def test_iterative_imputer_early_stopping():
rng = np.random.RandomState(0)
n = 50
d = 5
A = rng.rand(n, 1)
B = rng.rand(1, d)
X = np.dot(A, B)
nan_mask = rng.rand(n, d) < 0.5
X_missing = X.copy()
X_missing[nan_mask] = np.nan
imputer = IterativeImputer(max_iter=100,
tol=1e-2,
sample_posterior=False,
verbose=1,
random_state=rng)
X_filled_100 = imputer.fit_transform(X_missing)
assert len(imputer.imputation_sequence_) == d * imputer.n_iter_
imputer = IterativeImputer(max_iter=imputer.n_iter_,
sample_posterior=False,
verbose=1,
random_state=rng)
X_filled_early = imputer.fit_transform(X_missing)
assert_allclose(X_filled_100, X_filled_early, atol=1e-7)
imputer = IterativeImputer(max_iter=100,
tol=0,
sample_posterior=False,
verbose=1,
random_state=rng)
imputer.fit(X_missing)
assert imputer.n_iter_ == imputer.max_iter
def test_iterative_imputer_one_feature(X):
# check we exit early when there is a single feature
imputer = IterativeImputer().fit(X)
assert imputer.n_iter_ == 0
imputer = IterativeImputer()
imputer.fit([[1], [2]])
assert imputer.n_iter_ == 0
imputer.fit([[1], [np.nan]])
assert imputer.n_iter_ == 0
def test_iterative_imputer_verbose():
rng = np.random.RandomState(0)
n = 100
d = 3
X = _sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
imputer = IterativeImputer(missing_values=0, max_iter=1, verbose=1)
imputer.fit(X)
imputer.transform(X)
imputer = IterativeImputer(missing_values=0, max_iter=1, verbose=2)
imputer.fit(X)
imputer.transform(X)
def test_iterative_imputer_skip_non_missing(skip_complete):
# check the imputing strategy when missing data are present in the
# testing set only.
# taken from: https://github.com/scikit-learn/scikit-learn/issues/14383
rng = np.random.RandomState(0)
X_train = np.array([
[5, 2, 2, 1],
[10, 1, 2, 7],
[3, 1, 1, 1],
[8, 4, 2, 2]
])
X_test = np.array([
[np.nan, 2, 4, 5],
[np.nan, 4, 1, 2],
[np.nan, 1, 10, 1]
])
imputer = IterativeImputer(
initial_strategy='mean', skip_complete=skip_complete, random_state=rng
)
X_test_est = imputer.fit(X_train).transform(X_test)
if skip_complete:
# impute with the initial strategy: 'mean'
assert_allclose(X_test_est[:, 0], np.mean(X_train[:, 0]))
else:
assert_allclose(X_test_est[:, 0], [11, 7, 12], rtol=1e-4)
def test_iterative_imputer_no_missing():
rng = np.random.RandomState(0)
X = rng.rand(100, 100)
X[:, 0] = np.nan
m1 = IterativeImputer(max_iter=10, random_state=rng)
m2 = IterativeImputer(max_iter=10, random_state=rng)
pred1 = m1.fit(X).transform(X)
pred2 = m2.fit_transform(X)
# should exclude the first column entirely
assert_allclose(X[:, 1:], pred1)
# fit and fit_transform should both be identical
assert_allclose(pred1, pred2)
def test_iterative_imputer_transform_stochasticity():
pytest.importorskip("scipy", minversion="0.17.0")
rng1 = np.random.RandomState(0)
rng2 = np.random.RandomState(1)
n = 100
d = 10
X = _sparse_random_matrix(n, d, density=0.10,
random_state=rng1).toarray()
# when sample_posterior=True, two transforms shouldn't be equal
imputer = IterativeImputer(missing_values=0,
max_iter=1,
sample_posterior=True,
random_state=rng1)
imputer.fit(X)
X_fitted_1 = imputer.transform(X)
X_fitted_2 = imputer.transform(X)
# sufficient to assert that the means are not the same
assert np.mean(X_fitted_1) != pytest.approx(np.mean(X_fitted_2))
# when sample_posterior=False, and n_nearest_features=None
# and imputation_order is not random
# the two transforms should be identical even if rng are different
imputer1 = IterativeImputer(missing_values=0,
max_iter=1,
sample_posterior=False,
n_nearest_features=None,
imputation_order='ascending',
random_state=rng1)
imputer2 = IterativeImputer(missing_values=0,
max_iter=1,
sample_posterior=False,
n_nearest_features=None,
imputation_order='ascending',
random_state=rng2)
imputer1.fit(X)
imputer2.fit(X)
X_fitted_1a = imputer1.transform(X)
X_fitted_1b = imputer1.transform(X)
X_fitted_2 = imputer2.transform(X)
assert_allclose(X_fitted_1a, X_fitted_1b)
assert_allclose(X_fitted_1a, X_fitted_2)