_base.py

# Authors: Nicolas Tresegnie <nicolas.tresegnie@gmail.com>
#          Sergey Feldman <sergeyfeldman@gmail.com>
# License: BSD 3 clause

import numbers
import warnings
from collections import Counter
from functools import partial
from typing import Callable

import numpy as np
import numpy.ma as ma
from scipy import sparse as sp

from ..base import BaseEstimator, TransformerMixin, _fit_context
from ..utils._mask import _get_mask
from ..utils._missing import is_pandas_na, is_scalar_nan
from ..utils._param_validation import MissingValues, StrOptions
from ..utils.fixes import _mode
from ..utils.sparsefuncs import _get_median
from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, check_is_fitted


def _check_inputs_dtype(X, missing_values):
    if is_pandas_na(missing_values):
        # Allow using `pd.NA` as missing values to impute numerical arrays.
        return
    if X.dtype.kind in ("f", "i", "u") and not isinstance(missing_values, numbers.Real):
        raise ValueError(
            "'X' and 'missing_values' types are expected to be"
            " both numerical. Got X.dtype={} and "
            " type(missing_values)={}.".format(X.dtype, type(missing_values))
        )


def _most_frequent(array, extra_value, n_repeat):
    """Compute the most frequent value in a 1d array extended with
    [extra_value] * n_repeat, where extra_value is assumed to be not part
    of the array."""
    # Compute the most frequent value in array only
    if array.size > 0:
        if array.dtype == object:
            # scipy.stats.mode is slow with object dtype array.
            # Python Counter is more efficient
            counter = Counter(array)
            most_frequent_count = counter.most_common(1)[0][1]
            # tie breaking similarly to scipy.stats.mode
            most_frequent_value = min(
                value
                for value, count in counter.items()
                if count == most_frequent_count
            )
        else:
            mode = _mode(array)
            most_frequent_value = mode[0][0]
            most_frequent_count = mode[1][0]
    else:
        most_frequent_value = 0
        most_frequent_count = 0

    # Compare to array + [extra_value] * n_repeat
    if most_frequent_count == 0 and n_repeat == 0:
        return np.nan
    elif most_frequent_count < n_repeat:
        return extra_value
    elif most_frequent_count > n_repeat:
        return most_frequent_value
    elif most_frequent_count == n_repeat:
        # tie breaking similarly to scipy.stats.mode
        return min(most_frequent_value, extra_value)


class _BaseImputer(TransformerMixin, BaseEstimator):
    """Base class for all imputers.

    It adds automatically support for `add_indicator`.
    """

    _parameter_constraints: dict = {
        "missing_values": [MissingValues()],
        "add_indicator": ["boolean"],
        "keep_empty_features": ["boolean"],
    }

    def __init__(
        self, *, missing_values=np.nan, add_indicator=False, keep_empty_features=False
    ):
        self.missing_values = missing_values
        self.add_indicator = add_indicator
        self.keep_empty_features = keep_empty_features

    def _fit_indicator(self, X):
        """Fit a MissingIndicator."""
        if self.add_indicator:
            self.indicator_ = MissingIndicator(
                missing_values=self.missing_values, error_on_new=False
            )
            self.indicator_._fit(X, precomputed=True)
        else:
            self.indicator_ = None

    def _transform_indicator(self, X):
        """Compute the indicator mask.'

        Note that X must be the original data as passed to the imputer before
        any imputation, since imputation may be done inplace in some cases.
        """
        if self.add_indicator:
            if not hasattr(self, "indicator_"):
                raise ValueError(
                    "Make sure to call _fit_indicator before _transform_indicator"
                )
            return self.indicator_.transform(X)

    def _concatenate_indicator(self, X_imputed, X_indicator):
        """Concatenate indicator mask with the imputed data."""
        if not self.add_indicator:
            return X_imputed

        if sp.issparse(X_imputed):
            # sp.hstack may result in different formats between sparse arrays and
            # matrices; specify the format to keep consistent behavior
            hstack = partial(sp.hstack, format=X_imputed.format)
        else:
            hstack = np.hstack

