Why Gemfury?
Push, build, and install
RubyGems
npm packages
Python packages
Maven artifacts
PHP packages
Go Modules
Bower components
Debian packages
RPM packages
NuGet packages
from __future__ import division
import pytest
import numpy as np
from scipy import sparse
from scipy.stats import kstest
import io
from sklearn.utils._testing import assert_allclose
from sklearn.utils._testing import assert_allclose_dense_sparse
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
# make IterativeImputer available
from sklearn.experimental import enable_iterative_imputer # noqa
from sklearn.datasets import load_diabetes
from sklearn.impute import MissingIndicator
from sklearn.impute import SimpleImputer, IterativeImputer
from sklearn.dummy import DummyRegressor
from sklearn.linear_model import BayesianRidge, ARDRegression, RidgeCV
from sklearn.pipeline import Pipeline
from sklearn.pipeline import make_union
from sklearn.model_selection import GridSearchCV
from sklearn import tree
from sklearn.random_projection import _sparse_random_matrix
from sklearn.exceptions import ConvergenceWarning
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=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=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))
@pytest.mark.parametrize("strategy",
['mean', 'median', 'most_frequent', "constant"])
def test_imputation_shape(strategy):
# Verify the shapes of the imputed matrix for different strategies.
X = np.random.randn(10, 2)
X[::2] = np.nan
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)
@pytest.mark.parametrize("strategy", ["const", 101, None])
def test_imputation_error_invalid_strategy(strategy):
X = np.ones((3, 5))
X[0, 0] = np.nan
with pytest.raises(ValueError, match=str(strategy)):
imputer = SimpleImputer(strategy=strategy)
imputer.fit_transform(X)
@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent"])
def test_imputation_deletion_warning(strategy):
X = np.ones((3, 5))
X[:, 0] = np.nan
with pytest.warns(UserWarning, match="Deleting"):
imputer = SimpleImputer(strategy=strategy, verbose=True)
imputer.fit_transform(X)
@pytest.mark.parametrize("strategy", ["mean", "median",
"most_frequent", "constant"])
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 safe_median(arr, *args, **kwargs):
# np.median([]) raises a TypeError for numpy >= 1.10.1
length = arr.size if hasattr(arr, 'size') else len(arr)
return np.nan if length == 0 else np.median(arr, *args, **kwargs)
def safe_mean(arr, *args, **kwargs):
# np.mean([]) raises a RuntimeWarning for numpy >= 1.10.1
length = arr.size if hasattr(arr, 'size') else len(arr)
return np.nan if length == 0 else np.mean(arr, *args, **kwargs)
def test_imputation_mean_median():
# Test imputation using the mean and median strategies, when
# missing_values != 0.
rng = np.random.RandomState(0)
dim = 10
dec = 10
shape = (dim * dim, dim + dec)
zeros = np.zeros(shape[0])
values = np.arange(1, shape[0] + 1)
values[4::2] = - values[4::2]
tests = [("mean", np.nan, lambda z, v, p: safe_mean(np.hstack((z, v)))),
("median", np.nan,
lambda z, v, p: safe_median(np.hstack((z, v))))]
for strategy, test_missing_values, true_value_fun in tests:
X = np.empty(shape)
X_true = np.empty(shape)
true_statistics = np.empty(shape[1])
# Create a matrix X with columns
# - with only zeros,
# - with only missing values
# - with zeros, missing values and values
# And a matrix X_true containing all true values
for j in range(shape[1]):
nb_zeros = (j - dec + 1 > 0) * (j - dec + 1) * (j - dec + 1)
nb_missing_values = max(shape[0] + dec * dec
- (j + dec) * (j + dec), 0)
nb_values = shape[0] - nb_zeros - nb_missing_values
z = zeros[:nb_zeros]
p = np.repeat(test_missing_values, nb_missing_values)
v = values[rng.permutation(len(values))[:nb_values]]
