<reponame>andycon/PyMVPA
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#
#   See COPYING file distributed along with the PyMVPA package for the
#   copyright and license terms.
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"""Utility class to compute the transfer error of classifiers."""

__docformat__ = 'restructuredtext'

import mvpa2.support.copy as copy

import numpy as np

from StringIO import StringIO
from math import log10, ceil

from mvpa2.base import externals
from mvpa2.base.node import Node

from mvpa2.misc.errorfx import mean_power_fx, root_mean_power_fx, rms_error, \
     relative_rms_error, mean_mismatch_error, auc_error
from mvpa2.base import warning
from mvpa2.datasets import Dataset
from mvpa2.base.collections import Collectable
from mvpa2.base.state import ConditionalAttribute, ClassWithCollections, \
     UnknownStateError
from mvpa2.base.dochelpers import enhanced_doc_string, table2string
from mvpa2.clfs.stats import auto_null_dist

if __debug__:
    from mvpa2.base import debug

if externals.exists('scipy'):
    from scipy.stats.stats import nanmean
    from mvpa2.misc.stats import chisquare
    from scipy.stats import linregress, friedmanchisquare
    from mvpa2.misc.errorfx import corr_error, corr_error_prob
else:
    from mvpa2.clfs.stats import nanmean
    chisquare = None
    linregress = None

def _p2(x, prec=2, formatting=''):
    """Helper to print depending on the type nicely. For some
    reason %.2g for 100 prints exponential form which is ugly
    """
    if isinstance(x, int):
        s = "%d" % x
    elif isinstance(x, float):
        s = ("%%.%df" % prec % x).rstrip('0').rstrip('.').lstrip()
        if s == '':
            s = '0'
    else:
        s = "%s" % x
    return formatting + s



class SummaryStatistics(object):
    """Basic class to collect targets/predictions and report summary statistics

    It takes care about collecting the sets, which are just tuples
    (targets, predictions, estimates). While 'computing' the matrix, all
    sets are considered together.  Children of the class are
    responsible for computation and display.
    """

    _STATS_DESCRIPTION = (
        ('# of sets',
         'number of target/prediction sets which were provided', None),
        )


    def __init__(self, targets=None, predictions=None, estimates=None, sets=None):
        """Initialize SummaryStatistics

        targets or predictions cannot be provided alone (ie targets
        without predictions)

        Parameters
        ----------
        targets
         Optional set of targets
        predictions
         Optional set of predictions
        estimates
         Optional set of estimates (which served for prediction)
        sets
         Optional list of sets
        """
        self._computed = False
        """Flag either it was computed for a given set of data"""

        self.__sets = (sets, [])[int(sets is None)]
        """Datasets (target, prediction) to compute confusion matrix on"""

        self._stats = {}
        """Dictionary to keep statistics. Initialized here to please pylint"""

        if not targets is None or not predictions is None:
            if not targets is None and not predictions is None:
                self.add(targets=targets, predictions=predictions,
                         estimates=estimates)
            else:
                raise ValueError, \
                      "Please provide none or both targets and predictions"


    def add(self, targets, predictions, estimates=None):
        """Add new results to the set of known results"""
        if len(targets) != len(predictions):
            raise ValueError, \
                  "Targets[%d] and predictions[%d]" % (len(targets),
                                                       len(predictions)) + \
                  " have different number of samples"

        # extract value if necessary
        if isinstance(estimates, Collectable):
            try:
                estimates = estimates.value
            except UnknownStateError, e:
                estimates = None        # reset to None to be safe below
                if __debug__:
                    debug('CM', "Cannot get yet uncollected estimates from %s",
                          estimates)

        if estimates is not None and len(targets) != len(estimates):
            raise ValueError, \
                  "Targets[%d] and estimates[%d]" % (len(targets),
                                                  len(estimates)) + \
                  " have different number of samples"

        # enforce labels in predictions to be of the same datatype as in
        # targets, since otherwise we are getting doubles for unknown at a
        # given moment labels
        nonetype = type(None)
        for i in xrange(len(targets)):
            t1, t2 = type(targets[i]), type(predictions[i])
            # if there were no prediction made - leave None, otherwise
            # convert to appropriate type
            if t1 != t2 and t2 != nonetype:
                #warning("Obtained target %s and prediction %s are of " %
                #       (t1, t2) + "different datatypes.")
                if isinstance(predictions, tuple):
                    predictions = list(predictions)
                predictions[i] = t1(predictions[i])

        if estimates is not None:
            # assure that we have a copy, or otherwise further in-place
            # modifications might screw things up (some classifiers share
            # estimates and spit out results)
            estimates = copy.deepcopy(estimates)

        self.__sets.append( (targets, predictions, estimates) )
        self._computed = False


    ##REF: Name was automagically refactored
    def as_string(self, short=False, header=True, summary=True,
                 description=False):
        """'Pretty print' the matrix

        Parameters
        ----------
        short : bool
          if True, ignores the rest of the parameters and provides consise
          1 line summary
        header : bool
          print header of the table
        summary : bool
          print summary (accuracy)
        description : bool
          print verbose description of presented statistics
        """
        raise NotImplementedError


    def __str__(self):
        """String summary over the `SummaryStatistics`

        It would print description of the summary statistics if 'CM'
        debug target is active
        """
        if __debug__:
            description = ('CM' in debug.active)
        else:
            description = False
        return self.as_string(short=False, header=True, summary=True,
                             description=description)


    def __iadd__(self, other):
        """Add the sets from `other` s `SummaryStatistics` to current one
        """
        #print "adding ", other, " to ", self
        # need to do shallow copy, or otherwise smth like "cm += cm"
        # would loop forever and exhaust memory eventually
        othersets = copy.copy(other.__sets)
        for set in othersets:
            self.add(*set)#[0], set[1])
        return self


    def __add__(self, other):
        """Add two `SummaryStatistics`s
        """
        result = copy.deepcopy(self)
        result += other
        return result


    def compute(self):
        """Actually compute the confusion matrix based on all the sets"""
        if self._computed:
            return

        self._compute()
        self._computed = True


    def _compute(self):
        """Compute basic statistics
        """
        self._stats = {'# of sets' : len(self.sets)}


    @property
    def summaries(self):
        """Return a list of separate summaries per each stored set"""
        return [ self.__class__(sets=[x]) for x in self.sets ]


    @property
    def error(self):
        raise NotImplementedError


    @property
    def stats(self):
        self.compute()
        return self._stats


    def reset(self):
        """Cleans summary -- all data/sets are wiped out
        """
        self.__sets = []
        self._computed = False


    sets = property(lambda self:self.__sets)


class ROCCurve(object):
    """Generic class for ROC curve computation and plotting
    """

    def __init__(self, labels, sets=None):
        """
        Parameters
        ----------
        labels : list
          labels which were used (in order of estimates if multiclass,
          or 1 per class for binary problems (e.g. in SMLR))
        sets : list of tuples
          list of sets for the analysis
        """
        self._labels = labels
        self._sets = sets
        self.__computed = False


    def _compute(self):
        """Lazy computation if needed
        """
        if self.__computed:
            return
        # local bindings
        labels = self._labels
        Nlabels = len(labels)
        sets = self._sets

        # Handle degenerate cases politely
        if Nlabels < 2:
            warning("ROC was asked to be evaluated on data with %i"
                    " labels which is a degenerate case." % Nlabels)
            self._ROCs = []
            self._aucs = []
            return

        # take sets which have values in the shape we can handle
        def _check_values(set_):
            """Check if values are 'acceptable'"""
            if len(set_)<3:
                return False
            x = set_[2]                 # estimates
            # TODO: OPT: need optimization
            if (x is None) or len(x) == 0:
                return False          # undefined
            for v in x:
                try:
                    if Nlabels <= 2 and np.isscalar(v):
                        continue
                    if (isinstance(v, dict) or # not dict for pairs
                        ((Nlabels >= 2) and len(v) != Nlabels)): # 1 per each label for multiclass
                        return False
                except Exception, e:
                    # Something else which is not supported, like
                    # in shogun interface we don't yet extract values per each label or
                    # in pairs in the case of built-in multiclass
                    if __debug__:
                        debug('ROC', "Exception %s while checking "
                              "either %s are valid labels" % (str(e), x))
                    return False
            return True

        sets_wv = [x for x in sets if _check_values(x)]
        # check if all had estimates, if not -- complain
        Nsets_wv = len(sets_wv)
        if Nsets_wv > 0 and len(sets) != Nsets_wv:
            warning("Only %d sets have estimates assigned from %d sets. "
                    "ROC estimates might be incorrect." %
                    (Nsets_wv, len(sets)))
        # bring all values to the same 'shape':
        #  1 value per each label. In binary classifier, if only a single
        #  value is provided, add '0' for 0th label 'value'... it should
        #  work taking drunk Yarik logic ;-)
        # yoh: apparently it caused problems whenever we had just a single
        #      unique label in the sets. Introduced handling for
        #      NLabels == 1
        for iset, s in enumerate(sets_wv):
            # we will do inplace modification, thus go by index
            estimates = s[2]
            # we would need it to be a list to reassign element with a list
            if isinstance(estimates, np.ndarray) and len(estimates.shape)==1:
                # XXX ??? so we are going away from inplace modifications?
                estimates = list(estimates)
            rangev = None
            for i in xrange(len(estimates)):
                v = estimates[i]
                if np.isscalar(v):
                    if Nlabels == 1:
                        # ensure the right dimensionality
                        estimates[i] = np.array(v, ndmin=2)
                    elif Nlabels == 2:
                        def last_el(x):
                            """Helper function. Returns x if x is scalar, and
                            last element if x is not (ie list/tuple)"""
                            if np.isscalar(x): return x
                            else:             return x[-1]
                        if rangev is None:
                            # we need to figure out min/max estimates
                            # to invert for the 0th label
                            estimates_ = [last_el(x) for x in estimates]
                            rangev = np.min(estimates_) + np.max(estimates_)
                        estimates[i] = [rangev - v, v]
                    else:
                        raise ValueError, \
                              "Cannot have a single 'value' for multiclass" \
                              " classification. Got %s" % (v)
                elif len(v) != Nlabels:
                    raise ValueError, \
                          "Got %d estimates whenever there is %d labels" % \
                          (len(v), Nlabels)
            # reassign possibly adjusted estimates
            sets_wv[iset] = (s[0], s[1], np.asarray(estimates))


        # we need to estimate ROC per each label
        # XXX order of labels might not correspond to the one among 'estimates'
        #     which were used to make a decision... check
        rocs, aucs = [], []             # 1 per label
        for i,label in enumerate(labels):
            aucs_pl = []
            ROCs_pl = []
            for s in sets_wv:
                targets_pl = (np.asanyarray(s[0]) == label).astype(int)
                # XXX we might unify naming between AUC/ROC
                ROC = auc_error
                aucs_pl += [ROC([np.asanyarray(x)[i] for x in s[2]], targets_pl)]
                ROCs_pl.append(ROC)
            if len(aucs_pl)>0:
                rocs += [ROCs_pl]
                aucs += [nanmean(aucs_pl)]
                #aucs += [np.mean(aucs_pl)]

        # store results within the object
        self._ROCs =  rocs
        self._aucs = aucs
        self.__computed = True


    @property
    def aucs(self):
        """Compute and return set of AUC values 1 per label
        """
        self._compute()
        return self._aucs


    @property
    ##REF: Name was automagically refactored
    def rocs(self):
        self._compute()
        return self._ROCs


    def plot(self, label_index=0):
        """

        TODO: make it friendly to labels given by values?
              should we also treat labels_map?
        """
        externals.exists("pylab", raise_=True)
        import pylab as pl

        self._compute()

        labels = self._labels
        # select only rocs for the given label
        rocs = self.rocs[label_index]

        fig = pl.gcf()
        ax = pl.gca()

        pl.plot([0, 1], [0, 1], 'k:')

        for ROC in rocs:
            pl.plot(ROC.fp, ROC.tp, linewidth=1)

        pl.axis((0.0, 1.0, 0.0, 1.0))
        pl.axis('scaled')
        pl.title('Label %s. Mean AUC=%.2f' % (label_index, self.aucs[label_index]))

        pl.xlabel('False positive rate')
        pl.ylabel('True positive rate')


class ConfusionMatrix(SummaryStatistics):
    """Class to contain information and display confusion matrix.

    Implementation of the `SummaryStatistics` in the case of
    classification problem. Actual computation of confusion matrix is
    delayed until all data is acquired (to figure out complete set of
    labels). If testing data doesn't have a complete set of labels,
    but you like to include all labels, provide them as a parameter to
    the constructor.

    Confusion matrix provides a set of performance statistics (use
    as_string(description=True) for the description of abbreviations),
    as well ROC curve (http://en.wikipedia.org/wiki/ROC_curve)
    plotting and analysis (AUC) in the limited set of problems:
    binary, multiclass 1-vs-all.
    """

    _STATS_DESCRIPTION = (
        ('TP', 'true positive (AKA hit)', None),
        ('TN', 'true negative (AKA correct rejection)', None),
        ('FP', 'false positive (AKA false alarm, Type I error)', None),
        ('FN', 'false negative (AKA miss, Type II error)', None),
        ('TPR', 'true positive rate (AKA hit rate, recall, sensitivity)',
                'TPR = TP / P = TP / (TP + FN)'),
        ('FPR', 'false positive rate (AKA false alarm rate, fall-out)',
                'FPR = FP / N = FP / (FP + TN)'),
        ('ACC', 'accuracy', 'ACC = (TP + TN) / (P + N)'),
        ('SPC', 'specificity', 'SPC = TN / (FP + TN) = 1 - FPR'),
        ('PPV', 'positive predictive value (AKA precision)',
                'PPV = TP / (TP + FP)'),
        ('NPV', 'negative predictive value', 'NPV = TN / (TN + FN)'),
        ('FDR', 'false discovery rate', 'FDR = FP / (FP + TP)'),
        ('MCC', "Matthews Correlation Coefficient",
                "MCC = (TP*TN - FP*FN)/sqrt(P N P' N')"),
        ('F1',  'F1 score',
                "F1 = 2TP / (P + P') = 2TP / (2TP + FP + FN)"),
        ('AUC', "Area under (AUC) curve", None),
        ('CHI^2', "Chi-square of confusion matrix", None),
        ('LOE(ACC)', "Linear Order Effect in ACC across sets", None),
        ## ('Friedman(TPR)',
        ##  "Friedman CHI^2 test of TPRs consistencies across sets", None),
        ## ('Friedman(CM)',
        ##  "Friedman CHI^2 test of full CM (normalized) consistencies across sets", None),
        ) + SummaryStatistics._STATS_DESCRIPTION


    def __init__(self, labels=None, labels_map=None, **kwargs):
        """Initialize ConfusionMatrix with optional list of `labels`

        Parameters
        ----------
        labels : list
         Optional set of labels to include in the matrix
        labels_map : None or dict
         Dictionary from original dataset to show mapping into
         numerical labels
        targets
         Optional set of targets
        predictions
         Optional set of predictions
         """

        SummaryStatistics.__init__(self, **kwargs)

        if labels == None:
            labels = []

        self.__labels = labels
        """List of known labels"""
        self.__labels_in_custom_order = bool(len(labels))
        """So we know later on either we could resort them"""
        self.__labels_map = labels_map
        """Mapping from original into given labels"""
        self.__matrix = None
        """Resultant confusion matrix"""


    def __call__(self, predictions, targets, estimates=None, store=False):
        """Computes confusion matrix (counts)

        It would rely on previously provided 'labels' to define columns/rows
        of the matrix to assure consistency across multiple invocations.

        Parameters
        ----------
        store : bool, optional
          By default, this function does not modify an existing
          instance of the ConfusionMatrix, and is just used merely to
          provide a resultant confusion matrix.  If 'store' set to
          True, provided set of predictions and targets would be added
          to the sets.

