def test_formula_partial_weights():
data = DataFrame(np.random.standard_normal((500, 4)),
columns=['y1', 'y2', 'x1', 'x2'])
weights = DataFrame(np.random.chisquare(5, (500, 1)), columns=['eq2'])
formula = OrderedDict()
formula['eq1'] = 'y1 ~ 1 + x1'
formula['eq2'] = 'y2 ~ 1 + x1'
with warnings.catch_warnings(record=True) as w:
mod = SUR.from_formula(formula, data, weights=weights)
assert len(w) == 1
assert 'Weights' in w[0].message.args[0]
assert 'eq1' in w[0].message.args[0]
assert 'eq2' not in w[0].message.args[0]
mod.fit()
expected = np.ones((500, 1))
assert_allclose(mod._w[0], expected / expected.mean())
expected = weights.values[:, [0]]
assert_allclose(mod._w[1], expected / expected.mean())
formula = '{y1 ~ 1 + x1} {y2 ~ 1 + x2}'
weights = DataFrame(np.random.chisquare(5, (500, 1)), columns=['y2'])
with warnings.catch_warnings(record=True) as w:
mod = SUR.from_formula(formula, data, weights=weights)
assert len(w) == 1
assert 'y1' in w[0].message.args[0]
assert 'y2' not in w[0].message.args[0]
expected = np.ones((500, 1))
assert_allclose(mod._w[0], expected / expected.mean())
expected = weights.values[:, [0]]
assert_allclose(mod._w[1], expected / expected.mean())
def test_formula_partial_weights():
data = DataFrame(np.random.standard_normal((500, 4)),
columns=["y1", "y2", "x1", "x2"])
weights = DataFrame(np.random.chisquare(5, (500, 1)), columns=["eq2"])
formula = {"eq1": "y1 ~ 1 + x1", "eq2": "y2 ~ 1 + x1"}
with warnings.catch_warnings(record=True) as w:
mod = SUR.from_formula(formula, data, weights=weights)
assert len(w) == 1
assert "Weights" in w[0].message.args[0]
assert "eq1" in w[0].message.args[0]
assert "eq2" not in w[0].message.args[0]
mod.fit()
expected = np.ones((500, 1))
assert_allclose(mod._w[0], expected / expected.mean())
expected = weights.values[:, [0]]
assert_allclose(mod._w[1], expected / expected.mean())
formula = "{y1 ~ 1 + x1} {y2 ~ 1 + x2}"
weights = DataFrame(np.random.chisquare(5, (500, 1)), columns=["y2"])
with warnings.catch_warnings(record=True) as w:
mod = SUR.from_formula(formula, data, weights=weights)
assert len(w) == 1
assert "y1" in w[0].message.args[0]
assert "y2" not in w[0].message.args[0]
expected = np.ones((500, 1))
assert_allclose(mod._w[0], expected / expected.mean())
expected = weights.values[:, [0]]
assert_allclose(mod._w[1], expected / expected.mean())
def test_formula():
data = DataFrame(np.random.standard_normal((500, 4)),
columns=['y1', 'y2', 'x1', 'x2'])
formula = {'eq1': 'y1 ~ 1 + x1', 'eq2': 'y2 ~ 1 + x2'}
mod = SUR.from_formula(formula, data)
mod.fit()
formula = '{y1 ~ 1 + x1} {y2 ~ 1 + x2}'
mod = SUR.from_formula(formula, data)
mod.fit(cov_type='heteroskedastic')
formula = '''
{y1 ~ 1 + x1}
{y2 ~ 1 + x2}
'''
mod = SUR.from_formula(formula, data)
mod.fit(cov_type='heteroskedastic')
formula = '''
{eq.a:y1 ~ 1 + x1}
{second: y2 ~ 1 + x2}
'''
mod = SUR.from_formula(formula, data)
res = mod.fit(cov_type='heteroskedastic')
assert 'eq.a' in res.equation_labels
assert 'second' in res.equation_labels
def test_formula():
data = DataFrame(np.random.standard_normal((500, 4)),
columns=["y1", "y2", "x1", "x2"])
formula = {"eq1": "y1 ~ 1 + x1", "eq2": "y2 ~ 1 + x2"}
mod = SUR.from_formula(formula, data)