true_statistics[j] = true_value_fun(z, v, p)
# Create the columns
X[:, j] = np.hstack((v, z, p))
if 0 == test_missing_values:
# XXX unreached code as of v0.22
X_true[:, j] = np.hstack((v,
np.repeat(
true_statistics[j],
nb_missing_values + nb_zeros)))
else:
X_true[:, j] = np.hstack((v,
z,
np.repeat(true_statistics[j],
nb_missing_values)))
# Shuffle them the same way
np.random.RandomState(j).shuffle(X[:, j])
np.random.RandomState(j).shuffle(X_true[:, j])
# Mean doesn't support columns containing NaNs, median does
if strategy == "median":
cols_to_keep = ~np.isnan(X_true).any(axis=0)
else:
cols_to_keep = ~np.isnan(X_true).all(axis=0)
X_true = X_true[:, cols_to_keep]
_check_statistics(X, X_true, strategy,
true_statistics, test_missing_values)
def test_imputation_median_special_cases():
# Test median imputation with sparse boundary cases
X = np.array([
[0, np.nan, np.nan], # odd: implicit zero
[5, np.nan, np.nan], # odd: explicit nonzero
[0, 0, np.nan], # even: average two zeros
[-5, 0, np.nan], # even: avg zero and neg
[0, 5, np.nan], # even: avg zero and pos
[4, 5, np.nan], # even: avg nonzeros
[-4, -5, np.nan], # even: avg negatives
[-1, 2, np.nan], # even: crossing neg and pos
]).transpose()
X_imputed_median = np.array([
[0, 0, 0],
[5, 5, 5],
[0, 0, 0],
[-5, 0, -2.5],
[0, 5, 2.5],
[4, 5, 4.5],
[-4, -5, -4.5],
[-1, 2, .5],
]).transpose()
statistics_median = [0, 5, 0, -2.5, 2.5, 4.5, -4.5, .5]
_check_statistics(X, X_imputed_median, "median",
statistics_median, np.nan)
@pytest.mark.parametrize("strategy", ["mean", "median"])
@pytest.mark.parametrize("dtype", [None, object, str])
def test_imputation_mean_median_error_invalid_type(strategy, dtype):
X = np.array([["a", "b", 3],
[4, "e", 6],
["g", "h", 9]], dtype=dtype)
msg = "non-numeric data:\ncould not convert string to float: '"
with pytest.raises(ValueError, match=msg):
imputer = SimpleImputer(strategy=strategy)
imputer.fit_transform(X)
@pytest.mark.parametrize("strategy", ["mean", "median"])
@pytest.mark.parametrize("type", ['list', 'dataframe'])
def test_imputation_mean_median_error_invalid_type_list_pandas(strategy, type):
X = [["a", "b", 3],
[4, "e", 6],
["g", "h", 9]]
if type == 'dataframe':
pd = pytest.importorskip("pandas")
X = pd.DataFrame(X)
msg = "non-numeric data:\ncould not convert string to float: '"
with pytest.raises(ValueError, match=msg):
imputer = SimpleImputer(strategy=strategy)
imputer.fit_transform(X)
@pytest.mark.parametrize("strategy", ["constant", "most_frequent"])
@pytest.mark.parametrize("dtype", [str, np.dtype('U'), np.dtype('S')])
def test_imputation_const_mostf_error_invalid_types(strategy, dtype):
# Test imputation on non-numeric data using "most_frequent" and "constant"
# strategy
X = np.array([
[np.nan, np.nan, "a", "f"],
[np.nan, "c", np.nan, "d"],
[np.nan, "b", "d", np.nan],
[np.nan, "c", "d", "h"],
], dtype=dtype)
err_msg = "SimpleImputer does not support data"
with pytest.raises(ValueError, match=err_msg):
imputer = SimpleImputer(strategy=strategy)
imputer.fit(X).transform(X)
def test_imputation_most_frequent():
# Test imputation using the most-frequent strategy.
X = np.array([
[-1, -1, 0, 5],
[-1, 2, -1, 3],
[-1, 1, 3, -1],
[-1, 2, 3, 7],
])
X_true = np.array([
[2, 0, 5],
[2, 3, 3],
[1, 3, 3],
[2, 3, 7],
])
# scipy.stats.mode, used in SimpleImputer, doesn't return the first most
# frequent as promised in the doc but the lowest most frequent. When this
# test will fail after an update of scipy, SimpleImputer will need to be
# updated to be consistent with the new (correct) behaviour
_check_statistics(X, X_true, "most_frequent", [np.nan, 2, 3, 3], -1)
@pytest.mark.parametrize("marker", [None, np.nan, "NAN", "", 0])
def test_imputation_most_frequent_objects(marker):
