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Fixed multiple docstring issues #84

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Merged
merged 2 commits into from
May 17, 2019
Merged

Fixed multiple docstring issues #84

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b9e69fb
Fixed multiple docstring issues
ngayraud May 10, 2019
f67f8fd
Fixed pep8 related issue
ngayraud May 14, 2019
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123 changes: 65 additions & 58 deletions ot/da.py
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Original file line number Diff line number Diff line change
Expand Up @@ -473,22 +473,24 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian',
Weight for the linear OT loss (>0)
eta : float, optional
Regularization term for the linear mapping L (>0)
bias : bool,optional
Estimate linear mapping with constant bias
kerneltype : str,optional
kernel used by calling function ot.utils.kernel (gaussian by default)
sigma : float, optional
Gaussian kernel bandwidth.
bias : bool,optional
Estimate linear mapping with constant bias
verbose : bool, optional
Print information along iterations
verbose2 : bool, optional
Print information along iterations
numItermax : int, optional
Max number of BCD iterations
stopThr : float, optional
Stop threshold on relative loss decrease (>0)
numInnerItermax : int, optional
Max number of iterations (inner CG solver)
stopInnerThr : float, optional
Stop threshold on error (inner CG solver) (>0)
verbose : bool, optional
Print information along iterations
stopThr : float, optional
Stop threshold on relative loss decrease (>0)
log : bool, optional
record log if True

Expand Down Expand Up @@ -643,7 +645,8 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None,
The function estimates the optimal linear operator that aligns the two
empirical distributions. This is equivalent to estimating the closed
form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)`
and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark 2.29 in [15].
and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark
2.29 in [15].

The linear operator from source to target :math:`M`

Expand Down Expand Up @@ -1184,25 +1187,25 @@ class SinkhornTransport(BaseTransport):
algorithm if no it has not converged
tol : float, optional (default=10e-9)
The precision required to stop the optimization algorithm.
mapping : string, optional (default="barycentric")
The kind of mapping to apply to transport samples from a domain into
another one.
if "barycentric" only the samples used to estimate the coupling can
be transported from a domain to another one.
verbose : bool, optional (default=False)
Controls the verbosity of the optimization algorithm
log : int, optional (default=False)
Controls the logs of the optimization algorithm
metric : string, optional (default="sqeuclidean")
The ground metric for the Wasserstein problem
norm : string, optional (default=None)
If given, normalize the ground metric to avoid numerical errors that
can occur with large metric values.
distribution : string, optional (default="uniform")
distribution_estimation : callable, optional (defaults to the uniform)
The kind of distribution estimation to employ
verbose : int, optional (default=0)
Controls the verbosity of the optimization algorithm
log : int, optional (default=0)
Controls the logs of the optimization algorithm
out_of_sample_map : string, optional (default="ferradans")
The kind of out of sample mapping to apply to transport samples
from a domain into another one. Currently the only possible option is
"ferradans" which uses the method proposed in [6].
limit_max: float, optional (defaul=np.infty)
Controls the semi supervised mode. Transport between labeled source
and target samples of different classes will exhibit an infinite cost
and target samples of different classes will exhibit an cost defined
by this variable

Attributes
----------
Expand Down Expand Up @@ -1287,22 +1290,19 @@ class EMDTransport(BaseTransport):

