def uniform_hyperrectangle_user(data, domain, center_pts_per_edge=1):
r"""
Creates a simple funciton appoximation of :math:`\rho_{\mathcal{D},M}`
where :math:`\rho{\mathcal{D}, M}` is a uniform probablity density over the
hyperrectangular domain specified by domain.
Since :math:`\rho_\mathcal{D}` is a uniform distribution on a
hyperrectangle we should we able to represent it exactly with
:math:`M=3^{m}` where m is the dimension of the data space or rather
``len(d_distr_samples) == 3**mdim``.
:param data: Array containing QoI data where the QoI is mdim diminsional
:type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
:param domain: The domain overwhich :math:`\rho_\mathcal{D}` is
uniform.
:type domain: :class:`numpy.ndarray` of shape (2, mdim)
:param list() center_pts_per_edge: number of center points per edge and
additional two points will be added to create the bounding layer
:rtype: tuple
:returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
is the :class:`~scipy.spatial.KDTree` for d_distr_samples
"""
# make sure the shape of the data and the domain are correct
data = util.fix_dimensions_data(data)
domain = util.fix_dimensions_data(domain, data.shape[1])
domain_center = np.mean(domain, 0)
domain_lengths = np.max(domain, 0) - np.min(domain, 0)
return uniform_hyperrectangle_binsize(data, domain_center, domain_lengths,
center_pts_per_edge)
def simple_fun_uniform(points, volumes, rect_domain):
"""
Given a set of points, the volumes associated with these points, and
``rect_domain`` creates a simple function approximation of a uniform
distribution over the hyperrectangle defined by ``rect_domain``.
:param points: points used to define the voronoi tesselation (only the
points that define regions of finite volumes)
:type points: :class:`numpy.ndarrray` of shape (num_points, mdim)
:param list() volumes: finite volumes associated with ``points``
:type points: :class:`numpy.ndarray` of shape (num_points,)
:param rect_domain: minima and maxima of each dimension defining the
hyperrectangle of uniform probability
:type rect_domain: :class:`numpy.ndarray` of shape (mdim, 2)
:rtype: tuple
:returns: (rho_D_M, points, d_Tree) where ``rho_D_M`` and
``points`` are (mdim, M) :class:`~numpy.ndarray` and
`d_Tree` is the :class:`~scipy.spatial.KDTree` for points
"""
util.fix_dimensions_data(points)
inside = np.logical_and(
np.all(np.greater_equal(points, rect_domain[:, 0]), axis=1),
np.all(np.less_equal(points, rect_domain[:, 1]), axis=1))
rho_D_M = np.zeros(volumes.shape)
# normalize on Lambda not D
rho_D_M[inside] = volumes[inside] / np.sum(volumes[inside])
d_Tree = spatial.KDTree(points)
return (rho_D_M, points, d_Tree)
def uniform_hyperrectangle_user(data, domain, center_pts_per_edge=1):
r"""
Creates a simple funciton appoximation of :math:`\rho_{\mathcal{D},M}`
where :math:`\rho{\mathcal{D}, M}` is a uniform probablity density over the
hyperrectangular domain specified by domain.
Since :math:`\rho_\mathcal{D}` is a uniform distribution on a
hyperrectangle we should we able to represent it exactly with
:math:`M=3^{m}` where m is the dimension of the data space or rather
``len(d_distr_samples) == 3**mdim``.
:param data: Array containing QoI data where the QoI is mdim diminsional
:type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
:param domain: The domain overwhich :math:`\rho_\mathcal{D}` is
uniform.
:type domain: :class:`numpy.ndarray` of shape (2, mdim)
:param list() center_pts_per_edge: number of center points per edge and
additional two points will be added to create the bounding layer
:rtype: tuple
:returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
is the :class:`~scipy.spatial.KDTree` for d_distr_samples
"""
# make sure the shape of the data and the domain are correct
data = util.fix_dimensions_data(data)
domain = util.fix_dimensions_data(domain, data.shape[1])
domain_center = np.mean(domain, 0)
domain_lengths = np.max(domain, 0) - np.min(domain, 0)
return uniform_hyperrectangle_binsize(data, domain_center, domain_lengths,
center_pts_per_edge)
def simple_fun_uniform(points, volumes, rect_domain):
"""
Given a set of points, the volumes associated with these points, and
``rect_domain`` creates a simple function approximation of a uniform
distribution over the hyperrectangle defined by ``rect_domain``.
:param points: points used to define the voronoi tesselation (only the
points that define regions of finite volumes)
:type points: :class:`numpy.ndarrray` of shape (num_points, mdim)
:param list() volumes: finite volumes associated with ``points``
:type points: :class:`numpy.ndarray` of shape (num_points,)
:param rect_domain: minima and maxima of each dimension defining the
hyperrectangle of uniform probability
:type rect_domain: :class:`numpy.ndarray` of shape (mdim, 2)
:rtype: tuple
:returns: (rho_D_M, points, d_Tree) where ``rho_D_M`` and
``points`` are (mdim, M) :class:`~numpy.ndarray` and
`d_Tree` is the :class:`~scipy.spatial.KDTree` for points
"""
util.fix_dimensions_data(points)
inside = np.logical_and(np.all(np.greater_equal(points, rect_domain[:, 0]),
axis=1), np.all(np.less_equal(points, rect_domain[:, 1]), axis=1))
rho_D_M = np.zeros(volumes.shape)
# normalize on Lambda not D
rho_D_M[inside] = volumes[inside]/np.sum(volumes[inside])
d_Tree = spatial.KDTree(points)
return (rho_D_M, points, d_Tree)
def regular_partition_uniform_distribution_rectangle_domain(
data_set, rect_domain, cells_per_dimension=1):
r"""
Creates a simple function appoximation of :math:`\rho_{\mathcal{D},M}`
where :math:`\rho{\mathcal{D}, M}` is a uniform probablity density over the
hyperrectangular domain specified by ``rect_domain``.
Since :math:`\rho_\mathcal{D}` is a uniform distribution on a
hyperrectangle we are able to represent it exactly.
:param data_set: Sample set that the probability measure is defined for.
:type data_set: :class:`~bet.sample.discretization` or
:class:`~bet.sample.sample_set` or :class:`~numpy.ndarray`
:param rect_domain: The domain overwhich :math:`\rho_\mathcal{D}` is
uniform.
:type rect_domain: :class:`numpy.ndarray` of shape (2, mdim)
:param list cells_per_dimension: number of cells per dimension
:rtype: :class:`~bet.sample.rectangle_sample_set`
:returns: sample_set object defining simple function approximation
"""
# make sure the shape of the data and the domain are correct
(num, dim, values) = check_inputs_no_reference(data_set)
data = values
rect_domain = util.fix_dimensions_data(rect_domain, data.shape[1])
domain_center = np.mean(rect_domain, 0)
domain_lengths = np.max(rect_domain, 0) - np.min(rect_domain, 0)
return regular_partition_uniform_distribution_rectangle_size(
data_set, domain_center, domain_lengths, cells_per_dimension)
def uniform_data(data):
r"""
Creates a simple function approximation of :math:`\rho_{\mathcal{D},M}`
where :math:`\rho_{\mathcal{D},M}` is a uniform probability density over
the entire ``data_domain``. Here the ``data_domain`` is the union of
voronoi cells defined by ``data``. In other words we assign each sample the
same probability, so ``M = len(data)`` or rather ``len(d_distr_samples) ==
len(data)``. The purpose of this method is to approximate uniform
distributions over irregularly shaped domains.
:param data: Array containing QoI data where the QoI is mdim diminsional
:type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
:param list() center_pts_per_edge: number of center points per edge and
additional two points will be added to create the bounding layer
:rtype: tuple
:returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
is the :class:`~scipy.spatial.KDTree` for d_distr_samples
"""
data = util.fix_dimensions_data(data)
d_distr_prob = np.ones((data.shape[0],), dtype=np.float)/data.shape[0]
d_Tree = spatial.KDTree(data)
return (d_distr_prob, data, d_Tree)
def uniform_data(data):
r"""
Creates a simple function approximation of :math:`\rho_{\mathcal{D},M}`
where :math:`\rho_{\mathcal{D},M}` is a uniform probability density over
the entire ``data_domain``. Here the ``data_domain`` is the union of
voronoi cells defined by ``data``. In other words we assign each sample the
same probability, so ``M = len(data)`` or rather ``len(d_distr_samples) ==
len(data)``. The purpose of this method is to approximate uniform
distributions over irregularly shaped domains.