        if X_indicator is None:
            raise ValueError(
                "Data from the missing indicator are not provided. Call "
                "_fit_indicator and _transform_indicator in the imputer "
                "implementation."
            )

        return hstack((X_imputed, X_indicator))

    def _concatenate_indicator_feature_names_out(self, names, input_features):
        if not self.add_indicator:
            return names

        indicator_names = self.indicator_.get_feature_names_out(input_features)
        return np.concatenate([names, indicator_names])

    def _more_tags(self):
        return {"allow_nan": is_scalar_nan(self.missing_values)}


class SimpleImputer(_BaseImputer):
    """Univariate imputer for completing missing values with simple strategies.

    Replace missing values using a descriptive statistic (e.g. mean, median, or
    most frequent) along each column, or using a constant value.

    Read more in the :ref:`User Guide <impute>`.

    .. versionadded:: 0.20
       `SimpleImputer` replaces the previous `sklearn.preprocessing.Imputer`
       estimator which is now removed.

    Parameters
    ----------
    missing_values : int, float, str, np.nan, None or pandas.NA, default=np.nan
        The placeholder for the missing values. All occurrences of
        `missing_values` will be imputed. For pandas' dataframes with
        nullable integer dtypes with missing values, `missing_values`
        can be set to either `np.nan` or `pd.NA`.

    strategy : str or Callable, default='mean'
        The imputation strategy.

        - If "mean", then replace missing values using the mean along
          each column. Can only be used with numeric data.
        - If "median", then replace missing values using the median along
          each column. Can only be used with numeric data.
        - If "most_frequent", then replace missing using the most frequent
          value along each column. Can be used with strings or numeric data.
          If there is more than one such value, only the smallest is returned.
        - If "constant", then replace missing values with fill_value. Can be
          used with strings or numeric data.
        - If an instance of Callable, then replace missing values using the
          scalar statistic returned by running the callable over a dense 1d
          array containing non-missing values of each column.

        .. versionadded:: 0.20
           strategy="constant" for fixed value imputation.

        .. versionadded:: 1.5
           strategy=callable for custom value imputation.

    fill_value : str or numerical value, default=None
        When strategy == "constant", `fill_value` is used to replace all
        occurrences of missing_values. For string or object data types,
        `fill_value` must be a string.
        If `None`, `fill_value` will be 0 when imputing numerical
        data and "missing_value" for strings or object data types.

    copy : bool, 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`:

        - If `X` is not an array of floating values;
        - If `X` is encoded as a CSR matrix;
        - If `add_indicator=True`.

    add_indicator : bool, 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
        missing values at fit/train time, the feature won't appear on
        the missing indicator even if there are missing values at
        transform/test time.

    keep_empty_features : bool, default=False
        If True, features that consist exclusively of missing values when
        `fit` is called are returned in results when `transform` is called.
        The imputed value is always `0` except when `strategy="constant"`
        in which case `fill_value` will be used instead.

        .. versionadded:: 1.2

    Attributes
    ----------
    statistics_ : array of shape (n_features,)
        The imputation fill value for each feature.
        Computing statistics can result in `np.nan` values.
        During :meth:`transform`, features corresponding to `np.nan`
        statistics will be discarded.

    indicator_ : :class:`~sklearn.impute.MissingIndicator`
        Indicator used to add binary indicators for missing values.
        `None` if `add_indicator=False`.

    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X`
        has feature names that are all strings.

        .. versionadded:: 1.0

    See Also
    --------
    IterativeImputer : Multivariate imputer that estimates values to impute for
        each feature with missing values from all the others.
    KNNImputer : Multivariate imputer that estimates missing features using
        nearest samples.

    Notes
    -----
    Columns which only contained missing values at :meth:`fit` are discarded
    upon :meth:`transform` if strategy is not `"constant"`.

