, te_data, eng_para, col_name, grid_size, \
optimizer, batch_size, hidden_size, mlp_feature, \
nb_epoch, prediction, model_name, is_train):
# Load the dataset
print 'Loading dataset ...'
tr_feature, tr_label, tr_ids = mlp_feature(tr_data, eng_para, True, col_name)
te_feature, te_label, te_ids = mlp_feature(te_data, eng_para, True, col_name)
rg = RoadGrid(np.vstack((tr_label, te_label)), grid_size)
tr_label = rg.transform(tr_label)
# te_label = rg.transform(te_label)
## !!! maybe here need to ensure train data are the same shape as test data
train_size, n_con = tr_feature.shape
test_size, n_con = te_feature.shape
n_dis = len(tr_ids)
# Create neural network model
print 'Preprocessing data ...'
# Standardize continous input
# tr_feature, te_feature = preprocess(tr_feature, te_feature)
tr_feature, te_feature = preprocess(tr_feature, te_feature)
# te_feature = preprocess(te_feature)
tr_input = {'con_input' : tr_feature, 'output' : tr_label}
te_input = {'con_input' : te_feature}
# Prepare embedding input
dis_dims, vocab_sizes = [], []
for ii, tr_ids_, te_ids_ in zip(range(n_dis), tr_ids, te_ids): # make sure tr_ids contain several different discrete features
vocab_size, vocab_dict = make_vocab(tr_ids_, te_ids_)
tr_id_idx_, te_id_idx_ = [], []
dis_dim = len(tr_ids_)
for i in range(dis_dim):
tr_id_idx_ += map(lambda x: vocab_dict[x], tr_ids_[i])
te_id_idx_ += map(lambda x: vocab_dict[x], te_ids_[i])
tr_ids = np.array(tr_id_idx_, dtype=np.int32).reshape(dis_dim, train_size).transpose()
te_ids = np.array(te_id_idx_, dtype=np.int32).reshape(dis_dim, test_size).transpose()
## Add discrete feature to dict
tr_input['emb_input%d' % ii] = tr_ids
te_input['emb_input%d' % ii] = te_ids
dis_dims.append(dis_dim)
vocab_sizes.append(vocab_size)
print 'Building model and compiling functions ...'
# Define network structure
grid_info = rg.grid_center
network = build_mlp(n_con, n_dis, dis_dims, vocab_sizes, len(grid_info), hidden_size)
#network.compile(loss={'output': 'categorical_crossentropy'}, optimizer=SGD(lr=1e-2, momentum=0.9, nesterov=True))
network.compile(loss={'output': 'categorical_crossentropy'}, optimizer=optimizer)
# Build network
# pickle_name = 'MLP-softmax-0.4.pickle'
pickle_name = model_name
if is_train:
history = network.fit(tr_input, nb_epoch=nb_epoch, batch_size=batch_size, verbose=1)
# Dump Network
with open('model/'+pickle_name, 'wb') as f:
pickle.dump(network, f, -1)
else:
# Load Network
f = open('model/'+pickle_name)
network = pickle.load(f)
# Make prediction
## 1. weighted
if prediction == 'weighted':
te_pred = np.asarray(network.predict(te_input)['output'])
te_pred = te_pred.dot(grid_info)
# Generate report
# gen_report(te_label, te_pred, pickle_name, [type(optimizer), batch_size, hidden_size, 'Weighted'])
elif prediction == 'argmax':
## 2. argmax
te_pred = np.asarray(network.predict(te_input)['output'])
te_pred = np.argmax(te_pred, axis=1)
te_pred = [grid_info[idx] for idx in te_pred]
# Generate report
# gen_report(te_label, te_pred, pickle_name, [type(optimizer), batch_size, hidden_size, 'Argmax'])
else:
te_pred = None
return te_pred
def train():
"""
Training systole and diastole models.
"""
print('Loading and compiling models...')
model_systole = get_model()
model_diastole = get_model()
print('Loading training data...')
X, y = load_train_data()
print('Pre-processing images...')
X = preprocess(X)
# split to training and test
X_train, y_train, X_test, y_test = split_data(X, y, split_ratio=0.2)
nb_iter = 200
epochs_per_iter = 1
batch_size = 32
calc_crps = 1 # calculate CRPS every n-th iteration (set to 0 if CRPS estimation is not needed)
# remember min val. losses (best iterations), used as sigmas for submission
min_val_loss_systole = sys.float_info.max
min_val_loss_diastole = sys.float_info.max
print('-'*50)
print('Training...')
print('-'*50)
for i in range(nb_iter):
print('-'*50)
print('Iteration {0}/{1}'.format(i + 1, nb_iter))
print('-'*50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
print('Fitting systole model...')
hist_systole = model_systole.fit(X_train_aug, y_train[:, 0], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, y_test[:, 0]))
print('Fitting diastole model...')
hist_diastole = model_diastole.fit(X_train_aug, y_train[:, 1], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, y_test[:, 1]))
# sigmas for predicted data, actually loss function values (RMSE)
loss_systole = hist_systole.history['loss'][-1]
loss_diastole = hist_diastole.history['loss'][-1]
val_loss_systole = hist_systole.history['val_loss'][-1]
val_loss_diastole = hist_diastole.history['val_loss'][-1]
if calc_crps > 0 and i % calc_crps == 0:
print('Evaluating CRPS...')
pred_systole = model_systole.predict(X_train, batch_size=batch_size, verbose=1)
pred_diastole = model_diastole.predict(X_train, batch_size=batch_size, verbose=1)
val_pred_systole = model_systole.predict(X_test, batch_size=batch_size, verbose=1)
val_pred_diastole = model_diastole.predict(X_test, batch_size=batch_size, verbose=1)
# CDF for train and test data (actually a step function)
cdf_train = real_to_cdf(np.concatenate((y_train[:, 0], y_train[:, 1])))
cdf_test = real_to_cdf(np.concatenate((y_test[:, 0], y_test[:, 1])))
# CDF for predicted data
cdf_pred_systole = real_to_cdf(pred_systole, loss_systole)
cdf_pred_diastole = real_to_cdf(pred_diastole, loss_diastole)
cdf_val_pred_systole = real_to_cdf(val_pred_systole, val_loss_systole)
cdf_val_pred_diastole = real_to_cdf(val_pred_diastole, val_loss_diastole)
# evaluate CRPS on training data
crps_train = crps(cdf_train, np.concatenate((cdf_pred_systole, cdf_pred_diastole)))
print('CRPS(train) = {0}'.format(crps_train))
# evaluate CRPS on test data
crps_test = crps(cdf_test, np.concatenate((cdf_val_pred_systole, cdf_val_pred_diastole)))
print('CRPS(test) = {0}'.format(crps_test))
print('Saving weights...')
