def test_imputation_error_sparse_0(strategy):
# check that error are raised when missing_values = 0 and input is sparse
X = np.ones((3, 5))
X[0] = 0
X = sparse.csc_matrix(X)
imputer = SimpleImputer(strategy=strategy, missing_values=0)
with pytest.raises(ValueError, match="Provide a dense array"):
imputer.fit(X)
imputer.fit(X.toarray())
with pytest.raises(ValueError, match="Provide a dense array"):
imputer.transform(X)
def _check_statistics(X, X_true, strategy, statistics, missing_values):
"""Utility function for testing imputation for a given strategy.
Test with dense and sparse arrays
Check that:
- the statistics (mean, median, mode) are correct
- the missing values are imputed correctly"""
err_msg = "Parameters: strategy = %s, missing_values = %s, " \
"sparse = {0}" % (strategy, missing_values)
assert_ae = assert_array_equal
if X.dtype.kind == 'f' or X_true.dtype.kind == 'f':
assert_ae = assert_array_almost_equal
# Normal matrix
imputer = SimpleImputer(missing_values, strategy=strategy)
X_trans = imputer.fit(X).transform(X.copy())
assert_ae(imputer.statistics_, statistics, err_msg=err_msg.format(False))
assert_ae(X_trans, X_true, err_msg=err_msg.format(False))
# Sparse matrix
imputer = SimpleImputer(missing_values, strategy=strategy)
imputer.fit(sparse.csc_matrix(X))
X_trans = imputer.transform(sparse.csc_matrix(X.copy()))
if sparse.issparse(X_trans):
X_trans = X_trans.toarray()
assert_ae(imputer.statistics_, statistics, err_msg=err_msg.format(True))
assert_ae(X_trans, X_true, err_msg=err_msg.format(True))
def test_iterative_imputer_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
imputer = IterativeImputer(missing_values=0,
max_iter=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 imputer will
# only use the initial imputer for that feature at transform
assert_allclose(
imputer.transform(X_test)[:, 0],
initial_imputer.transform(X_test)[:, 0])