def test_empty_data(self):
with self.assertRaises(ValueError):
variable_dropout(LinearRegression(),
pd.DataFrame({
'a': [],
'b': []
}), [])
def test_y_longer(self):
with self.assertRaises(ValueError):
variable_dropout(LinearRegression(),
pd.DataFrame({
'a': [1, 2, 3],
'b': [4, 5, 6]
}), [0, 1, 2, 3])
def test_same_results_with_same_random_state(self):
rng = random.RandomState(0)
X = pd.DataFrame({
'linear': rng.randint(-5, 5, 2000),
'square': rng.randint(-5, 5, 2000),
'noise': rng.randint(-5, 5, 2000)
})
y = [(row.square**2 - 2 * row.linear + 1 + 0.1 * rng.randn())
for row in X.itertuples()]
y = [val > np.mean(y) for val in y]
model = LogisticRegression(random_state=rng)
model.fit(X, y)
importance_first = variable_dropout(model,
X,
y,
n_iters=1,
loss_function=hinge_loss,
random_state=0)
importance_second = variable_dropout(model,
X,
y,
n_iters=1,
loss_function=hinge_loss,
random_state=0)
self.assertEqual(list(importance_first), list(importance_second))
def test_negative_iters(self):
with self.assertRaises(ValueError):
variable_dropout(LinearRegression(),
pd.DataFrame({
'a': [1, 2, 3],
'b': [4, 5, 6]
}), [0, 1, 2],
n_iters=-1)
def test_random_noise_least_important_for_regression(self):
rng = random.RandomState(0)
X = pd.DataFrame({
'linear': rng.randint(-5, 5, 2000),
'square': rng.randint(-5, 5, 2000),
'noise': rng.randint(-5, 5, 2000)
})
y = [
row.square**2 - 2 * row.linear + 1 + 0.1 * rng.randn()
for row in X.itertuples()
]
model = LinearRegression(n_jobs=-1)
model.fit(X, y)
importance = variable_dropout(model, X, y, random_state=rng)
self.assertGreater(importance['linear'], importance['noise'])
self.assertGreater(importance['square'], importance['noise'])
def test_random_noise_least_important_for_classification(self):
rng = random.RandomState(0)
X = pd.DataFrame({
'linear': rng.randint(-5, 5, 2000),
'square': rng.randint(-5, 5, 2000),
'noise': rng.randint(-5, 5, 2000)
})
y = [(row.square**2 - 2 * row.linear + 1 + 0.1 * rng.randn())
for row in X.itertuples()]
y = [val > np.mean(y) for val in y]
model = LogisticRegression(random_state=rng)
model.fit(X, y)
importance = variable_dropout(model,
X,
y,
loss_function=hinge_loss,
random_state=rng)
self.assertGreater(importance['linear'], importance['noise'])
self.assertGreater(importance['square'], importance['noise'])
def test_bad_estimator(self):
with self.assertRaises(ValueError):
variable_dropout([], pd.DataFrame({
'a': [1, 2, 3],
'b': [4, 5, 6]
}), [0, 1, 2])