bsfit.nodes.models.bayes.StandardBayes¶

class bsfit.nodes.models.bayes.StandardBayes(initial_params: Dict[str, list], prior_shape: str, prior_mode: float, readout: str)[source]¶

Bases: bsfit.nodes.models.abstract_model.Model

Standard Bayesian model class

__init__(initial_params: Dict[str, list], prior_shape: str, prior_mode: float, readout: str)[source]¶

instantiate Standard Bayesian model

Parameters

initial_params (Dict) –

initial_params = {
    "k_llh": list,
    "k_prior": list,
    "p_rand": list,
    "k_m": list,
}

prior_shape (str) – prior’s function (“vonMisesPrior”)
prior_mode (float) – prior’s mode (e.g., 225)
readout (str) – decision process (“map”: max a posteriori readout)

Methods

`__init__`(initial_params, prior_shape, ...)	instantiate Standard Bayesian model
`fit`(dataset)	fit the model
`get_attributes`()	get model attributes
`predict`(data, granularity)	generate the model's predictions
`simulate`(dataset, granularity, centering, ...)	simulate predictions

fit(dataset: pandas.core.frame.DataFrame)[source]¶

fit the model

Parameters

dataset (pd.DataFrame) –

dataset with columns:

'stim_mean': the stimulus feature (e.g., motion direction)
'stim_std': the stimulus feature strength
'prior_mode': mode of prior
'prior_std': std of the prior
'prior_shape': density function of the prior (e.g., "vonMisesPrior")
'estimate': the estimate data

init_p (dict) – initial parameters

Usage:

from bsfit.nodes.models.bayes import StandardBayes
initial_params = {
    "k_llh": [2.7, 10.7, 33],
    "k_prior": [2.7, 33],
    "p_rand": [0],
    "k_m": [2000],
}
model = StandardBayes(
    initial_params=initial_params,
    prior_shape="vonMisesPrior",
    prior_mode=225,
    readout="map")
model.fit(dataset)

Returns: the fitted model
Return type: (Model)

get_attributes()¶

get model attributes

Parameters: self (Model) – the model
Returns: list the model attributes
Return type: (list)

predict(data: tuple, granularity: str) → dict[source]¶

generate the model’s predictions

Parameters

data (tuple) – the data to predict, tuple of (stim_mean, stim_estimate)
granularity (str) – (“trial” or “mean”) to predict single choices or their stats

Returns

the model predictions

Return type

(dict)

simulate(dataset: pandas.core.frame.DataFrame, granularity: str, centering: bool, **kwargs: dict)[source]¶

simulate predictions

Parameters

dataset (pd.DataFrame) –

dataset:

'stim_mean': the stimulus feature (e.g., motion direction)
'stim_std': the stimulus feature strength
'prior_mode': the stimulus feature density's mode
'prior_std': the stimulus feature density's std
'prior_shape': prior's density function (e.g., "vonMisesPrior")
'estimate' (optional): the estimate data

granularity (str) – (“trial” or “mean”)
centering (bool) – center or not plot’s x-axis

Kwargs:: n_repeats (int): the number of repeats of conditions, when granularity=”trial”

Returns: results of the simulation
Return type: (dict)