208 lines
7.1 KiB
Python
208 lines
7.1 KiB
Python
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"""Compatibility fixes for older version of python, numpy and scipy
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If you add content to this file, please give the version of the package
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at which the fixe is no longer needed.
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"""
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# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
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# Gael Varoquaux <gael.varoquaux@normalesup.org>
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# Fabian Pedregosa <fpedregosa@acm.org>
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# Lars Buitinck
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#
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# License: BSD 3 clause
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import warnings
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import os
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import errno
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import numpy as np
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import scipy.sparse as sp
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import scipy
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try:
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from inspect import signature
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except ImportError:
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from ..externals.funcsigs import signature
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def _parse_version(version_string):
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version = []
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for x in version_string.split('.'):
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try:
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version.append(int(x))
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except ValueError:
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# x may be of the form dev-1ea1592
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version.append(x)
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return tuple(version)
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euler_gamma = getattr(np, 'euler_gamma',
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0.577215664901532860606512090082402431)
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np_version = _parse_version(np.__version__)
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sp_version = _parse_version(scipy.__version__)
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# Remove when minimum required NumPy >= 1.10
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try:
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if (not np.allclose(np.divide(.4, 1, casting="unsafe"),
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np.divide(.4, 1, casting="unsafe", dtype=np.float64))
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or not np.allclose(np.divide(.4, 1), .4)):
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raise TypeError('Divide not working with dtype: '
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'https://github.com/numpy/numpy/issues/3484')
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divide = np.divide
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except TypeError:
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# Compat for old versions of np.divide that do not provide support for
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# the dtype args
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def divide(x1, x2, out=None, dtype=None):
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out_orig = out
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if out is None:
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out = np.asarray(x1, dtype=dtype)
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if out is x1:
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out = x1.copy()
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else:
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if out is not x1:
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out[:] = x1
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if dtype is not None and out.dtype != dtype:
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out = out.astype(dtype)
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out /= x2
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if out_orig is None and np.isscalar(x1):
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out = np.asscalar(out)
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return out
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try:
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with warnings.catch_warnings(record=True):
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# Don't raise the numpy deprecation warnings that appear in
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# 1.9, but avoid Python bug due to simplefilter('ignore')
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warnings.simplefilter('always')
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sp.csr_matrix([1.0, 2.0, 3.0]).max(axis=0)
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except (TypeError, AttributeError):
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# in scipy < 14.0, sparse matrix min/max doesn't accept an `axis` argument
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# the following code is taken from the scipy 0.14 codebase
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def _minor_reduce(X, ufunc):
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major_index = np.flatnonzero(np.diff(X.indptr))
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value = ufunc.reduceat(X.data, X.indptr[major_index])
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return major_index, value
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def _min_or_max_axis(X, axis, min_or_max):
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N = X.shape[axis]
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if N == 0:
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raise ValueError("zero-size array to reduction operation")
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M = X.shape[1 - axis]
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mat = X.tocsc() if axis == 0 else X.tocsr()
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mat.sum_duplicates()
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major_index, value = _minor_reduce(mat, min_or_max)
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not_full = np.diff(mat.indptr)[major_index] < N
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value[not_full] = min_or_max(value[not_full], 0)
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mask = value != 0
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major_index = np.compress(mask, major_index)
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value = np.compress(mask, value)
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from scipy.sparse import coo_matrix
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if axis == 0:
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res = coo_matrix((value, (np.zeros(len(value)), major_index)),
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dtype=X.dtype, shape=(1, M))
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else:
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res = coo_matrix((value, (major_index, np.zeros(len(value)))),
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dtype=X.dtype, shape=(M, 1))
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return res.A.ravel()
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def _sparse_min_or_max(X, axis, min_or_max):
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if axis is None:
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if 0 in X.shape:
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raise ValueError("zero-size array to reduction operation")
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zero = X.dtype.type(0)
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if X.nnz == 0:
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return zero
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m = min_or_max.reduce(X.data.ravel())
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if X.nnz != np.product(X.shape):
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m = min_or_max(zero, m)
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return m
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if axis < 0:
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axis += 2
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if (axis == 0) or (axis == 1):
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return _min_or_max_axis(X, axis, min_or_max)
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else:
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raise ValueError("invalid axis, use 0 for rows, or 1 for columns")
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def sparse_min_max(X, axis):
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return (_sparse_min_or_max(X, axis, np.minimum),
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_sparse_min_or_max(X, axis, np.maximum))
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else:
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def sparse_min_max(X, axis):
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return (X.min(axis=axis).toarray().ravel(),
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X.max(axis=axis).toarray().ravel())
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if sp_version < (0, 15):
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# Backport fix for scikit-learn/scikit-learn#2986 / scipy/scipy#4142
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from ._scipy_sparse_lsqr_backport import lsqr as sparse_lsqr
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else:
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from scipy.sparse.linalg import lsqr as sparse_lsqr # noqa
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try: # SciPy >= 0.19
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from scipy.special import comb, logsumexp
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except ImportError:
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from scipy.misc import comb, logsumexp # noqa
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def parallel_helper(obj, methodname, *args, **kwargs):
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"""Workaround for Python 2 limitations of pickling instance methods"""
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return getattr(obj, methodname)(*args, **kwargs)
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if 'exist_ok' in signature(os.makedirs).parameters:
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makedirs = os.makedirs
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else:
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def makedirs(name, mode=0o777, exist_ok=False):
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"""makedirs(name [, mode=0o777][, exist_ok=False])
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Super-mkdir; create a leaf directory and all intermediate ones. Works
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like mkdir, except that any intermediate path segment (not just the
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rightmost) will be created if it does not exist. If the target
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directory already exists, raise an OSError if exist_ok is False.
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Otherwise no exception is raised. This is recursive.
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"""
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try:
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os.makedirs(name, mode=mode)
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except OSError as e:
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if (not exist_ok or e.errno != errno.EEXIST
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or not os.path.isdir(name)):
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raise
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if np_version < (1, 12):
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class MaskedArray(np.ma.MaskedArray):
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# Before numpy 1.12, np.ma.MaskedArray object is not picklable
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# This fix is needed to make our model_selection.GridSearchCV
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# picklable as the ``cv_results_`` param uses MaskedArray
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def __getstate__(self):
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"""Return the internal state of the masked array, for pickling
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purposes.
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"""
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cf = 'CF'[self.flags.fnc]
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data_state = super(np.ma.MaskedArray, self).__reduce__()[2]
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return data_state + (np.ma.getmaskarray(self).tostring(cf),
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self._fill_value)
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else:
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from numpy.ma import MaskedArray # noqa
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# To be removed once this fix is included in six
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try:
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from collections.abc import Sequence as _Sequence # noqa
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from collections.abc import Iterable as _Iterable # noqa
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from collections.abc import Mapping as _Mapping # noqa
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from collections.abc import Sized as _Sized # noqa
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except ImportError: # python <3.3
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from collections import Sequence as _Sequence # noqa
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from collections import Iterable as _Iterable # noqa
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from collections import Mapping as _Mapping # noqa
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from collections import Sized as _Sized # noqa
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