    In a prediction context, simple imputation usually performs poorly when
    associated with a weak learner. However, with a powerful learner, it can
    lead to as good or better performance than complex imputation such as
    :class:`~sklearn.impute.IterativeImputer` or :class:`~sklearn.impute.KNNImputer`.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.impute import SimpleImputer
    >>> imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')
    >>> imp_mean.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]])
    SimpleImputer()
    >>> X = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]]
    >>> print(imp_mean.transform(X))
    [[ 7.   2.   3. ]
     [ 4.   3.5  6. ]
     [10.   3.5  9. ]]

    For a more detailed example see
    :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py`.
    """

    _parameter_constraints: dict = {
        **_BaseImputer._parameter_constraints,
        "strategy": [
            StrOptions({"mean", "median", "most_frequent", "constant"}),
            callable,
        ],
        "fill_value": "no_validation",  # any object is valid
        "copy": ["boolean"],
    }

    def __init__(
        self,
        *,
        missing_values=np.nan,
        strategy="mean",
        fill_value=None,
        copy=True,
        add_indicator=False,
        keep_empty_features=False,
    ):
        super().__init__(
            missing_values=missing_values,
            add_indicator=add_indicator,
            keep_empty_features=keep_empty_features,
        )
        self.strategy = strategy
        self.fill_value = fill_value
        self.copy = copy

    def _validate_input(self, X, in_fit):
        if self.strategy in ("most_frequent", "constant"):
            # If input is a list of strings, dtype = object.
            # Otherwise ValueError is raised in SimpleImputer
            # with strategy='most_frequent' or 'constant'
            # because the list is converted to Unicode numpy array
            if isinstance(X, list) and any(
                isinstance(elem, str) for row in X for elem in row
            ):
                dtype = object
            else:
                dtype = None
        else:
            dtype = FLOAT_DTYPES

        if not in_fit and self._fit_dtype.kind == "O":
            # Use object dtype if fitted on object dtypes
            dtype = self._fit_dtype

        if is_pandas_na(self.missing_values) or is_scalar_nan(self.missing_values):
            force_all_finite = "allow-nan"
        else:
            force_all_finite = True

        try:
            X = self._validate_data(
                X,
                reset=in_fit,
                accept_sparse="csc",
                dtype=dtype,
                force_all_finite=force_all_finite,
                copy=self.copy,
            )
        except ValueError as ve:
            if "could not convert" in str(ve):
                new_ve = ValueError(
                    "Cannot use {} strategy with non-numeric data:\n{}".format(
                        self.strategy, ve
                    )
                )
                raise new_ve from None
            else:
                raise ve

        if in_fit:
            # Use the dtype seen in `fit` for non-`fit` conversion
            self._fit_dtype = X.dtype

        _check_inputs_dtype(X, self.missing_values)
        if X.dtype.kind not in ("i", "u", "f", "O"):
            raise ValueError(
                "SimpleImputer does not support data with dtype "
                "{0}. Please provide either a numeric array (with"
                " a floating point or integer dtype) or "
                "categorical data represented either as an array "
                "with integer dtype or an array of string values "
                "with an object dtype.".format(X.dtype)
            )

        if sp.issparse(X) and self.missing_values == 0:
            # missing_values = 0 not allowed with sparse data as it would
            # force densification
            raise ValueError(
                "Imputation not possible when missing_values "
                "== 0 and input is sparse. Provide a dense "
                "array instead."
            )

        if self.strategy == "constant":
            if in_fit and self.fill_value is not None:
                fill_value_dtype = type(self.fill_value)
                err_msg = (
                    f"fill_value={self.fill_value!r} (of type {fill_value_dtype!r}) "
                    f"cannot be cast to the input data that is {X.dtype!r}. Make sure "
                    "that both dtypes are of the same kind."
                )
            elif not in_fit:
                fill_value_dtype = self.statistics_.dtype
                err_msg = (
                    f"The dtype of the filling value (i.e. {fill_value_dtype!r}) "
                    f"cannot be cast to the input data that is {X.dtype!r}. Make sure "
                    "that the dtypes of the input data is of the same kind between "
                    "fit and transform."
                )
            else:
                # By default, fill_value=None, and the replacement is always
                # compatible with the input data
                fill_value_dtype = X.dtype

            # Make sure we can safely cast fill_value dtype to the input data dtype
            if not np.can_cast(fill_value_dtype, X.dtype, casting="same_kind"):
                raise ValueError(err_msg)

        return X

    @_fit_context(prefer_skip_nested_validation=True)
    def fit(self, X, y=None):
        """Fit the imputer on `X`.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            Input data, where `n_samples` is the number of samples and
            `n_features` is the number of features.

        y : Ignored
            Not used, present here for API consistency by convention.

