def test_mice_additive_matrix():
rng = np.random.RandomState(0)
n = 100
d = 10
A = rng.randn(n, d)
B = rng.randn(n, d)
X_filled = np.zeros(A.shape)
for i in range(d):
for j in range(d):
X_filled[:, (i+j) % d] += (A[:, i] + B[:, j]) / 2
# a quarter is randomly missing
nan_mask = rng.rand(n, d) < 0.25
X_missing = X_filled.copy()
X_missing[nan_mask] = np.nan
# split up data
n = n // 2
X_train = X_missing[:n]
X_test_filled = X_filled[n:]
X_test = X_missing[n:]
imputer = MICEImputer(n_imputations=25,
n_burn_in=10,
verbose=True,
random_state=rng).fit(X_train)
X_test_est = imputer.transform(X_test)
assert_allclose(X_test_filled, X_test_est, atol=0.01)
def test_mice_additive_matrix():
rng = np.random.RandomState(0)
n = 100
d = 10
A = rng.randn(n, d)
B = rng.randn(n, d)
X_filled = np.zeros(A.shape)
for i in range(d):
for j in range(d):
X_filled[:, (i + j) % d] += (A[:, i] + B[:, j]) / 2
# a quarter is randomly missing
nan_mask = rng.rand(n, d) < 0.25
X_missing = X_filled.copy()
X_missing[nan_mask] = np.nan
# split up data
n = n // 2
X_train = X_missing[:n]
X_test_filled = X_filled[n:]
X_test = X_missing[n:]
imputer = MICEImputer(n_imputations=25,
n_burn_in=10,
verbose=True,
random_state=rng).fit(X_train)
X_test_est = imputer.transform(X_test)
assert_allclose(X_test_filled, X_test_est, atol=0.01)
def test_mice_transform_stochasticity():
rng = np.random.RandomState(0)
n = 100
d = 10
X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
imputer = MICEImputer(missing_values=0,
n_imputations=1,
n_burn_in=1,
random_state=rng)
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))
def test_mice_transform_stochasticity():
rng = np.random.RandomState(0)
n = 100
d = 10
X = sparse_random_matrix(n, d, density=0.10,
random_state=rng).toarray()
imputer = MICEImputer(missing_values=0,
n_imputations=1,
n_burn_in=1,
random_state=rng)
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))
def test_mice_missing_at_transform(strategy):
rng = np.random.RandomState(0)
n = 100
d = 10
X_train = rng.randint(low=0, high=3, size=(n, d))
X_test = rng.randint(low=0, high=3, size=(n, d))
X_train[:, 0] = 1 # definitely no missing values in 0th column
X_test[0, 0] = 0 # definitely missing value in 0th column
mice = MICEImputer(missing_values=0,
n_imputations=1,
n_burn_in=1,
initial_strategy=strategy,
random_state=rng).fit(X_train)
initial_imputer = SimpleImputer(missing_values=0,
strategy=strategy).fit(X_train)
# if there were no missing values at time of fit, then mice will
# only use the initial imputer for that feature at transform
assert np.all(mice.transform(X_test)[:, 0] ==
initial_imputer.transform(X_test)[:, 0])
def test_mice_missing_at_transform(strategy):
rng = np.random.RandomState(0)
n = 100
d = 10
X_train = rng.randint(low=0, high=3, size=(n, d))
X_test = rng.randint(low=0, high=3, size=(n, d))
X_train[:, 0] = 1 # definitely no missing values in 0th column
X_test[0, 0] = 0 # definitely missing value in 0th column
mice = MICEImputer(missing_values=0,
n_imputations=1,
n_burn_in=1,
initial_strategy=strategy,
random_state=rng).fit(X_train)
initial_imputer = SimpleImputer(missing_values=0,
strategy=strategy).fit(X_train)
# if there were no missing values at time of fit, then mice will
# only use the initial imputer for that feature at transform
assert np.all(
mice.transform(X_test)[:, 0] == initial_imputer.transform(X_test)[:,
0])
def test_mice_transform_recovery(rank):
rng = np.random.RandomState(0)
n = 100
d = 100
A = rng.rand(n, rank)
B = rng.rand(rank, d)
X_filled = np.dot(A, B)
# half is randomly missing
nan_mask = rng.rand(n, d) < 0.5
X_missing = X_filled.copy()
X_missing[nan_mask] = np.nan
# split up data in half
n = n // 2
X_train = X_missing[:n]
X_test_filled = X_filled[n:]
X_test = X_missing[n:]
imputer = MICEImputer(n_imputations=10,
n_burn_in=10,
verbose=True,
random_state=rng).fit(X_train)
X_test_est = imputer.transform(X_test)
assert_allclose(X_test_filled, X_test_est, rtol=1e-5, atol=0.1)
def test_mice_transform_recovery(rank):
rng = np.random.RandomState(0)
n = 100
d = 100
A = rng.rand(n, rank)
B = rng.rand(rank, d)
X_filled = np.dot(A, B)
# half is randomly missing
nan_mask = rng.rand(n, d) < 0.5
X_missing = X_filled.copy()
X_missing[nan_mask] = np.nan
# split up data in half
n = n // 2
X_train = X_missing[:n]
X_test_filled = X_filled[n:]
X_test = X_missing[n:]
imputer = MICEImputer(n_imputations=10,
n_burn_in=10,
verbose=True,
random_state=rng).fit(X_train)
X_test_est = imputer.transform(X_test)
assert_allclose(X_test_filled, X_test_est, rtol=1e-5, atol=0.1)