/
generate_benchmark_params.py
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/
generate_benchmark_params.py
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"""Benchmarking parameters generation file."""
import argparse
from itertools import chain, product
import pandas as pd
parser = argparse.ArgumentParser(
description="Generate parameters for which benchmark is run"
)
parser.add_argument(
"-m",
"--manifold",
type=str,
default="all",
help="Manifold for which benchmark is run. 'all' denotes all manifolds present.",
)
parser.add_argument(
"-n",
"--n_samples",
type=int,
default=10,
help="Number of samples for which benchmark is run",
)
args = parser.parse_args()
def spd_manifold_params(n_samples):
"""Generate spd manifold benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "SPDMatrices"
manifold_args = [(2,), (5,), (10,)]
module = "geomstats.geometry.spd_matrices"
def spd_affine_metric_params():
params = []
metric = "SPDMetricAffine"
power_args = [-0.5, 1, 0.5]
metric_args = list(product([item for item, in manifold_args], power_args))
manifold_args_re = [
item for item in manifold_args for i in range(len(power_args))
]
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args_re, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
def spd_bures_wasserstein_metric_params():
params = []
metric = "SPDMetricBuresWasserstein"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
def spd_euclidean_metric_params():
params = []
metric = "SPDMetricEuclidean"
power_args = [-0.5, 1, 0.5]
metric_args = list(product([item for item, in manifold_args], power_args))
manifold_args_re = [
item for item in manifold_args for i in range(len(power_args))
]
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args_re, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
def spd_log_euclidean_metric_params():
params = []
metric = "SPDMetricLogEuclidean"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return list(
chain(
*[
spd_bures_wasserstein_metric_params(),
spd_affine_metric_params(),
spd_euclidean_metric_params(),
spd_log_euclidean_metric_params(),
]
)
)
def stiefel_params(n_samples):
"""Generate stiefel benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "Stiefel"
manifold_args = [(3, 2), (4, 3)]
module = "geomstats.geometry.stiefel"
def stiefel_canonical_metric_params():
params = []
metric = "StiefelCanonicalMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return stiefel_canonical_metric_params()
def pre_shape_params(n_samples):
"""Generate pre shape benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "PreShapeSpace"
manifold_args = [(3, 3), (5, 5)]
module = "geomstats.geometry.pre_shape"
def pre_shape_metric_params():
params = []
metric = "PreShapeMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return pre_shape_metric_params()
def positive_lower_triangular_matrices_params(n_samples):
"""Generate positive lower triangular matrices benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "PositiveLowerTriangularMatrices"
manifold_args = [(3,), (5,)]
module = "geomstats.geometry.positive_lower_triangular_matrices"
def cholesky_metric_params():
params = []
metric = "CholeskyMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return cholesky_metric_params()
def minkowski_params(n_samples):
"""Generate minkowski benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "Minkowski"
manifold_args = [(3,), (5,)]
module = "geomstats.geometry.minkowski"
def minkowski_metric_params():
params = []
metric = "MinkowskiMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return minkowski_metric_params()
def matrices_params(n_samples):
"""Generate matrices benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "Matrices"
manifold_args = [(3, 3), (5, 5)]
module = "geomstats.geometry.matrices"
def matrices_metric_params():
params = []
metric = "MatricesMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return matrices_metric_params()
def hypersphere_params(n_samples):
"""Generate hypersphere benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "Hypersphere"
manifold_args = [(3,), (5,)]
module = "geomstats.geometry.hypersphere"
def hypersphere_metric_params():
params = []
metric = "HypersphereMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return hypersphere_metric_params()
def grassmanian_params(n_samples):
"""Generate grassmanian parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "Grassmannian"
manifold_args = [(4, 3), (5, 4)]
module = "geomstats.geometry.grassmannian"
def grassmannian_canonical_metric_params():
params = []
metric = "GrassmannianCanonicalMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return grassmannian_canonical_metric_params()
def full_rank_correlation_matrices_params(n_samples):
"""Generate full rank correlation matrices benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "FullRankCorrelationMatrices"
manifold_args = [(3,), (5,)]
module = "geomstats.geometry.full_rank_correlation_matrices"
def full_rank_correlation_affine_quotient_metric_params():
params = []
metric = "FullRankCorrelationAffineQuotientMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return full_rank_correlation_affine_quotient_metric_params()
def hyperboloid_params(n_samples):
"""Generate hyperboloid benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "Hyperboloid"
manifold_args = [(3,), (5,)]
module = "geomstats.geometry.hyperboloid"
def hyperboloid_metric_params():
params = []
metric = "HyperboloidMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return hyperboloid_metric_params()
def poincare_ball_params(n_samples):
"""Generate poincare ball benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "PoincareBall"
manifold_args = [(3,), (5,)]
module = "geomstats.geometry.poincare_ball"
def poincare_ball_metric_params():
params = []
metric = "PoincareBallMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return poincare_ball_metric_params()
def poincare_half_space_params(n_samples):
"""Generate poincare half space benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "PoincareHalfSpace"
manifold_args = [(3,), (5,)]
module = "geomstats.geometry.poincare_half_space"
def poincare_half_space_metric_params():
params = []
metric = "PoincareHalfSpaceMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return poincare_half_space_metric_params()
def poincare_polydisk_params(n_samples):
"""Generate poincare polydisk benchmarking parameters.
Parameters
----------
n_samples : int
Number of samples to be used.
Returns
-------
_ : list.
List of params.
"""
manifold = "PoincarePolydisk"
manifold_args = [(3,), (5,)]
module = "geomstats.geometry.poincare_polydisk"
def poincare_poly_disk_metric_params():
params = []
metric = "PoincarePolydiskMetric"
metric_args = manifold_args
kwargs = {}
common = manifold, module, metric, n_samples, kwargs
for manifold_arg, metric_arg in zip(manifold_args, metric_args):
params += [common + (manifold_arg, metric_arg)]
return params
return poincare_poly_disk_metric_params()
manifolds = [
"spd_manifold",
"stiefel",
"pre_shape",
"positive_lower_triangular_matrices",
"minkowski",
"matrices",
"hypersphere",
"grassmanian",
"hyperboloid",
"poincare_ball",
"poincare_half_space",
]
def generate_benchmark_params(manifold="all", n_samples=10):
"""Generate parameters for benchmarking.
Parameters
----------
manifold : str
Manifold name or all.
Optional, default "all".
n_samples : int
Number of samples.
Optional, default 10.
"""
params_list = []
manifolds_list = manifolds if manifold == "all" else [manifold]
params_list = [
globals()[manifold + "_params"](n_samples) for manifold in manifolds_list
]
params_list = list(chain(*params_list))
df = pd.DataFrame(
params_list,
columns=[
"manifold",
"module",
"metric",
"n_samples",
"exp_kwargs",
"manifold_args",
"metric_args",
],
)
df.to_pickle("benchmark_params.pkl")
print("Generated params at benchmark_params.pkl.")
def main():
"""Generate Benchmark Params."""
generate_benchmark_params(args.manifold, args.n_samples)
if __name__ == "__main__":
main()