        Returns
        -------
        self : object
            Fitted estimator.
        """
        X = self._validate_input(X, in_fit=True)

        # default fill_value is 0 for numerical input and "missing_value"
        # otherwise
        if self.fill_value is None:
            if X.dtype.kind in ("i", "u", "f"):
                fill_value = 0
            else:
                fill_value = "missing_value"
        else:
            fill_value = self.fill_value

        if sp.issparse(X):
            self.statistics_ = self._sparse_fit(
                X, self.strategy, self.missing_values, fill_value
            )
        else:
            self.statistics_ = self._dense_fit(
                X, self.strategy, self.missing_values, fill_value
            )

        return self

    def _sparse_fit(self, X, strategy, missing_values, fill_value):
        """Fit the transformer on sparse data."""
        missing_mask = _get_mask(X, missing_values)
        mask_data = missing_mask.data
        n_implicit_zeros = X.shape[0] - np.diff(X.indptr)

        statistics = np.empty(X.shape[1])

        if strategy == "constant":
            # for constant strategy, self.statistics_ is used to store
            # fill_value in each column
            statistics.fill(fill_value)
        else:
            for i in range(X.shape[1]):
                column = X.data[X.indptr[i] : X.indptr[i + 1]]
                mask_column = mask_data[X.indptr[i] : X.indptr[i + 1]]
                column = column[~mask_column]

                # combine explicit and implicit zeros
                mask_zeros = _get_mask(column, 0)
                column = column[~mask_zeros]
                n_explicit_zeros = mask_zeros.sum()
                n_zeros = n_implicit_zeros[i] + n_explicit_zeros

                if len(column) == 0 and self.keep_empty_features:
                    # in case we want to keep columns with only missing values.
                    statistics[i] = 0
                else:
                    if strategy == "mean":
                        s = column.size + n_zeros
                        statistics[i] = np.nan if s == 0 else column.sum() / s

                    elif strategy == "median":
                        statistics[i] = _get_median(column, n_zeros)

                    elif strategy == "most_frequent":
                        statistics[i] = _most_frequent(column, 0, n_zeros)

                    elif isinstance(strategy, Callable):
                        statistics[i] = self.strategy(column)

        super()._fit_indicator(missing_mask)

        return statistics

    def _dense_fit(self, X, strategy, missing_values, fill_value):
        """Fit the transformer on dense data."""
        missing_mask = _get_mask(X, missing_values)
        masked_X = ma.masked_array(X, mask=missing_mask)

        super()._fit_indicator(missing_mask)

        # Mean
        if strategy == "mean":
            mean_masked = np.ma.mean(masked_X, axis=0)
            # Avoid the warning "Warning: converting a masked element to nan."
            mean = np.ma.getdata(mean_masked)
            mean[np.ma.getmask(mean_masked)] = 0 if self.keep_empty_features else np.nan

            return mean

        # Median
        elif strategy == "median":
            median_masked = np.ma.median(masked_X, axis=0)
            # Avoid the warning "Warning: converting a masked element to nan."
            median = np.ma.getdata(median_masked)
            median[np.ma.getmaskarray(median_masked)] = (
                0 if self.keep_empty_features else np.nan
            )

            return median

        # Most frequent
        elif strategy == "most_frequent":
            # Avoid use of scipy.stats.mstats.mode due to the required
            # additional overhead and slow benchmarking performance.
            # See Issue 14325 and PR 14399 for full discussion.

