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# -*- coding: utf-8 -*-
"""
Various function that can be usefull
"""
# Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import multiprocessing
from functools import reduce
import time
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics.pairwise import euclidean_distances
import sys
import warnings
try:
from inspect import signature
except ImportError:
from .externals.funcsigs import signature
__time_tic_toc = time.time()
def tic():
""" Python implementation of Matlab tic() function """
global __time_tic_toc
__time_tic_toc = time.time()
def toc(message='Elapsed time : {} s'):
""" Python implementation of Matlab toc() function """
t = time.time()
print(message.format(t - __time_tic_toc))
return t - __time_tic_toc
def toq():
""" Python implementation of Julia toc() function """
t = time.time()
return t - __time_tic_toc
def kernel(x1, x2, method='gaussian', sigma=1, **kwargs):
"""Compute kernel matrix"""
if method.lower() in ['gaussian', 'gauss', 'rbf']:
K = np.exp(-dist(x1, x2) / (2 * sigma**2))
return K
def unif(n):
""" return a uniform histogram of length n (simplex)
Parameters
----------
n : int
number of bins in the histogram
Returns
-------
h : np.array (n,)
histogram of length n such that h_i=1/n for all i
"""
return np.ones((n,)) / n
def clean_zeros(a, b, M):
""" Remove all components with zeros weights in a and b
"""
M2 = M[a > 0, :][:, b > 0].copy() # copy force c style matrix (froemd)
a2 = a[a > 0]
b2 = b[b > 0]
return a2, b2, M2
def dist(x1, x2=None, metric='sqeuclidean'):
"""Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist
Parameters
----------
x1 : np.array (n1,d)
matrix with n1 samples of size d
x2 : np.array (n2,d), optional
matrix with n2 samples of size d (if None then x2=x1)
metric : str, fun, optional
name of the metric to be computed (full list in the doc of scipy), If a string,
the distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock',
'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski',
'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski', 'yule'.
Returns
-------
M : np.array (n1,n2)
distance matrix computed with given metric
"""
if x2 is None:
x2 = x1
if metric == "sqeuclidean":
return euclidean_distances(x1, x2, squared=True)
return cdist(x1, x2, metric=metric)
def dist0(n, method='lin_square'):
"""Compute standard cost matrices of size (n,n) for OT problems
Parameters
----------
n : int
size of the cost matrix
method : str, optional
Type of loss matrix chosen from:
* 'lin_square' : linear sampling between 0 and n-1, quadratic loss
Returns
-------
M : np.array (n1,n2)
distance matrix computed with given metric
"""
res = 0
if method == 'lin_square':
x = np.arange(n, dtype=np.float64).reshape((n, 1))
res = dist(x, x)
return res
def cost_normalization(C, norm=None):
""" Apply normalization to the loss matrix
Parameters
----------
C : np.array (n1, n2)
The cost matrix to normalize.
norm : str
type of normalization from 'median','max','log','loglog'. Any other
value do not normalize.
Returns
-------
C : np.array (n1, n2)
The input cost matrix normalized according to given norm.
"""
if norm == "median":
C /= float(np.median(C))
elif norm == "max":
C /= float(np.max(C))
elif norm == "log":
C = np.log(1 + C)
elif norm == "loglog":
C = np.log(1 + np.log(1 + C))
return C
def dots(*args):
""" dots function for multiple matrix multiply """
return reduce(np.dot, args)
def fun(f, q_in, q_out):
""" Utility function for parmap with no serializing problems """
while True:
i, x = q_in.get()
if i is None:
break
q_out.put((i, f(x)))
def parmap(f, X, nprocs=multiprocessing.cpu_count()):
""" paralell map for multiprocessing """
q_in = multiprocessing.Queue(1)
q_out = multiprocessing.Queue()
proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out))
for _ in range(nprocs)]
for p in proc:
p.daemon = True
p.start()
sent = [q_in.put((i, x)) for i, x in enumerate(X)]
[q_in.put((None, None)) for _ in range(nprocs)]
res = [q_out.get() for _ in range(len(sent))]
[p.join() for p in proc]
return [x for i, x in sorted(res)]
def check_params(**kwargs):
"""check_params: check whether some parameters are missing
"""
missing_params = []
check = True
for param in kwargs:
if kwargs[param] is None:
missing_params.append(param)
if len(missing_params) > 0:
print("POT - Warning: following necessary parameters are missing")
for p in missing_params:
print("\n", p)
check = False
return check
def check_random_state(seed):
"""Turn seed into a np.random.RandomState instance
Parameters
----------
seed : None | int | instance of RandomState
If seed is None, return the RandomState singleton used by np.random.
If seed is an int, return a new RandomState instance seeded with seed.
If seed is already a RandomState instance, return it.
Otherwise raise ValueError.
"""
if seed is None or seed is np.random:
return np.random.mtrand._rand
if isinstance(seed, (int, np.integer)):
return np.random.RandomState(seed)
if isinstance(seed, np.random.RandomState):
return seed
raise ValueError('{} cannot be used to seed a numpy.random.RandomState'
' instance'.format(seed))
class deprecated(object):
"""Decorator to mark a function or class as deprecated.
deprecated class from scikit-learn package
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/deprecation.py
Issue a warning when the function is called/the class is instantiated and
adds a warning to the docstring.
