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"""Sparse Multinomial Logistic Regression classifier."""

__docformat__ = 'restructuredtext'

import numpy as np

from mvpa2 import _random_seed
from mvpa2.base import warning, externals
from mvpa2.clfs.base import Classifier, accepts_dataset_as_samples
from mvpa2.measures.base import Sensitivity
from mvpa2.misc.exceptions import ConvergenceError
from mvpa2.base.param import Parameter
from mvpa2.base.constraints import *
from mvpa2.base.state import ConditionalAttribute
from mvpa2.datasets.base import Dataset

from mvpa2.support.due import due, Doi

__all__ = [ "SMLR", "SMLRWeights" ]


_DEFAULT_IMPLEMENTATION = "Python"
if externals.exists('ctypes'):
    # Uber-fast C-version of the stepwise regression
    try:
        from mvpa2.clfs.libsmlrc import stepwise_regression as _cStepwiseRegression
        _DEFAULT_IMPLEMENTATION = "C"
    except OSError as e:
        warning("Failed to load fast implementation of SMLR.  May be you "
                "forgotten to build it.  We will use much slower pure-Python "
                "version. Original exception was %s" % (e,))
        _cStepwiseRegression = None
else:
    _cStepwiseRegression = None
    warning("SMLR implementation without ctypes is overwhelmingly slow."
            " You are strongly advised to install python-ctypes")

if __debug__:
    from mvpa2.base import debug

def _label2oneofm(labels, ulabels):
    """Convert labels to one-of-M form.

    TODO: Might be useful elsewhere so could migrate into misc/
    """

    # allocate for the new one-of-M labels
    new_labels = np.zeros((len(labels), len(ulabels)))

    # loop and convert to one-of-M
    for i, c in enumerate(ulabels):
        new_labels[labels == c, i] = 1

    return new_labels



class SMLR(Classifier):
    """Sparse Multinomial Logistic Regression `Classifier`.

    This is an implementation of the SMLR algorithm published in
    :ref:`Krishnapuram et al., 2005 <KCF+05>` (2005, IEEE Transactions
    on Pattern Analysis and Machine Intelligence).  Be sure to cite
    that article if you use this classifier for your work.
    """

    __tags__ = [ 'smlr', 'linear', 'has_sensitivity', 'binary',
                 'multiclass', 'oneclass', 'does_feature_selection',
                 'random_tie_breaking']
    # XXX: later 'kernel-based'?

    lm = Parameter(.1, constraints=EnsureFloat() & EnsureRange(min=1e-10),
             doc="""The penalty term lambda.  Larger values will give rise to
             more sparsification.""")

    convergence_tol = Parameter(1e-3, 
             constraints=EnsureFloat() & EnsureRange(min=1e-10, max=1.0),
             doc="""When the weight change for each cycle drops below this value
             the regression is considered converged.  Smaller values
             lead to tighter convergence.""")

    resamp_decay = Parameter(0.5, 
             constraints=EnsureFloat() & EnsureRange(min=0.0, max=1.0),
             doc="""Decay rate in the probability of resampling a zero weight.
             1.0 will immediately decrease to the min_resamp from 1.0, 0.0
             will never decrease from 1.0.""")

    min_resamp = Parameter(0.001, 
             constraints=EnsureFloat() & EnsureRange(min=1e-10, max=1.0),
             doc="Minimum resampling probability for zeroed weights")

    maxiter = Parameter(10000, constraints=EnsureInt() & EnsureRange(min=1),
             doc="""Maximum number of iterations before stopping if not
             converged.""")

    has_bias = Parameter(True, constraints='bool',
             doc="""Whether to add a bias term to allow fits to data not through
             zero""")

    fit_all_weights = Parameter(True, constraints='bool',
             doc="""Whether to fit weights for all classes or to the number of
            classes minus one.  Both should give nearly identical results, but
            if you set fit_all_weights to True it will take a little longer
            and yield weights that are fully analyzable for each class.  Also,
            note that the convergence rate may be different, but convergence
            point is the same.""")

