def test_rad_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
assert data.inequality("rad") == 0.2600392437248902
def test_gap_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
pline = 0.5 * np.median(data.data.values)
assert data.poverty("gap", pline=pline) == 0.13715275200855706
def test_gap_valid_pline():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
with pytest.raises(ValueError):
data.poverty("gap", pline=-1)
def test_herfindahl_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
assert (data.concentration("herfindahl",
normalized=True) == 0.0011776319218515382)
assert (data.concentration("herfindahl",
normalized=False) == 0.004507039815445367)
def test_rosenbluth_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
np.testing.assert_allclose(data.concentration("rosenbluth"),
0.00506836225627098)
def test_concentration_ratio_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
assert data.concentration.concentration_ratio(k=20) == 0.12913322818634668
def test_concentration_ratio_call_equal_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
call_result = data.concentration("concentration_ratio", k=20)
method_result = data.concentration.concentration_ratio(k=20)
assert call_result == method_result
def test_gini_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
np.testing.assert_allclose(data.inequality("gini"), 0.34232535781966483)
def test_bonferroni_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
np.testing.assert_allclose(data.inequality("bonferroni"),
0.507498668487682)
def test_sdlog_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
assert data.inequality("sdlog") == 1.057680329912003
def test_merhan_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
assert data.inequality("merhan") == 0.5068579435513223
def test_sdlog_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
assert data.inequality.sdlog() == 1.057680329912003
def test_cv_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
assert data.inequality("cv") == 0.5933902127888603
def test_cv_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
assert data.inequality.cv() == 0.5933902127888603
def test_ratio_invalid_alpha():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
with pytest.raises(ValueError):
data.inequality.ratio(alpha=-1)
with pytest.raises(ValueError):
data.inequality.ratio(alpha=2)
def test_piesch_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
np.testing.assert_allclose(data.inequality("piesch"), 0.25015872424726393)
def test_entropy_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
np.testing.assert_allclose(data.inequality.entropy(), 0.3226715241069237)
def test_piesch_call_equal_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
call_result = data.inequality("piesch")
method_result = data.inequality.piesch()
assert call_result == method_result
def test_concentration_ratio_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
assert (data.concentration("concentration_ratio",
k=20) == 0.12913322818634668)
def test_gini_call_equal_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
call_result = data.inequality("gini")
method_result = data.inequality.gini()
assert call_result == method_result
def test_invalid():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
with pytest.raises(AttributeError):
data.concentration("foo")
def test_kolm_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
assert data.inequality("kolm", alpha=1) == 0.04278027786607911
def test_herfindahl_call_equal_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
call_result = data.concentration("herfindahl")
method_result = data.concentration.herfindahl()
assert call_result == method_result
def test_kolm_call_equal_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
call_result = data.inequality("kolm", alpha=1)
method_result = data.inequality.kolm(alpha=1)
assert call_result == method_result
def test_rosenbluth_call_equal_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
call_result = data.concentration("rosenbluth")
method_result = data.concentration.rosenbluth()
assert call_result == method_result
def test_ratio_call():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
assert data.inequality("ratio", alpha=0.5) == 0.31651799363507865
def test_gap_call_equal_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
pline = 0.5 * np.median(data.data.values)
call_result = data.poverty("gap", pline=pline)
method_result = data.poverty.gap(pline=pline)
assert call_result == method_result
def test_ratio_call_equal_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
call_result = data.inequality("ratio", alpha=0.5)
method_result = data.inequality.ratio(alpha=0.5)
assert call_result == method_result
def test_gap_extreme_values():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
pline_min = np.min(data.data.values) / 2
pline_max = np.max(data.data.values) + 1
assert data.poverty("gap", pline=pline_min) == 0
assert data.poverty("gap", pline=pline_max) <= 1
def test_rrange_call_equal_method():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
call_result = data.inequality("rrange")
method_result = data.inequality.rrange()
assert call_result == method_result