macrostat.core.Behavior#

class macrostat.core.Behavior(parameters: Parameters, scenarios: Scenarios, variables: Variables, record: bool = False, scenario: int = 0, differentiable: bool = False, debug: bool = False)[source]#

Bases: Module

Base class for the behavior of the MacroStat model.

__init__(parameters: Parameters, scenarios: Scenarios, variables: Variables, record: bool = False, scenario: int = 0, differentiable: bool = False, debug: bool = False)[source]#

Initialize the behavior of the MacroStat model.

Parameters:

Methods

__init__(parameters, scenarios, variables[, ...])

Initialize the behavior of the MacroStat model.

add_module(name, module)

Add a child module to the current module.

apply(fn)

Apply fn recursively to every submodule (as returned by .children()) as well as self.

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

buffers([recurse])

Return an iterator over module buffers.

children()

Return an iterator over immediate children modules.

compile(*args, **kwargs)

Compile this Module's forward using torch.compile().

cpu()

Move all model parameters and buffers to the CPU.

cuda([device])

Move all model parameters and buffers to the GPU.

diffmax(x1, x2)

Smooth approximation to the minimum B: https://mathoverflow.net/questions/35191/a-differentiable-approximation-to-the-minimum-function

diffmax_v(x)

Smooth approximation to the maximum for a tensor.

diffmin(x1, x2)

Smooth approximation to the minimum B: https://mathoverflow.net/questions/35191/a-differentiable-approximation-to-the-minimum-function

diffmin_v(x)

Smooth approximation to the minimum.

diffwhere(condition, x1, x2)

Where condition that is differentiable with respect to the condition.

double()

Casts all floating point parameters and buffers to double datatype.

eval()

Set the module in evaluation mode.

extra_repr()

Return the extra representation of the module.

float()

Casts all floating point parameters and buffers to float datatype.

forward()

Forward pass of the behavior.

get_buffer(target)

Return the buffer given by target if it exists, otherwise throw an error.

get_extra_state()

Return any extra state to include in the module's state_dict.

get_parameter(target)

Return the parameter given by target if it exists, otherwise throw an error.

get_submodule(target)

Return the submodule given by target if it exists, otherwise throw an error.

half()

Casts all floating point parameters and buffers to half datatype.

initialize()

Initialize the behavior.

ipu([device])

Move all model parameters and buffers to the IPU.

load_state_dict(state_dict[, strict, assign])

Copy parameters and buffers from state_dict into this module and its descendants.

modules()

Return an iterator over all modules in the network.

mtia([device])

Move all model parameters and buffers to the MTIA.

named_buffers([prefix, recurse, ...])

Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

named_children()

Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

named_modules([memo, prefix, remove_duplicate])

Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

named_parameters([prefix, recurse, ...])

Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

parameters([recurse])

Return an iterator over module parameters.

register_backward_hook(hook)

Register a backward hook on the module.

register_buffer(name, tensor[, persistent])

Add a buffer to the module.

register_forward_hook(hook, *[, prepend, ...])

Register a forward hook on the module.

register_forward_pre_hook(hook, *[, ...])

Register a forward pre-hook on the module.

register_full_backward_hook(hook[, prepend])

Register a backward hook on the module.

register_full_backward_pre_hook(hook[, prepend])

Register a backward pre-hook on the module.

register_load_state_dict_post_hook(hook)

Register a post-hook to be run after module's load_state_dict() is called.

register_load_state_dict_pre_hook(hook)

Register a pre-hook to be run before module's load_state_dict() is called.

register_module(name, module)

Alias for add_module().

register_parameter(name, param)

Add a parameter to the module.

register_state_dict_post_hook(hook)

Register a post-hook for the state_dict() method.

register_state_dict_pre_hook(hook)

Register a pre-hook for the state_dict() method.

requires_grad_([requires_grad])

Change if autograd should record operations on parameters in this module.

set_extra_state(state)

Set extra state contained in the loaded state_dict.

set_submodule(target, module)

Set the submodule given by target if it exists, otherwise throw an error.

share_memory()

See torch.Tensor.share_memory_().

state_dict(*args[, destination, prefix, ...])

Return a dictionary containing references to the whole state of the module.

step(t, scenario)

Step function of the behavior.

tanhmask(x)

Convert a variable into 0 (x<0) and 1 (x>0)

to(*args, **kwargs)

Move and/or cast the parameters and buffers.

to_empty(*, device[, recurse])

Move the parameters and buffers to the specified device without copying storage.

train([mode])

Set the module in training mode.

type(dst_type)

Casts all parameters and buffers to dst_type.

xpu([device])

Move all model parameters and buffers to the XPU.

zero_grad([set_to_none])

Reset gradients of all model parameters.

Attributes

T_destination

call_super_init

dump_patches

training

diffmax(x1, x2)[source]#

Smooth approximation to the minimum B: https://mathoverflow.net/questions/35191/a-differentiable-approximation-to-the-minimum-function

Requires:

self.hyper[‘max_constant’] as a large number

Parameters:

  • x1 (torch.Tensor) – The first variable to be compared.

  • x2 (torch.Tensor) – The second variable to be compared.

diffmax_v(x)[source]#

Smooth approximation to the maximum for a tensor. See diffmax

Requires:

self.hyper[‘max_constant’] as a large number

Parameters:

x (torch.Tensor) – The variable to be converted.

diffmin(x1, x2)[source]#

Smooth approximation to the minimum B: https://mathoverflow.net/questions/35191/a-differentiable-approximation-to-the-minimum-function

Requires:

self.hyper[‘min_constant’] as a large number

Parameters:

  • x1 (torch.Tensor) – The first variable to be compared.

  • x2 (torch.Tensor) – The second variable to be compared.

diffmin_v(x)[source]#

Smooth approximation to the minimum. See diffmin

Parameters:

  • x (torch.Tensor) – The variable to be converted.

  • Requires – self.hyper[‘min_constant’] as a large number

diffwhere(condition, x1, x2)[source]#

Where condition that is differentiable with respect to the condition.

Requires:

self.hyper[‘diffwhere’] = True self.hyper[‘sigmoid_constant’] as a large number

Note: For non-NaN/inf, where(x > eps, z, y) is (x - eps > 0) * (z - y) + y, so we can use the sigmoid function to approximate the where function.

Parameters:

  • condition (torch.Tensor) – Condition to be evaluated expressed as x - eps

  • x1 (torch.Tensor) – Value to be returned if condition is True

  • x2 (torch.Tensor) – Value to be returned if condition is False

forward()[source]#

Forward pass of the behavior.

This should include the model’s main loop, and is implemented as a placeholder. The idea is for users to implement an initialize() and step() function, which will be called by the forward() function.

If there are additional steps necessary, users may wish to overwrite this function.

initialize()[source]#

Initialize the behavior.

This should include the model’s initialization steps, and set all of the necessary state variables. They only need to be set for one period, and will then be copied to the history and prior to be used in the step function.

step(t: int, scenario: dict)[source]#

Step function of the behavior.

This should include the model’s main loop.

Parameters:

  • t (int) – The current timestep.

  • scenario (dict) – The scenario information for the current timestep.

tanhmask(x)[source]#

Convert a variable into 0 (x<0) and 1 (x>0)

Requires:

self.hyper[‘tanh_constant’] as a large number

Parameters:

x (torch.Tensor) – The variable to be converted.