def test_bootstrap_input_array_samples_several(self, sample_array_type):
res = bootstrap(sample_array_type,
num_iter=100,
resample_size=100,
metrics={"mean": np.mean})
# if all means are the same there could be two issues: not actually calculating the mean from the sample
# OR not actually sampling with replacement
counter = create_count_of_sampled_items(res)
assert len(dict(counter)) > 1
def test_bootstrap_input_list_has_at_least_one_duplicate(
self, sample_list_type):
res = bootstrap(sample_list_type,
num_iter=100,
resample_size=100,
metrics={"mean": np.mean})
counter = create_count_of_sampled_items(res)
assert any(
[True if count > 1 else False for count in counter.values()])
# checking if the mean lies around the true mean of the given 'sample' - this cannot be tested without fixing
# the seed, which I do not want to in this implementation
print(
len(
np.array(res)
[[abs(metric['mean'] - 50) >= 5 for metric in res]]) < 15)
import os
import sys
SRC_ROOT = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
sys.path.append(SRC_ROOT)
from src.bootstrap import bootstrap, get_application
from src.settings import settings
bootstrap()
application = get_application()
if __name__ == '__main__':
application.run(debug=settings.DEBUG,
host=settings.HOST,
port=settings.PORT,
threaded=False)
def main():
'''
Main function to be called
'''
return bootstrap()
def test_main(self):
'''
placeholder test
'''
assert bootstrap() is 'foo'
def simulation(
population_function,
metric_functions: Dict[str, Any],
num_sample_draws: int,
conf_interval: Callable[[Iterable[Any], float],
ConfidenceInterval] = confidence_interval,
pop_size: int = 100000,
sample_size: int = 1000,
resample_size: int = 1000,
num_bootstraps: int = 1000,
coverage: float = 0.95
) -> Tuple[List[Dict[str, MetricResult]], Dict[str, Any]]:
"""
Simulates several sample draws from the actual population to see if about 90% of the time the measured metrics
actually lie within the calculated confidence interval given by the bootstrapping procedure.
Args:
population_function: the function that returns a population of size pop_size
metric_functions: a list of functions that take an iterable as input and return a single value as output
num_sample_draws: How often do we want to simulate sampling from the distribution? (without replacement)
conf_interval: the function which calculates the confidence interval from the bootstrapping procedure. There are
several ways of calculating a confidence interval.
pop_size: size of the true population
sample_size: size of the sample for which we simulate
resample_size: size of the bootstrap resamples (it is recommended to use the sample size or at least 50% of it)
num_bootstraps: how often the bootstrap resamples should be drawn
coverage: the coverage of the confidence interval
Returns:
A Tuple sim_res, metrics:
`sim_res` is a list of dictionaries of MetricResults, which tells us the confidence
interval for this bootstrap and if the population metric is within the confidence interval.
`metrics` is a dictionary of the population metrics.
"""
sim_res = []
# Could also expect this as input, such that several simulations can run on one population. Cannot set a seed
# for distribution sampling
pop, metrics = get_population_and_metrics(
population_function,
pop_size=pop_size,
metric_functions=metric_functions)
for _ in tqdm(range(num_sample_draws)):
sample = get_sample(pop, sample_size=sample_size)
res = bootstrap(sample,
num_iter=num_bootstraps,
resample_size=resample_size,
metrics=metric_functions)
bs_res = _calculate_metric_for_bootstrap_sample(
res,
conf_interval=conf_interval,
coverage=coverage,
metrics=metrics)
# for metric_name, _ in metric_functions.items():
# bs_metric_res = [single_res[metric_name] for single_res in res]
# conf = conf_interval(bs_metric_res, coverage)
# pop_metric_in_conf = True if conf.lower_bound <= metrics[metric_name] <= conf.upper_bound else False
#
# bs_res[metric_name] = MetricResult(metric_confidence=conf, pop_metric_in_conf=pop_metric_in_conf)
sim_res.append(bs_res)
return sim_res, metrics
