def test_1D_continuous_rug_plot(self):
ale_eff = aleplot_1D_continuous(
X=self.X_cleaned,
model=self.model,
feature="x1",
grid_size=5,
include_CI=True,
)
fig, ax = plot_1D_continuous_eff(ale_eff, self.X_cleaned)
## the rug
rug_plot_data = ax.lines[1].get_xydata()
# a line for each data point in X
self.assertEqual(rug_plot_data.shape[0], self.X_cleaned.shape[0])
# y position is always at the lowest eff value (including the values
# of the confidence interval)
self.assertCountEqual(
np.unique(rug_plot_data[:, 1]),
[ale_eff.drop("size", axis=1, inplace=False).min().min()],
)
# x position should always be plotted within the bin it belongs to
# (less than the upper bin limit and more than the lower bin limit)
self.assertTrue(
np.all(ale_eff.index[pd.cut(
self.X_cleaned["x1"], ale_eff.index,
include_lowest=True).cat.codes + 1] > rug_plot_data[:, 0])
and np.all(ale_eff.index[pd.cut(
self.X_cleaned["x1"], ale_eff.index,
include_lowest=True).cat.codes] < rug_plot_data[:, 0]))
def test_1D_continuous_line_plot(self):
ale_eff = aleplot_1D_continuous(X=self.X,
model=self.model,
feature="x1",
grid_size=5,
include_CI=True)
fig, ax = plot_1D_continuous_eff(ale_eff, self.X)
## effect line
eff_plt_data = ax.lines[0].get_xydata()
# the x values should be the bins
self.assertCountEqual(eff_plt_data[:, 0], ale_eff.index)
# the y values should be the effect
self.assertCountEqual(eff_plt_data[:, 1], ale_eff.eff)
def test_1D_continuous_ci_plot(self):
ale_eff = aleplot_1D_continuous(X=self.X,
model=self.model,
feature="x1",
grid_size=5,
include_CI=True)
fig, ax = plot_1D_continuous_eff(ale_eff, self.X)
ci_plot_data = pd.DataFrame(
ax.collections[0].get_paths()
[0].vertices).drop_duplicates().groupby(0).agg(['min', 'max'])
ci_plot_data.index.name = 'x1'
ci_plot_data.columns = ['lowerCI_95%', 'upperCI_95%']
self.assertTrue(
np.all(
ale_eff.loc[ale_eff.index[1]:,
['lowerCI_95%', 'upperCI_95%']] == ci_plot_data))
def test_1D_continuous_ci_plot(self):
ale_eff = aleplot_1D_continuous(
X=self.X_cleaned,
model=self.model,
feature="x1",
grid_size=5,
include_CI=True,
)
fig, ax = plot_1D_continuous_eff(ale_eff, self.X_cleaned)
ci_plot_data = (pd.DataFrame(
ax.collections[0].get_paths()
[0].vertices).drop_duplicates().groupby(0).agg(["min", "max"]))
ci_plot_data.index.name = "x1"
ci_plot_data.columns = ["lowerCI_95%", "upperCI_95%"]
self.assertTrue(
np.all(
ale_eff.loc[ale_eff.index[1]:,
["lowerCI_95%", "upperCI_95%"]] == ci_plot_data))
def test_1D_continuous_rug_plot(self):
ale_eff = aleplot_1D_continuous(X=self.X,
model=self.model,
feature="x1",
grid_size=5,
include_CI=True)
fig, ax = plot_1D_continuous_eff(ale_eff, self.X)
## the rug
rug_plot_data = ax.lines[1].get_xydata()
# a line for each data point in X
self.assertEqual(rug_plot_data.shape[0], self.X.shape[0])
# y position is always at the lower eff value
self.assertCountEqual(np.unique(rug_plot_data[:, 1]),
[ale_eff.eff.min()])
# x position should always be plotted within the bin it belongs to
# (less than the upper bin limit and more than the lower bin limit)
self.assertTrue(
np.all(ale_eff.index[pd.cut(
self.X["x1"], ale_eff.index, include_lowest=True).cat.codes +
1] > rug_plot_data[:, 0])
and np.all(ale_eff.index[pd.cut(
self.X["x1"], ale_eff.index, include_lowest=True).cat.codes] <
rug_plot_data[:, 0]))