* Adding Collector for handling data sampling/collection before dataset/dataloader
* Modify domain by adding sample_mode, variables as property * Small change concatenate -> cat in lno/avno * Create different factory classes for conditions
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Dario Coscia
committed by
Nicola Demo
Nicola Demo
parent
aef5a5d590
commit
1bd3f40f54
@@ -1,12 +1,11 @@
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""" Module for AbstractProblem class """
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from abc import ABCMeta, abstractmethod
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from ..utils import merge_tensors, check_consistency
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from ..utils import check_consistency
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from ..domain import DomainInterface
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from ..condition.domain_equation_condition import DomainEquationCondition
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from ..collector import Collector
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from copy import deepcopy
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import torch
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from .. import LabelTensor
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class AbstractProblem(metaclass=ABCMeta):
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"""
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@@ -20,27 +19,25 @@ class AbstractProblem(metaclass=ABCMeta):
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def __init__(self):
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self._discretized_domains = {}
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for name, domain in self.domains.items():
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if isinstance(domain, (torch.Tensor, LabelTensor)):
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self._discretized_domains[name] = domain
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# create collector to manage problem data
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self.collector = Collector(self)
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# create hook conditions <-> problems
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for condition_name in self.conditions:
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self.conditions[condition_name].set_problem(self)
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self.conditions[condition_name].problem = self
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# # variable storing all points
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self.input_pts = {}
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# store in collector all the available fixed points
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# note that some points could not be stored at this stage (e.g. when
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# sampling locations). To check that all data points are ready for
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# training all type self.collector.full, which returns true if all
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# points are ready.
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self.collector.store_fixed_data()
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# # varible to check if sampling is done. If no location
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# # element is presented in Condition this variable is set to true
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# self._have_sampled_points = {}
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for condition_name in self.conditions:
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self._discretized_domains[condition_name] = False
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# # put in self.input_pts all the points that we don't need to sample
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self._span_condition_points()
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@property
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def input_pts(self):
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return self.collector.data_collections
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def __deepcopy__(self, memo):
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"""
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Implements deepcopy for the
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@@ -85,19 +82,6 @@ class AbstractProblem(metaclass=ABCMeta):
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def input_variables(self, variables):
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raise RuntimeError
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@property
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@abstractmethod
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def domains(self):
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"""
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The domain(s) where the conditions of the AbstractProblem are valid.
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If more than one domain type is passed, a list of Location is
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retured.
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:return: the domain(s) of ``self``
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:rtype: list[Location]
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"""
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pass
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@property
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@abstractmethod
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def output_variables(self):
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@@ -114,34 +98,8 @@ class AbstractProblem(metaclass=ABCMeta):
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"""
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return self._conditions
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def _span_condition_points(self):
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"""
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Simple function to get the condition points
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"""
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for condition_name in self.conditions:
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condition = self.conditions[condition_name]
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if hasattr(condition, "input_points"):
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samples = condition.input_points
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self.input_pts[condition_name] = samples
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self._discretized_domains[condition_name] = True
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if hasattr(self, "unknown_parameter_domain"):
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# initialize the unknown parameters of the inverse problem given
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# the domain the user gives
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self.unknown_parameters = {}
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for i, var in enumerate(self.unknown_variables):
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range_var = self.unknown_parameter_domain.range_[var]
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tensor_var = (
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torch.rand(1, requires_grad=True) * range_var[1]
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+ range_var[0]
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)
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self.unknown_parameters[var] = torch.nn.Parameter(
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tensor_var
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)
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def discretise_domain(
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self, n, mode="random", variables="all", domains="all"
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self, n, mode="random", variables="all", locations="all"
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):
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"""
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Generate a set of points to span the `Location` of all the conditions of
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@@ -172,119 +130,38 @@ class AbstractProblem(metaclass=ABCMeta):
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``CartesianDomain``.
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"""
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# check consistecy n
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# check consistecy n, mode, variables, locations
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check_consistency(n, int)
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# check consistency mode
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check_consistency(mode, str)
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if mode not in ["random", "grid", "lh", "chebyshev", "latin"]:
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check_consistency(variables, str)
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check_consistency(locations, str)
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# check correct sampling mode
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if mode not in DomainInterface.available_sampling_modes:
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raise TypeError(f"mode {mode} not valid.")