# save weights so they can be loaded later
model_systole.save_weights('weights_systole.hdf5', overwrite=True)
model_diastole.save_weights('weights_diastole.hdf5', overwrite=True)
# for best (lowest) val losses, save weights
if val_loss_systole < min_val_loss_systole:
min_val_loss_systole = val_loss_systole
model_systole.save_weights('weights_systole_best.hdf5', overwrite=True)
if val_loss_diastole < min_val_loss_diastole:
min_val_loss_diastole = val_loss_diastole
model_diastole.save_weights('weights_diastole_best.hdf5', overwrite=True)
# save best (lowest) val losses in file (to be later used for generating submission)
with open('val_loss.txt', mode='w+') as f:
f.write(str(min_val_loss_systole))
f.write('\n')
f.write(str(min_val_loss_diastole))
def run(self, load_pipeline=False, train_more=False):
# Load/pretrain the net
if load_pipeline:
# Load the processing pipeline
nn_input_shape = self.get_nn_input_shape()
nnet, numer, denom, scaler = utils.load_processing_pipeline(
self._filename_pipeline_base,
self._is_scaling_needed,
nn_type=self._nn_type,
nn_input_shape=nn_input_shape,
nn_output_shape=self._num_event_types,
num_max_training_epochs=self._num_max_training_epochs)
if train_more:
# Load the training data
X_train_raw, labels_train = utils.load_data(
data_filename=self._filename_train,
signal_col_ids=self._signal_col_ids,
label_col_ids=self._label_col_ids,
decimation_factor=self._decimation_factor)
# Preprocess the data
numer, denom, scaler = utils.init_preprocessors(
X_raw=X_train_raw,
freq_sampling=self._freq_sampling,
freq_cut_lo=self._freq_cut_lo,
freq_cut_hi=self._freq_cut_hi,
M_fir=self._M_fir,
artifact_threshold=self._artifact_threshold)
if not self._is_scaling_needed:
scaler = None
X_train_preproc, labels_train = utils.preprocess(
X_train_raw, labels_train,
tdfilt_numer=numer, tdfilt_denom=denom,
# reref_channel_id=params.REREF_CHANNEL_ID,
artifact_threshold=self._artifact_threshold,
# power=True,
# mov_avg_window_size=params.MOVING_AVG_WINDOW_SIZE_SECS,
scaler=scaler,
window_size=self._window_size_decimated_in_samples,
nn_type=self._nn_type)
# Train the NN
nnet = nnutils.train_nn_from_timeseries(
nnet, self._nn_type,
X_train_preproc, labels_train,
self._window_size_decimated_in_samples,
self._num_event_types,
self._num_train_data_instances,
plot_history=False)
# Save the pipeline
utils.save_processing_pipeline(
nnet, self._nn_type, numer, denom, scaler)
else: # If a new net is to be created
# Load the training data
logging.debug('%s Loading the training data...', TAG)
X_train_raw, labels_train = utils.load_data(
data_filename=self._filename_train,
signal_col_ids=self._signal_col_ids,
label_col_ids=self._label_col_ids,
decimation_factor=self._decimation_factor)
logging.debug('%s X_train_raw.shape: %s', TAG, str(X_train_raw.shape))
logging.debug('%s labels_train.shape: %s', TAG, str(labels_train.shape))
logging.debug('%s np.sum(labels_train, axis=0): %s', TAG, str(np.sum(labels_train, axis=0).tolist()))
logging.debug('%s Training data loaded.', TAG)
# Preprocess the data
numer, denom, scaler = utils.init_preprocessors(
X_train_raw,
self._freq_sampling,
self._freq_cut_lo,
self._freq_cut_hi,
self._M_fir,
artifact_threshold=self._artifact_threshold,
plot=False)
if not self._is_scaling_needed:
scaler = None
X_train_preproc, labels_train = utils.preprocess(
X_train_raw, labels_train,
tdfilt_numer=numer, tdfilt_denom=denom,
# reref_channel_id=params.REREF_CHANNEL_ID,
artifact_threshold=self._artifact_threshold,
# power=True,
# mov_avg_window_size=params.MOVING_AVG_WINDOW_SIZE_SECS,
scaler=scaler,
window_size=self._window_size_decimated_in_samples,
nn_type=self._nn_type)
# labels_train = labels_train
# Plot the training data
if self._is_plot_mode_on:
logging.debug('%s Plotting the preprocessed training data... %s', TAG, self._nn_type)
time_axis = np.arange(X_train_preproc.shape[0])
if 'gtec' in self._nn_type:
t_from = 0
t_to = t_from + 120 * self._freq_sampling
plot_cols = range(16)
#plot_cols = (1, 3, 9)
#plt.plot(time_axis[t_from:t_to], X_train_preproc[t_from:t_to, plot_rows, plot_cols], label='tdfilt')
#plt.plot(time_axis[t_from:t_to], X_train_raw[t_from:t_to, plot_cols], label='raw')
plt.plot(time_axis[t_from:t_to], X_train_preproc[t_from:t_to, plot_cols], label='tdfilt')
plt.plot(time_axis[t_from:t_to], 10.0*labels_train[t_from:t_to], linewidth=3, label='event')
elif 'biosemi' in self._nn_type:
t_from = 20000
t_to = t_from + 1000 * self._freq_sampling
#plot_rows = (6)
#plot_cols = (0, 1, 2, 3, 4, 5, 6, 7)
#plt.plot(time_axis[t_from:t_to], X_train_preproc[t_from:t_to, plot_rows, plot_cols], label='tdfilt')
#plt.plot(time_axis[t_from:t_to], X_train_raw[t_from:t_to, plot_cols], label='raw')
plt.plot(time_axis[t_from:t_to], X_train_preproc[t_from:t_to, plot_cols], label='tdfilt')
plt.plot(time_axis[t_from:t_to], -300000.0*labels_train[t_from:t_to], linewidth=3, label='event')
elif 'gal' in self._nn_type:
t_from = 0
t_to = t_from + 1000 * self._freq_sampling
#plot_rows = (6)
plot_cols = (0, 1, 2, 3, 4, 5, 6, 7)
#plt.plot(time_axis[t_from:t_to], X_train_preproc[t_from:t_to, plot_rows, plot_cols], label='tdfilt')