# Test imputation using the most-frequent strategy.
X = np.array([
[marker, marker, "a", "f"],
[marker, "c", marker, "d"],
[marker, "b", "d", marker],
[marker, "c", "d", "h"],
], dtype=object)
X_true = np.array([
["c", "a", "f"],
["c", "d", "d"],
["b", "d", "d"],
["c", "d", "h"],
], dtype=object)
imputer = SimpleImputer(missing_values=marker,
strategy="most_frequent")
X_trans = imputer.fit(X).transform(X)
assert_array_equal(X_trans, X_true)
@pytest.mark.parametrize("dtype", [object, "category"])
def test_imputation_most_frequent_pandas(dtype):
# Test imputation using the most frequent strategy on pandas df
pd = pytest.importorskip("pandas")
f = io.StringIO("Cat1,Cat2,Cat3,Cat4\n"
",i,x,\n"
"a,,y,\n"
"a,j,,\n"
"b,j,x,")
df = pd.read_csv(f, dtype=dtype)
X_true = np.array([
["a", "i", "x"],
["a", "j", "y"],
["a", "j", "x"],
["b", "j", "x"]
], dtype=object)
imputer = SimpleImputer(strategy="most_frequent")
X_trans = imputer.fit_transform(df)
assert_array_equal(X_trans, X_true)
@pytest.mark.parametrize("X_data, missing_value", [(1, 0), (1., np.nan)])
def test_imputation_constant_error_invalid_type(X_data, missing_value):
# Verify that exceptions are raised on invalid fill_value type
X = np.full((3, 5), X_data, dtype=float)
X[0, 0] = missing_value
with pytest.raises(ValueError, match="imputing numerical"):
imputer = SimpleImputer(missing_values=missing_value,
strategy="constant",
fill_value="x")
imputer.fit_transform(X)
def test_imputation_constant_integer():
# Test imputation using the constant strategy on integers
X = np.array([
[-1, 2, 3, -1],
[4, -1, 5, -1],
[6, 7, -1, -1],
[8, 9, 0, -1]
])
X_true = np.array([
[0, 2, 3, 0],
[4, 0, 5, 0],
[6, 7, 0, 0],
[8, 9, 0, 0]
])
imputer = SimpleImputer(missing_values=-1, strategy="constant",
fill_value=0)
X_trans = imputer.fit_transform(X)
assert_array_equal(X_trans, X_true)
@pytest.mark.parametrize("array_constructor", [sparse.csr_matrix, np.asarray])
def test_imputation_constant_float(array_constructor):
# Test imputation using the constant strategy on floats
X = np.array([
[np.nan, 1.1, 0, np.nan],
[1.2, np.nan, 1.3, np.nan],
[0, 0, np.nan, np.nan],
[1.4, 1.5, 0, np.nan]
])
X_true = np.array([
[-1, 1.1, 0, -1],
[1.2, -1, 1.3, -1],
[0, 0, -1, -1],
[1.4, 1.5, 0, -1]
])
X = array_constructor(X)
X_true = array_constructor(X_true)
imputer = SimpleImputer(strategy="constant", fill_value=-1)
X_trans = imputer.fit_transform(X)
assert_allclose_dense_sparse(X_trans, X_true)
@pytest.mark.parametrize("marker", [None, np.nan, "NAN", "", 0])
def test_imputation_constant_object(marker):
# Test imputation using the constant strategy on objects
X = np.array([
[marker, "a", "b", marker],
["c", marker, "d", marker],
["e", "f", marker, marker],
["g", "h", "i", marker]
], dtype=object)
X_true = np.array([
["missing", "a", "b", "missing"],
["c", "missing", "d", "missing"],
["e", "f", "missing", "missing"],
["g", "h", "i", "missing"]
], dtype=object)
imputer = SimpleImputer(missing_values=marker, strategy="constant",
fill_value="missing")
X_trans = imputer.fit_transform(X)
assert_array_equal(X_trans, X_true)
@pytest.mark.parametrize("dtype", [object, "category"])
def test_imputation_constant_pandas(dtype):
# Test imputation using the constant strategy on pandas df
pd = pytest.importorskip("pandas")
f = io.StringIO("Cat1,Cat2,Cat3,Cat4\n"
",i,x,\n"
"a,,y,\n"
"a,j,,\n"
"b,j,x,")
df = pd.read_csv(f, dtype=dtype)
X_true = np.array([
["missing_value", "i", "x", "missing_value"],
["a", "missing_value", "y", "missing_value"],
["a", "j", "missing_value", "missing_value"],
["b", "j", "x", "missing_value"]
], dtype=object)
imputer = SimpleImputer(strategy="constant")
X_trans = imputer.fit_transform(df)
assert_array_equal(X_trans, X_true)
@pytest.mark.parametrize("X", [[[1], [2]], [[1], [np.nan]]])
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_imputation_pipeline_grid_search():
