from __future__ import division

import numpy as np
import scipy.linalg
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.externals.joblib import Memory
from sklearn.externals.six import string_types, with_metaclass
from sklearn.externals.six.moves import xrange
from sklearn.utils import check_array

def get_memory(memory):
    if isinstance(memory, string_types):
        return Memory(memory, verbose=0)
    return memory


class _Meta(type):
    def __getitem__(cls, indices):
        return cls(indices)

class PairwisePicker(BaseEstimator, TransformerMixin, with_metaclass(_Meta)):
    '''
    Picks a subset of the passed-in matrix. Useful for choosing one of several
    divergences computed by
    :class:`skl_groups.divergences.KNNDivergenceEstimator`, for example.

    Rather than ``PairwisePicker((0, slice(2, 3)))``, you can also do
    ``PairwisePicker[0, 2:3]``.

    Parameters
    ----------
    indices : tuple of integers / slice objects / etc
        The indices to subset the input with.
    '''
    _pairwise = True  # TODO: not really
    _pairwise_output = True

    def __init__(self, indices):
        self.indices = indices

    def fit(self, X=None, y=None):
        "Do nothing; this transformer is stateless."
        return self

    def transform(self, X):
        "Subsets the given matrix."
        return X[self.indices]


class Symmetrize(BaseEstimator, TransformerMixin):
    '''
    Symmetrizes pairwise affinity/distance/whatever matrices, by taking
    the mean of itself and its transpose.

    Parameters
    ----------
    copy : boolean, optional, default True
        If false, invalidates the passed-in matrix.

    Notes
    -----
    ``copy=False`` currently doesn't do anything.
    '''

    _pairwise = True
    _pairwise_output = True

    def __init__(self, copy=True):
        self.copy = copy

    def fit(self, X, y=None):
        "Raises NotImplementedError."
        raise NotImplementedError("Symmetrize can only fit_transform")

    def transform(self, X):
        "Does nothing, so that it works nicely in pipelines."
        return X

    def fit_transform(self, X, y=None):
        '''
        Symmetrizes X.

        Parameters
        ----------
        X : array, shape [n, n]
            The pairwise inputs to symmetrize.

        Returns
        -------
        X : array, shape [n, n]
            The symmetrized pairwise outputs.
        '''
        # TODO: figure out a no-copy version of this...
        n = X.shape[0]
        if X.shape != (n, n):
            raise TypeError("Input must be a square matrix.")

        X = X + X.T
        X /= 2
        return X


class RBFize(BaseEstimator, TransformerMixin):
    r'''
    Turns a distance matrix into an RBF kernel:
    :math:`K(x, y) = \exp\left( - \gamma \lVert x - y \rVert^2 \right)`.

    For the output to be a valid kernel, the inputs should either be
    Euclidean distances, e.g. the output of
    :func:`sklearn.metrics.pairwise.euclidean_distances`,
    or be isometrically embeddable into a Euclidean space.
    If not, you should ensure that the result is positive semidefinite before
    using it as a kernel, for example via :class:`ProjectPSD`.

    Parameters
    ----------

    gamma : float, optional, default 1
        The :math:`\gamma` value to use in the kernel.

        Defaults to 1, but this is not a very good value to use;
        two reasonable heuristics are 1 / feature dimension
        or 1 / the median distance value.
        You probably want to cross-validate on a range of possible values;
        see also :attr:`scale_by_median`.

    scale_by_median : boolean, optional, default False
        If True, scale :attr:`gamma` by the squared median input distance.

    squared : boolean, optional, default False
        Whether the inputs are treated as distances or squared distances.

    copy : boolean, optional, default True
        If False, data passed to :meth:`transform` is overwritten.

    Attributes
    ----------

    `median_` : float
        If :attr:`scale_by_median`, the median distance. Otherwise, not set.