#plt.plot(time_axis[t_from:t_to], X_train_raw[t_from:t_to, plot_cols], label='raw')
plt.plot(time_axis[t_from:t_to], X_train_preproc[t_from:t_to, plot_cols], label='tdfilt')
plt.plot(time_axis[t_from:t_to], 10.0*labels_train[t_from:t_to], linewidth=3, label='event')
else:
logging.critical('%s Unknown source make.', TAG)
plt.legend(loc='lower right')
plt.show()
# Init the NN
nn_input_shape = self.get_nn_input_shape()
nnet, _ = nnfactory.create_nn(
nn_type=self._nn_type,
nn_input_shape=nn_input_shape,
nn_output_shape=self._num_event_types,
num_max_training_epochs=self._num_max_training_epochs)
# Train the NN
logging.debug('%s Training the NN...', TAG)
nnet = nnutils.train_nn_from_timeseries(
nnet, self._nn_type,
X_train_preproc, labels_train,
self._window_size_decimated_in_samples,
self._num_event_types,
self._num_train_data_instances,
plot_history=False)
logging.debug('%s Training the NN finished.', TAG)
# Save the pipeline
utils.save_processing_pipeline(
nnet, self._nn_type,
numer, denom, scaler)
# Load the test data
logging.debug('%s Loading the test data...', TAG)
X_test_raw, labels_test = utils.load_data(
data_filename=self._filename_test,
signal_col_ids=self._signal_col_ids,
label_col_ids=self._label_col_ids,
decimation_factor=self._decimation_factor)
if self._is_runtest_mode_on:
X_test_raw = X_test_raw[0:X_test_raw.shape[0]/4]
labels_test = labels_test[0:labels_test.shape[0]/4]
# Pre-process the test data
if not self._is_scaling_needed:
scaler = None
X_test_preproc, labels_test = utils.preprocess(
X_test_raw, labels_test,
tdfilt_numer=numer, tdfilt_denom=denom,
#reref_channel_id=params.REREF_CHANNEL_ID,
artifact_threshold=self._artifact_threshold,
#power=True,
#mov_avg_window_size=params.MOVING_AVG_WINDOW_SIZE_SECS,
scaler=scaler,
window_size=self._window_size_decimated_in_samples,
nn_type=self._nn_type)
#X_test_preproc = X_test_preproc[0:40000, :]
#labels_test = labels_test[0:40000, :]
# Dummy set for testing
#X_test_preproc = X_train = np.tile(np.reshape(labels_test[:, 0], (labels_test.shape[0], 1)), [1, params.NUM_CHANNELS])
# Test the net
batch_iter_test_valid = TimeSeriesBatchIterator(
data=X_test_preproc, labels=None,
nn_type=self._nn_type,
window_size_samples=self._window_size_decimated_in_samples,
nn_output_shape=self._num_event_types,
batch_size=params.BATCH_SIZE)
nnet.batch_iterator_train = None
nnet.batch_iterator_test = batch_iter_test_valid
indices_test = np.arange(X_test_preproc.shape[0])
logging.debug('%s Testing the net...', TAG)
utils.log_timestamp()
predictions = nnet.predict_proba(indices_test)
utils.log_timestamp()
logging.debug('%s Predictions size: %d, %d', TAG, predictions.shape[0], predictions.shape[1])
logging.debug('%s np.sum(predictions): %f', TAG, np.sum(predictions))
# Find the thresholds
#tpr_targets = (0.5, 0.5, 0.5, 0.5, 0.5, 0.5)
tpr_targets = (0.5, 0.5)
p_thresholds = utils.calculate_auroc(
labels_test, predictions,
self._event_name_list,
tpr_targets,
self._nn_type,
plot=self._is_plot_mode_on)
#p_thresholds = (0.1, 0.1, 0.1)
logging.debug('%s p_thresholds: %s', TAG, str(p_thresholds))
# Simulate control signal
if self._is_control_simulation_on:
TimeSeriesProcessor.create_control_signal(
labels_test, predictions, p_thresholds,
self._nn_type);
''' setting parameters according to the README '''
prob = svm_problem(train_labels, train_images)
param = svm_parameter('-q')
param_best = svm_parameter('-c 32 -g 0.0078125 -q')
param_linear = svm_parameter('-t 0 -q')
param_poly = svm_parameter('-t 1 -g 1 -q')
param_rbf = svm_parameter('-g 0.0078125 -q')
model = svm_train(prob, param)
"""
''' precompute-kernel in generate by precompute-kernel.py '''
pre_train_labels, pre_train_images = svm_read_problem('../../../lab5/data/precompute-kernel-train')
pre_test_labels, pre_test_images = svm_read_problem('../../../lab5/data/precompute-kernel-test')
print('File loaded')
prob_pre = svm_problem(pre_train_labels, pre_train_images, isKernel=True)
param_pre = svm_parameter('-t 4')
model = svm_train(prob_pre, param_pre)
"""
''' get support vectors '''
n = model.get_sv_indices()
n = [i-1 for i in n]
''' draw support vectors and dots in 2D space with PCA '''
images, labels = preprocess(path='../../../lab5/data/')
pca(images, labels, special=n)
# preprocess the training text
from utils import preprocess
with open("raw.txt") as fin:
text = fin.read()
print preprocess(text)
def cursor_func(
freq_sampling,
num_signal_channels,
num_event_types,
window_size_in_samples):
logging.debug('%s cursor_func(.) entered.', TAG)
len_padding = 5 * freq_sampling
cursor_radius = 26
w = 2 * math.pi / 10
# Initialize the time-domain filter
#numer, denom = get_time_domain_filters(8.0, 12.0, 0.5)
# Init the NN
if is_control_mode:
filename_base = '../models/MIBBCI_NN_medium_bestsofar'
filename_nn = filename_base + '.npz'
nnet = nnutils.load_nn(nnutils.create_nn_medium, filename_nn)
# Init the preproc stuff
if is_control_mode:
filename_p = filename_base + '.p'
scaler = cPickle.load(open(filename_p, 'rb'))
print 'Loaded scaler.mean_, scaler.var_:', scaler.mean_, scaler.var_