        Returns
        -------
        numpy.ndarray
           counts of hits with rows -- predictions, columns -- targets
        """
        labels = self.__labels
        if labels is None or not len(labels):
            raise RuntimeError("ConfusionMatrix must have labels assigned prior"
                               "__call__()")
        # verify that we know all the labels
        labels_set = set(labels)
        if not (labels_set.issuperset(targets)
                and labels_set.issuperset(predictions)):
            raise ValueError("Known labels %r does not include some labels "
                             "found in predictions %r or targets %r provided"
                             % (labels_set, set(predictions), set(targets)))

        Nlabels = len(labels_set)
        cm = np.zeros( (Nlabels, Nlabels), dtype=int )

        rev_map = dict([ (x[1], x[0]) for x in enumerate(labels)])
        for t,p in zip(targets, predictions):
            cm[rev_map[p], rev_map[t]] += 1

        if store:
            self.add(targets=targets, predictions=predictions, estimates=estimates)
        return cm

    # XXX might want to remove since summaries does the same, just without
    #     supplying labels
    @property
    def matrices(self):
        """Return a list of separate confusion matrix per each stored set"""
        return [ self.__class__(labels=self.labels,
                                labels_map=self.labels_map,
                                sets=[x]) for x in self.sets]


    def _compute(self):
        """Actually compute the confusion matrix based on all the sets"""

        super(ConfusionMatrix, self)._compute()

        if __debug__:
            if not self.__matrix is None:
                debug("LAZY",
                      "Have to recompute %s#%s" \
                        % (self.__class__.__name__, id(self)))


        # TODO: BinaryClassifier might spit out a list of predictions for each
        # value need to handle it... for now just keep original labels
        try:
            # figure out what labels we have
            labels = \
                list(reduce(lambda x, y: x.union(set(y[0]).union(set(y[1]))),
                            self.sets,
                            set(self.__labels)))
        except:
            labels = self.__labels


        # Check labels_map if it was provided if it covers all the labels
        labels_map = self.__labels_map
        if labels_map is not None:
            labels_set = set(labels)
            map_labels_set = set(labels_map.values())

            if not map_labels_set.issuperset(labels_set):
                warning("Provided labels_map %s is not coherent with labels "
                        "provided to ConfusionMatrix. No reverse mapping "
                        "will be provided" % labels_map)
                labels_map = None

        # Create reverse map
        labels_map_rev = None
        if labels_map is not None:
            labels_map_rev = {}
            for k,v in labels_map.iteritems():
                v_mapping = labels_map_rev.get(v, [])
                v_mapping.append(k)
                labels_map_rev[v] = v_mapping
        self.__labels_map_rev = labels_map_rev

        labels.sort()

        if self.__labels is None or not len(self.__labels):
            self.__labels = labels          # just store the recomputed labels
        else:
            # we should append them to already known ones
            # Otherwise order of labels known before might be altered
            add_labels = [x for x in labels if not (x in self.__labels)]
            if len(add_labels):
                self.__labels += add_labels
            labels = self.__labels      # and use them later on

            if not self.__labels_in_custom_order:
                labels.sort()

        Nlabels, Nsets = len(labels), len(self.sets)

        if __debug__:
            debug("CM", "Got labels %s" % labels)

        # Create a matrix for all votes
        mat_all = np.zeros( (Nsets, Nlabels, Nlabels), dtype=int )

        # create total number of samples of each label counts
        # just for convinience I guess since it can always be
        # computed from mat_all
        counts_all = np.zeros( (Nsets, Nlabels) )

        # reverse mapping from label into index in the list of labels
        rev_map = dict([ (x[1], x[0]) for x in enumerate(labels)])
        for iset, set_ in enumerate(self.sets):
            for t,p in zip(*set_[:2]):
                mat_all[iset, rev_map[p], rev_map[t]] += 1


        # for now simply compute a sum of votes across different sets
        # we might do something more sophisticated later on, and this setup
        # should easily allow it
        self.__matrix = np.sum(mat_all, axis=0)
        self.__Nsamples = np.sum(self.__matrix, axis=0)
        self.__Ncorrect = sum(np.diag(self.__matrix))

        TP = np.diag(self.__matrix)
        offdiag = self.__matrix - np.diag(TP)
        stats = {
            '# of labels' : Nlabels,
            'TP' : TP,
            'FP' : np.sum(offdiag, axis=1),
            'FN' : np.sum(offdiag, axis=0)}

        stats['CORR']  = np.sum(TP)
        stats['TN']  = stats['CORR'] - stats['TP']
        stats['P']  = stats['TP'] + stats['FN']
        stats['N']  = np.sum(stats['P']) - stats['P']
        stats["P'"] = stats['TP'] + stats['FP']
        stats["N'"] = stats['TN'] + stats['FN']
        stats['TPR'] = stats['TP'] / (1.0*stats['P'])
        # reset nans in TPRs to 0s whenever there is no entries
        # for those labels among the targets
        stats['TPR'][stats['P'] == 0] = 0
        stats['PPV'] = stats['TP'] / (1.0*stats["P'"])
        stats['NPV'] = stats['TN'] / (1.0*stats["N'"])
        stats['FDR'] = stats['FP'] / (1.0*stats["P'"])
        stats['SPC'] = (stats['TN']) / (1.0*stats['FP'] + stats['TN'])
        stats['F1'] = 2.*stats['TP'] / (stats["P"] + stats["P'"])

        MCC_denom = np.sqrt(1.0*stats['P']*stats['N']*stats["P'"]*stats["N'"])
        nz = MCC_denom!=0.0
        stats['MCC'] = np.zeros(stats['TP'].shape)
        stats['MCC'][nz] = \
                 (stats['TP'] * stats['TN'] - stats['FP'] * stats['FN'])[nz] \
                  / MCC_denom[nz]

        stats['ACC'] = np.sum(TP)/(1.0*np.sum(stats['P']))
        # TODO: STD of accuracy and corrected one according to
        #    Nadeau and Bengio [50]
        stats['ACC%'] = stats['ACC'] * 100.0
        if chisquare:
            # indep_rows to assure reasonable handling of disbalanced
            # cases
            stats['CHI^2'] = chisquare(self.__matrix, exp='indep_rows')
        if linregress and Nsets > 3:
            # Lets see if there is possible order effect in accuracy
            # (e.g. it goes down through splits)

            # simple linear regression
            ACC_per_set = [np.sum(np.diag(m))/np.sum(m).astype(float)
                           for m in mat_all]
            stats['LOE(ACC):slope'], stats['LOE(ACC):inter'], \
                stats['LOE(ACC):r'], stats['LOE(ACC):p'], _ = \
                linregress(np.arange(Nsets), ACC_per_set)

            TPRs_per_set = [np.diag(m)/np.sum(m, axis=0).astype(float)
                            for m in mat_all]
            # Confusion ratios (both TPs or FPs)
            # we want to divide each column but sum in the column
            CM_per_set = [np.ravel(m/np.sum(m, axis=0).astype(float)[None, :])
                          for m in mat_all]

            ## stats['Friedman(TPR):chi^2'], stats['Friedman(TPR):p'] = \
            ##                               friedmanchisquare(*TPRs_per_set)
            ## stats['Friedman(CM):chi^2'], stats['Friedman(CM):p'] = \
            ##                              friedmanchisquare(*CM_per_set)

        #
        # ROC computation if available
        ROC = ROCCurve(labels=labels, sets=self.sets)
        aucs = ROC.aucs
        if len(aucs)>0:
            stats['AUC'] = aucs
            if len(aucs) != Nlabels:
                raise RuntimeError, \
                      "We must got a AUC per label. Got %d instead of %d" % \
                      (len(aucs), Nlabels)
            self.ROC = ROC
        else:
            # we don't want to provide ROC if it is bogus
            stats['AUC'] = [np.nan] * Nlabels
            self.ROC = None


        # compute mean stats
        for k,v in stats.items():
            stats['mean(%s)' % k] = np.mean(v)

        self._stats.update(stats)


    ##REF: Name was automagically refactored
    def as_string(self, short=False, header=True, summary=True,
                 description=False):
        """'Pretty print' the matrix

        Parameters
        ----------
        short : bool
          if True, ignores the rest of the parameters and provides consise
          1 line summary
        header : bool
          print header of the table
        summary : bool
          print summary (accuracy)
        description : bool
          print verbose description of presented statistics
        """
        if len(self.sets) == 0:
            return "Empty"

        self.compute()

        # some shortcuts
        labels = self.__labels
        labels_map_rev = self.__labels_map_rev
        matrix = self.__matrix

        labels_rev = []
        if labels_map_rev is not None:
            labels_rev = [','.join([str(x) for x in labels_map_rev[l]])
                                   for l in labels]

        out = StringIO()
        # numbers of different entries
        Nlabels = len(labels)
        Nsamples = self.__Nsamples.astype(int)

        stats = self._stats
        if short:
            return "%(# of sets)d sets %(# of labels)d labels " \
                   " ACC:%(ACC).2f" \
                   % stats

        Ndigitsmax = int(ceil(log10(max(Nsamples))))
        Nlabelsmax = max( [len(str(x)) for x in labels] )

        # length of a single label/value
        L = max(Ndigitsmax+2, Nlabelsmax) #, len("100.00%"))
        res = ""

        stats_perpredict = ["P'", "N'", 'FP', 'FN', 'PPV', 'NPV', 'TPR',
                            'SPC', 'FDR', 'MCC', 'F1']
        # print AUC only if ROC was computed
        if self.ROC is not None: stats_perpredict += [ 'AUC' ]
        stats_pertarget = ['P', 'N', 'TP', 'TN']
        stats_summary = ['ACC', 'ACC%', '# of sets']
        # 'Friedman(TPR):p', 'Friedman(CM):p'


        #prefixlen = Nlabelsmax + 2 + Ndigitsmax + 1
        prefixlen = Nlabelsmax + 1
        pref = ' '*(prefixlen) # empty prefix

        if matrix.shape != (Nlabels, Nlabels):
            raise ValueError, \
                  "Number of labels %d doesn't correspond the size" + \
                  " of a confusion matrix %s" % (Nlabels, matrix.shape)

        # list of lists of what is printed
        printed = []
        underscores = [" %s" % ("-" * L)] * Nlabels
        if header:
            # labels
            printed.append(['@l----------.        '] + labels_rev)
            printed.append(['@lpredictions\\targets'] + labels)
            # underscores
            printed.append(['@l            `------'] \
                           + underscores + stats_perpredict)

        # matrix itself
        for i, line in enumerate(matrix):
            l = labels[i]
            if labels_rev != []:
                l = '@r%10s / %s' % (labels_rev[i], l)
            printed.append(
                [l] +
                [ str(x) for x in line ] +
                [ _p2(stats[x][i]) for x in stats_perpredict])

        if summary:
            ## Various alternative schemes ;-)
            # printed.append([''] + underscores)
            # printed.append(['@lPer target \ Means:'] + underscores + \
            #               [_p2(x) for x in mean_stats])
            # printed.append(['Means:'] + [''] * len(labels)
            #                + [_p2(x) for x in mean_stats])
            printed.append(['@lPer target:'] + underscores)
            for stat in stats_pertarget:
                printed.append([stat] + [
                    _p2(stats[stat][i]) for i in xrange(Nlabels)])

            # compute mean stats
            # XXX refactor to expose them in stats as well, as
            #     mean(FCC)
            mean_stats = np.mean(np.array([stats[k] for k in stats_perpredict]),
                                axis=1)
            printed.append(['@lSummary \ Means:'] + underscores
                           + [_p2(stats['mean(%s)' % x])
                              for x in stats_perpredict])

            if 'CHI^2' in self.stats:
                chi2t = stats['CHI^2']
                printed.append(['CHI^2'] + [_p2(chi2t[0])]
                               + ['@wp=%.2g' % chi2t[1]])

            for stat in stats_summary:
                if stat in stats:
                    printed.append([stat] + [_p2(stats[stat])])

            if 'LOE(ACC):inter' in stats:
                # Inject into sets line
                printed[-1] += [
                    '@w ACC(i) = %(LOE(ACC):inter).2g%(LOE(ACC):slope)+.2g*i'
                    % stats +
                    ' p=' + _p2(stats['LOE(ACC):p']) +
                    ' r=' + _p2(stats['LOE(ACC):r']) +
                    ' r^2=' + _p2(stats['LOE(ACC):r']**2)]

        table2string(printed, out)

        if description:
            out.write("\nStatistics computed in 1-vs-rest fashion per each " \
                      "target.\n")
            out.write("Abbreviations (for details see " \
                      "http://en.wikipedia.org/wiki/ROC_curve):\n")
            for d, val, eq in self._STATS_DESCRIPTION:
                out.write(" %-3s: %s\n" % (d, val))
                if eq is not None:
                    out.write("      " + eq + "\n")

        #out.write("%s" % printed)
        result = out.getvalue()
        out.close()
        return result


    def plot(self, labels=None, numbers=False, origin='upper',
             numbers_alpha=None, xlabels_vertical=True, numbers_kwargs={},
             **kwargs):
        """Provide presentation of confusion matrix in image

        Parameters
        ----------
        labels : list of int or str
          Optionally provided labels guarantee the order of
          presentation. Also value of None places empty column/row,
          thus provides visual groupping of labels (Thanks Ingo)
        numbers : bool
          Place values inside of confusion matrix elements
        numbers_alpha : None or float
          Controls textual output of numbers. If None -- all numbers
          are plotted in the same intensity. If some float -- it controls
          alpha level -- higher value would give higher contrast. (good
          value is 2)
        origin : str
          Which left corner diagonal should start
        xlabels_vertical : bool
          Either to plot xlabels vertical (benefitial if number of labels
          is large)
        numbers_kwargs : dict
          Additional keyword parameters to be added to numbers (if numbers
          is True)
        **kwargs
          Additional arguments given to imshow (\eg me cmap)

        Returns
        -------
         (fig, im, cb) -- figure, imshow, colorbar
        """

        externals.exists("pylab", raise_=True)
        import pylab as pl

        self.compute()
        labels_order = labels

        # some shortcuts
        labels = self.__labels
        labels_map = self.__labels_map
        labels_map_rev = self.__labels_map_rev
        matrix = self.__matrix

        # craft original mapping from a label into index in the matrix
        labels_indexes = dict([(x,i) for i,x in enumerate(labels)])

        labels_rev = []
        if labels_map_rev is not None:
            labels_rev = [','.join([str(x) for x in labels_map_rev[l]])
                                   for l in labels]
            labels_map_full = dict(zip(labels_rev, labels))

        if labels_order is not None:
            labels_order_filtered = filter(lambda x:x is not None, labels_order)
            labels_order_filtered_set = set(labels_order_filtered)
            # Verify if all labels provided in labels
            if set(labels) == labels_order_filtered_set:
                # We were provided numerical (most probably) set
                labels_plot = labels_order
            elif len(labels_rev) \
                     and set(labels_rev) == labels_order_filtered_set:
                # not clear if right whenever there were multiple labels
                # mapped into the same
                labels_plot = []
                for l in labels_order:
                    v = None
                    if l is not None: v = labels_map_full[l]
                    labels_plot += [v]
            else:
                raise ValueError, \
                      "Provided labels %s do not match set of known " \
                      "original labels (%s) or mapped labels (%s)" % \
                      (labels_order, labels, labels_rev)
        else:
            labels_plot = labels

        # where we have Nones?
        isempty = np.array([l is None for l in labels_plot])
        non_empty = np.where(np.logical_not(isempty))[0]
        # numbers of different entries
        NlabelsNN = len(non_empty)
        Nlabels = len(labels_plot)

        if matrix.shape != (NlabelsNN, NlabelsNN):
            raise ValueError, \
                  "Number of labels %d doesn't correspond the size" + \
                  " of a confusion matrix %s" % (NlabelsNN, matrix.shape)

        confusionmatrix = np.zeros((Nlabels, Nlabels))
        mask = confusionmatrix.copy()
        ticks = []
        tick_labels = []
        # populate in a silly way
        reordered_indexes = [labels_indexes[i] for i in labels_plot
                             if i is not None]
        for i, l in enumerate(labels_plot):
            if l is not None:
                j = labels_indexes[l]
                confusionmatrix[i, non_empty] = matrix[j, reordered_indexes]
                confusionmatrix[non_empty, i] = matrix[reordered_indexes, j]
                ticks += [i + 0.5]
                if labels_map_rev is not None:
                    tick_labels += ['/'.join(labels_map_rev[l])]
                else:
                    tick_labels += [str(l)]
            else:
                mask[i, :] = mask[:, i] = 1

        confusionmatrix = np.ma.MaskedArray(confusionmatrix, mask=mask)

        # turn off automatic update if interactive
        if pl.matplotlib.get_backend() == 'TkAgg':
            pl.ioff()

        fig = pl.gcf()
        ax = pl.gca()
        ax.axis('off')

