1367 lines
47 KiB
Python
1367 lines
47 KiB
Python
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from __future__ import division
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import pytest
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import numpy as np
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from scipy import sparse
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from scipy.stats import kstest
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import io
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from sklearn.utils._testing import assert_allclose
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from sklearn.utils._testing import assert_allclose_dense_sparse
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_array_almost_equal
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# make IterativeImputer available
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from sklearn.experimental import enable_iterative_imputer # noqa
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from sklearn.datasets import load_diabetes
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from sklearn.impute import MissingIndicator
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from sklearn.impute import SimpleImputer, IterativeImputer
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from sklearn.dummy import DummyRegressor
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from sklearn.linear_model import BayesianRidge, ARDRegression, RidgeCV
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from sklearn.pipeline import Pipeline
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from sklearn.pipeline import make_union
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from sklearn.model_selection import GridSearchCV
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from sklearn import tree
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from sklearn.random_projection import _sparse_random_matrix
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from sklearn.exceptions import ConvergenceWarning
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def _check_statistics(X, X_true,
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strategy, statistics, missing_values):
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"""Utility function for testing imputation for a given strategy.
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Test with dense and sparse arrays
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Check that:
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- the statistics (mean, median, mode) are correct
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- the missing values are imputed correctly"""
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err_msg = "Parameters: strategy = %s, missing_values = %s, " \
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"sparse = {0}" % (strategy, missing_values)
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assert_ae = assert_array_equal
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if X.dtype.kind == 'f' or X_true.dtype.kind == 'f':
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assert_ae = assert_array_almost_equal
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# Normal matrix
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imputer = SimpleImputer(missing_values=missing_values, strategy=strategy)
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X_trans = imputer.fit(X).transform(X.copy())
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assert_ae(imputer.statistics_, statistics,
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err_msg=err_msg.format(False))
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assert_ae(X_trans, X_true, err_msg=err_msg.format(False))
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# Sparse matrix
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imputer = SimpleImputer(missing_values=missing_values, strategy=strategy)
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imputer.fit(sparse.csc_matrix(X))
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X_trans = imputer.transform(sparse.csc_matrix(X.copy()))
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if sparse.issparse(X_trans):
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X_trans = X_trans.toarray()
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assert_ae(imputer.statistics_, statistics,
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err_msg=err_msg.format(True))
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assert_ae(X_trans, X_true, err_msg=err_msg.format(True))
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@pytest.mark.parametrize("strategy",
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['mean', 'median', 'most_frequent', "constant"])
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def test_imputation_shape(strategy):
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# Verify the shapes of the imputed matrix for different strategies.
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X = np.random.randn(10, 2)
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X[::2] = np.nan
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imputer = SimpleImputer(strategy=strategy)
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X_imputed = imputer.fit_transform(sparse.csr_matrix(X))
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assert X_imputed.shape == (10, 2)
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X_imputed = imputer.fit_transform(X)
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assert X_imputed.shape == (10, 2)
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iterative_imputer = IterativeImputer(initial_strategy=strategy)
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X_imputed = iterative_imputer.fit_transform(X)
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assert X_imputed.shape == (10, 2)
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@pytest.mark.parametrize("strategy", ["const", 101, None])
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def test_imputation_error_invalid_strategy(strategy):
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X = np.ones((3, 5))
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X[0, 0] = np.nan
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with pytest.raises(ValueError, match=str(strategy)):
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imputer = SimpleImputer(strategy=strategy)
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imputer.fit_transform(X)
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@pytest.mark.parametrize("strategy", ["mean", "median", "most_frequent"])
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def test_imputation_deletion_warning(strategy):
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X = np.ones((3, 5))
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X[:, 0] = np.nan
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with pytest.warns(UserWarning, match="Deleting"):
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imputer = SimpleImputer(strategy=strategy, verbose=True)
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imputer.fit_transform(X)
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@pytest.mark.parametrize("strategy", ["mean", "median",
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"most_frequent", "constant"])
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def test_imputation_error_sparse_0(strategy):
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# check that error are raised when missing_values = 0 and input is sparse
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X = np.ones((3, 5))
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X[0] = 0
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X = sparse.csc_matrix(X)
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imputer = SimpleImputer(strategy=strategy, missing_values=0)
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with pytest.raises(ValueError, match="Provide a dense array"):
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imputer.fit(X)
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imputer.fit(X.toarray())
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with pytest.raises(ValueError, match="Provide a dense array"):
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imputer.transform(X)
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def safe_median(arr, *args, **kwargs):
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# np.median([]) raises a TypeError for numpy >= 1.10.1
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length = arr.size if hasattr(arr, 'size') else len(arr)
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return np.nan if length == 0 else np.median(arr, *args, **kwargs)
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def safe_mean(arr, *args, **kwargs):
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# np.mean([]) raises a RuntimeWarning for numpy >= 1.10.1
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length = arr.size if hasattr(arr, 'size') else len(arr)
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return np.nan if length == 0 else np.mean(arr, *args, **kwargs)
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def test_imputation_mean_median():
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# Test imputation using the mean and median strategies, when
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# missing_values != 0.