# Init graphics
win = graphics.GraphWin('Cursor', params.IMAGE_W, params.IMAGE_H)
cursor = graphics.Circle(graphics.Point(params.IMAGE_W/2, params.IMAGE_H/2), cursor_radius)
cursor.setFill(graphics.color_rgb(params.CURSOR_COLOR_REST[0], params.CURSOR_COLOR_REST[1], params.CURSOR_COLOR_REST[2]))
cursor.setOutline(graphics.color_rgb(params.CURSOR_COLOR_REST[0], params.CURSOR_COLOR_REST[1], params.CURSOR_COLOR_REST[2]))
cursor.draw(win)
cursor_pos_prev = np.array([params.IMAGE_W/2, params.IMAGE_H/2])
cursor_pos = cursor_pos_prev
# Init event labels
event_arr_right = np.zeros((params.LEN_DATA_CHUNK_READ, num_event_types))
event_arr_right[:, params.EVENT_ID_RH] = np.ones(params.LEN_DATA_CHUNK_READ)
event_arr_left = np.zeros((params.LEN_DATA_CHUNK_READ, num_event_types))
event_arr_left[:, params.EVENT_ID_LH] = np.ones(params.LEN_DATA_CHUNK_READ)
event_arr_idle = np.zeros((params.LEN_DATA_CHUNK_READ, num_event_types))
event_arr_idle[:, params.EVENT_ID_IDLE] = np.ones(params.LEN_DATA_CHUNK_READ)
#event_arr_calib = np.zeros((params.LEN_DATA_CHUNK_READ, num_event_types))
#event_arr_calib[:, 3] = np.ones(params.LEN_DATA_CHUNK_READ)
cursor_event_list = []
cursor_color_arr_raw = np.zeros((int(params.LEN_PERIOD_SEC * freq_sampling / params.LEN_DATA_CHUNK_READ), 3))
color_counter = 0
for i in range(int(params.LEN_IDLE_SEC * freq_sampling / params.LEN_DATA_CHUNK_READ)):
cursor_color_arr_raw[color_counter, :] = params.CURSOR_COLOR_IDLE
cursor_event_list.append(event_arr_idle) # r, l, idle, calib
color_counter += 1
for i in range(int(params.LEN_RIGHT_SEC * freq_sampling / params.LEN_DATA_CHUNK_READ)):
cursor_color_arr_raw[color_counter, :] = params.CURSOR_COLOR_RIGHT
cursor_event_list.append(event_arr_right)
color_counter += 1
for i in range(int(params.LEN_IDLE_SEC * freq_sampling / params.LEN_DATA_CHUNK_READ)):
cursor_color_arr_raw[color_counter, :] = params.CURSOR_COLOR_IDLE
cursor_event_list.append(event_arr_idle)
color_counter += 1
for i in range(int(params.LEN_LEFT_SEC * freq_sampling / params.LEN_DATA_CHUNK_READ)):
cursor_color_arr_raw[color_counter, :] = params.CURSOR_COLOR_LEFT
cursor_event_list.append(event_arr_left)
color_counter += 1
conv_window = np.ones((params.LEN_COLOR_CONV_SEC * freq_sampling / params.LEN_DATA_CHUNK_READ, 1))\
/ (1 * int(params.LEN_COLOR_CONV_SEC * freq_sampling / params.LEN_DATA_CHUNK_READ))
cursor_color_arr_ud = np.flipud(cursor_color_arr_raw)
cursor_color_arr_ud_convd = signal.convolve(cursor_color_arr_ud.T, conv_window.T).T
cursor_color_arr_final = np.flipud(cursor_color_arr_ud_convd[0:cursor_color_arr_raw.shape[0], :])
if False:
plt.figure()
plt.plot(cursor_color_arr_raw)
#plt.plot(cursor_color_arr_ud[:, 0])
#plt.plot(cursor_color_arr_ud_convd[:, 0])
plt.plot(cursor_color_arr_final)
#plt.legend(['raw', 'ud', 'ud_convd', 'final'])
plt.show()
# Initialize the amplifier
if not is_simulation_mode:
print 'Initializing the amp...'
recorder = Recorder('lslamp', freq_sampling, params.LEN_REC_BUF_SEC, num_signal_channels)
thread_rec = threading.Thread(target=recorder.record)
thread_rec.start()
# Cursor control loop
X_raw_buf_live = np.zeros((int(freq_sampling*params.LEN_REC_BUF_SEC), num_signal_channels))
label_buf_live = np.zeros((int(freq_sampling*params.LEN_REC_BUF_SEC), num_event_types))
counter = 0
#while True:
while counter < (params.LEN_REC_SEC * freq_sampling / params.LEN_DATA_CHUNK_READ):
print 'counter: ', counter
# Clear the canvas
win.delete('all')
if not is_simulation_mode:
# Wait for new data and get it
data_last_chunk = recorder.get_new_data(params.LEN_DATA_CHUNK_READ, params.AMP_WAIT_SEC)
recorder.acknowledge_new_data()
print 'recorder.new_data_counter:', recorder.new_data_counter
else:
time.sleep(1.0 / (freq_sampling/params.LEN_DATA_CHUNK_READ))
data_last_chunk = 1000.0 * np.random.rand(int(params.LEN_DATA_CHUNK_READ), num_signal_channels)
#print 'Random data_last_chunk size:', data_last_chunk
# Insert the new sample into our time series
i_row_lb = int((counter+len_padding)*params.LEN_DATA_CHUNK_READ)
i_row_ub = int((counter+len_padding+1)*params.LEN_DATA_CHUNK_READ)
X_raw_buf_live[i_row_lb:i_row_ub, :] = data_last_chunk
#print 'data_last_chunk:', data_last_chunk
label_buf_live[i_row_lb:i_row_ub, :]\
= cursor_event_list[counter % int(params.LEN_PERIOD_SEC * freq_sampling / params.LEN_DATA_CHUNK_READ)]
# Calculating cursor step
i_row_ub = int((counter+len_padding+1)*params.LEN_DATA_CHUNK_READ)
i_row_lb = i_row_ub - int(window_size_in_samples)
if i_row_lb >= 0:
#print 'i_row_lb, i_row_ub:', i_row_lb, i_row_ub
#print 'X_raw_buf_live[i_row_lb:i_row_ub, :].shape:', X_raw_buf_live[i_row_lb:i_row_ub, :].shape
if is_control_mode:
X_window = utils.preprocess(X_raw_buf_live[i_row_lb:i_row_ub, :], scaler)
X_in = TimeSeriesBatchIterator.create_X_instance(X_window, conv_dim=1)
X_in = X_in.reshape(1, X_in.shape[0], X_in.shape[1])
#print 'X_window.shape:', X_window.shape
#print 'X_in.shape:', X_in.shape
cursor_step = calc_cursor_step(nnet, X_in.astype(np.float32))
else:
#X_window = X_raw_buf_live[i_row_lb:i_row_ub, :]
cursor_step = 0
cursor_pos = cursor_pos_prev + np.array([cursor_step, 0])
