ApproximateVariance

class hybrid_learning.concepts.train_eval.kpis.aggregating_kpis.ApproximateVariance(ddof=1, **binning_args)[source]

Bases: ApproximateMean

Approximate the variance in the data by binning the values. Approximation works the same as in the super class. See there for details.

Public Data Attributes:

Inherited from : py: class:ApproximateMean

bin_centers

1D tensor holding the values of the center points of each bin.
Inherited from : py: class:Module

dump_patches

This allows better BC support for load_state_dict().

T_destination

alias of TypeVar('T_destination', bound=Mapping[str, Tensor])

Public Methods:

value()

Return the approximate variance according to the binning so far.
Inherited from : py: class:ApproximateMean

value()

Return the approximate variance according to the binning so far.
Inherited from : py: class:Histogram

update(outputs[, _labels])

Update the bin count.

reset()

Reset the bin count.

value()

Return the approximate variance according to the binning so far.
Inherited from : py: class:_OnPredictions

forward(outputs[, _labels])

Calculate KPI without gradient.

update(outputs[, _labels])

Update the bin count.
Inherited from : py: class:AggregatingKpi

update(outputs[, _labels])

Update the bin count.

reset()

Reset the bin count.

value()

Return the approximate variance according to the binning so far.

value_and_reset()

Shorthand for subsequent calls to value and to reset.

forward(outputs[, _labels])

Calculate KPI without gradient.
Inherited from : py: class:Module

forward(outputs[, _labels])

Calculate KPI without gradient.

register_buffer(name, tensor[, persistent])

Adds a buffer to the module.

register_parameter(name, param)

Adds a parameter to the module.

add_module(name, module)

Adds a child module to the current module.

get_submodule(target)

Returns the submodule given by target if it exists, otherwise throws an error.

get_parameter(target)

Returns the parameter given by target if it exists, otherwise throws an error.

get_buffer(target)

Returns the buffer given by target if it exists, otherwise throws an error.

apply(fn)

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

cuda([device])

Moves all model parameters and buffers to the GPU.

xpu([device])

Moves all model parameters and buffers to the XPU.

cpu()

Moves all model parameters and buffers to the CPU.

type(dst_type)

Casts all parameters and buffers to dst_type.

float()

Casts all floating point parameters and buffers to float datatype.

double()

Casts all floating point parameters and buffers to double datatype.

half()

Casts all floating point parameters and buffers to half datatype.

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

to_empty(*, device)

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

to(*args, **kwargs)

Moves and/or casts the parameters and buffers.

register_backward_hook(hook)

Registers a backward hook on the module.

register_full_backward_hook(hook)

Registers a backward hook on the module.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the module.

register_forward_hook(hook)

Registers a forward hook on the module.

state_dict([destination, prefix, keep_vars])

Returns a dictionary containing a whole state of the module.

load_state_dict(state_dict[, strict])

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

parameters([recurse])

Returns an iterator over module parameters.

named_parameters([prefix, recurse])

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

buffers([recurse])

Returns an iterator over module buffers.

named_buffers([prefix, recurse])

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

children()

Returns an iterator over immediate children modules.

named_children()

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

modules()

Returns an iterator over all modules in the network.

named_modules([memo, prefix, remove_duplicate])

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

train([mode])

Sets the module in training mode.

eval()

Sets the module in evaluation mode.

requires_grad_([requires_grad])

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

zero_grad([set_to_none])

Sets gradients of all model parameters to zero.

share_memory()

See torch.Tensor.share_memory_()

extra_repr()

Set the extra representation of the module

Special Methods:

__init__([ddof])

Init.
Inherited from : py: class:Histogram

__init__([ddof])

Init.
Inherited from : py: class:AggregatingKpi

__init__([ddof])

Init.
Inherited from : py: class:Module

__init__([ddof])

Init.

__call__(*input, **kwargs)

Call self as a function.

__setstate__(state)

__getattr__(name)

__setattr__(name, value)

Implement setattr(self, name, value).

__delattr__(name)

Implement delattr(self, name).

__repr__()

Return repr(self).

__dir__()

Default dir() implementation.
Parameters

ddof (int) –

__init__(ddof=1, **binning_args)[source]

Init.

Parameters

  • n_bins – the number of bins to use

  • lower_bound – the expected lower bound of the output values

  • upper_bound – the expected upper bound of the output values

  • device – the device onto which to put the count tensor

  • ddof (int) –

value()[source]

Return the approximate variance according to the binning so far.

Return type

Optional[Tensor]

count: Tensor

Total amount of predictions per bin.

ddof: int

The delta degrees of freedom to use for divisors. The divisor is N-ddof, cf. the pandas implementation.

n_bins: int

The number of bins to use.

training: bool