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DOC Improve documentation of default values for imputers #15964

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Dec 26, 2019
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DOC Improve documentation of default values for imputers #15964

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16 changes: 8 additions & 8 deletions sklearn/impute/_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -126,7 +126,7 @@ class SimpleImputer(_BaseImputer):
The placeholder for the missing values. All occurrences of
`missing_values` will be imputed.

strategy : string, optional (default="mean")
strategy : string, default='mean'
The imputation strategy.

- If "mean", then replace missing values using the mean along
Expand All @@ -141,16 +141,16 @@ class SimpleImputer(_BaseImputer):
.. versionadded:: 0.20
strategy="constant" for fixed value imputation.

fill_value : string or numerical value, optional (default=None)
fill_value : string or numerical value, default=None
When strategy == "constant", fill_value is used to replace all
occurrences of missing_values.
If left to the default, fill_value will be 0 when imputing numerical
data and "missing_value" for strings or object data types.

verbose : integer, optional (default=0)
verbose : integer, default=0
Controls the verbosity of the imputer.

copy : boolean, optional (default=True)
copy : boolean, default=True
If True, a copy of X will be created. If False, imputation will
be done in-place whenever possible. Note that, in the following cases,
a new copy will always be made, even if `copy=False`:
Expand All @@ -159,7 +159,7 @@ class SimpleImputer(_BaseImputer):
- If X is encoded as a CSR matrix;
- If add_indicator=True.

add_indicator : boolean, optional (default=False)
add_indicator : boolean, default=False
If True, a :class:`MissingIndicator` transform will stack onto output
of the imputer's transform. This allows a predictive estimator
to account for missingness despite imputation. If a feature has no
Expand Down Expand Up @@ -470,23 +470,23 @@ class MissingIndicator(TransformerMixin, BaseEstimator):
`missing_values` will be indicated (True in the output array), the
other values will be marked as False.

features : str, optional
features : str, default=None
Whether the imputer mask should represent all or a subset of
features.

- If "missing-only" (default), the imputer mask will only represent
features containing missing values during fit time.
- If "all", the imputer mask will represent all features.

sparse : boolean or "auto", optional
sparse : boolean or "auto", default=None
Whether the imputer mask format should be sparse or dense.

- If "auto" (default), the imputer mask will be of same type as
input.
- If True, the imputer mask will be a sparse matrix.
- If False, the imputer mask will be a numpy array.

error_on_new : boolean, optional
error_on_new : boolean, default=None
If True (default), transform will raise an error when there are
features with missing values in transform that have no missing values
in fit. This is applicable only when ``features="missing-only"``.
Expand Down
26 changes: 13 additions & 13 deletions sklearn/impute/_iterative.py
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Expand Up @@ -52,7 +52,7 @@ class IterativeImputer(_BaseImputer):
If ``sample_posterior`` is True, the estimator must support
``return_std`` in its ``predict`` method.

missing_values : int, np.nan, optional (default=np.nan)
missing_values : int, np.nan, default=np.nan
The placeholder for the missing values. All occurrences of
``missing_values`` will be imputed.

Expand All @@ -62,18 +62,18 @@ class IterativeImputer(_BaseImputer):
``return_std`` in its ``predict`` method if set to ``True``. Set to
``True`` if using ``IterativeImputer`` for multiple imputations.

max_iter : int, optional (default=10)
max_iter : int, default=10
Maximum number of imputation rounds to perform before returning the
imputations computed during the final round. A round is a single
imputation of each feature with missing values. The stopping criterion
is met once `abs(max(X_t - X_{t-1}))/abs(max(X[known_vals]))` < tol,
where `X_t` is `X` at iteration `t. Note that early stopping is only
applied if ``sample_posterior=False``.

tol : float, optional (default=1e-3)
tol : float, default=1e-3
Tolerance of the stopping condition.

n_nearest_features : int, optional (default=None)
n_nearest_features : int, default=None
Number of other features to use to estimate the missing values of
each feature column. Nearness between features is measured using
the absolute correlation coefficient between each feature pair (after
Expand All @@ -83,12 +83,12 @@ class IterativeImputer(_BaseImputer):
imputed target feature. Can provide significant speed-up when the
number of features is huge. If ``None``, all features will be used.

initial_strategy : str, optional (default="mean")
initial_strategy : str, default='mean'
Which strategy to use to initialize the missing values. Same as the
``strategy`` parameter in :class:`sklearn.impute.SimpleImputer`
Valid values: {"mean", "median", "most_frequent", or "constant"}.

imputation_order : str, optional (default="ascending")
imputation_order : str, default='ascending'
The order in which the features will be imputed. Possible values:

"ascending"
Expand All @@ -102,34 +102,34 @@ class IterativeImputer(_BaseImputer):
"random"
A random order for each round.

skip_complete : boolean, optional (default=False)
skip_complete : boolean, default=False
If ``True`` then features with missing values during ``transform``
which did not have any missing values during ``fit`` will be imputed
with the initial imputation method only. Set to ``True`` if you have
many features with no missing values at both ``fit`` and ``transform``
time to save compute.

min_value : float, optional (default=None)
min_value : float, default=None
Minimum possible imputed value. Default of ``None`` will set minimum
to negative infinity.

max_value : float, optional (default=None)
max_value : float, default=None
Maximum possible imputed value. Default of ``None`` will set maximum
to positive infinity.

verbose : int, optional (default=0)
verbose : int, default=0
Verbosity flag, controls the debug messages that are issued
as functions are evaluated. The higher, the more verbose. Can be 0, 1,
or 2.

random_state : int, RandomState instance or None, optional (default=None)
random_state : int, RandomState instance or None, default=None
The seed of the pseudo random number generator to use. Randomizes
selection of estimator features if n_nearest_features is not None, the
``imputation_order`` if ``random``, and the sampling from posterior if
``sample_posterior`` is True. Use an integer for determinism.
See :term:`the Glossary <random_state>`.

add_indicator : boolean, optional (default=False)
add_indicator : boolean, default=False
If True, a :class:`MissingIndicator` transform will stack onto output
of the imputer's transform. This allows a predictive estimator
to account for missingness despite imputation. If a feature has no
Expand Down Expand Up @@ -443,7 +443,7 @@ def _get_abs_corr_mat(self, X_filled, tolerance=1e-6):
X_filled : ndarray, shape (n_samples, n_features)
Input data with the most recent imputations.

tolerance : float, optional (default=1e-6)
tolerance : float, default=1e-6
``abs_corr_mat`` can have nans, which will be replaced
with ``tolerance``.

Expand Down