pymc.fit#

pymc.fit(n=10000, method='advi', model=None, random_seed=None, start=None, start_sigma=None, inf_kwargs=None, **kwargs)[source]#

Handy shortcut for using inference methods in functional way

Parameters:

n: `int`

number of iterations

method: str or :class:`Inference`

string name is case insensitive in:

model: :class:`Model`

PyMC model for inference

random_seed: None or int

inf_kwargs: dict

additional kwargs passed to Inference

start: `dict[str, np.ndarray]` or `StartDict`

starting point for inference

start_sigma: `dict[str, np.ndarray]`

starting standard deviation for inference, only available for method ‘advi’

Returns:

Other Parameters:

score: bool: evaluate loss on each iteration or not
callbacks: list[function: (Approximation, losses, i) -> None]: calls provided functions after each iteration step
progressbar: bool: whether to show progressbar or not
progressbar_theme: Theme: Custom theme for the progress bar
obj_n_mc: `int`: Number of monte carlo samples used for approximation of objective gradients
tf_n_mc: `int`: Number of monte carlo samples used for approximation of test function gradients
obj_optimizer: function (grads, params) -> updates: Optimizer that is used for objective params
test_optimizer: function (grads, params) -> updates: Optimizer that is used for test function params
more_obj_params: `list`: Add custom params for objective optimizer
more_tf_params: `list`: Add custom params for test function optimizer
more_updates: `dict`: Add custom updates to resulting updates
total_grad_norm_constraint: `float`: Bounds gradient norm, prevents exploding gradient problem
fn_kwargs: `dict`: Add kwargs to pytensor.function (e.g. {‘profile’: True})
more_replacements: `dict`: Apply custom replacements before calculating gradients