:param data: Array containing QoI data where the QoI is mdim diminsional
:type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
:param list() center_pts_per_edge: number of center points per edge and
additional two points will be added to create the bounding layer
:rtype: tuple
:returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
is the :class:`~scipy.spatial.KDTree` for d_distr_samples
"""
data = util.fix_dimensions_data(data)
d_distr_prob = np.ones((data.shape[0],), dtype=np.float)/data.shape[0]
d_Tree = spatial.KDTree(data)
return (d_distr_prob, data, d_Tree)
def show_data_domain_2D(samples, data, Q_ref, ref_markers=None,
ref_colors=None, xlabel=r'$q_1$', ylabel=r'$q_2$',
triangles=None, save=True, interactive=False, filenames=None):
r"""
Plot the data domain D using a triangulation based on the generating
samples with a marker for various :math:`Q_{ref}`. Assumes that the first
dimension of data is :math:`q_1`.
:param samples: Samples to plot
:type samples: :class:`~numpy.ndarray` of shape (num_samples, ndim)
:param data: Data associated with ``samples``
:type data: :class:`numpy.ndarray`
:param Q_ref: reference data value
:type Q_ref: :class:`numpy.ndarray` of shape (M, 2)
:param list ref_markers: list of marker types for :math:`Q_{ref}`
:param list ref_colors: list of colors for :math:`Q_{ref}`
:param string xlabel: x-axis label
:param string ylabel: y-axis label
:param triangles: triangulation defined by ``samples``
:type triangles: :class:`tri.Triuangulation.triangles`
:param bool save: flag whether or not to save the figure
:param bool interactive: flag whether or not to show the figure
:param list filenames: file names for the unmarked and marked domain plots
"""
if ref_markers == None:
ref_markers = markers
if ref_colors == None:
ref_colors = colors
if type(triangles) == type(None):
triangulation = tri.Triangulation(samples[:, 0], samples[:, 1])
triangles = triangulation.triangles
if filenames == None:
filenames = ['domain_q1_q2_cs.eps', 'q1_q2_domain_Q_cs.eps']
Q_ref = util.fix_dimensions_data(Q_ref, 2)
# Create figure
plt.tricontourf(data[:, 0], data[:, 1], np.zeros((data.shape[0],)),
triangles=triangles, colors='grey')
plt.autoscale(tight=True)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.savefig(filenames[0], bbox_inches='tight', transparent=True,
pad_inches=0)
# Add truth markers
for i in xrange(Q_ref.shape[0]):
plt.scatter(Q_ref[i, 0], Q_ref[i, 1], s=60, c=ref_colors[i],
marker=ref_markers[i])
if save:
plt.savefig(filenames[1], bbox_inches='tight', transparent=True,
pad_inches=0)
if interactive:
plt.show()
else:
plt.close()
def test_fix_dimensions_data_nodim():
"""
Tests :meth`bet.util.fix_dimensions_domain` when `dim` is not specified
"""
values = [1, [1], range(2), np.empty((2,)), np.empty((2, 1)), np.empty((1, 2)), np.empty((5, 2)), np.empty((2, 5))]
shapes = [(1, 1), (1, 1), (2, 1), (2, 1), (2, 1), (1, 2), (5, 2), (2, 5)]
print len(values), len(shapes)
for value, shape in zip(values, shapes):
vector = util.fix_dimensions_data(value)
print vector, value
print vector.shape, shape
assert vector.shape == shape
def uniform_hyperrectangle_binsize(data, Q_ref, bin_size,
center_pts_per_edge=1):
r"""
Creates a simple function approximation of :math:`\rho_{\mathcal{D},M}`
where :math:`\rho_{\mathcal{D},M}` is a uniform probability density
centered at Q_ref with bin_size of the width of D.
Since rho_D is a uniform distribution on a hyperrectanlge we should be able
to represent it exactly with ``M = 3^mdim`` or rather
``len(d_distr_samples) == 3^mdim``.
:param bin_size: The size used to determine the width of the uniform
distribution
:type bin_size: double or list()
:param int num_d_emulate: Number of samples used to emulate using an MC
assumption
:param data: Array containing QoI data where the QoI is mdim diminsional
:type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
:param Q_ref: :math:`Q(\lambda_{reference})`
:type Q_ref: :class:`~numpy.ndarray` of size (mdim,)
:param list() center_pts_per_edge: number of center points per edge
and additional two points will be added to create the bounding layer
:rtype: tuple
:returns: (rho_D_M, d_distr_samples, d_Tree) where
``rho_D_M`` is (M,) and ``d_distr_samples`` are (M, mdim)
:class:`~numpy.ndarray` and `d_Tree` is the :class:`~scipy.spatial.KDTree`
for d_distr_samples
"""
data = util.fix_dimensions_data(data)
if not isinstance(center_pts_per_edge, collections.Iterable):
center_pts_per_edge = np.ones((data.shape[1],)) * center_pts_per_edge
else:
if not len(center_pts_per_edge) == data.shape[1]:
center_pts_per_edge = np.ones((data.shape[1],))
print 'Warning: center_pts_per_edge dimension mismatch.'
print 'Using 1 in each dimension.'
if np.any(np.less(center_pts_per_edge, 0)):
print 'Warning: center_pts_per_edge must be greater than 0'
if not isinstance(bin_size, collections.Iterable):
bin_size = bin_size*np.ones((data.shape[1],))
if np.any(np.less(bin_size, 0)):
print 'Warning: center_pts_per_edge must be greater than 0'
sur_domain = np.array([np.min(data, 0), np.max(data, 0)]).transpose()
points, _, rect_domain = vHist.center_and_layer1_points_binsize\
(center_pts_per_edge, Q_ref, bin_size, sur_domain)
edges = vHist.edges_regular(center_pts_per_edge, rect_domain, sur_domain)
_, volumes, _ = vHist.histogramdd_volumes(edges, points)
return vHist.simple_fun_uniform(points, volumes, rect_domain)
def uniform_hyperrectangle_binsize(data, Q_ref, bin_size, center_pts_per_edge=1):
r"""
Creates a simple function approximation of :math:`\rho_{\mathcal{D},M}`
where :math:`\rho_{\mathcal{D},M}` is a uniform probability density
centered at Q_ref with bin_size of the width of D.
Since rho_D is a uniform distribution on a hyperrectanlge we should be able
to represent it exactly with ``M = 3^mdim`` or rather
``len(d_distr_samples) == 3^mdim``.
:param bin_size: The size used to determine the width of the uniform
distribution
:type bin_size: double or list()
:param int num_d_emulate: Number of samples used to emulate using an MC
assumption
:param data: Array containing QoI data where the QoI is mdim diminsional
:type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
:param Q_ref: :math:`Q(\lambda_{reference})`
:type Q_ref: :class:`~numpy.ndarray` of size (mdim,)
:param list() center_pts_per_edge: number of center points per edge
and additional two points will be added to create the bounding layer
:rtype: tuple :returns: (rho_D_M, d_distr_samples, d_Tree) where
``rho_D_M`` is (M,) and ``d_distr_samples`` are (M, mdim)
:class:`~numpy.ndarray` and `d_Tree` is the :class:`~scipy.spatial.KDTree`
for d_distr_samples
"""
data = util.fix_dimensions_data(data)
if not isinstance(center_pts_per_edge, collections.Iterable):
center_pts_per_edge = np.ones((data.shape[1],)) * center_pts_per_edge
else:
if not len(center_pts_per_edge) == data.shape[1]:
center_pts_per_edge = np.ones((data.shape[1],))
print 'Warning: center_pts_per_edge dimension mismatch.'
print 'Using 1 in each dimension.'
if np.any(np.less(center_pts_per_edge, 0)):
print 'Warning: center_pts_per_edge must be greater than 0'
if not isinstance(bin_size, collections.Iterable):
bin_size = bin_size*np.ones((data.shape[1],))
if np.any(np.less(bin_size, 0)):
print 'Warning: center_pts_per_edge must be greater than 0'
sur_domain = np.array([np.min(data, 0), np.max(data, 0)]).transpose()
points, _, rect_domain = vHist.center_and_layer1_points_binsize(center_pts_per_edge,
Q_ref, bin_size, sur_domain)
edges = vHist.edges_regular(center_pts_per_edge, rect_domain, sur_domain)
_, volumes, _ = vHist.histogramdd_volumes(edges, points)
return vHist.simple_fun_uniform(points, volumes, rect_domain)
def test_fix_dimensions_data_dim():
"""
Tests :meth`bet.util.fix_dimensions_domain` when `dim` is specified
"""
values = [1, [1], np.arange(2), np.empty((2,)), np.empty((2, 1)), np.empty((1, 2)),
np.empty((5, 2)), np.empty((2, 5)), np.empty((5, 2)), np.empty((2, 5))]
shapes = [(1, 1), (1, 1), (1, 2), (1, 2), (1, 2), (1, 2), (5, 2), (5, 2), (2, 5),
(2, 5)]
dims = [1, 1, 2, 2, 2, 2, 2, 2, 5, 5]
for value, shape, dim in zip(values, shapes, dims):
vector = util.fix_dimensions_data(value, dim)
print(vector, value)
print(vector.shape, shape, dim)
assert vector.shape == shape
def uniform_partition_uniform_distribution_rectangle_domain(data_set,
rect_domain, M=50, num_d_emulate=1E6):
r"""
Creates a simple function approximation of :math:`\rho_{\mathcal{D}}`
where :math:`\rho_{\mathcal{D}}` is a uniform probability density on
a generalized rectangle defined by ``rect_domain``.
