def insert_patient(patient):
name_to_post_id = {}
client = MongoClient()
db = client['gingivere']
collection = db[patient]
d = collections.defaultdict(list)
for data in load_raw_data.walk_training_mats(patient):
post_item = copy.deepcopy(data)
channels = post_item['channels']
del post_item['data']
del post_item['channels']
for i, item in enumerate(data['data']):
post_item['data'] = item.tolist()
post_item['channel'] = channels[i]
name = "%02d_%s" % (i, data['file'])
post_id = collection.insert(post_item)
d['name'].append(name)
d['_id'].append(str(post_id))
d['state'].append(post_item['state'])
d['channel'].append(channels[i])
print("Just posted: " + name)
del post_item['_id']
df = pd.DataFrame(d)
shelve_api.insert(df, "labeled_" + patient)
from collections import defaultdict
import numpy as np
import raw_data_clf
import mongo_select
from tests import shelve_api
print("Training the CLF")
clf = raw_data_clf.RawClf('Dog_2')
clf.clear_data()
print()
print("Cleared the data")
print()
d = defaultdict(list)
for item in mongo_select.get_all('Dog_2'):
data = np.array(item['data']).astype('float32')
prediction = clf.predict(data)
d['pred'].append(prediction)
del item['data']
for key in item:
d[key].append(item[key])
print("Just predicted %s for %s" % (prediction[0], item['file']))
shelve_api.insert(d, 'preds')
insert_item['_id'] = count
count += 1
yield insert_item
def shelve(result, error):
if error:
print('error getting user!', error)
else:
name = "%02d_%s" % (i, result['file'])
d['name'].append(name)
d['_id'].append(result['_id'])
d['state'].append(result['state'])
d['channel'].append(result['channel'])
print("Just posted: " + name)
@gen.coroutine
def bulk_write():
global d
d = defaultdict(list)
collection.insert((i for i in insert_patient('Dog_2')), callback=shelve)
if __name__ == "__main__":
client = motor.MotorClient()
db = motor.MotorDatabase(client, 'gingivere')
collection = motor.MotorCollection(db, 'Dog_1')
tornado.ioloop.IOLoop.current().run_sync(bulk_write)
df = pd.DataFrame(d)
shelve_api.insert(df, 'test_dog_1')
return (np.min(mm), np.max(mm))
def walk_data(patient):
path = "D:/gingivere/data/"
for file in os.listdir(path):
if patient in file:
yield path + file
if __name__ == '__main__':
patients = [
"Dog_1", "Dog_2", "Dog_3", "Dog_4", "Dog_5", "Patient_1", "Patient_2"
]
# d_keys = ['data_length_sec', 'sampling_frequency', 'sequence', 'state', 'file']
num_cores = multiprocessing.cpu_count()
now = time.time()
if len(sys.argv) >= 2:
patient = sys.argv[1]
r = get_min_max(patient)
res = Parallel(n_jobs=num_cores)(delayed(preprocess_data)((i, r))
for i in walk_data(patient))
sapi.insert(res, "%s_len" % patient)
else:
for patient in patients:
r = get_min_max(patient)
res = Parallel(n_jobs=num_cores)(delayed(preprocess_data)((i, r))
for i in walk_data(patient))
sapi.insert(res, "%s_len" % patient)
print("Finished in", time.time() - now, "sec")
print(__doc__)
def process_data(input):
data = lrd.load_mat(*input)
name = data['file'].split('.')[0]
print(name)
store = pd.HDFStore("D:/gingivere/data/%s.h5" % name)
# pp = preprocessing.scale(data['data'])
df = pd.DataFrame(data['data'], index=data['channels'])
del data
store['data'] = df
store.close()
return np.asarray([df.values.min(), df.values.max()])
if __name__ == '__main__':
patients = ["Dog_1", "Dog_2", "Dog_3", "Dog_4", "Dog_5", "Patient_1", "Patient_2"]
num_cores = multiprocessing.cpu_count()
now = time.time()
if len(sys.argv) >= 2:
patient = sys.argv[1]
# res = Parallel(n_jobs=num_cores)(delayed(process_data)(i) for i in lrd.walk_files(patient))
res = []
for i in lrd.walk_files(patient):
res.append(process_data(i))
sapi.insert(res, "%s_mm" % patient)
else:
for patient in patients:
res = Parallel(n_jobs=num_cores)(delayed(process_data)(i) for i in lrd.walk_files(patient))
sapi.insert(res, "%s_mm" % patient)
print("Finished in", time.time()-now , "sec")
name = data['file'].split('.')[0]
print(name)
store = pd.HDFStore("D:/gingivere/data/%s.h5" % name)
# pp = preprocessing.scale(data['data'])
df = pd.DataFrame(data['data'], index=data['channels'])
del data
store['data'] = df
