本文整理汇总了Python中sklearn.impute.IterativeImputer方法的典型用法代码示例。如果您正苦于以下问题:Python impute.IterativeImputer方法的具体用法?Python impute.IterativeImputer怎么用?Python impute.IterativeImputer使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.impute
的用法示例。
在下文中一共展示了impute.IterativeImputer方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_iterative_imputer_zero_iters
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
def test_iterative_imputer_zero_iters():
rng = np.random.RandomState(0)
n = 100
d = 10
X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
missing_flag = X == 0
X[missing_flag] = np.nan
imputer = IterativeImputer(max_iter=0)
X_imputed = imputer.fit_transform(X)
# with max_iter=0, only initial imputation is performed
assert_allclose(X_imputed, imputer.initial_imputer_.transform(X))
# repeat but force n_iter_ to 0
imputer = IterativeImputer(max_iter=5).fit(X)
# transformed should not be equal to initial imputation
assert not np.all(imputer.transform(X) ==
imputer.initial_imputer_.transform(X))
imputer.n_iter_ = 0
# now they should be equal as only initial imputation is done
assert_allclose(imputer.transform(X),
imputer.initial_imputer_.transform(X))
示例2: test_iterative_imputer_clip
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
def test_iterative_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 = IterativeImputer(missing_values=0,
max_iter=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])
示例3: test_iterative_imputer_clip_truncnorm
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
def test_iterative_imputer_clip_truncnorm():
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
imputer = IterativeImputer(missing_values=0,
max_iter=2,
n_nearest_features=5,
sample_posterior=True,
min_value=0.1,
max_value=0.2,
verbose=1,
imputation_order='random',
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])
示例4: test_iterative_imputer_missing_at_transform
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
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])
示例5: test_iterative_imputer_transform_recovery
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
def test_iterative_imputer_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)
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 = IterativeImputer(max_iter=10,
verbose=1,
random_state=rng).fit(X_train)
X_test_est = imputer.transform(X_test)
assert_allclose(X_test_filled, X_test_est, atol=0.1)
示例6: test_imputation_shape
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
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)
iterative_imputer = IterativeImputer(initial_strategy=strategy)
X_imputed = iterative_imputer.fit_transform(X)
assert X_imputed.shape == (10, 2)
示例7: test_iterative_imputer_verbose
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
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)
示例8: test_iterative_imputer_all_missing
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
def test_iterative_imputer_all_missing():
n = 100
d = 3
X = np.zeros((n, d))
imputer = IterativeImputer(missing_values=0, max_iter=1)
X_imputed = imputer.fit_transform(X)
assert_allclose(X_imputed, imputer.initial_imputer_.transform(X))
示例9: test_iterative_imputer_imputation_order
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
def test_iterative_imputer_imputation_order(imputation_order):
rng = np.random.RandomState(0)
n = 100
d = 10
max_iter = 2
X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
X[:, 0] = 1 # this column should not be discarded by IterativeImputer
imputer = IterativeImputer(missing_values=0,
max_iter=max_iter,
n_nearest_features=5,
sample_posterior=False,
min_value=0,
max_value=1,
verbose=1,
imputation_order=imputation_order,
random_state=rng)
imputer.fit_transform(X)
ordered_idx = [i.feat_idx for i in imputer.imputation_sequence_]
assert (len(ordered_idx) // imputer.n_iter_ ==
imputer.n_features_with_missing_)
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) == max_iter * (d - 1)
示例10: test_iterative_imputer_truncated_normal_posterior
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
def test_iterative_imputer_truncated_normal_posterior():
# test that the values that are imputed using `sample_posterior=True`
# with boundaries (`min_value` and `max_value` are not None) are drawn
# from a distribution that looks gaussian via the Kolmogorov Smirnov test.
# note that starting from the wrong random seed will make this test fail
# because random sampling doesn't occur at all when the imputation
# is outside of the (min_value, max_value) range
pytest.importorskip("scipy", minversion="0.17.0")
rng = np.random.RandomState(42)
X = rng.normal(size=(5, 5))
X[0][0] = np.nan
imputer = IterativeImputer(min_value=0,
max_value=0.5,
sample_posterior=True,
random_state=rng)
imputer.fit_transform(X)
# generate multiple imputations for the single missing value
imputations = np.array([imputer.transform(X)[0][0] for _ in range(100)])
assert all(imputations >= 0)
assert all(imputations <= 0.5)
mu, sigma = imputations.mean(), imputations.std()
ks_statistic, p_value = kstest((imputations - mu) / sigma, 'norm')
if sigma == 0:
sigma += 1e-12
ks_statistic, p_value = kstest((imputations - mu) / sigma, 'norm')
# we want to fail to reject null hypothesis
# null hypothesis: distributions are the same
assert ks_statistic < 0.2 or p_value > 0.1, \
"The posterior does appear to be normal"
示例11: test_iterative_imputer_rank_one
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
def test_iterative_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 = IterativeImputer(max_iter=5,
verbose=1,
random_state=rng)
X_filled = imputer.fit_transform(X_missing)
assert_allclose(X_filled, X, atol=0.01)
示例12: test_iterative_imputer_additive_matrix
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
def test_iterative_imputer_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 = IterativeImputer(max_iter=10,
verbose=1,
random_state=rng).fit(X_train)
X_test_est = imputer.transform(X_test)
assert_allclose(X_test_filled, X_test_est, rtol=1e-3, atol=0.01)
示例13: test_iterative_imputer_error_param
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
def test_iterative_imputer_error_param(max_iter, tol, error_type, warning):
X = np.zeros((100, 2))
imputer = IterativeImputer(max_iter=max_iter, tol=tol)
with pytest.raises(error_type, match=warning):
imputer.fit_transform(X)
示例14: test_iterative_imputer_early_stopping
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
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-3,
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
示例15: build_column
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import IterativeImputer [as 别名]
def build_column(self, data):
imputer_type = self.cfg["type"]
if imputer_type == "iterative":
try:
from sklearn.experimental import enable_iterative_imputer # noqa
from sklearn.impute import IterativeImputer
except ImportError:
raise Exception(
"You must have at least scikit-learn 0.21.0 installed in order to use the Iterative Imputer!"
)
imputer = IterativeImputer()
elif imputer_type == "knn":
try:
from sklearn.impute import KNNImputer
except ImportError:
raise Exception(
"You must have at least scikit-learn 0.22.0 installed in order to use the Iterative Imputer!"
)
n_neighbors = self.cfg.get("n_neighbors") or 2
imputer = KNNImputer(n_neighbors=n_neighbors)
elif imputer_type == "simple":
try:
from sklearn.impute import SimpleImputer
except ImportError:
raise Exception(
"You must have at least scikit-learn 0.20.0 installed in order to use the Iterative Imputer!"
)
imputer = SimpleImputer()
else:
raise NotImplementedError(
"'{}' sklearn imputer not implemented yet!".format(imputer_type)
)
output = imputer.fit_transform(data[[self.col]])
return pd.DataFrame(output, columns=[self.col], index=data.index)[self.col]