    See Also
    --------
    sklearn.metrics.pairwise.rbf_kernel : computes this from feature vectors
    '''
    _pairwise = True
    _pairwise_output = True

    def __init__(self, gamma=1, scale_by_median=False, squared=False,
                 copy=True):
        self.gamma = gamma
        self.scale_by_median = scale_by_median
        self.copy = copy
        self.squared = squared

    def fit(self, X, y=None):
        '''
        If scale_by_median, find :attr:`median_`; otherwise, do nothing.

        Parameters
        ----------
        X : array
            The raw pairwise distances.
        '''

        X = check_array(X)
        if self.scale_by_median:
            self.median_ = np.median(X[np.triu_indices_from(X, k=1)],
                                     overwrite_input=True)
        elif hasattr(self, 'median_'):
            del self.median_
        return self

    def transform(self, X):
        '''
        Turns distances into RBF values.

        Parameters
        ----------
        X : array
            The raw pairwise distances.

        Returns
        -------
        X_rbf : array of same shape as X
            The distances in X passed through the RBF kernel.
        '''
        X = check_array(X)
        X_rbf = np.empty_like(X) if self.copy else X

        X_in = X
        if not self.squared:
            np.power(X_in, 2, out=X_rbf)
            X_in = X_rbf

        if self.scale_by_median:
            scale = self.median_ if self.squared else self.median_ ** 2
            gamma = self.gamma * scale
        else:
            gamma = self.gamma
        np.multiply(X_in, -gamma, out=X_rbf)

        np.exp(X_rbf, out=X_rbf)
        return X_rbf


class ProjectPSD(BaseEstimator, TransformerMixin):
    '''
    Projects a pairwise square symmetric affinity matrix to be positive
    semidefinite, by discarding any negative eigenvalues from its spectrum.

    `fit_transform()` does the actual projection. If you `transform` onto data
    different than the data originally `fit` on (not necessarily square), then
    the data will be processed in a way that attempts to treat test similarities
    consistently with training ones, using the method of [1].

    Parameters
    ----------
    min_eig : float, optional, default 0
        The minimum eigenvalue for the projected matrix. Because of
        floating-point inaccuracies, don't take this too literally.

    copy : boolean, optional, default True
        Operate on a copy of the passed-in matrix; otherwise, the original
        matrix will be invalidated.

    negatives_likely : boolean, optional, default True
        Optimize memory usage for the case where we expect there to be negative
        eigenvalues.

    memory : Instance of joblib.Memory or string (optional)
        Used to cache the eigendecomposition.
        By default, no caching is done. If a string is given, it is the
        path to the caching directory.

    Attributes
    ----------
    `clip_` : array of shape (n, n)
        The linear transformation corresponding to
        the clip operation on the training points.

    References
    ----------
    [1] Y. Chen, E. K. Garcia, M. R. Gupta, A. Rahimi, & L. Cazzanti (2009).
        Similarity-based classification: Concepts and algorithms.
        Journal of Machine Learning Research, 10, 747-776.
    '''

    _pairwise = True
    _pairwise_output = True

    def __init__(self, min_eig=0, copy=True, negatives_likely=True,
                 memory=Memory(cachedir=None, verbose=0)):
        self.min_eig = min_eig
        self.copy = copy
        self.negatives_likely = negatives_likely
        self.memory = memory

    def fit(self, X, y=None):
        '''
        Learn the linear transformation to clipped eigenvalues.

        Note that if min_eig isn't zero and any of the original eigenvalues
        were exactly zero, this will leave those eigenvalues as zero.

        Parameters
        ----------
        X : array, shape [n, n]
            The *symmetric* input similarities. If X is asymmetric, it will be
            treated as if it were symmetric based on its lower-triangular part.
        '''
        n = X.shape[0]
        if X.shape != (n, n):
            raise TypeError("Input must be a square matrix.")