# Test imputation within a pipeline + gridsearch.
X = _sparse_random_matrix(100, 100, density=0.10)
missing_values = X.data[0]
pipeline = Pipeline([('imputer',
SimpleImputer(missing_values=missing_values)),
('tree',
tree.DecisionTreeRegressor(random_state=0))])
parameters = {
'imputer__strategy': ["mean", "median", "most_frequent"]
}
Y = _sparse_random_matrix(100, 1, density=0.10).toarray()
gs = GridSearchCV(pipeline, parameters)
gs.fit(X, Y)
def test_imputation_copy():
# Test imputation with copy
X_orig = _sparse_random_matrix(5, 5, density=0.75, random_state=0)
# copy=True, dense => copy
X = X_orig.copy().toarray()
imputer = SimpleImputer(missing_values=0, strategy="mean", copy=True)
Xt = imputer.fit(X).transform(X)
Xt[0, 0] = -1
assert not np.all(X == Xt)
# copy=True, sparse csr => copy
X = X_orig.copy()
imputer = SimpleImputer(missing_values=X.data[0], strategy="mean",
copy=True)
Xt = imputer.fit(X).transform(X)
Xt.data[0] = -1
assert not np.all(X.data == Xt.data)
# copy=False, dense => no copy
X = X_orig.copy().toarray()
imputer = SimpleImputer(missing_values=0, strategy="mean", copy=False)
Xt = imputer.fit(X).transform(X)
Xt[0, 0] = -1
assert_array_almost_equal(X, Xt)
# copy=False, sparse csc => no copy
X = X_orig.copy().tocsc()
imputer = SimpleImputer(missing_values=X.data[0], strategy="mean",
copy=False)
Xt = imputer.fit(X).transform(X)
Xt.data[0] = -1
assert_array_almost_equal(X.data, Xt.data)
# copy=False, sparse csr => copy
X = X_orig.copy()
imputer = SimpleImputer(missing_values=X.data[0], strategy="mean",
copy=False)
Xt = imputer.fit(X).transform(X)
Xt.data[0] = -1
assert not np.all(X.data == Xt.data)
