def test_plot_comparision(self):
mg = MatrixGenerator()
X, y, costs = mg.generate(n_basic_cols=10, noise_sigmas=[0.1, 1])
lamb = 1
dvs = DiffVariableSelector()
dvs.fit(data=X,
target_variable=y,
costs=costs,
lamb=lamb,
j_criterion_func='mim')
model = LogisticRegression()
dvs.score(model, scoring_function=roc_auc_score)
dvs.plot_scores(compare_no_cost_method=True, budget=1, model=model)
def test_plot_without_comparision(self):
integer_matrix = np.random.randint(0, 10, (100, 10))
diverse_target = np.random.randint(0, 2, (100))
costs = [1.76, 0.19, 0.36, 0.96, 0.41, 0.17, 0.36, 0.75, 0.79, 1.38]
lamb = 1
dvs = DiffVariableSelector()
dvs.fit(data=integer_matrix,
target_variable=diverse_target,
costs=costs,
lamb=lamb,
j_criterion_func='mim')
model = LogisticRegression()
dvs.score(model, scoring_function=roc_auc_score)
dvs.plot_scores(budget=1)
def test_score(self):
integer_matrix = np.random.randint(0, 10, (100, 10))
diverse_target = np.random.randint(0, 2, (100))
costs = [1.76, 0.19, -0.36, 0.96, 0.41, 0.17, -0.36, 0.75, 0.79, -1.38]
lamb = 1
dvs = DiffVariableSelector()
dvs.fit(data=integer_matrix,
target_variable=diverse_target,
costs=costs,
lamb=lamb,
j_criterion_func='mim')
model = LogisticRegression()
dvs.score(model, scoring_function=roc_auc_score)
self.assertEqual(len(dvs.total_scores), len(costs))
def test_run_score_before_fit(self):
dvs = DiffVariableSelector()
model = LogisticRegression()
with self.assertRaises(AssertionError):
dvs.score(model, scoring_function=roc_auc_score)