mod.fit()
formula = "{y1 ~ 1 + x1} {y2 ~ 1 + x2}"
mod = SUR.from_formula(formula, data)
mod.fit(cov_type="heteroskedastic")
formula = """
{y1 ~ 1 + x1}
{y2 ~ 1 + x2}
"""
mod = SUR.from_formula(formula, data)
mod.fit(cov_type="heteroskedastic")
formula = """
{eq.a:y1 ~ 1 + x1}
{second: y2 ~ 1 + x2}
"""
mod = SUR.from_formula(formula, data)
res = mod.fit(cov_type="heteroskedastic")
assert "eq.a" in res.equation_labels
assert "second" in res.equation_labels
def test_formula_repeated_key():
data = DataFrame(np.random.standard_normal((500, 4)),
columns=['y1', 'y2', 'x1', 'x2'])
formula = '''
{first:y1 ~ 1 + x1}
{first: y2 ~ 1 + x2}
'''
mod = SUR.from_formula(formula, data)
res = mod.fit()
assert 'first' in res.equation_labels
assert 'first.0' in res.equation_labels
def test_formula_weights():
data = DataFrame(np.random.standard_normal((500, 4)),
columns=["y1", "y2", "x1", "x2"])
weights = DataFrame(np.random.chisquare(5, (500, 2)),
columns=["eq1", "eq2"])
formula = {"eq1": "y1 ~ 1 + x1", "eq2": "y2 ~ 1 + x1"}
mod = SUR.from_formula(formula, data, weights=weights)
mod.fit()
expected = weights.values[:, [0]]
assert_allclose(mod._w[0], expected / expected.mean())
expected = weights.values[:, [1]]
assert_allclose(mod._w[1], expected / expected.mean())
formula = "{y1 ~ 1 + x1} {y2 ~ 1 + x2}"
weights = DataFrame(np.random.chisquare(5, (500, 2)), columns=["y1", "y2"])
mod = SUR.from_formula(formula, data, weights=weights)
mod.fit()
expected = weights.values[:, [0]]
assert_allclose(mod._w[0], expected / expected.mean())
expected = weights.values[:, [1]]
assert_allclose(mod._w[1], expected / expected.mean())
def test_formula_repeated_key():
data = DataFrame(np.random.standard_normal((500, 4)),
columns=["y1", "y2", "x1", "x2"])
formula = """
{first:y1 ~ 1 + x1}
{first: y2 ~ 1 + x2}
"""
mod = SUR.from_formula(formula, data)
res = mod.fit()
assert "first" in res.equation_labels
assert "first.0" in res.equation_labels
def test_formula_weights():
data = DataFrame(np.random.standard_normal((500, 4)),
columns=['y1', 'y2', 'x1', 'x2'])
weights = DataFrame(np.random.chisquare(5, (500, 2)), columns=['eq1', 'eq2'])
formula = OrderedDict()
formula['eq1'] = 'y1 ~ 1 + x1'
formula['eq2'] = 'y2 ~ 1 + x1'
mod = SUR.from_formula(formula, data, weights=weights)
mod.fit()
expected = weights.values[:, [0]]
assert_allclose(mod._w[0], expected / expected.mean())
expected = weights.values[:, [1]]
assert_allclose(mod._w[1], expected / expected.mean())
formula = '{y1 ~ 1 + x1} {y2 ~ 1 + x2}'
weights = DataFrame(np.random.chisquare(5, (500, 2)), columns=['y1', 'y2'])
mod = SUR.from_formula(formula, data, weights=weights)
mod.fit()
expected = weights.values[:, [0]]
assert_allclose(mod._w[0], expected / expected.mean())
expected = weights.values[:, [1]]
assert_allclose(mod._w[1], expected / expected.mean())
def test_formula_errors():
data = DataFrame(np.random.standard_normal((500, 4)),
columns=['y1', 'y2', 'x1', 'x2'])
with pytest.raises(TypeError):
SUR.from_formula(np.ones(10), data)
def test_formula_errors():
data = DataFrame(np.random.standard_normal((500, 4)),
columns=["y1", "y2", "x1", "x2"])
with pytest.raises(TypeError):
SUR.from_formula(np.ones(10), data)