            # To be able access the elements by columns
            X = X.transpose()
            mask = missing_mask.transpose()

            if X.dtype.kind == "O":
                most_frequent = np.empty(X.shape[0], dtype=object)
            else:
                most_frequent = np.empty(X.shape[0])

            for i, (row, row_mask) in enumerate(zip(X[:], mask[:])):
                row_mask = np.logical_not(row_mask).astype(bool)
                row = row[row_mask]
                if len(row) == 0 and self.keep_empty_features:
                    most_frequent[i] = 0
                else:
                    most_frequent[i] = _most_frequent(row, np.nan, 0)

            return most_frequent

        # Constant
        elif strategy == "constant":
            # for constant strategy, self.statistcs_ is used to store
            # fill_value in each column
            return np.full(X.shape[1], fill_value, dtype=X.dtype)

        # Custom
        elif isinstance(strategy, Callable):
            statistics = np.empty(masked_X.shape[1])
            for i in range(masked_X.shape[1]):
                statistics[i] = self.strategy(masked_X[:, i].compressed())
            return statistics

    def transform(self, X):
        """Impute all missing values in `X`.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data to complete.

        Returns
        -------
        X_imputed : {ndarray, sparse matrix} of shape \
                (n_samples, n_features_out)
            `X` with imputed values.
        """
        check_is_fitted(self)

        X = self._validate_input(X, in_fit=False)
        statistics = self.statistics_

        if X.shape[1] != statistics.shape[0]:
            raise ValueError(
                "X has %d features per sample, expected %d"
                % (X.shape[1], self.statistics_.shape[0])
            )

        # compute mask before eliminating invalid features
        missing_mask = _get_mask(X, self.missing_values)

        # Decide whether to keep missing features
        if self.strategy == "constant" or self.keep_empty_features:
            valid_statistics = statistics
            valid_statistics_indexes = None
        else:
            # same as np.isnan but also works for object dtypes
            invalid_mask = _get_mask(statistics, np.nan)
            valid_mask = np.logical_not(invalid_mask)
            valid_statistics = statistics[valid_mask]
            valid_statistics_indexes = np.flatnonzero(valid_mask)

            if invalid_mask.any():
                invalid_features = np.arange(X.shape[1])[invalid_mask]
                # use feature names warning if features are provided
                if hasattr(self, "feature_names_in_"):
                    invalid_features = self.feature_names_in_[invalid_features]
                warnings.warn(
                    "Skipping features without any observed values:"
                    f" {invalid_features}. At least one non-missing value is needed"
                    f" for imputation with strategy='{self.strategy}'."
                )
                X = X[:, valid_statistics_indexes]

        # Do actual imputation
        if sp.issparse(X):
            if self.missing_values == 0:
                raise ValueError(
                    "Imputation not possible when missing_values "
                    "== 0 and input is sparse. Provide a dense "
                    "array instead."
                )
            else:
                # if no invalid statistics are found, use the mask computed
                # before, else recompute mask
                if valid_statistics_indexes is None:
                    mask = missing_mask.data
                else:
                    mask = _get_mask(X.data, self.missing_values)
                indexes = np.repeat(
                    np.arange(len(X.indptr) - 1, dtype=int), np.diff(X.indptr)
                )[mask]

                X.data[mask] = valid_statistics[indexes].astype(X.dtype, copy=False)
        else:
            # use mask computed before eliminating invalid mask
            if valid_statistics_indexes is None:
                mask_valid_features = missing_mask
            else:
                mask_valid_features = missing_mask[:, valid_statistics_indexes]
            n_missing = np.sum(mask_valid_features, axis=0)
            values = np.repeat(valid_statistics, n_missing)
            coordinates = np.where(mask_valid_features.transpose())[::-1]

            X[coordinates] = values

        X_indicator = super()._transform_indicator(missing_mask)

        return super()._concatenate_indicator(X, X_indicator)

    def inverse_transform(self, X):
        """Convert the data back to the original representation.

        Inverts the `transform` operation performed on an array.
        This operation can only be performed after :class:`SimpleImputer` is
        instantiated with `add_indicator=True`.

