def GetModels(model_folder, pattern_file_name):
'''Returns model dict.'''
# label_list = ['P', 'Ponset', 'Poffset',
# 'T', 'Toffset', 'Tonset']
label_list = [
'P', 'Ponset', 'Poffset', 'T', 'Toffset', 'Tonset', 'Ronset', 'Roffset'
]
# label_list = ['P', 'Ponset', 'Poffset']
# label_list = ['P', ]
bias_list = [
-0.19,
-0.195,
-0.185,
0.26,
0.27,
0.1,
-0.02,
0.02,
]
# Get model dict
models = list()
for target_label, bias in zip(label_list, bias_list):
model_file_name = os.path.join(model_folder, target_label + '.mdl')
walker_model = RandomWalker(target_label=target_label,
random_pattern_file_name=pattern_file_name)
walker_model.load_model(model_file_name)
models.append((walker_model, bias, target_label))
return models
def TrainingModels(target_label, model_file_name, training_list):
'''Randomly select num_training records to train, and test others.'''
qt = QTloader()
record_list = qt.getreclist()
testing_list = list(set(record_list) - set(training_list))
random_forest_config = dict(max_depth=10)
walker = RandomWalker(target_label=target_label,
random_forest_config=random_forest_config,
random_pattern_file_name=os.path.join(
os.path.dirname(model_file_name),
'random_pattern.json'))
start_time = time.time()
for record_name in training_list:
print 'Collecting features from record %s.' % record_name
sig = qt.load(record_name)
walker.collect_training_data(sig['sig'], qt.getExpert(record_name))
print 'random forest start training(%s)...' % target_label
walker.training()
print 'trianing used %.3f seconds' % (time.time() - start_time)
import joblib
start_time = time.time()
walker.save_model(model_file_name)
print 'Serializing model time cost %f' % (time.time() - start_time)
def estimates(self, num_estimates, start_node=None):
"""
Generate estimates of the property.
:param num_estimates: Maximum number of estimates to provide.
:param start_node: (Optional) Custom starting node.
:return: Yields estimates (Python generator style)
"""
rw = RandomWalker(self.G, self._edge_weight_func, self._accum_func)
if start_node is None:
start_node = self.compute_start_node()
prev_t = 0
for k, t, accum in rw.return_times(start_node, num_estimates):
met = self._compute_metric(start_node, k, t, accum)
yield k - 1, met, t - prev_t
prev_t = t
def TrainingModels(target_label, model_file_name, training_list):
'''Randomly select num_training records to train, and test others.
CP: Characteristic points
'''
qt = QTloader()
record_list = qt.getreclist()
testing_list = list(set(record_list) - set(training_list))
random_forest_config = dict(max_depth=10)
walker = RandomWalker(target_label=target_label,
random_forest_config=random_forest_config,
random_pattern_file_name=os.path.join(
os.path.dirname(model_file_name),
'random_pattern.json'))
start_time = time.time()
for record_name in training_list:
CP_file_name = os.path.join(
'/home/alex/code/Python/EcgCharacterPointMarks', target_label,
'%s_poslist.json' % record_name)
# Add expert marks
expert_marks = qt.getExpert(record_name)
CP_marks = [x for x in expert_marks if x[1] == target_label]
if len(CP_marks) == 0:
continue
# Add manual labels if possible
if os.path.exists(CP_file_name) == True:
with open(CP_file_name, 'r') as fin:
CP_info = json.load(fin)
poslist = CP_info['poslist']
if len(poslist) == 0:
continue
CP_marks.extend(zip(poslist, [
target_label,
] * len(poslist)))
print 'Collecting features from record %s.' % record_name
sig = qt.load(record_name)
walker.collect_training_data(sig['sig'], CP_marks)
print 'random forest start training(%s)...' % target_label
walker.training()
print 'trianing used %.3f seconds' % (time.time() - start_time)
import joblib
start_time = time.time()
walker.save_model(model_file_name)
print 'Serializing model time cost %f' % (time.time() - start_time)
def TrainingModels_Changgeng(target_label, model_file_name):
'''Randomly select num_training records to train, and test others.
