def test_time_series_observation(self):
def week_agg(date):
return date.year, date.isocalendar()[1]
def month_agg(date):
return date.year, date.month
ob = rnnmed.data.io.read_time_series_observation(
open("/mnt/veracrypt1/EHR_DATA/L270-90-raw-measurements.csv"),
min_sparsity=0.1)
import random
random.seed(10)
random.shuffle(ob)
n_visits = 10
generator = observations.time_observation_generator(ob,
n_visits=n_visits)
print(len(ob), ob.n_features)
from rnnmed.visit2visit import visit2visit
visit2visit(generator,
n_features=ob.n_features,
n_labels=ob.n_labels,
n_timesteps=n_visits,
n_hidden=128,
max_iter=1000)
def test_med_2_vec_predict(self):
observations = rnnmed.data.io.read_labeled_observations(
"test_data/mimic_demo.seq")
generator = ob.time_observation_generator(observations, n_visits=15)
np.set_printoptions(suppress=True)
visit2visit.visit2visit(generator,
n_labels=observations.n_labels,
n_features=observations.n_features,
n_timesteps=15,
transform=None)
def test_time_series(self):
import random
timeseries = rnnmed.data.io.read_time_series(
open("test_data/synthetic_control.txt"))
random.shuffle(timeseries)
print(timeseries[0])
generator = ts.timeseries_generator(timeseries)
# x, y = rnnmed.data.generate_time_batch(generator, batch_size=5)
np.set_printoptions(suppress=True)
visit2visit.visit2visit(generator,
n_features=timeseries.n_dimensions,
n_timesteps=timeseries.n_timesteps,
n_labels=timeseries.n_labels,
n_hidden=128,
max_iter=10000)