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rng = np.random.RandomState(0)
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dim = 10
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dec = 10
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shape = (dim * dim, dim + dec)
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zeros = np.zeros(shape[0])
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values = np.arange(1, shape[0] + 1)
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values[4::2] = - values[4::2]
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tests = [("mean", np.nan, lambda z, v, p: safe_mean(np.hstack((z, v)))),
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("median", np.nan,
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lambda z, v, p: safe_median(np.hstack((z, v))))]
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for strategy, test_missing_values, true_value_fun in tests:
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X = np.empty(shape)
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X_true = np.empty(shape)
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true_statistics = np.empty(shape[1])
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# Create a matrix X with columns
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# - with only zeros,
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# - with only missing values
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# - with zeros, missing values and values
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# And a matrix X_true containing all true values
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for j in range(shape[1]):
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nb_zeros = (j - dec + 1 > 0) * (j - dec + 1) * (j - dec + 1)
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nb_missing_values = max(shape[0] + dec * dec
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- (j + dec) * (j + dec), 0)
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nb_values = shape[0] - nb_zeros - nb_missing_values
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z = zeros[:nb_zeros]
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p = np.repeat(test_missing_values, nb_missing_values)
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v = values[rng.permutation(len(values))[:nb_values]]
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true_statistics[j] = true_value_fun(z, v, p)
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# Create the columns
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X[:, j] = np.hstack((v, z, p))
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if 0 == test_missing_values:
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# XXX unreached code as of v0.22
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X_true[:, j] = np.hstack((v,
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np.repeat(
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true_statistics[j],
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nb_missing_values + nb_zeros)))
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else:
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X_true[:, j] = np.hstack((v,
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z,
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np.repeat(true_statistics[j],
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nb_missing_values)))
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# Shuffle them the same way
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np.random.RandomState(j).shuffle(X[:, j])
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np.random.RandomState(j).shuffle(X_true[:, j])
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# Mean doesn't support columns containing NaNs, median does
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if strategy == "median":
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cols_to_keep = ~np.isnan(X_true).any(axis=0)
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else:
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cols_to_keep = ~np.isnan(X_true).all(axis=0)
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X_true = X_true[:, cols_to_keep]
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_check_statistics(X, X_true, strategy,
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true_statistics, test_missing_values)
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def test_imputation_median_special_cases():
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# Test median imputation with sparse boundary cases
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X = np.array([
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[0, np.nan, np.nan], # odd: implicit zero
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[5, np.nan, np.nan], # odd: explicit nonzero
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[0, 0, np.nan], # even: average two zeros
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[-5, 0, np.nan], # even: avg zero and neg
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[0, 5, np.nan], # even: avg zero and pos
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[4, 5, np.nan], # even: avg nonzeros
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[-4, -5, np.nan], # even: avg negatives
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[-1, 2, np.nan], # even: crossing neg and pos
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]).transpose()
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X_imputed_median = np.array([
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[0, 0, 0],
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[5, 5, 5],
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[0, 0, 0],
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[-5, 0, -2.5],
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[0, 5, 2.5],
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[4, 5, 4.5],
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[-4, -5, -4.5],
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[-1, 2, .5],
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]).transpose()
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statistics_median = [0, 5, 0, -2.5, 2.5, 4.5, -4.5, .5]
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_check_statistics(X, X_imputed_median, "median",
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statistics_median, np.nan)
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@pytest.mark.parametrize("strategy", ["mean", "median"])
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@pytest.mark.parametrize("dtype", [None, object, str])
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def test_imputation_mean_median_error_invalid_type(strategy, dtype):
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X = np.array([["a", "b", 3],
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[4, "e", 6],
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["g", "h", 9]], dtype=dtype)
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msg = "non-numeric data:\ncould not convert string to float: '"
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with pytest.raises(ValueError, match=msg):
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imputer = SimpleImputer(strategy=strategy)
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imputer.fit_transform(X)
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@pytest.mark.parametrize("strategy", ["mean", "median"])
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@pytest.mark.parametrize("type", ['list', 'dataframe'])