    implementation = Parameter(_DEFAULT_IMPLEMENTATION,
             constraints=EnsureChoice('C', 'Python'),
             doc="""Use C or Python as the implementation of
             stepwise_regression. C version brings significant speedup thus is
             the default one.""")

    ties = Parameter('random', constraints='str',
                     doc="""Resolve ties which could occur.  At the moment
                     only obvious ties resulting in identical weights
                     per two classes are detected and resolved
                     randomly by injecting small amount of noise into
                     the estimates of tied categories.
                     Set to False to avoid this behavior""")

    seed = Parameter(_random_seed, constraints=EnsureNone() | EnsureInt(),
             doc="""Seed to be used to initialize random generator, might be
             used to replicate the run""")

    unsparsify = Parameter(False, constraints='bool',
             doc="""***EXPERIMENTAL*** Whether to unsparsify the weights via
             regression. Note that it likely leads to worse classifier
             performance, but more interpretable weights.""")

    std_to_keep = Parameter(2.0, constraints='float',
             doc="""Standard deviation threshold of weights to keep when
             unsparsifying.""")

    def __init__(self, **kwargs):
        """Initialize an SMLR classifier.
        """

        """
        TODO:
         # Add in likelihood calculation
         # Add kernels, not just direct methods.
         """
        # init base class first
        Classifier.__init__(self, **kwargs)

        if _cStepwiseRegression is None and self.params.implementation == 'C':
            warning('SMLR: C implementation is not available.'
                    ' Using pure Python one')
            self.params.implementation = 'Python'

        # pylint friendly initializations
        self._ulabels = None
        """Unigue labels from the training set."""
        self.__weights_all = None
        """Contains all weights including bias values"""
        self.__weights = None
        """Just the weights, without the biases"""
        self.__biases = None
        """The biases, will remain none if has_bias is False"""


    ##REF: Name was automagically refactored
    def _python_stepwise_regression(self, w, X, XY, Xw, E,
                                  auto_corr,
                                  lambda_over_2_auto_corr,
                                  S,
                                  M,
                                  maxiter,
                                  convergence_tol,
                                  resamp_decay,
                                  min_resamp,
                                  verbose,
                                  seed = None):
        """The (much slower) python version of the stepwise
        regression.  I'm keeping this around for now so that we can
        compare results."""

        # get the data information into easy vars
        ns, nd = X.shape

        # initialize the iterative optimization
        converged = False
        incr = np.finfo(np.float).max
        non_zero, basis, m, wasted_basis, cycles = 0, 0, 0, 0, 0
        sum2_w_diff, sum2_w_old, w_diff = 0.0, 0.0, 0.0
        p_resamp = np.ones(w.shape, dtype=np.float)

        if seed is not None:
            # set the random seed
            np.random.seed(seed)

            if __debug__:
                debug("SMLR_", "random seed=%s" % seed)

        # perform the optimization
        while not converged and cycles < maxiter:
            # get the starting weight
            w_old = w[basis, m]

            # see if we're gonna update
            if (w_old != 0) or np.random.rand() < p_resamp[basis, m]:
                # let's do it
                # get the probability
                P = E[:, m]/S

                # set the gradient
                grad = XY[basis, m] - np.dot(X[:, basis], P)

                # calculate the new weight with the Laplacian prior
                w_new = w_old + grad/auto_corr[basis]

                # keep weights within bounds
                if w_new > lambda_over_2_auto_corr[basis]:
                    w_new -= lambda_over_2_auto_corr[basis]
                    changed = True
                    # unmark from being zero if necessary
                    if w_old == 0:
                        non_zero += 1
                        # reset the prob of resampling
                        p_resamp[basis, m] = 1.0
                elif w_new < -lambda_over_2_auto_corr[basis]:
                    w_new += lambda_over_2_auto_corr[basis]
                    changed = True
                    # unmark from being zero if necessary
                    if w_old == 0:
                        non_zero += 1
                        # reset the prob of resampling
                        p_resamp[basis, m] = 1.0
                else:
                    # gonna zero it out
                    w_new = 0.0

                    # decrease the p_resamp
                    p_resamp[basis, m] -= (p_resamp[basis, m] - \
                                           min_resamp) * resamp_decay