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# check consistency variables
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# check correct variables
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if variables == "all":
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variables = self.input_variables
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else:
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check_consistency(variables, str)
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if sorted(variables) != sorted(self.input_variables):
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TypeError(
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f"Wrong variables for sampling. Variables ",
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f"should be in {self.input_variables}.",
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)
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# # check consistency location # TODO: check if this is needed (from 0.1)
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# locations_to_sample = [
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# condition
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# for condition in self.conditions
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# if hasattr(self.conditions[condition], "location")
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# ]
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# if locations == "all":
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# # only locations that can be sampled
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# locations = locations_to_sample
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# else:
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# check_consistency(locations, str)
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# if sorted(locations) != sorted(locations_to_sample):
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if domains == "all":
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domains = [condition for condition in self.conditions]
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else:
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check_consistency(domains, str)
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print(domains)
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if sorted(domains) != sorted(self.conditions):
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TypeError(
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f"Wrong locations for sampling. Location ",
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f"should be in {locations_to_sample}.",
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)
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# sampling
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for d in domains:
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condition = self.conditions[d]
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# we try to check if we have already sampled
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try:
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already_sampled = [self.input_pts[d]]
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# if we have not sampled, a key error is thrown
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except KeyError:
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already_sampled = []
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# if we have already sampled fully the condition
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# but we want to sample again we set already_sampled
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# to an empty list since we need to sample again, and
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# self._have_sampled_points to False.
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if self._discretized_domains[d]:
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already_sampled = []
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self._discretized_domains[d] = False
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print(condition.domain)
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print(d)
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# build samples
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samples = [
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self.domains[d].sample(n=n, mode=mode, variables=variables)
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] + already_sampled
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pts = merge_tensors(samples)
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self.input_pts[d] = pts
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# the condition is sampled if input_pts contains all labels
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if sorted(self.input_pts[d].labels) == sorted(
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self.input_variables
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):
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# self._have_sampled_points[location] = True
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# self.input_pts[location] = self.input_pts[location].extract(
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# sorted(self.input_variables)
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# )
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self._have_sampled_points[d] = True
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def add_points(self, new_points):
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"""
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Adding points to the already sampled points.
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:param dict new_points: a dictionary with key the location to add the points
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and values the torch.Tensor points.
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"""
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if sorted(new_points.keys()) != sorted(self.conditions):
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TypeError(
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f"Wrong locations for new points. Location ",
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f"should be in {self.conditions}.",
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)
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for location in new_points.keys():
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# extract old and new points
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old_pts = self.input_pts[location]
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new_pts = new_points[location]
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# if they don't have the same variables error
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if sorted(old_pts.labels) != sorted(new_pts.labels):
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for variable in variables:
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if variable not in self.input_variables:
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TypeError(
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f"Not matching variables for old and new points "
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f"in condition {location}."
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f"Wrong variables for sampling. Variables ",
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f"should be in {self.input_variables}.",
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)
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if old_pts.labels != new_pts.labels:
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new_pts = torch.hstack(
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[new_pts.extract([i]) for i in old_pts.labels]
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)
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new_pts.labels = old_pts.labels
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# merging
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merged_pts = torch.vstack([old_pts, new_pts])
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merged_pts.labels = old_pts.labels
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self.input_pts[location] = merged_pts
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# check correct location
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if locations == "all":
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locations = [name for name in self.conditions.keys()]
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else:
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if not isinstance(locations, (list)):
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locations = [locations]
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for loc in locations:
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if not isinstance(self.conditions[loc], DomainEquationCondition):
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raise TypeError(
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f"Wrong locations passed, locations for sampling "
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f"should be in {[loc for loc in locations if not isinstance(self.conditions[loc], DomainEquationCondition)]}.",
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)
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# store data
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self.collector.store_sample_domains(n, mode, variables, locations)
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@@ -45,6 +45,20 @@ class InverseProblem(AbstractProblem):
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>>> 'data': Condition(CartesianDomain({'x': [0, 1]}), Equation(solution_data))
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"""
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def __init__(self):
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super().__init__()
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# storing unknown_parameters for optimization
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self.unknown_parameters = {}
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for i, var in enumerate(self.unknown_variables):
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range_var = self.unknown_parameter_domain.range_[var]
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tensor_var = (
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torch.rand(1, requires_grad=True) * range_var[1]
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+ range_var[0]
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)
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self.unknown_parameters[var] = torch.nn.Parameter(
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tensor_var
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)
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@abstractmethod
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def unknown_parameter_domain(self):
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"""
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