def test_chained_imputer_imputation_order(imputation_order):
rng = np.random.RandomState(0)
n = 100
d = 10
X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
X[:, 0] = 1 # this column should not be discarded by ChainedImputer
imputer = ChainedImputer(missing_values=0,
n_imputations=1,
n_burn_in=1,
n_nearest_features=5,
min_value=0,
max_value=1,
verbose=False,
imputation_order=imputation_order,
random_state=rng)
imputer.fit_transform(X)
ordered_idx = [i.feat_idx for i in imputer.imputation_sequence_]
if imputation_order == 'roman':
assert np.all(ordered_idx[:d-1] == np.arange(1, d))
elif imputation_order == 'arabic':
assert np.all(ordered_idx[:d-1] == np.arange(d-1, 0, -1))
elif imputation_order == 'random':
ordered_idx_round_1 = ordered_idx[:d-1]
ordered_idx_round_2 = ordered_idx[d-1:]
assert ordered_idx_round_1 != ordered_idx_round_2
elif 'ending' in imputation_order:
assert len(ordered_idx) == 2 * (d - 1)
def test_chained_imputer_imputation_order(imputation_order):
rng = np.random.RandomState(0)
n = 100
d = 10
X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
X[:, 0] = 1 # this column should not be discarded by ChainedImputer
imputer = ChainedImputer(missing_values=0,
n_imputations=1,
n_burn_in=1,
n_nearest_features=5,
min_value=0,
max_value=1,
verbose=False,
imputation_order=imputation_order,
random_state=rng)
imputer.fit_transform(X)
ordered_idx = [i.feat_idx for i in imputer.imputation_sequence_]
if imputation_order == 'roman':
assert np.all(ordered_idx[:d - 1] == np.arange(1, d))
elif imputation_order == 'arabic':
assert np.all(ordered_idx[:d - 1] == np.arange(d - 1, 0, -1))
elif imputation_order == 'random':
ordered_idx_round_1 = ordered_idx[:d - 1]
ordered_idx_round_2 = ordered_idx[d - 1:]
assert ordered_idx_round_1 != ordered_idx_round_2
elif 'ending' in imputation_order:
assert len(ordered_idx) == 2 * (d - 1)
def get_results_multiple_imputation_approach2(X_train, X_test, y_train,
y_test):
m = 5
multiple_predictions = []
for i in range(m):
# Fit the imputer for every i in m
# Be aware that you fit the imputer on the train data
# And apply to the test data
imputer = ChainedImputer(n_burn_in=99, n_imputations=1, random_state=i)
X_train_imputed = imputer.fit_transform(X_train)
X_test_imputed = imputer.transform(X_test)
# Perform the steps you wish to take before fitting the estimator
# Such as standardization
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train_imputed)
X_test_scaled = scaler.transform(X_test_imputed)
# Finally fit the estimator and calculate the predictions for every i
# in m. Save the predictions.
estimator = LinearRegression()
estimator.fit(X_train_scaled, y_train)
y_predict = estimator.predict(X_test_scaled)
multiple_predictions.append(y_predict)
# Average the predictions over the m loops
# Then calculate the error metric.
predictions_average = np.mean(multiple_predictions, axis=0)
mse_approach2 = mse(y_test, predictions_average)
return mse_approach2
def test_chained_imputer_no_missing():
rng = np.random.RandomState(0)
X = rng.rand(100, 100)
X[:, 0] = np.nan
m1 = ChainedImputer(n_imputations=10, random_state=rng)
m2 = ChainedImputer(n_imputations=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_chained_imputer_no_missing():
rng = np.random.RandomState(0)
X = rng.rand(100, 100)
X[:, 0] = np.nan
m1 = ChainedImputer(n_imputations=10, random_state=rng)
m2 = ChainedImputer(n_imputations=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_chained_imputer_predictors(predictor):
rng = np.random.RandomState(0)
n = 100
d = 10
X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
imputer = ChainedImputer(missing_values=0,
n_imputations=1,
n_burn_in=1,
predictor=predictor,
random_state=rng)
imputer.fit_transform(X)
# check that types are correct for predictors
hashes = []
for triplet in imputer.imputation_sequence_:
assert triplet.predictor
hashes.append(id(triplet.predictor))
# check that each predictor is unique
assert len(set(hashes)) == len(hashes)
def test_chained_imputer_predictors(predictor):
rng = np.random.RandomState(0)
n = 100
d = 10
X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
imputer = ChainedImputer(missing_values=0,
n_imputations=1,
n_burn_in=1,
predictor=predictor,
random_state=rng)
imputer.fit_transform(X)
# check that types are correct for predictors
hashes = []
for triplet in imputer.imputation_sequence_:
assert triplet.predictor
hashes.append(id(triplet.predictor))
# check that each predictor is unique
assert len(set(hashes)) == len(hashes)
def test_imputation_shape():
