import warnings
import numpy as np
from numpy.testing import assert_almost_equal
from nose.tools import raises
from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.utils import check_random_state
from stability_selection import StabilitySelection, plot_stability_path
def _generate_dummy_regression_data(p=1000, n=1000, k=5,
random_state=123321):
rng = check_random_state(random_state)
X = rng.normal(loc=0.0, scale=1.0, size=(n, p))
betas = np.zeros(p)
important_betas = np.sort(rng.choice(a=np.arange(p), size=k))
betas[important_betas] = rng.uniform(size=k)
y = np.matmul(X, betas)
return X, y, important_betas
def _generate_dummy_classification_data(p=1000, n=1000, k=5,
random_state=123321):
rng = check_random_state(random_state)
X = rng.normal(loc=0.0, scale=1.0, size=(n, p))
betas = np.zeros(p)
important_betas = np.sort(rng.choice(a=np.arange(p), size=k))
betas[important_betas] = rng.uniform(size=k)
probs = 1 / (1 + np.exp(-1 * np.matmul(X, betas)))
y = (probs > 0.5).astype(int)
return X, y, important_betas
def test_stability_selection_classification():
n, p, k = 1000, 1000, 5
X, y, important_betas = _generate_dummy_classification_data(n=n, k=k)
selector = StabilitySelection(lambda_grid=np.logspace(-5, -1, 25), verbose=1)
selector.fit(X, y)
chosen_betas = selector.get_support(indices=True)
X_r = selector.transform(X)
assert_almost_equal(important_betas, chosen_betas)
assert(X_r.shape == (n, k))
assert(selector.stability_scores_.shape == (p, selector.lambda_grid.shape[0]))
def test_stability_selection_regression():
n, p, k = 500, 1000, 5
X, y, important_betas = _generate_dummy_regression_data(n=n, k=k)
base_estimator = Pipeline([
('scaler', StandardScaler()),
('model', Lasso())
])
lambdas_grid = np.logspace(-1, 1, num=10)
selector = StabilitySelection(base_estimator=base_estimator,
lambda_name='model__alpha',
lambda_grid=lambdas_grid)
selector.fit(X, y)
chosen_betas = selector.get_support(indices=True)
assert_almost_equal(important_betas, chosen_betas)
def test_with_complementary_pairs_bootstrap():
n, p, k = 500, 1000, 5
X, y, important_betas = _generate_dummy_regression_data(n=n, k=k)
base_estimator = Pipeline([
('scaler', StandardScaler()),
('model', Lasso())
])
lambdas_grid = np.logspace(-1, 1, num=10)
selector = StabilitySelection(base_estimator=base_estimator,
lambda_name='model__alpha',
lambda_grid=lambdas_grid,
bootstrap_func='complementary_pairs')
selector.fit(X, y)
chosen_betas = selector.get_support(indices=True)
assert_almost_equal(important_betas, chosen_betas)
def test_with_stratified_bootstrap():
n, p, k = 1000, 1000, 5
X, y, important_betas = _generate_dummy_classification_data(n=n, k=k)
selector = StabilitySelection(lambda_grid=np.logspace(-5, -1, 25), verbose=1,
bootstrap_func='stratified')
selector.fit(X, y)
chosen_betas = selector.get_support(indices=True)
assert_almost_equal(important_betas, chosen_betas)
@raises(ValueError)
def test_different_shape():
n, p, k = 100, 200, 5
X, y, important_betas = _generate_dummy_regression_data(n=n, k=k)
base_estimator = Pipeline([
('scaler', StandardScaler()),
('model', Lasso())
])
lambdas_grid = np.logspace(-1, 1, num=10)
selector = StabilitySelection(base_estimator=base_estimator,
lambda_name='model__alpha',
lambda_grid=lambdas_grid)
selector.fit(X, y)
selector.transform(X[:, :-2])
def test_no_features():
n, p, k = 100, 200, 0
X, y, important_betas = _generate_dummy_regression_data(n=n, k=k)
base_estimator = Pipeline([
('scaler', StandardScaler()),
('model', Lasso())
])
lambdas_grid = np.logspace(-1, 1, num=10)
selector = StabilitySelection(base_estimator=base_estimator,
lambda_name='model__alpha',
lambda_grid=lambdas_grid)
selector.fit(X, y)
assert_almost_equal(selector.transform(X),
np.empty(0).reshape((X.shape[0], 0)))
def test_stability_plot():
n, p, k = 500, 200, 5
X, y, important_betas = _generate_dummy_regression_data(n=n, k=k)
base_estimator = Pipeline([
('scaler', StandardScaler()),
('model', Lasso())
])
lambdas_grid = np.logspace(-1, 1, num=10)
selector = StabilitySelection(base_estimator=base_estimator,
lambda_name='model__alpha',
lambda_grid=lambdas_grid)
selector.fit(X, y)
plot_stability_path(selector, threshold_highlight=0.5)
