def test_watts_replication():
ad = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
k = 2 # factor
pline = np.mean(ad.data.values)
y = k * ad.data["x"].tolist()
df2 = pd.DataFrame({"x": y})
ad2 = ApodeData(df2, income_column="x")
np.testing.assert_allclose(ad.poverty("watts", pline=pline),
ad2.poverty("watts", pline=pline))
def test_watts_symmetry():
ad = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
pline = np.mean(ad.data.values)
y = ad.data["x"].tolist()
np.random.shuffle(y)
df2 = pd.DataFrame({"x": y})
ad2 = ApodeData(df2, income_column="x")
assert ad.poverty(method="watts",
pline=pline) == ad2.poverty(method="watts", pline=pline)
def test_headcount_replication():
ad = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
k = 2 # factor
pline = np.mean(ad.data.values)
y = k * ad.data["x"].tolist()
df2 = pd.DataFrame({"x": y})
ad2 = ApodeData(df2, income_column="x")
assert ad.poverty("headcount", pline=pline) == ad2.poverty("headcount",
pline=pline)
def test_hagenaars_symmetry():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
pline = np.mean(data.data.values)
y = data.data["x"].tolist()
np.random.shuffle(y)
df2 = pd.DataFrame({"x": y})
dr2 = ApodeData(df2, income_column="x")
assert data.poverty(method="hagenaars",
pline=pline) == dr2.poverty(method="hagenaars",
pline=pline)
def test_bd_homogeneity():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
k = 2 # factor
pline = np.mean(data.data.values)
y = data.data["x"].tolist()
y = [yi * k for yi in y]
df2 = pd.DataFrame({"x": y})
dr2 = ApodeData(df2, income_column="x")
assert data.poverty("bd", pline=pline) == dr2.poverty("bd",
pline=pline * k)
def test_thon_homogeneity():
ad = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
k = 2 # factor
pline = np.mean(ad.data.values)
y = ad.data["x"].tolist()
y = [yi * k for yi in y]
df2 = pd.DataFrame({"x": y})
ad2 = ApodeData(df2, income_column="x")
assert ad.poverty("thon", pline=pline) == ad2.poverty("thon",
pline=pline * k)
def test_severity_replication():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
k = 2 # factor
pline = np.mean(data.data.values)
y = k * data.data["x"].tolist()
df2 = pd.DataFrame({"x": y})
dr2 = ApodeData(df2, income_column="x")
np.testing.assert_allclose(
data.poverty("severity", pline=pline),
dr2.poverty("severity", pline=pline),
)