The optional extra argument will be appended to the deprecation message
and the docstring. Note: to use this with the default value for extra, put
in an empty of parentheses:
>>> from ot.deprecation import deprecated
>>> @deprecated()
... def some_function(): pass
Parameters
----------
extra : string
to be added to the deprecation messages
"""
# Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary,
# but with many changes.
def __init__(self, extra=''):
self.extra = extra
def __call__(self, obj):
"""Call method
Parameters
----------
obj : object
"""
if isinstance(obj, type):
return self._decorate_class(obj)
else:
return self._decorate_fun(obj)
def _decorate_class(self, cls):
msg = "Class %s is deprecated" % cls.__name__
if self.extra:
msg += "; %s" % self.extra
# FIXME: we should probably reset __new__ for full generality
init = cls.__init__
def wrapped(*args, **kwargs):
warnings.warn(msg, category=DeprecationWarning)
return init(*args, **kwargs)
cls.__init__ = wrapped
wrapped.__name__ = '__init__'
wrapped.__doc__ = self._update_doc(init.__doc__)
wrapped.deprecated_original = init
return cls
def _decorate_fun(self, fun):
"""Decorate function fun"""
msg = "Function %s is deprecated" % fun.__name__
if self.extra:
msg += "; %s" % self.extra
def wrapped(*args, **kwargs):
warnings.warn(msg, category=DeprecationWarning)
return fun(*args, **kwargs)
wrapped.__name__ = fun.__name__
wrapped.__dict__ = fun.__dict__
wrapped.__doc__ = self._update_doc(fun.__doc__)
return wrapped
def _update_doc(self, olddoc):
newdoc = "DEPRECATED"
if self.extra:
newdoc = "%s: %s" % (newdoc, self.extra)
if olddoc:
newdoc = "%s\n\n%s" % (newdoc, olddoc)
return newdoc
def _is_deprecated(func):
"""Helper to check if func is wraped by our deprecated decorator"""
if sys.version_info < (3, 5):
raise NotImplementedError("This is only available for python3.5 "
"or above")
closures = getattr(func, '__closure__', [])
if closures is None:
closures = []
is_deprecated = ('deprecated' in ''.join([c.cell_contents
for c in closures
if isinstance(c.cell_contents, str)]))
return is_deprecated
class BaseEstimator(object):
"""Base class for most objects in POT
adapted from sklearn BaseEstimator class
Notes
-----
All estimators should specify all the parameters that can be set
at the class level in their ``__init__`` as explicit keyword
arguments (no ``*args`` or ``**kwargs``).
"""
@classmethod
def _get_param_names(cls):
"""Get parameter names for the estimator"""
# fetch the constructor or the original constructor before
# deprecation wrapping if any
init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
if init is object.__init__:
# No explicit constructor to introspect
return []
# introspect the constructor arguments to find the model parameters
# to represent
init_signature = signature(init)
# Consider the constructor parameters excluding 'self'
parameters = [p for p in init_signature.parameters.values()
if p.name != 'self' and p.kind != p.VAR_KEYWORD]
for p in parameters:
if p.kind == p.VAR_POSITIONAL:
raise RuntimeError("POT estimators should always "
"specify their parameters in the signature"
" of their __init__ (no varargs)."
" %s with constructor %s doesn't "
" follow this convention."
% (cls, init_signature))
# Extract and sort argument names excluding 'self'
return sorted([p.name for p in parameters])
def get_params(self, deep=True):
"""Get parameters for this estimator.
Parameters
----------
deep : boolean, optional
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : mapping of string to any
Parameter names mapped to their values.
"""
out = dict()
for key in self._get_param_names():
# We need deprecation warnings to always be on in order to
# catch deprecated param values.
# This is set in utils/__init__.py but it gets overwritten
# when running under python3 somehow.
warnings.simplefilter("always", DeprecationWarning)
try:
with warnings.catch_warnings(record=True) as w:
value = getattr(self, key, None)
if len(w) and w[0].category == DeprecationWarning:
# if the parameter is deprecated, don't show it
continue
finally:
warnings.filters.pop(0)
# XXX: should we rather test if instance of estimator?
if deep and hasattr(value, 'get_params'):
deep_items = value.get_params().items()
out.update((key + '__' + k, val) for k, val in deep_items)
out[key] = value
return out
def set_params(self, **params):
"""Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
``<component>__<parameter>`` so that it's possible to update each
component of a nested object.
Returns
-------
self
"""
if not params:
# Simple optimisation to gain speed (inspect is slow)
return self
valid_params = self.get_params(deep=True)
# for key, value in iteritems(params):
for key, value in params.items():
split = key.split('__', 1)
if len(split) > 1:
# nested objects case
name, sub_name = split
if name not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(name, self))
sub_object = valid_params[name]
sub_object.set_params(**{sub_name: value})
else:
# simple objects case
if key not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(key, self.__class__.__name__))
setattr(self, key, value)
return self
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