        # some customization depending on the origin
        xticks_position, yticks, ybottom = {
            'upper': ('top', [Nlabels-x for x in ticks], 0.1),
            'lower': ('bottom', ticks, 0.2)
            }[origin]


        # Plot
        axi = fig.add_axes([0.15, ybottom, 0.7, 0.7])
        im = axi.imshow(confusionmatrix, interpolation="nearest", origin=origin,
                        aspect='equal', extent=(0, Nlabels, 0, Nlabels),
                        **kwargs)

        # plot numbers
        if numbers:
            numbers_kwargs_ = {'fontsize': 10,
                               'horizontalalignment': 'center',
                               'verticalalignment': 'center'}
            maxv = float(np.max(confusionmatrix))
            colors = [im.to_rgba(0), im.to_rgba(maxv)]
            for i,j in zip(*np.logical_not(mask).nonzero()):
                v = confusionmatrix[j, i]
                # scale alpha non-linearly
                if numbers_alpha is None:
                    alpha = 1.0
                else:
                    # scale according to value
                    alpha = 1 - np.array(1 - v / maxv) ** numbers_alpha
                y = {'lower':j, 'upper':Nlabels-j-1}[origin]
                numbers_kwargs_['color'] = colors[int(v<maxv/2)]
                numbers_kwargs_.update(numbers_kwargs)
                pl.text(i+0.5, y+0.5, '%d' % v, alpha=alpha, **numbers_kwargs_)

        maxv = np.max(confusionmatrix)
        boundaries = np.linspace(0, maxv, np.min((maxv, 10)), True)

        # Label axes
        pl.xlabel("targets")
        pl.ylabel("predictions")

        pl.setp(axi, xticks=ticks, yticks=yticks,
               xticklabels=tick_labels, yticklabels=tick_labels)

        axi.xaxis.set_ticks_position(xticks_position)
        axi.xaxis.set_label_position(xticks_position)

        if xlabels_vertical:
            pl.setp(pl.getp(axi, 'xticklabels'), rotation='vertical')

        axcb = fig.add_axes([0.8, ybottom, 0.02, 0.7])
        cb = pl.colorbar(im, cax=axcb, format='%d', ticks = boundaries)

        if pl.matplotlib.get_backend() == 'TkAgg':
            pl.ion()
        pl.draw()
        # Store it primarily for testing
        self._plotted_confusionmatrix = confusionmatrix
        return fig, im, cb


    @property
    def error(self):
        self.compute()
        return 1.0-self.__Ncorrect*1.0/sum(self.__Nsamples)


    @property
    def labels(self):
        self.compute()
        return self.__labels


    ##REF: Name was automagically refactored
    def get_labels_map(self):
        return self.__labels_map


    ##REF: Name was automagically refactored
    def set_labels_map(self, val):
        if val is None or isinstance(val, dict):
            self.__labels_map = val
        else:
            raise ValueError, "Cannot set labels_map to %s" % val
        # reset it just in case
        self.__labels_map_rev = None
        self._computed = False


    @property
    def matrix(self):
        self.compute()
        return self.__matrix


    @property
    ##REF: Name was automagically refactored
    def percent_correct(self):
        self.compute()
        return 100.0*self.__Ncorrect/sum(self.__Nsamples)

    labels_map = property(fget=get_labels_map, fset=set_labels_map)


class ConfusionMatrixError(object):
    """Compute confusion matrix as an "error function"

    This class can be used to compute confusion matrices from classifier
    output inside cross-validation fold without the ``stats`` conditional
    attribute. Simply pass an instance of this class to the ``errorfx``
    argument of ``CrossValidation``.
    """
    def __init__(self, labels=None):
        """
        Parameters
        ----------
        labels : list
          Class labels for confusion matrix columns/rows
        """
        self.labels = labels

    def __call__(self, predictions, targets):
        cm = ConfusionMatrix(labels=list(self.labels),
                             targets=targets, predictions=predictions)
        #print cm.matrix
        # We have to add a degenerate leading dimension
        # so we could separate them into separate 'samples'
        return cm.matrix[None, :]


class Confusion(Node):
    """Compute a confusion matrix from predictions and targets (Node interface)

    This class is very similar to ``ConfusionMatrix`` and
    ``ConfusionMatrixError``.  However, in contrast to these this class can be
    used in any place that accepts ``Nodes`` -- most importantly others node's
    ``postproc`` functionality. This makes it very straightforward to compute
    confusion matrices from classifier output as an intermediate result and
    continue processing with other nodes. A sketch of a cross-validation setup
    using this functionality looks like this::

      CrossValidation(some_classifier,
                      some_partitioner,
                      errorfx=None,
                      postproc=Confusion())

    It is vital to set ``errorfx`` to ``None`` to preserve raw classifier
    prediction values in the output dataset to allow for proper data aggregation
    in a confusion matrix.
    """
    def __init__(self, attr='targets', labels=None, add_confusion_obj=False,
                 **kwargs):
        """
        Parameters
        ----------
        attr : str
          Sample attribute name where classification target values are stored
          for each prediction.
        labels : list or None
          Optional list of labels to compute a confusion matrix for. This can be
          useful if a particular  prediction dataset doesn't have all
          theoretically possible labels as targets.
        add_confusion_obj : bool
          If True, the ConfusionMatrix object will be added to the output
          dataset as attribute 'confusion_obj', i.e. ds.a.confusion_obj
        **kwargs
          All remaining argments will be passed on to the Node base-class.
        """
        Node.__init__(self, **kwargs)
        self._labels = labels
        self._target_attr = attr
        self._add_confusion_obj = add_confusion_obj

    def _call(self, ds):
        if not len(ds.shape) == 2 and ds.shape[1] == 1:
            raise ValueError("Confusion cannot deal with multi-dimensional "
                             "predictions, got shape %s." % ds.shape[1:])
        # MH: we could also make it iterate over individual chunks and create
        # matrix sets -- but not sure if there is a use case for the Node
        # interface
        # compute the confusion matrix
        cm = ConfusionMatrix(labels=list(self._labels),
                             predictions=ds.samples[:,0],
                             targets=ds.sa[self._target_attr].value)
        # figure out where to store the labels
        # by default the confusion matrix in the Dataset will look just like
        # a printed ConfusionMatrix
        if self.get_space() is None:
            fa_attr = 'targets'
            sa_attr = 'predictions'
        else:
            sa_attr = fa_attr = self.get_space()
        out = Dataset(cm.matrix,
                      sa={sa_attr: cm.labels},
                      fa={fa_attr: cm.labels})
        if self._add_confusion_obj:
            out.a['confusion_obj'] = cm

        return out


class BayesConfusionHypothesis(Node):
    """Bayesian hypothesis testing on confusion matrices.

    For multi-class classification a single accuracy value is often not a
    meaningful performance measure -- or at least hard to interpret. This class
    allows for convenient Bayesian hypothesis testing of confusion matrices.
    It computes the likelihood of discriminibility of any partitions of
    classes given a confusion matrix.

    The returned dataset contains at least one feature (the log likelihood of
    a hypothesis) and as many samples as (possible) partitions of classes.
    The actual partition configurations are stored in a sample attribute
    of nested lists. The top-level list contains discriminable groups of
    classes, whereas the second level lists contain groups of classes that
    cannot be discriminated under a given hypothesis. For example::

      [[0, 1], [2], [3, 4, 5]]

    This hypothesis represent the state where class 0 and 1 cannot be
    distinguish from each other, but both 0 and 1 together can be distinguished
    from class 2 and the group of 3, 4, and 5 -- where classes from the later
    group cannot be distinguished from one another.

    This algorithms is based on

        <NAME>., <NAME>. and <NAME>. (2012). Testing for
        Information with Brain Decoding. In: Pattern Recognition in NeuroImaging
        (PRNI), International Workshop on.
    """
    def __init__(self, alpha=None, labels_attr='predictions',
                 space='hypothesis', prior_Hs=None, log=True,
                 postprob=True, hypotheses=None, **kwargs):
        """
        Parameters
        ----------
        alpha : array
          Bayesian hyper-prior alpha (in a multivariate-Dirichlet sense)
        labels_attr : str
          Name of the sample attribute in the input dataset that contains
          the class labels corresponding to the confusion matrix rows. If an
          attribute with this name is not found, hypotheses will be reported
          based on confusion table row/column numbers, instead of their
          corresponding labels. If such an attribute is found in the input
          dataset, any ``hypotheses`` specification has to be specified
          using literal labels also.
        space : str
          Name of the sample attribute in the output dataset where the
          hypothesis partition configurations will be stored.
        prior_Hs : array
          Vector of priors for each hypotheses. Typically used in conjuction
          with an explicit set of possible hypotheses (see ``hypotheses``).
          If ``None`` a flat prior is assumed.
        log : bool
          Whether to return values (likelihood or posterior probabilities) in
          log scale to mitigate numerical precision problems with near-zero
          probabilities.
        postprob : bool
          Whether to return posterior probabilities p(hypothesis|confusion)
          instead of likelihood(confusion|hypothesis).
        hypotheses : list
          List of possible hypotheses. XXX needs work on how to specify them.
        **kwargs
          All remaining argments will be passed on to the Node base-class.
        """
        Node.__init__(self, space=space, **kwargs)
        self._alpha = alpha
        self._prior_Hs = prior_Hs
        self._labels_attr = labels_attr
        self._log = log
        self._postprob = postprob
        self._hypotheses = hypotheses

    def _call(self, ds):
        from mvpa2.support.bayes.partitioner import Partition
        from mvpa2.support.bayes.partial_independence import compute_logp_H

        hypotheses = self._hypotheses
        if hypotheses is None:
            # generate all possible hypotheses if none are given
            partitions = Partition(range(len(ds)))
        else:
            if self._labels_attr in ds.sa:
                # literal labels are given -> recode into digits to match
                # underlying API
                recode = dict([(e, i)
                    for i, e in enumerate(ds.sa[self._labels_attr].value)])
                partitions = [[[recode[label] for label in class_]
                                for class_ in hyp]
                                    for hyp in hypotheses]
            else:
                # use hypotheses as is -- all bets are off
                partitions = hypotheses

        if self._prior_Hs is None:
            # default: uniform prior on hypotheses: p(H_i)
            prior_Hs = np.ones(len(partitions)) / len(partitions)
        else:
            prior_Hs = self._prior_Hs

        # p(X|H_i) for all H
        logp_X_given_Hs = np.zeros(len(partitions))
        for i, psi in enumerate(partitions):
            # use Emanuele's toolbox
            logp_X_given_Hs[i] = compute_logp_H(ds.samples, psi, self._alpha)
        out = logp_X_given_Hs
        statfa = ['log(p(C|H))']

        if self._postprob:
            # convert into posterior probabilities: p(H|X)
            # normalization constant: p(X)
            logp_X = reduce(np.logaddexp, logp_X_given_Hs + np.log(prior_Hs))

            # p(H|X) from Bayes rule:
            log_posterior_Hs_given_X = logp_X_given_Hs + np.log(prior_Hs) - logp_X

            out = np.vstack((out, log_posterior_Hs_given_X)).T
            statfa.append('log(p(H|C))')

        if not self._log:
            # convert from log scale
            out = np.exp(out)
            # remove the log() from the stat label
            statfa = [s[4:-1] for s in statfa]

        if hypotheses is None:
            if self._labels_attr in ds.sa:
                # recode partition IDs into actual labels, if the necessary attr
                # is available
                hypotheses = Partition(ds.sa[self._labels_attr].value)
            else:
                hypotheses = partitions
            hypotheses = list(hypotheses)

        out = Dataset(out,
                      sa={self.get_space(): hypotheses,
                          'prior': prior_Hs},
                      fa={'stat': statfa})
        return out


class RegressionStatistics(SummaryStatistics):
    """Class to contain information and display on regression results.

    """

    _STATS_DESCRIPTION = (
        ('CCe', 'Error based on correlation coefficient',
         '1 - corr_coef'),
        ('CCp', 'Correlation coefficient (p-value)', None),
        ('RMSE', 'Root mean squared error', None),
        ('STD', 'Standard deviation', None),
        ('RMP', 'Root mean power (compare to RMSE of results)',
         'sqrt(mean( data**2 ))'),
        ) + SummaryStatistics._STATS_DESCRIPTION


    def __init__(self, **kwargs):
        """Initialize RegressionStatistics

        Parameters
        ----------
        targets
         Optional set of targets
        predictions
         Optional set of predictions
         """

        SummaryStatistics.__init__(self, **kwargs)


    def _compute(self):
        """Actually compute the confusion matrix based on all the sets"""

        super(RegressionStatistics, self)._compute()
        sets = self.sets
        Nsets = len(sets)

        stats = {}

        funcs = {
            'RMP_t': lambda p,t:root_mean_power_fx(t),
            'STD_t': lambda p,t:np.std(t),
            'RMP_p': lambda p,t:root_mean_power_fx(p),
            'STD_p': lambda p,t:np.std(p),
            'RMSE': rms_error,
            'RMSE/RMP_t': relative_rms_error
            }
        if externals.exists('scipy'):
            funcs['CCe'] = corr_error
            funcs['CCp'] = corr_error_prob

        for funcname, func in funcs.iteritems():
            funcname_all = funcname + '_all'
            stats[funcname_all] = []
            for i, (targets, predictions, estimates) in enumerate(sets):
                stats[funcname_all] += [func(predictions, targets)]
            stats[funcname_all] = np.array(stats[funcname_all])
            stats[funcname] = np.mean(stats[funcname_all])
            stats[funcname+'_std'] = np.std(stats[funcname_all])
            stats[funcname+'_max'] = np.max(stats[funcname_all])
            stats[funcname+'_min'] = np.min(stats[funcname_all])

        # create ``summary`` statistics, since some per-set statistics
        # might be uncomputable if a set contains just a single number
        # (like in the case of correlation coefficient)
        targets, predictions = [], []
        for i, (targets_, predictions_, estimates_) in enumerate(sets):
            targets += list(targets_)
            predictions += list(predictions_)

        for funcname, func in funcs.iteritems():
            funcname_all = 'Summary ' + funcname
            stats[funcname_all] = func(predictions, targets)

        self._stats.update(stats)


    def plot(self,
             plot=True, plot_stats=True,
             splot=True
             #labels=None, numbers=False, origin='upper',
             #numbers_alpha=None, xlabels_vertical=True,
             #numbers_kwargs={},
             #**kwargs
             ):
        """Provide presentation of regression performance in image

        Parameters
        ----------
        plot : bool
          Plot regular plot of values (targets/predictions)
        plot_stats : bool
          Print basic statistics in the title
        splot : bool
          Plot scatter plot