The simple function approximation is then defined by determining ``M``
Voronoi cells (i.e., "bins") partitioning :math:`\mathcal{D}`. These
bins are only implicitly defined by ``M ``samples in :math:`\mathcal{D}`.
Finally, the probabilities of each of these bins is computed by
sampling from :math:`\rho{\mathcal{D}}` and using nearest neighbor
searches to bin these samples in the ``M`` implicitly defined bins.
The result is the simple function approximation denoted by
:math:`\rho_{\mathcal{D},M}`.
Note that all computations in the measure-theoretic framework that
follow from this are for the fixed simple function approximation
:math:`\rho_{\mathcal{D},M}`.
:param int M: Defines number M samples in D used to define
:math:`\rho_{\mathcal{D},M}` The choice of M is something of an "art" -
play around with it and you can get reasonable results with a
relatively small number here like 50.
:param rect_domain: The support of the density
:type rect_domain: double or list
:param int num_d_emulate: Number of samples used to emulate using an MC
assumption
:param data_set: Sample set that the probability measure is defined for.
:type data_set: :class:`~bet.sample.discretization` or
:class:`~bet.sample.sample_set` or :class:`~numpy.ndarray`
:param Q_ref: :math:`Q(`\lambda_{reference})`
:type Q_ref: :class:`~numpy.ndarray` of size (mdim,)
:rtype: :class:`~bet.sample.voronoi_sample_set`
:returns: sample_set object defininng simple function approximation
"""
(num, dim, values) = check_inputs_no_reference(data_set)
data = values
rect_domain = util.fix_dimensions_data(rect_domain, data.shape[1])
domain_center = np.mean(rect_domain, 0)
domain_lengths = np.max(rect_domain, 0) - np.min(rect_domain, 0)
return uniform_partition_uniform_distribution_rectangle_size(data_set,
domain_center, domain_lengths, M, num_d_emulate)
def test_fix_dimensions_data_nodim():
"""
Tests :meth`bet.util.fix_dimensions_domain` when `dim` is not specified
"""
values = [
1, [1],
range(2),
np.empty((2, )),
np.empty((2, 1)),
np.empty((1, 2)),
np.empty((5, 2)),
np.empty((2, 5))
]
shapes = [(1, 1), (1, 1), (2, 1), (2, 1), (2, 1), (1, 2), (5, 2), (2, 5)]
print len(values), len(shapes)
for value, shape in zip(values, shapes):
vector = util.fix_dimensions_data(value)
print vector, value
print vector.shape, shape
assert vector.shape == shape
def test_fix_dimensions_data_dim():
"""
Tests :meth`bet.util.fix_dimensions_domain` when `dim` is specified
"""
values = [
1, [1],
range(2),
np.empty((2, )),
np.empty((2, 1)),
np.empty((1, 2)),
np.empty((5, 2)),
np.empty((2, 5)),
np.empty((5, 2)),
np.empty((2, 5))
]
shapes = [(1, 1), (1, 1), (1, 2), (1, 2), (1, 2), (1, 2), (5, 2), (5, 2),
(2, 5), (2, 5)]
dims = [1, 1, 2, 2, 2, 2, 2, 2, 5, 5]
for value, shape, dim in zip(values, shapes, dims):
vector = util.fix_dimensions_data(value, dim)
print vector, value
print vector.shape, shape, dim
assert vector.shape == shape
def regular_partition_uniform_distribution_rectangle_domain(data_set,
rect_domain,
cells_per_dimension=1):
r"""
Creates a simple function appoximation of :math:`\rho_{\mathcal{D},M}`
where :math:`\rho{\mathcal{D}, M}` is a uniform probablity density over the
hyperrectangular domain specified by ``rect_domain``.
Since :math:`\rho_\mathcal{D}` is a uniform distribution on a
hyperrectangle we are able to represent it exactly.
:param data_set: Sample set that the probability measure is defined for.
:type data_set: :class:`~bet.sample.discretization` or
:class:`~bet.sample.sample_set` or :class:`~numpy.ndarray`
:param rect_domain: The domain overwhich :math:`\rho_\mathcal{D}` is
uniform.
:type rect_domain: :class:`numpy.ndarray` of shape (2, mdim)
:param list cells_per_dimension: number of cells per dimension
:rtype: :class:`~bet.sample.rectangle_sample_set`
:returns: sample_set object defining simple function approximation
"""
# make sure the shape of the data and the domain are correct
(num, dim, values) = check_inputs_no_reference(data_set)
data = values
rect_domain = util.fix_dimensions_data(rect_domain, data.shape[1])
domain_center = np.mean(rect_domain, 0)
domain_lengths = np.max(rect_domain, 0) - np.min(rect_domain, 0)
return regular_partition_uniform_distribution_rectangle_size(data_set,
domain_center,
domain_lengths,
cells_per_dimension)
def uniform_hyperrectangle(data, Q_ref, bin_ratio, center_pts_per_edge=1):
r"""
Creates a simple function approximation of :math:`\rho_{\mathcal{D},M}`
where :math:`\rho_{\mathcal{D},M}` is a uniform probability density
centered at Q_ref with bin_ratio of the width
of D.
Since rho_D is a uniform distribution on a hyperrectanlge we should be able
to represent it exactly with ``M = 3^mdim`` or rather
``len(d_distr_samples) == 3^mdim``.
:param bin_ratio: The ratio used to determine the width of the
uniform distributiion as ``bin_size = (data_max-data_min)*bin_ratio``
:type bin_ratio: double or list()
:param int num_d_emulate: Number of samples used to emulate using an MC
assumption
:param data: Array containing QoI data where the QoI is mdim diminsional
:type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
:param Q_ref: :math:`Q(\lambda_{reference})`
:type Q_ref: :class:`~numpy.ndarray` of size (mdim,)
:param list() center_pts_per_edge: number of center points per edge and
additional two points will be added to create the bounding layer
:rtype: tuple
:returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
is the :class:`~scipy.spatial.KDTree` for d_distr_samples
"""
data = util.fix_dimensions_data(data)
if not isinstance(bin_ratio, collections.Iterable):
bin_ratio = bin_ratio*np.ones((data.shape[1], ))
bin_size = (np.max(data, 0) - np.min(data, 0))*bin_ratio
return uniform_hyperrectangle_binsize(data, Q_ref, bin_size,
center_pts_per_edge)
def uniform_hyperrectangle(data, Q_ref, bin_ratio, center_pts_per_edge=1):
r"""
Creates a simple function approximation of :math:`\rho_{\mathcal{D},M}`
where :math:`\rho_{\mathcal{D},M}` is a uniform probability density
centered at Q_ref with bin_ratio of the width
of D.
Since rho_D is a uniform distribution on a hyperrectanlge we should be able
to represent it exactly with ``M = 3^mdim`` or rather
``len(d_distr_samples) == 3^mdim``.
