def test_rawlsian_symmetry():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
y = data.data["x"].tolist()
np.random.shuffle(y)
df2 = pd.DataFrame({"x": y})
dr2 = ApodeData(df2, income_column="x")
assert data.welfare(method="rawlsian") == dr2.welfare(method="rawlsian")
def test_utilitarian_replication():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
k = 2 # factor
y = k * data.data["x"].tolist()
df2 = pd.DataFrame({"x": y})
dr2 = ApodeData(df2, income_column="x")
assert data.welfare("utilitarian") == dr2.welfare("utilitarian")
def test_theilt_replication():
ad = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
k = 2 # factor
y = k * ad.data["x"].tolist()
df2 = pd.DataFrame({"x": y})
ad2 = ApodeData(df2, income_column="x")
assert ad.welfare("theilt") == ad2.welfare("theilt")
def test_utilitarian_symmetry():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
y = data.data["x"].tolist()
np.random.shuffle(y)
df2 = pd.DataFrame({"x": y})
dr2 = ApodeData(df2, income_column="x")
np.testing.assert_allclose(data.welfare(method="utilitarian"),
dr2.welfare(method="utilitarian"))
def test_theilt_homogeneity():
data = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
k = 2 # factor
y = data.data["x"].tolist()
y = k * y
df2 = pd.DataFrame({"x": y})
dr2 = ApodeData(df2, income_column="x")
assert data.welfare("theilt") == dr2.welfare("theilt")
def test_utilitarian_homogeneity():
ad = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
k = 2 # factor
y = ad.data["x"].tolist()
y = k * y
df2 = pd.DataFrame({"x": y})
ad2 = ApodeData(df2, income_column="x")
assert ad.welfare("utilitarian") == ad2.welfare("utilitarian")
def test_theilt_symmetry():
ad = datasets.make_uniform(seed=42, size=300, mu=1, nbin=None)
y = ad.data["x"].tolist()
np.random.shuffle(y)
df2 = pd.DataFrame({"x": y})
ad2 = ApodeData(df2, income_column="x")
np.testing.assert_allclose(ad.welfare(method="theilt"),
ad2.welfare(method="theilt"))