"""Include various function to read results from hdf5 Files."""
import ast
import logging
import h5py
import numpy as np
from ..objective import Hdf5History, Objective, ObjectiveBase
from ..problem import Problem
from ..result import McmcPtResult, OptimizerResult, ProfilerResult, Result
logger = logging.getLogger(__name__)
def read_hdf5_profile(
f: h5py.File,
profile_id: str,
parameter_id: str,
) -> 'ProfilerResult':
"""Read HDF5 results per start.
Parameters
----------
f:
The HDF5 result file
profile_id:
specifies the profile start that is read
from the HDF5 file
parameter_id:
specifies the profile index that is read
from the HDF5 file
"""
result = ProfilerResult(np.array([]), np.array([]), np.array([]))
for profile_key in result.keys():
if profile_key in f[f'/profiling/{profile_id}/{parameter_id}']:
result[profile_key] = f[
f'/profiling/{profile_id}/{parameter_id}/{profile_key}'
][:]
elif profile_key in f[f'/profiling/{profile_id}/{parameter_id}'].attrs:
result[profile_key] = f[
f'/profiling/{profile_id}/{parameter_id}'
].attrs[profile_key]
return result
def read_hdf5_optimization(
f: h5py.File,
file_name: str,
opt_id: str,
) -> 'OptimizerResult':
"""Read HDF5 results per start.
Parameters
----------
f:
The HDF5 result file
file_name:
The name of the HDF5 file, needed to create HDF5History
opt_id:
Specifies the start that is read from the HDF5 file
"""
result = OptimizerResult()
for optimization_key in result.keys():
if optimization_key == 'history':
if optimization_key in f:
result['history'] = Hdf5History(id=opt_id, file=file_name)
result['history']._recover_options(file_name)
continue
if optimization_key in f[f'/optimization/results/{opt_id}']:
result[optimization_key] = f[
f'/optimization/results/{opt_id}/{optimization_key}'
][:]
elif optimization_key in f[f'/optimization/results/{opt_id}'].attrs:
result[optimization_key] = f[
f'/optimization/results/{opt_id}'
].attrs[optimization_key]
return result
[docs]class ProblemHDF5Reader:
"""
Reader of the HDF5 problem files written by ProblemHDF5Writer.
Attributes
----------
storage_filename:
HDF5 problem file name
"""
[docs] def __init__(self, storage_filename: str):
"""Initialize reader.
Parameters
----------
storage_filename: str
HDF5 problem file name
"""
self.storage_filename = storage_filename
[docs] def read(self, objective: ObjectiveBase = None) -> Problem:
"""Read HDF5 problem file and return pyPESTO problem object.
Parameters
----------
objective:
Objective function which is currently not saved to storage.
Returns
-------
problem:
A problem instance with all attributes read in.
"""
# create empty problem
if objective is None:
objective = Objective()
# raise warning that objective is not loaded.
logger.info(
'WARNING: You are loading a problem.\nThis problem'
' is not to be used without a separately created'
' objective.'
)
problem = Problem(objective, [], [])
with h5py.File(self.storage_filename, 'r') as f:
for problem_key in f['/problem']:
if problem_key == 'config':
continue
setattr(problem, problem_key, f[f'/problem/{problem_key}'][:])
for problem_attr in f['/problem'].attrs:
setattr(
problem, problem_attr, f['/problem'].attrs[problem_attr]
)
# h5 uses numpy for everything; convert to lists where necessary
problem.x_fixed_vals = [float(val) for val in problem.x_fixed_vals]
problem.x_fixed_indices = [int(ix) for ix in problem.x_fixed_indices]
problem.x_names = [name.decode() for name in problem.x_names]
return problem
[docs]class OptimizationResultHDF5Reader:
"""
Reader of the HDF5 result files written by OptimizationResultHDF5Writer.
Attributes
----------
storage_filename:
HDF5 result file name
"""
[docs] def __init__(self, storage_filename: str):
"""
Initialize reader.
Parameters
----------
storage_filename: str
HDF5 result file name
"""
self.storage_filename = storage_filename
self.results = Result()
[docs] def read(self) -> Result:
"""Read HDF5 result file and return pyPESTO result object."""
with h5py.File(self.storage_filename, "r") as f:
for opt_id in f['/optimization/results']:
result = read_hdf5_optimization(
f, self.storage_filename, opt_id
)
self.results.optimize_result.append(result)
self.results.optimize_result.sort()
return self.results
[docs]class SamplingResultHDF5Reader:
"""
Reader of the HDF5 result files written by SamplingResultHDF5Writer.