:param bin_ratio: The ratio used to determine the width of the
uniform distributiion as ``bin_size = (data_max-data_min)*bin_ratio``
:type bin_ratio: double or list()
:param int num_d_emulate: Number of samples used to emulate using an MC
assumption
:param data: Array containing QoI data where the QoI is mdim diminsional
:type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
:param Q_ref: :math:`Q(\lambda_{reference})`
:type Q_ref: :class:`~numpy.ndarray` of size (mdim,)
:param list() center_pts_per_edge: number of center points per edge and
additional two points will be added to create the bounding layer
:rtype: tuple
:returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree`
is the :class:`~scipy.spatial.KDTree` for d_distr_samples
"""
data = util.fix_dimensions_data(data)
if not isinstance(bin_ratio, collections.Iterable):
bin_ratio = bin_ratio*np.ones((data.shape[1], ))
bin_size = (np.max(data, 0) - np.min(data, 0))*bin_ratio
return uniform_hyperrectangle_binsize(data, Q_ref, bin_size,
center_pts_per_edge)
def show_data_domain_multi(sample_disc,
Q_ref=None,
Q_nums=None,
img_folder='figs/',
ref_markers=None,
ref_colors=None,
showdim=None,
file_extension=".png",
markersize=75):
r"""
Plots 2-D projections of the data domain D using a triangulation based on
the first two coordinates (parameters) of the generating samples where
:math:`Q={q_1, q_i}` for ``i=Q_nums``, with a marker for various
:math:`Q_{ref}`.
:param sample_disc: Object containing the samples to plot
:type sample_disc: :class:`~bet.sample.discretization`
:param Q_ref: reference data value
:type Q_ref: :class:`numpy.ndarray` of shape (M, mdim)
:param list Q_nums: dimensions of the QoI to plot
:param string img_folder: folder to save the plots to
:param list ref_markers: list of marker types for :math:`Q_{ref}`
:param list ref_colors: list of colors for :math:`Q_{ref}`
:param showdim: default 1. If int then flag to show all combinations with a
given dimension (:math:`q_i`) or if ``all`` show all combinations.
:type showdim: int or string
:param string file_extension: file extension
"""
if not isinstance(sample_disc, sample.discretization):
raise bad_object("Improper sample object")
# Set the default marker and colors
if ref_markers is None:
ref_markers = markers
if ref_colors is None:
ref_colors = colors
data_obj = sample_disc._output_sample_set
sample_obj = sample_disc._input_sample_set
if Q_ref is None:
Q_ref = data_obj._reference_value
# If no specific coordinate numbers are given for the data coordinates
# (e.g. i, where \q_i is a coordinate in the data space), then
# set them to be the the counting numbers.
if Q_nums is None:
Q_nums = list(range(data_obj.get_dim()))
# If no specific coordinate number of choice is given set to be the first
# coordinate direction.
if showdim is None:
showdim = 0
# Create a folder for these figures if it doesn't already exist
if not os.path.isdir(img_folder):
os.mkdir(img_folder)
# Make sure the shape of Q_ref is correct
if Q_ref is not None:
Q_ref = util.fix_dimensions_data(Q_ref, data_obj.get_dim())
# Create the triangulization to use to define the topology of the samples
# in the data space from the first two parameters in the parameter space
triangulation = tri.Triangulation(sample_obj.get_values()[:, 0],
sample_obj.get_values()[:, 1])
triangles = triangulation.triangles
# Create plots of the showdim^th QoI (q_{showdim}) with all other QoI (q_i)
if isinstance(showdim, int):
for i in Q_nums:
if i != showdim:
xlabel = r'$q_{' + str(showdim + 1) + r'}$'
ylabel = r'$q_{' + str(i + 1) + r'}$'
filenames = [
img_folder + 'domain_q' + str(showdim + 1) + '_q' +
str(i + 1), img_folder + 'q' + str(showdim + 1) + '_q' +
str(i + 1) + '_domain_Q_cs'
]
data_obj_temp = sample.sample_set(2)
data_obj_temp.set_values(data_obj.get_values()[:,
[showdim, i]])
sample_disc_temp = sample.discretization(
sample_obj, data_obj_temp)
if Q_ref is not None:
show_data_domain_2D(sample_disc_temp,
Q_ref[:, [showdim, i]],
ref_markers,
ref_colors,
xlabel=xlabel,
ylabel=ylabel,
triangles=triangles,
save=True,
interactive=False,
filenames=filenames,
file_extension=file_extension,
markersize=markersize)
else:
show_data_domain_2D(sample_disc_temp,
None,
ref_markers,
ref_colors,
xlabel=xlabel,
ylabel=ylabel,
triangles=triangles,
save=True,
interactive=False,
filenames=filenames,
file_extension=file_extension,
markersize=markersize)
# Create plots of all combinations of QoI in 2D
elif showdim == 'all' or showdim == 'ALL':
for x, y in combinations(Q_nums, 2):
xlabel = r'$q_{' + str(x + 1) + r'}$'
ylabel = r'$q_{' + str(y + 1) + r'}$'
filenames = [
img_folder + 'domain_q' + str(x + 1) + '_q' + str(y + 1),
img_folder + 'q' + str(x + 1) + '_q' + str(y + 1) +
'_domain_Q_cs'
]
data_obj_temp = sample.sample_set(2)
data_obj_temp.set_values(data_obj.get_values()[:, [x, y]])
sample_disc_temp = sample.discretization(sample_obj, data_obj_temp)
if Q_ref is not None:
show_data_domain_2D(sample_disc_temp,
Q_ref[:, [x, y]],
ref_markers,
ref_colors,
xlabel=xlabel,
ylabel=ylabel,
triangles=triangles,
save=True,
interactive=False,
filenames=filenames,
file_extension=file_extension,
markersize=markersize)
else:
show_data_domain_2D(sample_disc_temp,
None,
ref_markers,
ref_colors,
xlabel=xlabel,
ylabel=ylabel,
triangles=triangles,
save=True,
interactive=False,
filenames=filenames,
file_extension=file_extension,
markersize=markersize)
def show_data_domain_multi(samples, data, Q_ref, Q_nums=None,
img_folder='figs/', ref_markers=None,
ref_colors=None, showdim=None):
r"""
Plot the data domain D using a triangulation based on the generating
samples where :math:`Q={q_1, q_i}` for ``i=Q_nums``, with a marker for
various :math:`Q_{ref}`.
:param samples: Samples to plot
:type samples: :class:`~numpy.ndarray` of shape (num_samples, ndim). Only
uses the first two dimensions.
:param data: Data associated with ``samples``
:type data: :class:`numpy.ndarray`
:param Q_ref: reference data value
:type Q_ref: :class:`numpy.ndarray` of shape (M, mdim)
:param list Q_nums: dimensions of the QoI to plot
:param string img_folder: folder to save the plots to
:param list ref_markers: list of marker types for :math:`Q_{ref}`
:param list ref_colors: list of colors for :math:`Q_{ref}`
:param showdim: default 1. If int then flag to show all combinations with a
given dimension or if ``all`` show all combinations.
:type showdim: int or string
"""
if ref_markers == None:
ref_markers = markers
if ref_colors == None:
ref_colors = colors
if type(Q_nums) == type(None):
Q_nums = range(data.shape[1])
if showdim == None:
showdim = 0
if not os.path.isdir(img_folder):
os.mkdir(img_folder)
Q_ref = util.fix_dimensions_data(Q_ref, data.shape[1])
triangulation = tri.Triangulation(samples[:, 0], samples[:, 1])
triangles = triangulation.triangles
if type(showdim) == int:
for i in Q_nums:
xlabel = r'$q_{'+str(showdim+1)+r'}$'
ylabel = r'$q_{'+str(i+1)+r'}$'
filenames = [img_folder+'domain_q'+str(showdim+1)+'_q'+str(i+1)+'.eps',
img_folder+'q'+str(showdim+1)+'_q'+str(i+1)+'_domain_Q_cs.eps']
show_data_domain_2D(samples, data[:, [showdim, i]], Q_ref[:,
[showdim, i]], ref_markers, ref_colors, xlabel=xlabel,
ylabel=ylabel, triangles=triangles, save=True,
interactive=False, filenames=filenames)
elif showdim == 'all' or showdim == 'ALL':
for x, y in combinations(Q_nums, 2):
xlabel = r'$q_{'+str(x+1)+r'}$'
ylabel = r'$q_{'+str(y+1)+r'}$'
filenames = [img_folder+'domain_q'+str(x+1)+'_q'+str(y+1)+'.eps',
img_folder+'q'+str(x+1)+'_q'+str(y+1)+'_domain_Q_cs.eps']
show_data_domain_2D(samples, data[:, [x, y]], Q_ref[:,
[x, y]], ref_markers, ref_colors, xlabel=xlabel,
ylabel=ylabel, triangles=triangles, save=True,
interactive=False, filenames=filenames)
def show_data_domain_2D(samples,
data,
Q_ref,
ref_markers=None,
ref_colors=None,
xlabel=r'$q_1$',
ylabel=r'$q_2$',
triangles=None,
save=True,
interactive=False,
filenames=None):
r"""
Plot the data domain D using a triangulation based on the generating
samples with a marker for various :math:`Q_{ref}`. Assumes that the first
dimension of data is :math:`q_1`.