# Verify the shapes of the imputed matrix for different strategies.
X = np.random.randn(10, 2)
X[::2] = np.nan
for strategy in ['mean', 'median', 'most_frequent', "constant"]:
imputer = SimpleImputer(strategy=strategy)
X_imputed = imputer.fit_transform(sparse.csr_matrix(X))
assert X_imputed.shape == (10, 2)
X_imputed = imputer.fit_transform(X)
assert X_imputed.shape == (10, 2)
chained_imputer = ChainedImputer(initial_strategy=strategy)
X_imputed = chained_imputer.fit_transform(X)
assert X_imputed.shape == (10, 2)
def test_imputation_shape():
# Verify the shapes of the imputed matrix for different strategies.
X = np.random.randn(10, 2)
X[::2] = np.nan
for strategy in ['mean', 'median', 'most_frequent', "constant"]:
imputer = SimpleImputer(strategy=strategy)
X_imputed = imputer.fit_transform(sparse.csr_matrix(X))
assert X_imputed.shape == (10, 2)
X_imputed = imputer.fit_transform(X)
assert X_imputed.shape == (10, 2)
chained_imputer = ChainedImputer(initial_strategy=strategy)
X_imputed = chained_imputer.fit_transform(X)
assert X_imputed.shape == (10, 2)
def test_chained_imputer_rank_one():
rng = np.random.RandomState(0)
d = 100
A = rng.rand(d, 1)
B = rng.rand(1, d)
X = np.dot(A, B)
nan_mask = rng.rand(d, d) < 0.5
X_missing = X.copy()
X_missing[nan_mask] = np.nan
imputer = ChainedImputer(n_imputations=5,
n_burn_in=5,
verbose=True,
random_state=rng)
X_filled = imputer.fit_transform(X_missing)
assert_allclose(X_filled, X, atol=0.001)
def test_chained_imputer_rank_one():
rng = np.random.RandomState(0)
d = 100
A = rng.rand(d, 1)
B = rng.rand(1, d)
X = np.dot(A, B)
nan_mask = rng.rand(d, d) < 0.5
X_missing = X.copy()
X_missing[nan_mask] = np.nan
imputer = ChainedImputer(n_imputations=5,
n_burn_in=5,
verbose=True,
random_state=rng)
X_filled = imputer.fit_transform(X_missing)
assert_allclose(X_filled, X, atol=0.001)
def test_chained_imputer_clip():
rng = np.random.RandomState(0)
n = 100
d = 10
X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
imputer = ChainedImputer(missing_values=0,
n_imputations=1,
n_burn_in=1,
min_value=0.1,
max_value=0.2,
random_state=rng)
Xt = imputer.fit_transform(X)
assert_allclose(np.min(Xt[X == 0]), 0.1)
assert_allclose(np.max(Xt[X == 0]), 0.2)
assert_allclose(Xt[X != 0], X[X != 0])
def test_chained_imputer_clip():
rng = np.random.RandomState(0)
n = 100
d = 10
X = sparse_random_matrix(n, d, density=0.10,
random_state=rng).toarray()
imputer = ChainedImputer(missing_values=0,
n_imputations=1,
n_burn_in=1,
min_value=0.1,
max_value=0.2,
random_state=rng)
Xt = imputer.fit_transform(X)
assert_allclose(np.min(Xt[X == 0]), 0.1)
assert_allclose(np.max(Xt[X == 0]), 0.2)
assert_allclose(Xt[X != 0], X[X != 0])
def get_results_single_imputation(X_train, X_test, y_train, y_test):
# Apply imputation
imputer = ChainedImputer(n_burn_in=99, n_imputations=1, random_state=0)
X_train_imputed = imputer.fit_transform(X_train)
X_test_imputed = imputer.transform(X_test)
# Standardize data
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train_imputed)
X_test_scaled = scaler.transform(X_test_imputed)
# Perform estimation and prediction
estimator = LinearRegression()
estimator.fit(X_train_scaled, y_train)
y_predict = estimator.predict(X_test_scaled)
mse_single = mse(y_test, y_predict)
return mse_single
