_base.py

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

from __future__ import division

import warnings
import numbers

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

from ..base import BaseEstimator, TransformerMixin
from ..utils.sparsefuncs import _get_median
from ..utils.validation import check_is_fitted
from ..utils.validation import FLOAT_DTYPES
from ..utils.mask import _get_mask
from ..utils import is_scalar_nan
from ..utils import check_array


def _check_inputs_dtype(X, missing_values):
    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:
        with warnings.catch_warnings():
            # stats.mode raises a warning when input array contains objects due
            # to incapacity to detect NaNs. Irrelevant here since input array
            # has already been NaN-masked.
            warnings.simplefilter("ignore", RuntimeWarning)
            mode = stats.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:
        # Ties the breaks. Copy the behaviour of scipy.stats.mode
        if most_frequent_value < extra_value:
            return most_frequent_value
        else:
            return extra_value


class SimpleImputer(TransformerMixin, BaseEstimator):
    """Imputation transformer for completing missing values.

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

    Parameters
    ----------
    missing_values : number, string, np.nan (default) or None
        The placeholder for the missing values. All occurrences of
        `missing_values` will be imputed.

    strategy : string, optional (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 "constant", then replace missing values with fill_value. Can be
          used with strings or numeric data.

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

    fill_value : string or numerical value, optional (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)
        Controls the verbosity of the imputer.

    copy : boolean, optional (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 : boolean, optional (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.

    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 is False.

    See also
    --------
    IterativeImputer : Multivariate imputation of missing values.

    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. ]]

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

    """
    def __init__(self, missing_values=np.nan, strategy="mean",
                 fill_value=None, verbose=0, copy=True, add_indicator=False):
        self.missing_values = missing_values
        self.strategy = strategy
        self.fill_value = fill_value
        self.verbose = verbose
        self.copy = copy
        self.add_indicator = add_indicator

    def _validate_input(self, X):
        allowed_strategies = ["mean", "median", "most_frequent", "constant"]
        if self.strategy not in allowed_strategies:
            raise ValueError("Can only use these strategies: {0} "
                             " got strategy={1}".format(allowed_strategies,
                                                        self.strategy))

        if self.strategy in ("most_frequent", "constant"):
            dtype = None
        else:
            dtype = FLOAT_DTYPES

        if not is_scalar_nan(self.missing_values):
            force_all_finite = True
        else:
            force_all_finite = "allow-nan"

        try:
            X = check_array(X, 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):
                raise ValueError("Cannot use {0} strategy with non-numeric "
                                 "data. Received datatype :{1}."
                                 "".format(self.strategy, X.dtype.kind))
            else:
                raise ve

        _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))

        return X

    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.

        Returns
        -------
        self : SimpleImputer
        """
        X = self._validate_input(X)

        # 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

        # fill_value should be numerical in case of numerical input
        if (self.strategy == "constant" and
                X.dtype.kind in ("i", "u", "f") and
                not isinstance(fill_value, numbers.Real)):
            raise ValueError("'fill_value'={0} is invalid. Expected a "
                             "numerical value when imputing numerical "
                             "data".format(fill_value))

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

        if self.add_indicator:
            self.indicator_ = MissingIndicator(
                missing_values=self.missing_values, error_on_new=False)
            self.indicator_.fit(X)
        else:
            self.indicator_ = None

        return self

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

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

        if strategy == "constant":
            # for constant strategy, self.statistcs_ 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 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)
        return statistics

    def _dense_fit(self, X, strategy, missing_values, fill_value):
        """Fit the transformer on dense data."""
        mask = _get_mask(X, missing_values)
        masked_X = ma.masked_array(X, mask=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)] = 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)] = 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 = 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(np.bool)
                row = row[row_mask]
                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)

    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.
        """
        check_is_fitted(self)

        X = self._validate_input(X)

        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]))

        if self.add_indicator:
            X_trans_indicator = self.indicator_.transform(X)

        # Delete the invalid columns if strategy is not constant
        if self.strategy == "constant":
            valid_statistics = statistics
        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():
                missing = np.arange(X.shape[1])[invalid_mask]
                if self.verbose:
                    warnings.warn("Deleting features without "
                                  "observed values: %s" % missing)
                X = X[:, valid_statistics_indexes]

        # Do actual imputation
        if sparse.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:
                mask = _get_mask(X.data, self.missing_values)
                indexes = np.repeat(np.arange(len(X.indptr) - 1, dtype=np.int),
                                    np.diff(X.indptr))[mask]

                X.data[mask] = valid_statistics[indexes].astype(X.dtype,
                                                                copy=False)
        else:
            mask = _get_mask(X, self.missing_values)
            n_missing = np.sum(mask, axis=0)
            values = np.repeat(valid_statistics, n_missing)
            coordinates = np.where(mask.transpose())[::-1]

            X[coordinates] = values

        if self.add_indicator:
            hstack = sparse.hstack if sparse.issparse(X) else np.hstack
            X = hstack((X, X_trans_indicator))

        return X

    def _more_tags(self):
        return {'allow_nan': True}


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

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

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

    Parameters
    ----------
    missing_values : number, string, np.nan (default) or None
        The placeholder for the missing values. All occurrences of
        `missing_values` will be indicated (True in the output array), the
        other values will be marked as False.

    features : str, optional
        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
        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
        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"``.

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

    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]])

    """

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

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

        features_with_missing : ndarray, shape (n_features_with_missing)
            The features containing missing values.

        """
        if sparse.issparse(X):
            mask = _get_mask(X.data, self.missing_values)

            # The imputer mask will be constructed with the same sparse format
            # as X.
            sparse_constructor = (sparse.csr_matrix if X.format == 'csr'
                                  else sparse.csc_matrix)
            imputer_mask = sparse_constructor(
                (mask, X.indices.copy(), X.indptr.copy()),
                shape=X.shape, dtype=bool)
            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:
            imputer_mask = _get_mask(X, self.missing_values)

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

            if self.sparse is True:
                imputer_mask = sparse.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):
        if not is_scalar_nan(self.missing_values):
            force_all_finite = True
        else:
            force_all_finite = "allow-nan"
        X = check_array(X, 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 sparse.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):
        """Fit the transformer 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.

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

        """
        X = self._validate_input(X)
        self._n_features = X.shape[1]

        if self.features not in ('missing-only', 'all'):
            raise ValueError("'features' has to be either 'missing-only' or "
                             "'all'. Got {} instead.".format(self.features))

        if not ((isinstance(self.sparse, str) and
                self.sparse == "auto") or isinstance(self.sparse, bool)):
            raise ValueError("'sparse' has to be a boolean or 'auto'. "
                             "Got {!r} instead.".format(self.sparse))

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

        return missing_features_info[0]

    def fit(self, X, y=None):
        """Fit the transformer 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.

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

        return self

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

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

        Returns
        -------
        Xt : {ndarray or sparse matrix}, 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)
        X = self._validate_input(X)

        if X.shape[1] != self._n_features:
            raise ValueError("X has a different number of features "
                             "than during fitting.")

        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

    def fit_transform(self, X, y=None):
        """Generate missing values indicator for X.

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

        Returns
        -------
        Xt : {ndarray or sparse matrix}, 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 _more_tags(self):
        return {'allow_nan': True,
                'X_types': ['2darray', 'string']}