prfmodel.scaling.Baseline¶

class prfmodel.scaling.Baseline¶

Additive baseline scaling model.

Transforms a temporal response by adding a baseline.

Examples

>>> import numpy as np
>>> import pandas as pd
>>> params = pd.DataFrame({
...     "baseline": [5.0, 10.0, -3.0],
... })
>>> num_frames = 10
>>> inputs = np.ones((params.shape[0], num_frames))
>>> model = Baseline()
>>> resp = model(inputs, params)
>>> print(resp.shape)  # (num_units, num_frames)
(3, 10)

__call__(inputs: prfmodel.typing.Tensor | numpy.ndarray, parameters: pandas.DataFrame, dtype: str | None = None) → numpy.ndarray¶

Make predictions with the scaling model.

This is the public entry point; subclasses implement call() instead. Use call() when a backend tensor is required.

Parameters:

inputs (prfmodel.typing.Tensor or numpy.ndarray) – Input tensor with temporal response and shape (num_units, num_frames).
parameters (pandas.DataFrame) – Dataframe with columns containing different model parameters and rows containing parameter values for different units.
dtype (str, optional) – The dtype of the prediction result. If None (the default), uses the dtype from prfmodel.utils.get_dtype().

Returns:

The predicted model response with shape (num_units, num_frames) and dtype dtype.

Return type:

numpy.ndarray

Raises:

ValueError – If parameters is missing one or more of parameter_names.

call(inputs: prfmodel.typing.Tensor, parameters: prfmodel.utils.TensorFrame) → prfmodel.typing.Tensor¶

Predict the model response.

Parameters:

inputs (prfmodel.typing.Tensor) – Input tensor with temporal response and shape (num_units, num_frames).
parameters (TensorFrame) – Model parameters as tensors, supporting the same column selection as a pandas.DataFrame.

Returns:

The predicted model response with shape (num_units, num_frames) and dtype dtype.

Return type:

Tensor

check_parameter_names(parameters: pandas.DataFrame) → None¶

Check that required parameter names are supplied.

Parameters:: parameters (pandas.DataFrame) – Dataframe with columns containing different model parameters and rows containing parameter values for different units.
Raises:: ValueError – When a parameter name in the parameter_names attribute is not a column in parameters.

check_parameter_values(parameters: pandas.DataFrame) → None¶

Check that the parameter values lie inside the domain the model is defined on.

Parameters:: parameters (pandas.DataFrame) – Dataframe with columns containing different model parameters and rows containing parameter values for different units.
Raises:: ValueError – When a parameter that must be > 0 is zero or negative.

get_consumed_parameter_names(parameters: pandas.DataFrame) → list[str]¶

Return the parameter names the model reads from parameters.

A name covered by default_parameters is only read when the caller supplies a column for it; otherwise the default is merged in further down and the column would be absent here.

Parameters:: parameters (pandas.DataFrame) – Dataframe with columns containing different model parameters and rows containing parameter values for different units.
Returns:: Names of the parameters the model reads from parameters.
Return type:: list of str

property parameter_names: list[str]¶

Names of parameters used by the model.

Parameter names are: baseline.