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Introduce add_points method in AbstractProblem, removed unused comments in Collector class and add the test for add_points and codacy corrections

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FilippoOlivo
2025-02-06 17:29:37 +01:00
committed by Nicola Demo
parent 004cbc00c0
commit f578b2ed12
4 changed files with 56 additions and 109 deletions
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56
pina/collector.py
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@@ -62,7 +62,6 @@ class Collector:
# condition now is ready
self._is_conditions_ready[condition_name] = True
def store_sample_domains(self):
"""
# TODO: Add docstring
@@ -70,7 +69,7 @@ class Collector:
for condition_name in self.problem.conditions:
condition = self.problem.conditions[condition_name]
if not hasattr(condition, "domain"):
continue
continue
samples = self.problem.discretised_domains[condition.domain]
@@ -78,56 +77,3 @@ class Collector:
'input_points': samples,
'equation': condition.equation
}
# # get condition
# condition = self.problem.conditions[loc]
# condition_domain = condition.domain
# if isinstance(condition_domain, str):
# condition_domain = self.problem.domains[condition_domain]
# keys = ["input_points", "equation"]
# # if the condition is not ready, we get and store the data
# if not self._is_conditions_ready[loc]:
# # if it is the first time we sample
# if not self.data_collections[loc]:
# already_sampled = []
# # if we have sampled the condition but not all variables
# else:
# already_sampled = [
# self.data_collections[loc]['input_points']
# ]
# # if the condition is ready but we want to sample again
# else:
# self._is_conditions_ready[loc] = False
# already_sampled = []
# # get the samples
# samples = [
# condition_domain.sample(n=n, mode=mode,
# variables=variables)
# ] + already_sampled
# pts = merge_tensors(samples)
# if set(pts.labels).issubset(sorted(self.problem.input_variables)):
# pts = pts.sort_labels()
# if sorted(pts.labels) == sorted(self.problem.input_variables):
# self._is_conditions_ready[loc] = True
# values = [pts, condition.equation]
# self.data_collections[loc] = dict(zip(keys, values))
# else:
# raise RuntimeError(
# 'Try to sample variables which are not in problem defined '
# 'in the problem')
def add_points(self, new_points_dict):
"""
Add input points to a sampled condition
:param new_points_dict: Dictonary of input points (condition_name:
LabelTensor)
:raises RuntimeError: if at least one condition is not already sampled
"""
for k, v in new_points_dict.items():
if not self._is_conditions_ready[k]:
raise RuntimeError(
'Cannot add points on a non sampled condition')
self.data_collections[k]['input_points'] = LabelTensor.vstack(
[self.data_collections[k][
'input_points'], v])

40
pina/problem/abstract_problem.py
View File

@@ -6,6 +6,7 @@ from ..domain import DomainInterface
from ..condition.domain_equation_condition import DomainEquationCondition
from ..condition import InputPointsEquationCondition
from copy import deepcopy
from pina import LabelTensor
class AbstractProblem(metaclass=ABCMeta):
@@ -135,12 +136,11 @@ class AbstractProblem(metaclass=ABCMeta):
"""
The conditions of the problem.
"""
return self._conditions
return self.conditions
def discretise_domain(self,
n,
mode="random",
variables="all",
domains="all"):
"""
Generate a set of points to span the `Location` of all the conditions of
@@ -153,10 +153,8 @@ class AbstractProblem(metaclass=ABCMeta):
Available modes include: random sampling, ``random``;
latin hypercube sampling, ``latin`` or ``lh``;
chebyshev sampling, ``chebyshev``; grid sampling ``grid``.
:param variables: problem's variables to be sampled, defaults to 'all'.
:type variables: str | list[str]
:param domain: problem's domain from where to sample, defaults to 'all'.
:type locations: str
:param domains: problem's domain from where to sample, defaults to 'all'.
:type domains: str | list[str]
:Example:
>>> pinn.discretise_domain(n=10, mode='grid')
@@ -174,22 +172,12 @@ class AbstractProblem(metaclass=ABCMeta):
# check consistecy n, mode, variables, locations
check_consistency(n, int)
check_consistency(mode, str)
check_consistency(variables, str)
check_consistency(domains, (list, str))
# check correct sampling mode
# if mode not in DomainInterface.available_sampling_modes:
# raise TypeError(f"mode {mode} not valid.")
# check correct variables
if variables == "all":
variables = self.input_variables
for variable in variables:
if variable not in self.input_variables:
TypeError(
f"Wrong variables for sampling. Variables ",
f"should be in {self.input_variables}.",
)
# check correct location
if domains == "all":
domains = self.domains.keys()
@@ -198,14 +186,16 @@ class AbstractProblem(metaclass=ABCMeta):
for domain in domains:
self.discretised_domains[domain] = (
self.domains[domain].sample(n, mode, variables)
self.domains[domain].sample(n, mode)
)
# if not isinstance(self.conditions[loc], DomainEquationCondition):
# raise TypeError(
# f"Wrong locations passed, locations for sampling "
# f"should be in {[loc for loc in locations if isinstance(self.conditions[loc], DomainEquationCondition)]}.",
# )
# store data
# self.collector.store_sample_domains()
# self.collector.store_sample_domains(n, mode, variables, domain)
def add_points(self, new_points_dict):
"""
Add input points to a sampled condition
:param new_points_dict: Dictionary of input points (condition_name:
LabelTensor)
:raises RuntimeError: if at least one condition is not already sampled
"""
for k, v in new_points_dict.items():
self.discretised_domains[k] = LabelTensor.vstack(
[self.discretised_domains[k], v])