Attributes
----------
storage_filename:
HDF5 result file name
"""
[docs] def __init__(self, storage_filename: str):
"""Initialize reader.
Parameters
----------
storage_filename: str
HDF5 result file name
"""
self.storage_filename = storage_filename
self.results = Result()
[docs] def read(self) -> Result:
"""Read HDF5 result file and return pyPESTO result object."""
sample_result = {}
with h5py.File(self.storage_filename, "r") as f:
for key in f['/sampling/results']:
sample_result[key] = f[f'/sampling/results/{key}'][:]
for key in f['/sampling/results'].attrs:
sample_result[key] = f['/sampling/results'].attrs[key]
try:
self.results.sample_result = McmcPtResult(**sample_result)
except TypeError:
logger.warning(
"Warning: You tried loading a non-existent " "sampling result."
)
return self.results
[docs]class ProfileResultHDF5Reader:
"""
Reader of the HDF5 result files written by OptimizationResultHDF5Writer.
Attributes
----------
storage_filename:
HDF5 result file name
"""
[docs] def __init__(self, storage_filename: str):
"""
Initialize reader.
Parameters
----------
storage_filename:
HDF5 result file name
"""
self.storage_filename = storage_filename
self.results = Result()
[docs] def read(self) -> Result:
"""Read HDF5 result file and return pyPESTO result object."""
profiling_list = []
with h5py.File(self.storage_filename, "r") as f:
for profile_id in f['/profiling']:
profiling_list.append(
[None for _ in f[f'/profiling/{profile_id}']]
)
for parameter_id in f[f'/profiling/{profile_id}']:
if f[f'/profiling/{profile_id}/' f'{parameter_id}'].attrs[
'IsNone'
]:
continue
profiling_list[int(profile_id)][
int(parameter_id)
] = read_hdf5_profile(
f, profile_id=profile_id, parameter_id=parameter_id
)
self.results.profile_result.list = profiling_list
return self.results
[docs]def read_result(
filename: str,
problem: bool = True,
optimize: bool = False,
profile: bool = False,
sample: bool = False,
) -> Result:
"""Save the whole pypesto.Result object in an HDF5 file.
Parameters
----------
filename:
The HDF5 filename.
problem:
Read the problem.
optimize:
Read the optimize result.
profile:
Read the profile result.
sample:
Read the sample result.
Returns
-------
result:
Result object containing the results stored in HDF5 file.
"""
if not any([optimize, profile, sample]):
optimize = True
profile = True
sample = True
result = Result()
if problem:
pypesto_problem_reader = ProblemHDF5Reader(filename)
result.problem = pypesto_problem_reader.read()
if optimize:
pypesto_opt_reader = OptimizationResultHDF5Reader(filename)
try:
temp_result = pypesto_opt_reader.read()
result.optimize_result = temp_result.optimize_result
except KeyError:
logger.warning(
'Loading the optimization result failed. It is '
'highly likely that no optimization result exists '
f'within {filename}.'
)
if profile:
pypesto_profile_reader = ProfileResultHDF5Reader(filename)
try:
temp_result = pypesto_profile_reader.read()
result.profile_result = temp_result.profile_result
except KeyError:
logger.warning(
'Loading the profiling result failed. It is '
'highly likely that no profiling result exists '
f'within {filename}.'
)
if sample:
pypesto_sample_reader = SamplingResultHDF5Reader(filename)
try:
temp_result = pypesto_sample_reader.read()
result.sample_result = temp_result.sample_result
except KeyError:
logger.warning(
'Loading the sampling result failed. It is '
'highly likely that no sampling result exists '
f'within {filename}.'
)
return result
[docs]def load_objective_config(filename: str):
"""Load the objective information stored in f.
Parameters
----------
filename:
The name of the file in which the information are stored.
Returns
-------
A dictionary of the information, stored instead of the
actual objective in problem.objective.
"""
with h5py.File(filename, 'r') as f:
info_str = f['problem/config'][()].decode()
info = ast.literal_eval(info_str)
return info