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

import numbers
import warnings

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

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

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

    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)
        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

        hstack = sparse.hstack if sparse.issparse(X_imputed) else 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 _more_tags(self):
        return {'allow_nan': is_scalar_nan(self.missing_values)}


class SimpleImputer(_BaseImputer):
    """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):
        super().__init__(
            missing_values=missing_values,
            add_indicator=add_indicator
        )
        self.strategy = strategy
        self.fill_value = fill_value
        self.verbose = verbose
        self.copy = copy

    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):
                new_ve = ValueError("Cannot use {} strategy with non-numeric "
                                    "data:\n{}".format(self.strategy, ve))
                raise new_ve from None
            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)
        super()._fit_indicator(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)
        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)
        X_indicator = super()._transform_indicator(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]))

        # 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

        return super()._concatenate_indicator(X, X_indicator)


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']}