#print 'cursor_pos: ', cursor_pos
else:
cursor_pos = cursor_pos_prev
cursor_pos_point = graphics.Point(cursor_pos[0], cursor_pos[1])
cursor_pos_prev = cursor_pos
cursor = graphics.Circle(cursor_pos_point, cursor_radius)
color_temp = cursor_color_arr_final[counter % int(params.LEN_PERIOD_SEC * freq_sampling / params.LEN_DATA_CHUNK_READ)]
cursor.setFill(graphics.color_rgb(color_temp[0], color_temp[1], color_temp[2]))
cursor.setOutline(graphics.color_rgb(color_temp[0], color_temp[1], color_temp[2]))
cursor.draw(win)
counter += 1
# End of if
# End of while
# Stop recording
recorder.stop_recording()
# Close the window
win.close()
# Cut the padding from the data
i_row_lb = int(len_padding * params.LEN_DATA_CHUNK_READ)
i_row_ub = int((counter+len_padding)*params.LEN_DATA_CHUNK_READ)
X_raw_buf_cut = X_raw_buf_live[i_row_lb:i_row_ub, :]
label_buf_cut = label_buf_live[i_row_lb:i_row_ub, :]
# Save data to file
time_axis = np.arange(X_raw_buf_cut.shape[0]).reshape((X_raw_buf_cut.shape[0], 1))
print 'time_axis.shape:', time_axis.shape
data_merged = np.concatenate((time_axis, X_raw_buf_cut, label_buf_cut), axis=1)
print 'data_merged.shape: ', data_merged.shape
time_save = datetime.now()
np.savetxt('../data/MIBBCI_REC_{0}Hz_{1}{2:02}{3:02}_{4:02}h{5:02}m{6:02}s_RAW.csv'.format(
int(freq_sampling),
time_save.year, time_save.month, time_save.day,
time_save.hour, time_save.minute, time_save.second),
X=data_merged, fmt='%.8f', delimiter=",",
header='time, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, red, blue, idle',
comments='')
print 'cursor_func(.) terminates.'
y = "year"
aff = "affiliation"
affn = "aff_nullable"
numa = "number_of_authors"
coa = "coauthors"
cit = "cit_count"
s = json.load(open(config_file))
conn_str = ('driver=%s; server=%s; uid=%s; pwd=%s; db=%s' %
(s['driver'], s['server'], s['uid'], s['pwd'], s['db']))
conn = pyodbc.connect(conn_str)
"""
make the pairs
"""
df = pd.io.sql.read_sql(select1, conn)
df[ab] = df[ab].map(lambda s : " ".join([utils.preprocess(x) for x in json.loads(s).itervalues()]))
df[ti] = df[ti].map(lambda x: utils.preprocess(x))
# see
# http://stackoverflow.com/questions/13446480/python-pandas-remove-entries-based-on-the-number-of-occurrences#comment18556837_13447176
# for a better way?
counts = df.groupby(un).size()
counts = counts[counts != 1]
df = df[df[un].isin(counts.index.values)]
cursor = conn.cursor()
df[coa] = df.apply(lambda x: utils.query_coauths(cursor, int(x[pmid]), int(x[id])), axis=1)['pmid']
cursor.close()
ungroup = df.groupby(un)
bases_idx = []
matches_idx = []
def main():
# parse arguments
args=parse_args()
if args.cpu:
os.environ["CUDA_VISIBLE_DEVICES"] = ""
sess = tf.Session()
if args.is_train:
# read tfrecord files
glob_pattern=os.path.join(args.dataset_dir, '*.tfrecord')
tfrecords_list = glob.glob(glob_pattern)
# check dirs
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.logs_dir):
os.makedirs(args.logs_dir)
model=MobileNetV2(sess=sess, tf_files=tfrecords_list, num_sampes=args.num_samples,
epoch=args.epoch, batch_size=args.batch_size,
image_height=args.image_height, image_width=args.image_width,
n_classes=args.n_classes,
is_train=args.is_train, learning_rate=args.learning_rate,
lr_decay=args.lr_decay,beta1=args.beta1,
chkpt_dir=args.checkpoint_dir, logs_dir=args.logs_dir,
model_name=args.model_name, rand_crop=args.rand_crop)
model._build_train_graph()
model._train()
else:
model=MobileNetV2(sess=sess, tf_files='', num_sampes=args.num_samples,
epoch=args.epoch, batch_size=args.batch_size,
image_height=args.image_height, image_width=args.image_width,
n_classes=args.n_classes,
is_train=args.is_train, learning_rate=args.learning_rate,
lr_decay=args.lr_decay,beta1=args.beta1,
chkpt_dir=args.checkpoint_dir, logs_dir=args.logs_dir,
model_name=args.model_name, rand_crop=args.rand_crop)
model._build_test_graph()
saver=tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(args.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
saver.restore(sess, os.path.join(args.checkpoint_dir, ckpt_name))
print("[*] Success to read {}".format(ckpt_name))
else:
print("[*] Failed to find a checkpoint")
return
# get input and output tensors from graph
graph = tf.get_default_graph()
input_x = graph.get_tensor_by_name("input:0")
input_y = graph.get_tensor_by_name("label:0")
prob = graph.get_tensor_by_name("mobilenetv2/prob:0")
# prepare eval/test data and label
img=imread('data/test/t_1_0.jpeg')
img = imresize(img, (args.image_height, args.image_width))
img=preprocess(img)
print(img.dtype)
label=1
feed_dict={input_x:[img],input_y:[label]} # use [], because we need 4-D tensor
start=time.time()
res=sess.run(prob, feed_dict=feed_dict)[0] # index 0 for batch_size
print('prob: {}, class: {}'.format(res, np.argmax(res)))
print('time: {}'.format(time.time()-start))
def train(train_prefix_dir="/data/heart"):
"""
Training systole and diastole models.
"""
print('Loading and compiling models...')
model_systole = get_vgg_model()
model_diastole = get_vgg_model()
print('Loading training data...')
X, y = load_train_data(train_prefix_dir)
print('Pre-processing images...')