        Returns
        -------
         (fig, im, cb) -- figure, imshow, colorbar
        """
        externals.exists("pylab", raise_=True)
        import pylab as pl

        self.compute()
        # total number of plots
        nplots = plot + splot

        # turn off automatic update if interactive
        if pl.matplotlib.get_backend() == 'TkAgg':
            pl.ioff()

        fig = pl.gcf()
        pl.clf()
        sps = []                        # subplots

        nplot = 0
        if plot:
            nplot += 1
            sps.append(pl.subplot(nplots, 1, nplot))
            xstart = 0
            lines = []
            for s in self.sets:
                nsamples = len(s[0])
                xend = xstart+nsamples
                xs = xrange(xstart, xend)
                lines += [pl.plot(xs, s[0], 'b')]
                lines += [pl.plot(xs, s[1], 'r')]
                # vertical line
                pl.plot([xend, xend], [np.min(s[0]), np.max(s[0])], 'k--')
                xstart = xend
            if len(lines)>1:
                pl.legend(lines[:2], ('Target', 'Prediction'))
            if plot_stats:
                pl.title(self.as_string(short='very'))

        if splot:
            nplot += 1
            sps.append(pl.subplot(nplots, 1, nplot))
            for s in self.sets:
                pl.plot(s[0], s[1], 'o',
                       markeredgewidth=0.2,
                       markersize=2)
            pl.gca().set_aspect('equal')

        if pl.matplotlib.get_backend() == 'TkAgg':
            pl.ion()
        pl.draw()

        return fig, sps

    ##REF: Name was automagically refactored
    def as_string(self, short=False, header=True,  summary=True,
                 description=False):
        """'Pretty print' the statistics"""

        if len(self.sets) == 0:
            return "Empty"

        self.compute()

        stats = self.stats

        if short:
            if short == 'very':
                # " RMSE/RMP_t:%(RMSE/RMP_t).2f" \
                return "%(# of sets)d sets CCe=%(CCe).2f p=%(CCp).2g" \
                       " RMSE:%(RMSE).2f" \
                       " Summary (stacked data): " \
                       "CCe=%(Summary CCe).2f p=%(Summary CCp).2g" \
                       % stats
            else:
                return "%(# of sets)d sets CCe=%(CCe).2f+-%(CCe_std).3f" \
                       " RMSE=%(RMSE).2f+-%(RMSE_std).3f" \
                       " RMSE/RMP_t=%(RMSE/RMP_t).2f+-%(RMSE/RMP_t_std).3f" \
                       % stats

        stats_data = ['RMP_t', 'STD_t', 'RMP_p', 'STD_p']
        # CCp needs tune up of format so excluded
        stats_ = ['CCe', 'RMSE', 'RMSE/RMP_t']
        stats_summary = ['# of sets']

        out = StringIO()

        printed = []
        if header:
            # labels
            printed.append(['Statistics', 'Mean', 'Std', 'Min', 'Max'])
            # underscores
            printed.append(['----------', '-----', '-----', '-----', '-----'])

        def print_stats(printed, stats_):
            # Statistics itself
            for stat in stats_:
                s = [stat]
                for suffix in ['', '_std', '_min', '_max']:
                    s += [ _p2(stats[stat+suffix], 3) ]
                printed.append(s)

        printed.append(["Data:     "])
        print_stats(printed, stats_data)
        printed.append(["Results:  "])
        print_stats(printed, stats_)
        printed.append(["Summary:  "])
        printed.append(["CCe", _p2(stats['Summary CCe']), "", "p=", '%g' % stats['Summary CCp']])
        printed.append(["RMSE", _p2(stats['Summary RMSE'])])
        printed.append(["RMSE/RMP_t", _p2(stats['Summary RMSE/RMP_t'])])

        if summary:
            for stat in stats_summary:
                printed.append([stat] + [_p2(stats[stat])])

        table2string(printed, out)

        if description:
            out.write("\nDescription of printed statistics.\n"
                      " Suffixes: _t - targets, _p - predictions\n")

            for d, val, eq in self._STATS_DESCRIPTION:
                out.write(" %-3s: %s\n" % (d, val))
                if eq is not None:
                    out.write("      " + eq + "\n")

        result = out.getvalue()
        out.close()
        return result


    @property
    def error(self):
        self.compute()
        return self.stats['RMSE']



class ClassifierError(ClassWithCollections):
    """Compute (or return) some error of a (trained) classifier on a dataset.
    """

    confusion = ConditionalAttribute(enabled=False)
    """TODO Think that labels might be also symbolic thus can't directly
       be indicies of the array
    """

    training_stats = ConditionalAttribute(enabled=False,
        doc="Proxy training_stats from underlying classifier.")


    def __init__(self, clf, labels=None, train=True, **kwargs):
        """Initialization.

        Parameters
        ----------
        clf : Classifier
          Either trained or untrained classifier
        labels : list
          if provided, should be a set of labels to add on top of the
          ones present in testdata
        train : bool
          unless train=False, classifier gets trained if
          trainingdata provided to __call__
        """
        ClassWithCollections.__init__(self, **kwargs)
        self.__clf = clf

        self._labels = labels
        """Labels to add on top to existing in testing data"""

        self.__train = train
        """Either to train classifier if trainingdata is provided"""


    __doc__ = enhanced_doc_string('ClassifierError', locals(), ClassWithCollections)


    def __copy__(self):
        """TODO: think... may be we need to copy self.clf"""
        out = ClassifierError.__new__(TransferError)
        ClassifierError.__init__(out, self.clf)
        return out


    def _precall(self, testdataset, trainingdataset=None):
        """Generic part which trains the classifier if necessary
        """
        if not trainingdataset is None:
            if self.__train:
                # XXX can be pretty annoying if triggered inside an algorithm
                # where it cannot be switched of, but retraining might be
                # intended or at least not avoidable.
                # Additonally is_trained docs say:
                #   MUST BE USED WITH CARE IF EVER
                #
                # switching it off for now
                #if self.__clf.is_trained(trainingdataset):
                #    warning('It seems that classifier %s was already trained' %
                #            self.__clf + ' on dataset %s. Please inspect' \
                #                % trainingdataset)
                if self.ca.is_enabled('training_stats'):
                    self.__clf.ca.change_temporarily(
                        enable_ca=['training_stats'])
                self.__clf.train(trainingdataset)
                if self.ca.is_enabled('training_stats'):
                    self.ca.training_stats = \
                        self.__clf.ca.training_stats
                    self.__clf.ca.reset_changed_temporarily()

        if self.__clf.ca.is_enabled('trained_targets') \
               and not self.__clf.__is_regression__ \
               and not testdataset is None:
            newlabels = set(testdataset.sa[self.clf.get_space()].unique) \
                        - set(self.__clf.ca.trained_targets)
            if len(newlabels)>0:
                warning("Classifier %s wasn't trained to classify labels %s" %
                        (self.__clf, newlabels) +
                        " present in testing dataset. Make sure that you have" +
                        " not mixed order/names of the arguments anywhere")

        ### Here checking for if it was trained... might be a cause of trouble
        # XXX disabled since it is unreliable.. just rely on explicit
        # self.__train
        #    if  not self.__clf.is_trained(trainingdataset):
        #        self.__clf.train(trainingdataset)
        #    elif __debug__:
        #        debug('CERR',
        #              'Not training classifier %s since it was ' % `self.__clf`
        #              + ' already trained on dataset %s' % `trainingdataset`)


    def _call(self, testdataset, trainingdataset=None):
        raise NotImplementedError


    def _postcall(self, testdataset, trainingdataset=None, error=None):
        pass


    def __call__(self, testdataset, trainingdataset=None):
        """Compute the transfer error for a certain test dataset.

        If `trainingdataset` is not `None` the classifier is trained using the
        provided dataset before computing the transfer error. Otherwise the
        classifier is used in it's current state to make the predictions on
        the test dataset.

        Returns a scalar value of the transfer error.
        """
        self._precall(testdataset, trainingdataset)
        error = self._call(testdataset, trainingdataset)
        self._postcall(testdataset, trainingdataset, error)
        if __debug__:
            debug('CERR', 'Classifier error on %s: %.2f'
                  % (testdataset, error))
        return error


    def untrain(self):
        """Untrain the `*Error` which relies on the classifier
        """
        self.clf.untrain()


    @property
    def clf(self):
        return self.__clf


    @property
    def labels(self):
        return self._labels



class ConfusionBasedError(ClassifierError):
    """For a given classifier report an error based on internally
    computed error measure (given by some `ConfusionMatrix` stored in
    some conditional attribute of `Classifier`).

    This way we can perform feature selection taking as the error
    criterion either learning error, or transfer to splits error in
    the case of SplitClassifier
    """

    def __init__(self, clf, labels=None, confusion_state="training_stats",
                 **kwargs):
        """Initialization.

        Parameters
        ----------
        clf : Classifier
          Either trained or untrained classifier
        confusion_state
          Id of the conditional attribute which stores `ConfusionMatrix`
        labels : list
          if provided, should be a set of labels to add on top of the
          ones present in testdata
        """
        ClassifierError.__init__(self, clf, labels, **kwargs)

        self.__confusion_state = confusion_state
        """What state to extract from"""

        if not clf.ca.has_key(confusion_state):
            raise ValueError, \
                  "Conditional attribute %s is not defined for classifier %r" % \
                  (confusion_state, clf)
        if not clf.ca.is_enabled(confusion_state):
            if __debug__:
                debug('CERR', "Forcing state %s to be enabled for %r" %
                      (confusion_state, clf))
            clf.ca.enable(confusion_state)


    __doc__ = enhanced_doc_string('ConfusionBasedError', locals(),
                                ClassifierError)


    def _call(self, testdata, trainingdata=None):
        """Extract transfer error. Nor testdata, neither trainingdata is used
        """
        confusion = self.clf.ca[self.__confusion_state].value
        self.ca.confusion = confusion
        return confusion.error

# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
#   See COPYING file distributed along with the PyMVPA package for the
#   copyright and license terms.
#
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
"""Utility class to compute the transfer error of classifiers."""

__docformat__ = 'restructuredtext'

import mvpa2.support.copy as copy

import numpy as np

from StringIO import StringIO
from math import log10, ceil

from mvpa2.base import externals
from mvpa2.base.node import Node

from mvpa2.misc.errorfx import mean_power_fx, root_mean_power_fx, rms_error, \
     relative_rms_error, mean_mismatch_error, auc_error
from mvpa2.base import warning
from mvpa2.datasets import Dataset
from mvpa2.base.collections import Collectable
from mvpa2.base.state import ConditionalAttribute, ClassWithCollections, \
     UnknownStateError
from mvpa2.base.dochelpers import enhanced_doc_string, table2string
from mvpa2.clfs.stats import auto_null_dist

if __debug__:
    from mvpa2.base import debug

if externals.exists('scipy'):
    from scipy.stats.stats import nanmean
    from mvpa2.misc.stats import chisquare
    from scipy.stats import linregress, friedmanchisquare
    from mvpa2.misc.errorfx import corr_error, corr_error_prob
else:
    from mvpa2.clfs.stats import nanmean
    chisquare = None
    linregress = None

def _p2(x, prec=2, formatting=''):
    """Helper to print depending on the type nicely. For some
    reason %.2g for 100 prints exponential form which is ugly
    """
    if isinstance(x, int):
        s = "%d" % x
    elif isinstance(x, float):
        s = ("%%.%df" % prec % x).rstrip('0').rstrip('.').lstrip()
        if s == '':
            s = '0'
    else:
        s = "%s" % x
    return formatting + s



class SummaryStatistics(object):
    """Basic class to collect targets/predictions and report summary statistics

    It takes care about collecting the sets, which are just tuples
    (targets, predictions, estimates). While 'computing' the matrix, all
    sets are considered together.  Children of the class are
    responsible for computation and display.
    """

    _STATS_DESCRIPTION = (
        ('# of sets',
         'number of target/prediction sets which were provided', None),
        )


    def __init__(self, targets=None, predictions=None, estimates=None, sets=None):
        """Initialize SummaryStatistics

        targets or predictions cannot be provided alone (ie targets
        without predictions)

        Parameters
        ----------
        targets
         Optional set of targets
        predictions
         Optional set of predictions
        estimates
         Optional set of estimates (which served for prediction)
        sets
         Optional list of sets
        """
        self._computed = False
        """Flag either it was computed for a given set of data"""

        self.__sets = (sets, [])[int(sets is None)]
        """Datasets (target, prediction) to compute confusion matrix on"""

        self._stats = {}
        """Dictionary to keep statistics. Initialized here to please pylint"""

        if not targets is None or not predictions is None:
            if not targets is None and not predictions is None:
                self.add(targets=targets, predictions=predictions,
                         estimates=estimates)
            else:
                raise ValueError, \
                      "Please provide none or both targets and predictions"


    def add(self, targets, predictions, estimates=None):
        """Add new results to the set of known results"""
        if len(targets) != len(predictions):
            raise ValueError, \
                  "Targets[%d] and predictions[%d]" % (len(targets),
                                                       len(predictions)) + \
                  " have different number of samples"

        # extract value if necessary
        if isinstance(estimates, Collectable):
            try:
                estimates = estimates.value
            except UnknownStateError, e:
                estimates = None        # reset to None to be safe below
                if __debug__:
                    debug('CM', "Cannot get yet uncollected estimates from %s",
                          estimates)

        if estimates is not None and len(targets) != len(estimates):
            raise ValueError, \
                  "Targets[%d] and estimates[%d]" % (len(targets),
                                                  len(estimates)) + \
                  " have different number of samples"

        # enforce labels in predictions to be of the same datatype as in
        # targets, since otherwise we are getting doubles for unknown at a
        # given moment labels
        nonetype = type(None)
        for i in xrange(len(targets)):
            t1, t2 = type(targets[i]), type(predictions[i])
            # if there were no prediction made - leave None, otherwise
            # convert to appropriate type
            if t1 != t2 and t2 != nonetype:
                #warning("Obtained target %s and prediction %s are of " %
                #       (t1, t2) + "different datatypes.")
                if isinstance(predictions, tuple):
                    predictions = list(predictions)
                predictions[i] = t1(predictions[i])

        if estimates is not None:
            # assure that we have a copy, or otherwise further in-place
            # modifications might screw things up (some classifiers share
            # estimates and spit out results)
            estimates = copy.deepcopy(estimates)

        self.__sets.append( (targets, predictions, estimates) )
        self._computed = False


    ##REF: Name was automagically refactored
    def as_string(self, short=False, header=True, summary=True,
                 description=False):
        """'Pretty print' the matrix

        Parameters
        ----------
        short : bool
          if True, ignores the rest of the parameters and provides consise
          1 line summary
        header : bool
          print header of the table
        summary : bool
          print summary (accuracy)
        description : bool
          print verbose description of presented statistics
        """
        raise NotImplementedError


    def __str__(self):
        """String summary over the `SummaryStatistics`

        It would print description of the summary statistics if 'CM'
        debug target is active
        """
        if __debug__:
            description = ('CM' in debug.active)
        else:
            description = False
        return self.as_string(short=False, header=True, summary=True,
                             description=description)


    def __iadd__(self, other):
        """Add the sets from `other` s `SummaryStatistics` to current one
        """
        #print "adding ", other, " to ", self
        # need to do shallow copy, or otherwise smth like "cm += cm"
        # would loop forever and exhaust memory eventually
        othersets = copy.copy(other.__sets)
        for set in othersets:
            self.add(*set)#[0], set[1])
        return self


    def __add__(self, other):
        """Add two `SummaryStatistics`s
        """
        result = copy.deepcopy(self)
        result += other
        return result


    def compute(self):
        """Actually compute the confusion matrix based on all the sets"""
        if self._computed:
            return

        self._compute()
        self._computed = True


    def _compute(self):
        """Compute basic statistics
        """
        self._stats = {'# of sets' : len(self.sets)}


    @property
    def summaries(self):
        """Return a list of separate summaries per each stored set"""
        return [ self.__class__(sets=[x]) for x in self.sets ]


    @property
    def error(self):
        raise NotImplementedError


    @property
    def stats(self):
        self.compute()
        return self._stats


    def reset(self):
        """Cleans summary -- all data/sets are wiped out
        """
        self.__sets = []
        self._computed = False


    sets = property(lambda self:self.__sets)


class ROCCurve(object):
    """Generic class for ROC curve computation and plotting
    """

    def __init__(self, labels, sets=None):
        """
        Parameters
        ----------
        labels : list
          labels which were used (in order of estimates if multiclass,
          or 1 per class for binary problems (e.g. in SMLR))
        sets : list of tuples
          list of sets for the analysis
        """
        self._labels = labels
        self._sets = sets
        self.__computed = False


    def _compute(self):
        """Lazy computation if needed
        """
        if self.__computed:
            return
        # local bindings
        labels = self._labels
        Nlabels = len(labels)
        sets = self._sets

        # Handle degenerate cases politely
        if Nlabels < 2:
            warning("ROC was asked to be evaluated on data with %i"
                    " labels which is a degenerate case." % Nlabels)
            self._ROCs = []
            self._aucs = []
            return