store.close()
return np.asarray([df.values.min(), df.values.max()])
if __name__ == '__main__':
patients = [
"Dog_1", "Dog_2", "Dog_3", "Dog_4", "Dog_5", "Patient_1", "Patient_2"
]
num_cores = multiprocessing.cpu_count()
now = time.time()
if len(sys.argv) >= 2:
patient = sys.argv[1]
# res = Parallel(n_jobs=num_cores)(delayed(process_data)(i) for i in lrd.walk_files(patient))
res = []
for i in lrd.walk_files(patient):
res.append(process_data(i))
sapi.insert(res, "%s_mm" % patient)
else:
for patient in patients:
res = Parallel(n_jobs=num_cores)(delayed(process_data)(i)
for i in lrd.walk_files(patient))
sapi.insert(res, "%s_mm" % patient)
print("Finished in", time.time() - now, "sec")
# print(roc_auc_score(y_true, y_pred))
# print()
clf.fit(X, y)
return clf
if __name__ == '__main__':
patients = [
"Dog_1", "Dog_2", "Dog_3", "Dog_4", "Dog_5", "Patient_1", "Patient_2"
]
# d_keys = ['data_length_sec', 'sampling_frequency', 'sequence', 'state', 'file']
num_cores = multiprocessing.cpu_count()
now = time.time()
if len(sys.argv) >= 2:
patient = sys.argv[1]
res = Parallel(n_jobs=num_cores)(
delayed(load_training_data)(file)
for file in walk_training_data(patient))
X, y = sort_data(res)
clf = train_clf(X, y)
sapi.insert(clf, "%s_clf" % patient)
else:
for patient in patients:
res = Parallel(n_jobs=num_cores)(
delayed(load_training_data)(file)
for file in walk_training_data(patient))
X, y = sort_data(res)
clf = train_clf(X, y)
sapi.insert(clf, "%s_clf" % patient)
print("Finished in", time.time() - now, "sec")
from __future__ import print_function
from collections import defaultdict
import numpy as np
import raw_data_clf
import mongo_select
from tests import shelve_api
print("Training the CLF")
clf = raw_data_clf.RawClf('Dog_2')
clf.clear_data()
print()
print("Cleared the data")
print()
d = defaultdict(list)
for item in mongo_select.get_all('Dog_2'):
data = np.array(item['data']).astype('float32')
prediction = clf.predict(data)
d['pred'].append(prediction)
del item['data']
for key in item:
d[key].append(item[key])
print("Just predicted %s for %s" % (prediction[0], item['file']))
shelve_api.insert(d, 'preds')
print(file)
return len(X)
def get_min_max(patient):
mm = sapi.load("%s_mm" % patient)
return (np.min(mm), np.max(mm))
def walk_data(patient):
path = "D:/gingivere/data/"
for file in os.listdir(path):
if patient in file:
yield path + file
if __name__ == '__main__':
patients = ["Dog_1", "Dog_2", "Dog_3", "Dog_4", "Dog_5", "Patient_1", "Patient_2"]
# d_keys = ['data_length_sec', 'sampling_frequency', 'sequence', 'state', 'file']
num_cores = multiprocessing.cpu_count()
now = time.time()
if len(sys.argv) >= 2:
patient = sys.argv[1]
r = get_min_max(patient)
res = Parallel(n_jobs=num_cores)(delayed(preprocess_data)((i, r)) for i in walk_data(patient))
sapi.insert(res, "%s_len" % patient)
else:
for patient in patients:
r = get_min_max(patient)
res = Parallel(n_jobs=num_cores)(delayed(preprocess_data)((i, r)) for i in walk_data(patient))
sapi.insert(res, "%s_len" % patient)
print("Finished in", time.time()-now , "sec")
# print()
#
# y_true, y_pred = y, clf.predict(X)
# print(roc_auc_score(y_true, y_pred))
# y_pred = y_pred - y_pred.mean()
# y_pred = y_pred/y_pred.std()
# y_pred = [1/(1+math.pow(math.e, -.5*p)) for p in y_pred]
# print(roc_auc_score(y_true, y_pred))
# print()
clf.fit(X, y)
return clf
if __name__ == '__main__':
patients = ["Dog_1", "Dog_2", "Dog_3", "Dog_4", "Dog_5", "Patient_1", "Patient_2"]
# d_keys = ['data_length_sec', 'sampling_frequency', 'sequence', 'state', 'file']
num_cores = multiprocessing.cpu_count()
now = time.time()
if len(sys.argv) >= 2:
patient = sys.argv[1]
res = Parallel(n_jobs=num_cores)(delayed(load_training_data)(file) for file in walk_training_data(patient))
X, y = sort_data(res)
clf = train_clf(X, y)
sapi.insert(clf, "%s_clf" % patient)
else:
for patient in patients:
res = Parallel(n_jobs=num_cores)(delayed(load_training_data)(file) for file in walk_training_data(patient))
X, y = sort_data(res)
clf = train_clf(X, y)
sapi.insert(clf, "%s_clf" % patient)
print("Finished in", time.time()-now , "sec")
