def test_plot():
import matplotlib.pyplot as plt
y, x, w = gen_data(250, 3, True, pandas=True)
fmla = "y ~ 1 + x0 + x1 + x2"
data = pd.concat([y, x], axis=1)
mod = RollingWLS.from_formula(fmla, window=100, data=data, weights=w)
res = mod.fit()
fig = res.plot_recursive_coefficient()
assert isinstance(fig, plt.Figure)
res.plot_recursive_coefficient(variables=2, alpha=None, figsize=(30, 7))
res.plot_recursive_coefficient(variables="x0", alpha=None, figsize=(30, 7))
res.plot_recursive_coefficient(variables=[0, 2],
alpha=None,
figsize=(30, 7))
res.plot_recursive_coefficient(variables=["x0"],
alpha=None,
figsize=(30, 7))
res.plot_recursive_coefficient(variables=["x0", "x1", "x2"],
alpha=None,
figsize=(30, 7))
with pytest.raises(ValueError, match="variable x4 is not an integer"):
res.plot_recursive_coefficient(variables="x4")
fig = plt.Figure()
# Just silence the warning
with warnings.catch_warnings():
warnings.simplefilter("ignore")
out = res.plot_recursive_coefficient(fig=fig)
assert out is fig
res.plot_recursive_coefficient(alpha=None, figsize=(30, 7))
def test_plot():
import matplotlib.pyplot as plt
y, x, w = gen_data(250, 3, True, pandas=True)
fmla = 'y ~ 1 + x0 + x1 + x2'
data = pd.concat([y, x], axis=1)
mod = RollingWLS.from_formula(fmla, window=100, data=data, weights=w)
res = mod.fit()
fig = res.plot_recursive_coefficient()
assert isinstance(fig, plt.Figure)
res.plot_recursive_coefficient(variables=2, alpha=None, figsize=(30, 7))
res.plot_recursive_coefficient(variables='x0', alpha=None, figsize=(30, 7))
res.plot_recursive_coefficient(variables=[0, 2],
alpha=None,
figsize=(30, 7))
res.plot_recursive_coefficient(variables=['x0'],
alpha=None,
figsize=(30, 7))
res.plot_recursive_coefficient(variables=['x0', 'x1', 'x2'],
alpha=None,
figsize=(30, 7))
with pytest.raises(ValueError, match='variable x4 is not an integer'):
res.plot_recursive_coefficient(variables='x4')
fig = plt.Figure()
with pytest.warns(UserWarning, match="tight_layout"):
out = res.plot_recursive_coefficient(fig=fig)
assert out is fig
res.plot_recursive_coefficient(alpha=None, figsize=(30, 7))
def test_formula():
y, x, w = gen_data(250, 3, True, pandas=True)
fmla = "y ~ 1 + x0 + x1 + x2"
data = pd.concat([y, x], axis=1)
mod = RollingWLS.from_formula(fmla, window=100, data=data, weights=w)
res = mod.fit()
alt = RollingWLS(y, x, window=100)
alt_res = alt.fit()
assert_allclose(res.params, alt_res.params)
ols_mod = RollingOLS.from_formula(fmla, window=100, data=data)
ols_mod.fit()