                    # set number of non-zero
                    if w_old == 0:
                        changed = False
                        wasted_basis += 1
                    else:
                        changed = True
                        non_zero -= 1

                # process any changes
                if changed:
                    #print "w[%d, %d] = %g" % (basis, m, w_new)
                    # update the expected values
                    w_diff = w_new - w_old
                    Xw[:, m] = Xw[:, m] + X[:, basis]*w_diff
                    E_new_m = np.exp(Xw[:, m])
                    S += E_new_m - E[:, m]
                    E[:, m] = E_new_m

                    # update the weight
                    w[basis, m] = w_new

                    # keep track of the sqrt sum squared diffs
                    sum2_w_diff += w_diff*w_diff

                # add to the old no matter what
                sum2_w_old += w_old*w_old

            # update the class and basis
            m = np.mod(m+1, w.shape[1])
            if m == 0:
                # we completed a cycle of labels
                basis = np.mod(basis+1, nd)
                if basis == 0:
                    # we completed a cycle of features
                    cycles += 1

                    # assess convergence
                    incr = np.sqrt(sum2_w_diff) / \
                           (np.sqrt(sum2_w_old)+np.finfo(np.float).eps)

                    # save the new weights
                    converged = incr < convergence_tol

                    if __debug__:
                        debug("SMLR_", \
                              "cycle=%d ; incr=%g ; non_zero=%d ; " %
                              (cycles, incr, non_zero) +
                              "wasted_basis=%d ; " %
                              (wasted_basis) +
                              "sum2_w_old=%g ; sum2_w_diff=%g" %
                              (sum2_w_old, sum2_w_diff))

                    # reset the sum diffs and wasted_basis
                    sum2_w_diff = 0.0
                    sum2_w_old = 0.0
                    wasted_basis = 0


        if not converged:
            raise ConvergenceError("More than %d Iterations without convergence" % \
                (maxiter))

        # calcualte the log likelihoods and posteriors for the training data
        #log_likelihood = x

        return cycles


    @due.dcite(
        Doi('10.1109/TPAMI.2005.127'),
        path="mvpa2.clfs.smlr:SMLR",
        description="Sparse multinomial-logistic regression classifier",
        tags=["implementation"])
    def _train(self, dataset):
        """Train the classifier using `dataset` (`Dataset`).
        """
        targets_sa_name = self.get_space()    # name of targets sa
        targets_sa = dataset.sa[targets_sa_name]  # actual targets sa

        # Process the labels to turn into 1 of N encoding
        uniquelabels = targets_sa.unique
        labels = _label2oneofm(targets_sa.value, uniquelabels)
        self._ulabels = uniquelabels.copy()

        Y = labels
        M = len(self._ulabels)

        # get the dataset information into easy vars
        X = dataset.samples

        # see if we are adding a bias term
        if self.params.has_bias:
            if __debug__:
                debug("SMLR_", "hstacking 1s for bias")

            # append the bias term to the features
            X = np.hstack((X, np.ones((X.shape[0], 1), dtype=X.dtype)))

        if self.params.implementation.upper() == 'C':
            _stepwise_regression = _cStepwiseRegression
            #
            # TODO: avoid copying to non-contig arrays, use strides in ctypes?
            if not (X.flags['C_CONTIGUOUS'] and X.flags['ALIGNED']):
                if __debug__:
                    debug("SMLR_",
                          "Copying data to get it C_CONTIGUOUS/ALIGNED")
                X = np.array(X, copy=True, dtype=np.double, order='C')

            # currently must be double for the C code
            if X.dtype != np.double:
                if __debug__:
                    debug("SMLR_", "Converting data to double")
                # must cast to double
                X = X.astype(np.double)

        # set the feature dimensions
        elif self.params.implementation.upper() == 'PYTHON':
            _stepwise_regression = self._python_stepwise_regression
        else:
            raise ValueError(
                  "Unknown implementation %s of stepwise_regression" %
                  self.params.implementation)

        # set the feature dimensions
        ns, nd = X.shape

        # decide the size of weights based on num classes estimated
        if self.params.fit_all_weights:
            c_to_fit = M
        else:
            c_to_fit = M - 1