        Note that `inverse_transform` can only invert the transform in
        features that have binary indicators for missing values. If a feature
        has no missing values at `fit` time, the feature won't have a binary
        indicator, and the imputation done at `transform` time won't be
        inverted.

        .. versionadded:: 0.24

        Parameters
        ----------
        X : array-like of shape \
                (n_samples, n_features + n_features_missing_indicator)
            The imputed data to be reverted to original data. It has to be
            an augmented array of imputed data and the missing indicator mask.

        Returns
        -------
        X_original : ndarray of shape (n_samples, n_features)
            The original `X` with missing values as it was prior
            to imputation.
        """
        check_is_fitted(self)

        if not self.add_indicator:
            raise ValueError(
                "'inverse_transform' works only when "
                "'SimpleImputer' is instantiated with "
                "'add_indicator=True'. "
                f"Got 'add_indicator={self.add_indicator}' "
                "instead."
            )

        n_features_missing = len(self.indicator_.features_)
        non_empty_feature_count = X.shape[1] - n_features_missing
        array_imputed = X[:, :non_empty_feature_count].copy()
        missing_mask = X[:, non_empty_feature_count:].astype(bool)

        n_features_original = len(self.statistics_)
        shape_original = (X.shape[0], n_features_original)
        X_original = np.zeros(shape_original)
        X_original[:, self.indicator_.features_] = missing_mask
        full_mask = X_original.astype(bool)

        imputed_idx, original_idx = 0, 0
        while imputed_idx < len(array_imputed.T):
            if not np.all(X_original[:, original_idx]):
                X_original[:, original_idx] = array_imputed.T[imputed_idx]
                imputed_idx += 1
                original_idx += 1
            else:
                original_idx += 1

        X_original[full_mask] = self.missing_values
        return X_original

    def _more_tags(self):
        return {
            "allow_nan": is_pandas_na(self.missing_values)
            or is_scalar_nan(self.missing_values)
        }

    def get_feature_names_out(self, input_features=None):
        """Get output feature names for transformation.

        Parameters
        ----------
        input_features : array-like of str or None, default=None
            Input features.

            - If `input_features` is `None`, then `feature_names_in_` is
              used as feature names in. If `feature_names_in_` is not defined,
              then the following input feature names are generated:
              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
            - If `input_features` is an array-like, then `input_features` must
              match `feature_names_in_` if `feature_names_in_` is defined.

        Returns
        -------
        feature_names_out : ndarray of str objects
            Transformed feature names.
        """
        check_is_fitted(self, "n_features_in_")
        input_features = _check_feature_names_in(self, input_features)
        non_missing_mask = np.logical_not(_get_mask(self.statistics_, np.nan))
        names = input_features[non_missing_mask]
        return self._concatenate_indicator_feature_names_out(names, input_features)


class MissingIndicator(TransformerMixin, BaseEstimator):
    """Binary indicators for missing values.

    Note that this component typically should not be used in a vanilla
    :class:`~sklearn.pipeline.Pipeline` consisting of transformers and a
    classifier, but rather could be added using a
    :class:`~sklearn.pipeline.FeatureUnion` or
    :class:`~sklearn.compose.ColumnTransformer`.

    Read more in the :ref:`User Guide <impute>`.

    .. versionadded:: 0.20

    Parameters
    ----------
    missing_values : int, float, str, np.nan or None, default=np.nan
        The placeholder for the missing values. All occurrences of
        `missing_values` will be imputed. For pandas' dataframes with
        nullable integer dtypes with missing values, `missing_values`
        should be set to `np.nan`, since `pd.NA` will be converted to `np.nan`.

    features : {'missing-only', 'all'}, default='missing-only'
        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 : bool or 'auto', default='auto'
        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 : bool, default=True
        If `True`, :meth:`transform` will raise an error when there are
        features with missing values that have no missing values in
        :meth:`fit`. This is applicable only when `features='missing-only'`.

    Attributes
    ----------
    features_ : ndarray of shape (n_missing_features,) or (n_features,)
        The features indices which will be returned when calling
        :meth:`transform`. They are computed during :meth:`fit`. If
        `features='all'`, `features_` is equal to `range(n_features)`.