        # TODO: only get negative eigs somehow?
        memory = get_memory(self.memory)
        vals, vecs = memory.cache(scipy.linalg.eigh, ignore=['overwrite_a'])(
            X, overwrite_a=not self.copy)
        vals = vals.reshape(-1, 1)

        if self.min_eig == 0:
            inner = vals > self.min_eig
        else:
            with np.errstate(divide='ignore'):
                inner = np.where(vals >= self.min_eig, 1,
                                 np.where(vals == 0, 0, self.min_eig / vals))

        self.clip_ = np.dot(vecs, inner * vecs.T)
        return self

    def transform(self, X):
        '''
        Transforms X according to the linear transformation corresponding to
        clipping the input eigenvalues.

        Parameters
        ----------
        X : array, shape [n_test, n]
            The test similarities to training points.

        Returns
        -------
        Xt : array, shape [n_test, n]
            The transformed test similarites to training points.
        '''
        n = self.clip_.shape[0]
        if X.ndim != 2 or X.shape[1] != n:
            msg = "X should have {} columns, the number of samples at fit time"
            raise TypeError(msg.format(self.clip_.shape[0]))
        return np.dot(X, self.clip_)

    def fit_transform(self, X, y=None):
        '''
        Clips the negative eigenvalues of X.

        Parameters
        ----------
        X : array, shape [n, n]
            The *symmetric* input similarities. If X is asymmetric, it will be
            treated as if it were symmetric based on its lower-triangular part.

        Returns
        -------
        Xt : array, shape [n, n]
            The transformed training similarities; smallest eigenvalue will be
            at least `self.min_eig`.
        '''
        n = X.shape[0]
        if X.shape != (n, n):
            raise TypeError("Input must be a square matrix.")

        memory = get_memory(self.memory)
        discard_X = not self.copy and self.negatives_likely
        vals, vecs = memory.cache(scipy.linalg.eigh, ignore=['overwrite_a'])(
            X, overwrite_a=discard_X)
        vals = vals[:, None]

        self.clip_ = np.dot(vecs, (vals > self.min_eig) * vecs.T)

        if discard_X or vals[0, 0] < self.min_eig:
            del X
            np.maximum(vals, self.min_eig, out=vals)
            X = np.dot(vecs, vals * vecs.T)
            del vals, vecs

            # should be symmetric, but make sure because floats
            X = Symmetrize(copy=False).fit_transform(X)
        return X


class FlipPSD(BaseEstimator, TransformerMixin):
    '''
    Makes a pairwise symmetric square affinity matrix into a valid positive
    semidefinite kernel, by flipping the sign of any negative eigenvalues in
    its spectrum.

    `fit_transform()` does the actual projection. If you `transform` onto data
    different than the data originally `fit` on (not necessarily square), then
    the data will be processed in a way that attempts to treat test similarities
    consistently with training ones, using the method of [1].

    Parameters
    ----------
    copy : boolean, optional, default True
        Operate on a copy of the passed-in matrix; otherwise, the original
        matrix will be invalidated (for both ``fit()`` and ``transform()``).

    negatives_likely : boolean, optional, default True
        Optimize memory usage for the case where we expect there to be negative
        eigenvalues.

    memory : Instance of joblib.Memory or string (optional)
        Used to cache the eigendecomposition.
        By default, no caching is done. If a string is given, it is the
        path to the caching directory.

    Attributes
    ----------
    `flip_` : array of shape (n, n)
        The linear transformation corresponding
        to the flip operation on the training points.

    References
    ----------
    [1] Y. Chen, E. K. Garcia, M. R. Gupta, A. Rahimi, & L. Cazzanti (2009).
        Similarity-based classification: Concepts and algorithms.
        Journal of Machine Learning Research, 10, 747-776.
    '''

    _pairwise = True
    _pairwise_output = True

    def __init__(self, copy=True, negatives_likely=True,
                 memory=Memory(cachedir=None, verbose=0)):
        self.copy = copy
        self.negatives_likely = negatives_likely
        self.memory = memory

    def fit(self, X, y=None):
        '''
        Learn the linear transformation to flipped eigenvalues.

