def test_regression_2(self):
n = 3
sample = select_sample(self.X_train, n=n)
cp2 = individual_variable_profile(self.explainer_rf,
sample,
variables=['TAX', 'CRIM'])
self.assertEqual(len(cp2.profile), cp2._grid_points * 2 * n)
def test_select_sample_6(self):
sample_x, sample_y = select_sample(pd.DataFrame(self.x),
pd.DataFrame(self.y),
n=1)
pos = list(self.y).index(sample_y[0])
self.assertSequenceEqual(list(sample_x.iloc[0]), list(self.x[pos]))
def test_select_sample_4(self):
sample_x = select_sample(self.x, n=300)
self.assertEqual(len(sample_x), len(self.x))
def test_select_sample_5(self):
sample_x = select_sample(self.x, n=300)
sample_x_2 = select_sample(pd.DataFrame(self.x), n=300)
np.testing.assert_array_equal(sample_x, sample_x_2)
def test_select_sample_3(self):
sample_x, sample_y = select_sample(self.x, self.y, n=1)
pos = list(self.y).index(sample_y[0])
self.assertSequenceEqual(list(sample_x[0]), list(self.x[pos]))
def test_select_sample_2(self):
sample = select_sample(self.x, n=1)
self.assertIn(sample[0], self.x)
def test_select_sample(self):
(_, m) = self.x.shape
size = 2
sample = select_sample(self.x, n=size)
self.assertEqual(sample.shape, (size, m))
# model, data, labels, variable_names
return rf_model, X_train, y_train, list(boston['feature_names'])
if __name__ == "__main__":
(model, data, labels, variable_names) = random_forest_regression()
explainer_rf = explain(model, variable_names, data, labels)
cp_profile = individual_variable_profile(explainer_rf,
X_train[0],
y=y_train[0],
variables=['TAX', 'CRIM'])
plot(cp_profile)
sample = select_sample(X_train, n=3)
cp2 = individual_variable_profile(explainer_rf,
sample,
variables=['TAX', 'CRIM'])
plot(cp2)
neighbours = select_neighbours(X_train,
X_train[0],
variable_names=variable_names,
selected_variables=variable_names,
n=15)
cp3 = individual_variable_profile(explainer_rf,
neighbours,
variables=['LSTAT', 'RM'],
variable_splits={
'LSTAT': [10, 20, 30],
random_state=42)
gb_model.fit(x, y)
return gb_model, x, y, var_names
def supported_vector_machines_model():
svm_model = svm.SVR(C=0.01, gamma='scale')
svm_model.fit(x, y)
return svm_model, x, y, var_names
if __name__ == "__main__":
(linear_model, data, labels, variable_names) = linear_regression_model()
(gb_model, _, _, _) = gradient_boosting_model()
(svm_model, _, _, _) = supported_vector_machines_model()
explainer_linear = explain(linear_model, variable_names, data, y)
explainer_gb = explain(gb_model, variable_names, data, y)
explainer_svm = explain(svm_model, variable_names, data, y)
cp_profile = individual_variable_profile(explainer_linear, x[0], y[0])
plot(cp_profile, show_residuals=True)
sample_x, sample_y = select_sample(x, y, n=10)
cp2 = individual_variable_profile(explainer_gb, sample_x, y=sample_y)
cp3 = individual_variable_profile(explainer_gb, x[0], y[0])
plot(cp3, show_residuals=True)
plot(cp_profile, cp3, show_residuals=True)