py_irt.models package

Submodules

py_irt.models.one_param_logistic module

class py_irt.models.one_param_logistic.OneParamLog(*, priors: str, num_items: int, num_subjects: int, verbose: bool = False, device: str = 'cpu', vocab_size: Optional[int] = None, dropout: Optional[float] = None, hidden: Optional[int] = None, **kwargs)

Bases: IrtModel

1PL IRT model

export()

fit(models, items, responses, num_epochs)

Fit the IRT model with variational inference

fit_MCMC(models, items, responses, num_epochs)

Fit the IRT model with MCMC

get_guide()

get_model()

guide_hierarchical(models, items, obs)

Initialize a 1PL guide with hierarchical priors

guide_vague(models, items, obs)

Initialize a 1PL guide with vague priors

model_hierarchical(models, items, obs)

Initialize a 1PL model with hierarchical priors

model_vague(models, items, obs)

Initialize a 1PL model with vague priors

predict(subjects, items, params_from_file=None)

predict p(correct | params) for a specified list of model, item pairs

summary(traces, sites)

Aggregate marginals for MCM

py_irt.models.two_param_logistic module

class py_irt.models.two_param_logistic.TwoParamLog(*, priors: str, num_items: int, num_subjects: int, verbose=False, device: str = 'cpu', **kwargs)

Bases: IrtModel

2PL IRT model

export()

fit_MCMC(models, items, responses, num_epochs)

Fit the IRT model with MCMC

get_guide()

get_model()

guide_hierarchical(subjects, items, obs)

Initialize a 2PL guide with hierarchical priors

guide_vague(subjects, items, obs)

Initialize a 2PL guide with vague priors

model_hierarchical(subjects, items, obs)

Initialize a 2PL model with hierarchical priors

model_vague(subjects, items, obs)

Initialize a 2PL model with vague priors

predict(subjects, items, params_from_file=None)

predict p(correct | params) for a specified list of model, item pairs

summary(traces, sites)

Aggregate marginals for MCM

Module contents