def run_ensemble(params):
driver_id, verbose = params
results = []
for i, get_data, model, repeat in STACK:
results.append((run_model(
(i, driver_id, model, get_data, repeat)), WEIGHTS[i]))
predictions = np.array([r[0][0] * r[1] for r in results]).sum(0)
testY = results[0][0][1]
if verbose >= 1:
logging.info('finished driver %s' % driver_id)
return predictions, testY
def run_ensemble(params):
driver_id, verbose = params
results = []
for i, get_data, model, repeat in STACK:
results.append((
run_model((i, driver_id, model, get_data, repeat)),
WEIGHTS[i]
))
predictions = np.array([r[0][0] * r[1] for r in results]).sum(0)
testY = results[0][0][1]
if verbose >= 1:
logging.info('finished driver %s' % driver_id)
return predictions, testY
def compute_weights(params):
driver_id, verbose, stack_option = params
stack = STACK if stack_option == 's' else MODELS
predictions = {}
for i, get_data, model, repeat in stack:
start_time = time.time()
predictions[i], testY = run_model((i, driver_id, model, get_data, repeat))
if verbose == 2:
logging.info('%s: %.2f' % (i, time.time() - start_time))
if verbose >= 1:
logging.info('finished driver %s' % driver_id)
return driver_id, predictions, testY