# Note: If X is sparse and if missing_values=0, then a (dense) copy of X is
# made, even if copy=False.
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))
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_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))
@pytest.mark.parametrize(
"imputation_order",
['random', 'roman', 'ascending', 'descending', 'arabic']
)
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,
skip_complete=True,
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)
@pytest.mark.parametrize(
"estimator",
[None, DummyRegressor(), BayesianRidge(), ARDRegression(), RidgeCV()]
)
def test_iterative_imputer_estimators(estimator):
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,
estimator=estimator,
random_state=rng)
imputer.fit_transform(X)
# check that types are correct for estimators
hashes = []
for triplet in imputer.imputation_sequence_:
expected_type = (type(estimator) if estimator is not None
else type(BayesianRidge()))
assert isinstance(triplet.estimator, expected_type)
hashes.append(id(triplet.estimator))
# check that each estimator is unique
assert len(set(hashes)) == len(hashes)
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])
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])
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
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"
@pytest.mark.parametrize(
"strategy",
["mean", "median", "most_frequent"]
)
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])
def test_iterative_imputer_transform_stochasticity():
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)
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_rank_one():
rng = np.random.RandomState(0)
d = 50
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=0.02)
@pytest.mark.parametrize(
"rank",
[3, 5]
)
def test_iterative_imputer_transform_recovery(rank):
rng = np.random.RandomState(0)
n = 70
d = 70
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=5,
imputation_order='descending',
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)
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)
@pytest.mark.parametrize("max_iter, tol, error_type, warning", [
(-1, 1e-3, ValueError, 'should be a positive integer'),
(1, -1e-3, ValueError, 'should be a non-negative float')
])
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)
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_catch_warning():
# check that we catch a RuntimeWarning due to a division by zero when a
# feature is constant in the dataset
X, y = load_diabetes(return_X_y=True)
n_samples, n_features = X.shape
# simulate that a feature only contain one category during fit
X[:, 3] = 1
# add some missing values
rng = np.random.RandomState(0)
missing_rate = 0.15
for feat in range(n_features):
sample_idx = rng.choice(
np.arange(n_samples), size=int(n_samples * missing_rate),
replace=False
)
X[sample_idx, feat] = np.nan
imputer = IterativeImputer(n_nearest_features=5, sample_posterior=True)
with pytest.warns(None) as record:
X_fill = imputer.fit_transform(X, y)
assert not record.list
assert not np.any(np.isnan(X_fill))
@pytest.mark.parametrize(
"min_value, max_value, correct_output",
[(0, 100, np.array([[0] * 3, [100] * 3])),
(None, None, np.array([[-np.inf] * 3, [np.inf] * 3])),
(-np.inf, np.inf, np.array([[-np.inf] * 3, [np.inf] * 3])),
([-5, 5, 10], [100, 200, 300], np.array([[-5, 5, 10], [100, 200, 300]])),
([-5, -np.inf, 10], [100, 200, np.inf],
np.array([[-5, -np.inf, 10], [100, 200, np.inf]]))],
ids=["scalars", "None-default", "inf", "lists", "lists-with-inf"])
def test_iterative_imputer_min_max_array_like(min_value,
max_value,
correct_output):
# check that passing scalar or array-like
# for min_value and max_value in IterativeImputer works
X = np.random.RandomState(0).randn(10, 3)
imputer = IterativeImputer(min_value=min_value, max_value=max_value)
imputer.fit(X)
assert (isinstance(imputer._min_value, np.ndarray) and
isinstance(imputer._max_value, np.ndarray))
assert ((imputer._min_value.shape[0] == X.shape[1]) and
(imputer._max_value.shape[0] == X.shape[1]))
assert_allclose(correct_output[0, :], imputer._min_value)
assert_allclose(correct_output[1, :], imputer._max_value)