:param samples: Samples to plot
:type samples: :class:`~numpy.ndarray` of shape (num_samples, ndim)
:param data: Data associated with ``samples``
:type data: :class:`numpy.ndarray`
:param Q_ref: reference data value
:type Q_ref: :class:`numpy.ndarray` of shape (M, 2)
:param list ref_markers: list of marker types for :math:`Q_{ref}`
:param list ref_colors: list of colors for :math:`Q_{ref}`
:param string xlabel: x-axis label
:param string ylabel: y-axis label
:param triangles: triangulation defined by ``samples``
:type triangles: :class:`tri.Triuangulation.triangles`
:param bool save: flag whether or not to save the figure
:param bool interactive: flag whether or not to show the figure
:param list filenames: file names for the unmarked and marked domain plots
"""
if ref_markers == None:
ref_markers = markers
if ref_colors == None:
ref_colors = colors
if type(triangles) == type(None):
triangulation = tri.Triangulation(samples[:, 0], samples[:, 1])
triangles = triangulation.triangles
if filenames == None:
filenames = ['domain_q1_q2_cs.eps', 'q1_q2_domain_Q_cs.eps']
Q_ref = util.fix_dimensions_data(Q_ref, 2)
# Create figure
plt.tricontourf(data[:, 0],
data[:, 1],
np.zeros((data.shape[0], )),
triangles=triangles,
colors='grey')
plt.autoscale(tight=True)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.savefig(filenames[0],
bbox_inches='tight',
transparent=True,
pad_inches=0)
# Add truth markers
for i in xrange(Q_ref.shape[0]):
plt.scatter(Q_ref[i, 0],
Q_ref[i, 1],
s=60,
c=ref_colors[i],
marker=ref_markers[i])
if save:
plt.savefig(filenames[1],
bbox_inches='tight',
transparent=True,
pad_inches=0)
if interactive:
plt.show()
else:
plt.close()
def show_data_domain_multi(samples,
data,
Q_ref,
Q_nums=None,
img_folder='figs/',
ref_markers=None,
ref_colors=None,
showdim=None):
r"""
Plot the data domain D using a triangulation based on the generating
samples where :math:`Q={q_1, q_i}` for ``i=Q_nums``, with a marker for
various :math:`Q_{ref}`.
:param samples: Samples to plot
:type samples: :class:`~numpy.ndarray` of shape (num_samples, ndim). Only
uses the first two dimensions.
:param data: Data associated with ``samples``
:type data: :class:`numpy.ndarray`
:param Q_ref: reference data value
:type Q_ref: :class:`numpy.ndarray` of shape (M, mdim)
:param list Q_nums: dimensions of the QoI to plot
:param string img_folder: folder to save the plots to
:param list ref_markers: list of marker types for :math:`Q_{ref}`
:param list ref_colors: list of colors for :math:`Q_{ref}`
:param showdim: default 1. If int then flag to show all combinations with a
given dimension or if ``all`` show all combinations.
:type showdim: int or string
"""
if ref_markers == None:
ref_markers = markers
if ref_colors == None:
ref_colors = colors
if type(Q_nums) == type(None):
Q_nums = range(data.shape[1])
if showdim == None:
showdim = 0
if not os.path.isdir(img_folder):
os.mkdir(img_folder)
Q_ref = util.fix_dimensions_data(Q_ref, data.shape[1])
triangulation = tri.Triangulation(samples[:, 0], samples[:, 1])
triangles = triangulation.triangles
if type(showdim) == int:
for i in Q_nums:
xlabel = r'$q_{' + str(showdim + 1) + r'}$'
ylabel = r'$q_{' + str(i + 1) + r'}$'
filenames = [
img_folder + 'domain_q' + str(showdim + 1) + '_q' +
str(i + 1) + '.eps', img_folder + 'q' + str(showdim + 1) +
'_q' + str(i + 1) + '_domain_Q_cs.eps'
]
show_data_domain_2D(samples,
data[:, [showdim, i]],
Q_ref[:, [showdim, i]],
ref_markers,
ref_colors,
xlabel=xlabel,
ylabel=ylabel,
triangles=triangles,
save=True,
interactive=False,
filenames=filenames)
elif showdim == 'all' or showdim == 'ALL':
for x, y in combinations(Q_nums, 2):
xlabel = r'$q_{' + str(x + 1) + r'}$'
ylabel = r'$q_{' + str(y + 1) + r'}$'
filenames = [
img_folder + 'domain_q' + str(x + 1) + '_q' + str(y + 1) +
'.eps', img_folder + 'q' + str(x + 1) + '_q' + str(y + 1) +
'_domain_Q_cs.eps'
]
show_data_domain_2D(samples,
data[:, [x, y]],
Q_ref[:, [x, y]],
ref_markers,
ref_colors,
xlabel=xlabel,
ylabel=ylabel,
triangles=triangles,
save=True,
interactive=False,
filenames=filenames)
def show_data_domain_multi(sample_disc, Q_ref=None, Q_nums=None,
img_folder='figs/', ref_markers=None, ref_colors=None, showdim=None,
file_extension=".png", markersize=75):
r"""
Plots 2-D projections of the data domain D using a triangulation based on
the first two coordinates (parameters) of the generating samples where
:math:`Q={q_1, q_i}` for ``i=Q_nums``, with a marker for various
:math:`Q_{ref}`.
:param sample_disc: Object containing the samples to plot
:type sample_disc: :class:`~bet.sample.discretization`
:param Q_ref: reference data value
:type Q_ref: :class:`numpy.ndarray` of shape (M, mdim)
:param list Q_nums: dimensions of the QoI to plot
:param string img_folder: folder to save the plots to
:param list ref_markers: list of marker types for :math:`Q_{ref}`
:param list ref_colors: list of colors for :math:`Q_{ref}`
:param showdim: default 1. If int then flag to show all combinations with a
given dimension (:math:`q_i`) or if ``all`` show all combinations.
:type showdim: int or string
:param string file_extension: file extension
"""
if not isinstance(sample_disc, sample.discretization):
raise bad_object("Improper sample object")
# Set the default marker and colors
if ref_markers is None:
ref_markers = markers
if ref_colors is None:
ref_colors = colors
data_obj = sample_disc._output_sample_set
sample_obj = sample_disc._input_sample_set
if Q_ref is None:
Q_ref = data_obj._reference_value
# If no specific coordinate numbers are given for the data coordinates
# (e.g. i, where \q_i is a coordinate in the data space), then
# set them to be the the counting numbers.
if Q_nums is None:
Q_nums = list(range(data_obj.get_dim()))
