python-sklearn 0.14.1-2 / usr / share / pyshared / sklearn / utils / fixes.py

"""Compatibility fixes for older version of python, numpy and scipy

If you add content to this file, please give the version of the package
at which the fixe is no longer needed.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
#          Gael Varoquaux <gael.varoquaux@normalesup.org>
#          Fabian Pedregosa <fpedregosa@acm.org>
#          Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause

import collections
from operator import itemgetter
import inspect
from sklearn.externals import six

import numpy as np

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

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

        def update(self, other):
            """Adds counts for elements in other"""
            if isinstance(other, self.__class__):
                for x, n in six.iteritems(other):
                    self[x] += n
            else:
                for x in other:
                    self[x] += 1


def lsqr(X, y, tol=1e-3):
    import scipy.sparse.linalg as sp_linalg
    from ..utils.extmath import safe_sparse_dot

    if hasattr(sp_linalg, 'lsqr'):
        # scipy 0.8 or greater
        return sp_linalg.lsqr(X, y)
    else:
        n_samples, n_features = X.shape
        if n_samples > n_features:
            coef, _ = sp_linalg.cg(safe_sparse_dot(X.T, X),
                                   safe_sparse_dot(X.T, y),
                                   tol=tol)
        else:
            coef, _ = sp_linalg.cg(safe_sparse_dot(X, X.T), y, tol=tol)
            coef = safe_sparse_dot(X.T, coef)

        residues = y - safe_sparse_dot(X, coef)
        return coef, None, None, residues


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.asarray(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 = []
for x in np.__version__.split('.'):
    try:
        np_version.append(int(x))
    except ValueError:
        # x may be of the form dev-1ea1592
        np_version.append(x)
np_version = tuple(np_version)

if np_version[:2] < (1, 5):
    unique = _unique
else:
    unique = np.unique


def _bincount(X, weights=None, minlength=None):
    """Replacing np.bincount in numpy < 1.6 to provide minlength."""
    result = np.bincount(X, weights)
    if len(result) >= minlength:
        return result
    out = np.zeros(minlength, np.int)
    out[:len(result)] = result
    return out

if np_version[:2] < (1, 6):
    bincount = _bincount
else:
    bincount = np.bincount


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:
        import warnings
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", DeprecationWarning)
            return scipy.linalg.qr(A, econ=True, **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))

# little danse to see if np.copy has an 'order' keyword argument
if 'order' in inspect.getargspec(np.copy)[0]:
    def safe_copy(X):
        # Copy, but keep the order
        return np.copy(X, order='K')
else:
    # Before an 'order' argument was introduced, numpy wouldn't muck with
    # the ordering
    safe_copy = np.copy

try:
    if (not np.allclose(np.divide(.4, 1), np.divide(.4, 1, dtype=np.float))
            or not np.allclose(np.divide(.4, 1), .4)):
        raise TypeError('Divide not working with dtype: '
                        'https://github.com/numpy/numpy/issues/3484')
    divide = np.divide

except TypeError:
    # Compat for old versions of np.divide that do not provide support for
    # the dtype args
    def divide(x1, x2, out=None, dtype=None):
        out_orig = out
        if out is None:
            out = np.asarray(x1, dtype=dtype)
            if out is x1:
                out = x1.copy()
        else:
            if out is not x1:
                out[:] = x1
        if dtype is not None and out.dtype != dtype:
            out = out.astype(dtype)
        out /= x2
        if out_orig is None and np.isscalar(x1):
            out = np.asscalar(out)
        return out