        # take sets which have values in the shape we can handle
        def _check_values(set_):
            """Check if values are 'acceptable'"""
            if len(set_)<3:
                return False
            x = set_[2]                 # estimates
            # TODO: OPT: need optimization
            if (x is None) or len(x) == 0:
                return False          # undefined
            for v in x:
                try:
                    if Nlabels <= 2 and np.isscalar(v):
                        continue
                    if (isinstance(v, dict) or # not dict for pairs
                        ((Nlabels >= 2) and len(v) != Nlabels)): # 1 per each label for multiclass
                        return False
                except Exception, e:
                    # Something else which is not supported, like
                    # in shogun interface we don't yet extract values per each label or
                    # in pairs in the case of built-in multiclass
                    if __debug__:
                        debug('ROC', "Exception %s while checking "
                              "either %s are valid labels" % (str(e), x))
                    return False
            return True

        sets_wv = [x for x in sets if _check_values(x)]
        # check if all had estimates, if not -- complain
        Nsets_wv = len(sets_wv)
        if Nsets_wv > 0 and len(sets) != Nsets_wv:
            warning("Only %d sets have estimates assigned from %d sets. "
                    "ROC estimates might be incorrect." %
                    (Nsets_wv, len(sets)))
        # bring all values to the same 'shape':
        #  1 value per each label. In binary classifier, if only a single
        #  value is provided, add '0' for 0th label 'value'... it should
        #  work taking drunk Yarik logic ;-)
        # yoh: apparently it caused problems whenever we had just a single
        #      unique label in the sets. Introduced handling for
        #      NLabels == 1
        for iset, s in enumerate(sets_wv):
            # we will do inplace modification, thus go by index
            estimates = s[2]
            # we would need it to be a list to reassign element with a list
            if isinstance(estimates, np.ndarray) and len(estimates.shape)==1:
                # XXX ??? so we are going away from inplace modifications?
                estimates = list(estimates)
            rangev = None
            for i in xrange(len(estimates)):
                v = estimates[i]
                if np.isscalar(v):
                    if Nlabels == 1:
                        # ensure the right dimensionality
                        estimates[i] = np.array(v, ndmin=2)
                    elif Nlabels == 2:
                        def last_el(x):
                            """Helper function. Returns x if x is scalar, and
                            last element if x is not (ie list/tuple)"""
                            if np.isscalar(x): return x
                            else:             return x[-1]
                        if rangev is None:
                            # we need to figure out min/max estimates
                            # to invert for the 0th label
                            estimates_ = [last_el(x) for x in estimates]
                            rangev = np.min(estimates_) + np.max(estimates_)
                        estimates[i] = [rangev - v, v]
                    else:
                        raise ValueError, \
                              "Cannot have a single 'value' for multiclass" \
                              " classification. Got %s" % (v)
                elif len(v) != Nlabels:
                    raise ValueError, \
                          "Got %d estimates whenever there is %d labels" % \
                          (len(v), Nlabels)
            # reassign possibly adjusted estimates
            sets_wv[iset] = (s[0], s[1], np.asarray(estimates))


        # we need to estimate ROC per each label
        # XXX order of labels might not correspond to the one among 'estimates'
        #     which were used to make a decision... check
        rocs, aucs = [], []             # 1 per label
        for i,label in enumerate(labels):
            aucs_pl = []
            ROCs_pl = []
            for s in sets_wv:
                targets_pl = (np.asanyarray(s[0]) == label).astype(int)
                # XXX we might unify naming between AUC/ROC
                ROC = auc_error
                aucs_pl += [ROC([np.asanyarray(x)[i] for x in s[2]], targets_pl)]
                ROCs_pl.append(ROC)
            if len(aucs_pl)>0:
                rocs += [ROCs_pl]
                aucs += [nanmean(aucs_pl)]
                #aucs += [np.mean(aucs_pl)]

        # store results within the object
        self._ROCs =  rocs
        self._aucs = aucs
        self.__computed = True


    @property
    def aucs(self):
        """Compute and return set of AUC values 1 per label
        """
        self._compute()
        return self._aucs


    @property
    ##REF: Name was automagically refactored
    def rocs(self):
        self._compute()
        return self._ROCs


    def plot(self, label_index=0):
        """

        TODO: make it friendly to labels given by values?
              should we also treat labels_map?
        """
        externals.exists("pylab", raise_=True)
        import pylab as pl

        self._compute()

        labels = self._labels
        # select only rocs for the given label
        rocs = self.rocs[label_index]

        fig = pl.gcf()
        ax = pl.gca()

        pl.plot([0, 1], [0, 1], 'k:')

        for ROC in rocs:
            pl.plot(ROC.fp, ROC.tp, linewidth=1)

        pl.axis((0.0, 1.0, 0.0, 1.0))
        pl.axis('scaled')
        pl.title('Label %s. Mean AUC=%.2f' % (label_index, self.aucs[label_index]))

        pl.xlabel('False positive rate')
        pl.ylabel('True positive rate')


class ConfusionMatrix(SummaryStatistics):
    """Class to contain information and display confusion matrix.

    Implementation of the `SummaryStatistics` in the case of
    classification problem. Actual computation of confusion matrix is
    delayed until all data is acquired (to figure out complete set of
    labels). If testing data doesn't have a complete set of labels,
    but you like to include all labels, provide them as a parameter to
    the constructor.

    Confusion matrix provides a set of performance statistics (use
    as_string(description=True) for the description of abbreviations),
    as well ROC curve (http://en.wikipedia.org/wiki/ROC_curve)
    plotting and analysis (AUC) in the limited set of problems:
    binary, multiclass 1-vs-all.
    """

    _STATS_DESCRIPTION = (
        ('TP', 'true positive (AKA hit)', None),
        ('TN', 'true negative (AKA correct rejection)', None),
        ('FP', 'false positive (AKA false alarm, Type I error)', None),
        ('FN', 'false negative (AKA miss, Type II error)', None),
        ('TPR', 'true positive rate (AKA hit rate, recall, sensitivity)',
                'TPR = TP / P = TP / (TP + FN)'),
        ('FPR', 'false positive rate (AKA false alarm rate, fall-out)',
                'FPR = FP / N = FP / (FP + TN)'),
        ('ACC', 'accuracy', 'ACC = (TP + TN) / (P + N)'),
        ('SPC', 'specificity', 'SPC = TN / (FP + TN) = 1 - FPR'),
        ('PPV', 'positive predictive value (AKA precision)',
                'PPV = TP / (TP + FP)'),
        ('NPV', 'negative predictive value', 'NPV = TN / (TN + FN)'),
        ('FDR', 'false discovery rate', 'FDR = FP / (FP + TP)'),
        ('MCC', "Matthews Correlation Coefficient",
                "MCC = (TP*TN - FP*FN)/sqrt(P N P' N')"),
        ('F1',  'F1 score',
                "F1 = 2TP / (P + P') = 2TP / (2TP + FP + FN)"),
        ('AUC', "Area under (AUC) curve", None),
        ('CHI^2', "Chi-square of confusion matrix", None),
        ('LOE(ACC)', "Linear Order Effect in ACC across sets", None),
        ## ('Friedman(TPR)',
        ##  "Friedman CHI^2 test of TPRs consistencies across sets", None),
        ## ('Friedman(CM)',
        ##  "Friedman CHI^2 test of full CM (normalized) consistencies across sets", None),
        ) + SummaryStatistics._STATS_DESCRIPTION


    def __init__(self, labels=None, labels_map=None, **kwargs):
        """Initialize ConfusionMatrix with optional list of `labels`

        Parameters
        ----------
        labels : list
         Optional set of labels to include in the matrix
        labels_map : None or dict
         Dictionary from original dataset to show mapping into
         numerical labels
        targets
         Optional set of targets
        predictions
         Optional set of predictions
         """

        SummaryStatistics.__init__(self, **kwargs)

        if labels == None:
            labels = []

        self.__labels = labels
        """List of known labels"""
        self.__labels_in_custom_order = bool(len(labels))
        """So we know later on either we could resort them"""
        self.__labels_map = labels_map
        """Mapping from original into given labels"""
        self.__matrix = None
        """Resultant confusion matrix"""


    def __call__(self, predictions, targets, estimates=None, store=False):
        """Computes confusion matrix (counts)

        It would rely on previously provided 'labels' to define columns/rows
        of the matrix to assure consistency across multiple invocations.

        Parameters
        ----------
        store : bool, optional
          By default, this function does not modify an existing
          instance of the ConfusionMatrix, and is just used merely to
          provide a resultant confusion matrix.  If 'store' set to
          True, provided set of predictions and targets would be added
          to the sets.

        Returns
        -------
        numpy.ndarray
           counts of hits with rows -- predictions, columns -- targets
        """
        labels = self.__labels
        if labels is None or not len(labels):
            raise RuntimeError("ConfusionMatrix must have labels assigned prior"
                               "__call__()")
        # verify that we know all the labels
        labels_set = set(labels)
        if not (labels_set.issuperset(targets)
                and labels_set.issuperset(predictions)):
            raise ValueError("Known labels %r does not include some labels "
                             "found in predictions %r or targets %r provided"
                             % (labels_set, set(predictions), set(targets)))

        Nlabels = len(labels_set)
        cm = np.zeros( (Nlabels, Nlabels), dtype=int )

        rev_map = dict([ (x[1], x[0]) for x in enumerate(labels)])
        for t,p in zip(targets, predictions):
            cm[rev_map[p], rev_map[t]] += 1

        if store:
            self.add(targets=targets, predictions=predictions, estimates=estimates)
        return cm

    # XXX might want to remove since summaries does the same, just without
    #     supplying labels
    @property
    def matrices(self):
        """Return a list of separate confusion matrix per each stored set"""
        return [ self.__class__(labels=self.labels,
                                labels_map=self.labels_map,
                                sets=[x]) for x in self.sets]


    def _compute(self):
        """Actually compute the confusion matrix based on all the sets"""

        super(ConfusionMatrix, self)._compute()

        if __debug__:
            if not self.__matrix is None:
                debug("LAZY",
                      "Have to recompute %s#%s" \
                        % (self.__class__.__name__, id(self)))


        # TODO: BinaryClassifier might spit out a list of predictions for each
        # value need to handle it... for now just keep original labels
        try:
            # figure out what labels we have
            labels = \
                list(reduce(lambda x, y: x.union(set(y[0]).union(set(y[1]))),
                            self.sets,
                            set(self.__labels)))
        except:
            labels = self.__labels


        # Check labels_map if it was provided if it covers all the labels
        labels_map = self.__labels_map
        if labels_map is not None:
            labels_set = set(labels)
            map_labels_set = set(labels_map.values())

            if not map_labels_set.issuperset(labels_set):
                warning("Provided labels_map %s is not coherent with labels "
                        "provided to ConfusionMatrix. No reverse mapping "
                        "will be provided" % labels_map)
                labels_map = None

        # Create reverse map
        labels_map_rev = None
        if labels_map is not None:
            labels_map_rev = {}
            for k,v in labels_map.iteritems():
                v_mapping = labels_map_rev.get(v, [])
                v_mapping.append(k)
                labels_map_rev[v] = v_mapping
        self.__labels_map_rev = labels_map_rev

        labels.sort()

        if self.__labels is None or not len(self.__labels):
            self.__labels = labels          # just store the recomputed labels
        else:
            # we should append them to already known ones
            # Otherwise order of labels known before might be altered
            add_labels = [x for x in labels if not (x in self.__labels)]
            if len(add_labels):
                self.__labels += add_labels
            labels = self.__labels      # and use them later on

            if not self.__labels_in_custom_order:
                labels.sort()

        Nlabels, Nsets = len(labels), len(self.sets)

        if __debug__:
            debug("CM", "Got labels %s" % labels)

        # Create a matrix for all votes
        mat_all = np.zeros( (Nsets, Nlabels, Nlabels), dtype=int )

        # create total number of samples of each label counts
        # just for convinience I guess since it can always be
        # computed from mat_all
        counts_all = np.zeros( (Nsets, Nlabels) )

        # reverse mapping from label into index in the list of labels
        rev_map = dict([ (x[1], x[0]) for x in enumerate(labels)])
        for iset, set_ in enumerate(self.sets):
            for t,p in zip(*set_[:2]):
                mat_all[iset, rev_map[p], rev_map[t]] += 1


        # for now simply compute a sum of votes across different sets
        # we might do something more sophisticated later on, and this setup
        # should easily allow it
        self.__matrix = np.sum(mat_all, axis=0)
        self.__Nsamples = np.sum(self.__matrix, axis=0)
        self.__Ncorrect = sum(np.diag(self.__matrix))

        TP = np.diag(self.__matrix)
        offdiag = self.__matrix - np.diag(TP)
        stats = {
            '# of labels' : Nlabels,
            'TP' : TP,
            'FP' : np.sum(offdiag, axis=1),
            'FN' : np.sum(offdiag, axis=0)}

        stats['CORR']  = np.sum(TP)
        stats['TN']  = stats['CORR'] - stats['TP']
        stats['P']  = stats['TP'] + stats['FN']
        stats['N']  = np.sum(stats['P']) - stats['P']
        stats["P'"] = stats['TP'] + stats['FP']
        stats["N'"] = stats['TN'] + stats['FN']
        stats['TPR'] = stats['TP'] / (1.0*stats['P'])
        # reset nans in TPRs to 0s whenever there is no entries
        # for those labels among the targets
        stats['TPR'][stats['P'] == 0] = 0
        stats['PPV'] = stats['TP'] / (1.0*stats["P'"])
        stats['NPV'] = stats['TN'] / (1.0*stats["N'"])
        stats['FDR'] = stats['FP'] / (1.0*stats["P'"])
        stats['SPC'] = (stats['TN']) / (1.0*stats['FP'] + stats['TN'])
        stats['F1'] = 2.*stats['TP'] / (stats["P"] + stats["P'"])

        MCC_denom = np.sqrt(1.0*stats['P']*stats['N']*stats["P'"]*stats["N'"])
        nz = MCC_denom!=0.0
        stats['MCC'] = np.zeros(stats['TP'].shape)
        stats['MCC'][nz] = \
                 (stats['TP'] * stats['TN'] - stats['FP'] * stats['FN'])[nz] \
                  / MCC_denom[nz]

        stats['ACC'] = np.sum(TP)/(1.0*np.sum(stats['P']))
        # TODO: STD of accuracy and corrected one according to
        #    Nadeau and Bengio [50]
        stats['ACC%'] = stats['ACC'] * 100.0
        if chisquare:
            # indep_rows to assure reasonable handling of disbalanced
            # cases
            stats['CHI^2'] = chisquare(self.__matrix, exp='indep_rows')
        if linregress and Nsets > 3:
            # Lets see if there is possible order effect in accuracy
            # (e.g. it goes down through splits)

            # simple linear regression
            ACC_per_set = [np.sum(np.diag(m))/np.sum(m).astype(float)
                           for m in mat_all]
            stats['LOE(ACC):slope'], stats['LOE(ACC):inter'], \
                stats['LOE(ACC):r'], stats['LOE(ACC):p'], _ = \
                linregress(np.arange(Nsets), ACC_per_set)

            TPRs_per_set = [np.diag(m)/np.sum(m, axis=0).astype(float)
                            for m in mat_all]
            # Confusion ratios (both TPs or FPs)
            # we want to divide each column but sum in the column
            CM_per_set = [np.ravel(m/np.sum(m, axis=0).astype(float)[None, :])
                          for m in mat_all]

            ## stats['Friedman(TPR):chi^2'], stats['Friedman(TPR):p'] = \
            ##                               friedmanchisquare(*TPRs_per_set)
            ## stats['Friedman(CM):chi^2'], stats['Friedman(CM):p'] = \
            ##                              friedmanchisquare(*CM_per_set)

        #
        # ROC computation if available
        ROC = ROCCurve(labels=labels, sets=self.sets)
        aucs = ROC.aucs
        if len(aucs)>0:
            stats['AUC'] = aucs
            if len(aucs) != Nlabels:
                raise RuntimeError, \
                      "We must got a AUC per label. Got %d instead of %d" % \
                      (len(aucs), Nlabels)
            self.ROC = ROC
        else:
            # we don't want to provide ROC if it is bogus
            stats['AUC'] = [np.nan] * Nlabels
            self.ROC = None


        # compute mean stats
        for k,v in stats.items():
            stats['mean(%s)' % k] = np.mean(v)

        self._stats.update(stats)


    ##REF: Name was automagically refactored
    def as_string(self, short=False, header=True, summary=True,
                 description=False):
        """'Pretty print' the matrix

        Parameters
        ----------
        short : bool
          if True, ignores the rest of the parameters and provides consise
          1 line summary
        header : bool
          print header of the table
        summary : bool
          print summary (accuracy)
        description : bool
          print verbose description of presented statistics
        """
        if len(self.sets) == 0:
            return "Empty"

        self.compute()

        # some shortcuts
        labels = self.__labels
        labels_map_rev = self.__labels_map_rev
        matrix = self.__matrix

        labels_rev = []
        if labels_map_rev is not None:
            labels_rev = [','.join([str(x) for x in labels_map_rev[l]])
                                   for l in labels]

        out = StringIO()
        # numbers of different entries
        Nlabels = len(labels)
        Nsamples = self.__Nsamples.astype(int)

        stats = self._stats
        if short:
            return "%(# of sets)d sets %(# of labels)d labels " \
                   " ACC:%(ACC).2f" \
                   % stats

        Ndigitsmax = int(ceil(log10(max(Nsamples))))
        Nlabelsmax = max( [len(str(x)) for x in labels] )