CP: Characteristic points
'''
from changgengLoader import ECGLoader
ecg = ECGLoader(500, current_folderpath)
random_forest_config = dict(max_depth=10)
walker = RandomWalker(target_label=target_label,
random_forest_config=random_forest_config,
random_pattern_file_name=os.path.join(
os.path.dirname(model_file_name),
'random_pattern.json'))
start_time = time.time()
for record_ind in xrange(0, len(ecg.P_faillist)):
record_name = ecg.P_faillist[record_ind]
CP_file_name = os.path.join(current_folderpath, 'data', 'labels',
target_label,
'%s_poslist.json' % record_name)
CP_marks = []
# Add manual labels if possible
if os.path.exists(CP_file_name) == True:
with open(CP_file_name, 'r') as fin:
print 'Collecting features from record %s.' % record_name
CP_info = json.load(fin)
poslist = CP_info['poslist']
if len(poslist) == 0:
continue
CP_marks.extend(zip(poslist, [
target_label,
] * len(poslist)))
sig = ecg.load(record_ind)
walker.collect_training_data(sig[0], CP_marks)
print 'random forest start training(%s)...' % target_label
walker.training()
print 'trianing used %.3f seconds' % (time.time() - start_time)
import joblib
start_time = time.time()
walker.save_model(model_file_name)
print 'Serializing model time cost %f' % (time.time() - start_time)
def TrainingModels(target_label, model_file_name, training_list):
'''Randomly select num_training records to train, and test others.'''
qt = QTloader()
record_list = qt.getreclist()
testing_list = list(set(record_list) - set(training_list))
random_forest_config = dict(max_depth=10)
walker = RandomWalker(target_label=target_label,
random_forest_config=random_forest_config,
random_pattern_file_name=os.path.join(
os.path.dirname(model_file_name),
'random_pattern.json'))
start_time = time.time()
for record_name in training_list:
Tonset_file_name = os.path.join(
'/home/alex/code/Python/Tonset/results',
'%s_poslist.json' % record_name)
if os.path.exists(Tonset_file_name) == True:
with open(Tonset_file_name, 'r') as fin:
Tonset_info = json.load(fin)
poslist = Tonset_info['poslist']
if len(poslist) == 0:
continue
Tonset_marks = zip(poslist, [
'Tonset',
] * len(poslist))
else:
expert_marks = qt.getExpert(record_name)
Tonset_marks = [x for x in expert_marks if x[1] == 'Tonset']
if len(Tonset_marks) == 0:
continue
print 'Collecting features from record %s.' % record_name
sig = qt.load(record_name)
walker.collect_training_data(sig['sig'], Tonset_marks)
print 'random forest start training(%s)...' % target_label
walker.training()
print 'trianing used %.3f seconds' % (time.time() - start_time)
import joblib
start_time = time.time()
walker.save_model(model_file_name)
print 'Serializing model time cost %f' % (time.time() - start_time)
def Test1(target_label='P', num_training=25):
'''Test case 1: random walk.'''
qt = QTloader()
record_list = qt.getreclist()
training_list = random.sample(record_list, num_training)
testing_list = list(set(record_list) - set(training_list))
random_forest_config = dict(max_depth=10)
walker = RandomWalker(target_label=target_label,
random_forest_config=random_forest_config)
start_time = time.time()
for record_name in training_list:
print 'Collecting features from record %s.' % record_name
sig = qt.load(record_name)
walker.collect_training_data(sig['sig'], qt.getExpert(record_name))
print 'random forest start training...'
walker.training()
print 'trianing used %.3f seconds' % (time.time() - start_time)
for record_name in testing_list:
sig = qt.load(record_name)
raw_sig = sig['sig']
seed_position = random.randint(100, len(raw_sig) - 100)
plt.figure(1)
plt.clf()
plt.plot(sig['sig'], label=record_name)
plt.title(target_label)
for ti in xrange(0, 20):
seed_position += random.randint(1, 200)
print 'testing...(position: %d)' % seed_position
start_time = time.time()
results = walker.testing_walk(sig['sig'],
seed_position,
iterations=100,
stepsize=10)
print 'testing finished in %.3f seconds.' % (time.time() -
start_time)
pos_list, values = zip(*results)
predict_pos = np.mean(pos_list[len(pos_list) / 2:])
# amp_list = [raw_sig[int(x)] for x in pos_list]
amp_list = []
bias = raw_sig[pos_list[0]]
for pos in pos_list:
amp_list.append(bias)
bias -= 0.01
plt.plot(predict_pos,
raw_sig[int(predict_pos)],
'ro',
markersize=14,
label='predict position')
plt.plot(pos_list,
amp_list,
'r',
label='walk path',
markersize=3,
linewidth=8,
alpha=0.3)
plt.xlim(min(pos_list) - 100, max(pos_list) + 100)
plt.grid(True)
plt.legend()
plt.show(block=False)
pdb.set_trace()
def TrainingModels_Changgeng(target_label, model_file_name):
'''Randomly select num_training records to train, and test others.