X = preprocess(X)
# split to training and test
X_train, y_train, X_test, y_test = split_data(X, y, split_ratio=0.2)
nb_iter = 200
epochs_per_iter = 1
batch_size = 32
calc_crps = 1 # calculate CRPS every n-th iteration (set to 0 if CRPS estimation is not needed)
# remember min val. losses (best iterations), used as sigmas for submission
min_val_loss_systole = sys.float_info.max
min_val_loss_diastole = sys.float_info.max
print('-'*50)
print('Training...')
print('-'*50)
# Create Image Augmentation
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=15, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=True) # randomly flip images
# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
datagen.fit(X_train)
# Create model checkpointers for systole and diastole
systole_checkpointer_best = ModelCheckpoint(filepath="weights_systole_best.hdf5", verbose=1, save_best_only=True)
diastole_checkpointer_best = ModelCheckpoint(filepath="weights_diastole_best.hdf5", verbose=1, save_best_only=True)
systole_checkpointer = ModelCheckpoint(filepath="weights_systole.hdf5", verbose=1, save_best_only=False)
diastole_checkpointer = ModelCheckpoint(filepath="weights_diastole.hdf5", verbose=1, save_best_only=False)
# Create 600-dimentional y cdfs from observations
y_syst_train = np.array([(i < np.arange(600)) for i in y_train[:, 0]], dtype=np.uint8)
y_syst_test = np.array([(i < np.arange(600)) for i in y_test[:, 0]], dtype=np.uint8)
y_diast_train = np.array([(i < np.arange(600)) for i in y_train[:, 1]], dtype=np.uint8)
y_diast_test = np.array([(i < np.arange(600)) for i in y_test[:, 1]], dtype=np.uint8)
print('Fitting Systole Shapes')
hist_systole = model_systole.fit_generator(datagen.flow(X_train, y_syst_train, batch_size=batch_size),
samples_per_epoch=X_train.shape[0],
nb_epoch=nb_iter, show_accuracy=False,
validation_data=(X_test, y_syst_test),
callbacks=[systole_checkpointer, systole_checkpointer_best],
nb_worker=1)
print('Fitting Diastole Shapes')
hist_diastole = model_diastole.fit_generator(datagen.flow(X_train, y_diast_train, batch_size=batch_size),
samples_per_epoch=X_train.shape[0],
nb_epoch=nb_iter, show_accuracy=False,
validation_data=(X_test, y_diast_test),
callbacks=[diastole_checkpointer, diastole_checkpointer_best],
nb_worker=1)
loss_systole = hist_systole.history['loss'][-1]
loss_diastole = hist_diastole.history['loss'][-1]
val_loss_systole = hist_systole.history['val_loss'][-1]
val_loss_diastole = hist_diastole.history['val_loss'][-1]
if calc_crps > 0:
print('Evaluating CRPS...')
pred_systole = model_systole.predict(X_train, batch_size=batch_size, verbose=1)
pred_diastole = model_diastole.predict(X_train, batch_size=batch_size, verbose=1)
val_pred_systole = model_systole.predict(X_test, batch_size=batch_size, verbose=1)
val_pred_diastole = model_diastole.predict(X_test, batch_size=batch_size, verbose=1)
# CDF for train and test data (actually a step function)
cdf_train = real_to_cdf(np.concatenate((y_train[:, 0], y_train[:, 1])))
cdf_test = real_to_cdf(np.concatenate((y_test[:, 0], y_test[:, 1])))
# CDF for predicted data
cdf_pred_systole = real_to_cdf(pred_systole, loss_systole)
cdf_pred_diastole = real_to_cdf(pred_diastole, loss_diastole)
cdf_val_pred_systole = real_to_cdf(val_pred_systole, val_loss_systole)
cdf_val_pred_diastole = real_to_cdf(val_pred_diastole, val_loss_diastole)
# evaluate CRPS on training data
crps_train = crps(cdf_train, np.concatenate((cdf_pred_systole, cdf_pred_diastole)))
print('CRPS(train) = {0}'.format(crps_train))
# evaluate CRPS on test data
crps_test = crps(cdf_test, np.concatenate((cdf_val_pred_systole, cdf_val_pred_diastole)))
print('CRPS(test) = {0}'.format(crps_test))
# save best (lowest) val losses in file (to be later used for generating submission)
with open('val_loss.txt', mode='w+') as f:
f.write(str(min(hist_systole.history['val_loss'])))
f.write('\n')
f.write(str(min(hist_diastole.history['val_loss'])))
"""
def train():
"training ONE model for systole and diastole "
print('Loading and compiling models...')
model_systole = get_model1()
print('Loading models weights...')
model_systole.load_weights('weights_systole_best.hdf5')
print('Loading training data...')
X, y = load_train_data()
print('Pre-processing images...') # denoising filter
X = preprocess(X)
# split to training and test
X_train, y_train, X_test, y_test = split_data(X, y, split_ratio=0.2)
# save test subset
with open('y_test.txt', mode='w+') as f:
f.write(str(y_test))
f.write('\n')
nb_iter = 20
epochs_per_iter = 1
batch_size = 32 # if too small, will converge to unreliable models
# if too big, it wont fit into memory
calc = 4 # Every n-th iteration (0 if not needed)
# remember min val. losses (best iterations)
min_val_loss_systole = sys.float_info.max
print('-'*50)
print('Training...')
print('-'*50)
for i in range(nb_iter):
print('-'*50)
print('Iteration {0}/{1}'.format(i + 1, nb_iter))
print('-'*50)
print('Fitting diastole/systole model...')
hist_systole = model_systole.fit(X_train, y_train[:, :], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, y_test[:, :]))
# loss function values (RMSE)
loss_last = hist_systole.history['loss'][-1] # one number for the iter
val_loss_last = hist_systole.history['val_loss'][-1]
# since hist_systole.history['loss'] returns an array
# pick the last value with [-1]
loss = hist_systole.history['loss'][:] # all iter
val_loss = hist_systole.history['val_loss'][:]
with open('loss_last.txt', mode='a') as f:
f.write(str(loss_last))
f.write('\n')
with open('val_loss_last.txt', mode='a') as f:
f.write(str(val_loss_last))
f.write('\n')
with open('loss.txt', mode='a') as f:
f.write(str(loss))
f.write('\n')
with open('val_loss.txt', mode='a') as f:
f.write(str(val_loss))
f.write('\n')
# usually accuracy = correct predictions / total predictions
# using RMSE as a loss function, means if value of loss function is 20
# - that is an indicator the model usually misses the true value by ~20ml
if calc > 0 and i % calc == 0:
print('Getting predictions...')