        # Precompute what we can
        auto_corr = ((M - 1.) / (2. * M)) * (np.sum(X * X, 0))
        XY = np.dot(X.T, Y[:, :c_to_fit])
        lambda_over_2_auto_corr = (self.params.lm/2.)/auto_corr

        # set starting values
        w = np.zeros((nd, c_to_fit), dtype=np.double)
        Xw = np.zeros((ns, c_to_fit), dtype=np.double)
        E = np.ones((ns, c_to_fit), dtype=np.double)
        S = M * np.ones(ns, dtype=np.double)

        # set verbosity
        if __debug__:
            verbosity = int("SMLR_" in debug.active)
            debug('SMLR_', 'Calling stepwise_regression. Seed %s' % self.params.seed)
        else:
            verbosity = 0

        # call the chosen version of stepwise_regression
        cycles = _stepwise_regression(w,
                                      X,
                                      XY,
                                      Xw,
                                      E,
                                      auto_corr,
                                      lambda_over_2_auto_corr,
                                      S,
                                      M,
                                      self.params.maxiter,
                                      self.params.convergence_tol,
                                      self.params.resamp_decay,
                                      self.params.min_resamp,
                                      verbosity,
                                      self.params.seed)

        if cycles >= self.params.maxiter:
            # did not converge
            raise ConvergenceError(
                "More than %d iterations without convergence" %
                self.params.maxiter)

        # see if unsparsify the weights
        if self.params.unsparsify:
            # unsparsify
            w = self._unsparsify_weights(X, w)

        # resolve ties if present
        self.__ties = None
        if self.params.ties:
            if self.params.ties == 'random':
                # check if there is a tie showing itself as absent
                # difference between two w's
                wdot = np.dot(w.T, -w)
                ties = np.where(np.max(np.abs(wdot), axis=0) == 0)[0]
                if len(ties):
                    warning("SMLR: detected ties in categories %s.  Small "
                            "amount of noise will be injected into result "
                            "estimates upon prediction to break the ties"
                            % self._ulabels[ties])
                    self.__ties = ties
                    ## w_non0 = np.nonzero(w)
                    ## w_non0_max = np.max(np.abs(w[w_non0]))
                    ## w_non0_idx = np.unique(w_non0[0])
                    ## w_non0_len = len(w_non0_idx)
                    ## for f_idx in np.where(ties)[0]:
                    ##     w[w_non0_idx, f_idx] += \
                    ##          0.001 * np.random.normal(size=(w_non0_len,))
            else:
                raise ValueError("Do not know how to treat ties=%r"
                                 % (self.params.ties,))

        # save the weights
        self.__weights_all = w
        self.__weights = w[:dataset.nfeatures, :]

        if self.ca.is_enabled('feature_ids'):
            self.ca.feature_ids = np.where(np.max(np.abs(w[:dataset.nfeatures, :]),
                                             axis=1) > 0)[0]

        # and a bias
        if self.params.has_bias:
            self.__biases = w[-1, :]

        if __debug__:
            debug('SMLR', "train finished in %d cycles on data.shape=%s " %
                  (cycles, X.shape) +
                  "min:max(data)=%f:%f, got min:max(w)=%f:%f" %
                  (np.min(X), np.max(X), np.min(w), np.max(w)))

    def _unsparsify_weights(self, samples, weights):
        """Unsparsify weights via least squares regression."""
        # allocate for the new weights
        new_weights = np.zeros(weights.shape, dtype=np.double)

        # get the sample data we're predicting and the sum squared
        # total variance
        b = samples
        sst = np.power(b - b.mean(0),2).sum(0)

        # loop over each column
        for i in range(weights.shape[1]):
            w = weights[:, i]

            # get the nonzero ind
            ind = w != 0

            # get the features with non-zero weights
            a = b[:, ind]

            # predict all the data with the non-zero features
            betas = np.linalg.lstsq(a, b)[0]

            # determine the R^2 for each feature based on the sum
            # squared prediction error
            f = np.dot(a, betas)
            sse = np.power((b - f), 2).sum(0)
            rsquare = np.zeros(sse.shape, dtype=sse.dtype)
            gind = sst > 0
            rsquare[gind] = 1 - (sse[gind]/sst[gind])

