def test_no_boundaries_or_as_of_times(self):
with patch(
"triage.component.timechop.plotting.plt.show") as show_patch:
visualize_chops(self.chopper,
show_as_of_times=False,
show_boundaries=False)
assert show_patch.called
def __call__(self, args):
if args.validate_only:
try:
logger.info(f"Validating experiment [config file: {self.args.config}]")
self.experiment.validate()
logger.success(
f"Experiment ({self.experiment.experiment_hash})'s configuration file is OK!"
)
except Exception:
logger.exception(f"Validation failed!")
logger.info(
f"Experiment [config file: {self.args.config}] configuration file is incorrect"
)
elif args.show_timechop:
experiment_name = os.path.splitext(os.path.basename(self.args.config))[0]
project_storage = ProjectStorage(self.args.project_path)
timechop_store = project_storage.get_store(
["images"], f"{experiment_name}.png"
)
with timechop_store.open("wb") as fd:
visualize_chops(self.experiment.chopper, save_target=fd)
else:
try:
logger.info(f"Running Experiment ({self.experiment.experiment_hash})")
self.experiment.run()
logger.success(
f"Experiment ({self.experiment.experiment_hash}) ran through completion"
)
except Exception:
logger.exception("Something went wrong")
logger.info(f"Experiment [config file: {self.args.config}] run failed!")
def __call__(self, args):
experiment_config = yaml.load(args.config)
if 'temporal_config' not in experiment_config:
raise ValueError('Passed configuration must have `temporal_config` key '
'in order to visualize time chops')
chopper = Timechop(**(experiment_config['temporal_config']))
logging.info('Visualizing time chops')
visualize_chops(chopper)
def __call__(self, args):
experiment_config = yaml.load(args.config)
if "temporal_config" not in experiment_config:
raise ValueError(
"Passed configuration must have `temporal_config` key "
"in order to visualize time chops")
chopper = Timechop(**(experiment_config["temporal_config"]))
logging.info("Visualizing time chops")
visualize_chops(chopper)
def __call__(self, args):
if args.validate_only:
self.experiment.validate()
elif args.show_timechop:
experiment_name = os.path.splitext(
os.path.basename(self.args.config))[0]
project_storage = ProjectStorage(self.args.project_path)
timechop_store = project_storage.get_store(
["images"], f"{experiment_name}.png")
with timechop_store.open('wb') as fd:
visualize_chops(self.experiment.chopper, save_target=fd)
else:
self.experiment.run()
def visualize_timechop(config_file):
experiment_config = read_config_file(config_file)
temporal_config = experiment_config['temporal_config']
feature_start_time = temporal_config['feature_start_time']
feature_end_time = temporal_config['feature_end_time']
label_start_time = temporal_config['label_start_time']
label_end_time = temporal_config['label_end_time']
model_update_frequency = temporal_config['model_update_frequency']
training_as_of_date_frequencies = temporal_config[
'training_as_of_date_frequencies']
test_as_of_date_frequencies = temporal_config[
'test_as_of_date_frequencies']
max_training_histories = temporal_config['max_training_histories']
training_label_timespans = temporal_config['label_timespans']
test_label_timespans = temporal_config['label_timespans']
test_durations = temporal_config['test_durations']
chopper = Timechop(
feature_start_time=feature_start_time,
feature_end_time=feature_end_time,
label_start_time=label_start_time,
label_end_time=label_end_time,
model_update_frequency=model_update_frequency,
training_as_of_date_frequencies=training_as_of_date_frequencies,
max_training_histories=max_training_histories,
training_label_timespans=training_label_timespans,
test_as_of_date_frequencies=test_as_of_date_frequencies,
test_durations=test_durations,
test_label_timespans=test_label_timespans)
dateTimeObj = datetime.now()
timestr = dateTimeObj.strftime('%Y%m%d%H%M')
user = getpass.getuser()
cf = ntpath.basename(config_file)[0:10]
data_folder = '/mnt/data/experiment_data/'
save_path = os.path.join(data_folder, 'triage', 'elsal', 'timechops',
'{}_{}_{}.png'.format(user, timestr, cf))
visualize_chops(chopper=chopper,
show_as_of_times=True,
show_boundaries=True,
save_target=save_path)
# coding: utf-8
from triage.component.timechop.plotting import visualize_chops
from triage.component.timechop import Timechop
import yaml
import matplotlib.pyplot as plt
import numpy as np
if __name__ == '__main__':
with open('simple_config.yaml') as f:
experiment_config = yaml.load(f)
chopper = Timechop(**(experiment_config["temporal_config"]))
visualize_chops(chopper)
def test_default_args(self):
with patch(
"triage.component.timechop.plotting.plt.show") as show_patch:
visualize_chops(self.chopper)
assert show_patch.called
)
db_engine = create_engine(db_url)
# loading config file
with open('donors-choose-config.yaml', 'r') as fin:
config = yaml.load(fin)
# generating temporal config plot
chopper = Timechop(**config['temporal_config'])
# We aren't interested in seeing the entire feature_start_time represented
# in our timechop plot. That would hide the interesting information. So we
# set it to equal label_start_time for the plot.
chopper.feature_start_time = chopper.label_start_time
visualize_chops(chopper, save_target='triage_output/timechop.png')
# creating experiment object
experiment = MultiCoreExperiment(
config=config,
db_engine=db_engine,
project_path='s3://dsapp-education-migrated/donors-choose',
n_processes=32,
n_db_processes=4,
replace=False)
experiment.run()