Parameters
----------
mapping : string, optional (default="barycentric")
The kind of mapping to apply to transport samples from a domain into
another one.
if "barycentric" only the samples used to estimate the coupling can
be transported from a domain to another one.
metric : string, optional (default="sqeuclidean")
The ground metric for the Wasserstein problem
norm : string, optional (default=None)
If given, normalize the ground metric to avoid numerical errors that
can occur with large metric values.
distribution : string, optional (default="uniform")
The kind of distribution estimation to employ
verbose : int, optional (default=0)
Controls the verbosity of the optimization algorithm
log : int, optional (default=0)
log : int, optional (default=False)
Controls the logs of the optimization algorithm
distribution_estimation : callable, optional (defaults to the uniform)
The kind of distribution estimation to employ
out_of_sample_map : string, optional (default="ferradans")
The kind of out of sample mapping to apply to transport samples
from a domain into another one. Currently the only possible option is
"ferradans" which uses the method proposed in [6].
limit_max: float, optional (default=10)
Controls the semi supervised mode. Transport between labeled source
and target samples of different classes will exhibit an infinite cost
Expand Down Expand Up @@ -1387,28 +1387,32 @@ class SinkhornLpl1Transport(BaseTransport):
Entropic regularization parameter
reg_cl : float, optional (default=0.1)
Class regularization parameter
mapping : string, optional (default="barycentric")
The kind of mapping to apply to transport samples from a domain into
another one.
if "barycentric" only the samples used to estimate the coupling can
be transported from a domain to another one.
metric : string, optional (default="sqeuclidean")
The ground metric for the Wasserstein problem
norm : string, optional (default=None)
If given, normalize the ground metric to avoid numerical errors that
can occur with large metric values.
distribution : string, optional (default="uniform")
The kind of distribution estimation to employ
max_iter : int, float, optional (default=10)
The minimum number of iteration before stopping the optimization
algorithm if no it has not converged
max_inner_iter : int, float, optional (default=200)
The number of iteration in the inner loop
verbose : int, optional (default=0)
log : bool, optional (default=False)
Controls the logs of the optimization algorithm
tol : float, optional (default=10e-9)
Stop threshold on error (inner sinkhorn solver) (>0)
verbose : bool, optional (default=False)
Controls the verbosity of the optimization algorithm
metric : string, optional (default="sqeuclidean")
The ground metric for the Wasserstein problem
norm : string, optional (default=None)
If given, normalize the ground metric to avoid numerical errors that
can occur with large metric values.
distribution_estimation : callable, optional (defaults to the uniform)
The kind of distribution estimation to employ
out_of_sample_map : string, optional (default="ferradans")
The kind of out of sample mapping to apply to transport samples
from a domain into another one. Currently the only possible option is
"ferradans" which uses the method proposed in [6].
limit_max: float, optional (defaul=np.infty)
Controls the semi supervised mode. Transport between labeled source
and target samples of different classes will exhibit an infinite cost
and target samples of different classes will exhibit a cost defined by
limit_max.

Attributes
----------
Expand Down Expand Up @@ -1504,27 +1508,28 @@ class SinkhornL1l2Transport(BaseTransport):
Entropic regularization parameter
reg_cl : float, optional (default=0.1)
Class regularization parameter
mapping : string, optional (default="barycentric")
The kind of mapping to apply to transport samples from a domain into
another one.
if "barycentric" only the samples used to estimate the coupling can
be transported from a domain to another one.
metric : string, optional (default="sqeuclidean")
The ground metric for the Wasserstein problem
norm : string, optional (default=None)
If given, normalize the ground metric to avoid numerical errors that
can occur with large metric values.
distribution : string, optional (default="uniform")
The kind of distribution estimation to employ
max_iter : int, float, optional (default=10)
The minimum number of iteration before stopping the optimization
algorithm if no it has not converged
max_inner_iter : int, float, optional (default=200)
The number of iteration in the inner loop
verbose : int, optional (default=0)
tol : float, optional (default=10e-9)
Stop threshold on error (inner sinkhorn solver) (>0)
verbose : bool, optional (default=False)
Controls the verbosity of the optimization algorithm
log : int, optional (default=0)
log : bool, optional (default=False)
Controls the logs of the optimization algorithm
metric : string, optional (default="sqeuclidean")
The ground metric for the Wasserstein problem
norm : string, optional (default=None)
If given, normalize the ground metric to avoid numerical errors that
can occur with large metric values.
distribution_estimation : callable, optional (defaults to the uniform)
The kind of distribution estimation to employ
out_of_sample_map : string, optional (default="ferradans")
The kind of out of sample mapping to apply to transport samples
from a domain into another one. Currently the only possible option is
"ferradans" which uses the method proposed in [6].
limit_max: float, optional (default=10)
Controls the semi supervised mode. Transport between labeled source
and target samples of different classes will exhibit an infinite cost
Expand Down Expand Up @@ -1646,10 +1651,12 @@ class MappingTransport(BaseEstimator):
Max number of iterations (inner CG solver)
inner_tol : float, optional (default=1e-6)
Stop threshold on error (inner CG solver) (>0)
verbose : bool, optional (default=False)
Print information along iterations
log : bool, optional (default=False)
record log if True
verbose : bool, optional (default=False)
Print information along iterations
verbose2 : bool, optional (default=False)
Print information along iterations

Attributes
----------
Expand Down