        Parameters
        ----------
        X : array, shape [n, n]
            The *symmetric* input similarities. If X is asymmetric, it will be
            treated as if it were symmetric based on its lower-triangular part.
        '''
        n = X.shape[0]
        if X.shape != (n, n):
            raise TypeError("Input must be a square matrix.")

        # TODO: only get negative eigs somehow?
        memory = get_memory(self.memory)
        vals, vecs = memory.cache(scipy.linalg.eigh, ignore=['overwrite_a'])(
            X, overwrite_a=not self.copy)
        vals = vals[:, None]

        self.flip_ = np.dot(vecs, np.sign(vals) * vecs.T)
        return self

    def transform(self, X):
        '''
        Transforms X according to the linear transformation corresponding to
        flipping the input eigenvalues.

        Parameters
        ----------
        X : array, shape [n_test, n]
            The test similarities to training points.

        Returns
        -------
        Xt : array, shape [n_test, n]
            The transformed test similarites to training points.
        '''
        n = self.flip_.shape[0]
        if X.ndim != 2 or X.shape[1] != n:
            msg = "X should have {} columns, the number of samples at fit time"
            raise TypeError(msg.format(self.flip_.shape[0]))
        return np.dot(X, self.flip_)

    def fit_transform(self, X, y=None):
        '''
        Flips the negative eigenvalues of X.

        Parameters
        ----------
        X : array, shape [n, n]
            The *symmetric* input similarities. If X is asymmetric, it will be
            treated as if it were symmetric based on its lower-triangular part.

        Returns
        -------
        Xt : array, shape [n, n]
            The transformed training similarities.
        '''
        n = X.shape[0]
        if X.shape != (n, n):
            raise TypeError("Input must be a square matrix.")

        memory = get_memory(self.memory)
        discard_X = not self.copy and self.negatives_likely
        vals, vecs = memory.cache(scipy.linalg.eigh, ignore=['overwrite_a'])(
            X, overwrite_a=discard_X)
        vals = vals[:, None]

        self.clip_ = np.dot(vecs, np.sign(vals) * vecs.T)

        if discard_X or vals[0, 0] < 0:
            del X
            np.abs(vals, out=vals)
            X = np.dot(vecs, vals * vecs.T)
            del vals, vecs

            # should be symmetric, but make sure because floats
            X = Symmetrize(copy=False).fit_transform(X)
        return X


class ShiftPSD(BaseEstimator, TransformerMixin):
    '''
    Makes a pairwise square affinity matrix into a valid positive semidefinite
    kernel, by flipping the sign of any negative eigenvalues in its spectrum.

    `fit_transform()` does the actual projection. If you `transform` onto data
    different than the data originally `fit` on (not necessarily square), then
    nothing will be changed, because the shift operation only affects self-
    similarities.

    Parameters
    ----------
    min_eig : float, optional, default 0
        The minimum eigenvalue for the projected matrix. Because of
        floating-point inaccuracies, don't take this too literally.

    copy : boolean, optional, default True
        Operate on a copy of the passed-in matrix; otherwise, the original
        matrix will be invalidated (for both ``fit()`` and ``transform()``).

    memory : Instance of joblib.Memory or string (optional)
        Used to cache the eigendecomposition.
        By default, no caching is done. If a string is given, it is the
        path to the caching directory.

    Attributes
    ----------
    `train_` : array of shape [n, n]
        The training similarities.
        Stored so that `transform` can check if
        it's transforming the test data and act appropriately.

    `shift_` : float
        The amount to shift all the eigenvalues up by.
    '''

    _pairwise = True
    _pairwise_output = True

    def __init__(self, min_eig=0, copy=True,
                 memory=Memory(cachedir=None, verbose=0)):
        self.min_eig = min_eig
        self.copy = copy
        self.memory = memory