def test_quantile_filtering():
exp = Experiment({})
df = pd.DataFrame.from_dict({ 'earnings' : np.array([0,0,1,2]) / np.array([0,0,1,1]) })
flags = exp._quantile_filtering(df, ['earnings'], 90, 'upper')
assert flags.tolist() == [False, False, False, True]
flags = exp._quantile_filtering(df, ['earnings'], 10, 'lower')
assert flags.tolist() == [False, False, True, False]
def test_quantile_filtering_two_sided():
exp = Experiment({})
df = pd.DataFrame.from_dict({'earnings': list(range(10))})
flags = exp._quantile_filtering(df, ['earnings'],
{'earnings': ('two-sided', 80.0)})
results = flags.tolist()
assert results == [True] + [False] * 8 + [True]
def test_quantile_filtering_lower_old():
exp = Experiment({})
data = np.array([0, 0, 1, 2]) / np.array([0, 0, 1, 1])
df = pd.DataFrame.from_dict({'earnings': data})
flags = exp._quantile_filtering(df, ['earnings'],
{'earnings': ('lower', 10.)})
assert flags.tolist() == [False, False, True, False]
def test_quantile_filtering_two_sided_asym():
exp = Experiment({})
data = list(range(-8, 0)) + list(range(16))
df = pd.DataFrame.from_dict({'earnings': data})
flags = exp._quantile_filtering(df, ['earnings'],
{'earnings': ('two-sided-asym', 50.0)})
results = flags.tolist()
assert results == [True] * 2 + [False] * 18 + [True] * 4
def test_quantile_filtering_upper():
exp = Experiment({})
data = np.array([0.0] * 2 + list(range(10))) / np.array([0.0] * 2 +
[1.0] * 10)
df = pd.DataFrame.from_dict({'earnings': data})
flags = exp._quantile_filtering(df, ['earnings'],
{'earnings': ('upper', 90.0)})
assert flags.tolist() == [False] * 11 + [True]