    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X`
        has feature names that are all strings.

        .. versionadded:: 1.0

    See Also
    --------
    SimpleImputer : Univariate imputation of missing values.
    IterativeImputer : Multivariate imputation of missing values.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.impute import MissingIndicator
    >>> X1 = np.array([[np.nan, 1, 3],
    ...                [4, 0, np.nan],
    ...                [8, 1, 0]])
    >>> X2 = np.array([[5, 1, np.nan],
    ...                [np.nan, 2, 3],
    ...                [2, 4, 0]])
    >>> indicator = MissingIndicator()
    >>> indicator.fit(X1)
    MissingIndicator()
    >>> X2_tr = indicator.transform(X2)
    >>> X2_tr
    array([[False,  True],
           [ True, False],
           [False, False]])
    """

    _parameter_constraints: dict = {
        "missing_values": [MissingValues()],
        "features": [StrOptions({"missing-only", "all"})],
        "sparse": ["boolean", StrOptions({"auto"})],
        "error_on_new": ["boolean"],
    }

    def __init__(
        self,
        *,
        missing_values=np.nan,
        features="missing-only",
        sparse="auto",
        error_on_new=True,
    ):
        self.missing_values = missing_values
        self.features = features
        self.sparse = sparse
        self.error_on_new = error_on_new

    def _get_missing_features_info(self, X):
        """Compute the imputer mask and the indices of the features
        containing missing values.

        Parameters
        ----------
        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
            The input data with missing values. Note that `X` has been
            checked in :meth:`fit` and :meth:`transform` before to call this
            function.

        Returns
        -------
        imputer_mask : {ndarray, sparse matrix} of shape \
        (n_samples, n_features)
            The imputer mask of the original data.

        features_with_missing : ndarray of shape (n_features_with_missing)
            The features containing missing values.
        """
        if not self._precomputed:
            imputer_mask = _get_mask(X, self.missing_values)
        else:
            imputer_mask = X

        if sp.issparse(X):
            imputer_mask.eliminate_zeros()

            if self.features == "missing-only":
                n_missing = imputer_mask.getnnz(axis=0)

            if self.sparse is False:
                imputer_mask = imputer_mask.toarray()
            elif imputer_mask.format == "csr":
                imputer_mask = imputer_mask.tocsc()
        else:
            if not self._precomputed:
                imputer_mask = _get_mask(X, self.missing_values)
            else:
                imputer_mask = X

            if self.features == "missing-only":
                n_missing = imputer_mask.sum(axis=0)

            if self.sparse is True:
                imputer_mask = sp.csc_matrix(imputer_mask)

        if self.features == "all":
            features_indices = np.arange(X.shape[1])
        else:
            features_indices = np.flatnonzero(n_missing)

        return imputer_mask, features_indices

    def _validate_input(self, X, in_fit):
        if not is_scalar_nan(self.missing_values):
            force_all_finite = True
        else:
            force_all_finite = "allow-nan"
        X = self._validate_data(
            X,
            reset=in_fit,
            accept_sparse=("csc", "csr"),
            dtype=None,
            force_all_finite=force_all_finite,
        )
        _check_inputs_dtype(X, self.missing_values)
        if X.dtype.kind not in ("i", "u", "f", "O"):
            raise ValueError(
                "MissingIndicator does not support data with "
                "dtype {0}. Please provide either a numeric array"
                " (with a floating point or integer dtype) or "
                "categorical data represented either as an array "
                "with integer dtype or an array of string values "
                "with an object dtype.".format(X.dtype)
            )

        if sp.issparse(X) and self.missing_values == 0:
            # missing_values = 0 not allowed with sparse data as it would
            # force densification
            raise ValueError(
                "Sparse input with missing_values=0 is "
                "not supported. Provide a dense "
                "array instead."
            )

        return X

    def _fit(self, X, y=None, precomputed=False):
        """Fit the transformer on `X`.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Input data, where `n_samples` is the number of samples and
            `n_features` is the number of features.
            If `precomputed=True`, then `X` is a mask of the input data.

        precomputed : bool
            Whether the input data is a mask.

