a0cbf1c44a
* Reimplement conditions * Refactor datasets and implement LabelBatch --------- Co-authored-by: Dario Coscia <dariocos99@gmail.com>
123 lines
4.2 KiB
Python
123 lines
4.2 KiB
Python
"""Condition module."""
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import warnings
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from .data_condition import DataCondition
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from .domain_equation_condition import DomainEquationCondition
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from .input_equation_condition import InputEquationCondition
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from .input_target_condition import InputTargetCondition
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from ..utils import custom_warning_format
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# Set the custom format for warnings
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warnings.formatwarning = custom_warning_format
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warnings.filterwarnings("always", category=DeprecationWarning)
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def warning_function(new, old):
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"""Handle the deprecation warning.
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:param new: Object to use instead of the old one.
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:type new: str
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:param old: Object to deprecate.
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:type old: str
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"""
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warnings.warn(
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f"'{old}' is deprecated and will be removed "
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f"in future versions. Please use '{new}' instead.",
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DeprecationWarning,
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)
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class Condition:
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"""
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The class ``Condition`` is used to represent the constraints (physical
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equations, boundary conditions, etc.) that should be satisfied in the
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problem at hand. Condition objects are used to formulate the
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PINA :obj:`pina.problem.abstract_problem.AbstractProblem` object.
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Conditions can be specified in four ways:
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1. By specifying the input and output points of the condition; in such a
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case, the model is trained to produce the output points given the input
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points. Those points can either be torch.Tensor, LabelTensors, Graph
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2. By specifying the location and the equation of the condition; in such
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a case, the model is trained to minimize the equation residual by
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evaluating it at some samples of the location.
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3. By specifying the input points and the equation of the condition; in
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such a case, the model is trained to minimize the equation residual by
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evaluating it at the passed input points. The input points must be
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a LabelTensor.
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4. By specifying only the data matrix; in such a case the model is
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trained with an unsupervised costum loss and uses the data in training.
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Additionaly conditioning variables can be passed, whenever the model
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has extra conditioning variable it depends on.
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Example::
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>>> from pina import Condition
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>>> condition = Condition(
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... input=input,
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... target=target
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... )
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>>> condition = Condition(
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... domain=location,
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... equation=equation
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... )
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>>> condition = Condition(
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... input=input,
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... equation=equation
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... )
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>>> condition = Condition(
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... input=data,
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... conditional_variables=conditional_variables
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... )
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"""
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__slots__ = list(
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set(
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InputTargetCondition.__slots__
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+ InputEquationCondition.__slots__
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+ DomainEquationCondition.__slots__
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+ DataCondition.__slots__
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)
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)
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def __new__(cls, *args, **kwargs):
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if len(args) != 0:
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raise ValueError(
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"Condition takes only the following keyword "
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f"arguments: {Condition.__slots__}."
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)
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# back-compatibility 0.1
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keys = list(kwargs.keys())
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if "location" in keys:
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kwargs["domain"] = kwargs.pop("location")
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warning_function(new="domain", old="location")
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if "input_points" in keys:
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kwargs["input"] = kwargs.pop("input_points")
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warning_function(new="input", old="input_points")
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if "output_points" in keys:
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kwargs["target"] = kwargs.pop("output_points")
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warning_function(new="target", old="output_points")
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sorted_keys = sorted(kwargs.keys())
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if sorted_keys == sorted(InputTargetCondition.__slots__):
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return InputTargetCondition(**kwargs)
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if sorted_keys == sorted(InputEquationCondition.__slots__):
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return InputEquationCondition(**kwargs)
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if sorted_keys == sorted(DomainEquationCondition.__slots__):
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return DomainEquationCondition(**kwargs)
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if (
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sorted_keys == sorted(DataCondition.__slots__)
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or sorted_keys[0] == DataCondition.__slots__[0]
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):
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return DataCondition(**kwargs)
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raise ValueError(f"Invalid keyword arguments {kwargs.keys()}.")
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