        # length of a single label/value
        L = max(Ndigitsmax+2, Nlabelsmax) #, len("100.00%"))
        res = ""

        stats_perpredict = ["P'", "N'", 'FP', 'FN', 'PPV', 'NPV', 'TPR',
                            'SPC', 'FDR', 'MCC', 'F1']
        # print AUC only if ROC was computed
        if self.ROC is not None: stats_perpredict += [ 'AUC' ]
        stats_pertarget = ['P', 'N', 'TP', 'TN']
        stats_summary = ['ACC', 'ACC%', '# of sets']
        # 'Friedman(TPR):p', 'Friedman(CM):p'


        #prefixlen = Nlabelsmax + 2 + Ndigitsmax + 1
        prefixlen = Nlabelsmax + 1
        pref = ' '*(prefixlen) # empty prefix

        if matrix.shape != (Nlabels, Nlabels):
            raise ValueError, \
                  "Number of labels %d doesn't correspond the size" + \
                  " of a confusion matrix %s" % (Nlabels, matrix.shape)

        # list of lists of what is printed
        printed = []
        underscores = [" %s" % ("-" * L)] * Nlabels
        if header:
            # labels
            printed.append(['@l----------.        '] + labels_rev)
            printed.append(['@lpredictions\\targets'] + labels)
            # underscores
            printed.append(['@l            `------'] \
                           + underscores + stats_perpredict)

        # matrix itself
        for i, line in enumerate(matrix):
            l = labels[i]
            if labels_rev != []:
                l = '@r%10s / %s' % (labels_rev[i], l)
            printed.append(
                [l] +
                [ str(x) for x in line ] +
                [ _p2(stats[x][i]) for x in stats_perpredict])

        if summary:
            ## Various alternative schemes ;-)
            # printed.append([''] + underscores)
            # printed.append(['@lPer target \ Means:'] + underscores + \
            #               [_p2(x) for x in mean_stats])
            # printed.append(['Means:'] + [''] * len(labels)
            #                + [_p2(x) for x in mean_stats])
            printed.append(['@lPer target:'] + underscores)
            for stat in stats_pertarget:
                printed.append([stat] + [
                    _p2(stats[stat][i]) for i in xrange(Nlabels)])

            # compute mean stats
            # XXX refactor to expose them in stats as well, as
            #     mean(FCC)
            mean_stats = np.mean(np.array([stats[k] for k in stats_perpredict]),
                                axis=1)
            printed.append(['@lSummary \ Means:'] + underscores
                           + [_p2(stats['mean(%s)' % x])
                              for x in stats_perpredict])

            if 'CHI^2' in self.stats:
                chi2t = stats['CHI^2']
                printed.append(['CHI^2'] + [_p2(chi2t[0])]
                               + ['@wp=%.2g' % chi2t[1]])

            for stat in stats_summary:
                if stat in stats:
                    printed.append([stat] + [_p2(stats[stat])])

            if 'LOE(ACC):inter' in stats:
                # Inject into sets line
                printed[-1] += [
                    '@w ACC(i) = %(LOE(ACC):inter).2g%(LOE(ACC):slope)+.2g*i'
                    % stats +
                    ' p=' + _p2(stats['LOE(ACC):p']) +
                    ' r=' + _p2(stats['LOE(ACC):r']) +
                    ' r^2=' + _p2(stats['LOE(ACC):r']**2)]

        table2string(printed, out)

        if description:
            out.write("\nStatistics computed in 1-vs-rest fashion per each " \
                      "target.\n")
            out.write("Abbreviations (for details see " \
                      "http://en.wikipedia.org/wiki/ROC_curve):\n")
            for d, val, eq in self._STATS_DESCRIPTION:
                out.write(" %-3s: %s\n" % (d, val))
                if eq is not None:
                    out.write("      " + eq + "\n")

        #out.write("%s" % printed)
        result = out.getvalue()
        out.close()
        return result


    def plot(self, labels=None, numbers=False, origin='upper',
             numbers_alpha=None, xlabels_vertical=True, numbers_kwargs={},
             **kwargs):
        """Provide presentation of confusion matrix in image

        Parameters
        ----------
        labels : list of int or str
          Optionally provided labels guarantee the order of
          presentation. Also value of None places empty column/row,
          thus provides visual groupping of labels (Thanks Ingo)
        numbers : bool
          Place values inside of confusion matrix elements
        numbers_alpha : None or float
          Controls textual output of numbers. If None -- all numbers
          are plotted in the same intensity. If some float -- it controls
          alpha level -- higher value would give higher contrast. (good
          value is 2)
        origin : str
          Which left corner diagonal should start
        xlabels_vertical : bool
          Either to plot xlabels vertical (benefitial if number of labels
          is large)
        numbers_kwargs : dict
          Additional keyword parameters to be added to numbers (if numbers
          is True)
        **kwargs
          Additional arguments given to imshow (\eg me cmap)

        Returns
        -------
         (fig, im, cb) -- figure, imshow, colorbar
        """

        externals.exists("pylab", raise_=True)
        import pylab as pl

        self.compute()
        labels_order = labels

        # some shortcuts
        labels = self.__labels
        labels_map = self.__labels_map
        labels_map_rev = self.__labels_map_rev
        matrix = self.__matrix

        # craft original mapping from a label into index in the matrix
        labels_indexes = dict([(x,i) for i,x in enumerate(labels)])

        labels_rev = []
        if labels_map_rev is not None:
            labels_rev = [','.join([str(x) for x in labels_map_rev[l]])
                                   for l in labels]
            labels_map_full = dict(zip(labels_rev, labels))

        if labels_order is not None:
            labels_order_filtered = filter(lambda x:x is not None, labels_order)
            labels_order_filtered_set = set(labels_order_filtered)
            # Verify if all labels provided in labels
            if set(labels) == labels_order_filtered_set:
                # We were provided numerical (most probably) set
                labels_plot = labels_order
            elif len(labels_rev) \
                     and set(labels_rev) == labels_order_filtered_set:
                # not clear if right whenever there were multiple labels
                # mapped into the same
                labels_plot = []
                for l in labels_order:
                    v = None
                    if l is not None: v = labels_map_full[l]
                    labels_plot += [v]
            else:
                raise ValueError, \
                      "Provided labels %s do not match set of known " \
                      "original labels (%s) or mapped labels (%s)" % \
                      (labels_order, labels, labels_rev)
        else:
            labels_plot = labels

        # where we have Nones?
        isempty = np.array([l is None for l in labels_plot])
        non_empty = np.where(np.logical_not(isempty))[0]
        # numbers of different entries
        NlabelsNN = len(non_empty)
        Nlabels = len(labels_plot)

        if matrix.shape != (NlabelsNN, NlabelsNN):
            raise ValueError, \
                  "Number of labels %d doesn't correspond the size" + \
                  " of a confusion matrix %s" % (NlabelsNN, matrix.shape)

        confusionmatrix = np.zeros((Nlabels, Nlabels))
        mask = confusionmatrix.copy()
        ticks = []
        tick_labels = []
        # populate in a silly way
        reordered_indexes = [labels_indexes[i] for i in labels_plot
                             if i is not None]
        for i, l in enumerate(labels_plot):
            if l is not None:
                j = labels_indexes[l]
                confusionmatrix[i, non_empty] = matrix[j, reordered_indexes]
                confusionmatrix[non_empty, i] = matrix[reordered_indexes, j]
                ticks += [i + 0.5]
                if labels_map_rev is not None:
                    tick_labels += ['/'.join(labels_map_rev[l])]
                else:
                    tick_labels += [str(l)]
            else:
                mask[i, :] = mask[:, i] = 1

        confusionmatrix = np.ma.MaskedArray(confusionmatrix, mask=mask)

        # turn off automatic update if interactive
        if pl.matplotlib.get_backend() == 'TkAgg':
            pl.ioff()

        fig = pl.gcf()
        ax = pl.gca()
        ax.axis('off')

        # some customization depending on the origin
        xticks_position, yticks, ybottom = {
            'upper': ('top', [Nlabels-x for x in ticks], 0.1),
            'lower': ('bottom', ticks, 0.2)
            }[origin]


        # Plot
        axi = fig.add_axes([0.15, ybottom, 0.7, 0.7])
        im = axi.imshow(confusionmatrix, interpolation="nearest", origin=origin,
                        aspect='equal', extent=(0, Nlabels, 0, Nlabels),
                        **kwargs)

        # plot numbers
        if numbers:
            numbers_kwargs_ = {'fontsize': 10,
                               'horizontalalignment': 'center',
                               'verticalalignment': 'center'}
            maxv = float(np.max(confusionmatrix))
            colors = [im.to_rgba(0), im.to_rgba(maxv)]
            for i,j in zip(*np.logical_not(mask).nonzero()):
                v = confusionmatrix[j, i]
                # scale alpha non-linearly
                if numbers_alpha is None:
                    alpha = 1.0
                else:
                    # scale according to value
                    alpha = 1 - np.array(1 - v / maxv) ** numbers_alpha
                y = {'lower':j, 'upper':Nlabels-j-1}[origin]
                numbers_kwargs_['color'] = colors[int(v<maxv/2)]
                numbers_kwargs_.update(numbers_kwargs)
                pl.text(i+0.5, y+0.5, '%d' % v, alpha=alpha, **numbers_kwargs_)

        maxv = np.max(confusionmatrix)
        boundaries = np.linspace(0, maxv, np.min((maxv, 10)), True)

        # Label axes
        pl.xlabel("targets")
        pl.ylabel("predictions")

        pl.setp(axi, xticks=ticks, yticks=yticks,
               xticklabels=tick_labels, yticklabels=tick_labels)

        axi.xaxis.set_ticks_position(xticks_position)
        axi.xaxis.set_label_position(xticks_position)

        if xlabels_vertical:
            pl.setp(pl.getp(axi, 'xticklabels'), rotation='vertical')

        axcb = fig.add_axes([0.8, ybottom, 0.02, 0.7])
        cb = pl.colorbar(im, cax=axcb, format='%d', ticks = boundaries)

        if pl.matplotlib.get_backend() == 'TkAgg':
            pl.ion()
        pl.draw()
        # Store it primarily for testing
        self._plotted_confusionmatrix = confusionmatrix
        return fig, im, cb


    @property
    def error(self):
        self.compute()
        return 1.0-self.__Ncorrect*1.0/sum(self.__Nsamples)


    @property
    def labels(self):
        self.compute()
        return self.__labels


    ##REF: Name was automagically refactored
    def get_labels_map(self):
        return self.__labels_map


    ##REF: Name was automagically refactored
    def set_labels_map(self, val):
        if val is None or isinstance(val, dict):
            self.__labels_map = val
        else:
            raise ValueError, "Cannot set labels_map to %s" % val
        # reset it just in case
        self.__labels_map_rev = None
        self._computed = False


    @property
    def matrix(self):
        self.compute()
        return self.__matrix


    @property
    ##REF: Name was automagically refactored
    def percent_correct(self):
        self.compute()
        return 100.0*self.__Ncorrect/sum(self.__Nsamples)

    labels_map = property(fget=get_labels_map, fset=set_labels_map)


class ConfusionMatrixError(object):
    """Compute confusion matrix as an "error function"

    This class can be used to compute confusion matrices from classifier
    output inside cross-validation fold without the ``stats`` conditional
    attribute. Simply pass an instance of this class to the ``errorfx``
    argument of ``CrossValidation``.
    """
    def __init__(self, labels=None):
        """
        Parameters
        ----------
        labels : list
          Class labels for confusion matrix columns/rows
        """
        self.labels = labels

    def __call__(self, predictions, targets):
        cm = ConfusionMatrix(labels=list(self.labels),
                             targets=targets, predictions=predictions)
        #print cm.matrix
        # We have to add a degenerate leading dimension
        # so we could separate them into separate 'samples'
        return cm.matrix[None, :]


class Confusion(Node):
    """Compute a confusion matrix from predictions and targets (Node interface)

    This class is very similar to ``ConfusionMatrix`` and
    ``ConfusionMatrixError``.  However, in contrast to these this class can be
    used in any place that accepts ``Nodes`` -- most importantly others node's
    ``postproc`` functionality. This makes it very straightforward to compute
    confusion matrices from classifier output as an intermediate result and
    continue processing with other nodes. A sketch of a cross-validation setup
    using this functionality looks like this::

      CrossValidation(some_classifier,
                      some_partitioner,
                      errorfx=None,
                      postproc=Confusion())

    It is vital to set ``errorfx`` to ``None`` to preserve raw classifier
    prediction values in the output dataset to allow for proper data aggregation
    in a confusion matrix.
    """
    def __init__(self, attr='targets', labels=None, add_confusion_obj=False,
                 **kwargs):
        """
        Parameters
        ----------
        attr : str
          Sample attribute name where classification target values are stored
          for each prediction.
        labels : list or None
          Optional list of labels to compute a confusion matrix for. This can be
          useful if a particular  prediction dataset doesn't have all
          theoretically possible labels as targets.
        add_confusion_obj : bool
          If True, the ConfusionMatrix object will be added to the output
          dataset as attribute 'confusion_obj', i.e. ds.a.confusion_obj
        **kwargs
          All remaining argments will be passed on to the Node base-class.
        """
        Node.__init__(self, **kwargs)
        self._labels = labels
        self._target_attr = attr
        self._add_confusion_obj = add_confusion_obj

    def _call(self, ds):
        if not len(ds.shape) == 2 and ds.shape[1] == 1:
            raise ValueError("Confusion cannot deal with multi-dimensional "
                             "predictions, got shape %s." % ds.shape[1:])
        # MH: we could also make it iterate over individual chunks and create
        # matrix sets -- but not sure if there is a use case for the Node
        # interface
        # compute the confusion matrix
        cm = ConfusionMatrix(labels=list(self._labels),
                             predictions=ds.samples[:,0],
                             targets=ds.sa[self._target_attr].value)
        # figure out where to store the labels
        # by default the confusion matrix in the Dataset will look just like
        # a printed ConfusionMatrix
        if self.get_space() is None:
            fa_attr = 'targets'
            sa_attr = 'predictions'
        else:
            sa_attr = fa_attr = self.get_space()
        out = Dataset(cm.matrix,
                      sa={sa_attr: cm.labels},
                      fa={fa_attr: cm.labels})
        if self._add_confusion_obj:
            out.a['confusion_obj'] = cm

        return out


class BayesConfusionHypothesis(Node):
    """Bayesian hypothesis testing on confusion matrices.

    For multi-class classification a single accuracy value is often not a
    meaningful performance measure -- or at least hard to interpret. This class
    allows for convenient Bayesian hypothesis testing of confusion matrices.
    It computes the likelihood of discriminibility of any partitions of
    classes given a confusion matrix.

    The returned dataset contains at least one feature (the log likelihood of
    a hypothesis) and as many samples as (possible) partitions of classes.
    The actual partition configurations are stored in a sample attribute
    of nested lists. The top-level list contains discriminable groups of
    classes, whereas the second level lists contain groups of classes that
    cannot be discriminated under a given hypothesis. For example::

      [[0, 1], [2], [3, 4, 5]]

    This hypothesis represent the state where class 0 and 1 cannot be
    distinguish from each other, but both 0 and 1 together can be distinguished
    from class 2 and the group of 3, 4, and 5 -- where classes from the later
    group cannot be distinguished from one another.

