def test_search_config():
df = generate_fake_observations(n_observations=1)
# test against default config template
assert 'treatment' in config.search_config(df, 'experiment', 'treatment')
assert 'metric' in config.search_config(df, 'experiment', 'measures')
assert 'attr_0' in config.search_config(df, 'experiment', 'attributes')
def proportions_data_large():
return generate_fake_observations(
distribution='bernoulli',
n_treatments=3,
n_attributes=4,
n_observations=10000
)
#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from abra import (Experiment, HypothesisTest, HypothesisTestSuite,
MultipleComparisonCorrection, CustomMetric)
from abra.utils import generate_fake_observations
proportions_data_large = generate_fake_observations(distribution='bernoulli',
n_treatments=3,
n_attributes=4,
n_observations=10000)
proportions_data_small = generate_fake_observations(distribution='bernoulli',
n_treatments=6,
n_observations=6 * 50)
means_data = generate_fake_observations(distribution='gaussian',
n_treatments=6,
n_observations=6 * 50)
counts_data = generate_fake_observations(distribution='poisson',
n_treatments=3,
n_observations=3 * 100)
def test_multiple_comparison():
p_values = np.arange(.001, .1, .01)
mc = MultipleComparisonCorrection(p_values, method='b')
assert mc.alpha_corrected < mc.alpha_orig
def counts_data():
return generate_fake_observations(
distribution='poisson',
n_treatments=3,
n_observations=3 * 100
)
def means_data():
return generate_fake_observations(
distribution='gaussian',
n_treatments=6,
n_observations=6 * 50
)
def proportions_data_small():
return generate_fake_observations(
distribution='bernoulli',
n_treatments=6,
n_observations=6 * 50
)