@pytest.mark.parametrize(
"min_value, max_value, err_msg",
[(100, 0, "min_value >= max_value."),
(np.inf, -np.inf, "min_value >= max_value."),
([-5, 5], [100, 200, 0], "_value' should be of shape")])
def test_iterative_imputer_catch_min_max_error(min_value, max_value, err_msg):
# check that passing scalar or array-like
# for min_value and max_value in IterativeImputer works
X = np.random.random((10, 3))
imputer = IterativeImputer(min_value=min_value, max_value=max_value)
with pytest.raises(ValueError, match=err_msg):
imputer.fit(X)
@pytest.mark.parametrize(
"min_max_1, min_max_2",
[([None, None], [-np.inf, np.inf]),
([-10, 10], [[-10] * 4, [10] * 4])],
ids=["None-vs-inf", "Scalar-vs-vector"])
def test_iterative_imputer_min_max_array_like_imputation(min_max_1, min_max_2):
# Test that None/inf and scalar/vector give the same imputation
X_train = np.array([
[np.nan, 2, 2, 1],
[10, np.nan, np.nan, 7],
[3, 1, np.nan, 1],
[np.nan, 4, 2, np.nan]])
X_test = np.array([
[np.nan, 2, np.nan, 5],
[2, 4, np.nan, np.nan],
[np.nan, 1, 10, 1]])
imputer1 = IterativeImputer(min_value=min_max_1[0],
max_value=min_max_1[1],
random_state=0)
imputer2 = IterativeImputer(min_value=min_max_2[0],
max_value=min_max_2[1],
random_state=0)
X_test_imputed1 = imputer1.fit(X_train).transform(X_test)
X_test_imputed2 = imputer2.fit(X_train).transform(X_test)
assert_allclose(X_test_imputed1[:, 0], X_test_imputed2[:, 0])
@pytest.mark.parametrize(
"skip_complete", [True, False]
)
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)
@pytest.mark.parametrize(
"X_fit, X_trans, params, msg_err",
[(np.array([[-1, 1], [1, 2]]), np.array([[-1, 1], [1, -1]]),
{'features': 'missing-only', 'sparse': 'auto'},
'have missing values in transform but have no missing values in fit'),
(np.array([[-1, 1], [1, 2]]), np.array([[-1, 1], [1, 2]]),
{'features': 'random', 'sparse': 'auto'},
"'features' has to be either 'missing-only' or 'all'"),
(np.array([[-1, 1], [1, 2]]), np.array([[-1, 1], [1, 2]]),
{'features': 'all', 'sparse': 'random'},
"'sparse' has to be a boolean or 'auto'"),
(np.array([['a', 'b'], ['c', 'a']], dtype=str),
np.array([['a', 'b'], ['c', 'a']], dtype=str),
{}, "MissingIndicator does not support data with dtype")]
)
def test_missing_indicator_error(X_fit, X_trans, params, msg_err):
indicator = MissingIndicator(missing_values=-1)
indicator.set_params(**params)
with pytest.raises(ValueError, match=msg_err):
indicator.fit(X_fit).transform(X_trans)
@pytest.mark.parametrize(
"missing_values, dtype, arr_type",
[(np.nan, np.float64, np.array),
(0, np.int32, np.array),
(-1, np.int32, np.array),
(np.nan, np.float64, sparse.csc_matrix),
(-1, np.int32, sparse.csc_matrix),
(np.nan, np.float64, sparse.csr_matrix),
(-1, np.int32, sparse.csr_matrix),
(np.nan, np.float64, sparse.coo_matrix),
(-1, np.int32, sparse.coo_matrix),
(np.nan, np.float64, sparse.lil_matrix),
(-1, np.int32, sparse.lil_matrix),
(np.nan, np.float64, sparse.bsr_matrix),
(-1, np.int32, sparse.bsr_matrix)
])
@pytest.mark.parametrize(
"param_features, n_features, features_indices",
[('missing-only', 3, np.array([0, 1, 2])),
('all', 3, np.array([0, 1, 2]))])
def test_missing_indicator_new(missing_values, arr_type, dtype, param_features,
n_features, features_indices):
X_fit = np.array([[missing_values, missing_values, 1],
[4, 2, missing_values]])
X_trans = np.array([[missing_values, missing_values, 1],
[4, 12, 10]])
X_fit_expected = np.array([[1, 1, 0], [0, 0, 1]])
X_trans_expected = np.array([[1, 1, 0], [0, 0, 0]])
# convert the input to the right array format and right dtype
X_fit = arr_type(X_fit).astype(dtype)
X_trans = arr_type(X_trans).astype(dtype)
X_fit_expected = X_fit_expected.astype(dtype)
X_trans_expected = X_trans_expected.astype(dtype)
indicator = MissingIndicator(missing_values=missing_values,
features=param_features,
sparse=False)
X_fit_mask = indicator.fit_transform(X_fit)
X_trans_mask = indicator.transform(X_trans)
assert X_fit_mask.shape[1] == n_features
assert X_trans_mask.shape[1] == n_features
assert_array_equal(indicator.features_, features_indices)
assert_allclose(X_fit_mask, X_fit_expected[:, features_indices])
assert_allclose(X_trans_mask, X_trans_expected[:, features_indices])