# If no specific coordinate number of choice is given set to be the first
# coordinate direction.
if showdim is None:
showdim = 0
# Create a folder for these figures if it doesn't already exist
if not os.path.isdir(img_folder):
os.mkdir(img_folder)
# Make sure the shape of Q_ref is correct
if Q_ref is not None:
Q_ref = util.fix_dimensions_data(Q_ref, data_obj.get_dim())
# Create the triangulization to use to define the topology of the samples
# in the data space from the first two parameters in the parameter space
triangulation = tri.Triangulation(sample_obj.get_values()[:, 0],
sample_obj.get_values()[:, 1])
triangles = triangulation.triangles
# Create plots of the showdim^th QoI (q_{showdim}) with all other QoI (q_i)
if isinstance(showdim, int):
for i in Q_nums:
if i != showdim:
xlabel = r'$q_{' + str(showdim + 1) + r'}$'
ylabel = r'$q_{' + str(i + 1) + r'}$'
filenames = [img_folder + 'domain_q' + str(showdim + 1) + '_q' +
str(i + 1), img_folder + 'q' + str(showdim + 1) +
'_q' + str(i + 1) + '_domain_Q_cs']
data_obj_temp = sample.sample_set(2)
data_obj_temp.set_values(
data_obj.get_values()[:, [showdim, i]])
sample_disc_temp = sample.discretization(
sample_obj, data_obj_temp)
if Q_ref is not None:
show_data_domain_2D(sample_disc_temp, Q_ref[:, [showdim, i]],
ref_markers, ref_colors, xlabel=xlabel,
ylabel=ylabel, triangles=triangles,
save=True, interactive=False,
filenames=filenames,
file_extension=file_extension,
markersize=markersize)
else:
show_data_domain_2D(sample_disc_temp, None, ref_markers,
ref_colors, xlabel=xlabel, ylabel=ylabel,
triangles=triangles, save=True,
interactive=False,
filenames=filenames,
file_extension=file_extension,
markersize=markersize)
# Create plots of all combinations of QoI in 2D
elif showdim == 'all' or showdim == 'ALL':
for x, y in combinations(Q_nums, 2):
xlabel = r'$q_{' + str(x + 1) + r'}$'
ylabel = r'$q_{' + str(y + 1) + r'}$'
filenames = [img_folder + 'domain_q' + str(x + 1) + '_q' +
str(y + 1), img_folder + 'q' + str(x + 1) + '_q' +
str(y + 1) + '_domain_Q_cs']
data_obj_temp = sample.sample_set(2)
data_obj_temp.set_values(data_obj.get_values()[:, [x, y]])
sample_disc_temp = sample.discretization(sample_obj, data_obj_temp)
if Q_ref is not None:
show_data_domain_2D(sample_disc_temp, Q_ref[:, [x, y]],
ref_markers, ref_colors, xlabel=xlabel, ylabel=ylabel,
triangles=triangles, save=True, interactive=False,
filenames=filenames,
file_extension=file_extension,
markersize=markersize)
else:
show_data_domain_2D(sample_disc_temp, None, ref_markers,
ref_colors, xlabel=xlabel, ylabel=ylabel,
triangles=triangles, save=True, interactive=False,
filenames=filenames,
file_extension=file_extension,
markersize=markersize)
def show_data_domain_2D(sample_disc,
Q_ref=None,
ref_markers=None,
ref_colors=None,
xlabel=r'$q_1$',
ylabel=r'$q_2$',
triangles=None,
save=True,
interactive=False,
filenames=None,
file_extension=".png",
markersize=75):
r"""
Plots 2-D a single data domain D using a triangulation based on the first
two coordinates (parameters) of the generating samples where :math:`Q={q_1,
q_i}` for ``i=Q_nums``, with a marker for various :math:`Q_{ref}`. Assumes
that the first dimension of data is :math:`q_1`.
.. note::
Do not specify the file extension in BOTH ``filenames`` and
``file_extension``.
:param sample_disc: Object containing the samples to plot
:type sample_disc: :class:`~bet.sample.discretization`
or :class:`~bet.sample.sample_set_base`
:param Q_ref: reference data value
:type Q_ref: :class:`numpy.ndarray` of shape (M, 2)
:param list ref_markers: list of marker types for :math:`Q_{ref}`
:param list ref_colors: list of colors for :math:`Q_{ref}`
:param string xlabel: x-axis label
:param string ylabel: y-axis label
:param triangles: triangulation defined by ``samples``
:type triangles: :class:`tri.Triuangulation.triangles`
:param bool save: flag whether or not to save the figure
:param bool interactive: flag whether or not to show the figure
:param list filenames: file names for the unmarked and marked domain plots
:param string file_extension: file extension
"""
if not isinstance(sample_disc, sample.discretization):
raise bad_object("Improper sample object")
data_obj = sample_disc._output_sample_set
sample_obj = sample_disc._input_sample_set
if Q_ref is None:
Q_ref = data_obj._reference_value
# Set the default marker and colors
if ref_markers is None:
ref_markers = markers
if ref_colors is None:
ref_colors = colors
# If no specific coordinate numbers are given for the data coordinates
# (e.g. i, where \q_i is a coordinate in the data space), then
# set them to be the the counting numbers.
if triangles is None:
triangulation = tri.Triangulation(sample_obj.get_values()[:, 0],
sample_obj.get_values()[:, 1])
triangles = triangulation.triangles
# Set default file names
if filenames is None:
filenames = ['domain_q1_q2_cs', 'q1_q2_domain_Q_cs']
# Make sure the shape of Q_ref is correct
if Q_ref is not None:
Q_ref = util.fix_dimensions_data(Q_ref, 2)
# Create figure
plt.tricontourf(data_obj.get_values()[:, 0],
data_obj.get_values()[:, 1],
np.zeros((data_obj.get_values().shape[0], )),
triangles=triangles,
colors='grey')
plt.autoscale(tight=True)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if "." not in filenames[0]:
full_filenames0 = filenames[0] + file_extension
else:
full_filenames0 = filenames[0]
if "." not in filenames[1]:
full_filenames1 = filenames[1] + file_extension
else:
full_filenames1 = filenames[1]
plt.savefig(full_filenames0,
bbox_inches='tight',
transparent=True,
pad_inches=.2)
# Add truth markers
if Q_ref is not None:
for i in range(Q_ref.shape[0]):
plt.scatter(Q_ref[i, 0],
Q_ref[i, 1],
s=60,
c=ref_colors[i],
marker=ref_markers[i])
plt.tight_layout()
if save:
plt.savefig(full_filenames1,
bbox_inches='tight',
transparent=True,
pad_inches=.2)
if interactive:
plt.show()
else:
plt.close()
def show_data_domain_2D(samples, data, Q_ref=None, ref_markers=None,
ref_colors=None, xlabel=r'$q_1$', ylabel=r'$q_2$',
triangles=None, save=True, interactive=False, filenames=None):
r"""
Plots 2-D a single data domain D using a triangulation based on the first
two coordinates (parameters) of the generating samples where :math:`Q={q_1,
q_i}` for ``i=Q_nums``, with a marker for various :math:`Q_{ref}`. Assumes
that the first dimension of data is :math:`q_1`.
:param samples: Samples to plot
:type samples: :class:`~numpy.ndarray` of shape (num_samples, ndim). Only
uses the first two dimensions.
:param data: Data associated with ``samples``
:type data: :class:`numpy.ndarray`
:param Q_ref: reference data value
:type Q_ref: :class:`numpy.ndarray` of shape (M, 2)
:param list ref_markers: list of marker types for :math:`Q_{ref}`
:param list ref_colors: list of colors for :math:`Q_{ref}`
:param string xlabel: x-axis label
:param string ylabel: y-axis label
:param triangles: triangulation defined by ``samples``
:type triangles: :class:`tri.Triuangulation.triangles`
:param bool save: flag whether or not to save the figure
:param bool interactive: flag whether or not to show the figure
:param list filenames: file names for the unmarked and marked domain plots
"""
# Set the default marker and colors
if ref_markers == None:
ref_markers = markers
if ref_colors == None:
ref_colors = colors
# If no specific coordinate numbers are given for the data coordinates
# (e.g. i, where \q_i is a coordinate in the data space), then
# set them to be the the counting numbers.
if triangles is None:
triangulation = tri.Triangulation(samples[:, 0], samples[:, 1])
triangles = triangulation.triangles
# Set default file names
if filenames == None:
filenames = ['domain_q1_q2_cs.eps', 'q1_q2_domain_Q_cs.eps']
# Make sure the shape of Q_ref is correct
if Q_ref is not None:
Q_ref = util.fix_dimensions_data(Q_ref, 2)
# Create figure
plt.tricontourf(data[:, 0], data[:, 1], np.zeros((data.shape[0],)),
triangles=triangles, colors='grey')
plt.autoscale(tight=True)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.savefig(filenames[0], bbox_inches='tight', transparent=True,
pad_inches=.2)
# Add truth markers
if Q_ref is not None:
for i in xrange(Q_ref.shape[0]):
plt.scatter(Q_ref[i, 0], Q_ref[i, 1], s=60, c=ref_colors[i],
marker=ref_markers[i])
if save:
plt.savefig(filenames[1], bbox_inches='tight', transparent=True,
pad_inches=.2)
if interactive:
plt.show()
else:
plt.close()
def show_data_domain_multi(samples, data, Q_ref=None, Q_nums=None,
img_folder='figs/', ref_markers=None,
ref_colors=None, showdim=None):
r"""
Plots 2-D projections of the data domain D using a triangulation based on
the first two coordinates (parameters) of the generating samples where
:math:`Q={q_1, q_i}` for ``i=Q_nums``, with a marker for various
:math:`Q_{ref}`.
