# Authors: The scikit-learn developers # SPDX-License-Identifier: 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_n_features, check_is_fitted, validate_data, ) 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 __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.allow_nan = is_scalar_nan(self.missing_values) return tags 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 `. .. 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 .. versionchanged:: 1.6 Currently, when `keep_empty_feature=False` and `strategy="constant"`, empty features are not dropped. This behaviour will change in version 1.8. Set `keep_empty_feature=True` to preserve this behaviour. 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): ensure_all_finite = "allow-nan" else: ensure_all_finite = True try: X = validate_data( self, X, reset=in_fit, accept_sparse="csc", dtype=dtype, force_writeable=True if not in_fit else None, ensure_all_finite=ensure_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": # TODO(1.8): Remove FutureWarning and add `np.nan` as a statistic # for empty features to drop them later. if not self.keep_empty_features and any( [all(missing_mask[:, i].data) for i in range(missing_mask.shape[1])] ): warnings.warn( "Currently, when `keep_empty_feature=False` and " '`strategy="constant"`, empty features are not dropped. ' "This behaviour will change in version 1.8. Set " "`keep_empty_feature=True` to preserve this behaviour.", FutureWarning, ) # 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": # TODO(1.8): Remove FutureWarning and add `np.nan` as a statistic # for empty features to drop them later. if not self.keep_empty_features and ma.getmask(masked_X).all(axis=0).any(): warnings.warn( "Currently, when `keep_empty_feature=False` and " '`strategy="constant"`, empty features are not dropped. ' "This behaviour will change in version 1.8. Set " "`keep_empty_feature=True` to preserve this behaviour.", FutureWarning, ) # 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 __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.allow_nan = is_pandas_na(self.missing_values) or is_scalar_nan( self.missing_values ) return tags 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 `. .. 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): ensure_all_finite = True else: ensure_all_finite = "allow-nan" X = validate_data( self, X, reset=in_fit, accept_sparse=("csc", "csr"), dtype=None, ensure_all_finite=ensure_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 _check_n_features(self, 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 __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.allow_nan = True tags.input_tags.string = True tags.transformer_tags.preserves_dtype = [] return tags