            # derrive new weights by combining the betas and weights
            # scaled by the rsquare
            new_weights[:, i] = np.dot(w[ind], betas)*rsquare

        # take the tails
        tozero = np.abs(new_weights) < self.params.std_to_keep * np.std(new_weights)
        orig_zero = weights == 0.0
        if orig_zero.sum() < tozero.sum():
            # should not end up with fewer than start
            tozero = orig_zero
        new_weights[tozero] = 0.0

        if __debug__:
            debug('SMLR_', "Start nonzero: %d; Finish nonzero: %d" %
                  ((weights != 0).sum(), (new_weights != 0).sum()))

        return new_weights


    ##REF: Name was automagically refactored
    def _get_feature_ids(self):
        """Return ids of the used features
        """
        return np.where(np.max(np.abs(self.__weights), axis=1)>0)[0]

    @accepts_dataset_as_samples
    def _predict(self, data):
        """Predict the output for the provided data.
        """
        # see if we are adding a bias term
        if self.params.has_bias:
            # append the bias term to the features
            data = np.hstack((data,
                             np.ones((data.shape[0], 1), dtype=data.dtype)))

        # append the zeros column to the weights if necessary
        if self.params.fit_all_weights:
            w = self.__weights_all
        else:
            w = np.hstack((self.__weights_all,
                          np.zeros((self.__weights_all.shape[0], 1))))

        # determine the probability values for making the prediction
        dot_prod = np.dot(data, w)
        E = np.exp(dot_prod)
        if self.__ties is not None:
            # 1e-5 should be adequate since anyways this is done
            # already after exponentiation
            E[:, self.__ties] += \
                 1e-5 * np.random.normal(size=(len(E), len(self.__ties)))
        S = np.sum(E, 1)

        if __debug__:
            debug('SMLR', "predict on data.shape=%s min:max(data)=%f:%f " %
                  (repr(data.shape), np.min(data), np.max(data)) +
                  "min:max(w)=%f:%f min:max(dot_prod)=%f:%f min:max(E)=%f:%f" %
                  (np.min(w), np.max(w), np.min(dot_prod), np.max(dot_prod),
                   np.min(E), np.max(E)))
        values = E / S[:, np.newaxis] #.repeat(E.shape[1], axis=1)
        self.ca.estimates = values

        # generate predictions
        predictions = np.asarray([self._ulabels[np.argmax(vals)]
                                 for vals in values])
        # no need to assign conditional attribute here -- would be done
        # in Classifier._postpredict anyway
        #self.predictions = predictions

        return predictions


    ##REF: Name was automagically refactored
    def get_sensitivity_analyzer(self, **kwargs):
        """Returns a sensitivity analyzer for SMLR."""
        return SMLRWeights(self, **kwargs)


    biases = property(lambda self: self.__biases)
    weights = property(lambda self: self.__weights)



class SMLRWeights(Sensitivity):
    """`SensitivityAnalyzer` that reports the weights SMLR trained
    on a given `Dataset`.

    By default SMLR provides multiple weights per feature (one per label in
    training dataset). By default, all weights are combined into a single
    sensitivity value. Please, see the `FeaturewiseMeasure` constructor
    arguments how to custmize this behavior.
    """

    _LEGAL_CLFS = [ SMLR ]


    def _call(self, dataset=None):
        """Extract weights from SMLR classifier.

        SMLR always has weights available, so nothing has to be computed here.
        """
        clf = self.clf
        # transpose to have the number of features on the second axis
        # (as usual)
        weights = clf.weights.T

        if __debug__:
            debug('SMLR',
                  "Extracting weights for %d-class SMLR" %
                  (len(weights) + 1) +
                  "Result: min=%f max=%f" %
                  (np.min(weights), np.max(weights)))

        # limit the labels to the number of sensitivity sets, to deal
        # with the case of `fit_all_weights=False`
        ds = Dataset(weights,
                     sa={clf.get_space(): clf._ulabels[:len(weights)]})

        if clf.params.has_bias:
            ds.sa['biases'] = clf.biases
        return ds