:param samples: Samples to plot
:type samples: :class:`~numpy.ndarray` of shape (num_samples, ndim). Only
uses the first two dimensions.
:param data: Data associated with ``samples``
:type data: :class:`numpy.ndarray`
:param Q_ref: reference data value
:type Q_ref: :class:`numpy.ndarray` of shape (M, mdim)
:param list Q_nums: dimensions of the QoI to plot
:param string img_folder: folder to save the plots to
:param list ref_markers: list of marker types for :math:`Q_{ref}`
:param list ref_colors: list of colors for :math:`Q_{ref}`
:param showdim: default 1. If int then flag to show all combinations with a
given dimension (:math:`q_i`) or if ``all`` show all combinations.
:type showdim: int or string
"""
# Set the default marker and colors
if ref_markers == None:
ref_markers = markers
if ref_colors == None:
ref_colors = colors
# If no specific coordinate numbers are given for the data coordinates
# (e.g. i, where \q_i is a coordinate in the data space), then
# set them to be the the counting numbers.
if Q_nums is None:
Q_nums = range(data.shape[1])
# If no specific coordinate number of choice is given set to be the first
# coordinate direction.
if showdim == None:
showdim = 0
# Create a folder for these figures if it doesn't already exist
if not os.path.isdir(img_folder):
os.mkdir(img_folder)
# Make sure the shape of Q_ref is correct
if Q_ref is not None:
Q_ref = util.fix_dimensions_data(Q_ref, data.shape[1])
# Create the triangulization to use to define the topology of the samples
# in the data space from the first two parameters in the parameter space
triangulation = tri.Triangulation(samples[:, 0], samples[:, 1])
triangles = triangulation.triangles
# Create plots of the showdim^th QoI (q_{showdim}) with all other QoI (q_i)
if isinstance(showdim, int):
for i in Q_nums:
xlabel = r'$q_{'+str(showdim+1)+r'}$'
ylabel = r'$q_{'+str(i+1)+r'}$'
filenames = [img_folder+'domain_q'+str(showdim+1)+'_q'+\
str(i+1)+'.eps', img_folder+'q'+str(showdim+1)+\
'_q'+str(i+1)+'_domain_Q_cs.eps']
if Q_ref is not None:
show_data_domain_2D(samples, data[:, [showdim, i]], Q_ref[:,
[showdim, i]], ref_markers, ref_colors, xlabel=xlabel,
ylabel=ylabel, triangles=triangles, save=True,
interactive=False, filenames=filenames)
else:
show_data_domain_2D(samples, data[:, [showdim, i]], None,
ref_markers, ref_colors, xlabel=xlabel, ylabel=ylabel,
triangles=triangles, save=True, interactive=False,
filenames=filenames)
# Create plots of all combinations of QoI in 2D
elif showdim == 'all' or showdim == 'ALL':
for x, y in combinations(Q_nums, 2):
xlabel = r'$q_{'+str(x+1)+r'}$'
ylabel = r'$q_{'+str(y+1)+r'}$'
filenames = [img_folder+'domain_q'+str(x+1)+'_q'+str(y+1)+'.eps',
img_folder+'q'+str(x+1)+'_q'+str(y+1)+'_domain_Q_cs.eps']
if Q_ref is not None:
show_data_domain_2D(samples, data[:, [x, y]], Q_ref[:, [x, y]],
ref_markers, ref_colors, xlabel=xlabel, ylabel=ylabel,
triangles=triangles, save=True, interactive=False,
filenames=filenames)
else:
show_data_domain_2D(samples, data[:, [x, y]], None,
ref_markers, ref_colors, xlabel=xlabel, ylabel=ylabel,
triangles=triangles, save=True, interactive=False,
filenames=filenames)
def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
r"""
Creates a simple function approximation of :math:`\rho_{\mathcal{D}}`
where :math:`\rho_{\mathcal{D}}` is a uniform probability density on
a generalized rectangle centered at Q_ref.
The support of this density is defined by bin_ratio, which determines
the size of the generalized rectangle by scaling the circumscribing
generalized rectangle of :math:`\mathcal{D}`.
The simple function approximation is then defined by determining M
Voronoi cells (i.e., "bins") partitioning :math:`\mathcal{D}`. These
bins are only implicitly defined by M samples in :math:`\mathcal{D}`.
Finally, the probabilities of each of these bins is computed by
sampling from :math:`\rho{\mathcal{D}}` and using nearest neighbor
searches to bin these samples in the M implicitly defined bins.
The result is the simple function approximation denoted by
:math:`\rho_{\mathcal{D},M}`.
Note that all computations in the measure-theoretic framework that
follow from this are for the fixed simple function approximation
:math:`\rho_{\mathcal{D},M}`.
:param int M: Defines number M samples in D used to define
:math:`\rho_{\mathcal{D},M}` The choice of M is something of an "art" -
play around with it and you can get reasonable results with a
relatively small number here like 50.
:param bin_ratio: The ratio used to determine the width of the
uniform distributiion as ``bin_size = (data_max-data_min)*bin_ratio``
:type bin_ratio: double or list()
:param int num_d_emulate: Number of samples used to emulate using an MC
assumption
:param data: Array containing QoI data where the QoI is mdim
diminsional
:type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
:param Q_ref: :math:`Q(`\lambda_{reference})`
:type Q_ref: :class:`~numpy.ndarray` of size (mdim,)
:rtype: tuple
:returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree` is
the :class:`~scipy.spatial.KDTree` for d_distr_samples
"""
data = util.fix_dimensions_data(data)
bin_size = (np.max(data, 0) - np.min(data, 0))*bin_ratio
r'''
Create M samples defining M Voronoi cells (i.e., "bins") in D used to
define the simple function approximation :math:`\rho_{\mathcal{D},M}`.
This does not have to be random, but here we assume this to be the case.
We can choose these samples deterministically but that fails to scale with
dimension efficiently.
Note that these M samples are chosen for the sole purpose of determining
the bins used to create the approximation to :math:`rho_{\mathcal{D}}`.
We call these M samples "d_distr_samples" because they are samples on the
data space and the distr implies these samples are chosen to create the
approximation to the probability measure (distribution) on D.
Note that we create these samples in a set containing the hyperrectangle in
order to get output cells with zero probability. If all of the
d_dstr_samples were taken from within the support of
:math:`\rho_{\mathcal{D}}` then each of the M bins would have positive
probability. This would in turn imply that the support of
:math:`\rho_{\Lambda}` is all of :math:`\Lambda`.
'''
if comm.rank == 0:
d_distr_samples = 1.5*bin_size*(np.random.random((M,
data.shape[1]))-0.5)+Q_ref
else:
d_distr_samples = np.empty((M, data.shape[1]))
comm.Bcast([d_distr_samples, MPI.DOUBLE], root=0)
r'''
Compute probabilities in the M bins used to define
:math:`\rho_{\mathcal{D},M}` by Monte Carlo approximations
that in this context amount to binning with nearest neighbor
approximations the num_d_emulate samples taken from
:math:`\rho_{\mathcal{D}}`.
'''
# Generate the samples from :math:`\rho_{\mathcal{D}}`
num_d_emulate = int(num_d_emulate/comm.size)+1
d_distr_emulate = bin_size*(np.random.random((num_d_emulate,
data.shape[1]))-0.5) + Q_ref
# Bin these samples using nearest neighbor searches
d_Tree = spatial.KDTree(d_distr_samples)
(_, k) = d_Tree.query(d_distr_emulate)
count_neighbors = np.zeros((M,), dtype=np.int)
for i in range(M):
count_neighbors[i] = np.sum(np.equal(k, i))
# Use the binning to define :math:`\rho_{\mathcal{D},M}`
ccount_neighbors = np.copy(count_neighbors)
comm.Allreduce([count_neighbors, MPI.INT], [ccount_neighbors, MPI.INT],
op=MPI.SUM)
count_neighbors = ccount_neighbors
rho_D_M = count_neighbors.astype(np.float64) / \
float(num_d_emulate*comm.size)
'''
NOTE: The computation of q_distr_prob, q_distr_emulate, q_distr_samples
above, while possibly informed by the sampling of the map Q, do not require
solving the model EVER! This can be done "offline" so to speak. The results
can then be stored and accessed later by the algorithm using a completely
different set of parameter samples and model solves.