pred_systole = model_systole.predict(X_train, batch_size=batch_size, verbose=1)
# npy 1283 x 2
val_pred_systole = model_systole.predict(X_test, batch_size=batch_size, verbose=1)
# npy 320 x 2
# save predictions
with open('pred_systole.txt', mode='a') as f:
f.write(str(pred_systole))
f.write('\n')
with open('val_pred_systole.txt', mode='a') as f:
f.write(str(val_pred_systole))
f.write('\n')
# save weights so they can be loaded later
print('Saving weights...')
model_systole.save_weights('weights_systole.hdf5', overwrite=True)
# for best (lowest) val losses, save weights
if val_loss_last < min_val_loss_systole:
min_val_loss_systole = val_loss_last
model_systole.save_weights('weights_systole_best.hdf5', overwrite=True)
# save best (lowest) val losses in file (to be later used for submission)
with open('min_val_loss.txt', mode='w+') as f:
f.write(str(min_val_loss_systole))
f.write('\n')
def train(sequenceLabeler, data):
sequenceLabeler.learn(preprocess([data]))
def predict_box(box_path, model_path, out_path, *, box_dset="/box", epoch=None, verbose=True, overwrite=False, save_confmaps=False):
"""
Predict and save peak coordinates for a box.
:param box_path: path to HDF5 file with box dataset
:param model_path: path to Keras weights file or run folder with weights subfolder
:param out_path: path to HDF5 file to save results to
:param box_dset: name of HDF5 dataset containing box images
:param epoch: epoch to use if run folder provided instead of Keras weights file
:param verbose: if True, prints some info and statistics during procesing
:param overwrite: if True and out_path exists, file will be overwritten
:param save_confmaps: if True, saves the full confidence maps as additional datasets in the output file (very slow)
"""
if verbose:
print("model_path:", model_path)
# Find model weights
model_name = None
weights_path = model_path
if os.path.isdir(model_path):
model_name = os.path.basename(model_path)
weights_paths, epochs, val_losses = find_weights(model_path)
if epoch is None:
weights_path = weights_paths[np.argmin(val_losses)]
elif epoch == "final":
weights_path = os.path.join(model_path, "final_model.h5")
else:
weights_path = weights_paths[epoch]
# Input data
box = h5py.File(box_path,"r")[box_dset]
num_samples = box.shape[0]
if verbose:
print("Input:", box_path)
print("box.shape:", box.shape)
# Create output path
if out_path[-3:] != ".h5":
if model_name is None:
out_path = os.path.join(out_path, os.path.basename(box_path))
else:
out_path = os.path.join(out_path, model_name, os.path.basename(box_path))
os.makedirs(os.path.dirname(out_path), exist_ok=True)
model_name = os.path.basename(model_path)
if verbose:
print("Output:", out_path)
t0_all = time()
if os.path.exists(out_path):
if overwrite:
os.remove(out_path)
print("Deleted existing output.")
else:
print("Error: Output path already exists.")
return
# Load and prepare model
model = keras.models.load_model(weights_path)
model_peaks = convert_to_peak_outputs(model, include_confmaps=save_confmaps)
if verbose:
print("weights_path:", weights_path)
print("Loaded model: %d layers, %d params" % (len(model.layers), model.count_params()))
# Load data and preprocess (normalize)
t0 = time()
X = preprocess(box[:])
if verbose:
print("Loaded [%.1fs]" % (time() - t0))
# Evaluate
t0 = time()
if save_confmaps:
Ypk, confmaps = model_peaks.predict(X)
# Quantize
confmaps_min = confmaps.min()
confmaps_max = confmaps.max()
confmaps = (confmaps - confmaps_min) / (confmaps_max - confmaps_min)
confmaps = (confmaps * 255).astype('uint8')
# Reshape
confmaps = np.transpose(confmaps, (0, 3, 2, 1))
else:
Ypk = model_peaks.predict(X)
prediction_runtime = time() - t0
if verbose:
print("Predicted [%.1fs]" % prediction_runtime)
# Save
t0 = time()
with h5py.File(out_path, "w") as f:
f.attrs["num_samples"] = num_samples
f.attrs["img_size"] = X.shape[1:]
f.attrs["box_path"] = box_path
f.attrs["box_dset"] = box_dset
f.attrs["model_path"] = model_path
f.attrs["weights_path"] = weights_path
f.attrs["model_name"] = model_name
ds_pos = f.create_dataset("positions_pred", data=Ypk[:,:2,:].astype("int32"), compression="gzip", compression_opts=1)
ds_pos.attrs["description"] = "coordinate of peak at each sample"
ds_pos.attrs["dims"] = "(sample, [x, y], joint) === (sample, [column, row], joint)"
ds_conf = f.create_dataset("conf_pred", data=Ypk[:,2,:].squeeze(), compression="gzip", compression_opts=1)
ds_conf.attrs["description"] = "confidence map value in [0, 1.0] at peak"
ds_conf.attrs["dims"] = "(sample, joint)"
if save_confmaps:
ds_confmaps = f.create_dataset("confmaps", data=confmaps, compression="gzip", compression_opts=1)
ds_confmaps.attrs["description"] = "confidence maps"
ds_confmaps.attrs["dims"] = "(sample, channel, width, height)"
ds_confmaps.attrs["range_min"] = confmaps_min
ds_confmaps.attrs["range_max"] = confmaps_max
total_runtime = time() - t0_all
f.attrs["total_runtime_secs"] = total_runtime
f.attrs["prediction_runtime_secs"] = prediction_runtime
if verbose:
print("Saved [%.1fs]" % (time() - t0))
print("Total runtime: %.1f mins" % (total_runtime / 60))
print("Performance: %.3f FPS" % (num_samples / total_runtime))
exit(1)
if __name__ == '__main__':
if len(sys.argv) < 3:
_usage(sys.argv)
action = sys.argv[1]
k = 0
if action not in _ACTIONS:
_usage(sys.argv)
try:
k = int(sys.argv[2])
except ValueError:
_usage(sys.argv)
if action == 'preprocess':
preprocess(_DATA_FILE, encoding=_ENCODING, k=k)
if action == 'graph':
print("Loading graphs...")
with open('data/graphs_%d.dat' % k, 'rb') as f:
graphs = pickle.loads(f.read())
while True:
try:
index = int(input('Enter note number (ctrl+d to end program): '))
g = graphs[index]
print("Writing image to out.png...")