CP: Characteristic points
'''
import glob
annot_jsonIDs = glob.glob(
os.path.join(current_folderpath, 'data', 'labels', target_label,
'*.json'))
annot_jsonIDs = [os.path.split(x)[-1] for x in annot_jsonIDs]
annot_jsonIDs = [x.split('.')[0] for x in annot_jsonIDs]
# skip failed records
faillist = [
8999, 8374, 6659, 6655, 6059, 5395, 1401, 1269, 737, 75, 9524, 9476
]
faillist = [str(x) for x in faillist]
annot_jsonIDs = list(set(annot_jsonIDs) - set(faillist))
from changgengLoader import ECGLoader
ecg = ECGLoader(500, current_folderpath)
random_forest_config = dict(max_depth=10)
walker = RandomWalker(target_label=target_label,
random_forest_config=random_forest_config,
random_pattern_file_name=os.path.join(
os.path.dirname(model_file_name),
'random_pattern.json'))
start_time = time.time()
for record_ind in xrange(0, len(annot_jsonIDs)):
record_name = annot_jsonIDs[record_ind]
CP_file_name = os.path.join(current_folderpath, 'data', 'labels',
target_label, '%s.json' % record_name)
CP_marks = []
# Add manual labels if possible
if os.path.exists(CP_file_name) == True:
with open(CP_file_name, 'r') as fin:
CP_info = json.load(fin)
poslist = CP_info['poslist']
poslist = [int(x / 2) for x in poslist]
mat_file_name = CP_info['mat_file_name']
if len(poslist) == 0:
continue
CP_marks.extend(zip(poslist, [
target_label,
] * len(poslist)))
print 'Collecting features from record %s.' % record_name
sig = ecg.load(record_name)
raw_sig = sig[0]
import scipy.signal
resampled_sig = scipy.signal.resample_poly(raw_sig, 1, 2)
raw_sig = resampled_sig
# debug
# plt.figure(1)
# plt.plot(raw_sig, label = 'signal')
# plt.plot(xrange(0, len(raw_sig), 2), resampled_sig, label = 'resmaple')
# plt.legend()
# plt.grid(True)
# plt.title(record_name)
# plt.show()
walker.collect_training_data(raw_sig, CP_marks)
# Add QT training samples
# ContinueAddQtTrainingSamples(walker, target_label)
print 'random forest start training(%s)...' % target_label
walker.training()
print 'trianing used %.3f seconds' % (time.time() - start_time)
import joblib
start_time = time.time()
walker.save_model(model_file_name)
print 'Serializing model time cost %f' % (time.time() - start_time)
def RoundTest(target_label, result_folder, num_training = 75):
'''Randomly select num_training records to train, and test others.'''
qt = QTloader()
record_list = qt.getreclist()
must_train_list = [
"sel35",
"sel36",
"sel31",
"sel38",
"sel39",
"sel820",
"sel51",
"sele0104",
"sele0107",
"sel223",
"sele0607",
"sel102",
"sele0409",
"sel41",
"sel40",
"sel43",
"sel42",
"sel45",
"sel48",
"sele0133",
"sele0116",
"sel14172",
"sele0111",
"sel213",
"sel14157",
"sel301"
]
num_training -= len(must_train_list)
record_list = list(set(record_list) - set(must_train_list))
training_list = must_train_list
if num_training > 0:
training_list.extend(random.sample(record_list, num_training))
testing_list = list(set(record_list) - set(training_list))
random_forest_config = dict(
max_depth = 10)
walker = RandomWalker(target_label = target_label,
random_forest_config = random_forest_config)
start_time = time.time()
for record_name in training_list:
print 'Collecting features from record %s.' % record_name
sig = qt.load(record_name)
walker.collect_training_data(sig['sig'], qt.getExpert(record_name))
print 'random forest start training...'
walker.training()
print 'trianing used %.3f seconds' % (time.time() - start_time)
for record_name in testing_list:
print 'testing record %s...' % record_name
record_result = list()
sig = qt.load(record_name)
raw_sig = sig['sig']
record_result.append((record_name, testing(walker, raw_sig)))
raw_sig = sig['sig2']
record_result.append((record_name + '_sig2', testing(walker, raw_sig)))
# Write to json
with open(os.path.join(result_folder, '%s.json' % record_name), 'w') as fout:
json.dump(record_result, fout, indent = 4)