assert X_fit_mask.dtype == bool
assert X_trans_mask.dtype == bool
assert isinstance(X_fit_mask, np.ndarray)
assert isinstance(X_trans_mask, np.ndarray)
indicator.set_params(sparse=True)
X_fit_mask_sparse = indicator.fit_transform(X_fit)
X_trans_mask_sparse = indicator.transform(X_trans)
assert X_fit_mask_sparse.dtype == bool
assert X_trans_mask_sparse.dtype == bool
assert X_fit_mask_sparse.format == 'csc'
assert X_trans_mask_sparse.format == 'csc'
assert_allclose(X_fit_mask_sparse.toarray(), X_fit_mask)
assert_allclose(X_trans_mask_sparse.toarray(), X_trans_mask)
@pytest.mark.parametrize(
"arr_type",
[sparse.csc_matrix, sparse.csr_matrix, sparse.coo_matrix,
sparse.lil_matrix, sparse.bsr_matrix])
def test_missing_indicator_raise_on_sparse_with_missing_0(arr_type):
# test for sparse input and missing_value == 0
missing_values = 0
X_fit = np.array([[missing_values, missing_values, 1],
[4, missing_values, 2]])
X_trans = np.array([[missing_values, missing_values, 1],
[4, 12, 10]])
# convert the input to the right array format
X_fit_sparse = arr_type(X_fit)
X_trans_sparse = arr_type(X_trans)
indicator = MissingIndicator(missing_values=missing_values)
with pytest.raises(ValueError, match="Sparse input with missing_values=0"):
indicator.fit_transform(X_fit_sparse)
indicator.fit_transform(X_fit)
with pytest.raises(ValueError, match="Sparse input with missing_values=0"):
indicator.transform(X_trans_sparse)
@pytest.mark.parametrize("param_sparse", [True, False, 'auto'])
@pytest.mark.parametrize("missing_values, arr_type",
[(np.nan, np.array),
(0, np.array),
(np.nan, sparse.csc_matrix),
(np.nan, sparse.csr_matrix),
(np.nan, sparse.coo_matrix),
(np.nan, sparse.lil_matrix)
])
def test_missing_indicator_sparse_param(arr_type, missing_values,
param_sparse):
# check the format of the output with different sparse parameter
X_fit = np.array([[missing_values, missing_values, 1],
[4, missing_values, 2]])
X_trans = np.array([[missing_values, missing_values, 1],
[4, 12, 10]])
X_fit = arr_type(X_fit).astype(np.float64)
X_trans = arr_type(X_trans).astype(np.float64)
indicator = MissingIndicator(missing_values=missing_values,
sparse=param_sparse)
X_fit_mask = indicator.fit_transform(X_fit)
X_trans_mask = indicator.transform(X_trans)
if param_sparse is True:
assert X_fit_mask.format == 'csc'
assert X_trans_mask.format == 'csc'
elif param_sparse == 'auto' and missing_values == 0:
assert isinstance(X_fit_mask, np.ndarray)
assert isinstance(X_trans_mask, np.ndarray)
elif param_sparse is False:
assert isinstance(X_fit_mask, np.ndarray)
assert isinstance(X_trans_mask, np.ndarray)
else:
if sparse.issparse(X_fit):
assert X_fit_mask.format == 'csc'
assert X_trans_mask.format == 'csc'
else:
assert isinstance(X_fit_mask, np.ndarray)
assert isinstance(X_trans_mask, np.ndarray)
def test_missing_indicator_string():
X = np.array([['a', 'b', 'c'], ['b', 'c', 'a']], dtype=object)
indicator = MissingIndicator(missing_values='a', features='all')
X_trans = indicator.fit_transform(X)
assert_array_equal(X_trans, np.array([[True, False, False],
[False, False, True]]))
@pytest.mark.parametrize(
"X, missing_values, X_trans_exp",
[(np.array([['a', 'b'], ['b', 'a']], dtype=object), 'a',
np.array([['b', 'b', True, False], ['b', 'b', False, True]],
dtype=object)),
(np.array([[np.nan, 1.], [1., np.nan]]), np.nan,
np.array([[1., 1., True, False], [1., 1., False, True]])),
(np.array([[np.nan, 'b'], ['b', np.nan]], dtype=object), np.nan,
np.array([['b', 'b', True, False], ['b', 'b', False, True]],
dtype=object)),
(np.array([[None, 'b'], ['b', None]], dtype=object), None,
np.array([['b', 'b', True, False], ['b', 'b', False, True]],
dtype=object))]
)
def test_missing_indicator_with_imputer(X, missing_values, X_trans_exp):
trans = make_union(
SimpleImputer(missing_values=missing_values, strategy='most_frequent'),
MissingIndicator(missing_values=missing_values)
)
X_trans = trans.fit_transform(X)
assert_array_equal(X_trans, X_trans_exp)
@pytest.mark.parametrize("imputer_constructor",
[SimpleImputer, IterativeImputer])
@pytest.mark.parametrize(
"imputer_missing_values, missing_value, err_msg",
[("NaN", np.nan, "Input contains NaN"),
("-1", -1, "types are expected to be both numerical.")])
