def test_exceptions(self):
mod_not_fit = RandomForestRegressor()
# dataset should be compatible with the model
with self.assertRaises(Exception) as mod_ex_2:
aleplot_1D_discrete(self.X, mod_not_fit, "x4")
mod_ex_msg = "Please check that your model is fitted, and accepts X as input."
self.assertEqual(mod_ex_2.exception.args[0], mod_ex_msg)
def test_binsizes(self):
ale_eff = aleplot_1D_discrete(X=self.X,
model=self.model,
feature="x4",
include_CI=False)
self.assertCountEqual(ale_eff.loc[:, "size"],
[27, 14, 16, 26, 20, 17, 18, 23, 21, 18])
def test_outputshape_noCI(self):
ale_eff = aleplot_1D_discrete(X=self.X,
model=self.model,
feature="x4",
include_CI=False)
self.assertEqual(ale_eff.shape, (10, 2))
self.assertCountEqual(ale_eff.columns, ["eff", "size"])
def test_1D_continuous_bar_plot(self):
ale_eff = aleplot_1D_discrete(X=self.X,
model=self.model,
feature="x4",
include_CI=True)
fig, ax, ax2 = plot_1D_discrete_eff(ale_eff, self.X)
self.assertCountEqual(ale_eff['size'],
[bar.get_height() for bar in ax2.patches])
def test_1D_continuous_line_plot(self):
ale_eff = aleplot_1D_discrete(X=self.X,
model=self.model,
feature="x4",
include_CI=True)
fig, ax, ax2 = plot_1D_discrete_eff(ale_eff, self.X)
self.assertCountEqual(ax.lines[0].get_xydata()[:, 0], ale_eff.index)
self.assertCountEqual(ax.lines[0].get_xydata()[:, 1], ale_eff.eff)
def test_bins(self):
ale_eff = aleplot_1D_discrete(X=self.X,
model=self.model,
feature="x4",
include_CI=False)
self.assertCountEqual(
ale_eff.index,
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
)
def test_outputshape_withCI(self):
ale_eff = aleplot_1D_discrete(X=self.X,
model=self.model,
feature="x4",
include_CI=True,
C=0.9)
self.assertEqual(ale_eff.shape, (10, 4))
self.assertCountEqual(ale_eff.columns,
["eff", "size", "lowerCI_90%", "upperCI_90%"])
def test_1D_continuous_ci_plot(self):
ale_eff = aleplot_1D_discrete(X=self.X,
model=self.model,
feature="x4",
include_CI=True)
fig, ax, ax2 = plot_1D_discrete_eff(ale_eff, self.X)
self.assertCountEqual(np.round(ax.lines[1].get_xydata()[1:, 1], 8),
np.round(ale_eff['lowerCI_95%'][1:], 8))
self.assertCountEqual(np.round(ax.lines[2].get_xydata()[1:, 1], 8),
np.round(ale_eff['upperCI_95%'][1:], 8))
def test_effvalues(self):
ale_eff = aleplot_1D_discrete(X=self.X,
model=self.model,
feature="x4",
include_CI=False)
self.assertCountEqual(
np.round(ale_eff.loc[:, "eff"], 8),
[
-1.20935606,
-0.82901158,
-0.42415507,
-0.24192617,
0.04098572,
0.32370623,
0.56468117,
0.61378063,
0.6786663,
0.69330051,
],
)
def test_effvalues(self):
ale_eff = aleplot_1D_discrete(X=self.X_cleaned,
model=self.model,
feature="x4",
include_CI=False)
self.assertCountEqual(
np.round(ale_eff.loc[:, "eff"], 8),
[
-1.15833245,
-0.87518863,
-0.17627267,
-0.03183705,
0.21772015,
0.3103579,
0.37654819,
0.38187692,
0.54013521,
0.59118777,
],
)
def test_CIvalues(self):
ale_eff = aleplot_1D_discrete(X=self.X,
model=self.model,
feature="x4",
include_CI=True,
C=0.9)
# assert that the first bin do not have a CI
self.assertTrue(np.isnan(ale_eff.loc[ale_eff.index[0], "lowerCI_90%"]))
self.assertTrue(np.isnan(ale_eff.loc[ale_eff.index[0], "upperCI_90%"]))
# check the values of the CI
self.assertCountEqual(
np.round(ale_eff.loc[ale_eff.index[1]:, "lowerCI_90%"], 8),
[
-0.91916875,
-0.54755861,
-0.29067159,
-0.04528913,
0.2512252,
0.48115024,
0.58654446,
0.65270079,
0.66807024,
],
)
self.assertCountEqual(
np.round(ale_eff.loc[ale_eff.index[1]:, "upperCI_90%"], 8),
[
-0.73885441,
-0.30075154,
-0.19318075,
0.12726056,
0.39618726,
0.6482121,
0.64101679,
0.70463181,
0.71853079,
],
)
def test_CIvalues(self):
ale_eff = aleplot_1D_discrete(X=self.X_cleaned,
model=self.model,
feature="x4",
include_CI=True,
C=0.9)
# assert that the first bin do not have a CI
self.assertTrue(np.isnan(ale_eff.loc[ale_eff.index[0], "lowerCI_90%"]))
self.assertTrue(np.isnan(ale_eff.loc[ale_eff.index[0], "upperCI_90%"]))
# check the values of the CI
self.assertCountEqual(
np.round(ale_eff.loc[ale_eff.index[1]:, "lowerCI_90%"], 8),
[
-0.92693686,
-0.30837293,
-0.05872927,
0.17298345,
0.23835091,
0.34097451,
0.35996381,
0.47381797,
0.56927921,
],
)
self.assertCountEqual(
np.round(ale_eff.loc[ale_eff.index[1]:, "upperCI_90%"], 8),
[
-0.8234404,
-0.0441724,
-0.00494484,
0.26245684,
0.38236488,
0.41212186,
0.40379003,
0.60645245,
0.61309633,
],
)
def test_indexname(self):
ale_eff = aleplot_1D_discrete(X=self.X,
model=self.model,
feature="x4",
include_CI=False)
self.assertEqual(ale_eff.index.name, "x4")