    This algorithms is based on

        <NAME>., <NAME>. and <NAME>. (2012). Testing for
        Information with Brain Decoding. In: Pattern Recognition in NeuroImaging
        (PRNI), International Workshop on.
    """
    def __init__(self, alpha=None, labels_attr='predictions',
                 space='hypothesis', prior_Hs=None, log=True,
                 postprob=True, hypotheses=None, **kwargs):
        """
        Parameters
        ----------
        alpha : array
          Bayesian hyper-prior alpha (in a multivariate-Dirichlet sense)
        labels_attr : str
          Name of the sample attribute in the input dataset that contains
          the class labels corresponding to the confusion matrix rows. If an
          attribute with this name is not found, hypotheses will be reported
          based on confusion table row/column numbers, instead of their
          corresponding labels. If such an attribute is found in the input
          dataset, any ``hypotheses`` specification has to be specified
          using literal labels also.
        space : str
          Name of the sample attribute in the output dataset where the
          hypothesis partition configurations will be stored.
        prior_Hs : array
          Vector of priors for each hypotheses. Typically used in conjuction
          with an explicit set of possible hypotheses (see ``hypotheses``).
          If ``None`` a flat prior is assumed.
        log : bool
          Whether to return values (likelihood or posterior probabilities) in
          log scale to mitigate numerical precision problems with near-zero
          probabilities.
        postprob : bool
          Whether to return posterior probabilities p(hypothesis|confusion)
          instead of likelihood(confusion|hypothesis).
        hypotheses : list
          List of possible hypotheses. XXX needs work on how to specify them.
        **kwargs
          All remaining argments will be passed on to the Node base-class.
        """
        Node.__init__(self, space=space, **kwargs)
        self._alpha = alpha
        self._prior_Hs = prior_Hs
        self._labels_attr = labels_attr
        self._log = log
        self._postprob = postprob
        self._hypotheses = hypotheses

    def _call(self, ds):
        from mvpa2.support.bayes.partitioner import Partition
        from mvpa2.support.bayes.partial_independence import compute_logp_H

        hypotheses = self._hypotheses
        if hypotheses is None:
            # generate all possible hypotheses if none are given
            partitions = Partition(range(len(ds)))
        else:
            if self._labels_attr in ds.sa:
                # literal labels are given -> recode into digits to match
                # underlying API
                recode = dict([(e, i)
                    for i, e in enumerate(ds.sa[self._labels_attr].value)])
                partitions = [[[recode[label] for label in class_]
                                for class_ in hyp]
                                    for hyp in hypotheses]
            else:
                # use hypotheses as is -- all bets are off
                partitions = hypotheses

        if self._prior_Hs is None:
            # default: uniform prior on hypotheses: p(H_i)
            prior_Hs = np.ones(len(partitions)) / len(partitions)
        else:
            prior_Hs = self._prior_Hs

        # p(X|H_i) for all H
        logp_X_given_Hs = np.zeros(len(partitions))
        for i, psi in enumerate(partitions):
            # use Emanuele's toolbox
            logp_X_given_Hs[i] = compute_logp_H(ds.samples, psi, self._alpha)
        out = logp_X_given_Hs
        statfa = ['log(p(C|H))']

        if self._postprob:
            # convert into posterior probabilities: p(H|X)
            # normalization constant: p(X)
            logp_X = reduce(np.logaddexp, logp_X_given_Hs + np.log(prior_Hs))

            # p(H|X) from Bayes rule:
            log_posterior_Hs_given_X = logp_X_given_Hs + np.log(prior_Hs) - logp_X

            out = np.vstack((out, log_posterior_Hs_given_X)).T
            statfa.append('log(p(H|C))')

        if not self._log:
            # convert from log scale
            out = np.exp(out)
            # remove the log() from the stat label
            statfa = [s[4:-1] for s in statfa]

        if hypotheses is None:
            if self._labels_attr in ds.sa:
                # recode partition IDs into actual labels, if the necessary attr
                # is available
                hypotheses = Partition(ds.sa[self._labels_attr].value)
            else:
                hypotheses = partitions
            hypotheses = list(hypotheses)

        out = Dataset(out,
                      sa={self.get_space(): hypotheses,
                          'prior': prior_Hs},
                      fa={'stat': statfa})
        return out


class RegressionStatistics(SummaryStatistics):
    """Class to contain information and display on regression results.

    """

    _STATS_DESCRIPTION = (
        ('CCe', 'Error based on correlation coefficient',
         '1 - corr_coef'),
        ('CCp', 'Correlation coefficient (p-value)', None),
        ('RMSE', 'Root mean squared error', None),
        ('STD', 'Standard deviation', None),
        ('RMP', 'Root mean power (compare to RMSE of results)',
         'sqrt(mean( data**2 ))'),
        ) + SummaryStatistics._STATS_DESCRIPTION


    def __init__(self, **kwargs):
        """Initialize RegressionStatistics

        Parameters
        ----------
        targets
         Optional set of targets
        predictions
         Optional set of predictions
         """

        SummaryStatistics.__init__(self, **kwargs)


    def _compute(self):
        """Actually compute the confusion matrix based on all the sets"""

        super(RegressionStatistics, self)._compute()
        sets = self.sets
        Nsets = len(sets)

        stats = {}

        funcs = {
            'RMP_t': lambda p,t:root_mean_power_fx(t),
            'STD_t': lambda p,t:np.std(t),
            'RMP_p': lambda p,t:root_mean_power_fx(p),
            'STD_p': lambda p,t:np.std(p),
            'RMSE': rms_error,
            'RMSE/RMP_t': relative_rms_error
            }
        if externals.exists('scipy'):
            funcs['CCe'] = corr_error
            funcs['CCp'] = corr_error_prob

        for funcname, func in funcs.iteritems():
            funcname_all = funcname + '_all'
            stats[funcname_all] = []
            for i, (targets, predictions, estimates) in enumerate(sets):
                stats[funcname_all] += [func(predictions, targets)]
            stats[funcname_all] = np.array(stats[funcname_all])
            stats[funcname] = np.mean(stats[funcname_all])
            stats[funcname+'_std'] = np.std(stats[funcname_all])
            stats[funcname+'_max'] = np.max(stats[funcname_all])
            stats[funcname+'_min'] = np.min(stats[funcname_all])

        # create ``summary`` statistics, since some per-set statistics
        # might be uncomputable if a set contains just a single number
        # (like in the case of correlation coefficient)
        targets, predictions = [], []
        for i, (targets_, predictions_, estimates_) in enumerate(sets):
            targets += list(targets_)
            predictions += list(predictions_)

        for funcname, func in funcs.iteritems():
            funcname_all = 'Summary ' + funcname
            stats[funcname_all] = func(predictions, targets)

        self._stats.update(stats)


    def plot(self,
             plot=True, plot_stats=True,
             splot=True
             #labels=None, numbers=False, origin='upper',
             #numbers_alpha=None, xlabels_vertical=True,
             #numbers_kwargs={},
             #**kwargs
             ):
        """Provide presentation of regression performance in image

        Parameters
        ----------
        plot : bool
          Plot regular plot of values (targets/predictions)
        plot_stats : bool
          Print basic statistics in the title
        splot : bool
          Plot scatter plot

        Returns
        -------
         (fig, im, cb) -- figure, imshow, colorbar
        """
        externals.exists("pylab", raise_=True)
        import pylab as pl

        self.compute()
        # total number of plots
        nplots = plot + splot

        # turn off automatic update if interactive
        if pl.matplotlib.get_backend() == 'TkAgg':
            pl.ioff()

        fig = pl.gcf()
        pl.clf()
        sps = []                        # subplots

        nplot = 0
        if plot:
            nplot += 1
            sps.append(pl.subplot(nplots, 1, nplot))
            xstart = 0
            lines = []
            for s in self.sets:
                nsamples = len(s[0])
                xend = xstart+nsamples
                xs = xrange(xstart, xend)
                lines += [pl.plot(xs, s[0], 'b')]
                lines += [pl.plot(xs, s[1], 'r')]
                # vertical line
                pl.plot([xend, xend], [np.min(s[0]), np.max(s[0])], 'k--')
                xstart = xend
            if len(lines)>1:
                pl.legend(lines[:2], ('Target', 'Prediction'))
            if plot_stats:
                pl.title(self.as_string(short='very'))

        if splot:
            nplot += 1
            sps.append(pl.subplot(nplots, 1, nplot))
            for s in self.sets:
                pl.plot(s[0], s[1], 'o',
                       markeredgewidth=0.2,
                       markersize=2)
            pl.gca().set_aspect('equal')

        if pl.matplotlib.get_backend() == 'TkAgg':
            pl.ion()
        pl.draw()

        return fig, sps

    ##REF: Name was automagically refactored
    def as_string(self, short=False, header=True,  summary=True,
                 description=False):
        """'Pretty print' the statistics"""

        if len(self.sets) == 0:
            return "Empty"

        self.compute()

        stats = self.stats

        if short:
            if short == 'very':
                # " RMSE/RMP_t:%(RMSE/RMP_t).2f" \
                return "%(# of sets)d sets CCe=%(CCe).2f p=%(CCp).2g" \
                       " RMSE:%(RMSE).2f" \
                       " Summary (stacked data): " \
                       "CCe=%(Summary CCe).2f p=%(Summary CCp).2g" \
                       % stats
            else:
                return "%(# of sets)d sets CCe=%(CCe).2f+-%(CCe_std).3f" \
                       " RMSE=%(RMSE).2f+-%(RMSE_std).3f" \
                       " RMSE/RMP_t=%(RMSE/RMP_t).2f+-%(RMSE/RMP_t_std).3f" \
                       % stats

        stats_data = ['RMP_t', 'STD_t', 'RMP_p', 'STD_p']
        # CCp needs tune up of format so excluded
        stats_ = ['CCe', 'RMSE', 'RMSE/RMP_t']
        stats_summary = ['# of sets']

        out = StringIO()

        printed = []
        if header:
            # labels
            printed.append(['Statistics', 'Mean', 'Std', 'Min', 'Max'])
            # underscores
            printed.append(['----------', '-----', '-----', '-----', '-----'])

        def print_stats(printed, stats_):
            # Statistics itself
            for stat in stats_:
                s = [stat]
                for suffix in ['', '_std', '_min', '_max']:
                    s += [ _p2(stats[stat+suffix], 3) ]
                printed.append(s)

        printed.append(["Data:     "])
        print_stats(printed, stats_data)
        printed.append(["Results:  "])
        print_stats(printed, stats_)
        printed.append(["Summary:  "])
        printed.append(["CCe", _p2(stats['Summary CCe']), "", "p=", '%g' % stats['Summary CCp']])
        printed.append(["RMSE", _p2(stats['Summary RMSE'])])
        printed.append(["RMSE/RMP_t", _p2(stats['Summary RMSE/RMP_t'])])

        if summary:
            for stat in stats_summary:
                printed.append([stat] + [_p2(stats[stat])])

        table2string(printed, out)

        if description:
            out.write("\nDescription of printed statistics.\n"
                      " Suffixes: _t - targets, _p - predictions\n")

            for d, val, eq in self._STATS_DESCRIPTION:
                out.write(" %-3s: %s\n" % (d, val))
                if eq is not None:
                    out.write("      " + eq + "\n")

        result = out.getvalue()
        out.close()
        return result


    @property
    def error(self):
        self.compute()
        return self.stats['RMSE']



class ClassifierError(ClassWithCollections):
    """Compute (or return) some error of a (trained) classifier on a dataset.
    """

    confusion = ConditionalAttribute(enabled=False)
    """TODO Think that labels might be also symbolic thus can't directly
       be indicies of the array
    """

    training_stats = ConditionalAttribute(enabled=False,
        doc="Proxy training_stats from underlying classifier.")


    def __init__(self, clf, labels=None, train=True, **kwargs):
        """Initialization.

        Parameters
        ----------
        clf : Classifier
          Either trained or untrained classifier
        labels : list
          if provided, should be a set of labels to add on top of the
          ones present in testdata
        train : bool
          unless train=False, classifier gets trained if
          trainingdata provided to __call__
        """
        ClassWithCollections.__init__(self, **kwargs)
        self.__clf = clf

        self._labels = labels
        """Labels to add on top to existing in testing data"""

        self.__train = train
        """Either to train classifier if trainingdata is provided"""


    __doc__ = enhanced_doc_string('ClassifierError', locals(), ClassWithCollections)


    def __copy__(self):
        """TODO: think... may be we need to copy self.clf"""
        out = ClassifierError.__new__(TransferError)
        ClassifierError.__init__(out, self.clf)
        return out


    def _precall(self, testdataset, trainingdataset=None):
        """Generic part which trains the classifier if necessary
        """
        if not trainingdataset is None:
            if self.__train:
                # XXX can be pretty annoying if triggered inside an algorithm
                # where it cannot be switched of, but retraining might be
                # intended or at least not avoidable.
                # Additonally is_trained docs say:
                #   MUST BE USED WITH CARE IF EVER
                #
                # switching it off for now
                #if self.__clf.is_trained(trainingdataset):
                #    warning('It seems that classifier %s was already trained' %
                #            self.__clf + ' on dataset %s. Please inspect' \
                #                % trainingdataset)
                if self.ca.is_enabled('training_stats'):
                    self.__clf.ca.change_temporarily(
                        enable_ca=['training_stats'])
                self.__clf.train(trainingdataset)
                if self.ca.is_enabled('training_stats'):
                    self.ca.training_stats = \
                        self.__clf.ca.training_stats
                    self.__clf.ca.reset_changed_temporarily()

        if self.__clf.ca.is_enabled('trained_targets') \
               and not self.__clf.__is_regression__ \
               and not testdataset is None:
            newlabels = set(testdataset.sa[self.clf.get_space()].unique) \
                        - set(self.__clf.ca.trained_targets)
            if len(newlabels)>0:
                warning("Classifier %s wasn't trained to classify labels %s" %
                        (self.__clf, newlabels) +
                        " present in testing dataset. Make sure that you have" +
                        " not mixed order/names of the arguments anywhere")

        ### Here checking for if it was trained... might be a cause of trouble
        # XXX disabled since it is unreliable.. just rely on explicit
        # self.__train
        #    if  not self.__clf.is_trained(trainingdataset):
        #        self.__clf.train(trainingdataset)
        #    elif __debug__:
        #        debug('CERR',
        #              'Not training classifier %s since it was ' % `self.__clf`
        #              + ' already trained on dataset %s' % `trainingdataset`)


    def _call(self, testdataset, trainingdataset=None):
        raise NotImplementedError


    def _postcall(self, testdataset, trainingdataset=None, error=None):
        pass


    def __call__(self, testdataset, trainingdataset=None):
        """Compute the transfer error for a certain test dataset.

        If `trainingdataset` is not `None` the classifier is trained using the
        provided dataset before computing the transfer error. Otherwise the
        classifier is used in it's current state to make the predictions on
        the test dataset.

        Returns a scalar value of the transfer error.
        """
        self._precall(testdataset, trainingdataset)
        error = self._call(testdataset, trainingdataset)
        self._postcall(testdataset, trainingdataset, error)
        if __debug__:
            debug('CERR', 'Classifier error on %s: %.2f'
                  % (testdataset, error))
        return error


    def untrain(self):
        """Untrain the `*Error` which relies on the classifier
        """
        self.clf.untrain()


    @property
    def clf(self):
        return self.__clf


    @property
    def labels(self):
        return self._labels



class ConfusionBasedError(ClassifierError):
    """For a given classifier report an error based on internally
    computed error measure (given by some `ConfusionMatrix` stored in
    some conditional attribute of `Classifier`).

    This way we can perform feature selection taking as the error
    criterion either learning error, or transfer to splits error in
    the case of SplitClassifier
    """

    def __init__(self, clf, labels=None, confusion_state="training_stats",
                 **kwargs):
        """Initialization.