def test_inconsistent_dtype_X_missing_values(imputer_constructor,
imputer_missing_values,
missing_value,
err_msg):
# regression test for issue #11390. Comparison between incoherent dtype
# for X and missing_values was not raising a proper error.
rng = np.random.RandomState(42)
X = rng.randn(10, 10)
X[0, 0] = missing_value
imputer = imputer_constructor(missing_values=imputer_missing_values)
with pytest.raises(ValueError, match=err_msg):
imputer.fit_transform(X)
def test_missing_indicator_no_missing():
# check that all features are dropped if there are no missing values when
# features='missing-only' (#13491)
X = np.array([[1, 1],
[1, 1]])
mi = MissingIndicator(features='missing-only', missing_values=-1)
Xt = mi.fit_transform(X)
assert Xt.shape[1] == 0
def test_missing_indicator_sparse_no_explicit_zeros():
# Check that non missing values don't become explicit zeros in the mask
# generated by missing indicator when X is sparse. (#13491)
X = sparse.csr_matrix([[0, 1, 2],
[1, 2, 0],
[2, 0, 1]])
mi = MissingIndicator(features='all', missing_values=1)
Xt = mi.fit_transform(X)
assert Xt.getnnz() == Xt.sum()
@pytest.mark.parametrize("imputer_constructor",
[SimpleImputer, IterativeImputer])
def test_imputer_without_indicator(imputer_constructor):
X = np.array([[1, 1],
[1, 1]])
imputer = imputer_constructor()
imputer.fit(X)
assert imputer.indicator_ is None
@pytest.mark.parametrize(
"arr_type",
[
sparse.csc_matrix, sparse.csr_matrix, sparse.coo_matrix,
sparse.lil_matrix, sparse.bsr_matrix
]
)
def test_simple_imputation_add_indicator_sparse_matrix(arr_type):
X_sparse = arr_type([
[np.nan, 1, 5],
[2, np.nan, 1],
[6, 3, np.nan],
[1, 2, 9]
])
X_true = np.array([
[3., 1., 5., 1., 0., 0.],
[2., 2., 1., 0., 1., 0.],
[6., 3., 5., 0., 0., 1.],
[1., 2., 9., 0., 0., 0.],
])
imputer = SimpleImputer(missing_values=np.nan, add_indicator=True)
X_trans = imputer.fit_transform(X_sparse)
assert sparse.issparse(X_trans)
assert X_trans.shape == X_true.shape
assert_allclose(X_trans.toarray(), X_true)
@pytest.mark.parametrize(
"order, idx_order",
[
("ascending", [3, 4, 2, 0, 1]),
("descending", [1, 0, 2, 4, 3])
]
)
def test_imputation_order(order, idx_order):
# regression test for #15393
rng = np.random.RandomState(42)
X = rng.rand(100, 5)
X[:50, 1] = np.nan
X[:30, 0] = np.nan
X[:20, 2] = np.nan
X[:10, 4] = np.nan
with pytest.warns(ConvergenceWarning):
trs = IterativeImputer(max_iter=1,
imputation_order=order,
random_state=0).fit(X)
idx = [x.feat_idx for x in trs.imputation_sequence_]
assert idx == idx_order