def test_apply_constraints_unscaled(self):
n_values = [3]
categorical_features = [0]
encoder = DummyEncoder(n_values, categorical_features, ['a'], [])
encoder.fit([[0, 17]])
X_scaler = StandardScaler()
constraint_helper = ParamConstraintHelper(X_scaler, encoder,
init_flip_prob=0.3,
flip_prob_decay=0.5)
X = [0.1, 0.2, 0.3, 17]
X_expected = [0, 0, 1, 17]
X_corrected = constraint_helper.apply_constraints(X, scaled=False, rescale=False)
self.assertTrue(np.all(X_corrected == X_expected))
def test_randomize_categorical_features(self):
# variable 0 is categorical, 3 values
# variable 1 is not categorical
# variable 2 is categorical, 4 values
cat_var_0_levels = 3
cat_var_2_levels = 4
cat_var_0_idx = 0
cat_var_2_idx = 2
n_values = [cat_var_0_levels, cat_var_2_levels]
categorical_features = [cat_var_0_idx, cat_var_2_idx]
encoder = DummyEncoder(n_values, categorical_features, ['a', 'b'], [])
encoder.fit([[0, 17, 0]])
X_scaler = StandardScaler()
constraint_helper = ParamConstraintHelper(X_scaler, encoder,
init_flip_prob=0.3,
flip_prob_decay=0.5)
# row is a sample encoded set of features,
# note that the non-categorical variable is on the right
row = np.array([0, 0, 1, 1, 0, 0, 0, 17], dtype=float)
trials = 20
cat_var_0_counts = np.zeros(cat_var_0_levels)
cat_var_2_counts = np.zeros(cat_var_2_levels)
for _ in range(trials):
# possibly flip the categorical features
row = constraint_helper.randomize_categorical_features(row, scaled=False, rescale=False)
# check that result is valid for cat_var_0
cat_var_0_dummies = row[0: cat_var_0_levels]
self.assertTrue(np.all(np.logical_or(cat_var_0_dummies == 0, cat_var_0_dummies == 1)))
self.assertEqual(np.sum(cat_var_0_dummies), 1)
cat_var_0_counts[np.argmax(cat_var_0_dummies)] += 1
# check that result is valid for cat_var_2
cat_var_2_dummies = row[cat_var_0_levels: cat_var_0_levels + cat_var_2_levels]
self.assertTrue(np.all(np.logical_or(cat_var_2_dummies == 0, cat_var_2_dummies == 1)))
self.assertEqual(np.sum(cat_var_2_dummies), 1)
cat_var_2_counts[np.argmax(cat_var_2_dummies)] += 1
self.assertEqual(row[-1], 17)
for ct in cat_var_0_counts:
self.assertTrue(ct > 0)
for ct in cat_var_2_counts:
self.assertTrue(ct > 0)
def test_apply_constraints(self):
n_values = [3]
categorical_features = [0]
encoder = DummyEncoder(n_values, categorical_features, ['a'], [])
encoder.fit([[0, 17]])
X_scaler = StandardScaler()
X = np.array([[0, 0, 1, 17], [1, 0, 0, 17]], dtype=float)
X_scaled = X_scaler.fit_transform(X)
constraint_helper = ParamConstraintHelper(X_scaler, encoder,
init_flip_prob=0.3,
flip_prob_decay=0.5)
row = X_scaled[0]
new_row = np.copy(row)
new_row[0: 3] += 0.1 # should still represent [0, 0, 1] encoding
row_corrected = constraint_helper.apply_constraints(new_row)
self.assertTrue(np.all(row == row_corrected))