'''
return (rho_D_M, d_distr_samples, d_Tree)
def show_data_domain_2D(sample_disc, Q_ref=None, ref_markers=None,
ref_colors=None, xlabel=r'$q_1$', ylabel=r'$q_2$',
triangles=None, save=True, interactive=False,
filenames=None, file_extension=".png", markersize=75):
r"""
Plots 2-D a single data domain D using a triangulation based on the first
two coordinates (parameters) of the generating samples where :math:`Q={q_1,
q_i}` for ``i=Q_nums``, with a marker for various :math:`Q_{ref}`. Assumes
that the first dimension of data is :math:`q_1`.
.. note::
Do not specify the file extension in BOTH ``filenames`` and
``file_extension``.
:param sample_disc: Object containing the samples to plot
:type sample_disc: :class:`~bet.sample.discretization`
or :class:`~bet.sample.sample_set_base`
:param Q_ref: reference data value
:type Q_ref: :class:`numpy.ndarray` of shape (M, 2)
:param list ref_markers: list of marker types for :math:`Q_{ref}`
:param list ref_colors: list of colors for :math:`Q_{ref}`
:param string xlabel: x-axis label
:param string ylabel: y-axis label
:param triangles: triangulation defined by ``samples``
:type triangles: :class:`tri.Triuangulation.triangles`
:param bool save: flag whether or not to save the figure
:param bool interactive: flag whether or not to show the figure
:param list filenames: file names for the unmarked and marked domain plots
:param string file_extension: file extension
"""
if not isinstance(sample_disc, sample.discretization):
raise bad_object("Improper sample object")
data_obj = sample_disc._output_sample_set
sample_obj = sample_disc._input_sample_set
if Q_ref is None:
Q_ref = data_obj._reference_value
# Set the default marker and colors
if ref_markers is None:
ref_markers = markers
if ref_colors is None:
ref_colors = colors
# If no specific coordinate numbers are given for the data coordinates
# (e.g. i, where \q_i is a coordinate in the data space), then
# set them to be the the counting numbers.
if triangles is None:
triangulation = tri.Triangulation(sample_obj.get_values()[:, 0],
sample_obj.get_values()[:, 1])
triangles = triangulation.triangles
# Set default file names
if filenames is None:
filenames = ['domain_q1_q2_cs', 'q1_q2_domain_Q_cs']
# Make sure the shape of Q_ref is correct
if Q_ref is not None:
Q_ref = util.fix_dimensions_data(Q_ref, 2)
# Create figure
plt.tricontourf(data_obj.get_values()[:, 0], data_obj.get_values()[:, 1],
np.zeros((data_obj.get_values().shape[0],)),
triangles=triangles, colors='grey')
plt.autoscale(tight=True)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if "." not in filenames[0]:
full_filenames0 = filenames[0]+file_extension
else:
full_filenames0 = filenames[0]
if "." not in filenames[1]:
full_filenames1 = filenames[1]+file_extension
else:
full_filenames1 = filenames[1]
plt.savefig(full_filenames0, bbox_inches='tight', transparent=True,
pad_inches=.2)
# Add truth markers
if Q_ref is not None:
for i in range(Q_ref.shape[0]):
plt.scatter(Q_ref[i, 0], Q_ref[i, 1], s=60, c=ref_colors[i],
marker=ref_markers[i])
plt.tight_layout()
if save:
plt.savefig(full_filenames1, bbox_inches='tight', transparent=True,
pad_inches=.2)
if interactive:
plt.show()
else:
plt.close()
def unif_unif(data, Q_ref, M=50, bin_ratio=0.2, num_d_emulate=1E6):
r"""
Creates a simple function approximation of :math:`\rho_{\mathcal{D}}`
where :math:`\rho_{\mathcal{D}}` is a uniform probability density on
a generalized rectangle centered at Q_ref.
The support of this density is defined by bin_ratio, which determines
the size of the generalized rectangle by scaling the circumscribing
generalized rectangle of :math:`\mathcal{D}`.
The simple function approximation is then defined by determining M
Voronoi cells (i.e., "bins") partitioning :math:`\mathcal{D}`. These
bins are only implicitly defined by M samples in :math:`\mathcal{D}`.
Finally, the probabilities of each of these bins is computed by
sampling from :math:`\rho{\mathcal{D}}` and using nearest neighbor
searches to bin these samples in the M implicitly defined bins.
The result is the simple function approximation denoted by
:math:`\rho_{\mathcal{D},M}`.
Note that all computations in the measure-theoretic framework that
follow from this are for the fixed simple function approximation
:math:`\rho_{\mathcal{D},M}`.
:param int M: Defines number M samples in D used to define
:math:`\rho_{\mathcal{D},M}` The choice of M is something of an "art" -
play around with it and you can get reasonable results with a
relatively small number here like 50.
:param bin_ratio: The ratio used to determine the width of the
uniform distributiion as ``bin_size = (data_max-data_min)*bin_ratio``
:type bin_ratio: double or list()
:param int num_d_emulate: Number of samples used to emulate using an MC
assumption
:param data: Array containing QoI data where the QoI is mdim
diminsional
:type data: :class:`~numpy.ndarray` of size (num_samples, mdim)
:param Q_ref: :math:`Q(`\lambda_{reference})`
:type Q_ref: :class:`~numpy.ndarray` of size (mdim,)
:rtype: tuple
:returns: (rho_D_M, d_distr_samples, d_Tree) where ``rho_D_M`` is (M,) and
``d_distr_samples`` are (M, mdim) :class:`~numpy.ndarray` and `d_Tree` is
the :class:`~scipy.spatial.KDTree` for d_distr_samples
"""
data = util.fix_dimensions_data(data)
bin_size = (np.max(data, 0) - np.min(data, 0))*bin_ratio
r'''
Create M samples defining M Voronoi cells (i.e., "bins") in D used to
define the simple function approximation :math:`\rho_{\mathcal{D},M}`.
This does not have to be random, but here we assume this to be the case.
We can choose these samples deterministically but that fails to scale with
dimension efficiently.
Note that these M samples are chosen for the sole purpose of determining
the bins used to create the approximation to :math:`rho_{\mathcal{D}}`.
We call these M samples "d_distr_samples" because they are samples on the
data space and the distr implies these samples are chosen to create the
approximation to the probability measure (distribution) on D.
Note that we create these samples in a set containing the hyperrectangle in
order to get output cells with zero probability. If all of the
d_dstr_samples were taken from within the support of
:math:`\rho_{\mathcal{D}}` then each of the M bins would have positive
probability. This would in turn imply that the support of
:math:`\rho_{\Lambda}` is all of :math:`\Lambda`.
'''
if comm.rank == 0:
d_distr_samples = 1.5*bin_size*(np.random.random((M,
data.shape[1]))-0.5)+Q_ref
else:
d_distr_samples = np.empty((M, data.shape[1]))
comm.Bcast([d_distr_samples, MPI.DOUBLE], root=0)
r'''
Compute probabilities in the M bins used to define
:math:`\rho_{\mathcal{D},M}` by Monte Carlo approximations
that in this context amount to binning with nearest neighbor
approximations the num_d_emulate samples taken from
:math:`\rho_{\mathcal{D}}`.
'''
# Generate the samples from :math:`\rho_{\mathcal{D}}`
num_d_emulate = int(num_d_emulate/comm.size)+1
d_distr_emulate = bin_size*(np.random.random((num_d_emulate,
data.shape[1]))-0.5) + Q_ref
# Bin these samples using nearest neighbor searches
d_Tree = spatial.KDTree(d_distr_samples)
(_, k) = d_Tree.query(d_distr_emulate)
count_neighbors = np.zeros((M,), dtype=np.int)
for i in range(M):
count_neighbors[i] = np.sum(np.equal(k, i))
# Use the binning to define :math:`\rho_{\mathcal{D},M}`
ccount_neighbors = np.copy(count_neighbors)
comm.Allreduce([count_neighbors, MPI.INT], [ccount_neighbors, MPI.INT],
op=MPI.SUM)
count_neighbors = ccount_neighbors
rho_D_M = count_neighbors.astype(np.float64) / float(num_d_emulate*comm.size)
'''
NOTE: The computation of q_distr_prob, q_distr_emulate, q_distr_samples
above, while possibly informed by the sampling of the map Q, do not require
solving the model EVER! This can be done "offline" so to speak. The results
can then be stored and accessed later by the algorithm using a completely
different set of parameter samples and model solves.
'''
return (rho_D_M, d_distr_samples, d_Tree)