parameterspace.transformations.log_zero_one

LogZeroOneFloat

Maps bounded interval to [0,1] via a logarithmic transformation.

This means that all the priors used with a parameter effectively model the exponent of the actual quantity.

This class should be used for ContinuousParameters.

from_dict(json_dict) inherited

[summary]

Source code in parameterspace/transformations/log_zero_one.py

@staticmethod
def from_dict(json_dict: dict):
    """
    [summary]
    """
    transformation_class = json_dict["class_name"]
    module_str, class_str = transformation_class.rsplit(".", 1)
    module = importlib.import_module(module_str)
    model_class = getattr(module, class_str)
    return model_class(*json_dict["init_args"], **json_dict["init_kwargs"])

inverse(self, numerical_value)

Convert the numerical representation back to the true value with the proper type.

Parameters:

Name	Type	Description	Default
numerical_value	float	Transformed/Numerical representation of a value.	required

Returns:

Type	Description
float	The value corresponding to the given value. Type depends no the kind of transformation.

Source code in parameterspace/transformations/log_zero_one.py

def inverse(self, numerical_value: float) -> float:
    return float(
        np.clip(
            np.exp(self.log_bounds[0] + numerical_value * (self.log_interval_size)),
            self.input_bounds[0],
            self.input_bounds[1],
        )
    )

jacobian_factor(self, numerical_value)

Factor to correct the likelihood based on the non-linear transformation.

Parameters:

Name	Type	Description	Default
numerical_value	float	Transformed/Numerical representation of a value.	required

Returns:

Type	Description
float	Jacobian factor to properly transform the likelihood.

Source code in parameterspace/transformations/log_zero_one.py

def jacobian_factor(self, numerical_value: float) -> float:
    return 1.0 / (self.log_interval_size * self.inverse(numerical_value))

to_dict(self) inherited

[summary]

Source code in parameterspace/transformations/log_zero_one.py

def to_dict(self):
    """
    [summary]
    """
    json_dict = {
        "class_name": type(self).__module__ + "." + type(self).__qualname__,
        "init_args": self._init_args,
        "init_kwargs": self._init_kwargs,
    }
    return json_dict

LogZeroOneInteger

Maps a bounded interval of integers to [0, 1] via a logarithmic transformation.

This means that all the priors used with a parameter effectively model the exponent of the actual quantity.

This class should be used for IntegerParameters.

from_dict(json_dict) inherited

[summary]

Source code in parameterspace/transformations/log_zero_one.py

@staticmethod
def from_dict(json_dict: dict):
    """
    [summary]
    """
    transformation_class = json_dict["class_name"]
    module_str, class_str = transformation_class.rsplit(".", 1)
    module = importlib.import_module(module_str)
    model_class = getattr(module, class_str)
    return model_class(*json_dict["init_args"], **json_dict["init_kwargs"])

inverse(self, numerical_value)

[summary]

Source code in parameterspace/transformations/log_zero_one.py

def inverse(self, numerical_value: float) -> int:
    """
    [summary]
    """
    integer_value = np.around(
        np.exp(self.log_bounds[0] + numerical_value * (self.log_interval_size))
    )
    # clip result to bound due to rounding problems when the numerical value is 0.0
    return int(np.clip(integer_value, *self.input_bounds))

jacobian_factor(self, numerical_value)

Factor to correct the likelihood based on the non-linear transformation.

Parameters:

Name	Type	Description	Default
numerical_value	float	Transformed/Numerical representation of a value.	required

Returns:

Type	Description
float	Jacobian factor to properly transform the likelihood.

Source code in parameterspace/transformations/log_zero_one.py

def jacobian_factor(self, numerical_value: float) -> float:
    return 1.0 / (self.log_interval_size * self.inverse(numerical_value))

to_dict(self) inherited

[summary]

Source code in parameterspace/transformations/log_zero_one.py

def to_dict(self):
    """
    [summary]
    """
    json_dict = {
        "class_name": type(self).__module__ + "." + type(self).__qualname__,
        "init_args": self._init_args,
        "init_kwargs": self._init_kwargs,
    }
    return json_dict