scikit-learn/sklearn/utils/fixes.py

"""Compatibility fixes for older version of python, numpy and scipy"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
#          Gael Varoquaux <gael.varoquaux@normalesup.org>
#          Fabian Pedregosa <fpedregosa@acm.org>
# License: BSD

import collections
import numpy as np
from operator import itemgetter


try:
    from itertools import product
except ImportError:
    def product(*args, **kwds):
        pools = map(tuple, args) * kwds.get('repeat', 1)
        result = [[]]
        for pool in pools:
            result = [x + [y] for x in result for y in pool]
        for prod in result:
            yield tuple(prod)


try:
    Counter = collections.Counter
except AttributeError:
    # Partial replacement for Python 2.7 Counter
    class Counter(collections.defaultdict):
        def __init__(self, **kwargs):
            super(Counter, self).__init__(int, **kwargs)

        def most_common(self):
            return sorted(self.iteritems(), key=itemgetter(1), reverse=True)


def _unique(ar, return_index=False, return_inverse=False):
    """A replacement for the np.unique that appeared in numpy 1.4.

    While np.unique existed long before, keyword return_inverse was
    only added in 1.4.
    """
    try:
        ar = ar.flatten()
    except AttributeError:
        if not return_inverse and not return_index:
            items = sorted(set(ar))
            return np.asarray(items)
        else:
            ar = np.asanyarray(ar).flatten()

    if ar.size == 0:
        if return_inverse and return_index:
            return ar, np.empty(0, np.bool), np.empty(0, np.bool)
        elif return_inverse or return_index:
            return ar, np.empty(0, np.bool)
        else:
            return ar

    if return_inverse or return_index:
        perm = ar.argsort()
        aux = ar[perm]
        flag = np.concatenate(([True], aux[1:] != aux[:-1]))
        if return_inverse:
            iflag = np.cumsum(flag) - 1
            iperm = perm.argsort()
            if return_index:
                return aux[flag], perm[flag], iflag[iperm]
            else:
                return aux[flag], iflag[iperm]
        else:
            return aux[flag], perm[flag]

    else:
        ar.sort()
        flag = np.concatenate(([True], ar[1:] != ar[:-1]))
        return ar[flag]

np_version = np.__version__.split('.')
if int(np_version[0]) < 2 and int(np_version[1]) < 5:
    unique = _unique
else:
    unique = np.unique


def _copysign(x1, x2):
    """Slow replacement for np.copysign, which was introduced in numpy 1.4"""
    return np.abs(x1) * np.sign(x2)

if not hasattr(np, 'copysign'):
    copysign = _copysign
else:
    copysign = np.copysign


def _in1d(ar1, ar2, assume_unique=False):
    """Replacement for in1d that is provided for numpy >= 1.4"""
    if not assume_unique:
        ar1, rev_idx = unique(ar1, return_inverse=True)
        ar2 = np.unique(ar2)
    ar = np.concatenate((ar1, ar2))
    # We need this to be a stable sort, so always use 'mergesort'
    # here. The values from the first array should always come before
    # the values from the second array.
    order = ar.argsort(kind='mergesort')
    sar = ar[order]
    equal_adj = (sar[1:] == sar[:-1])
    flag = np.concatenate((equal_adj, [False]))
    indx = order.argsort(kind='mergesort')[:len(ar1)]

    if assume_unique:
        return flag[indx]
    else:
        return flag[indx][rev_idx]

if not hasattr(np, 'in1d'):
    in1d = _in1d
else:
    in1d = np.in1d


def qr_economic(A, **kwargs):
    """Compat function for the QR-decomposition in economic mode

    Scipy 0.9 changed the keyword econ=True to mode='economic'
    """
    import scipy.linalg
    # trick: triangular solve has introduced in 0.9
    if hasattr(scipy.linalg, 'solve_triangular'):
        return scipy.linalg.qr(A, mode='economic', **kwargs)
    else:
        return scipy.linalg.qr(A, econ=True, **kwargs)


def arpack_eigsh(A, **kwargs):
    """Compat function for sparse symmetric eigen vectors decomposition

    Scipy 0.9 renamed eigen_symmetric to eigsh in
    scipy.sparse.linalg.eigen.arpack
    """
    from scipy.sparse.linalg.eigen import arpack
    if hasattr(arpack, 'eigsh'):
        return arpack.eigsh(A, **kwargs)
    else:
        return arpack.eigen_symmetric(A, **kwargs)


def savemat(file_name, mdict, oned_as="column", **kwargs):
    """MATLAB-format output routine that is compatible with SciPy 0.7's.

    0.7.2 (or .1?) added the oned_as keyword arg with 'column' as the default
    value. It issues a warning if this is not provided, stating that "This will
    change to 'row' in future versions."
    """
    import scipy.io
    try:
        return scipy.io.savemat(file_name, mdict, oned_as=oned_as, **kwargs)
    except TypeError:
        return scipy.io.savemat(file_name, mdict, **kwargs)

try:
    from numpy import count_nonzero
except ImportError:
    def count_nonzero(X):
        return len(np.flatnonzero(X))