        Parameters
        ----------
        clf : Classifier
          Either trained or untrained classifier
        confusion_state
          Id of the conditional attribute which stores `ConfusionMatrix`
        labels : list
          if provided, should be a set of labels to add on top of the
          ones present in testdata
        """
        ClassifierError.__init__(self, clf, labels, **kwargs)

        self.__confusion_state = confusion_state
        """What state to extract from"""

        if not clf.ca.has_key(confusion_state):
            raise ValueError, \
                  "Conditional attribute %s is not defined for classifier %r" % \
                  (confusion_state, clf)
        if not clf.ca.is_enabled(confusion_state):
            if __debug__:
                debug('CERR', "Forcing state %s to be enabled for %r" %
                      (confusion_state, clf))
            clf.ca.enable(confusion_state)


    __doc__ = enhanced_doc_string('ConfusionBasedError', locals(),
                                ClassifierError)


    def _call(self, testdata, trainingdata=None):
        """Extract transfer error. Nor testdata, neither trainingdata is used
        """
        confusion = self.clf.ca[self.__confusion_state].value
        self.ca.confusion = confusion
        return confusion.error
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # #   See COPYING file distributed along with the PyMVPA package for the #   copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## Utility class to compute the transfer error of classifiers. Helper to print depending on the type nicely. For some     reason %.2g for 100 prints exponential form which is ugly Basic class to collect targets/predictions and report summary statistics      It takes care about collecting the sets, which are just tuples     (targets, predictions, estimates). While 'computing' the matrix, all     sets are considered together.  Children of the class are     responsible for computation and display. Initialize SummaryStatistics          targets or predictions cannot be provided alone (ie targets         without predictions)          Parameters         ----------         targets          Optional set of targets         predictions          Optional set of predictions         estimates          Optional set of estimates (which served for prediction)         sets          Optional list of sets Flag either it was computed for a given set of data Datasets (target, prediction) to compute confusion matrix on Dictionary to keep statistics. Initialized here to please pylint Add new results to the set of known results # extract value if necessary # reset to None to be safe below # enforce labels in predictions to be of the same datatype as in # targets, since otherwise we are getting doubles for unknown at a # given moment labels # if there were no prediction made - leave None, otherwise # convert to appropriate type #warning("Obtained target %s and prediction %s are of " % #       (t1, t2) + "different datatypes.") # assure that we have a copy, or otherwise further in-place # modifications might screw things up (some classifiers share # estimates and spit out results) ##REF: Name was automagically refactored 'Pretty print' the matrix          Parameters         ----------         short : bool           if True, ignores the rest of the parameters and provides consise           1 line summary         header : bool           print header of the table         summary : bool           print summary (accuracy)         description : bool           print verbose description of presented statistics String summary over the `SummaryStatistics`          It would print description of the summary statistics if 'CM'         debug target is active Add the sets from `other` s `SummaryStatistics` to current one #print "adding ", other, " to ", self # need to do shallow copy, or otherwise smth like "cm += cm" # would loop forever and exhaust memory eventually #[0], set[1]) Add two `SummaryStatistics`s Actually compute the confusion matrix based on all the sets Compute basic statistics Return a list of separate summaries per each stored set Cleans summary -- all data/sets are wiped out Generic class for ROC curve computation and plotting Parameters         ----------         labels : list           labels which were used (in order of estimates if multiclass,           or 1 per class for binary problems (e.g. in SMLR))         sets : list of tuples           list of sets for the analysis Lazy computation if needed # local bindings # Handle degenerate cases politely # take sets which have values in the shape we can handle Check if values are 'acceptable' # estimates # TODO: OPT: need optimization # undefined # not dict for pairs # 1 per each label for multiclass # Something else which is not supported, like # in shogun interface we don't yet extract values per each label or # in pairs in the case of built-in multiclass # check if all had estimates, if not -- complain # bring all values to the same 'shape': #  1 value per each label. In binary classifier, if only a single #  value is provided, add '0' for 0th label 'value'... it should #  work taking drunk Yarik logic ;-) # yoh: apparently it caused problems whenever we had just a single #      unique label in the sets. Introduced handling for #      NLabels == 1 # we will do inplace modification, thus go by index # we would need it to be a list to reassign element with a list # XXX ??? so we are going away from inplace modifications? # ensure the right dimensionality Helper function. Returns x if x is scalar, and                             last element if x is not (ie list/tuple) # we need to figure out min/max estimates # to invert for the 0th label # reassign possibly adjusted estimates # we need to estimate ROC per each label # XXX order of labels might not correspond to the one among 'estimates' #     which were used to make a decision... check # 1 per label # XXX we might unify naming between AUC/ROC #aucs += [np.mean(aucs_pl)] # store results within the object Compute and return set of AUC values 1 per label ##REF: Name was automagically refactored TODO: make it friendly to labels given by values?               should we also treat labels_map? # select only rocs for the given label Class to contain information and display confusion matrix.      Implementation of the `SummaryStatistics` in the case of     classification problem. Actual computation of confusion matrix is     delayed until all data is acquired (to figure out complete set of     labels). If testing data doesn't have a complete set of labels,     but you like to include all labels, provide them as a parameter to     the constructor.      Confusion matrix provides a set of performance statistics (use     as_string(description=True) for the description of abbreviations),     as well ROC curve (http://en.wikipedia.org/wiki/ROC_curve)     plotting and analysis (AUC) in the limited set of problems:     binary, multiclass 1-vs-all. ## ('Friedman(TPR)', ##  "Friedman CHI^2 test of TPRs consistencies across sets", None), ## ('Friedman(CM)', ##  "Friedman CHI^2 test of full CM (normalized) consistencies across sets", None), Initialize ConfusionMatrix with optional list of `labels`          Parameters         ----------         labels : list          Optional set of labels to include in the matrix         labels_map : None or dict          Dictionary from original dataset to show mapping into          numerical labels         targets          Optional set of targets         predictions          Optional set of predictions List of known labels So we know later on either we could resort them Mapping from original into given labels Resultant confusion matrix Computes confusion matrix (counts)          It would rely on previously provided 'labels' to define columns/rows         of the matrix to assure consistency across multiple invocations.          Parameters         ----------         store : bool, optional           By default, this function does not modify an existing           instance of the ConfusionMatrix, and is just used merely to           provide a resultant confusion matrix.  If 'store' set to           True, provided set of predictions and targets would be added           to the sets.          Returns         -------         numpy.ndarray            counts of hits with rows -- predictions, columns -- targets # verify that we know all the labels # XXX might want to remove since summaries does the same, just without #     supplying labels Return a list of separate confusion matrix per each stored set Actually compute the confusion matrix based on all the sets #%s" \ # TODO: BinaryClassifier might spit out a list of predictions for each # value need to handle it... for now just keep original labels # figure out what labels we have # Check labels_map if it was provided if it covers all the labels # Create reverse map # just store the recomputed labels # we should append them to already known ones # Otherwise order of labels known before might be altered # and use them later on # Create a matrix for all votes # create total number of samples of each label counts # just for convinience I guess since it can always be # computed from mat_all # reverse mapping from label into index in the list of labels # for now simply compute a sum of votes across different sets # we might do something more sophisticated later on, and this setup # should easily allow it # reset nans in TPRs to 0s whenever there is no entries # for those labels among the targets # TODO: STD of accuracy and corrected one according to #    Nadeau and Bengio [50] # indep_rows to assure reasonable handling of disbalanced # cases # Lets see if there is possible order effect in accuracy # (e.g. it goes down through splits) # simple linear regression # Confusion ratios (both TPs or FPs) # we want to divide each column but sum in the column ## stats['Friedman(TPR):chi^2'], stats['Friedman(TPR):p'] = \ ##                               friedmanchisquare(*TPRs_per_set) ## stats['Friedman(CM):chi^2'], stats['Friedman(CM):p'] = \ ##                              friedmanchisquare(*CM_per_set) # # ROC computation if available # we don't want to provide ROC if it is bogus # compute mean stats ##REF: Name was automagically refactored 'Pretty print' the matrix          Parameters         ----------         short : bool           if True, ignores the rest of the parameters and provides consise           1 line summary         header : bool           print header of the table         summary : bool           print summary (accuracy)         description : bool           print verbose description of presented statistics # some shortcuts # numbers of different entries # of sets)d sets %(# of labels)d labels " \ # length of a single label/value #, len("100.00%")) # print AUC only if ROC was computed # 'Friedman(TPR):p', 'Friedman(CM):p' #prefixlen = Nlabelsmax + 2 + Ndigitsmax + 1 # empty prefix # list of lists of what is printed # labels # underscores # matrix itself ## Various alternative schemes ;-) # printed.append([''] + underscores) # printed.append(['@lPer target \ Means:'] + underscores + \ #               [_p2(x) for x in mean_stats]) # printed.append(['Means:'] + [''] * len(labels) #                + [_p2(x) for x in mean_stats]) # compute mean stats # XXX refactor to expose them in stats as well, as #     mean(FCC) # Inject into sets line #out.write("%s" % printed) Provide presentation of confusion matrix in image          Parameters         ----------         labels : list of int or str           Optionally provided labels guarantee the order of           presentation. Also value of None places empty column/row,           thus provides visual groupping of labels (Thanks Ingo)         numbers : bool           Place values inside of confusion matrix elements         numbers_alpha : None or float           Controls textual output of numbers. If None -- all numbers           are plotted in the same intensity. If some float -- it controls           alpha level -- higher value would give higher contrast. (good           value is 2)         origin : str           Which left corner diagonal should start         xlabels_vertical : bool           Either to plot xlabels vertical (benefitial if number of labels           is large)         numbers_kwargs : dict           Additional keyword parameters to be added to numbers (if numbers           is True)         **kwargs           Additional arguments given to imshow (\eg me cmap)          Returns         -------          (fig, im, cb) -- figure, imshow, colorbar # some shortcuts # craft original mapping from a label into index in the matrix # Verify if all labels provided in labels # We were provided numerical (most probably) set # not clear if right whenever there were multiple labels # mapped into the same # where we have Nones? # numbers of different entries # populate in a silly way # turn off automatic update if interactive # some customization depending on the origin # Plot # plot numbers # scale alpha non-linearly # scale according to value # Label axes # Store it primarily for testing ##REF: Name was automagically refactored ##REF: Name was automagically refactored # reset it just in case ##REF: Name was automagically refactored Compute confusion matrix as an "error function"      This class can be used to compute confusion matrices from classifier     output inside cross-validation fold without the ``stats`` conditional     attribute. Simply pass an instance of this class to the ``errorfx``     argument of ``CrossValidation``. Parameters         ----------         labels : list           Class labels for confusion matrix columns/rows #print cm.matrix # We have to add a degenerate leading dimension # so we could separate them into separate 'samples' Compute a confusion matrix from predictions and targets (Node interface)      This class is very similar to ``ConfusionMatrix`` and     ``ConfusionMatrixError``.  However, in contrast to these this class can be     used in any place that accepts ``Nodes`` -- most importantly others node's     ``postproc`` functionality. This makes it very straightforward to compute     confusion matrices from classifier output as an intermediate result and     continue processing with other nodes. A sketch of a cross-validation setup     using this functionality looks like this::        CrossValidation(some_classifier,                       some_partitioner,                       errorfx=None,                       postproc=Confusion())      It is vital to set ``errorfx`` to ``None`` to preserve raw classifier     prediction values in the output dataset to allow for proper data aggregation     in a confusion matrix. Parameters         ----------         attr : str           Sample attribute name where classification target values are stored           for each prediction.         labels : list or None           Optional list of labels to compute a confusion matrix for. This can be           useful if a particular  prediction dataset doesn't have all           theoretically possible labels as targets.         add_confusion_obj : bool           If True, the ConfusionMatrix object will be added to the output           dataset as attribute 'confusion_obj', i.e. ds.a.confusion_obj         **kwargs           All remaining argments will be passed on to the Node base-class. # MH: we could also make it iterate over individual chunks and create # matrix sets -- but not sure if there is a use case for the Node # interface # compute the confusion matrix # figure out where to store the labels # by default the confusion matrix in the Dataset will look just like # a printed ConfusionMatrix Bayesian hypothesis testing on confusion matrices.      For multi-class classification a single accuracy value is often not a     meaningful performance measure -- or at least hard to interpret. This class     allows for convenient Bayesian hypothesis testing of confusion matrices.     It computes the likelihood of discriminibility of any partitions of     classes given a confusion matrix.      The returned dataset contains at least one feature (the log likelihood of     a hypothesis) and as many samples as (possible) partitions of classes.     The actual partition configurations are stored in a sample attribute     of nested lists. The top-level list contains discriminable groups of     classes, whereas the second level lists contain groups of classes that     cannot be discriminated under a given hypothesis. For example::        [[0, 1], [2], [3, 4, 5]]      This hypothesis represent the state where class 0 and 1 cannot be     distinguish from each other, but both 0 and 1 together can be distinguished     from class 2 and the group of 3, 4, and 5 -- where classes from the later     group cannot be distinguished from one another.      This algorithms is based on          <NAME>., <NAME>. and <NAME>. (2012). Testing for         Information with Brain Decoding. In: Pattern Recognition in NeuroImaging         (PRNI), International Workshop on. Parameters         ----------         alpha : array           Bayesian hyper-prior alpha (in a multivariate-Dirichlet sense)         labels_attr : str           Name of the sample attribute in the input dataset that contains           the class labels corresponding to the confusion matrix rows. If an           attribute with this name is not found, hypotheses will be reported           based on confusion table row/column numbers, instead of their           corresponding labels. If such an attribute is found in the input           dataset, any ``hypotheses`` specification has to be specified           using literal labels also.         space : str           Name of the sample attribute in the output dataset where the           hypothesis partition configurations will be stored.         prior_Hs : array           Vector of priors for each hypotheses. Typically used in conjuction           with an explicit set of possible hypotheses (see ``hypotheses``).           If ``None`` a flat prior is assumed.         log : bool           Whether to return values (likelihood or posterior probabilities) in           log scale to mitigate numerical precision problems with near-zero           probabilities.         postprob : bool           Whether to return posterior probabilities p(hypothesis|confusion)           instead of likelihood(confusion|hypothesis).         hypotheses : list           List of possible hypotheses. XXX needs work on how to specify them.         **kwargs           All remaining argments will be passed on to the Node base-class. # generate all possible hypotheses if none are given # literal labels are given -> recode into digits to match # underlying API # use hypotheses as is -- all bets are off # default: uniform prior on hypotheses: p(H_i) # p(X|H_i) for all H # use Emanuele's toolbox # convert into posterior probabilities: p(H|X) # normalization constant: p(X) # p(H|X) from Bayes rule: # convert from log scale # remove the log() from the stat label # recode partition IDs into actual labels, if the necessary attr # is available Class to contain information and display on regression results. Initialize RegressionStatistics          Parameters         ----------         targets          Optional set of targets         predictions          Optional set of predictions Actually compute the confusion matrix based on all the sets # create ``summary`` statistics, since some per-set statistics # might be uncomputable if a set contains just a single number # (like in the case of correlation coefficient) #labels=None, numbers=False, origin='upper', #numbers_alpha=None, xlabels_vertical=True, #numbers_kwargs={}, #**kwargs Provide presentation of regression performance in image          Parameters         ----------         plot : bool           Plot regular plot of values (targets/predictions)         plot_stats : bool           Print basic statistics in the title         splot : bool           Plot scatter plot          Returns         -------          (fig, im, cb) -- figure, imshow, colorbar # total number of plots # turn off automatic update if interactive # subplots # vertical line ##REF: Name was automagically refactored 'Pretty print' the statistics # " RMSE/RMP_t:%(RMSE/RMP_t).2f" \ # of sets)d sets CCe=%(CCe).2f p=%(CCp).2g" \ # of sets)d sets CCe=%(CCe).2f+-%(CCe_std).3f" \ # CCp needs tune up of format so excluded # labels # underscores # Statistics itself Compute (or return) some error of a (trained) classifier on a dataset. TODO Think that labels might be also symbolic thus can't directly        be indicies of the array Initialization.          Parameters         ----------         clf : Classifier           Either trained or untrained classifier         labels : list           if provided, should be a set of labels to add on top of the           ones present in testdata         train : bool           unless train=False, classifier gets trained if           trainingdata provided to __call__ Labels to add on top to existing in testing data Either to train classifier if trainingdata is provided TODO: think... may be we need to copy self.clf Generic part which trains the classifier if necessary # XXX can be pretty annoying if triggered inside an algorithm # where it cannot be switched of, but retraining might be # intended or at least not avoidable. # Additonally is_trained docs say: #   MUST BE USED WITH CARE IF EVER # # switching it off for now #if self.__clf.is_trained(trainingdataset): #    warning('It seems that classifier %s was already trained' % #            self.__clf + ' on dataset %s. Please inspect' \ #                % trainingdataset) ### Here checking for if it was trained... might be a cause of trouble # XXX disabled since it is unreliable.. just rely on explicit # self.__train #    if  not self.__clf.is_trained(trainingdataset): #        self.__clf.train(trainingdataset) #    elif __debug__: #        debug('CERR', #              'Not training classifier %s since it was ' % `self.__clf` #              + ' already trained on dataset %s' % `trainingdataset`) Compute the transfer error for a certain test dataset.          If `trainingdataset` is not `None` the classifier is trained using the         provided dataset before computing the transfer error. Otherwise the         classifier is used in it's current state to make the predictions on         the test dataset.          Returns a scalar value of the transfer error. Untrain the `*Error` which relies on the classifier For a given classifier report an error based on internally     computed error measure (given by some `ConfusionMatrix` stored in     some conditional attribute of `Classifier`).      This way we can perform feature selection taking as the error     criterion either learning error, or transfer to splits error in     the case of SplitClassifier Initialization.          Parameters         ----------         clf : Classifier           Either trained or untrained classifier         confusion_state           Id of the conditional attribute which stores `ConfusionMatrix`         labels : list           if provided, should be a set of labels to add on top of the           ones present in testdata What state to extract from Extract transfer error. Nor testdata, neither trainingdata is used