g.draw('out.png')
print("Done")
print()
except (ValueError, KeyError):
continue
except (KeyboardInterrupt, EOFError):
# Load the pipeline
filename_base = '../models/MIBBCI_NN_20160406_14h17m59s'
nnet, numer, denom, scaler = utils.load_pipeline(filename_base)
if is_net_to_train_more:
# Load the training data
X_train_raw, labels_train = utils.load_data(data_filename_train_list)
# Preprocess the data
numer, denom, scaler = utils.init_preprocessors(X_train_raw)
X_train_preproc, labels_train = utils.preprocess(
X_train_raw, labels_train,
decimation_factor=params.DECIMATION_FACTOR_PREPROC,
tdfilt_numer=numer, tdfilt_denom=denom,
# reref_channel_id=params.REREF_CHANNEL_ID,
# power=True,
# mov_avg_window_size=params.MOVING_AVG_WINDOW_SIZE_SECS,
scaler=scaler)
# labels_train = labels_train
# Epoch the data
X_epoch_list_train_rh = utils.create_epochs(
X_train_preproc, labels_train[:, 0], params.EPOCH_OFFSET_SAMPLES)
X_epoch_list_train_lh = utils.create_epochs(
X_train_preproc, labels_train[:, 1], params.EPOCH_OFFSET_SAMPLES)
X_epoch_list_train_all = []
X_epoch_list_train_all.extend(X_epoch_list_train_rh)
X_epoch_list_train_all.extend(X_epoch_list_train_lh)
label_list_train_all = []
label_list_train_all.extend([1.0, 0.0] * len(X_epoch_list_train_rh))
def train():
print('Loading and compiling models...')
model_systole = get_model()
model_diastole = get_model()
print('Loading training data...')
X, y = load_train_data()
print('Pre-processing images...')
X = preprocess(X)
X_train, y_train, X_test, y_test = split_data(X, y, split_ratio=0.2)
nb_iter = 200
epochs_per_iter = 1
batch_size = 32
calc_crps = 1
min_val_loss_systole = sys.float_info.max
min_val_loss_diastole = sys.float_info.max
print('-'*50)
print('Training...')
print('-'*50)
for i in range(nb_iter):
print('-'*50)
print('Iteration {0}/{1}'.format(i + 1, nb_iter))
print('-'*50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
print('Fitting systole model...')
hist_systole = model_systole.fit(X_train_aug, y_train[:, 0], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, y_test[:, 0]))
print('Fitting diastole model...')
hist_diastole = model_diastole.fit(X_train_aug, y_train[:, 1], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, y_test[:, 1]))
loss_systole = hist_systole.history['loss'][-1]
loss_diastole = hist_diastole.history['loss'][-1]
val_loss_systole = hist_systole.history['val_loss'][-1]
val_loss_diastole = hist_diastole.history['val_loss'][-1]
if calc_crps > 0 and i % calc_crps == 0:
print('Evaluating CRPS...')
pred_systole = model_systole.predict(X_train, batch_size=batch_size, verbose=1)
pred_diastole = model_diastole.predict(X_train, batch_size=batch_size, verbose=1)
val_pred_systole = model_systole.predict(X_test, batch_size=batch_size, verbose=1)
val_pred_diastole = model_diastole.predict(X_test, batch_size=batch_size, verbose=1)
cdf_train = real_to_cdf(np.concatenate((y_train[:, 0], y_train[:, 1])))
cdf_test = real_to_cdf(np.concatenate((y_test[:, 0], y_test[:, 1])))
cdf_pred_systole = real_to_cdf(pred_systole, loss_systole)
cdf_pred_diastole = real_to_cdf(pred_diastole, loss_diastole)
cdf_val_pred_systole = real_to_cdf(val_pred_systole, val_loss_systole)
cdf_val_pred_diastole = real_to_cdf(val_pred_diastole, val_loss_diastole)
crps_train = crps(cdf_train, np.concatenate((cdf_pred_systole, cdf_pred_diastole)))
print('CRPS(train) = {0}'.format(crps_train))
crps_test = crps(cdf_test, np.concatenate((cdf_val_pred_systole, cdf_val_pred_diastole)))
print('CRPS(test) = {0}'.format(crps_test))
print('Saving weights...')
model_systole.save_weights('weights_systole.hdf5', overwrite=True)
model_diastole.save_weights('weights_diastole.hdf5', overwrite=True)
if val_loss_systole < min_val_loss_systole:
min_val_loss_systole = val_loss_systole
model_systole.save_weights('weights_systole_best.hdf5', overwrite=True)
if val_loss_diastole < min_val_loss_diastole:
min_val_loss_diastole = val_loss_diastole
model_diastole.save_weights('weights_diastole_best.hdf5', overwrite=True)
with open('val_loss.txt', mode='w+') as f:
f.write(str(min_val_loss_systole))
f.write('\n')
f.write(str(min_val_loss_diastole))
labels.append(label)
cluster[label] += d.real
count[label] += 1
print('Each cluster:', count)
''' Update centers and check whether it has converged '''
cluster = [cluster[i] / float(count[i]) for i in range(K)]
if np.array_equal(center, cluster):
break
center = cluster
return labels
print('Fetching data ...')
t = time()
X_train, T_train = preprocess()
table = dot_table(X_train, X_train, 'dot_table.npy')
print('Time:', time() - t)
if LOAD and os.path.isfile('w.npy') and os.path.isfile('v.npy'):
print('Loading eigenvector ...')
t = time()
w = np.load(open('w.npy', 'rb'))
v = np.load(open('v.npy', 'rb'))
print('Time:', time() - t)
else:
kernel = {'linear':linear, 'rbf':rbf, 'linearbf':linearbf}[KERNEL]
print('Generating L ...')
t = time()
N = len(table)
def getSVC(df, random_split=None):
X, Y = to_array(df.drop("validation", axis=1))
scaler = StandardScaler()
X = scaler.fit_transform(X)
tr_ind = df[df["validation"]==0].index.values.astype(int)
val_ind = df[df["validation"]==1].index.values.astype(int)
custom_CV_iterator = [(tr_ind, val_ind)]
print("Create a Random Forest Classifier")
print("__Parameter searching...")
# TODOs: cross-validation for best hyper parameter
clf = GridSearchCV(SVC(probability=False),
param_grid=TUNED_PARAMS,
scoring='roc_auc',
n_jobs=10,
verbose=5,
cv=custom_CV_iterator
)
clf.fit(X, Y)
print("Best score: {}".format(clf.best_score_))
print("Best parameters: {}".format(clf.best_params_))
return clf, scaler
if __name__ == "__main__":
output_fname = sys.argv[1]
df, test_df = preprocess()
model, scaler = getSVC(df)
write_ans(model, test_df, ofname=output_fname, scaler=scaler)