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def test_imputation_mean_median_error_invalid_type_list_pandas(strategy, type):
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X = [["a", "b", 3],
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[4, "e", 6],
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["g", "h", 9]]
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if type == 'dataframe':
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pd = pytest.importorskip("pandas")
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X = pd.DataFrame(X)
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msg = "non-numeric data:\ncould not convert string to float: '"
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with pytest.raises(ValueError, match=msg):
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imputer = SimpleImputer(strategy=strategy)
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imputer.fit_transform(X)
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@pytest.mark.parametrize("strategy", ["constant", "most_frequent"])
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@pytest.mark.parametrize("dtype", [str, np.dtype('U'), np.dtype('S')])
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def test_imputation_const_mostf_error_invalid_types(strategy, dtype):
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# Test imputation on non-numeric data using "most_frequent" and "constant"
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# strategy
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X = np.array([
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[np.nan, np.nan, "a", "f"],
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[np.nan, "c", np.nan, "d"],
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[np.nan, "b", "d", np.nan],
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[np.nan, "c", "d", "h"],
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], dtype=dtype)
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err_msg = "SimpleImputer does not support data"
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with pytest.raises(ValueError, match=err_msg):
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imputer = SimpleImputer(strategy=strategy)
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imputer.fit(X).transform(X)
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def test_imputation_most_frequent():
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# Test imputation using the most-frequent strategy.
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X = np.array([
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[-1, -1, 0, 5],
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[-1, 2, -1, 3],
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[-1, 1, 3, -1],
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[-1, 2, 3, 7],
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])
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X_true = np.array([
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[2, 0, 5],
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[2, 3, 3],
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[1, 3, 3],
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[2, 3, 7],
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])
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# scipy.stats.mode, used in SimpleImputer, doesn't return the first most
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# frequent as promised in the doc but the lowest most frequent. When this
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# test will fail after an update of scipy, SimpleImputer will need to be
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# updated to be consistent with the new (correct) behaviour
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_check_statistics(X, X_true, "most_frequent", [np.nan, 2, 3, 3], -1)
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@pytest.mark.parametrize("marker", [None, np.nan, "NAN", "", 0])
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def test_imputation_most_frequent_objects(marker):
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# Test imputation using the most-frequent strategy.
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X = np.array([
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[marker, marker, "a", "f"],
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[marker, "c", marker, "d"],
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[marker, "b", "d", marker],
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[marker, "c", "d", "h"],
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], dtype=object)
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X_true = np.array([
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["c", "a", "f"],
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["c", "d", "d"],
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["b", "d", "d"],
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["c", "d", "h"],
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], dtype=object)
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imputer = SimpleImputer(missing_values=marker,
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strategy="most_frequent")
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X_trans = imputer.fit(X).transform(X)
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assert_array_equal(X_trans, X_true)
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@pytest.mark.parametrize("dtype", [object, "category"])
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def test_imputation_most_frequent_pandas(dtype):
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# Test imputation using the most frequent strategy on pandas df
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pd = pytest.importorskip("pandas")
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f = io.StringIO("Cat1,Cat2,Cat3,Cat4\n"
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",i,x,\n"
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"a,,y,\n"
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"a,j,,\n"
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"b,j,x,")
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df = pd.read_csv(f, dtype=dtype)
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X_true = np.array([
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["a", "i", "x"],
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["a", "j", "y"],
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["a", "j", "x"],
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["b", "j", "x"]
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|
|
], 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)
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||
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X = rng.rand(100, 5)
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X[:50, 1] = np.nan
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X[:30, 0] = np.nan
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||
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X[:20, 2] = np.nan
|
||
|
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X[:10, 4] = np.nan
|
||
|
|
|
||
|
|
with pytest.warns(ConvergenceWarning):
|
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|
|
trs = IterativeImputer(max_iter=1,
|
||
|
|
imputation_order=order,
|
||
|
|
random_state=0).fit(X)
|
||
|
|
idx = [x.feat_idx for x in trs.imputation_sequence_]
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||
|
|
assert idx == idx_order
|