        Returns
        -------
        imputer_mask : {ndarray, sparse matrix} of shape (n_samples, \
        n_features)
            The imputer mask of the original data.
        """
        if precomputed:
            if not (hasattr(X, "dtype") and X.dtype.kind == "b"):
                raise ValueError("precomputed is True but the input data is not a mask")
            self._precomputed = True
        else:
            self._precomputed = False

        # Need not validate X again as it would have already been validated
        # in the Imputer calling MissingIndicator
        if not self._precomputed:
            X = self._validate_input(X, in_fit=True)
        else:
            # only create `n_features_in_` in the precomputed case
            self._check_n_features(X, reset=True)

        self._n_features = X.shape[1]

        missing_features_info = self._get_missing_features_info(X)
        self.features_ = missing_features_info[1]

        return missing_features_info[0]

    @_fit_context(prefer_skip_nested_validation=True)
    def fit(self, X, y=None):
        """Fit the transformer on `X`.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Input data, where `n_samples` is the number of samples and
            `n_features` is the number of features.

        y : Ignored
            Not used, present for API consistency by convention.

        Returns
        -------
        self : object
            Fitted estimator.
        """
        self._fit(X, y)

        return self

    def transform(self, X):
        """Generate missing values indicator for `X`.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input data to complete.

        Returns
        -------
        Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) \
        or (n_samples, n_features_with_missing)
            The missing indicator for input data. The data type of `Xt`
            will be boolean.
        """
        check_is_fitted(self)

        # Need not validate X again as it would have already been validated
        # in the Imputer calling MissingIndicator
        if not self._precomputed:
            X = self._validate_input(X, in_fit=False)
        else:
            if not (hasattr(X, "dtype") and X.dtype.kind == "b"):
                raise ValueError("precomputed is True but the input data is not a mask")

        imputer_mask, features = self._get_missing_features_info(X)

        if self.features == "missing-only":
            features_diff_fit_trans = np.setdiff1d(features, self.features_)
            if self.error_on_new and features_diff_fit_trans.size > 0:
                raise ValueError(
                    "The features {} have missing values "
                    "in transform but have no missing values "
                    "in fit.".format(features_diff_fit_trans)
                )

            if self.features_.size < self._n_features:
                imputer_mask = imputer_mask[:, self.features_]

        return imputer_mask

    @_fit_context(prefer_skip_nested_validation=True)
    def fit_transform(self, X, y=None):
        """Generate missing values indicator for `X`.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input data to complete.

        y : Ignored
            Not used, present for API consistency by convention.

        Returns
        -------
        Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) \
        or (n_samples, n_features_with_missing)
            The missing indicator for input data. The data type of `Xt`
            will be boolean.
        """
        imputer_mask = self._fit(X, y)

        if self.features_.size < self._n_features:
            imputer_mask = imputer_mask[:, self.features_]

        return imputer_mask

    def get_feature_names_out(self, input_features=None):
        """Get output feature names for transformation.

        Parameters
        ----------
        input_features : array-like of str or None, default=None
            Input features.

            - If `input_features` is `None`, then `feature_names_in_` is
              used as feature names in. If `feature_names_in_` is not defined,
              then the following input feature names are generated:
              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
            - If `input_features` is an array-like, then `input_features` must
              match `feature_names_in_` if `feature_names_in_` is defined.

        Returns
        -------
        feature_names_out : ndarray of str objects
            Transformed feature names.
        """
        check_is_fitted(self, "n_features_in_")
        input_features = _check_feature_names_in(self, input_features)
        prefix = self.__class__.__name__.lower()
        return np.asarray(
            [
                f"{prefix}_{feature_name}"
                for feature_name in input_features[self.features_]
            ],
            dtype=object,
        )

    def _more_tags(self):
        return {
            "allow_nan": True,
            "X_types": ["2darray", "string"],
            "preserves_dtype": [],
        }